Intergovernmental Panel on Climate Change
UNEP

WMO

Good Practice Guidance
for Land Use,
Land-Use Change and Forestry
Edited by
Jim Penman, Michael Gytarsky, Taka Hiraishi, Thelma Krug, Dina Kruger,
Riitta Pipatti, Leandro Buendia, Kyoko Miwa, Todd Ngara,
Kiyoto Tanabe and Fabian Wagner

IPCC National Greenhouse Gas Inventories ProgrammePublished by the Institute for Global Environmental Strategies (IGES) for the IPCC
© The Intergovernmental Panel on Climate Change (IPCC), 2003.
All rights reserved. No part of this publication may be reproduced or transmitted for commercial purposes in any
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Contents

CONTENTS

Acknowledgement ................................................................................................................................................ iv
Preface ................................................................................................................................................................... v
Chapters
1

Overview ................................................................................................................................................... 1.1

2

Basis for Consistent Representation of Land Areas .................................................................................. 2.1

3

LUCF Sector Good Practice Guidance ...................................................................................................... 3.1

4

Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol ......................... 4.1

5

Cross-Cutting Issues .................................................................................................................................. 5.1

Annexes
Annex A

Glossary ...................................................................................................................................... G.1

Annex B

Basic Information ......................................................................................................................... B.1

Annex C

Abbreviations and Acronyms .................................................................................................... AA.1

Annex D

List of Reviewers ...................................................................................................................... LR.1

IPCC Good Practice Guidance for LULUCF

iii

Acknowledgement

ACKNOWLEDGEMENT
The preparation of the Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF)
has been a major undertaking by the IPCC National Greenhouse Gas Inventories Programme (NGGIP). The
success in producing this report has depended foremost on the knowledge, enthusiasm and co-operation of theCoordinating Lead Authors, Lead Authors and Contributing Authors – some 150 worldwide. We wish to thankthe authors for the time and effort devoted to the task, as well as the commitment to the IPCC process.
Review editors have ensured that the process for consideration of the comments has been appropriate. We would
like to thank them for this important task.
UNFCCC Secretariat staff, Roberto Acosta, Claudio Forner and Heikki Granholm participated in the preparationof the report giving background and guidance on issues related to the Convention, the Kyoto Protocol and the
Marrakesh Accords. We wish to thank them for their valuable input.
The Steering Group, consisting of the IPCC TFI Co-chairs Taka Hiraishi and Thelma Krug, and Michael
Gytarsky (Russian Federation), Dina Kruger (USA) and Jim Penman (UK), has guided the work and ensured
internal consistency within the report as well as consistency with the Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories. We would like to express our gratitude for their skilful leadership and guidance
throughout the preparation of the report.
Five authors/experts meetings were held in Eisenach, Germany; Tampere, Finland; Rio de Janeiro, Brazil; Kuala
Lumpur, Malaysia; and Sydney, Australia during the course of preparation of the report. We wish to thank thehost countries and hosting organisations for co-organising these meetings.
Two combined government/expert reviews were organised during the preparation of the report; the first during
the period 2 December 2002 to 27 January 2003, and the second during 2 May 2003 to 27 June 2003. The firstreview resulted in almost 6,000 comments, the second in some 4,000 comments. The comments have provided
additional expertise to the work in a constructive way, and consideration of the comments improved the drafts
considerably. We wish to thank all reviewers for their comments.
The NGGIP Technical Support Unit (TSU Head Riitta Pipatti, Programme Officers Leandro Buendia, Kyoko
Miwa, Todd Ngara, Kiyoto Tanabe and Fabian Wagner, Administrative Assistant Ayako Hongo, Project
Secretary Akiko Kawase, and IT Officer John Lane) provided guidance as well as technical and organisational
support for the project. They worked tirelessly with the authors as they managed the final editing of the report.
We wish to thank them for their hard and competent work.
IPCC Secretariat (Rudie Bourgeois, Annie Courtin and Chantal Ettori) provided assistance for organising themeetings and for the reviews. We wish to thank them for their effort and flexibility in responding to the needs tothe authors and the TSU during the tight schedule.
Last but not least, we wish to thank the IPCC Chair Rajendra Pachauri, IPCC Secretary Geoff Love (until
August 2003), Acting Secretary Renate Christ and the Task Force Bureau on Inventories (Co-chairs, and Ian
Carruthers, Soobaraj N. Sok Appadu, Kirit Parikh, Dhari Al-Ajimi, Jamidu Katima, Javier Hanna Figueroa (until
June 2003), Sergio Gonzalez-Martineaux, Art Jaques, Dina Kruger, Helen Plume, Audun Rosland and Saad
Khorfan) for their support, advice and encouragement.

G.O.P. Obasi

K. Töpfer

Secretary-General
World Meteorological Organisation

Executive Director
United Nations Environmental Programme

iv

IPCC Good Practice Guidance for LULUCF

Preface

PREFACEThis report on Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF) is the
response to the invitation by the United Nations Framework Convention on Climate Change (UNFCCC)1 to theIntergovernmental Panel on Climate Change (IPCC)2 to develop good practice guidance for land use, land-use
change and forestry (LULUCF). GPG-LULUCF provides supplementary methods and good practice guidancefor estimating, measuring, monitoring and reporting on carbon stock changes and greenhouse gas emissions from
LULUCF activities under Article 3, paragraphs 3 and 4, and Articles 6 and 12 of the Kyoto Protocol.
The GPG-LULUCF assists countries in producing inventories for the land use, land-use change and forestry
sector that are neither over- nor underestimates so far as can be judged, and in which uncertainties are reduced as
far as practicable. It supports the development of inventories that are transparent, documented, consistent overtime, complete, comparable, assessed for uncertainties, subject to quality control and quality assurance, andefficient in the use of resources.
The GPG-LULUCF is consistent with the existing good practice guidance for the other sector and addresses:•

Choice of estimation method within the context of the IPCC Guidelines;•

Quality assurance and quality control procedures to provide cross-checks during the inventory compilation;•

Data and information to be documented, archived and reported to facilitate review and assessment of
inventory estimates;•

Quantification of uncertainties at the source or sink category level and for the inventory as a whole, so that
resources available can be directed toward reducing uncertainties over time, and the improvement can betracked.

In addition, GPG-LULUCF provides guidance related to the specific features of the LULUCF sector on
consistent representation of land areas, sampling for area estimates and for estimating emissions and removals,
verification, and guidance on how to complement the Convention reporting for the LULUCF sector to meet the
supplementary requirements under the Kyoto Protocol.
The development of good practice guidance for LULUCF sector is a step in the IPCC’s on-going programme ofinventory development and will also support future revisions of the IPCC Guidelines themselves.

1 Decision 11/CP.7 (Land use, land-use change and forestry) in FCCC/CP/2001/13/Add.1, paragraphs 3(a) and 3 (b), page 55.
2 IPCC was established jointly by the World Meteorological Organization (WMO) and the United Nations Environment

Programme (UNEP) to•••

Make periodic assessments of the science, impacts and the socio-economic aspects of climate change and of
adaptation and mitigation options to address it;
Assess, and develop as necessary, methodologies such as the IPCC Guidelines for National Greenhouse Gas
Inventories;
Provide, on request, scientific/technical/socio-economic advice to the Conference of the Parties to the United
Nations Framework Convention on Climate Change (UNFCCC) and its bodies.

IPCC Good Practice Guidance for LULUCFv

1

OVERVIEWIPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

AUTHORS AND REVIEW EDITORS
Co-ordinating Lead authors
Jim Penman (UK)Michael Gytarsky (Russia), Taka Hiraishi (Japan), Thelma Krug (Brazil), and Dina Kruger (USA)

Review Editors
Ian Carruthers (Australia) and Carlos Lopez (Cuba)

1.2

IPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

Contents
1.1

INTRODUCTION

1.4

1.2

GOOD PRACTICE GUIDANCE FOR LAND USE, LAND-USE CHANGE
AND FORESTRY (LULUCF)

1.4

DEFINITION OF INVENTORIES CONSISTENT WITH GOOD PRACTICEGUIDANCE

1.6

1.4

RELATIONSHIP TO THE IPCC GUIDELINES

1.8

1.5

OUTLINE OF PRESENT DOCUMENT

1.8

1.6

USING THE GUIDANCE - PRACTICAL ADVICE FOR INVENTORY AGENCIESAND OTHERS

1.10

POLICY RELEVANCE

1.11

1.3

1.7

IPCC Good Practice Guidance for LULUCF

1.3

Chapter 1: Overview

1.1

INTRODUCTION

In 1998, the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) invited theIntergovernmental Panel on Climate Change (IPCC) to produce good practice guidance to the Revised 1996
IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines)1. Since the Parties had alreadyagreed to use2 the IPCC Guidelines for estimating greenhouse gas emissions and removals, the role of goodpractice guidance was not to replace the IPCC Guidelines, but rather to provide advice consistent with them.
The IPCC finished its work in time for the first volume of the Good Practice Guidance and Uncertainty
Management in National Greenhouse Gas Inventories (GPG2000) 3 to be accepted at the IPCC Plenary meeting
held in Montreal in May 2000. The Conference of the Parties (COP) to the UNFCCC as well as its Subsidiary
Body for Scientific and Technological Advice (SBSTA) subsequently endorsed 4 GPG2000. The COP has
referred extensively to GPG2000 in subsequent decisions, including those collectively referred to as the
Marrakesh Accords5, which were achieved at its seventh session. The Marrakesh Accords also invited the IPCC
to develop good practice guidance for land use, land-use change and forestry (LULUCF), which is not coveredin GPG2000. The mandate for this work, the definition of good practice in this context, its relationship to the
IPCC Guidelines, and the practical consequences for inventory agencies are described in more detail below in
Sections 1.2, 1.3, 1.4 and 1.6 respectively. Sections 1.5 and 1.7 contain an outline of the present document and a
discussion of its policy relevance.

1.2

GOOD PRACTICE GUIDANCE FOR LAND USE,
LAND-USE CHANGE AND FORESTRY(LULUCF)The GPG2000 did not cover the land-use change and forestry (LUCF) activities described in Chapter 5 of the
IPCC Guidelines6 because during the time that the GPG2000 was being prepared, the IPCC was also preparing
the Special Report on Land Use, Land-Use Change, and Forestry (SR LULUCF). Parallel work on Good
Practice Guidance for LULUCF would have carried a risk of inconsistency with the Special Report. Furthermore,significant negotiations on LULUCF were underway in the UNFCCC process, and the IPCC recognised that it
would be better to develop Good Practice Guidance for LULUCF in the light of the outcome of thesenegotiations.
The LULUCF negotiations relating to the implementation of the Kyoto Protocol were completed (except for
those relating to rules and modalities for afforestation and reforestation activities under the clean development
mechanism) during the second part of the COP6, and at COP7, which took place respectively in Bonn (July 2001)

1

Intergovernmental Panel on Climate Change (IPCC). (1997). Houghton J.T., Meira Filho L.G., Lim B., Tréanton K.,
Mamaty I., Bonduki Y., Griggs D.J. and Callander B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse
Inventories. IPCC/OECD/IEA, Paris, France.

2

Notably the Report of the Fourth Session of the Subsidiary Body for Scientific and Technological Advice(FCCC/SBSTA/1996/20), paragraph 30; decisions 2/CP.3 and 3/CP.5 (UNFCCC reporting guidelines for preparation ofnational communications by Parties included in Annex I to the Convention, part I: UNFCCC reporting guidelines onannual inventories), 18/CP.8 revising the guidelines adopted under 3/CP.5, and 17/CP.8 adopting improved guidelines forthe preparation of national communications from Parties not included in Annex I to the Convention.

3

Intergovernmental Panel on Climate Change (IPCC) (2000). Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B.,
Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., and Tanabe K. (Eds). Good Practice Guidanceand Uncertainty Management in National Greenhouse Gas Inventories. IPCC/OECD/IEA/IGES, Hayama, Japan.

4

Report of the Twelfth session of the SBSTA (FCCC/SBSTA/2000/5), paragraph 40 and decisions 3/CP.5 and 19/CP.8.

5

Decisions 1/CP.7 to 24/CP.7, decision 21/CP.7 refers specifically to the use of Good Practice Guidance in the context of
the Kyoto Protocol.

6

The IPCC Guidelines refer to Land-Use Change and Forestry (LUCF), but Land Use, Land-Use Change and Forestry(LULUCF) has become the usual term in UNFCCC negotiations and was adopted for the title of IPCC’s 2000 Special
Report on the subject. LUCF is used in this report when referring specifically to the IPCC Guidelines.

1.4

IPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

and Marrakesh (November 2001). Paragraph 3 in the Decision 11/CP.77 agreed at COP7 contains the requests to
the IPCC (see Box 1.2.1).

BOX 1.2.1
INVITATION TO THE IPCC IN THE MARRAKESH ACCORDS, DECISION 11/CP.7

The Conference of Parties…..
3. Invites the Intergovernmental Panel on Climate Change (IPCC):(a) To elaborate methods to estimate, measure, monitor, and report changes in carbon stocks andanthropogenic greenhouse gas emissions by sources and removals by sinks resulting from land use,land-use change and forestry activities under Article 3, paragraphs 3 and 4, and Articles 6 and 12of the Kyoto Protocol, on the basis of the Revised 1996 Intergovernmental Panel on Climate
Change Guidelines for National Greenhouse Gas Inventories, taking into account the present
decision (11/CP.7), and draft decision -/CMP.1 (Land use, land-use change and forestry) attachedhereto, to be submitted for consideration and possible adoption to the Conference of the Parties at
its ninth session;(b) To prepare a report on good practice guidance and uncertainty management relating to themeasurement, estimation, assessment of uncertainties, monitoring and reporting of net carbon
stock changes and anthropogenic greenhouse gas emissions by sources and removals by sinks inland use, land-use change and forestry sector, taking into consideration the present decision(11/CP.7) and draft decision -/CMP.1 (Land use, land-use change and forestry) attached hereto, tobe submitted for consideration and possible adoption to the Conference of the Parties at its ninth
session;(c) To develop definitions for direct human-induced ‘degradation’ of forests and ‘devegetation’ ofother vegetation types and methodological options to inventory and report on emissions resulting
from these activities, to be submitted for consideration and possible adoption to the Conference of
the Parties at its ninth session; and(d) To develop practicable methodologies to factor out direct human-induced changes in carbon
stocks and greenhouse gas emissions by sources and removals by sinks from changes in carbon
stocks and greenhouse gas emissions by sources and removals by sinks due to indirect humaninduced and natural effects (such as those from carbon dioxide fertilization and nitrogen
deposition), and effects due to past practices in forests (pre-reference year), to be submitted to theConference of the Parties at its tenth session.
...

The invitations in paragraphs 3(a) and 3(b) of 11/CP.7 are closely linked, and therefore the IPCC has responded
to them by producing a single report on Good Practice Guidance for LULUCF, on the basis of the IPCC
Guidelines. This single report completes the set of good practice guidance for all sectors of the IPCC Guidelines.
The first volume of the good practice guidance (GPG2000) covers other sectors of the IPCC Guidelines –namely Energy, Industrial Processes, Agriculture and Waste.
The IPCC is addressing the requests under the paragraphs 3(c) and 3(d) of 11/CP.7 separately, and this Good
Practice Guidance for LULUCF does not rely on them for its application.

7

The designation 11/CP.7 means the 11th decision adopted by the COP to the UNFCCC at its 7th session. The
designation -/CMP.1 refers to draft decisions which will be considered by the COP when it meets for the first time serving
as the Meeting of the Parties to the Kyoto Protocol.

IPCC Good Practice Guidance for LULUCF

1.5

Chapter 1: Overview

1.3

DEFINITION OF INVENTORIES CONSISTENT
WITH GOOD PRACTICE GUIDANCEGPG2000 defines inventories consistent with good practice as those which contain neither over- nor underestimates so far as can be judged, and in which uncertainties are reduced as far as is practicable8.
When applied to LULUCF, this definition from GPG2000 should ensure that estimates of carbon stock changes,
emissions by sources and removals by sinks, even if uncertain, are bona fide estimates, in the sense of notcontaining any biases that could have been identified and eliminated, and that uncertainties have been reduced as
far as practicable given national circumstances. Estimates of this type are presumably the best attainable, given
current scientific knowledge and available resources. Good practice aims to satisfy the definition by providing
guidance on:•

Choice of estimation method within the context of the IPCC Guidelines;•

Quality assurance and quality control procedures to provide cross-checks during inventory compilation;•

Data and information to be documented, archived and reported to facilitate review and assessment of
inventory estimates; and•Quantification of uncertainties at the source or sink category level and for the inventory as a whole, so that
resources available can be directed toward reducing uncertainties over time, and the improvement can betracked.

Good practice guidance further supports the development of inventories that are transparent, documented,
consistent over time, complete, comparable, assessed for uncertainties, subject to quality control and assurance,
efficient in the use of resources available to inventory agencies, and in which uncertainties are reduced as better
information becomes available.
GPG2000 introduced a method to identify the key sources that should be prioritised by using more detailed(higher tier) estimation methods where resources are available, because of their significance in affecting absolutelevel or trend in emissions, their uncertainty, or qualitative factors such as unexpectedly high or low estimates.
Chapter 5.4 of this report extends the key source analysis to LULUCF categories. The approach augments the
key source categories identified without consideration of LULUCF by those identified as key by analysis of thewhole inventory including LULUCF categories. Activities under Articles 3.3 and 3.4 of the Kyoto Protocol are
key if the associated Chapter 3 category is key, or if the effect of activities spread over several Chapter 3
categories is larger than Chapter 3 categories that are key, or on qualitative grounds. The outcome of the key
category analysis is then used in decision trees to guide the choice of estimation method for use in preparing theinventory. Figure 1.1 shows an example decision tree (the abbreviations LF, LG, LC, LW, LS and LO in Figure
1.1 are explained in the “Abbreviations and Acronyms” at the end of this report).

8

See GPG2000 Section 1.3.

1.6

IPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

Figure 1.1

Decision tree for identification of appropriate tier-level for land converted toanother land-use category (example given for land converted to forest land,
LF)

Repeat for each land use category:
- LF
- LG
- LC
- LW
- LS
- LO

Are there
any land conversions toforest land?
(Note 1)

No
Report “Not Occurring”

Yes

Repeat for each gas:
- CO2 (carbon)
- CH4
- N2O

Use tier level mostappropriate foravailable data

No

Is LF
a key category?
(Note 2)
Yes

Repeat for each sub-category*:- Biomass- Dead organic matter- SoilAsk for each
sub-category under
LF (Note 3): Is this subcategory significant?
(Note 4)

No

Are
country-specificdata available?

Yes
No

No

Yes

Are
country-specific data
available?

Develop or obtainrepresentative data
and EFs

Yes

Yes

Are
advancedmethods and detailed
data for LF available
in yourcountry?

No

Use advanced methods
and detailed countryspecific data (Note 5)

Use country-specificdata (Note 5)

Use default data
(Note 5)(Tier 3)(Tier 2)(Tier 1)

Note 1: The use of 20 years, as a threshold, is consistent with the defaults contained in the IPCC Guidelines. Countries may use different
periods where appropriate to national circumstances.
Note 2: The concept of key categories is explained in Chapter 5, Subsection 5.4 (Methodological Choice – Identification of Key
Categories).
Note 3: See Table 3.1.2 for the characterisation of sub-categories.
Note 4: A sub-category is significant if it accounts for 25-30% of emissions/removals for the overall category.
Note 5: See Box 3.1.1 for definition of Tier levels.
* If a country reports harvested wood products (HWP) as a separate pool, it should be treated as a sub-category.

IPCC Good Practice Guidance for LULUCF

1.7

Chapter 1: Overview

1.4

RELATIONSHIP TO THE IPCC GUIDELINES

As explained in the introduction, good practice guidance needs to be consistent with the IPCC Guidelines since
the Parties have agreed to use the latter for estimation of greenhouse gas emissions and removals. Good PracticeGuidance for LULUCF defines consistency with the IPCC Guidelines, using the following three criteria9:(i)

Specific source or sink categories addressed by the Good Practice Guidance for LULUCF can betraced back to categories in the IPCC Guidelines.

(ii)

Good practice guidance for LULUCF uses the same functional forms for the equations that areused in the IPCC Guidelines, or their equivalent.

(iii)

Good practice guidance for LULUCF allows corrections of any errors or deficiencies that havebeen identified in the IPCC Guidelines.

Good Practice Guidance for LULUCF has some interlinkages with GPG2000 in estimation of agriculturalemissions, particularly nitrous oxide from soils, and must maintain consistency with the advice already agreed
upon.
Good Practice Guidance for LULUCF has some additional, though limited and specific, flexibility following theconclusions of the 15th meeting of the Subsidiary Body for Scientific and Technological Advice (SBSTA), heldin association with COP7 in Marrakesh. Having noted with appreciation the progress of IPCC’s work on
LULUCF, the SBSTA:…encouraged the IPCC to ensure that any elaboration of, or change to, the reporting of
categories in Chapter 510 of the 1996 Revised IPCC Guidelines for National Greenhouse Gas
Inventories allows for a comparison of information reported using Good Practice Guidancewith previous inventory reporting under the Convention.11
SBSTA suggested this flexibility for the scientific reason that the IPCC Guidelines treat all soils as one reporting
category, which tends to separate soil organic matter from associated living biomass stocks in the inventory
calculations, leading to possible inconsistencies in the estimates due partly to different handling of categories.
This advice from SBSTA allows some rearrangement in the Good Practice Guidance for LULUCF, as long as
the ability to trace back the inventory estimates to the reporting categories in Chapter 5 of the IPCC Guidelines
is retained. The development of Good Practice Guidance for LULUCF has made use of this flexibility, while
paying careful attention to the need to ensure consistency with Chapter 5 of the IPCC Guidelines.
Criteria (i) to (iii) allow for inclusion of additional source or sink categories on managed land where these arecovered under the “Other” category of Chapter 5 of the IPCC Guidelines. Default emission or removal factors
and model parameters have been updated where these can be linked to particular national circumstances anddocumented. Advice on more complex methods than those described in the IPCC Guidelines is also provided,
since the latter anticipate use of such methods12.
Good Practice Guidance for LULUCF must also serve the needs of the Kyoto Protocol, which introduces
LULUCF activities that are a subset of the activities covered in Chapter 5 of the IPCC Guidelines. These
activities have more precise requirements on definitions, geographical reporting, carbon pools and greenhousegases to be accounted and Good Practice Guidance for LULUCF provides ways to meet these requirements.

1.5

OUTLINE OF PRESENT DOCUMENT

The chapters of the Good Practice Guidance for LULUCF are organised as follows:
Chapter 1 Overview
This Chapter sets out the mandate for Good Practice Guidance for LULUCF, defines and describes the history
of IPCC good practice guidance and its relationship to the IPCC Guidelines, summarises the practical advice
provided to inventory agencies, and discusses policy relevance.
9

GPG2000, page 1.6.

10

The Chapter 5 categories referred to are Changes in Forest and Woody Biomass Stocks (5A), Forest and Grassland
Conversion (5B), Abandonment of Managed Lands (5C), CO2 Emissions and Removals from Soil (5D) and Other (5E).

11

Report of SBSTA 15, FCCC/SBSTA/2001/8, paragraph 29(b).

12IPCC Guidelines (Reference Manual), page 5.4.

1.8

IPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

Chapter 2 Basis for consistent representation of land areas
The IPCC Guidelines contain little, if any, discussion on how to estimate land areas and changes in land area
associated with LUCF activities. In practice, countries use a variety of sources including agricultural census data,
forest inventories, and remote sensing data, but definitions that different authorities use in assembling the data
are not always consistent. Chapter 2 therefore provides advice on different approaches for representing land area
depending on the data available. The term “approach” used in Chapter 2 is distinct from the term “tier” used in
Chapters 3 to 5. The approaches are not presented as a hierarchy, although the requirements of Article 3.3 and3.4 under the Kyoto Protocol imply the need for additional supplementary spatial data if Approaches 1 or 2 areused for estimating and reporting on these activities. Using the approaches, singly or in combination, will help
ensure the reliability of the area estimates, avoid overlaps and gaps.
The discussion is in terms of six broad categories of land use namely forest land, cropland, grassland, wetlands,
settlements, and other land that provide the basis for more detailed discussion in the chapters that follow.
Unmanaged as well as managed areas are considered to help ensure consistency of area estimates, althoughemissions and removals are only estimated in respect of managed areas, as required by the IPCC Guidelines.
Chapter 3 LUCF sector good practice guidance
Chapter 3 is organised following the six broad land-use categories identified in Chapter 2. Land may remain in
any of these categories (e.g., grassland) or its use may change to another category (e.g., from forest to cropland).
Chapter 3 provides advice on the estimation of emissions and removals of CO2 and non-CO2 greenhouse gases
for both situations, taking account of the long term average carbon stocks associated with particular land uses,
and the time taken for carbon stocks to adjust to the new equilibrium following a change in land use. Chapter 3
maintains consistency with the advice in GPG2000 on estimation of nitrous oxide emissions from land. Decision
trees guide the choice of method according to national circumstances. Simple tables are provided to assist
countries with the linkage to the IPCC Guidelines and good practices on the default methods in the IPCC
Guidelines are clearly identified. There are short summary sections on forest and grassland conversion. The
chapter also provides appendices covering wetlands and settlements, for which the IPCC Guidelines provide
only limited advice and harvested wood products (HWP), which remain under consideration by the UNFCCC.
The status of the appendices is further discussed in Section 1.7.
Chapter 4 Supplementary methods and good practice guidance arising from the Kyoto Protocol
The human-induced activities agreed under Article 3.3 of the Kyoto Protocol (afforestation, reforestation anddeforestation since 1990), and the activities which Parties may elect to use under Article 3.4 (forest management,
cropland management, grazing land management, revegetation) have specific supplementary requirements on
temporal and spatial boundaries, identification of areas, avoidance of double counting, inclusion of carbon pools,
and dealing with possible definitional differences between LULUCF activities under the Kyoto Protocol andcategories under the UNFCCC reporting. These requirements imply the need for supplementary information
beyond the information reported in inventories under the Convention. Chapter 4 explains how to use the methods
described in the other chapters, and where necessary provides additional methods, to meet these supplementary
requirements. Chapter 4 also provides advice on identification of project boundaries and sampling strategies for
project activities under Articles 6 and 12 of the Kyoto Protocol. The good practice advice for LULUCF related
project activities covers only estimation of carbon stock changes and emissions and removals of greenhousegases within the project boundary; there is no consideration of non-permanence, additionality 13 , leakage,baseline definition or socio-economic and environmental impacts, because these items are under consideration
by SBSTA14.
Chapter 5 Cross-cutting issues
Inventory development is a resource-intensive enterprise, which means that inventory agencies may need toprioritise efforts to improve the estimates by focusing on the more important categories, both in terms of thecontribution made to the overall level of emissions and removals, and the contribution to the trend. Chapter 5
provides advice on this, applying the key category concept in GPG2000 to cover sinks. The Chapter also has
sections on quality assurance and quality control, reconstruction of missing data, time series consistency,
collecting and analysing data by sampling, quantification and combination of uncertainties, and verification bymeans of comparison with inventories in other countries, independently compiled datasets, modelling
approaches and direct measurements on land and/or atmosphere.

13

Whether the emission reductions or removals are additional to those which would have occurred in the absence of the
project.

14

Decision 17/CP.7 in FCCC/CP/2001/13/Add.2.

IPCC Good Practice Guidance for LULUCF

1.9

Chapter 1: Overview

Glossary
Provides definitions of technical terms commonly used in the Guidance.

1.6

USING THE GUIDANCE - PRACTICAL ADVICEFOR INVENTORY AGENCIES AND OTHERS

Practical advice for using this good practice guidance report is given below. The advice summarises how to use
the guidance in preparing inventories for submission to the UNFCCC, the additional steps relevant to Parties
reporting under Articles 3.3 and 3.4 of the Kyoto Protocol, and the use of the guidance for projects under
Articles 6 and 12 of the Kyoto Protocol.
UNFCCC Inventory preparation
Inventory agencies, when preparing the national greenhouse gas inventory for the LULUCF Sector for annual
reporting under the UNFCCC, should follow steps 1 to 6:
1.

Use the approaches in Chapter 2 (Basis for Consistent Representation of Land Areas), singly or in
combination, to estimate land areas for each land-use category relevant to the country. For each land-use
category, inventory agencies should complement the advice in Chapter 2 with the more detailed guidance in
Chapters 3 and 4 on the preparation of specific emission and removal estimates and, if relevant, thereporting on the activities under the Kyoto Protocol.

2.

Follow the good practice guidance in Chapter 3 (LUCF Sector Good Practice Guidance) to estimate theemissions and removals of greenhouse gases for each land use, land-use change and pool relevant to thecountry. The decision trees in this chapter guide choices of method in terms of tiers. The tier structure usedin the IPCC Guidelines (Tier 1, Tier 2 and Tier 3) is hierarchical, with higher tiers implying increased
accuracy of the method and/or emissions factor and other parameters used in the estimation of the emissions
and removals. Key categories should be identified following the guidance in Chapter 5 and the results taken
into account in the application of the decision trees.

3.

If necessary, in some cases, collect additional data (if required to implement a particular tier) to improve
emission factors, other parameters and activity data.

4.

Estimate uncertainties at the 95% confidence level, using sectoral advice and the detailed guidance in
Chapter 5.

5.

Report the emissions and removals in the reporting tables provided in Chapter 3 Annex 3A.2 taking into
account any modifications by SBSTA15 and any additional information as specified under each category.

6.

Implement QA/QC procedures as described in the generic guidance in Chapter 5 and specific advice undereach category, including documentation and archiving of the information used to produce the nationalemission and removal estimates.

Kyoto Protocol requirements
Inventory agencies, when preparing the supplementary information for annual reporting of carbon stock changes
and emissions and removals of greenhouse gas emissions resulting from the activities under Article 3.3 andArticle 3.4 of the Kyoto Protocol, should additionally:
7.

Assess the extent to which the data assembled for the existing national inventory (following steps 1 to 6
above) can meet the supplementary data requirements set out in the supplementary guidance provided in
Chapter 4 of this report, taking into account national choices on definitions and activities elected under
Article 3.4, and the requirements in geographical location.

8.

Following this assessment collect or collate any additional information necessary to meet the supplementary
data requirements, using the advice in Chapter 4 and the references it contains to other Chapters.

9.

Follow the advice in Chapter 4 on reporting and documentation when providing the supplementary
information in the national inventory report.

National circumstances will determine the sequence in which the reporting information is compiled. For example,
it is possible to start with the UNFCCC inventory (with the additional spatial information required for Kyoto
Protocol reporting) and expand it to the reporting under the Kyoto Protocol, or it is possible to use a system that
15

SBSTA 18 requested the UNFCCC secretariat to develop common reporting format for its consideration, in consultation
with IPCC - see paragraph 2 in FCCC/SBSTA/2003/10.

1.10

IPCC Good Practice Guidance for LULUCF

Chapter 1: Overview

generates the information for both UNFCCC and Kyoto Protocol reporting. The precise sequence of steps 1 to 6
and 7 to 9 does not matter as long as the substance is covered.
Projects
Project participants, independent entities and operational entities should use the advice in Chapter 4, Section 4.3,
as needed, in the overall context of relevant decisions of the COP, when designing, validating and verifying
methods to measure and monitor changes in carbon stocks and non-CO2 greenhouse gases associated withprojects activities.

1.7

POLICY RELEVANCE

This Overview and Chapters 2, 3 and 5 are relevant to all countries as they prepare estimates of emissions andremovals from the LULUCF Sector, whether or not they ratify the Kyoto Protocol. The first two sections of
Chapter 4 provide supplementary information to that in Chapters 2, 3 and 5, which is relevant only to Annex I
countries that have ratified the Kyoto Protocol. Section 4.3 (LULUCF Projects) is relevant to all countries thatwill undertake projects under the Articles 6 or 12 of the Kyoto Protocol.
While many categories within the LULUCF sector are well established and relatively straightforward to estimate,
LULUCF is a complex area, and it was clear from the outset that some issues remain under consideration forsome emission/removal categories. In particular:•

SBSTA has set out a policy process on harvested wood products (HWP) accounting and reporting that may
lead to decisions by the COP and/or COP/MOP16. However, although the default assumption is that HWP
pools are not increasing, the IPCC Guidelines allow inclusion of HWP in national inventories if a country
can document that existing stocks of long-term forest products are increasing. Good practice guidance has
therefore been elaborated for the HWP pool. The material provided is in an appendix rather than part of themain text, since SBSTA is still considering this issue. The appendix makes no judgement about possible
future decisions on reporting or accounting.

•Settlements and wetlands are land-use categories for which limited methodological guidance was provided
in the IPCC Guidelines, but a great deal of scientific work has been done since these Guidelines werecompleted in 1996. This applies also to non-CO2 emissions from drainage and rewetting of forests soils. For
these categories and sources, the IPCC determined that good practice guidance reflecting the newer
scientific information should be developed, but that it should be presented in an appendix to indicate its
preliminary nature. The main text on these sections provided sufficient advice to estimate the contributionthat conversions to these categories make to national inventories.

Countries do not have to prepare estimates for categories contained in appendices, although they can do so ifthey desire. The IPCC intends this approach to reflect the prevailing scientific and policy contexts, in a mannerthat provides useful information to countries as they prepare their inventories while recognising that it is the
COP’s role to establish general guidelines for inventory reporting and accounting in the UNFCCC context.
•

The IPCC Guidelines do not explicitly include losses from natural disturbances in managed forests althoughomitting the effect of these disturbances would overestimate carbon uptakes as calculated by themethodology in the Guidelines. Good Practice Guidance therefore provides guidance on how to account forthem.

For Kyoto Protocol reporting, Chapter 4 is intended to provide policy-neutral scientific operationalisation of the
COP7 agreement in terms of annual reporting17. In some cases this has required judgement. In particular:•

In the treatment of the geographical identification issue the phrase The geographical location of theboundaries of the areas that encompass18 is interpreted as consistent with either a sampling approach withina geographical boundary, or complete enumeration of units of area subject to the carbon stock changes andemissions or removals of greenhouse gases due to the activities to be reported.

16

Conclusions related to emissions from forest harvesting and wood products (Report of the fifteenth session of SBSTA, heldat Marrakesh from 29 October to 6 November 2001, paragraph 29(m), page 14). The COP/MOP is the Conference ofParties to the UNFCCC serving as the Meeting of Parties to the Kyoto Protocol.

17

The terms estimation, reporting and accounting have distinct meanings. Estimation is the process of calculating emissions,and reporting the process of providing the estimates to the UNFCCC. Accounting refers to the rules for comparing
emissions and removals as reported with commitments. GPG2000 and this report deal with estimation and reporting issues,
but not accounting for which detailed rules have been established under the Marrakesh Accords.

18

FCCC/CP/2001/13/Add.3, page 22, paragraph 6(a).

IPCC Good Practice Guidance for LULUCF

1.11

Chapter 1: Overview•

The use of the key category concept and the choice of methodology in relationship to Articles 3.3 and 3.4
activities has been developed in a logical fashion as described in Section 1.3 above, but would not pre-empt
any decision as to whether all activities under Articles 3.3 or 3.4 should be treated as key.

•

Although it is good practice for Article 3.4 activities to match the dominant land use, in some cases (e.g.,agroforestry systems) land could fall under either forest management (which is limited by capping) or
cropland/grazing land management (which is subject to net-net accounting). In such cases Good PracticeGuidance for LULUCF suggests that countries should establish national criteria to be applied consistently
over time.

•

Net-net accounting is taken to require comparison between emissions and removals from the elected
activities in the base year and the commitment period, which could lead to comparison of areas that differ insize. Alternative approaches, where areas are changing, would be to normalise to constant area, or maintain
constant area over time, possibly the base year area – though this third approach would bring in effects ofactivities not covered by the Marrakesh Accords, and could increase uncertainties by making the estimation
more complex.

Elaboration of the Marrakesh Accords decision on these (or indeed any other matter) would be for the COP;
however, the IPCC believes that the interpretations should be acceptable because of the review process andbecause throughout the development of this report, the IPCC has maintained contact with the Convention
process via formal reporting of progress at SBSTA, side events, and attendance at workshops. The developmentof Good Practice Guidance for LULUCF is a step in the IPCC’s on-going programme of inventory development
and will also support future revisions of the IPCC Guidelines themselves.

1.12

IPCC Good Practice Guidance for LULUCF

2

BASIS FOR CONSISTENTREPRESENTATION OF LAND AREAS

IPCC Good Practice Guidance for LULUCF

Chapter 2: Basis for Consistent Representation of Land Areas

AUTHORS AND REVIEW EDITORS
Co-ordinating Lead Authors
Ronnie Milne (UK) and Bubu Pateh Jallow (The Gambia)

Lead Authors
Dominique Arrouays (France), Peter Beets (New Zealand), Paul Drichi (Uganda), Ismail Bin Harun (Malaysia),
James Hrubovcak (USA), Ted Huffman (Canada), William Irving (USA), Michael Koehl (Germany), Erda Lin(China), Lennart Olsson (Sweden), Jim Penman (UK), Ryosuke Shibasaki (Japan), Brian Turner (Australia),
Julio C.Vargas (Ecuador) and Ernesto F. Viglizzo (Argentina).

Contributing Author
Ralph Alig (USA)

Review Editors
Mike Apps (Canada) and Jose Domingo Miguez (Brazil)

2.2

IPCC Good Practice Guidance for LULUCF

Contents

Contents
2.1

INTRODUCTION

2.5

2.2

LAND-USE CATEGORIES

2.5

2.3

REPRESENTING LAND AREAS

2.7

2.3.1

Introduction ..................................................................................................................................... 2.7

2.3.2

Three Approaches ........................................................................................................................... 2.7

2.3.2.1 Approach 1: Basic land-use data ............................................................................................... 2.7
2.3.2.2 Approach 2: Survey of land use and land-use change .............................................................. 2.9
2.3.2.3 Approach 3: Geographically explicit land use data ................................................................. 2.12
2.3.3

Using the Approaches ................................................................................................................... 2.14

2.3.4

Uncertainties Associated with the Approaches ............................................................................. 2.17

2.4

DEVELOPMENT OF LAND-USE DATABASES

2.18

2.4.1

Use of Data Prepared for Other Purposes ..................................................................................... 2.18

2.4.2

Collection of New Data by Sampling Methods............................................................................. 2.19

2.4.3

Collection of New Data in Complete Inventories ......................................................................... 2.19

2.4.4

Tools for Data Collection.............................................................................................................. 2.19

2.4.4.1 Remote sensing (RS) techniques ............................................................................................. 2.19
2.4.4.2 Ground-based surveys ............................................................................................................. 2.21
Annex 2A.1

Examples of Approaches in Individual Countries

2.23

Annex 2A.2

Examples of International Land-Cover Datasets

2.27

References

2.29

IPCC Good Practice Guidance for LULUCF

2.3

Chapter 2: Basis for Consistent Representation of Land Areas

FiguresFigure 2.3.1

Overview of Approach 3: Direct and repeated assessments of land use from full
spatial coverage. ............................................................................................................ 2.12

Figure 2.3.2

Decision tree for use of existing data in the land area approaches ................................ 2.15

Figure 2.3.3

Decision tree for choosing land area approach for countries with no existing data…….2.16

Figure 2A.1.1 Stages in preparation of New Zealand Land-Cover Databases ..................................... 2.25

TablesTable 2.3.1

Example of Approach 1: Available Land-Use data with complete territorial coverage .... 2.8

Table 2.3.2

Illustrative example of sub-division of data for Approach 1 .......................................... 2.9

Table 2.3.3

Illustrative example of tabulating all transitions for Approach 2 including
nationally defined sub-categories ..............................................................................

2.10

Table 2.3.4

Illustrative example of Approach 2 data in a LUC matrix with category subdivisions. 2.11

Table 2.3.5

Simplified land-use change matrix for example Approach 2 ........................................ 2.11

Table 2.3.6

Summary of uncertainties under Approaches 1 to 3...................................................... 2.17

Table 2A.1.1 Land Use and Land-Use change matrix for USA .......................................................... 2.23
Table 2A.1.2 Land-Use change matrix for Scotland between 1984 and 1990 .................................... 2.24

2.4

IPCC Good Practice Guidance for LULUCF

Land-Use Categories

2.1

INTRODUCTION

Information about land area is needed to estimate carbon stocks and emissions and removals of greenhouse gases
associated with Land Use, Land-Use Change and Forestry (LULUCF) activities. This chapter seeks to provide
guidance on the selection of suitable methods for identifying and representing land areas as consistently as
possible in inventory calculations.
In practice, countries use methods including annual census, periodic surveys and remote sensing to obtain area
data. Starting from this position, Chapter 2 provides good practice guidance on three approaches for representing
land area. The approaches are intended to provide the area data specified in Chapters 3 and 4 for estimating andreporting greenhouse gas inventories for different categories of land. The approaches are also intended to makethe best use of available data and models, and to reduce, as far as practicable, possible overlaps and omissions inreporting land areas. The approaches described here should minimize the chance that some areas of land appear
under more than one activity whilst others are overlooked. The approaches and guidance presented here allow
informed decisions on these matters to be made by those preparing greenhouse gas inventories but are notintended to be definitive or exhaustive. Good practice approaches for representing areas should have thefollowing general characteristics:•

Firstly, the approaches should be adequate, i.e., capable of representing carbon stock changes andgreenhouse gas emissions and removals and the relations between these and land use and land-use changes.

•

Secondly, they should be consistent, i.e., capable of representing management and land-use change
consistently over time, without being unduly affected either by artificial discontinuities in time series data or
by effects due to interference of sampling data with rotational or cyclical patterns of land use (e.g., theharvest-regrowth cycle in forestry, or managed cycles of tillage intensity in cropland).

•Thirdly, the approaches should be complete, which means that all land area within a country should be
included, with increases in some areas balanced by decreases in others where this occurs in reality, andshould recognise subsets of land used for estimation and reporting according to definitions agreed in the
Marrakesh Accords for Parties to the Kyoto Protocol.

•

Finally, the approaches should be transparent, i.e., data sources, definitions, methodologies and assumptions
should be clearly described.

2.2

LAND-USE CATEGORIES

Six broad categories1 of land are described in this section. These may be considered as top-level categories forrepresenting land areas within a country. The categories are consistent with the IPCC Guidelines and therequirements of Articles 3.3 and 3.4 of the Kyoto Protocol, and may be further subdivided as described in
Chapters 3 and 4 of this report. The categories are broad enough to classify all land areas in most countries andto accommodate differences in national classification systems. These national classification systems should be
used consistently over time. The categories are intended for use in conjunction with the approaches described insubsequent sections of this chapter to facilitate consistent estimation of land use over time. This does not imply
that carbon stock changes or greenhouse gas emissions and removals need be estimated or reported for areas
where this is not required by the IPCC Guidelines or for some countries, the Marrakesh Accords2.
It is recognized that the names of these land categories are a mixture of land cover (e.g., Forest land, Grassland,
Wetlands) and land use (e.g., Cropland, Settlements) classes. For convenience, they are here referred to as landuse categories. These particular categories have been selected because they are:•Reasonably consistent with the IPCC Guidelines;•

Robust as a basis for carbon estimation;•

Reasonably mappable by remote sensing methods; and1

The basic categories are generally consistent with on-going work on harmonizing forest-related definitions by Food andAgriculture Organisation (FAO), IPCC, International Union of Forestry Research Organizations (IUFRO) and Centre forInternational Forestry Research (CIFOR) (FAO 2002), with definitions for forestry and other land use types by the UnitedStates Geological Survey (USGS (2001)), FAO (1986, 1995) described by IPCC (2000), and with the definitions adopted
for land use under the Kyoto Protocol and Marrakesh Accords (FCCC/CP/2001/13/Add.1, p58).

2 Carbon stock changes and greenhouse gas emissions on unmanaged land are not reported under the IPCC Guidelines,
although reporting is required when unmanaged land is subject to land use conversion.

IPCC Good Practice Guidance for LULUCF

2.5

Chapter 2: Basis for Consistent Representation of Land Areas•

Complete in that all land areas should be represented in one or another category.

Care will be needed in inferring land use from these categories. For example, in some countries significant areasof the forest land category may be grazed, and firewood may be collected from scattered trees in the grasslandcategory lands. These areas with different use may be significant enough for countries to consider themseparately in which case it is good practice to make these additional classes subcategories of the suggested highlevel categories and to ensure that all land is accounted for.
Countries will use their own definitions of these categories, which may, of course, refer to internationallyaccepted definitions, such as those by FAO, Ramsar, etc. For that reason no definitions are given here beyond
broad descriptions. Managed land may be distinguished from that unmanaged by fulfilling not only the
production but also ecological and social functions. The detailed definitions and the national approach todistinguishing between unmanaged and managed land should be described in a transparent manner.
The top-level land categories for greenhouse gas (GHG) inventory reporting are:(i) Forest landThis category includes all land with woody vegetation consistent with thresholds used to define forest land in thenational GHG inventory, sub-divided into managed and unmanaged, and also by ecosystem type as specified in
the IPCC Guidelines3. It also includes systems with vegetation that currently fall below, but are expected to
exceed, the threshold of the forest land category.
(ii) CroplandThis category includes arable and tillage land, and agro-forestry systems where vegetation falls below thethresholds used for the forest land category, consistent with the selection of national definitions.
(iii) Grassland
This category includes rangelands and pasture land that is not considered as cropland. It also includes systems
with vegetation that fall below the threshold used in the forest land category and are not expected to exceed,
without human intervention, the threshold used in the forest land category. The category also includes all
grassland from wild lands to recreational areas as well as agricultural and silvi-pastural systems, subdivided into
managed and unmanaged consistent with national definitions.
(iv) Wetlands
This category includes land that is covered or saturated by water for all or part of the year (e.g., peatland) andthat does not fall into the forest land, cropland, grassland or settlements categories. The category can be
subdivided into managed and unmanaged according to national definitions. It includes reservoirs as a managed
sub-division and natural rivers and lakes as unmanaged sub-divisions.
(v) Settlements
This category includes all developed land, including transportation infrastructure and human settlements of any
size, unless they are already included under other categories. This should be consistent with the selection ofnational definitions.
(vi) Other land 4
This category includes bare soil, rock, ice, and all unmanaged land areas that do not fall into any of the other fivecategories. It allows the total of identified land areas to match the national area, where data are available.
When applying these categories, inventory agencies should classify land under only one category to prevent
double counting. If a country's land classification system does not match categories (i) to (vi) as described above,it is good practice to combine or disaggregate the existing land classes of this system of land-use classification inorder to use the categories presented here, and to report on the procedure adopted. It is also good practice tospecify national definitions for all categories used in the inventory and report any threshold or parameter values
used in the definitions. Where national land classification systems are being changed or developed for the firsttime, it is good practice to ensure their compatibility with land-use classes (i) to (vi).
The broad categories listed above provide the framework for the further sub-division by activity, management
regime, climatic zone and ecosystem type as necessary to meet the needs of the methods for assessing carbon
stock changes and greenhouse gas emissions and removals described in Chapter 3 (LUCF Sector Good Practice3

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

4Carbon pools would not need to be assessed for this category, but it is included for checking overall consistency of land area.

2.6

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Guidance) and Chapter 4 (Supplementary Methods and Good Practice Guidance arising from the Kyoto Protocol)
and allows comparison with IPCC Guidelines categories 5A to 5E. Section 3.1.2 and Table 3.1.1 (Mapping
between the sections of Chapter 5 of the IPCC Guidelines and the sections of Chapter 3 of this report) describe
how to relate the structure of methods described in this report to those of the IPCC Guidelines.

2.3

REPRESENTING LAND AREAS

2.3.1

Introduction

This section describes three approaches for representing land areas using the broad categories defined in theprevious section. They are presented below in order of increasing information content. Approach 1 identifies thetotal area for each individual land-use category, but does not provide detailed information on changes of area
between categories and is not spatially explicit other than at the national or regional level. Approach 2 introduces
tracking of land-use changes between categories. Approach 3 extends Approach 2 by allowing land-use changes
to be tracked on a spatial basis.
The approaches are not presented as hierarchical tiers; they are not mutually exclusive, and the mix ofapproaches selected by an inventory agency should reflect calculation needs and national circumstances. One
approach may be applied uniformly to all areas and land-use categories within a country, or different approaches
may be applied to different regions or categories or in different time intervals. In all cases, it is good practice tocharacterise and account for all relevant land areas in a country. Using good practice in the application of any of
the approaches will increase accuracy and precision in area estimation for inventory purposes. Decision trees toassist in selecting an appropriate approach or mix of approaches are given in Section 2.3.3 (Using the Approaches).
All approaches require collection of data for estimating the historical trends in land use, which are needed for theinventory methods described in the IPCC Guidelines and Chapters 3 and 4 of this report. The amount ofhistorical data required will be based on the amount of time needed for stored carbon to reach equilibrium (often
20 years in the IPCC default methods, but longer for temperate and boreal systems). Where independent data are
available, it is good practice to verify estimates based on interpolation or extrapolation using the methods set out
in Chapter 5, Section 5.7 of this report. All approaches are capable of producing input to uncertainty calculations
discussed in Chapter 5 (Cross-cutting Issues).
A hypothetical example of each approach is provided below along with the description, and real-world examples
are provided in Annex 2A.1.

2.3.2
2.3.2.1

Three Approaches
A PPROACH 1: B ASIC LAND - USE DATA

Approach 1 is probably the most common approach used at present for preparing estimates of emissions andremovals under IPCC Guidelines categories 5A-5E. It uses area datasets likely to have been prepared for otherpurposes such as forestry or agricultural statistics. Frequently, several datasets will be combined to cover all land
classifications and regions of a country. The absence of a unified data system can lead to double counting oromission, since the agencies involved may use different definitions of specific land use for assembling their
databases. This report suggests ways to deal with this. Coverage must obviously be complete enough to include
all land areas affected by the activities set out in Chapter 5 of the IPCC Guidelines, but might not extend tocategories such as unmanaged ecosystems, wetlands or settlements.
When implementing Approach 1, it is good practice to:•Harmonise definitions between the existing independent databases and also with the broad land-use
categories of Section 2.2 (Land-Use Categories) to minimise gaps and overlaps. For example, if woodlandon farms were included both in forestry and agricultural datasets, overlaps might occur. In order toharmonise data, the woodland should be counted only once for greenhouse gas inventory purposes, taking
into account the forest definitions adopted nationally. Information on possible overlaps for the purposes ofharmonisation should be available from agencies responsible for surveys. Harmonisation of definitions does
not mean that agencies should abandon definitions that are of use to them. It is consistent with good practiceto establish the relationship between definitions in use with the aim of eliminating double counting andomissions. This should be done throughout the dataset to maintain time series consistency.

IPCC Good Practice Guidance for LULUCF

2.7

Chapter 2: Basis for Consistent Representation of Land Areas•

Ensure that the land-use categories used can identify all relevant activities. For example, if a country needs
to track a land-use activity such as forest management, then the classification system should be able todistinguish managed from unmanaged forest areas.

•

Ensure that data acquisition methods are reliable, well documented methodologically, timely, at an
appropriate scale, and from reputable sources. Reliability can be achieved by using surveys that can be
related to the harmonised definitions. Ground surveys can be cross-checked where independent data sources
are available and will be needed for checking the accuracy of remote sensing data, where used (See Chapter
5.7-Verification). International datasets are also available for cross-checking (see Annex 2A.2).

•

Ensure the consistent application of category definitions between time periods. For example, countries
should check whether the definition of forest has changed over time in terms of canopy cover and otherthresholds. If changes are identified, it is good practice to correct the data using the back-casting methods
described in Chapter 5 of this report to ensure consistency throughout the time series, and report on actions
taken.

•

Construct uncertainty estimates for those land category areas and changes in area that will be used in theestimation of carbon stock changes, emissions and removals (see Chapter 5 Section 5.3.4.1).

•

Assess whether the sum of the areas in the land classification databases is consistent with the total territorialarea, given the level of data uncertainty. If coverage is complete, then the net sum of all the changes
between two time periods should be zero to within the uncertainties involved. In cases where coverage isincomplete, the difference between the area covered and the territorial area should, in general, be stable orvary slowly with time, again to within the uncertainties expected in the data. If the balancing term varies
rapidly, or (in the case of complete coverage) sums are not equal, it is good practice to investigate, explain,
and make any corrections necessary. These checks on the total area should take into account the expected
uncertainties in the annual or periodic surveys or censuses involved. Information on expected uncertainties
should be obtained from the agencies responsible for the surveys. Usually there will be remaining
differences between the sum of areas accounted for by the available data and the national area. It is goodpractice to keep track of these differences and to provide an explanation for the likely causes. Carbon stockchanges and emissions and removals of greenhouse gases implied by variation through time of thesedifferences may be due to land-use change and may therefore need to be accounted for in the GHG
inventory as required by the methods set out in Chapters 3 and 4.

Tables 2.3.1 and 2.3.2 show summary land area data for a hypothetical country (total area 140 Mha) using
locally relevant land classifications. Table 2.3.1 is prepared at the level of categories (i) to (vi) and Table 2.3.2
depicts the same information with example subdivisions to estimate the effect of various activities using themethods in Chapter 3. Table 2.3.2 also indicates where in Chapter 3 the inventory methods can be found. It isgood practice to prepare tables similar to Table 2.3.1 or 2.3.2 as part of the quality assurance and quality control(QA/QC) procedures as set out in Chapter 5.

TABLE 2.3.1
EXAMPLE OF APPROACH 1:
AVAILABLE LAND -USE DATA WITH COMPLETE TERRITORIAL COVERAGE
Time 1Land-Use Change
between Time 1 and Time 2

Time 2

F

=

18

F

=

19

Forest

=

+1

G

=

84

G

=

82

Grassland

=

-2

C

=

31

C

=

29

Cropland

=

-2

W

=

0

W

=

0

Wetlands

=

0

S

=

5

S

=

8

Settlements

=

+3

O

=

2

O

=

2

Other land

=

0

Sum

=

140

Sum

=

140

Sum

=

0

Note: F = Forest land, G = Grassland, C = Cropland, W = Wetlands, S = Settlements, O = Other land. Numbers
represent area units (Mha in this example).

2.8

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

TABLE 2.3.2
ILLUSTRATIVE EXAMPLE OF SUB-DIVISION OF DATA FOR APPROACH 1

Land-Use Category
Land-Use Subcategory

Initial
land
area
Mha

Forest land total
Forest land
(Unmanaged)

Final
Net Good practice Guidance
Methods Section
Comment on subdivision by activity
land Change(illustrative only)
area in area Number in Chapter 3of this Report
Mha Mha

18

19

1

5

5

0

Forest land zone A(with deforestation)

7

4

-3

3.2.1/3.4.2/3.6

Forest land zone B

6

6

0

3.2.1

No LUC. Could require subdivision fordifferent management regimes etc.

Afforestation

0

4

4

3.2.2

Could require subdivision e.g. byecosystem type

84

82

-2

Unimproved grassland

65

63

-2

3.4.1/3.2.2/3.6

Fall in area indicates LUC. Could
require subdivision for different
management regimes etc.

Improved grassland

19

19

0

3.4.1

31

29

-2

31

29

-2

Wetlands total

0

0

0

Settlements total

Grassland total

Cropland total
All CroplandNot included in the inventory estimates

3.3.1/3.2.2/3.6

5

8

3

Existing Settlements

5

5

0

3.6

New Settlements

0

3

3

3.6

2

2

0

3.7.1

Other land total
Balancing term

0

0

0

TOTAL

140

140

0

No LUC. Could require subdivision fordifferent management regimes etc.
Fall in area indicates LUC. Could
require subdivision for different
management regimes etc.

Unmanaged - not in inventory
estimates

Note: “Initial” is the category at a time previous to the date for which the assessment is made and “Final” is the category at the date ofassessment. Activities for which location data are not available should be identified by further sub-division of an appropriate Land
Category.

Determination of the area of land-use change in each category is based on the difference in area at two points intime, either with partial or full land area coverage. No specification of inter-category changes is possible under
Approach 1 unless supplementary data are available (which would of course introduce a mix with Approach 2).
The land-use distribution data may come originally from sample survey data, maps or censuses (such as
landowner surveys), but will probably not be spatially explicit 5 in the form used. The sum of all land-use
categories may not equal the total area of the country or region under consideration, and the net result of landuse changes may not equal zero. The final result of this approach is a table of land use at given points in time.

2.3.2.2

A PPROACH 2: S URVEY OF LAND USE AND LAND - USE
CHANGEThe essential feature of Approach 2 is that it provides a national or regional-scale assessment of not only thelosses or gains in the area of specific land categories but what these changes represent (i.e., changes from and toa category). Thus, Approach 2 includes more information on changes between categories. Tracking land-use
changes in this explicit manner will normally require estimation of initial and final land-use categories, as well5

When considering the possibility of adopting Approach 2 or 3, it is useful to investigate with the data collection agencies
whether the original data sources contain spatially explicit data. For example, forest inventories are usually derived fromspatially explicit data sources.

IPCC Good Practice Guidance for LULUCF

2.9

Chapter 2: Basis for Consistent Representation of Land Areas

as of total area of unchanged land by category. The final result of this approach can be presented as a nonspatially explicit land-use change matrix. The matrix form is a compact format for representing the areas thathave come under different transitions between all possible land-use categories. Existing land-use databases may
have sufficient detail for this approach, or it may be necessary to obtain data through sampling. The input data
may or may not have originally been spatially explicit (i.e., mapped or otherwise geographically referenced).
Sample data will be extrapolated using the ratio to the total relevant area or the total relevant population. Data
will require periodic re-survey of a statistically and spatially valid sample of sites chosen according to theprinciples set out in Section 5.3 (Sampling) of Chapter 5.
Although Approach 2 is more data intensive than Approach 1, it can account for all land-use transitions. Thismeans that emission and removal factors or parameters for rate of change of carbon can be chosen to reflect
differences in the rate of changes in carbon in the opposing directions of transitions between any two categories,
and differences in initial carbon stocks associated with different land uses can be taken into account. For
example, the rate of soil organic carbon loss will commonly be much higher through ploughing than the rate ofre-accumulation if cultivation is subsequently abandoned, and initial carbon stocks may be lower for transitions
from cropland than from pasture.
Good practice points described for Approach 1 also apply to Approach 2, although at a greater level of detail,since the pattern of land-use change is available, not just the net change into or out of each land category or
subcategory.
Approach 2 is illustrated in Table 2.3.3 using the data from the Approach 1 example (Table 2.3.2) by adding
information on all the transitions taking place. Such data can be written in the more compact form of a matrix
and this is presented in Table 2.3.4. To illustrate the added value of Approach 2 and this land-use change matrix
format, the data of Table 2.3.4 is given in Table 2.3.5 without the subdivision of the land-use categories and thiscan be compared with the more limited information from Approach 1 in Table 2.3.1. In Table 2.3.5, the changes
into and out of land categories can be tracked, whereas in Table 2.3.1 only the net changes in a broad category
are detectable. When using Approach 2, it is good practice to prepare a table like Table 2.3.4 or 2.3.5 as part of
QA/QC procedures as set out in Chapter 5.
TABLE 2.3.3
ILLUSTRATIVE EXAMPLE OF TABULATING ALL TRANSITIONS FOR APPROACH 2
INCLUDING NATIONALLY DEFINED SUB-CATEGORIES
Initial Land Use

Final Land Use

Forest land (Unmanaged)

Forest land (Unmanaged)

5Good Practice Guidance Methods
Section No. in Chapter 3 of thisReportExcluded from GHG inventory
3.2.1

Forest land (Managed)

Land Area Mha

Forest land(Managed)

10(Forest zone A Table 2.3.2)

4(Forest zone B Table 2.3.2)

6

Forest land (Managed)

Grassland (Rough grazing)

2

3.4.2

Forest land (Managed)

Settlements

1

3.6

Grassland (Rough grazing)

Grassland (Rough grazing)

56

3.4.1

Grassland (Rough grazing)

Grassland (Improved)

2

3.4.1

Grassland (Rough grazing)

Forest land (Managed)

1

3.2.2

Grassland (Rough grazing)

Settlements

1

3.6

Grassland (Improved)

Grassland (Improved)

22

3.4.1

Grassland (Improved)

Forest land (Managed)

2

3.2.2

CroplandCropland

29

3.3.1

Cropland

Forest land (Managed)

1

3.2.2

CroplandSettlements

1

3.6

Wetlands

Wetlands

0

Settlements

Settlements

5

3.6

Other land

Other land

2

Excluded from GHG inventory

TOTAL

140

Note: Data are subdivided version of those in Table 2.3.2. Sub-categories are nationally defined and are illustrative only. “Initial”
indicates the category at a time previous to the date for which the assessment is made and “Final” the category at the date of assessment.

2.10

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

TABLE 2.3.4
ILLUSTRATIVE EXAMPLE OF APPROACH 2 DATA IN A LUC MATRIX WITH CATEGORY SUBDIVISIONS

Final

Initial Forest land Forest land
(Unmanaged) (Managed)

Forest land
(Unmanaged)
Forest land
(Managed)

Grassland
Grassland
Other(RoughCropland Wetlands Settlements(Improved)
land
grazing)

5

Final area
5

Grassland(Rough grazing)

10

1

2

56

Grassland(Improved)
Cropland

2

1

14
58

2

22

24
29

29
0

Wetlands
1

Settlements

1

0

1

5

Other land

822

Initial area

5

13

60

24

31

0

5

2

140

NET change

0

+1

-2

0

-2

0

+3

0

0

Note: Column and row totals show net changes in land use as presented in Table 2.3.2 but subdivided into national subcategories as in Table 2.3.3.
“Initial” indicates the category at a time previous to the date for which the assessment is made and “Final” the category at the date of assessment. Netchanges (bottom row) are the final area minus the initial area for each of the (sub) categories shown at the head of the corresponding column. Blank
entry indicates no land-use change for this transition.

TABLE 2.3.5
SIMPLIFIED LAND-USE CHANGE MATRIX FOR EXAMPLE APPROACH 2
Land-Use Change Matrix
Initial
Final

F

G

C
1

F

15

3

G

2

80

C

W

S

O

Final sum
19
82
29

29

W
S

1

1

1

5

18

84

31

5

O
Initial sum

8
2

2

2

140

Note:
F = Forest land, G = Grassland, C = Cropland,
W = Wetlands,
S = Settlements, O = Other land
Numbers represent area units (Mha in this example).
There is no Wetlands in this example. Blank entry indicates no land use change.

Further subcategorisations, for example by forest species or combinations of species and soil type, are likely tobe required by many countries when they implement this Approach, in order to provide data on the land areas
needed for estimating carbon stock changes taking account of the guidance in Chapter 3. Table 2.3.3 illustrates
possible subdivisions, and indicates where in Chapter 3 to find methodological guidance on particular land usesor transitions.

IPCC Good Practice Guidance for LULUCF

2.11

Chapter 2: Basis for Consistent Representation of Land Areas

2.3.2.3

APPROACH 3: GEOGRAPHICALLY EXPLICIT LAND USE DATA

Approach 3 (summarised in Figure 2.3.1) requires spatially explicit observations of land use and land-use change.
The data may be obtained either by sampling of geographically located points, a complete tally (wall-to-wall
mapping), or a combination of the two.
Approach 3 is comprehensive and relatively simple conceptually but data intensive to implement. The target area
is subdivided into spatial units such as grid cells or polygons appropriate to the scale of land-use variation andthe unit size required for sampling or complete enumeration. The spatial units must be used consistently overtime or bias will be introduced into the sampling. The spatial units should be sampled using pre-existing map
data (usually within a Geographic Information System (GIS)) and/or in the field and the land uses should beobserved or inferred and recorded at the time intervals required by Chapter 3 or 4 methods. If wall-to-wall
mapping is used, a polygon based approach can be used equivalently to a grid approach, see Figure 2.3.1.
Observations may be from remote sensing, site visits, oral interviews, or questionnaires. Sampling units may bepoints, or areas from 0.1 ha to a square kilometre or more, depending on the sample design. Units can be
sampled statistically on a sparser interval than would be used for the complete coverage, chosen at regular or
irregular intervals, and can be concentrated in areas where land-use change is expected. Recorded data could beof land use at a point or within a sampling unit on each occasion but could also include land-use change data
within a sampling unit between the sampling years.
For effective implementation of Approach 3, the sampling needs to be sufficient to allow spatial interpolationand thus production of a map of land use. Sampling methods and associated uncertainties are discussed in thesampling section of Chapter 5 (Section 5.3). All LULUCF activities in each spatial unit or collection of the units
are then tracked over time (periodically but not necessarily annually) and recorded individually, usually within a
GIS. Because Approach 3 is similar to Approach 2, summary Table 2.3.4 or 2.3.5 as described under Approach 2
should be prepared for this approach as part of QA/QC procedures as set out in Chapter 5.

Figure 2.3.1

Overview of Approach 3: Direct and repeated assessments of land use fromfull spatial coverage

Description
Under Approach 3 the country is subdivided into spatial units such as grid cells or small polygons. In thisexample grid cells are used for subdivision of the area. The grid cells are sampled by remote sensing andground survey, in order to establish the areas of the land use whose estimated extent is shown by the greylines below the grid. Remote sensing enables complete coverage of all grid cells (Figure 2.3.1A) in theinterpretation of land use. Ground surveys will be carried out in a sample of grid cells and can be used toestablish land use directly as well as to help interpret remote sensed data. The sample of grid cells can be
distributed regularly (Figure 2.3.1B) or irregularly (Figure 2.3.1C), for example, to give greater coverage
where LUC is more likely. Maps can be prepared using the grid cells, which can also be aggregated into
polygons (Figure 2.3.1D). The final result of the approach is a spatially explicit land-use change matrix.

Time 1Time 2

Figure 2.3.1A

G
G
G
G

2.12

G
G
G
G
G
F

G
G
G
G
F
F
F

G
F
F
F
F
F

F
F
F
F
C
F

F
C
C
C
C

C
C
C
C
C

C
S

C
S

C
C

G
G
G
G

G
G
G
G
G
G

F
F
G
G
G
G
G

F
F
F
F
G
G

F
F
F
F
C
C

C
C
C
C
C

C
C
C
C
C

S
S

S
S

C
C

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Figure 2.3.1.B

G
G

G
G

G
G

G
F

C

F
G

G

C
F

G
C

C

F
F

C
S

F

F

G
G

C
F

F
F
G

F

G
C

C
C

G
G

S
S

C

C
C

C

C

Figure 2.3.1C

G
G
G

G

G

G
F

G
G
F

G
F
F

F
F

C
F

F
C
C
C
C

C
S

C
S

C

C

G
G

G

G

G
G

F
F
F

G
G
G

F
F

C
G

C
C
C
C

S
S

S
S

C

C

Figure 2.3.1D

F

G
F

F

S
C

G

S S
C

G

Note: F = Forest land, G = Grassland, C = Cropland, W = Wetlands, S = Settlements, O = Other land.

Data, using either a grid or polygons, at a fine scale could directly account for units of land on which
afforestation, reforestation or deforestation has occurred under Article 3.3. Gridded data may be available fromremote sensing and will normally be combined with ancillary mapped data (such as forest inventories or soilmaps) to improve the accuracy of land-use classification. The building of models to relate remote sensing toground truth data is a highly skilled process, and hence is discussed in more detail in Section 2.4.4.1 (Remotesensing techniques).

IPCC Good Practice Guidance for LULUCF

2.13

Chapter 2: Basis for Consistent Representation of Land Areas

When using Approach 3, it is good practice to:•

Use a sampling strategy consistent with the approaches and advice provided in Section 2.4.2 and Section 5.3
of Chapter 5. This strategy should ensure that the data are unbiased and can be scaled up where necessary.
The number and location of the sampling units may need to change over time in order to remain
representative. Advice on time evolution is given in Section 5.3.3 (Sampling design) in Chapter 5.

•

Where remote sensing data are used, develop a method for its interpretation into land categories using
ground reference data as set out in Section 2.4.4.1 (Remote sensing techniques). Conventional forest
inventories or other survey data can be used for this. It is necessary to avoid possible misclassification ofland types – e.g., wetlands may be difficult to distinguish from forest land using remote sensing data alone
thereby requiring ancillary data such as soil type or topography. Hence map accuracy can be established bymeans of ground reference data as outlined in the same section. The conventional technique is to establish amatrix6 showing, for any given classification of land, the probability of misclassification as one of the othercandidate classifications.

•

Construct confidence intervals for those land category areas and changes in area that will be used in theestimation of carbon stock changes, emissions and removals (see Chapter 5 Section 5.3.4.1).

•

Derive summary tables of the national areas under different land-use change (similar to those described forApproach 2 for QA/QC purposes.

2.3.3

Using the Approaches

Figures 2.3.2 and 2.3.3 are decision trees to assist in choosing an appropriate approach or mix of approaches for
identifying land-use areas. All three approaches can, if implemented consistently with the requirements in
Chapters 3 to 5, be used to produce greenhouse gas emission and removal estimates that are consistent with goodpractice. In general, Approach 3 will allow for the spatial representation required as an input to spatially basedcarbon models (described in Chapter 3).
The use of one or more approaches in a country will depend on, amongst other factors, spatial variability, thesize and accessibility of remote areas, the history of biogeographical data collection, the availability of remotesensing staff and resources (outsourced, if necessary) and the availability of spatially explicit carbon data and/or
models. Most countries will have some existing land-use data and the decision tree in Figure 2.3.2 is provided toassist in using this data in ways that meet the guidance in this Chapter. There are three key decisions to be taken:
is spatially explicit data required for Kyoto Protocol reporting, do the data cover the whole country and do they
provide an adequate time series.
For the few countries with no existing data, the decision tree in Figure 2.3.3 is provided to assist in choosing anappropriate approach or mix of approaches. Broadly speaking, good accessibility to all land area and/or limited
remote sensing resources are indicators for greater emphasis on field survey methods to develop land-use
databases. Countries with more difficult access to some locations but with access to good remote sensing data,
should consider Approach 3 with an emphasis on remote sensing. Approach 2 may be more appropriate incountries where the land area is large but resources to handle the extensive high resolution data required byApproach 3 are not available. Countries with poor accessibility and limited remote sensing resources are unlikely
to be able to develop databases suitable for Approach 2 or 3 but should be able to use Approach 1, either from
FAO data (database on land use and land cover) or other internationally available databases (e.g., see Annex
2A.2).
Different Approaches may be more effective over different time periods, or may be required for different
reporting purposes. Chapter 5 provides methods to carry out matching of the time series between the different
periods or uses that are likely to be necessary.

6

Sometimes called the confusion matrix.

2.14

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

Figure 2.3.2

Decision tree for use of existing data in the land area approaches

Identify a primaryexisting land-use
dataset for the country

Is theNo
primary
data set spatially
explicit for alllandareas?

Is
spatially
explicit dataneeded for someland areas?
(cf. Chp.4)

Yes

Yes

No

Primary dataset
acceptable forspatial explicitness

Can ageographically
mixed set ofApproaches(1/2 & 3)
be used?
Yes

Modify primarydataset to use mixed
Approaches (1/2 & 3)
or Approach 3

Does
the (modified)primary dataset cover
the wholecountry?

NoCan
the gaps in thearea coverage be filledusing other existingdata sets?

Yes

Does
the (modified)No
primary dataset provide acomplete time series forthe country?

(Modified) Primary dataset will
be usable for Approach 3

Document the choiceof methods(Approaches)

Obtain and use theadditional spatial
information using
guidance in thischapterCollect new data forthe gaps in area
coverage using
guidance in thischapterCan data
No
for missing years
be supplemented withexisting secondary
data sets?
Yes

Use a mix ofapproaches to build acomplete time series(see Chapter 5 ontime seriesconsistency)

IPCC Good Practice Guidance for LULUCF

Yes

No

Collect spatial
information
where required
using guidancein this chapter

No

Yes

Combine primaryand secondary
datasets using
guidance in thischapterYes

Does
the dataset
have any underlying spatial
information?

Can
new data becollected to fill thegaps in area
coverage?

No

Yes(Modified) Primarydataset acceptablefor coverageNo

Use international
datasets tominimize gaps incoverage anddocument results

CanNo
new data becollected to complete
the time
series?
Yes

Collect new data
for the gaps in
the time series
using guidance inthis chapter

Use techniques
outlined in
Chapter 5 forinterpolation andextrapolation toestimate fluxes
for missing years

2.15

Chapter 2: Basis for Consistent Representation of Land AreasIs
KP reporting
required?
Yes
Implement
Approach 3 forcomplete landarea using
guidance inthis chapter

Willdata be collectedannually?
Yes
Approach 3

No

No

Yes

Are
No
resources available
for Approach 3?

Useinterpolation
methods from
Chapter 5 toestimate fluxes

Approach 2

Yes

Will
data be collectedannually?

Use sampled
images inApproach 2No

Useinterpolation
methods from
Chapter 5 toestimate fluxes

Yes

Use international
datasets

No

Are
resources available
for landownerquestionnaires?

Can
No
Yes
remote sensing data
be obtained?

NoAre
all locations easily
accessible?

Figure 2.3.3 Decision tree for choosing land area approach for countries with no existing data

2.16

Yes

Use landownerquestionnaires tocollect Approach
1 or 2 data

Will
data be collectedannually?

Yes

Approach 1 or 2

No

Use groundsurvey tocollect Approach
1 or 2 data

Useinterpolation
methods from
Chapter 5 toestimate fluxes

IPCC Good Practice Guidance for LULUCF

Representing Land Areas

2.3.4

Uncertainties Associated with the Approaches

Good practice requires uncertainties to be reduced as far as practicable and Chapter 5.2 (Identifying andquantifying uncertainties) sets out methods to quantify them. These methods require area uncertainty estimatesas an input. Although the uncertainty associated with the Approaches 1 to 3 obviously depends on how they are
implemented and on the quality of the data available, it is possible to give an indication of what can be achievedin practice. Table 2.3.6 sets out the sources of uncertainty involved, the basis for reducing uncertainties andindicative levels of uncertainty under conditions that might be encountered in practice.
The sources of uncertainty of area will tend to increase from Approach 1 to Approach 3, because successively
more data are brought into the assessment. This does not imply that uncertainty increases, however, because of
the additional cross-checks that are made possible by the new data, and because of the general reduction in
uncertainties due to cancellation of errors familiar in statistics. The main difference between Approach 1, andApproaches 2 and 3 is that percentage uncertainties on changes in land area are likely to be greater in Approach
1. This is because in Approach 1 changes in land use are derived from differences in total areas. Under Approach
1, the uncertainty in the difference will be between 1 and 1.4 times the uncertainty in areas being compared,
depending on the degree of correlation between the surveys. Approach 3 produces detailed spatially explicit
information; which may be required e.g., for some modelling approaches, or for reporting Kyoto Protocol
activities. In these cases additional spatial information would be needed if Approach 1 or 2 is being used for landarea identification. Kyoto Protocol requirements are identified in Chapter 4, Section 4.2.2.

TABLE 2.3.6
SUMMARY OF UNCERTAINTIES UNDER APPROACHES 1 TO 3

Approach 1

Sources of uncertainty

Ways to reduce uncertainty

Indicative uncertainty
following checks

Sources of uncertainty may include
some or all of the following,
depending on the nature of thesource of data:•

Check for consistent
relationship with national
area

Order of a few % to order of
10% for total land area in eachcategory.

•

Correct for differences indefinitions•

Consult statisticalagencies on likely
uncertainties involved•

Compare withinternational datasets•

Error in census returns•

Differences in definition
between agencies•

Sampling design•

Interpretation of samples

In addition:
Cross-checks on area changes
between categories cannot be
conducted under Approach 1 andthis will tend to increase
uncertainties.

Greater % uncertainty forchanges in area derived fromsuccessive surveys.

Systematic errors may besignificant when data preparedfor other purposes is used.

Approach 2

As Approach 1 with ability to carryout cross-checks

As above plus consistency
checks between inter-categorychanges within the matrix

Order of a few % to order of
10% for total land area in eachcategory, and greater forchanges in area, since these arederived directly

Approach 3

As Approach 2 plus uncertainties
linked to interpretation of remotesensing data, where used

As Approach 2 plus formal
analysis of uncertainties using
principles set out in Chapter 5

As Approach 2, but areas
involved can be identified
geographically. However, using
Approach 3 the amount of
uncertainty can be determined
more accurately, than forApproach 2.

IPCC Good Practice Guidance for LULUCF

2.17

Chapter 2: Basis for Consistent Representation of Land Areas

2.4 DEVELOPMENT OF LAND-USE DATABASES
There are three broad ways to develop the land-use databases needed for greenhouse gas inventories:•

Use of existing databases prepared for other purposes;•

Use of sampling, and•Use of complete land inventories.

The following subsections provide general good practice advice on the use of these types of data forconsideration by inventory agencies in consultation with other agencies responsible for provision of statistical
data at the national level. Inventory preparers might not be involved in the detailed collection of remote sensing
data or ground survey data, but can use the guidance provided here to help plan inventory improvements andcommunicate with experts in these areas.

2.4.1

Use of Data Prepared for Other Purposes

Two types of available databases may be used to classify land. In many countries, national datasets of the typediscussed below will be available. Otherwise, inventory agencies may use international datasets. Both types of
databases are described below.
Natio nal da ta ba se s
Approaches 1 and 2 will usually be based on existing data, updated annually or periodically. Typical sources ofdata include forest inventories, agricultural census and other surveys, censuses for urban and natural land, land
registry data and maps. Use of this information is illustrated by the examples in Annex 2A.1: Examples ofApproaches in individual countries. Good practice in using data of this type is set out in Section 2.3.2.1.
In t er na t io na l da t a ba se s
Several projects have been undertaken to develop international land-use and land-cover datasets at regional to
global scales (Annex 2A.2 lists some of these datasets). Almost all of these datasets are stored as raster data7
generated using different kinds of satellite remote sensing imagery, complemented by ground reference dataobtained by field survey or comparison with existing statistics/maps. These datasets can be used for:•Estimating spatial distribution of land use. Conventional inventories usually provide only the total sum ofland-use area by classes. Spatial distribution can be reconstructed using international land-use and landcover data as auxiliary data where national data are not available.

•

Reliability assessment of the existing land-use datasets. Comparison between independent national andinternational datasets can indicate apparent discrepancies and understanding these may increase confidence
in national data and/or improve the usability of the international data if required for purposes such as
extrapolation.

When using an international dataset, it is good practice to consider the following:•

The classification scheme (e.g., definition of land-use classes and their relations) may differ from that in thenational system. The equivalence between the classification systems used by the country and the systems
described in Section 2.2 (Land-Use Categories) therefore needs to be established by contacting theinternational agency and comparing their definitions with those used nationally.

•

Spatial resolution (typically 1km nominally but sometimes an order of magnitude more in practice) may becoarse, so national data may need aggregating to improve comparability.

•

Classification accuracy and errors in geo-referencing may exist, though several accuracy tests are usuallyconducted at sample sites. The agencies responsible should have details on classification issues and tests
undertaken.

•

As with national data, interpolation or extrapolation will probably be needed to develop estimates for thetime periods to match the dates required for reporting to the UNFCCC or under the Kyoto Protocol.

7

Raster data means information stored on a regular grid of points, as opposed to polygon data, which is information stored as
the coordinates of an outline area sharing a common attribute.

2.18

IPCC Good Practice Guidance for LULUCF

Development of Land-Use Databases

2.4.2

Collection of New Data by Sampling Methods

Sampling techniques for estimating areas and area changes are applied in situations where total tallies by direct
measurements in the field or assessments by remote sensing techniques are not feasible or would give inaccurate
results. It is good practice to apply sampling concepts that are based on sampling set out in Section 3 of Chapter 5, andthus allow for estimation procedures that are consistent and unbiased and result in estimates that are precise.
As discussed in Section 3 of Chapter 5, good practice on sampling usually involves a set of sampling units thatare located on a regular grid within the inventory area. A land-use class is then assigned to each sampling unit.
Sampling units can be used to derive the proportions of land-use categories within the inventory area.
Multiplying the proportions by the total area provides estimates of the area of each land-use category. Where thetotal area is not known it is assumed that each sampling unit represents a specific area. The area of the land-use
category can then be estimated via the number of sampling units that fall into this category.
Where sampling for areas is repeated at successive occasions, area changes over time can be derived to constructland-use change matrices.
Applying a sample-based approach for area assessment enables the calculation of sampling errors andconfidence intervals that quantify the reliability of the area estimates in each category. It is good practice to use
the confidence interval to verify if observed category area changes are statistically significant and reflect
meaningful changes.

2.4.3

Collection of New Data in Complete Inventories

A complete inventory of land use of all areas in a country will entail obtaining maps of land use throughout thecountry at regular intervals.
This can be achieved by using remote sensing techniques. As outlined under Approach 3 (Section 2.3.2.3), thedata will be most easily used in a GIS based on a set of grid cells or polygons supported by ground truth dataneeded to achieve unbiased interpretation. If the resolution of these data is sufficiently fine then they may allow
direct use for Kyoto Protocol reporting of relevant activities. Coarser scale data could be used to build Approach
1 or 2 data for the whole country or appropriate regions.
A complete inventory could also be achieved by surveying all landowners and each would need to provide
suitable data where they own many different blocks of land. Inherent problems in the method include obtaining
data at scales smaller than the size of the owner’s land as well as difficulties with ensuring complete coveragewith no overlaps.

2.4.4
2.4.4.1

Tools for Data Collection
R EMOTE SENSING (RS) TECHNIQUES

Remotely sensed data, as discussed here, are those acquired by sensors (optical or radar) on board satellites, orby cameras equipped with optical or infrared films, installed in aircraft. These data are usually classified toprovide estimates of the land cover and its corresponding area, and usually require ground survey data to provide
an estimate of the classification accuracy. Classification can be done either by visual analysis of the imagery or
photographs, or by digital (computer-based) methods. The strengths of remote sensing come from its ability toprovide spatially explicit information and repeated coverage including the possibility of covering large areas aswell as remote areas that are difficult to access otherwise. Archives of past remote sensing data also span several
decades and can therefore be used to reconstruct past time series of land cover and land use. The challenge of
remote sensing is related to the problem of interpretation: the images need to be translated into meaningful
information on land use and land management. Depending on the satellite sensor, the acquisition of data may be
impaired by the presence of atmospheric clouds and haze. Another concern, particularly when comparing dataover long time periods, is that remote sensing systems may change. Remote sensing is mainly useful for
obtaining area estimates of land-cover/use categories and for assisting in the identification of relatively
homogeneous areas that can guide the selection of sampling schemes and the number of samples to be collected.
For additional information on remote sensing and spatial statistics, see Cressie (1993) and Lillesand et al (1999).

IPCC Good Practice Guidance for LULUCF

2.19

Chapter 2: Basis for Consistent Representation of Land Areas

Types of remote sensi ng data
The most important types of RS data are 1) aerial photographs, 2) satellite imagery using visible and/or nearinfrared bands, and 3) satellite or airborne radar imagery (see Table 5.7.2 for features of main remote sensing
platforms). Combinations of different types of remote sensing data (e.g., visible/infrared and radar; different
spatial or spectral resolutions) might very well be used for assessing different land-use categories or regions. A
complete remote sensing system for tracking land-use change could include many sensor and data typecombinations at a variety of resolutions.
Important criteria for selecting remote sensing data and products are:•

Adequate land-use classification scheme;•

Appropriate spatial resolution (The smallest spatial unit for assessing land-use changes under the Kyoto
Protocol is 0.05 ha);•Appropriate temporal resolution for estimating of land-use and carbon stock changes;•

Availability of accuracy assessment;•

Transparent methods applied in data acquisition and processing; and•Consistency and availability over time.

1 . Ae r ia l p ho t o g r a p h s
Analysis of aerial photographs can reveal forest tree species and forest structure from which relative age
distribution and tree health (e.g., needle loss in coniferous forests, leaf loss and stress in deciduous forests) may
be inferred. In agriculture analysis, RS can show crop species, crop stress, and tree cover in agro-forestry
systems. The smallest spatial unit possible to assess depends on the type of aerial photos used, but for standard
products it is often as small as 1 square metre.
2 . Sa t e ll it e i ma g e s in v i si bl e a nd nea r i nf ra r e d w a v e le ng t h sComplete land use or land cover of large areas (national or regional), if not available otherwise, may befacilitated by the use of satellite images. The possibility exists of obtaining long time series of data from thedesired area since the satellite continuously and regularly passes over it. The images usually generate a detailed
mosaic of distinct categories, but the labelling into proper land-cover/use categories commonly requires groundreference data from maps or field surveys. The smallest unit to be identified depends on the spatial resolution of
the sensor and the scale of work. The most common sensor systems have a spatial resolution of 20 – 30 metres.
At a spatial resolution of 30 metres, for example, units as small as 1ha can be identified. Data from higher
resolution satellites is also available.
3. Ra dar i mag ery
The most common type of radar data are from the so-called Synthetic Aperture Radar (SAR) systems that operate at
microwave frequencies. A major advantage of such systems is that they can penetrate clouds and haze, and acquire
data during night-time. They may therefore be the only reliable source of remote sensing data in many areas of theworld with quasi-permanent cloud cover. By using different parts of the spectrum and different polarisations, SAR
systems may be able to distinguish land-cover categories (e.g., forest/non-forest), or the biomass content of vegetation,although there are at present some limitations at high biomass due to signal saturation.

Ground ref erence dat a
In order to make use of remote sensing data for inventories, and in particular to relate land cover to land use it isgood practice to complement the remotely sensed data with ground reference data (often called ground truthdata). Ground reference data can either be collected independently, or be obtained from forest or agricultural
inventories. Land uses that are rapidly changing over the estimation period or that have vegetation cover known
to be easily misclassified should be more intensively ground-truthed than other areas. This can only be done byusing ground reference data, preferably from actual ground surveys collected independently but high-resolution
photographs may also be useful.

Integration of remote sensing and GIS
Visual interpretation of images is often used for identifying sampling sites for forestry inventories. The method
is simple, and reliable. However, it is labour intensive and therefore restricted to limited areas, and may beaffected by subjective interpretations by different operators.

2.20

IPCC Good Practice Guidance for LULUCF

Development of Land-Use Databases

Full use of remote sensing generally requires integration of the extensive coverage that remote sensing can provide
with ground-based point measurements or map data to represent areas associated with particular land uses in space andtime. This is generally achieved most cost effectively using a geographical information system (GIS).

Land-cover classif ication using remotely s ensed data
Classification of land cover using remotely sensed data may be done by visual or digital (computer based)
analysis. Each one presents advantages and disadvantages. Visual analysis of imagery allows for humaninference through the evaluation of overall characteristics of the scene (analysis of the contextual aspects in theimage). Digital classification, on the other hand, allows several manipulations to be performed with the data,such as merging of different spectral data, which can help to improve modelling of the biophysical ground data(such as tree diameter, height, basal area, biomass) using the remotely sensed data. In addition, digital analysis
allows for the immediate computation of areas associated with the different land categories. It has developed
rapidly over the past decade, along with the associated technical computer development, making hardware,
software and also the satellite data readily available at low cost in most countries, although capacity to use thesedata and facilities may have to be outsourced, particularly in mapping at national level.

Detection of land use change using RS
Remote sensing can be used to detect locations of change related to LULUCF. Methods for land-use change
detection can be divided into two categories (Singh (1989)):
Post-classification change detection: This refers to techniques where two or more predefined land-cover/use
classifications exist from different points in time, and where the changes are detected, usually by subtraction of
the datasets. The techniques are straightforward but are also very sensitive to inconsistencies in interpretationand classification of the land categories.
Pre-classification change detection: This refers to more sophisticated and biophysical approaches to change
detection. Differences between spectral response data from two or more points in time are compared bystatistical methods and these differences are used to provide information on land-cover/use changes. Thisapproach is less sensitive to interpretation inconsistencies and can detect much more subtle changes than the
post-classification approaches, but is less straightforward and requires access to the original remote sensing data.

Evaluation of mapping accuracy
Whenever a map of land cover/use is being used, it is good practice to acquire information about the reliabilityof the map. When such maps are generated from classification of remote sensing data, it should be recognisedthat the reliability of the map is likely to vary between the different land categories. Some categories may beuniquely distinguished while others may easily be confounded with others. For example, coniferous forest isoften more accurately classified than deciduous forest because its reflectance characteristics are more distinct,
while deciduous forest may easily be confounded with, for example, grassland or cropland. Similarly, it is often
difficult to ascertain changes in land management practices through remote sensing. For example, it may bedifficult to detect a change from conventional to conservation tillage on a specific land area.
It is therefore good practice to estimate the accuracy of land-use/land-cover maps on a category-by-categorybasis. A number of sample points on the map and their corresponding real world categories are used to create a
confusion matrix (See Approach 3; Footnote 6) with the diagonal showing the probability of correct
identification and the off-diagonal elements showing the relative probability of misclassification of a land
category into one of the other possible categories. The confusion matrix expresses not only the accuracy of themap but it is also possible to determine which categories are easily confounded with each other. Based on the
confusion matrix, a number of accuracy indices can be derived (Congalton, 1991). It is good practice to present
an estimate of the accuracy of the land-use/cover map category-by-category and a confusion matrix may be
employed for this purpose where remote sensing is used. Multi-temporal analysis (analysis of images taken at
different times to determine the stability of land-use classification) can also be used to improve classification
accuracy, particularly in cases where ground truth data are limited.

2.4.4.2

G ROUND - BASED SURVEYS

Ground-based surveys may be used to gather and record information on land use, and for use as independent
ground-truth data for remote sensing classification. Prior to the advent of remote sensing techniques such as
aerial photography and satellite imagery, ground-based surveys were the only means of generating maps. The
process is essentially one of visiting the area under study and recording visible and/or other physical attributes ofthe landscape for mapping purposes. Digitisation of boundaries and symbolising attributes are used to make hard
copy field notes and historical maps useful in Geographic Information Systems (GIS). This is done via protocols

IPCC Good Practice Guidance for LULUCF

2.21

Chapter 2: Basis for Consistent Representation of Land Areason minimum land area delineation and attribute categorisation that are linked to the scale of the resultant map
and its intended use.
Very precise measurements of area and location can be made using a combination of survey equipment such as
theodolites, tape measures, distance wheels and electronic distance measuring devices. Development of Global
Positioning Systems (GPS) means that location information can be recorded in the field directly into electronic
format using portable computer devices. Data are downloaded to an office computer for registration andcoordination with other layers of information for spatial analysis.
Landowner interviews and questionnaires are used to collect socio-economic and land management information,
but may also provide data on land use and land-use change. With this census approach, the data collection
agency depends on the knowledge and records of landowners (or users) to provide reliable data. Typically, theresident is visited and interviewed by a representative of the collection agency and data are recorded in apredetermined format, or a questionnaire is issued to the land-user for completion. The respondent is usuallyencouraged to use any relevant records or maps they may have, but questions may also be used to elicit
information directly (Swanson et al., 1997).
Census surveys are probably the oldest form of data collection methods (Darby, 1970). Land-user surveys can be
conducted on the entire population or a sample of suitable size. Modern applications employ a full range ofvalidation and accuracy assessment techniques. The survey may be undertaken through personal visits, telephoneinterviews (often with computer-assisted prompts) or mail-out questionnaires. Land-user surveys start with theformulation of data and information needs into a series of simple and clear questions soliciting concise andunequivocal responses. The questions are tested on a sample of the population in order to ensure that they are
understandable and to identify any local technical terminology variations. For sample applications, the entire
study area is spatially stratified by appropriate ecological and/or administrative land units, and by significant
categorical differences within the population (e.g., private versus corporate, large versus small, pulp versus
lumber, etc.). For responses dealing with land areas and management practices, some geographic location,
whether precise coordinates, cadastral description or at least ecological or administrative units should be requiredof the respondent. Post-survey validation of results is conducted by searching for statistical anomalies,
comparing with independent data sources, conducting a sample of follow-up verification questionnaires or
conducting a sample of on-site verification surveys. Finally, presentation of results must follow the initialstratification parameters.

2.22

IPCC Good Practice Guidance for LULUCF

Annex 2A.1

Annex 2A.1 Examples of Approaches in individual countries
2A.1.1

Use of Existing Resource Inventories by USA(Approaches 1, 2 and 3)

In the United States, the National Resources Inventory (NRI) is designed to assess soil, water, and related
environmental resources on non-Federal lands (Nusser and Goebel, 1997; Fuller, 1999)8. The NRI uses datafrom several sources to verify estimates. A Geographic Information System (GIS) for the United States is used tohold the inventory and includes the total surface area, water area, and Federal land. Data from other sources e.g.,soils databases and other inventories such as the Forest Inventory and Analysis (FIA), can be linked to the NRI9.
While sampling techniques for the NRI and FIA are similar, differing objectives require different sampling grids
and make the estimates from the two inventory systems statistically independent. The raw sampled data could,
however, be used as a basis for Approach 3.
The data (See Table 2A.1.1) are sufficient to provide a land-use change matrix (Approach 2) that illustrates
several important land use and land-use change characteristics for the United States. First, comparing the 1997
total to the 1992 total for each of the broad land-use categories depicts the net change in land use. For example,
the amount of cropland declined by 2.1 million hectares from 1992 to1997, falling from 154.7 million hectares to152.6 million hectares, while the amount of non-Federally owned range and forests remained relatively stable.
These aspects of land use could also have been seen from an Approach 1 database. In addition, the total area of
the United States remains fixed from 1992 to 1997 at almost 800 million hectares, and thus any area increases ina one land-use category must be offset by area declines in other categories as could have been provided in an
Approach 2 structure.
However, the data can also describe land-use change dynamics using its Approach 2 structure. The diagonal andoff-diagonal elements in Table 2A.1.1 show how much land has remained in a land category and how much landhas changed use respectively. Comprehensive measures of changes in land use (the off-diagonal elements) canbe extremely important for carbon estimation and reporting. For example, the total amount of non-Federal forest
land remained relatively stable from 1992 to1997, increasing by about 400 000 hectares. However, the land-use
change elements show that 1.9 million hectares of non-Federal forest land were converted to settlements while
2.5 million hectares of pastureland were converted to forest land. Therefore, inferring small changes in carbon
stock based on small changes in overall land use could be incorrect if the individual land-use dynamics (e.g.,
Forest land to settlements and pastureland to forests) are relatively large.
TABLE 2A.1.1
LAND USE AND LAND-USE CHANGE MATRIX FOR USA
Final

Crop

CRP

Pasture

Range
(NF)

Forest
(NF)

Other
Rural

Settlements

Water andFederal

1997
Total

146.8

0.9

3.5

0.8

0.3

0.3

--

--

152.6

CRP

0.8

12.3

--

--

--

--

--

--

13.2

Pasture

3.7

0.3

43.2

0.3

0.8

0.3

--

--

48.6

Range (NF)

0.6

0.1

0.6

162.3

0.5

0.2

--

--

164.4

Forest (NF)

0.8

--

2.5

0.6

160.1

0.6

--

--

164.5

Other Rural

0.7

--

0.4

0.3

0.4

18.9

--

--

20.7

Settlements

1.2

--

0.8

0.5

1.9

0.2

35.2

--

39.8

Water and Federal Land

0.1

--

--

0.1

0.2

--

--

182.6

183.1

1992 Total

154.7

13.8

51.0

165

164.1

20.5

35.2

182.8

787.4

InitialCrop

Note: (i) Data from the 1997 NRI and excludes Alaska. (ii) NF is Non-Federal. Areas are millions of hectares. (iii) CRP represents land
enrolled in the Conservation Reserve Program. (iv) Some row and column totals do not add up due to rounding errors.

8

The NRI is conducted by the U.S. Department of Agriculture’s (USDA) Natural Resources Conservation Service, in
cooperation with the Iowa State University Statistical Laboratory. More information on the NRI is found at:
http://www.nhq.nrcs.usda.gov/technical/NRI/1997/.

9

The FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with
State and Private Forestry and National Forest Systems. More information on the FIA is found at: http://fia.fs.fed.us/.

IPCC Good Practice Guidance for LULUCF

2.23

Chapter 2: Basis for Consistent Representation of Land Areas

2A.1.2

Use of Agricultural Data for the Argentine Pampas(Approaches 1 and 2)

Since 1881, various national agricultural censuses involving 100% of farms in the Argentine pampas have been
undertaken. Data on land use were organized at the level of political districts in each of the 24 provinces. A
particular study on land-use change in the pampas across one century of agricultural transformation was recently
published (Viglizzo et al., 2001). Later results show that the Argentine pampas behaved as a net source ofgreenhouse gas emitter over much of the period in response to the conversion of natural grasslands into grazing
lands and croplands. However, emissions tend to decline since 1960 due to the adoption of conservation soil
management techniques, mainly reduced- and no-tillage methods - (Bernardos et al., 2001).These data can beused in the implementation of Approach 1 or 2.

2A.1.3Use of Land Registry Data in China (Approach 1)

China uses Approaches 1 and 2 for land-use change data, including forest inventories every 5 years, agricultural
censuses and other surveys. In particular, China is implementing a household contract system for returning
cultivated land to woodland. An individual contract system is being introduced whereby households are assigned
tasks, receive subsidies and own the trees and other vegetation that they plant. The programme aims at planting
about 5 million hectares with trees from year 2000 to 2010. The contracts for this scheme have been used tomake a database of specific land-use changes.

2A.1.4Land-use Matrices in the United Kingdom (Approaches
1, 2 and 3)

In the United Kingdom, land-use change matrices have been constructed from field survey data (Barr et al. 1993,
Haines-Young, 2000). Three surveys have now been completed, in 1984, 1990 and 1998. Each sample was a 1
km square area and 384 of these were used in 1984 to provide a stratified sampling of 32 eco-climatic zones.
These sample squares were revisited in 1990 and 1998 and about another 140 were added for the campaign in1990 and another 50 for 1998 to improve the coverage of the eco-climatic zones. Initially land-use /cover classes
unique to the survey were developed, but in 1998 alternative types common to other agencies in the UK wereused. The saved data for 1984 and 1990 have now been reclassified into the new classes. Each 1 km sample wasvisited by surveyors who, starting from existing 1:10 560 maps, drew outlines of different land cover/use parcels,numbered the parcels and recorded a range of information for each parcel. Subsequently, the maps were digitised
and the area of each parcel calculated from the digital data. When a square was revisited some years later, thedigitised maps, with the older parcel boundaries, became the starting point for recording of changes in theparcels. Thus data were built up, not only of the areas of land-cover/use classes in each sampling year, but of thetransitions occurring between each class. Regional and national estimates of land cover/use and change werethen made by weighted averaging of the samples against the occurrence in the different eco-climatic zones.
LUC matrices for England, Scotland and Wales between 1984 and 1990 were constructed for a simplified set ofland-use categories (Farm, Natural, Urban, Woods, Other) and have been used for estimating emissions andremovals for Category 5D (CO2 emissions and uptake by soils from LUC and management) of the UK
greenhouse gas inventory. An example is shown in Table 2A.1.2.
TABLE 2A.1.2
LAND-USE CHANGE MATRIX FOR SCOTLAND BETWEEN 1984 AND 1990
1990
1984

Farm

Natural

Urban

Woods

Other

1990 Total

Farm
Natural
Urban
Woods
Other
1984 Total

1 967
113
14
9
0
2 103

81
4 779
4
77
0
4 941

6
5
276
1
0
288

6
32
1
981
0
1 020

0
0
0
0
141
141

2 060
4 929
2 95
1 068
141
8 493

Note: Areas are thousands of hectares

The uncertainty in estimating land use and land-use change for regions using this method of sampling has been
described by Barr et al. (1993). If the variation in land use or change across a region is known or can be
estimated by an approximate value then the number of samples needed for a specified level of confidence in theregional total area for that land use or change can be estimated from statistical theory (Cochran, 1977).

2.24

IPCC Good Practice Guidance for LULUCF

Annex 2A.1

2A.1.5The New Zealand Example of Implementation of LandUse/Cover Database from Remote Sensing (Approach 3)

The first New Zealand land-use /Cover Database (NZLCDB) was completed in June 2000 from satellite images
acquired, mainly during the summer of 1996/97. For New Zealand, an appropriate period of time for detecting
significant land-cover changes is considered to be five years. Landsat Enhanced Thematic Mapper Plus (7 ETM+)
is the preferred sensor with in-fill from Système Probatoire d’Observation de la Terre (SPOT) as necessary.
Work commenced in 2001/02 on image acquisition and analyses, which will continue through until 2003/04 to
produce NZLCDB2, following stages outlined below.
The cost of Land-Cover Database 2 (NZLCDB2) is of the order of US$1 500 000 for 270 000 km2 i.e., US$5.6
per km2 and it will provide:•

A complete set of multi-spectral and ortho-corrected satellite imagery covering New Zealand sharpened to
15m spatial resolution;•

A revised NZLCDB1 digital GIS map of land-cover classes with identified classification and generalisation
errors corrected;•

A new NZLCDB2 digital GIS map of land-cover classes compatible with NZLCDB1 "parent classes";•

A digital GIS map recording changes identified in land cover for New Zealand at the 1 ha minimum
mapping unit, and•An accuracy assessment of NZLCDB2 including an error matrix to estimate data quality both spatially andby class.

A fuller description of the New Zealand Land-Cover Database project, which will be updated as the projectprogresses, can be found at http://www.mfe.govt.nz/issues/land/land-cover-dbase/index.html. The stages ofcompletion of the database are shown in Figure 2A.1.1.
Figure 2A.1.1 Stages in preparation of New Zealand Land-Cover Databases

Stage 1
Image Acquisition- Summer 2001 / 02

Stage 7
Completed LCDB
Completed
LCDB22- Compile Metadata- Produce User Manual- Distribute via Database Custodian- Institute Database MaintenanceStage 2
Image Processing- Pan sharpen ETM+ imagery to 15m- Ortho-rectify imagery- Run TopographicNormalisation
Normalisation

Stage 3Spectral Integrity check-Perform
PerformSpectral
SpectralIntegrity
IntegrityCheckCheck
ofofLCDB1LCDB1(1996)(1996)withwith2001
2001/ /02
02
imagery
imagery-Highlight
Highlightpixels
pixelsthat
thatdeviate
deviatefrom
from-assigned
assignedLCDB1
LCDB1class
class

IPCC Good Practice Guidance for LULUCF

Stage 6
Accuracy Assessment- Decide on Sampling Intensity byclass, acquire relevant aerial
photography and ancillary grounddata to complete the work
work.
- Publish Accuracy Assessment

Stage 4LCDB 2 GIS processing
Stage 4a- Correct LCDB1 classificationand generalisationerrors
errorsactualland
land-cover
cover- Confirmactualchanges between
betweenimage
imagedates
dates- Code type of land-coverland cover change--Supply
SupplyLCDB2
LCDB2classification
classificationfor
for
Field
FieldCheck
Check
Stage 4bStage 4b- Edit vectors from Field Check

Stage 5
Field Checking- Verify draft land
land-cover
cover classes- Supply recommended edits todraft classification / additional
ground data to contractors at
Stage 42.25

Chapter 2: Basis for Consistent Representation of Land Areas

2A.1.6The Australian Multi-Temporal Landsat Database for
Carbon Accounting (Approach 3)

The Australian Greenhouse Office (AGO) through its National Carbon Accounting System (NCAS) has
developed a national scale multi-temporal remote sensing programme which is an example of Approach 3, eventhough its primary purpose is to identify areas of land impacted by forest cover change rather than full land-use
mapping. Using Landsat satellite data for twelve national passes between 1972 and 2002, the forest cover statusof land units is monitored over time, at better than a one-hectare resolution. Initially a Year 2000 mosaic ofscenes was constructed for the whole continent (369 scenes) as a base dataset to which other time series wereregistered.
Consistent geographic resolution and spectral calibration of satellite data allows for objective statistical analysis
on a single land unit (pixel) through time. Remote sensing experts experienced in interpreting the Australianvegetation developed the analytical methods (Furby, 2002) that were refined over two rounds of pilot testing(Furby and Woodgate, 2002). The pilot testing was also used to train private sector providers who subsequently
competitively bid for the work.
In addition to the highly prescriptive methodology and performance standards, an independent quality assurance
programme has been implemented to ensure a consistent output standard. A Continuous Improvement andVerification Programme also monitors the quality of results and provides guidance on future improvements.
Because the methodology uses a conditional probability approach, the full time series is readily subjected to any
improvements identified.
The efficiency in processing methods developed for the programme has enabled the addition of new national
passes to the time series at a cost of approximately half a million US dollars.
The forest cover change data is integrated into a carbon/nitrogen cycle process model which is spatially operated
from within a Geographic Information System. In this way, carbon accounting of this sector is readily
accomplished.
Further information can be found in various NCAS Technical Reports available on the AGO Website: http://
www.greenhouse.gov.au/ncas.

2.26

IPCC Good Practice Guidance for LULUCF

Annex 2A.2

ANNEX 2A.2 Examples of international land cover datasets
EXAMPLES OF INTERNATIONAL LAND COVER DATASETS
Dataset nameAARS Global 4-Minute Land Cover

IGBP-DIS Global 1km Land Cover Data
Set

Global Land Cover Dataset

AuthorCenter for Environmental Remote
Sensing, Chiba University

IGBP/DIS

USGS, USA

GLCF (Global Land Cover Facility)

Brief descriptionof contents

This classification is derived fromLand cover classes are identified through Advanced Very High Resolution
clustering NOAA AVHRR monthly data. Radiometer (AVHRR) 1km data andancillary data.

The data set is derived from a flexible
data base structure and seasonal landcover regions concepts

Metrics describing the temporal dynamics
of vegetation were applied to 1984 PAL
data at 8km resolution to derive a global
land-cover classification product using adecision tree classifier.

Classification
schemeOriginal classification scheme is applied.
Compatible with IGBP/DIS classification It consists of 17 classes.
scheme.

A convergence of evidence approach isused to determine the land cover type foreach seasonal land cover class.

The classification was derived by testing
several metrics that describe the temporal
dynamics of vegetation over an annual
cycle.

Data format(vector/raster)

RasterRasterRasterRasterSpatial coverage

GlobalGlobalGlobalGlobalData acquisition
year

1990

1992-1993

April 1992-March 19931987

Spatial resolution
4min x 4min.
or grid size

1km x 1km

1km x 1km

8km x 8km

Revision interval(for time-series
datasets)

Not applicableNot applicableNot applicableNot applicable

Qualitydescription

Ground truth data are compared against
the dataset.

High-resolution satellite imagery used tostatistically validate the dataset.

Sample point accuracy: 59.4% Areaweighted accuracy: 66.9% (Scepan,1999).

No description

Contact address
and referenceURLtateishi@rsirc.cr.chiba-u.ac.jp
http://ceres.cr.chiba-u.ac.jp:8080/usrdir/you/ICHP/index.html

alan.belward@jrc.it
http://www.ngdc.noaa.gov/paleo/igbpdis/frame/coreprojects/index.html

icac@usgs.govhttp://edcdaac.usgs.gov/glc
http://glcf.umiacs.umd.edu/data.html
c/globe_int.html.

IPCC Good Practice Guidance for LULUCF

Global Land Cover Dataset

2.27

Chapter 2: Basis for Consistent Representation of Land Areas

Examples of international land cover datasets (Continued)
1º Land Cover Map from AVHRRCORINE land cover (CLC) database

Digital Chart of the World

Global Map

Dr. Ruth DeFries University of Maryland
at College Park, USA

European Environmental Agency

ESRI Products

Produced by National Mapping
Organizations, and Compiled by ISCGM.

Dataset nameAuthor

The data set describes the geographical
It provides a pan-European inventory of
distributions of eleven major cover types biophysical land cover. CORINE land
Brief description
based on inter-annual variations in NDVI. cover is a key database for integratedof contents
environmental assessment.

It is a worldwide base map of coastlines, Digital geographic information in 1 km
boundaries, land cover, etc. Contains more resolution covering the whole land withthan 200 attributes arranged into 17
standardized specifications and available
thematic layers with text annotations forto everyone at marginal cost.
geographical features.

Classification
schemeIt consists of the digital 13 class map

Uses a 44 class nomenclature.

8 Agriculture/ Extraction features and 7
surface cover features.

Refer to http://www.iscgm.org/gmspecifications11.pdf

Data format(vector/raster)

RasterRasterVector Polygons

Raster and Vector

GlobalAustria, Belgium, Bulgaria, Czech
Global coverage
Republic, Denmark, Finland, France,
Germany, Greece, Hungary, Ireland, Italy,
Luxembourg, Netherlands, Poland ,Portugal, Romania, Slovakia, Spain,
United Kingdom, Parts of Morocco andTunisia.

Participating countries (90 in number)

1987

Depends on the country (overall time span Based on ONCs of US Defense Mapping
is around 1985-95)
Agency. Period 1970-80. Refer to theCompilation date layer.

Depends on the participating nations.

1 x 1 degree

250m by 250m grid database which has
been aggregated from the original vector
data at 1:100,000.

1:1,000,000 scale

1km x 1km grids

CLC Update Project of 2000 for updating
it to the 1990's data

Not applicable

Approximately five-year intervals

Spatial coverage

Data acquisitionyear
Spatial resolution
or grid size

Revision interval Not applicable(for time-series
datasets)
No description
Qualitydescription
Contact address landcov@geog.umd.edu
and reference
http://www.geog.umd.edu/landcover/1dURL
map.html

2.28

No specific information available. Refer to Data quality information exists at three
http://dataservice.eea.eu.int/dataservice/oth levels within the database: feature, layer
er/land_cover/lcsource.asp for country
and source.
wise information.

Refer to http://www.iscgm.org/gmspecifications11.pdf.

dataservice@eea.eu.int

sec@iscgm.org

http://dataservice.eea.eu.int/dataservice/me
tadetails.asp?table=landcover and i=1

http://www.esri.com/data/index.html

http://www.iscgm.org/

IPCC Good Practice Guidance for LULUCF

References

References
Barr C.J., Bunce R.G.H., Clarke R.T., Furse M.T., Gillespie M.K., Groom G.G., Hallam C.J., Hornung M.,
Howard D.C. and Ness M.J. (1993). Countryside Survey 1990, Main Report. Department of theEnvironment, London.
Bernardos J.N., Viglizzo E.F., Jouvet V., Lértora F.A., Pordomingo S.J., and Aid F.D. (2001). The use of EPIC
model to study the agroecological change during 93 years of farming transformation in the Argentine
pampas. Agricultural Systems, 69: pp. 215-234.
Cochran W.G. (1977). Sampling Techniques. J. Wiley and Sons, New York, 428 p 9.
Congalton R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote
Sensing of Environment, Vol 37: No 1, pp. 35-46.
Cressie N.A.C. (1993). Statistics for Spatial Data. John Wiley and Sons, New York.
Darby H.C. (1970). Doomsday Book – The first land utilization survey. The Geographical Magazine, Vol. 42:
No.6, pp. 416 – 423.
FAO (1986). Programme for the 1990 World Census of Agriculture. FAO Statistical Development Series 2,
Food and Agriculture Organisation, Rome Italy, 90 pp.
FAO (1995). Planning for Sustainable use of Land Resources: Towards a New Approach. Land and Water
Bulletin 2, Food and Agriculture Organisation, Rome Italy, 60 pp.
FAO (2002) Proceedings of Expert Meeting on Harmonizing forest-related definitions for use by various
stakeholders.
FoodandAgriculture
Organisation,
Rome
Italy.
Availableat
http://www.fao.org/forestry/fop/fopw/Climate/doc/Y3431E.pdf.
Fuller W.A. (1999). Estimation procedures for the United States National Resources Inventory, 1999.
Proceedings of the Survey Methods Section, Statistical Society of Canada. Available at
http://www.nhq.nrcs.usda.gov/NRI/1997/stat_estimate.htm.
Furby S. (2002). Land Cover Change: Specification for Remote Sensing Analysis. National Carbon Accounting
System Technical Report No. 9, Australian Greenhouse Office, Canberra, Australia (402pp).
Furby S. and Woodgate P. (2002). Remote Sensing Analysis of Land Cover Change – Pilot Testing ofTechniques (Furby and Woodgate ed.) National Carbon Accounting System Technical Report No. 16,
Australian Greenhouse Office, Canberra, Australia (354pp).
Haines-Young R.H. and 23 others (2000). Accounting for nature: assessing habitats in the UK countryside.
Department of the Environment, Transport and the Regions, London. ISBN 1 85112 460 8.
Intergovernmental Panel on Climate Change (IPCC). (2000). Watson R., Noble I.R., Bolin B., Ravindranath
N.H., Verardo D.J. and Dokken D.J. (Eds) Land use, Land-use Change, and Forestry: A Special Report.
Cambridge University Press. Cambridge, UK.
Lillesand T.M. and Kiefer R. W., (1999). Remote Sensing and Image Interpretation, John Wiley and Sons,
2.29
New York.
Nusser S. M., and Goebel J.J. (1997). The National Resources Inventory: A Long-Term Multi-Resource
Monitoring Programme. Environmental and Ecological Statistics. Vol. 4, pp. 181-204.
Singh A., (1989). Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing, 10 no.
6: 989 – 1003.
Swanson B.E., R.P. Bentz and A.J., Sofranco (Eds.). (1997). Improving agricultural extension. A reference
manual. Food and Agriculture Organization of the United Nations, Rome.
USGS (2001) http://edcdaac.usgs.gov/glcc/globe_int.html
Viglizzo E.F., Lértora F., Pordomingo S.J., BernardosJ.N., Roberto Z.E. and Del Valle H. (2001). Ecologicallessons and applications from one century of low external-input farming in the pampas of Argentina.
Agriculture, Ecosystems and Environment, 83: 65-81.

IPCC Good Practice Guidance for LULUCF

2.29

3

LUCF SECTOR GOOD PRACTICEGUIDANCE

IPCC Good Practice Guidance for LULUCF

Chapter 3: LUCF Sector Good Practice Guidance

AUTHORS AND REVIEW EDITORS
Co-ordinating Lead Authors
Gert-Jan Nabuurs (The Netherlands), N.H. Ravindranath (India), and Keith Paustian (USA)Annette Freibauer (Germany), William Hohenstein (USA), and Willy Makundi (Tanzania)

Lead Authors
Harald Aalde (Norway), Abdelazim Yassin Abdelgadir (Sudan), Anwar Sheikhaddin Abdu Khalil (Bahrain),James Barton (New Zealand), Kathryn Bickel (USA), Samsudin Bin-Musa (Malaysia), Dominique Blain(Canada), Rizaldi Boer (Indonesia), Kenneth Byrne (Ireland), Carlos Cerri (Brazil), Lorenzo Ciccarese (Italy),David-Cruz Choque (Bolivia), Eric Duchemin (Canada), Lucien Dja (Ivory Coast), Justin Ford-Robertson (New
Zealand), Wojciech Galinski (Poland), Jean-Claude Germon (France), Hector Ginzo (Argentina), Michael
Gytarsky (Russian Federation), Linda Heath (USA), Denis Loustau (France), Tijani Mandouri (Morocco), Josef
Mindas (Slovakia), Kim Pingoud (Finland), John Raison (Australia), Vladimir Savchenko (Belarus), Dieter
Schöne (UN-FAO), Risto Sievanen (Finland), Kenneth Skog (USA), Keith Smith (UK), and Deying Xu (China)

Contributing Authors
Mark Bakker (France), Martial Bernoux (France/Brazil), Jagtar Bhatti (Canada), Rich Conant (USA), Mark
Harmon (USA), Yasuhiko Hirakawa (Japan), Toshiro Iehara (Japan), Moriyoshi Ishizuka (Japan), Esteban
Jobbagy (Argentina), Jukka Laine (Finland), Marna van der Merwe (South Africa), Indu K. Murthy (India),David Nowak (USA), Steve Ogle (USA), P. Sudha (India), Bob Scholes (South Africa), and Xiaoquan Zhang(China)

Review Editors
Sergio Gonzalez-Martineaux (Chile), Anke Herold (Germany), and Audun Rosland (Norway)

3.2

IPCC Good Practice Guidance for LULUCF

Chapter 3: LUCF Sector Good Practice GuidanceContents
3.1

INTRODUCTION

3.11

3.1.1

Inventory and Reporting Steps ...................................................................................................... 3.11

3.1.2

Linkage between this Chapter and the IPCC Guidelines’ Reporting Categories .......................... 3.11

3.1.2.1 Changes in forest and other woody biomass stocks................................................................. 3.13
3.1.2.2 Forest and grassland conversion.............................................................................................. 3.13
3.1.2.3 Abandonment of croplands, pastures, or other managed lands................................................ 3.13
3.1.2.4 CO2 emissions and removals from soils .................................................................................. 3.14
3.1.2.5 Other categories of reporting and specific cases...................................................................... 3.14
3.1.3

Definitions of Carbon Pools.......................................................................................................... 3.14

3.1.4

General Methods ........................................................................................................................... 3.15

3.1.5

Tier Levels .................................................................................................................................... 3.16

3.1.6

Choice of Method.......................................................................................................................... 3.17

3.1.7

Reporting....................................................................................................................................... 3.20

3.1.8

Generic Climatic Zones................................................................................................................. 3.20

3.2

FOREST LAND
3.2.1

3.23

Forest Land Remaining Forest Land ............................................................................................. 3.23

3.2.1.1 Change in carbon stocks in living biomass.............................................................................. 3.24
3.2.1.2 Change in carbon stocks in dead organic matter ..................................................................... 3.32
3.2.1.3 Change in carbon stocks in soils.............................................................................................. 3.38
3.2.1.4 Non-CO2 greenhouse gas emissions ........................................................................................ 3.45
3.2.2

Land Converted to Forest Land..................................................................................................... 3.50

3.2.2.1 Change in carbon stocks in living biomass.............................................................................. 3.51
3.2.2.2 Change in carbon stocks in dead organic matter ..................................................................... 3.56
3.2.2.3 Change in carbon stocks in soils.............................................................................................. 3.60
3.2.2.4 Non-CO2 greenhouse gas emissions ........................................................................................ 3.65
3.2.3

Completeness ................................................................................................................................ 3.66

3.2.4

Developing a Consistent Time Series ........................................................................................... 3.66

3.2.5

Reporting and Documentation ...................................................................................................... 3.66

3.2.6

Inventory Quality Assurance/Quality Control (QA/QC)............................................................... 3.68

3.3

CROPLAND
3.3.1

3.69

Cropland Remaining Cropland...................................................................................................... 3.69

3.3.1.1 Change in carbon stocks in living biomass.............................................................................. 3.70
3.3.1.2 Change in carbon stocks in soils.............................................................................................. 3.74
3.3.1.3 Non-CO2 greenhouse gas emissions ........................................................................................ 3.82
3.3.2

Land Converted to Cropland ......................................................................................................... 3.83

IPCC Good Practice Guidance for LULUCF

3.3

Chapter 3: LUCF Sector Good Practice Guidance

3.3.2.1 Change in carbon stocks living biomass.................................................................................. 3.84
3.3.2.2 Change in carbon stocks in soils.............................................................................................. 3.89
3.3.2.3 Non-CO2 greenhouse gas emissions ........................................................................................ 3.93
3.3.3

Completeness ................................................................................................................................ 3.95

3.3.4

Developing a Consistent Time Series ........................................................................................... 3.95

3.3.5

Reporting and Documentation ...................................................................................................... 3.96

3.3.6

Inventory Quality Assurance/Quality Control (QA/QC)............................................................... 3.96

3.3.7

Estimation of Revised GPG Tier 1 Defaults for Mineral Soil C Emissions/Removals
for Cropland .................................................................................................................................. 3.97

3.4

GRASSLAND
3.4.1

3.105

Grassland Remaining Grassland ................................................................................................. 3.105

3.4.1.1 Change in carbon stocks in living biomass............................................................................ 3.106
3.4.1.2 Change in carbon stocks in soils............................................................................................ 3.111
3.4.1.3 Non-CO2 greenhouse gas emissions ...................................................................................... 3.120
3.4.2

Land Converted to Grassland ...................................................................................................... 3.120

3.4.2.1 Change in carbon stocks in biomass ...................................................................................... 3.121
3.4.2.2 Change in carbon stocks in soils............................................................................................ 3.126
3.4.2.3 Non-CO2 greenhouse gases ................................................................................................... 3.130
3.4.3

Completeness .............................................................................................................................. 3.130

3.4.4

Developing a Consistent Time Series ......................................................................................... 3.131

3.4.5

Reporting and Documentation .................................................................................................... 3.131

3.4.6

Inventory Quality Assurance/Quality Control (QA/QC)............................................................. 3.131

3.4.7

Estimation of Revised GPG Tier 1 Defaults for Grassland Management ................................... 3.132

3.5

WETLANDS

3.135

3.5.1

Wetlands Remaining Wetlands ................................................................................................... 3.135

3.5.2

Land Converted to Wetlands....................................................................................................... 3.135

3.5.2.1 Change in carbon stocks in land converted to peat extraction ............................................... 3.136
3.5.2.2 Change in carbon stocks in land converted to flooded land (Reservoirs).............................. 3.140
3.5.3

Completeness .............................................................................................................................. 3.140

3.5.4

Developing a Consistent Time Series ......................................................................................... 3.140

3.5.5

Reporting and Documentation .................................................................................................... 3.141

3.5.6

Inventory Quality Assurance/Quality Control (QA/QC)............................................................. 3.141

3.6

SETTLEMENTS

3.143

3.6.1

Settlements Remaining Settlements ............................................................................................ 3.143

3.6.2

Land Converted to Settlements ................................................................................................... 3.143

3.7

OTHER LAND

3.145

3.7.1

Other Land Remaining Other Land............................................................................................. 3.145

3.7.2

Land Converted to Other Land.................................................................................................... 3.145

3.7.2.1 Change in carbon stocks in living biomass............................................................................ 3.145
3.7.2.2 Change in carbon stocks in soils............................................................................................ 3.147

3.4

IPCC Good Practice Guidance for LULUCF

Chapter 3: LUCF Sector Good Practice Guidance

3.7.3

Completeness .............................................................................................................................. 3.149

3.7.4

Developing a Consistent Time Series ......................................................................................... 3.149

3.7.5

Reporting and Documentation .................................................................................................... 3.149

3.7.6

Inventory Quality Assurance/Quality Control (QA/QC)............................................................. 3.149

Annex 3A.1

Biomass Default Tables for Section 3.2 Forest Land

3.151

Annex 3A.2

Reporting Tables and Worksheets

3.187

Appendix 3a.1 Harvested Wood Products: Basis for Future
Methodological Development

3.257

Appendix 3a.2 Non-CO2 Emissions from Drainage and Rewetting of Forest
Soils: Basis For Future Methodological Development

3.273

Appendix 3a.3 Wetlands Remaining Wetlands: Basis for Future Methodological Development

3.277

Appendix 3a.4 Settlements: Basis for Future Methodological
Development

3.295

References

3.301

IPCC Good Practice Guidance for LULUCF

3.5

Chapter 3: LUCF Sector Good Practice Guidance

Equations
Equation 3.1.1

Annual carbon stock change in a given pool as a function of gains and losses ......... 3.16

Equation 3.1.2

Annual carbon stock change in a given pool.............................................................. 3.16

Equation 3.2.1

Annual emissions or removals from forest land remaining forest land...................... 3.23

Equation 3.2.2

Annual change in carbon stocks in living biomass in forest land remaining
forest land (Default Method)...................................................................................... 3.24

Equation 3.2.3

Annual change in carbon stocks in living biomass in forest land remaining
forest land (Stock Change Method) ........................................................................... 3.24

Equation 3.2.4

Annual increase in carbon stocks due to biomass increment in forest
land remaining forest land......................................................................................... 3.25

Equation 3.2.5

Average annual increment in biomass ....................................................................... 3.26

Equation 3.2.6

Annual decrease in carbon stocks due to biomass loss in forest landremaining forest land ................................................................................................. 3.26

Equation 3.2.7

Annual carbon loss due to commercial fellings ......................................................... 3.27

Equation 3.2.8

Annual carbon loss due to fuelwood gathering.......................................................... 3.27

Equation 3.2.9

Annual other losses of carbon.................................................................................... 3.28

Equation 3.2.10 Annual change in carbon stocks in dead organic matter in forest landremaining forest land ................................................................................................. 3.32
Equation 3.2.11 Annual change in carbon stocks in dead wood in forest land remaining
forest land (Option 1)................................................................................................ 3.33
Equation 3.2.12 Annual change in carbon stocks in dead wood in forest landremaining forest land (Option 2)................................................................................ 3.34
Equation 3.2.13 Annual change in carbon stocks in litter in forest land remaining forest land ........... 3.35
Equation 3.2.14 Annual change in carbon stocks in mineral soils in forest landremaining forest land ................................................................................................. 3.40
Equation 3.2.15 CO2 emissions from drained organic forest soils ....................................................... 3.42
Equation 3.2.16 Soil organic carbon content........................................................................................ 3.43
Equation 3.2.17 Direct N2O emissions from managed forests ............................................................. 3.46
Equation 3.2.18 Direct N2O emissions from forest fertilisation........................................................... 3.46
Equation 3.2.19 Estimation of non-CO2 emissions from C released.................................................... 3.49
Equation 3.2.20 Estimation of GHGs directly released in fires ........................................................... 3.49
Equation 3.2.21 Annual change in carbon stocks in land converted to forest land ............................. 3.51
Equation 3.2.22 Annual change in carbon stocks in living biomass in land converted toforest land (Tier 1) ..................................................................................................... 3.51
Equation 3.2.23 Annual increase in carbon stocks in living biomass in land converted
to forest land .............................................................................................................. 3.52
Equation 3.2.24 Annual decrease in carbon stocks in living biomass due to losses in land
converted to forest land.............................................................................................. 3.52
Equation 3.2.25 Annual change in carbon stocks in living biomass in land converted toforest land (Tier 2) ..................................................................................................... 3.53
Equation 3.2.26 Change in carbon stocks in living biomass in land annually converted toforest land .................................................................................................................. 3.53

3.6

IPCC Good Practice Guidance for LULUCF

Chapter 3: LUCF Sector Good Practice Guidance

Equation 3.2.27 Annual change in carbon stocks in dead wood in land converted to forest land........ 3.57
Equation 3.2.28 Annual change in carbon stocks in dead wood in land converted to forest land........ 3.57
Equation 3.2.29 Annual change in carbon stocks in litter in land converted to forest land.................. 3.58
Equation 3.2.30 Annual change in carbon stocks in litter in land converted to forest land.................. 3.58
Equation 3.2.31 Annual change in carbon stocks in mineral soils in land converted to forest land..... 3.62
Equation 3.2.32 Annual change in carbon stocks in mineral soils upon afforestation1 ....................... 3.63
Equation 3.2.33 CO2 emissions from drained organic soils in land converted to forest land............... 3.63
Equation 3.3.1

Annual change in carbon stocks in cropland remaining cropland.............................. 3.70

Equation 3.3.2

Annual change in carbon stocks in soils in cropland remaining cropland ................. 3.74

Equation 3.3.3

Annual change in carbon stocks in mineral soils for a single cropland system ......... 3.75

Equation 3.3.4

Annual change in carbon stocks in mineral soils in cropland remaining cropland .... 3.78

Equation 3.3.5

CO2 emissions from cultivated organic soils in cropland remaining cropland .......... 3.79

Equation 3.3.6

Annual carbon emissions from agricultural lime application .................................... 3.80

Equation 3.3.7

Total change in carbon stocks in land converted to cropland .................................... 3.83

Equation 3.3.8

Annual change in carbon stocks in living biomass in land converted to cropland..... 3.85

Equation 3.3.9

Change in carbon stocks as a result of clearing biomass in a land use conversion .... 3.86

Equation 3.3.10 Carbon losses from biomass burning, on-site and off-site ......................................... 3.86
Equation 3.3.11 Carbon losses from biomass decay ............................................................................ 3.87
Equation 3.3.12 Annual change in carbon stocks in soils in land converted to cropland..................... 3.89
Equation 3.3.13 Total annual emissions of N2O from mineral soils in land converted to cropland..... 3.93
Equation 3.3.14 N2O emissions as a result of the disturbance associated with land-use
conversion of forest, grassland, or other land to cropland ......................................... 3.94
Equation 3.3.15 Annual nitrogen released by net soil organic mineralisation as a resultof the disturbance (based on soil C mineralised) ....................................................... 3.94
Equation 3.4.1

Annual change in carbon stocks in grassland remaining grassland ......................... 3.105

Equation 3.4.2

Annual change in carbon stocks in living biomass in grassland
remaining grassland ................................................................................................. 3.106

Equation 3.4.3

Annual change in carbon stocks in living biomass in grassland
remaining grassland ................................................................................................. 3.107

Equation 3.4.4

Annual change in living biomass – Rate Approach ................................................. 3.107

Equation 3.4.5

Annual change in living biomass – Difference Approach ....................................... 3.108

Equation 3.4.6

Total biomass ........................................................................................................... 3.108

Equation 3.4.7

Annual change in carbon stocks in soils in grassland remaining grassland ............. 3.111

Equation 3.4.8

Annual change in carbon stocks in mineral soils for a single grassland system ...... 3.112

Equation 3.4.9

Annual change in carbon stocks in mineral soils in total grassland
remaining grassland ................................................................................................. 3.113

Equation 3.4.10 CO2 emissions from cultivated organic soils in grassland remaining grassland ...... 3.114
Equation 3.4.11 Annual carbon emissions from agricultural lime application .................................. 3.115
Equation 3.4.12 Total change in carbon stocks in land converted to grassland ................................. 3.120
Equation 3.4.13 Annual change in carbon stocks in living biomass in land converted to grassland.. 3.122
Equation 3.4.14 Change in carbon stocks as a result of biomass clearing during
land use conversion.................................................................................................. 3.123
Equation 3.4.15 Carbon losses from biomass burning, on-site and off-site ....................................... 3.123

IPCC Good Practice Guidance for LULUCF

3.7

Chapter 3: LUCF Sector Good Practice Guidance

Equation 3.4.16 Carbon losses from biomass decay .......................................................................... 3.124
Equation 3.4.17 Annual change in carbon stocks in soils in land converted to grassland (LG)......... 3.126
Equation 3.5.1

Change in carbon stocks in land converted to wetlands........................................... 3.136

Equation 3.5.2

Annual change in carbon stocks in land converted to peat extraction...................... 3.136

Equation 3.5.3

Annual change in carbon stocks in living biomass in land converted topeat extraction.......................................................................................................... 3.137

Equation 3.5.4

Annual change in carbon stocks in soils in land converted to peat extraction ......... 3.137

Equation 3.5.5

Annual change in carbon stocks in soils due to drainage of organic soils
converted to peat extraction ..................................................................................... 3.138

Equation 3.5.6

Annual change in carbon stocks in living biomass in land converted
to flooded land ......................................................................................................... 3.140

Equation 3.6.1

Annual change in carbon stocks in living biomass in forest land converted
to settlements (FS) ................................................................................................... 3.143

Equation 3.7.1

Annual change in carbon stocks in land converted to “Other Land” ....................... 3.145

Equation 3.7.2

Annual change in carbon stocks in living biomass in land
converted to “Other Land”....................................................................................... 3.146

Equation 3.7.3

Annual change in carbon stocks in mineral soils in land converted
to “Other Land”........................................................................................................ 3.148

Figures

3.8

Figure 3.1.1

Decision Tree for Identification of Appropriate Tier-Level for Land
Remaining in the same Land Use Category (Example given for Forest
Land Remaining Forest Land, FF) ................................................................................ 3.18

Figure 3.1.2

Decision Tree for Identification of Appropriate Tier-Level for Land
Converted to another Land Use Category (Example given for Land
Converted to Forest Land, LF) ...................................................................................... 3.19

Figure 3.2.1

Two temporally averaged values of soil organic carbon corresponding to different
combinations of forest soils, management practices and disturbance regimes.............. 3.39

IPCC Good Practice Guidance for LULUCF

Chapter 3: LUCF Sector Good Practice Guidance

TablesTable 3.1.1

Mapping between the sections of Chapter 5 of the 1996 IPCC Guidelines
and the sections of Chapter 3 of this report ................................................................... 3.12

Table 3.1.2

Definitions for terrestrial pools used in Chapter 3......................................................... 3.15

Table 3.1.3

Sub-categories within a given land use section ............................................................. 3.20

Table 3.2.1

Updated defaults for litter carbon stocks and transition period . .................................. 3.36

Table 3.2.2

Updated defaults of natural mortality rates, dead wood stocks, and live:deadratios ............................................................................................................................ 3.37

Table 3.2.3

Default values for CO2-C emission factor for drained organic soils
in managed forests......................................................................................................... 3.42

Table 3.2.4

Default reference (under native vegetation) soil organic C stocks (SOCRef) ................ 3.43

Table 3.2.5

Sources of uncertainty in estimating CO2 emission/removal from forest soil
and DOM pools ............................................................................................................. 3.61

Table 3.3.1

Tier descriptions for subcategories under cropland remaining cropland ....................... 3.70

Table 3.3.2

Default coefficients for aboveground woody biomass and harvest cycles incropping systems containing perennial species ............................................................. 3.71

Table 3.3.3

Default reference (under native vegetation) soil organic C stocks (SOCRef) ................ 3.76

Table 3.3.4

Relative stock change factors (FLU, FMG, and FI) (over 20 years) for
different management activities on cropland ................................................................ 3.77

Table 3.3.5

Annual emission factors (EF) for cultivated organic soils ............................................ 3.79

Table 3.3.6

Tier descriptions for subcategories under land converted to cropland (LC).................. 3.84

Table 3.3.7

Default biomass carbon stocks removed due to land conversion to cropland ............... 3.87

Table 3.3.8

Default biomass carbon stocks present on land converted to cropland in
the year following conversion ....................................................................................... 3.88

Table 3.3.9

Relative soil stock change factors (FLU, FMG, FI) for land-use
conversions to cropland................................................................................................. 3.92

Table 3.4.1

Tier descriptions for subcategories under grassland remaining grassland................... 3.106

Table 3.4.2

Default estimates for standing biomass grassland (as dry matter) andaboveground net primary production, classified by IPCC climate zones. ................... 3.109

Table 3.4.3

Default expansion factors (root-to-shoot [R:S] ratios) for the majorsavannah/rangeland ecosystems of the world.............................................................. 3.110

Table 3.4.4

Default reference (under native vegetation) soil organic c stocks (SOCRef) ................ 3.117

Table 3.4.5

Relative stock change factors for grassland management ........................................... 3.118

Table 3.4.6

Annual emission factors (EF) for managed grassland organic soils............................ 3.118

Table 3.4.7

Tier descriptions for subcategories under land converted to grassland ....................... 3.122

Table 3.4.8

Default biomass carbon stocks removed due to land conversion to grassland ............ 3.124

Table 3.4.9

Default biomass carbon stocks present on land converted to grassland. ..................... 3.125

Table 3.4.10

Relative soil stock change factors for land-use conversions to grassland ................... 3.128

Table 3.5.1

Sections and appendices addressing major greenhouse gas emissions frommanaged wetlands in this report .................................................................................. 3.135

Table 3.5.2

Emission factors and associated uncertainty for organic soils after drainage.............. 3.138

IPCC Good Practice Guidance for LULUCF

3.9

Chapter 3: LUCF Sector Good Practice Guidance

Boxes

3.10

Box 3.1.1

Framework of Tier Structure in the Good Practice Guidance ....................................... 3.17

Box 3.2.1

Organic soils, peat lands and wetlands .......................................................................... 3.38

Box 3.3.1

Good practice in derivation of country-specific emission factors ................................. 3.95

IPCC Good Practice Guidance for LULUCF

Introduction

3.1

INTRODUCTION

Chapter 3 provides guidance on the estimation of emissions and removals of CO2 and non-CO2 for the Land Use,
Land-Use Change and Forestry (LULUCF) sector, covering Chapter 5 of the Revised 1996 IPCC Guidelines for
National Greenhouse Gas Inventories (IPCC Guidelines).
This chapter provides two significant advances:(i)

It introduces three hierarchical tiers of methods that range from default data and simple equations
to the use of country-specific data and models to accommodate national circumstances. These tiers,
if properly implemented, successively reduce uncertainty and increase accuracy.

(ii)

It uses the land-use categories (of Chapter 2) to organise the methodologies and to facilitate: a)transparent reporting, b) association of above and below ground carbon pools (at the higher tiers),
whilst allowing comparison with reporting of the IPCC Guidelines.

The methodologies in this report are organised by land-use categories (six sections), by broad carbon pools andnon-CO2 gases, and by tier, and are consistent with the other chapters of the report.

3.1.1

Inventory and Reporting Steps

The overall sequence of steps for inventorying and reporting emissions and removals is outlined below. It isgood practice for countries to follow these steps and those provided in each section of this chapter to estimate
emissions and removals:(i)

Drawing on the three approaches for representing areas in Chapter 2, estimate the land areas ineach land use category for time period required.

(ii)

Conduct key category assessment for the relevant LULUCF categories using the guidance provided
in Chapters 3 and 5. Within the categories designated as key, assess which non-CO2 gases andcarbon pools are significant, and prioritise such pools in terms of methodological choice.

(iii)

Ensure that the requirements in terms of emission and removal factors and activity data appropriate
to the tier level are being met.

(iv)

Quantify emissions and removals and estimate the uncertainty in each estimate, as set out in
Chapter 5 and the sector specific data provided in this Chapter.

(v)

Use the reporting tables to report emissions and removals estimates. Utilize the worksheets whereappropriate (see Annex 3A.2).

(vi)

Document and archive all information used to produce the national emissions and removals
estimates following specific instructions under each land use category, carbon pool, non-CO2
source, and land use change.

(vii)

Implement quality control checks, verification, and expert peer review of the emission estimatesfollowing specific guidance under each land use category, pool or non-CO2 gas (see also Chapter 5,
for broad guidance).

3.1.2

Linkage between this Chapter and the IPCC
Guidelines’ Reporting Categories

Chapter 3 is divided into six sections based on land-use categories; each section is further divided into two
subsections based on the status and recent history of the land use.
•

The first subsection is for lands that begin and end an inventory period in the same use.

•

The second subsection is for land conversions to the land use covered by the section.

Table 3.1.1 shows the sections and subsections of this chapter in relationship to the IPCC Guidelines. This
provides a basis for comparison, which is described in more detail below.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3.1.1
MAPPING BETWEEN THE SECTIONS OF CHAPTER 5 OF THE 1996 IPCC GUIDELINES
AND THE SECTIONS OF CHAPTER 3 OF THIS REPORTLand Usein the Initial Time period

Land Usein the Reporting ( current) Year

Chapter 3
Subsection 1

IPCC Guidelines 2

Forest land
Cropland
Grassland
Wetlands
Settlements
Other land

Forest land
Forest land
Forest land
Forest land
Forest land
Forest land

3.2.1
3.2.2
3.2.2
3.2.2
3.2.2
3.2.2

5A
5 A, 5 C, 5 D
5 A, 5 C, 5 D
5 A, 5 C, 5 D
5 A, 5 C, 5 D
5 A, 5 C, 5 D

Cropland
Forest land
Grassland
Wetlands
Settlements
Other land

CroplandCroplandCroplandCroplandCroplandCropland

3.3.1
3.3.2
3.3.2
3.3.2
3.3.2.
3.3.2.

5 A, 5 D
5 B, 5 D
5 B, 5 D
5D
5D
5D

Grassland
Forest land
CroplandWetlands
Settlements
Other land

Grassland
Grassland
Grassland
Grassland
Grassland
Grassland

3.4.1
3.4.2
3.4.2
3.4.2
3.4.2
3.4.2

5 A, 5 D
5 B, 5 D
5 C, 5 D
5 C, 5 D
5 C, 5 D
5 C, 5 D

Wetlands
Forest land
Cropland
Grassland
Settlements
Other land

Wetlands
Wetlands
Wetlands
Wetlands
Wetlands
Wetlands

3.5.1
3.5.2
3.5.2
3.5.2
3.5.2
3.5.2

5 A, 5 E
5B
5E
5B
5E
5E

Settlements
Forest land
Cropland
Grassland
Wetlands
Other land

Settlements
Settlements
Settlements
Settlements
Settlements
Settlements

3.6.1
3.6.2
3.6.2
3.6.2
3.6.2
3.6.2

5A
5B
5E
5B
5E
5E

Other land
Forest land
Cropland
Grassland
Wetlands
Settlements

Other land
Other land
Other land
Other land
Other land
Other land

3.7.1
3.7.2
3.7.2
3.7.2
3.7.2
3.7.2

5A
5B
5E
5B
5E
5E

1

Combines both soils and biomass, those in bold represent the ‘Forest and grassland conversion’ of the IPCC Guidelines.

2

The IPCC Guidelines cover the following categories: 5 A Changes in Forest and Other Woody Biomass Stocks; 5 B Forest andGrassland Conversion; 5 C Abandonment of Managed Lands; 5 D Emissions and Removals from Soils, and 5 E Other (Reporting
Instructions p. 1.14 - 1.16)

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Introduction

3.1.2.1

C HANGES IN FOREST AND OTHER WO ODY BIOMASSSTOCKSAs with the IPCC Guidelines, the Good Practice Guidance covers managed forests which can be defined in thefollowing terms:
Forest management is the process of planning and implementing practices for stewardship and use of
the forest aimed at fulfilling relevant ecological, economic and social functions of the forest...Amanaged forest is a forest subject to forest management1.
This definition implies that managed forests are subject to periodic or ongoing human interventions and that they
include the full range of management practices from commercial timber production to stewardship in noncommercial purposes. Section 3.2.1 covers forest land remaining forest land. Management and conversion toforests is covered in Section 3.2.2 Land Converted to Forest land.
The section Forest land provides guidance for all carbon pools and non-CO2 gases with exception of theharvested wood products (HWP). The IPCC Guidelines contain references to the treatment of HWP, andcountries choosing to estimate carbon stock changes within the harvested wood products pool can findmethodological advice in Appendix 3a.1. The IPCC Guidelines briefly address ‘Other Woody Biomass Stocks’,e.g., perennial biomass in croplands and grazing lands, as well as trees in urban areas. Guidance on this topic iselaborated in the Good Practice Guidance within the sections entitled “Changes in Biomass Carbon Pools.”
Changes in carbon stocks of perennial woody biomass are addressed in relevant biomass sections of each land
use category. Urban trees are addressed in the Section 3.6 and in Appendix 3a.4.

3.1.2.2

F OREST AND GRASSLAND CONVERSIO N

The Forest and Grassland Conversion Section of the IPCC Guidelines includes conversion of existing forests
and natural grasslands to other land uses such as cropland. Forests can be cleared to convert land to a wide
variety of other uses, but a predominant cause is conversion to pasture and croplands, which was the focus of the
IPCC Guidelines, with an emphasis on changes in carbon in biomass pools. Land use conversions are treatedsystematically in this report, organised by final land use. Guidance is provided under each section titled “Lands
Converted to any other land-use category”, and is given separately for changes in all carbon pools.
A summary estimate of conversion from forests or grassland to other uses can be constructed by totaling eachindividual conversion from these categories to another land-use category. For CO2 emissions and removals fromforest conversion, the total can be arrived at by summing Equations: 3.3.7, 3.4.12, 3.5.1, 3.6.1, and 3.7.1 forconversions from forest land to each category. Similarly, for grassland conversion, the total can be arrived at bysumming the same equations for conversions from grassland. It is good practice to estimate and report separately
the sum of all forest land conversions (deforestation) and grassland conversions to other final land uses. A
reporting table is provided for this in Annex 3A.2 (Table 3A.2.1B).

3.1.2.3

A BANDONMENT OF CROPLANDS , PASTURES , OR OTHER
MANAGED LANDSThe IPCC Guidelines focus mainly on lands that re-accumulate carbon in biomass as they return to a quasinatural state following abandonment or active reforestation. However, land can also remain constant or degrade
further with respect to carbon re-accumulation.
Croplands and grasslands can be abandoned or actively converted to several different land uses, affecting the netchange in carbon in biomass. Therefore, guidance on estimating changes in biomass is located in a number ofplaces depending on the type of land use it changed to. The range of specific land use transitions can be summed
for an aggregate assessment of carbon changes from abandonment of cropland, pastures, or other managed lands,
as indicated in Table 3.1.1.

1

Proceedings of the Expert Meeting on Harmonising Forest Related Definitions, Rome, Sept 2002 (FAO 2003).

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Chapter 3: LUCF Sector Good Practice Guidance

3.1.2.4

CO 2 EMISSIONS AND REMOVALS FROM SOILSThe IPCC Guidelines further divide this topic into: a) Cultivation of Mineral Soils; b) Cultivation of Organic
Soils; and c) Liming of Agricultural Soils. In general, in this chapter, each land use section addresses changes insoil carbon for that land use either remaining in the same use, or recently converted to that land use.
Guidance on estimating soil carbon stock changes due to management practices is covered under Cropland
Remaining Cropland, and Grassland Remaining Grassland, each at the sub section entitled ‘Changes in Carbon
Stocks in Soils’, within which guidance is provided separately for mineral and organic soils. Changes in soil
carbon stocks as a result of lands being converted into cropland or grassland are also covered, under theconversion subsections. A total assessment of soil carbon stock changes due to cultivation of mineral soils is thesum of changes in carbon stocks over a finite period following changes in management that impact soil carbon.
Drainage of peatland soils for forest establishment is found in the Forest land soil section. All greenhouse gas
emissions from Wetlands Remaining Wetlands are presented in Appendix 3a.3. Cultivation of organic soils inthe sense of peat extraction is handled under lands converted to peat extraction in Section 3.5 of this report.
Methodological guidance on liming of agricultural soils is addressed as in the IPCC Guidelines.

3.1.2.5

O THER CATEGORI ES OF REPORTI NG AND SPECIFIC CASESThe IPCC Guidelines briefly describe general issues and methodological approaches for other categories. The
issues are often complex and agreed methodologies were not available at the time the IPCC Guidelines werebeing prepared. This chapter addresses some of these categories in more depth. “Other possible categories” asdiscussed in the IPCC Guidelines explicitly includes belowground biomass, natural disturbances (including fire),
shifting cultivation, and flooding and drainage of wetlands. Information on estimating CO2 emission andremovals and non-CO2 emissions from managed wetland (including peatlands and flooded lands), and for
Settlements Remaining Settlements, are addressed in Appendix 3a.3 and 3a.4, respectively, because the methods
and available data for these land use types are preliminary. Estimation methods for belowground biomass areincluded explicitly in the section covering carbon stock changes in forest biomass (Sections 3.2.1.1 and 3.2.2.1)
and options for including belowground biomass in non-forest land uses are provided in other sections as well.
Non-CO2 emissions from drainage and rewetting of forest soils are addressed in Appendix 3a.2.
The Good Practice Guidance does not alter the basic default assumptions that land use changes have a linear
impact on soil organic matter for 20 years before a new equilibrium is reached (Tier 1), with possible
successions of 20 year periods to deal with longer time constants in temperate and boreal zones. This means that,when a piece of land changes use, then it is followed in that ‘changed status’ for 20 years, with each year 1/20 of
the CO2 and non-CO2 effects reported. Tier 3 modeling approaches may utilize different assumptions. Landshould be reported in a conversion category for 20 years, and then moved to a “remaining category”, unless afurther change occurs.
Natural disturbances (e.g., storms, fires, insects but only on managed lands) are included for their CO2 and nonCO2 effects. Where natural disturbances on unmanaged lands are followed by a land use change, then the CO2
and non-CO2 effects of the natural disturbance are to be reported.

3.1.3

Definitions of Carbon Pools

The methodologies in this report are organised first by land-use categories, as described above, and second by
broad pools. Table 3.1.2 provides a generic representation of these pools occurring in a terrestrial ecosystem.
Each of these pools is discussed in the IPCC Guidelines, although in some cases with only minimal guidance.

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Introduction

TABLE 3.1.2
DEFINITIONS FOR TERRESTRIAL POOLS USED IN CHAPTER 3
Pool 2
LivingBiomass

Dead Organic
MatterSoils

Description (see also notes below in italics)
3

Abovegroundbiomass

All living biomass above the soil including stem, stump, branches, bark, seeds, andfoliage.

Belowgroundbiomass

All living biomass of live roots. Fine roots of less than (suggested) 2mm diameter areoften excluded because these often cannot be distinguished empirically from soilorganic matter or litter.

Dead wood

Includes all non-living woody biomass not contained in the litter, either standing,lying on the ground, or in the soil. Dead wood includes wood lying on the surface,dead roots, and stumps larger than or equal to 10 cm in diameter or any otherdiameter used by the country.

LitterIncludes all non-living biomass with a diameter less than a minimum diameter chosen
by the country (for example 10 cm), lying dead, in various states of decomposition
above the mineral or organic soil. This includes the litter, fumic, and humic layers.
Live fine roots (of less than the suggested diameter limit for below-ground biomass)
are included in litter where they cannot be distinguished from it empirically.

Soil organic
matterIncludes organic carbon in mineral and organic soils (including peat) to a specified
depth chosen by the country and applied consistently through the time series. Livefine roots (of less than the suggested diameter limit for below-ground biomass) are
included with soil organic matter where they cannot be distinguished from it
empirically.

Note: In cases where forest understorey is a relatively small component of the aboveground biomass carbon pool, it is acceptable for the methodologies and associateddata used in some tiers to exclude it, provided the tiers are used in a consistentmanner throughout the inventory time series as specified in Chapter 5.

Note: National circumstances may necessitate slight modifications to the pool definitions used here. Where modified
definitions are used, it is good practice to report upon them clearly, to ensure that modified definitions are usedconsistently over time, and to demonstrate that pools are neither omitted nor double counted.

3.1.4

General Methods

Chapter 3 uses the same basic methodological approaches as in the IPCC Guidelines. As stated in the IPCC
Guidelines:
The fundamental basis for the methodology rests upon two linked themes: i) the flux of CO2 to orfrom the atmosphere is assumed to be equal to changes in carbon stocks in existing biomass and soils,
and ii) changes in carbon stocks can be estimated by first establishing rates of change in land use
and the practice used to bring about the change (e.g., burning, clear-cutting, selective cut, etc.).
Second, simple assumptions or data are applied about their impact on carbon stocks and thebiological response to a given land use.
The first order approach described above is the foundation for the basic methodologies presented in this chapter
for calculating changes in carbon pools. This approach can be generalised and applied to all carbon pools (i.e.,
aboveground biomass, belowground biomass, dead wood, litter, and soils), subdivided as necessary to capture
differences between ecosystems, climatic zones and management practice. Equation 3.1.1 illustrates the generalapproach for estimating carbon stock change based on rates of carbon losses and gains by area of land use.
In most first order approximations, the “activity data” are in terms of area of land use or land use change. The
generic guidance is to multiply the activity data by a carbon stock coefficient or “emission factor” to provide thesource/or sink estimates. Guidance is provided for all relevant carbon pools and changes of land use from one
type to another. The full range of possible changes in land use from one type to another is covered systematicallyand default transition periods are provided.

2

The default assumption in IPCC Guidelines is that carbon removed in wood and other biomass from forests is oxidised in
the year of removal. Countries may report on HWP pools if they can document that existing stocks of forest products are infact increasing. Appendix 3a.1 provides guidance to countries and information that could be used in future methodologicaldevelopment subject to decisions by UNFCCC.

3

Expressed in tonnes dry weight.

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Chapter 3: LUCF Sector Good Practice Guidance

EQUATION 3.1.1
ANNUAL CARBON STOCK CHANGE IN A GIVEN POOL AS A FUNCTION OF GAINS AND LOSSES∆C = ∑ijk [Aijk ● (CI – CL)ijk]
Where:∆C = carbon stock change in the pool, tonnes C yr-1
A

= area of land, ha

ijk = corresponds to climate type i, forest type j, management practice k, etc…
CI = rate of gain of carbon, tonnes C ha-1 yr-1
CL = rate of loss of carbon , tonnes C ha-1 yr-1
An alternative approach is proposed in the IPCC Guidelines where carbon stocks are measured at two points intime to assess carbon stock changes. Equation 3.1.2 illustrates the generic approach for estimating carbon stockchange in this way. This latter approach is presented in this chapter as an option in some instances.
EQUATION 3.1.2
ANNUAL CARBON STOCK CHANGE IN A GIVEN POOL
∆C = ∑ijk (Ct2 – Ct1 ) / (t2 – t1)ijk
Where:
Ct1 = carbon stock in the pool at time t1, tonnes C
Ct2 = carbon stock in the pool at time t2, tonnes C
Even though national reporting of sources and sinks is required annually, it does not mean that national
inventories have to be carried out annually for all pools, since data from national inventories done on 5 to 10
year cycles, can be interpolated. Chapter 5 provides guidance on how to use interpolation and extrapolation tomerge sources of data.
Several sources of non-CO2 greenhouse gas emissions from land use were discussed in the Agriculture Chapter(Chapter 4) of the IPCC Guidelines and the related parts of the GPG2000. Chapter 4 of IPCC Guidelines and
GPG2000 cover CH4 and N2O emissions from savanna burning and agricultural residue burning, direct andindirect N2O emissions from agricultural soils, and CH4 emissions from rice production. Guidance on
greenhouse gas emissions from the biomass fraction in waste disposed at solid waste disposal sites or incinerated
is provided in the Waste Chapter of IPCC Guidelines and GPG2000.
This good practice guidance provides additional information on how to apply and expand the Agriculture
Chapter of the IPCC Guidelines and GPG2000 to these additional categories of land uses and land use change:•

Non-CO2 (N2O and CH4) from forest fire (Section 3.2.1.4);•

N2O from managed (fertilized) forests (Section 3.2.1.4);•

N2O from drainage of forest soils (Appendix 3a.2);•

N2O and CH4 from managed wetland (Appendix 3a.3); and•Soil emissions of N2O following land use conversion (Sections 3.3.2.3 and 3.4.2.3).

3.1.5

Tier Levels

This chapter provides users with three methodological tiers for estimating greenhouse gas emissions andremovals for each source. Tiers correspond to a progression from the use of simple equations with default datato country-specific data 4 in more complex national systems. Three general tiers are summarised in Box 3.1.1.
Tiers implicitly progress from least to greatest levels of certainty in estimates as a function of methodologicalcomplexity, regional specificity of model parameters, and spatial resolution and extent of activity data. Complete

4

Country-specific data may require subdivision to capture different ecosystems and site qualities, climatic zones andmanagement practice within a single land category.

3.16

IPCC Good Practice Guidance for LULUCF

Introduction

guidance is provided for the implementation of Tier 1. Regardless of tier level, countries should document whattiers were used for various categories and pools as well as the emission factors, and activity data used to prepare
the estimate. For higher tiers, inventory agencies may need to provide additional documentation to support
decisions to use more sophisticated methodologies or country-defined parameters. Moving from lower to higher
tiers will usually require increased resources, and institutional and technical capacity.
BOX 3.1.1
FRAMEWORK OF TIER STRUCTURE IN THE GOOD PRACTICE GUIDANCEThe Tier 1 approach employs the basic method provided in the IPCC Guidelines (Workbook) andthe default emission factors provided in the IPCC Guidelines (Workbook and Reference Manual)with updates in this chapter of the report. For some land uses and pools that were only mentioned
in the IPCC Guidelines (i.e., the default was an assumed zero emissions or removals), updates areincluded in this report if new scientific information is available. Tier 1 methodologies usually use
activity data that are spatially coarse, such as nationally or globally available estimates ofdeforestation rates, agricultural production statistics, and global land cover maps.
Tier 2 can use the same methodological approach as Tier 1 but applies emission factors andactivity data which are defined by the country for the most important land uses/activities. Tier 2
can also apply stock change methodologies based on country-specific data. Country-definedemission factors/activity data are more appropriate for the climatic regions and land use systems inthat country. Higher resolution activity data are typically used in Tier 2 to correspond withcountry-defined coefficients for specific regions and specialised land-use categories.
At Tier 3, higher order methods are used including models and inventory measurement systems
tailored to address national circumstances, repeated over time, and driven by high-resolution
activity data and disaggregated at sub-national to fine grid scales. These higher order methods
provide estimates of greater certainty than lower tiers and have a closer link between biomass andsoil dynamics. Such systems may be GIS-based combinations of age, class/production datasystems with connections to soil modules, integrating several types of monitoring. Pieces of land
where a land-use change occurs can be tracked over time. In most cases these systems have a
climate dependency, and thus provide source estimates with interannual variability. Models should
undergo quality checks, audits, and validations.

3.1.6

Choice of Method

It is good practice to use methods that provide the highest levels of certainty, while using available resources as
efficiently as possible. The decision about what tier to use and where to expand resources for inventory
improvement should take into account whether the land use is a key category, as described in Chapter 5, Section
5.4 in this report. Guidance on methodological choice is provided in a set of decision trees, which are designed
to assess whether a source/sink category is a key category and which pools within a key category are consideredsignificant. Decision trees are applied at the sub-category level which corresponds roughly to carbon pools andsources of non-CO2 gases (see Table 3.1.3 for a list of subcategories). It is important to note that the key
category analysis is an iterative process and that initial estimates are needed for each sub-category to perform theanalysis. Figure 3.1.1 provides a generic decision tree to determine the appropriate methodological tier for lands
that begin and end an inventory period in the same use. This decision tree should be applied to subcategories
described in Sections 3.2.1, 3.3.1, 3.4.1, 3.5.1, 3.6.1, and 3.7.1. The figure uses Section 3.2.1, Forest land
Remaining Forest land, as an example. Figure 3.1.2 provides a generic decision tree to determine the appropriate
methodological tier for lands that changes uses during the inventory period, using Section 3.2.2, Lands
Converted to Forest land, as an example. This decision tree should be applied to subcategories described in
Sections 3.2.2., 3.3.2, 3.4.2, 3.5.2., 3.6.2., and 3.7.2.
The abbreviations FF, GG, CC, WW, SS, OO used in Figure 3.1.1 denote land-use categories undergoing noconversions; and the abbreviations LF, LG, LC, LW, LS, LO in Figure 3.1.2 denote land conversions to theseland-use categories:
FF
GG
CC
WW
SS
OO

=
=
=
=
=
=

forest land remaining forest landgrassland remaining grassland
cropland remaining croplandwetlands remaining wetlands
settlements remaining settlements
other land remaining other land

LF
LG
LC
LW
LS
LO

=
=
=
=
=
=

lands converted to forest land
lands converted to grassland
lands converted to croplandlands converted to wetlands
lands converted to settlements
lands converted to other land

These abbreviations have been used throughout Chapter 3 as subscripts for symbols in the equations.

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Chapter 3: LUCF Sector Good Practice Guidance

Figure 3.1.1

Decision tree for identification of appropriate tier-level for land remaining inthe same land use category (example given for forest land remaining forest
land, FF)

Repeat for each land use category:
- FF
- GG
- CC
- WW
- SS
- OO

Do managed
forests exist?
(Note 1)

No
Report “Not Occurring”

Yes

Repeat for each gas:
- CO2 (carbon)- CH4- N 2OIs FF akey category?
(Note 2)

No

Use tier level mostappropriate for available
data

Yes
Repeat for each subcategory*:- Biomass- Dead organic matter- SoilsAsk
for each subcategory under FF (Note 3):
Is this subcategory
significant?
(Note 4)

No

Are
No
country-specific data
available?
Yes

Yes
No

Are
country-specific data
available?

Develop or obtainrepresentative data
and EFs
Yes

Yes

Are
advanced methods
and detailed data for FF
available in yourcountry?

No

Use advanced methods
and detailed countryspecific data (Note 5)

Use countryspecific data
(Note 5)Use default data
(Note 5)(Tier 3)(Tier 2)(Tier 1)

Note 1: The use of 20 years, as a threshold, is consistent with the defaults contained in IPCC Guidelines. Countries may use different
periods where appropriate to national circumstances.
Note 2: The concept of key categories is explained in Chapter 5, Subsection 5.4 (Methodological Choice – Identification of Key
Categories).
Note 3: See Table 3.1.2 for the characterisation of subcategories.
Note 4: A subcategory is significant if it accounts for 25-30% of emissions/removals for the overall category.
Note 5: See Box 3.1.1 for definition of Tier levels.
* If a country reports harvested wood products (HWP) as a separate pool, it should be treated as a subcategory.

3.18

IPCC Good Practice Guidance for LULUCF

Introduction

Figure 3.1.2

Decision tree for identification of appropriate tier-level for land converted toanother land use category (example given for land converted to forest land,
LF)

Repeat for each land use category:
- LF
- LG
- LC
- LW
- LS
- LO

Are there
any land conversions toforest land?
(Note 1)

No
Report “Not Occurring”

Yes

Repeat for each gas:
- CO2 (carbon)
- CH4
- N2O

No

Is LF
a key category?
(Note 2)

Use tier level mostappropriate foravailable data

Yes
Repeat for each subcategory*:- Biomass- Dead organic matter- Soils

Ask for eachsubcategory under
LF (Note 3): Is this subcategory significant?
(Note 4)

No

Yes
No

Are
country-specificdata available?

No

Yes

Are
country-specific data
available?

Develop or obtainrepresentative data
and EFs

Yes

Yes

Are
advancedmethods and detailed
data for LF available
in yourcountry?

Use advanced methods
and detailed countryspecific data (Note 5)(Tier 3)No

Use countryspecific data
(Note 5)Use default data
(Note 5)(Tier 2)(Tier 1)

Note 1: The use of 20 years, as a threshold, is consistent with the defaults contained in the IPCC Guidelines. Countries may use different
periods where appropriate to national circumstances.
Note 2: The concept of key categories is explained in Chapter 5, Subsection 5.4 (Methodological Choice – Identification of Key
Categories).
Note 3: See Table 3.1.2 for the characterisation of subcategories.
Note 4: A subcategory is significant if it accounts for 25-30% of emissions/removals for the overall category.
Note 5: See Box 3.1.1 for definition of Tier levels.
* If a country reports harvested wood products (HWP) as a separate pool, it should be treated as a subcategory.

IPCC Good Practice Guidance for LULUCF

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3.1.3
SUBCATEGORIES WITHIN A GIVEN LAND USE SECTION

Gas

Subcategory

CO2

Living Biomass
Dead Organic Matter
Soils

N2O

Fire
Soil Organic Matter Mineralization
Nitrogen Inputs
Cultivation of organic soils

CH4

3.1.7

Fire

Reporting

It is good practice to conduct key category assessments for each land use category using the guidance provided
in this chapter and in Chapter 5 Section 5.4:•

Within each land use category designated as key, to assess which subcategories are significant; and•Use the results of this analysis to determine what categories and subcategories should be prioritised in termsof methodological choice.

Reporting categories are divided into greenhouse gases and land uses i.e., lands remaining in a use and lands
converted to that use. Category estimates are a compilation of individual subcategories. Table 3.1.3 shows thesubcategories within each reporting category. The reporting tables are given in Annex 3A.2. When compilingemissions and sinks estimates from land use, land-use change, and forestry with other elements of national
greenhouse gas inventories, consistent signs (+/-) must be followed. In final reporting tables, emissions(decrease in the carbon stock, non-CO2 emissions) are always positive (+) and removals (increase in the carbon
stock) negative (-). For calculating initial estimates, this chapter follows the convention used in Chapter 5 of the
IPCC Guidelines in which net increases of carbon stocks are positive (+) and net decreases are negative (-). As
is the case in the IPCC Guidelines, the signs of these values need to be converted in the final reporting tables inorder to maintain consistency with other sections of national inventory reports.

Units
Units of CO2 emissions/removals and emissions of non-CO2 gases are reported in gigagrams (Gg). To convert
tonnes C to Gg CO2, multiply the value by 44/12 and 10-3. To convert unit from kg N2O-N to Gg N2O, multiply
the value by 44/28 and 10-6.

Convention
For the purpose of reporting, which is consistent with the IPCC Guidelines, the signs for removal (uptake) arealways (-) and for emissions (+).

3.1.8

Generic Climatic Zones

Some default values in this chapter are provided by climatic zones. Figure 3.1.3 provides the global delineationof these zones. In comparison to the IPCC Guidelines this figure only holds polar/boreal as additional classes.

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Introduction

Figure 3.1.3 Delineation of major climate zones, updated from the IPCC Guidelines. Temperature zones are defined by mean annual temperature (MAT):
Polar/boreal (MAT<0 °C), Cold temperate (MAT 0-10 °C), Warm temperate (MAT 10-20 °C) and Tropical (MAT>20 °C). Moisture regimes for boreal and temperate zones aredefined by the ratio of mean annual precipitation (MAP) and potential evapotranspiration (PET): Dry (MAP/PET < 1) and Wet (MAP/PET > 1); and for tropical zones by
precipitation alone: Dry (MAP < 1000 mm), Moist (MAP 1000-2000 mm) and Wet (MAP > 2000 mm).
Precipitation and temperature data are from UNEP-GRID.

http://www.grid.unep.ch/data/grid/climate.php

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Forest Land

3.2

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

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

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

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

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

3.2.1

Forest Land Remaining Forest Land

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

1

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

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

3.2.1.1

C HANGE IN CARB ON STOCKS IN LIVI NG BIOMASS

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

3.2.1.1.1

METHODOLOGI CAL ISSUES

3.2.1.1.1.1

Choice of Method

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

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

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

GW = IV ● D ● BEF1

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

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

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

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

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

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Forest Land

Lfuelwood = annual carbon loss due to fuelwood gathering, tonnes C yr-1 (See Equation 3.2.8)
Lother losses = annual other losses of carbon, tonnes C yr-1 (See Equation 3.2.9)

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

= annually extracted volume, roundwood, m3 yr-1

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

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

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

(ii)

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

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

3.2.1.1.1.2

Choice of Emission/Removal Factors

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

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Forest Land

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

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Forest Land

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

Table 3A.1.12 provides default values of combustion factor to be used as (1– fBL) in case the country has
good growing stock biomass data; in this case the share lost is used;•

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

Tier 2: Under Tier 2, biomass growing stock changes due to major disturbances will be taken into account by
forest category, type of disturbance and intensity. Average values for biomass stocks are obtained from national
data.
Tier 3: Estimation of growth rate using two inventories and the loss of biomass from disturbances that have
happened between the inventories are included. If the year of the disturbance is unknown, the result will be a
reduction of the average growth rate for the period. If disturbances occur after the last inventory, losses will haveto be calculated similar to Tier 2 approach.
A database on rate and impact of natural disturbances by type, for all European countries (Schelhaas et al.,2001), can be found at: http://www.efi.fi/projects/dfde
A UNEP database on global burnt area can be found at:
http://www.grid.unep.ch/activities/earlywarning/preview/ims/gba/
However, one should note that the UNEP database is only valid for year 2000. In many countries interannual
variability in burned area is large, so these figures will not provide a representative average.

3.2.1.1.1.3

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

3.2.1.1.1.4

Uncertainty Assessm ent

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

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

3.2.1.2

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

∆CFFDOM = ∆CFFDW + ∆CFF LT
Where:

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Forest Land

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

3.2.1.2.1

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

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

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

3.2.1.2.1.1

Choice of Method

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

= area of managed forest land remaining forest land, ha

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Chapter 3: LUCF Sector Good Practice Guidance

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

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

Needleleaf
Evergreen

Broadleaf
Deciduous

Needleleaf
Evergreen

Litter carbon stock ofmature forests

Length oftransition period(tonnes C ha-1)25
31(10-58)(6-86)(years)Broadleaf
Deciduous

Needleleaf
Evergreen

Broadleaf
Deciduous

Needleleaf
Evergreen

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

50

80

0.5

0.4

1.2

1.6

Boreal, moist

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

50

80

0.8

0.7

2.0

2.8

Cold Temperate,dry

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

50

80

0.6

0.4

1.4

1.4

Cold temperate,
moist

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

50

50

0.3

0.5

0.8

1.3

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

75

75

0.4

0.3

1.4

1.0

Warm temperate,
moist

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

50

30

0.3

0.7

0.6

1.1

Subtropical

2.8
(2-3)

4.1

20

20

0.1

0.2

0.1

0.2

Tropical

2.1
(1-3)

5.2

20

20

0.1

0.3

0.1

0.3

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

b

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

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

3.2.1.2.1.2

Choice of Emission/Removal Factors

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

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

Tropical forest

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

Evergreen forest

0.0116

1.059/49

Deciduous forest

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

0.682/29

BiomeaTropical forest

Coefficient of Variation/Number of stands
0.616/61

Coefficient of Variation/Number of stands
2.12/37

Evergreen forest

43.4

1.12/64

Deciduous forest

34.7

1.00/62

Average (median) dead:live ratio

Coefficient of Variation/Number of stands

Tropical forest

0.11

0.75/10Evergreen forest

0.20

1.33/18

Deciduous forest

0.14

0.77/19

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

For delineation of biomes, see Figure 3.1.3.

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

3.2.1.2.1.3

Choice of Activity Data

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

3.2.1.2.1.4

Uncertainty Assessm ent

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

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

3.2.1.3

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

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

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Forest Land3.2.1.3.1

METHODOLOGI CAL ISSUES

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

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

(ii)

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

(iii)

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

Soil organic carbon(g C pe r ha)

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

S OC jS OC i

T ime

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

3.2.1.3.1.1

Choice of Method

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

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

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Forest Landfdist regime (i) = adjustment factor reflecting the effect of a change in the disturbance regime to state i withrespect to the native forest, dimensionless
The transition from SOCi to SOCj is assumed to take place over a transition period of T years (default = 20
years). In other words ∆C > 0 as long as fewer than T years have elapsed since the onset of changes in forest
type, management practices, or disturbance regime. The total SOC changes in any year equals the sum of theannual emissions/removals for all forest lands having undergone changes in forest types, management practices
or disturbance regimes for a period of time shorter than T years.
The decision tree in Figure 3.1.1 (Section 3.1) provides assistance in the selection of the appropriate tier level forthe implementation of estimation procedures.
Tier 1: This tier is used for countries using the default procedure in the IPCC Guidelines, or for which thissubcategory is not significant, and little or no country-specific data exist on the SOC of mineral forest soils under
dominant forest types, management practices and disturbance regimes. Under Tier 1, it is assumed that when
forest remains forest the carbon stock in soil organic matter does not change, regardless of changes in forest
management, types, and disturbance regimes (i.e. SOCj = SOCi = … = SOCn) in other words that the carbon
stock in mineral soil remains constant so long as the land remains forest.
Tier 2: Countries where this subcategory is significant should develop or select representative adjustment factors
fforest type, fman intensity, and fdist regime reflecting the impact on mineral SOC of different forest types, management
practices or disturbance regimes, and SOCref for their own native, unmanaged forest ecosystems. Domestic
values for the transition period T should be developed, and the assumption of linear rates of SOC change can be
modified to better reflect the actual temporal dynamics of soil carbon sequestration or release.
Tier 3: Tier 3 is appropriate for countries where emissions/removals in the mineral soils of managed forests areimportant, while current knowledge and available data allow the development of an accurate and comprehensive
domestic estimation methodology. This involves the development, validation and implementation of a domestic
monitoring scheme and/or modelling tool and its associated parameters. The basic elements of any countryspecific approach are (adapted from Webbnet Land Resource Services Pty ltd, 1999):•

Stratification by climatic zones, major forest types and management regimes coherent with those used forother sections of the inventory, especially the other carbon pools under this Section 3.2.1;•

Determination of dominant soil types in each stratum;•

Characterisation of corresponding soil carbon pools, identification of determinant processes in SOC input
and output rates and the conditions under which these processes occur; and•Determination and implementation of suitable methods to estimate carbon emissions/removals from forest
soils for each stratum on an operational basis, including validation procedures; methodologicalconsiderations should include the combination of monitoring activities – such as repeated forest soil
inventories - and modelling studies, and the establishment of benchmark sites. Further guidance on good soil
monitoring practices is available in the scientific literature (Kimble et al., 2003; Lal et al., 2001; McKenzie
et al., 2000), and Section 5.3 provides generic guidance on sampling techniques. Models developed or
adapted for this purpose should be peer-reviewed, and validated with observations representative of theecosystems under study and independent from the calibration data.

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

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

1.36

0.82 – 3.82

Temperate forests

0.68

0.41 – 1.91

Boreal forests

0.16

0.08 – 1.09

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

3.2.1.3.1.2

Choice of Emission/Removal Factors

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

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Forest Land

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

LAC soils2

Sandy soils3

Spodic soils4

Volcanic soils5

Wetlands
soils6

Boreal

68

NA

10#

117

20#

146

#

Cold temperate, dry

50

33

34

NA

20

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry
Warm temperate,
moist
Tropical, dry

38

24

19

NA

70#

88

63

34

NA

80

38

35

31

NA

50#

Tropical, moist

65

47

39

NA

70#

NA

#

Tropical, wet

44

60

66

130

87

88

86

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

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

2

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

3

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

4

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

5

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

6

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

EQUATION 3.2.16
SOIL ORGANIC CARBON CONTENT

SOC =horizon = n∑horizon =1

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

horizon = 1

Where:
SOC = representative soil organic carbon content for the forest type and soil of interest, tonnes C ha-1
SOChorizon = soil organic carbon content for a constituent soil horizon, tonnes C ha-1
[SOC] = concentration of soil organic carbon in a given soil mass obtained from lab analyses, g C (kgsoil)-1
Bulk Density = soil mass per sample volume, tonnes soil m-3 (equivalent to Mg m-3)
Depth = horizon depth or thickness of soil layer, mfrag = % volume of coarse fragments/100, dimensionless 2

Country- or region-specific values should be elaborated for the stable SOCi, SOCj, for the major combinations offorest types, management practices and disturbance regimes. Priority should be given to the factors that have the2[SOC] is usually determined on the fine earth fraction (commonly < 2 mm). The bulk density should be corrected for the
proportion of the soil volume occupied by coarse fragments (e.g. particles with a diameter > 2 mm).

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3.43

Chapter 3: LUCF Sector Good Practice Guidancelargest overall effect, taking into account the impact on forest SOC and the extent of affected forests.
Management practices can be coarsely labelled as intensive (e.g. plantation forestry with intensive site
preparation and fertilisation) or extensive (natural forests with minimum intervention); these categories can also
be redefined according to national circumstances. The development of adjustment factors is likely to be based on
intensive studies at experimental sites and sampling plots involving replicated, paired site comparisons (Johnson
et al., 2002; Olsson et al., 1996; see also the reviews by Johnson & Curtis, 2001 and Hoover, 2003.) In practice,
it may not be always possible to separate the effects of a different forest types, intensive management practices
and altered disturbance regimes, in which case some adjustment factors can be combined into a single modifier.
If a country has well-documented data for different forest types under different management regimes it might bepossible to derive SOCi directly without using reference carbon stocks and adjustment factors. Estimating theeffect of changing disturbance regimes over vast areas through sampling studies may create intractable logisticalproblems. Modelling studies provide an alternative approach for the derivation of these adjustment factors(Bhatti et al., 2001).
The duration of transition periods T between stable SOCi can be estimated from long-term monitoring ofchanges in forest SOC. The assumption of a linear rate of carbon stock changes during the transition from one
forest type/management regime to another can also be reassessed.
Tier 3: Country-specific methodologies and parameters are expected to be based on rigorous monitoring
programs, coupled with empirical and/or process modelling studies. The national system must represent all
significant forest types, management practices and disturbance regimes. Models must be validated withindependent observations from country or region-specific studies that cover the range of climatic conditions, soil
types and management practices. The same quality criteria as described under Tier 2 apply to SOC data.
Documentation on the structure, update frequency and procedures, and QA/QC procedures of SOC databases
should also be available.
O R GA N IC SO I L S
The parameters to be estimated are emission factor(s) for CO2 from drained organic forest soils: EFDrainage.
Tier 1: Table 3.2.3 provides default values for EFDrainage, derived from corresponding values for the conversion
to pastures/forests in the IPCC Guidelines, (Reference Manual, Section 5.3.9). These values apply for as long as
a drained organic soil remains.
Tier 2: Countries which develop their own emission factors or adopt ones that are different from the default
values should provide scientifically-based evidence of their reliability and representativeness, document theexperimental procedures used to derive them, and provide uncertainty estimates.

3.2.1.3.1.3

Choice of Activity Data

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

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Forest Land

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

3.2.1.3.1.4

Uncertainty Assessm ent

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

3.2.1.4

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

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

IPCC Good Practice Guidance for LULUCF

3.45

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

3.2.1.4.1

METHODOLOGI CAL ISSUES

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

3.2.1.4.1.1

Choice of Method

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

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

3.2.1.4.1.2

Choice of Emission/Removal Factors

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

3.2.1.4.1.3

Choice of Activity Data

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

3.2.1.4.1.4

Uncertainty Assessm ent

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

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

3.2.1.4.2

METHODOLOGI CAL ISSUES

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

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

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Forest Land

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

3.2.1.4.2.1

Choice of Method

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

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

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

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

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

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

C

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

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

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

Fire impact in unmanaged forest lands should not be reported.

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Chapter 3: LUCF Sector Good Practice Guidance

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

3.2.1.4.2.2

Choice of Removals/ Emission Factors

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

3.2.1.4.2.3

Choice of Activity Data

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

3.2.1.4.2.4

Uncertainty Assessm ent

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

3.2.2

Land Converted to Forest Land

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

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

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

3.2.2.1

C HANGE IN CARB ON STOCKS IN LIVI NG BIOMASS

3.2.2.1.1

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

3.2.2.1.1.1

Choice of Method

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

6

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

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

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

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

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Forest Land

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

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

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

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

3.2.2.1.1.2

Choice of Emission/Removal Factors

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

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Forest Land

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

3.2.2.1.1.3

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

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Chapter 3: LUCF Sector Good Practice Guidance

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

3.2.2.1.1.4

Uncertainty Assessm ent

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

3.2.2.2

C HANGE IN CARB ON STOCKS IN DEAD ORGANIC MATTER

3.2.2.2.1

METHODOLOGI CAL ISSUES

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

3.2.2.2.1.1

Choice of Method

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

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

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

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Forest Land3.2.2.2.1.2

Choice of Emission/Removal Factors

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

3.2.2.2.1.3

Choice of Activity Data

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

3.2.2.2.1.4

Uncertainty Assessm ent

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

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

3.2.2.3

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

3.2.2.3.1

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

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

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

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

Characteristics

Treatment

Activity data
Omission of managed forest
areas

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

Document and monitor forest types, management
practices and disturbances.

Omission of relevant changes
in events or practices.

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

State and document; discuss likely effect on estimate
validity

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

Areas or locations are not accurately mapped

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

Lack of proper stratification

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

Enhance the power of the sampling design throughimproved stratification

Use of default classification

National land-use classification incompatible
with IPCC default

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

Default values do not represent national
circumstances

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

Sampling design

Stratification, sampling intensity, incompletely
capture spatial variability

Quantify random uncertainty (see Chapter 5 or
GPG2000)

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

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

Layer thickness

Only superficial (0-30 cm) soil samples werecollected

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

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

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

Inconsistent identification of soil horizons orreference depths

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

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

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

Coarse fragments

No assessment of the volume or mass of coarse
fragments

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

Carbon concentration

Analytical methods for C analyses havechanged

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

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

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

Follow guidance in Chapter 5 for scaling-up

3.2.2.3.1.1

Choice of Method

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

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

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

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Forest Land

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

3.2.2.3.1.2

Choice of Emission/Removal Factors

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

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

3.2.2.3.1.3

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

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Forest Land

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

3.2.2.3.1.4

Uncertainty Assessm ent

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

3.2.2.4

N ON -CO 2 GREENH OUSE GAS EMISSIO NS

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

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

3.2.3

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

3.2.4

Developing a Consistent Time Series

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

3.2.5

Reporting and Documentation

The categories described in Section 3.2 can be reported using the reporting tables in Annex 3A.2. The general
requirements for reporting and documentation are set out in Chapter 5 of this report and in general it is a goodpractice to archive and document all data and information (such as figures, statistics, sources of assumptions,modeling approaches, uncertainty analyses validation studies, inventory methods, research experiments,

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Forest Landmeasurements arising from field site studies, associated protocols, and other basis of basic data) applied to
produce the national emissions/removals inventory. Elaborations on pool definition should be reported, anddefinitions relevant to determining the extent of the managed land included in the inventory, together withevidence that these definitions have been applied consistently over time.
Documentation is also needed for demonstrating completeness, consistency of time series data and methods forinterpolating between samples and methods for interpolating between samples and years, and for recalculating,and avoidance of double counting as well as for performing QA/QC.
As Parties decide to progress through higher tier levels, whose calculation methods and data are not described in
the IPCC Guidelines or characterised by more disaggregated approaches, additional documentation is required tosupport the use of more advanced and accurate methodologies, country-defined parameters, and high resolution
maps and data sets. However, at all tier levels, explanation is needed for decisions regarding choice of
methodology, coefficients, and activity data. The aim is to facilitate reconstruction of the estimates by
independent third parties, but it may prove impractical to include all documentation necessary in the national
inventory report. The inventory should therefore include summaries of approaches and methods used, andreferences to source of data such that the reported emissions estimates are transparent and steps adopted in theircalculation may be retraced.
Documentation is particularly important where the approach, calculation methods and data are not described in
the IPCC Guidelines, as in higher tier or more disaggregated approaches. In addition, it is a good practice toprovide documentation on:
Emission factors: Sources of the emission factors that were used (specific IPCC default values or otherwise)
have to be quoted. If country- or region-specific emission factors were used, and if new methods (other than thedefault IPCC methods) were used, the scientific basis of these emission factors and methods should becompletely described and documented. This includes defining the input parameters and describing the process bywhich these emission factors and methods are derived, as well as describing sources and magnitudes of
uncertainties. Inventory agencies using country-specific emission factors should provide information of the basis
for the selection of a different factor, describe how it was derived, compare it to other published emission
factors, explain any significant differences, and attempt to place bounds on the uncertainty.
Activity data: Sources of all activity data, such as areas, soil types and characteristics and vegetation covers,
used in the calculations should be provided (i.e. complete citations for the statistical databases from which data
were drawn). Reference to the metadata for the databases are useful, including information on dates andfrequency of data collection, sampling procedures, analytical procedures used to obtain soil characteristics andminimum detectable change in organic carbon, and estimates of accuracy and precision. When activity data were
not obtained directly from databases, the information and assumptions that were used to derive the activity data
should be provided, as well as estimates of the uncertainty associated to the derived activity data. This applies inparticular when scaling up procedures were used to derive large-scale estimates; in these cases the statisticalprocedures should be described along with the associated uncertainty.
Results of model simulations: If inventory agencies used data output from models in their estimation
procedures, the rationale for model selection and use should be provided. It is a good practice to provide
complete citations of peer-reviewed publications in which the model is described, and modelling results areinterpreted and validated. Detailed information should be provided to enable reviewers to assess the model’svalidity, including the general modelling approach, key model assumptions, input and output data, parametervalues and parameterisation procedures, confidence intervals of model outputs, and the outcome of any
sensitivity analysis conducted on the output.
Analysis of emissions: Significant fluctuations in emissions between years should be explained. A distinction
should be made between changes in activity levels and changes in emission coefficients from year to year, andthe reasons for these changes documented. If different emission factors are used for different years, the reasonsfor this should be explained and documented.
Non-CO2 greenhouse gases: the requirements on reporting follow three same principles as for CO2, butparticular attention needs to be given to methods for avoidance of omission or double counting with respect toagriculture and between forest land remaining forest land, and transitions to forest land. Clarity is also needed on
coverage, between emissions estimated using the guidance in this chapter and any use made of the guidance in
the Annex 3A.2 (Reporting Tables and Worksheets). In view of the uncertainties clarity in methods andreporting may help advance scientific knowledge as well as serve the purposes of inventory review.

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3.2.6

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

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Cropland

3.3

CROPLANDThis section provides Good Practice Guidance on inventorying and reporting greenhouse gas emissions andremovals in ‘cropland remaining cropland (CC)’ and ‘land converted to cropland’ (LC). Cropland includes allannual and perennial crops as well as temporary fallow land (i.e., land set at rest for one or several years before
being cultivated again). Annual crops may include cereals, oils seeds, vegetables, root crops and forages.
Perennial crops can include trees and shrubs, in combination with herbaceous crops (e.g. agroforestry) or as
orchards, vineyards and plantations such as cocoa, coffee, tea, oil palm, coconut, rubber trees, and bananas,
except where these lands meet the criteria for categorisation as forest land.1 Arable land which is normally used
for cultivation of annual crops but which is temporarily used for forage crops or grazing as part of an annual
crop-pasture rotation is included under cropland.
The amount of carbon stored in and emitted or removed from permanent cropland depends on crop type,management practices, and soil and climate variables. For example, annual crops (e.g. cereals, vegetables) are
harvested each year, so there is no long-term storage of carbon in biomass. However, perennial woody
vegetation in orchards, vineyards, and agroforestry systems can store significant carbon in long-lived biomass,
the amount depending on species type, density, growth rates, and harvesting and pruning practices. Carbon
stocks in soils can be significant and changes in stocks can occur in conjunction with most management practices,
including crop type and rotation, tillage, drainage, residue management and organic amendments.
The conversion of other land uses into cropland can affect carbon stocks and other greenhouse gases in a variety
of ways. Land-use conversions to cropland from forest land, grassland and wetlands usually result in a net loss ofcarbon from biomass and soils to the atmosphere. However, cropland established on previously sparsely
vegetated or highly disturbed lands (e.g. mined lands) can result in a net gain in both biomass and soil carbon.
The term land-use conversion refers only to lands coming from one type of use into another. In cases where
existing perennial cropland is replanted to the same or different crops, the land use remains cropland; therefore,
the carbon stock changes should be estimated using the methods for cropland remaining cropland, as describedin Section 3.3.1 below.
For cropland remaining cropland, emissions of methane (CH4) and nitrous oxide (N2O) from the management of
permanent agricultural lands are covered in Chapter 4 of the IPCC report on Good Practice Guidance andUncertainty Management in National Greenhouse Gas Inventories (GPG2000). This report provides guidanceon inventorying and reporting of N2O emissions from land-use conversions to cropland as a result of soiloxidation.
In this section, guidance on the use of basic and advanced methodologies for inventorying and reporting
emissions and removals for cropland remaining cropland and land converted to cropland is provided for biomass
and soil carbon pools. Methodologies follow a hierarchical tier structure where Tier 1 methods use default values,
typically with limited disaggregation of area data. Tier 2 corresponds to use of country-specific coefficients andtypically finer scale area disaggregation, which will reduce uncertainty in emission/removal estimates. Tier 3
methods refer to the use of country-specific approaches, which may include process models and detailed
inventory measurements. Where possible, default values from the IPCC Guidelines are updated and new default
values are provided based on the most up-to-date research findings.

3.3.1

Cropland Remaining Cropland

Emissions and removals from cropland remaining cropland can include two subcategories of CO2
emissions/removals. Equation 3.3.1 summarises net emissions or removals of carbon from cropland remaining
cropland for these subcategories: changes in carbon stocks in living biomass (Section 3.3.1.1) and changes incarbon stocks in soils (3.3.1.2). As noted above, emissions of CH4 and N2O are estimated as part of theAgriculture Chapter in the IPCC Guidelines and GPG2000. Table 3.3.1 summarises the methodological tiers foreach of the two subcategories covered below.

1

As described in Chapter 2, Section 2.2 (Land categories), the IPCC does not provide a single definition for forest or otherland uses. Rather, countries should determine their own definition for the purposes of inventory reporting. It is goodpractice to use clear definitions in the inventory report (include threshold values, e.g. for tree cover, land area, and treeheight) and to ensure that the categorisation is consistent across inventory reports and with other land use definitions.

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EQUATION 3.3.1
ANNUAL CHANGE IN CARBON STOCKS IN CROPLAND REMAINING CROPLAND∆CCC = ∆CCCLB + ∆CCCSoils
Where:∆CCC = annual change in carbon stocks in cropland remaining cropland, tonnes C yr-1
∆CCCLB= annual change in carbon stocks in living biomass, tonnes C yr-1

∆CCCSoils = annual change in carbon stocks in soils, tonnes C yr-1
To convert tonnes C to Gg CO2, multiply the value by 44/12 and 10-3. For the convention (signs), refer to Section
3.1.7 or Annex 3A.2 (Reporting Tables and Worksheets).
TABLE 3.3.1
TIER DESCRIPTIONS FOR SUBCATEGORIES UNDER CROPLAND REMAINING CROPLAND
Tier

Tier 1

Tier 2Tier 3Use default coefficients forcarbon accumulation and loss
rates. The average area of
perennial woody crops isestimated by climate region.

Use at least some country-specificvalues for carbon accumulation andloss rates. Use detailed annual or
periodic surveys to estimate the areaof land in perennial woody crops,
disaggregated to scales that matchthe country-specific rates. Consider
including belowground biomass inestimate, if data are available. May
rely on alternate approach of
measuring or estimating carbon
stocks at two points in time, in lieu
of developing rates of change incarbon stocks.

Use highly disaggregated area
estimates for detailed
categories of perennial woody
crops (e.g., coffee, orchards,
intercropping systems).

For both mineral and organic soils
use some combination of default
and/or country-specific coefficientsand area estimates of increasingly
finer spatial resolution. For
emissions from liming, use emission
factors differentiated by forms oflime.

Use country-specific approachat fine spatial scale (e.g.,modeling, measurement)

Subcategories
Living Biomass(for perennial
woody crops)

Applies country-specific rates
or estimates of carbon stockchanges in the specificperennial woody crop systems.
May use a country-specificapproach at fine spatial scale(e.g., modeling, measurement)
provided it yields a moreaccurate estimate of carbon
stock changes.

Soils

For changes in soil carbon
from mineral soils use default
coefficients. Areas should be
stratified by climate and soil
type. For changes in soil
carbon from organic soils use
default coefficients andstratify the areas by climatic
region. For emissions fromliming, use default emission
factors as given in IPCC
Guidelines.

3.3.1.1

C HANGE IN CARB ON STOCKS IN LIVI NG BIOMASS

Carbon can be stored in the biomass of croplands that contain perennial woody vegetation, including, but notlimited to, monocultures such as coffee, oil palm, coconut, and rubber plantations, and fruit and nut orchards,
and polycultures such as agroforestry systems. The basic methodology for estimating changes in woody biomass
is provided in the IPCC Guidelines Section 5.2.2 (Changes in Forest and Other Woody Biomass Stocks) and in
Section 3.2.1.1 (Changes in Carbon Stocks in Living Biomass) under Section 3.2.1 (Forest land Remaining
Forest land) of this report. This section elaborates these methodologies with respect to estimating changes incarbon stocks in living biomass in cropland remaining cropland.

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Cropland

3.3.1.1.1 METHODOLOGICAL ISSUES
The change in biomass is only estimated for perennial woody crops. For annual crops, increase in biomass stocks
in a single year is assumed equal to biomass losses from harvest and mortality in that same year - thus there is no
net accumulation of biomass carbon stocks.
The principal equation for total change in carbon stocks of living biomass in perennial woody crops on cropland(∆CCCLB) is the same as Equation 3.2.2 in Section 3.2.1 (Forest land Remaining Forest land), with the only
difference being that estimates of carbon stock changes apply to aboveground biomass only because limited data
are available on belowground biomass. Default growth and loss rates are given in Table 3.3.2.
TABLE 3.3.2
DEFAULT COEFFICIENTS FOR ABOVEGROUND WOODY BIOMASS AND HARVEST CYCLES IN CROPPING SYSTEMS CONTAINING
PERENNIAL SPECIESClimate region

Abovegroundbiomass carbon
stock at harvest-1

Harvest/Maturity
cycle

Biomass
accumulation rate(G)
-1

Biomass carbon loss
(L)

Error range1(tonnes C ha-1)-1(tonnes C ha )(yr)(tonnes C ha yr )Temperate (allmoisture regimes)

63

30

2.1

63

+ 75%

Tropical, dry

9

5

1.8

9

+ 75%

Tropical, moist

21

8

2.6

21

+ 75%

Tropical, wet

50

5

10.0

50

+ 75%

Note: Values are derived from the literature survey and synthesis published by Schroeder (1994).
1

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

Currently, there is not sufficient information to provide a basic approach with default parameters to estimate
carbon stock changes in dead organic matter pools in cropland remaining cropland.

3.3.1.1.1.1 Choice of Method
To estimate change in carbon in cropland biomass (∆CCCLB), there are two alternative approaches: (a) estimate
annual rates of growth and loss (Equation 3.2.2 in Forest land section) or (b) estimate carbon stocks at two points
in time (Equation 3.2.3 also in Forest land section). The first approach is developed below as the basic Tier 1
method; it can also serve as a Tier 2 or 3 method with refinements described below. The second approach isdeveloped as either a Tier 2 or Tier 3 method.
As described in more detail below, Tier 1 is based on highly aggregated area estimates for generic perennial
woody crops using default carbon accumulation rates and carbon losses. A Tier 2 estimate, in contrast, will
generally develop estimates for the major woody crop types by climate zones, using country-specific carbon
accumulation rates and stock losses where possible or country-specific estimates of carbon stocks at two points
in time. A Tier 3 estimate will use a highly disaggregated Tier 2 approach or a country-specific method
involving process modeling and/or detailed measurement. All countries should strive for improving inventory
and reporting approaches by advancing to the highest tier possible given national circumstances. It is goodpractice for countries to use a Tier 2 or Tier 3 approach if carbon emissions and removals in cropland remaining
cropland is a key category and if the subcategory of living biomass is considered significant based on principles
outlined in Chapter 5. Countries should use the decision tree in Figure 3.1.1 to help with the choice of method.
Tier 1: The basic method is to multiply the area of perennial woody cropland by a net estimate of biomass
accumulation from growth and subtract losses associated with harvest or other removals (according to Equation
3.2.2. in the Forest land section). Losses are estimated by multiplying a carbon stock value by the area ofcropland on which perennial woody crops are harvested or removed.
Default Tier 1 assumptions are: all carbon in perennial woody biomass removed (e.g., biomass cleared andreplanted with a different crop) is emitted in the year of removal; and perennial woody crops accumulate carbon
for an amount of time equal to a nominal harvest/maturity cycle. The latter assumption implies that perennial
woody crops accumulate biomass for a finite period until they are removed through harvest or reach a steadystate where there is no net accumulation of carbon in biomass because growth rates have slowed and incremental
gains from growth are offset by losses from natural mortality, pruning or other losses.
At Tier 1, default factors, which are discussed in more detail in Section 3.3.1.1.1.2 and Table 3.3.2., are applied
to nationally derived estimates of land areas (A in Equation 3.2.4. in Forest land section).

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Chapter 3: LUCF Sector Good Practice Guidance

Example 1: In the inventory year, 90,000 hectares of perennial woody crops are cultivated in a
tropical moist environment, while 10,000 ha are removed. The immature perennial woody
cropland area accumulates carbon at a rate of approximately 2.6 tonnes of C ha-1 yr-1. The area
harvested loses all carbon in biomass stocks in the year of removal. Default carbon stock losses fora tropical moist perennial woody cropland are 21 tonnes C ha-1 yr-1. Using equation 3.2.2, anestimated 234,000 tonnes C accumulates per year and 210,000 tonnes C are lost. The net change incarbon stocks in the tropical moist environment are 24,000 tonnes C yr-1.
Tier 2: One of two alternative approaches can be used at Tier 2. In principle, either approach should yield thesame answer.
The approaches include:•

Extending Tier 1 by matching more disaggregated area estimates (e.g., by specific perennial woody crop
types and detailed climate regions) with at least some country-specific carbon accumulation and harvest dataapplicable at the same scale. Countries should prioritize development of country-specific parameters byfocusing on either the most common perennial woody crops or the systems with relatively high levels of
perennial woody biomass per unit of land (i.e., high carbon stocks). Guidance on developing countryspecific parameters is provided in Section 3.3.1.1.1.2; or,•Estimating total carbon stocks in perennial woody crops at regular time intervals (following Equation 3.2.3of the Forest land section).

Tier 3: Tier 3 approaches are either highly disaggregated Tier 2 approaches that are parameterized withcountry-specific carbon stock and carbon stock change values or they are country-specific methods such as useof models or repeated measurements of stocks such as those obtained using detailed forest inventories (seeSection 3.2.1.1.1). For example, well validated and species-specific growth models and detailed information on
harvest and pruning practices could be used to estimate annual growth rates, analogous to Equation 3.2.2. This
would require information on the area of woody biomass crops by species and age class, as well as data on
climate, soil and other growth limiting conditions for specific areas. Alternatively, periodic sampling-based stock
estimates (and associated models), similar to those used in detailed forest inventories could be applied toestimate stock changes as in Equation 3.2.3.

3.3.1.1.1.2 Choice of Emission/Removal Factors
Emission/removal factors for this methodology include the biomass accumulation (G) and loss rates (L). Table
3.3.2 provides default values for G and L across four general climate regions based on a published review ofcarbon stock research on agroforestry systems (Schroeder, 1994). Additional data in Table 3.3.2 highlightunderlying assumptions of the default data (e.g., time to harvest/maturity) and demonstrate how the defaults
were derived. The default annual growth rate (G) is derived by dividing biomass stocks at maturity by the timefrom crop establishment to harvest/maturity. The default annual loss rate is equal to biomass stocks at harvest,
which are assumed removed entirely in the year of removal. For an individual country, these defaults are highly
uncertain as they represent generic perennial woody biomass crop systems for broad climatic regions. Woody
crops vary greatly in their uses, growth and harvest rates, and degree of association with other non-woody crops
and thus the application of simple default factors will only coarsely approximate carbon changes.
When using the Tier 2 approach, biomass stocks, harvest cycles and carbon accumulation rates can be estimatedfrom country or region specific research results on perennial woody crop systems conducted by national experts.
Woody crops vary greatly, from annually harvested species used for green manure and fuel wood to potentiallylong-lived woody crops such as fruit orchards. It is important in deriving estimates of biomass accumulation
rates to recognize that net increases in biomass stocks will occur primarily during the first years following initialestablishment or regrowth of the woody crops. While some longer-lived orchard crops may not be subject to aregular removal and replanting cycle, losses due to pruning and tree replacement are likely to largely offset new
growth so that in mature crops net biomass stock increases will be near zero. Thus, at the country-level, net
increases in biomass carbon stocks would occur primarily where the area of cropland with woody crops isincreasing relative to other land uses having lower carbon stocks or where the proportion of land subject toremovals is less than the average dictated by the normal harvest frequency (e.g. if the land area is dominated byyoung, recently established woody crops). Conversely, net biomass losses at the country-level would occur when
woody crops are replaced by other annual cropland systems or when the harvest frequency of woody crops isincreasing.
To further improve estimates of carbon accumulation in perennial woody crop biomass, countries may conduct
field research to measure carbon stock changes or accumulation rates. Research studies should be based on
sound scientific principles and follow general approaches laid out by other similar studies (Dixon et al., 1993;
Schroeder, 1994; Schroth et al., 2002; and Masera et al., 2003). Results from field research should be compared

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Croplandto estimates of carbon accumulation rates from other sources to verify that they are within documented ranges.
Reported carbon accumulation rates may be modified based on additional data and expert opinion, provided
clear rationale and documentation are included in the inventory report.

3.3.1.1.1.3 Choice of Activity Data
Activity data in this section refer to estimates of land areas (AG, AL) of growing stock and harvested land inperennial woody crops. Chapter 2 provides general guidance on approaches for obtaining and categorising areaby different land use classes. For estimating emissions and removals from this source, countries need to obtainarea estimates for land in perennial woody crops, disaggregated as required to correspond to the available
emission factors and other parameters.
Tier 1: Under Tier 1, annual or periodic surveys are used in conjunction with the approaches outlined in
Chapter 2 to estimate the average annual area of established perennial woody crops and the average annual areaof perennial woody crops that are harvested or removed. The area estimates are further subdivided into generalclimate regions to match the default G and L values. Under Tier 1 calculations, international statistics such as
FAO databases, IPCC Guidelines and other sources can be used to estimate the area of land in perennial woody
crops.
Tier 2: For the Tier 2 method, more detailed annual or periodic surveys are used to estimate the areas of land indifferent classes of perennial woody biomass crops. Areas are further classified into relevant categories such that
all major combinations of perennial woody crop types and climatic regions are represented with area estimatesfor each. These area estimates must match any country-specific carbon accumulation and loss values developed
for the Tier 2 method. If country-specific finer resolution data are only partially available, countries areencouraged to extrapolate to the entire land base of perennial woody crops using sound assumptions from best
available knowledge.
Tier 3: Tier 3 requires high-resolution activity data disaggregated at sub-national to fine grid scales. Similar to
Tier 2, land area is classified into specific types of perennial woody crops by major climate and soil categories
and other potentially important regional variables (e.g., regional patterns of management practices). If possible,
spatially explicit area estimates are used to facilitate complete coverage of the perennial woody cropland andensure that areas are not over- or underestimated. Furthermore, spatially explicit area estimates can be related to
locally relevant carbon accumulation and removal rates, and restocking and management impacts, improving theaccuracy of estimates.

3.3.1.1.1.4 Uncertai nty Assessment
The following discussion provides guidance on approaches for assessing uncertainty associated with each tiermethod described in Section 3.3.1.1.1.1.
Tier 1: The sources of uncertainty when using the Tier 1 method include the degree of accuracy in land area
estimates and in the default carbon accumulation and loss rates. A published compilation of research on carbon
stocks in agroforestry systems was used to derive the default data provided in Section 3.3.1.1.1.2 (Schroeder,
1994). While defaults were derived from multiple studies, their associated uncertainty ranges were not includedin the publication. Therefore, a default uncertainty level of + 75% of the parameter value has been assigned
based on expert judgement. This information can be used with a measure of uncertainty in area estimates from
Chapter 2 of this Report to assess the uncertainty in estimates of carbon emissions and removals in croplandbiomass using the Tier 1 methodology for uncertainty analysis in Chapter 5.2 (Identifying and quantifying
uncertainties).
Tier 2: The Tier 2 method will reduce overall uncertainty because country-defined rates should provide moreaccurate estimates of carbon accumulation and loss for crop systems and climatic regions within nationalboundaries. It is good practice to calculate error estimates (i.e., standard deviations, standard error, or ranges) for
country-defined carbon accumulation rates and to use these variables in a basic uncertainty assessment. It is goodpractice for countries to assess error ranges in country-specific coefficients and compare them to those of default
carbon accumulation coefficients. If country-defined rates have equal or greater error ranges than default
coefficients, then it is good practice to use a Tier 1 approach and to further refine country-defined rates withmore field measurements.
Tier 2 approaches may also use finer resolution activity data, such as area estimates for different climatic regions
or for specific cropping systems within national boundaries. The finer-resolution data will reduce uncertainty
levels when associated with carbon accumulation factors defined for those finer-scale land bases (e.g., when areaof coffee plantations is multiplied by a coffee plantation coefficient, rather than by a generic agroforestry
default).
Tier 3: Tier 3 approaches will provide the greatest level of certainty relative to Tiers 1 and 2 approaches. It isgood practice to calculate standard deviations, standard errors, or ranges for all country-defined biomass growth

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Chapter 3: LUCF Sector Good Practice Guidance

and loss rates. It is also good practice to assess the measurement error in land area estimates for each land basecategory. Countries should consider developing probability density functions for model parameters to use inMonte Carlo simulations.

3.3.1.2

C HANGE IN CARB ON STOCKS IN SOILS

3.3.1.2.1 METHODOLOGICAL ISSUESThe IPCC Guidelines provide methods for estimating CO2 Emissions and Uptake by Soils from Land-Use andManagement (Section 5.3) that can be applied to all land uses, including cropland. The methodology considers
organic carbon stock changes (CO2 emissions or removals) for mineral soils, CO2 emissions from organic soils(i.e. peat soils) and emissions of CO2 from liming of agricultural soils.
In the IPCC Guidelines, carbon stocks are measured to a default depth of 30cm and do not include C in surface
residue (i.e. dead organic matter) or changes in inorganic carbon (i.e. carbonate minerals). In most cropland soils,
surface residue is either absent (due to incorporation with tillage) or represents a minor stock. Other depths may
be used at higher tiers, but depth must in all cases be used consistently over time.
The summary Equation 3.3.2 for estimating the change in organic carbon stocks in soils is shown below:
EQUATION 3.3.2
ANNUAL CHANGE IN CARBON STOCKS IN SOILS IN CROPLAND REMAINING CROPLAND∆CCCSoils = ∆CCCMineral – ∆CCCOrganic – ∆CCCLime
Where:∆CCCSoils = annual change in carbon stocks in soils in cropland remaining cropland, tonnes C yr-1
∆CCCMineral = annual change in carbon stocks in mineral soils, tonnes C yr-1
∆CCCOrganic= annual carbon emissions from cultivated organic soils (estimated as net annual flux), tonnes C yr-1
∆CCCLime = annual C emissions from agricultural lime application, tonnes C yr-1
For Tiers 1 and 2 methods, changes in dead organic matter and inorganic carbon should be assumed to be zero. Ifdead organic matter is included in a Tier 3 approach, measurements should be based on the lowest amounts
present during an annual cycle to avoid including fresh post-harvest residues that represent a transient organic
matter pool. Selection of the most suitable tier will depend on: 1) type and level of detail of activity data on
agricultural management and changes in management over time, 2) availability of suitable information toestimate base C stocks and stock change and emission factors, 3) availability of dedicated national inventory
systems designed for soils.
All countries should strive for improving inventory and reporting approaches by advancing to the highest tierpossible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if
carbon emissions and removals in cropland remaining cropland is a key category and if the subcategory of soilorganic matter is considered significant based on principles outlined in Chapter 5. Countries should use thedecision tree in Figure 3.1.1 to help with the choice of method.

3.3.1.2.1.1 Choice of Method
The method used to estimate carbon stock changes in mineral soils is different from the method used for organicsoils. It is also possible that countries will use different tiers to prepare estimates of the separate components on
this subcategory, given availability of resources. Thus, mineral soils, organic soils, and emissions from liming
are discussed separately below.

Mineral Soils
For mineral soils, the estimation method is based on changes in soil C stocks over a finite period followingchanges in management that impact soil C, as shown in Equation 3.3.3. Previous soil C stocks (SOC(0-T)) and soil
C stocks in the inventory year (SOC0) for the area of a cropland system in the inventory are estimated fromreference carbon stocks (Table 3.3.3) and stock change factors (Table 3.3.4), applied for the respective time
points. Here a cropland system refers to a specific climate, soil and management combination. Annual rates ofemissions (source) or removals (sink) are calculated as the difference in stocks (over time) divided by theinventory time period. The default time period is 20 years.

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Cropland

EQUATION 3.3.3
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILS FOR A SINGLE CROPLAND SYSTEM∆CCCMineral = [(SOC0 – SOC(0 –T)) ● A] / T
SOC = SOCREF ● FLU ● FMG ● FI
Where:∆CCCMineral = annual change in carbon stocks in mineral soils, tonnes C yr-1
SOC0 = soil organic carbon stock in the inventory year, tonnes C ha-1
SOC(0-T) = soil organic carbon stock T years prior to the inventory, tonnes C ha-1
T = inventory time period, yr (default is 20 yr)
A = land area of each parcel, ha
SOCREF = the reference carbon stock, tonnes C ha-1; see Table 3.3.3
FLU = stock change factor for land use or land-use change type, dimensionless; see Table 3.3.4
FMG = stock change factor for management regime, dimensionless; see Table 3.3.4
FI = stock change factor for input of organic matter, dimensionless; see Table 3.3.4
The types of land use and management factors supplied are very broadly defined and include: 1) a land use factor(FLU) that reflects C stock changes associated with type of land use, 2) a management factor (FMG) that forpermanent cropland represents different types of tillage and 3) an input factor (FI) representing different levels of
C inputs to soil. For cropland, FLU describes base C stocks for long-term cultivated soils, paddy rice cultivationand for temporary cropland set-asides, relative to native (uncultivated) soil C stocks. If the area was in other landuse (e.g. forest land, grazing land) at the beginning of the inventory period, then guidance provided under
Section 3.3.2, Land Converted to Cropland, should be followed.
The calculation steps for determining SOC0 and SOC(0-T) and net soil C stock change per ha of land area are as
follows:
Step 1: Select the reference carbon stock value (SOCREF), based on climate and soil type, for each area of land
being inventoried.
Step 2: Select the type of cropland use (long-term cultivated, paddy rice, set-aside) present at beginning of theinventory period (e.g. 20 years ago), together with tillage (FMG) and C input levels (FI). These factors,
multiplied by the reference soil C stock, provide the estimate of ‘initial’ soil C stock (SOC(0-T)) for theinventory period.
Step 3: Calculate SOC0 by repeating step 2 using the same reference carbon stock (SOCREF), but with land use,
tillage and input factors that represent conditions in the (current) inventory year.
Step 4: Calculate the average annual change in soil C stock for the area over the inventory period (∆CCCMineral)

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Chapter 3: LUCF Sector Good Practice Guidance

Example: For a Mollisol soil in a warm temperate moist climate, SOCREF is 88 tonnes C ha-1. On
an area of land under long-term annual cropping, previously managed with intensive tillage andlow C input level, the carbon stock at the beginning of the inventory period is calculated as(SOCREF ● FLU ● FMG ● FI, ) = 88 tonnes C ha-1● 0.71 ● 1 ● 0.91 = 56.9 tonnes C ha-1. Under thecurrent management of annual cropping with no tillage and medium C input level the carbon stockis calculated as 88 tonnes C ha-1 ● 0.71 ● 1.16 ● 1 = 72.5 tonnes C ha-1. Thus the average annualchange in soil C stock for the area over the inventory period is calculated as (72.5 tonnes C ha-1 –56.9 tonnes C ha-1) / 20 yrs = 0.78 tonnes C ha-1 yr-1.

TABLE 3.3.3
DEFAULT REFERENCE (UNDER NATIVE VEGETATION) SOIL ORGANIC C STOCKS (SOCREF)(TONNES C PER HA FOR 0-30 CM DEPTH)
HAC soils1

LAC soils2

Sandy soils3

Spodic soils4

Volcanic
soils5

Wetlandsoils6

Boreal

68

NA

10#

117

20#

146

Cold temperate, dry

50

33

34

NA

20#

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry

38

24

19

NA

70#

Warm temperate, moist

88

63

34

NA

80

Tropical, dry

38

35

31

NA

50#

Tropical, moist

65

47

39

NA

70#

NA

#

Region87

88

Tropical, wet

44

60

66

130

86

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

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

2

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

3
Includes all soils (regardless of taxonomic classification) having > 70% sand and < 8% clay, based on standard textural analyses (inWRB classification includes Arenosols,; in USDA classification includes Psamments).
4

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

5

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

6

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

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Cropland

TABLE 3.3.4
RELATIVE STOCK CHANGE FACTORS (FLU, FMG, AND FI) (OVER 20 YEARS) FOR DIFFERENT MANAGEMENT ACTIVITIES ON
CROPLAND [SEE SECTION 3.3.7 FOR METHODS AND DATA SOURCES USED IN FACTOR DERIVATION]
Factor
value
type
Land
use
(FLU)

Land
use

Level

’96
IPCC
default

Temperate

0.7,0.64

Moisture
Regime1GPG
reviseddefault

Error2,3

Description

Dry

0.82

+ 10%

Wet

0.71

+ 12%

Dry

0.69

+ 38%

Wet

0.58

+ 42%

Represents area that has been continuously
managed for >20 yrs, to predominantly
annual crops. Input and tillage factors arealso applied to estimate carbon stockchanges. Land use factor was estimatedrelative to use of full tillage and nominal(‘medium”) carbon input levels.

Longterm
cultivatedTropical

0.6, 0.5

Paddy
rice

Temperate
andTropical

1.1

Set aside(< 20 yrs)

Temperate
andTropical

0.8

Temperate

(FLU)
Land
use

Temperature
regime(FLU)
TillageDry and
Wet

1.1

+ 90%

Dry

0.93

+ 10%

Wet

0.82

+ 18%

1.0

Dry and
Wet

1.0

NA

Tropical

0.9, 0.8

Dry and
Wet

1.0

NA

Temperate

1.05

Dry

1.03

+ 6%

Wet

1.09

+ 6%

Tropical

1.0

Full
(FMG)

Tillage
Reduced(FMG)

Tillage

Temperate

1.1

Tropical

1.1

Temperate

0.9

Tropical

0.8No-till
(FMG)

Input
Low
(FI)

Input

Dry

1.10

+ 10%

Wet

1.16

+ 8%

Dry

1.10

+ 6%

Wet

1.16

+ 4%

Dry

1.17

+ 8%

Wet

1.23

+ 8%

Dry

0.92

+ 4%

Wet

0.91

+ 8%

Dry

0.92

+ 4%

Wet

0.91

+ 4%

1.0

NA

Temperate

1.0

Dry andWet

Tropical

0.9

Dry and
Wet

1.0

NA

Dry

1.07

+ 10%

Wet

1.11

+ 10%

Dry

1.34

+ 12%

Wet

1.38

+ 8%

Medium
(FI)

Input
(FI)

Input
(FI)

High –
without
manureHigh –
withmanureTemperate
andTropicalTemperate
andTropical

1.1

1.2

Long-term (> 20 year) annual cropping of
wetland (paddy rice). Can include doublecropping with non-flooded crops. For
paddy rice, tillage and input factors are not
used.
Represents temporary set aside of annually
cropland (e.g. conservation reserves) orother idle cropland that has been
revegetated with perennial grasses.
Substantial soil disturbance with full
inversion and/or frequent (within year)
tillage operations. At planting time, little(e.g. <30%) of the surface is covered byresidues.
Primary and/or secondary tillage but with
reduced soil disturbance (usually shallow
and without full soil inversion). Normallyleaves surface with >30% coverage byresidues at planting.
Direct seeding without primary tillage,
with only minimal soil disturbance in theseeding zone. Herbicides are typicallyused for weed control.
Low residue return due to removal ofresidues (via collection or burning),
frequent bare-fallowing or production ofcrops yielding low residues (e.g.
vegetables, tobacco, cotton)
Representative for annual cropping withcereals where all crop residues are
returned to the field. If residues are
removed then supplemental organic matter(e.g. manure) is added.
Represents significantly greater crop
residue inputs due to production of highresidue yielding crops, use of green
manures, cover crops, improved vegetatedfallows, frequent use of perennial grasses
in annual crop rotations, but without
manure applied (see row below)
Represents high input of crop residues
together with regular addition of animal
manure (see row above).

1

Where data were sufficient, separate values were determined for temperate and tropical temperature regimes and dry and wet moistureregimes. Temperate and tropical zones correspond to those defined in the Chapter 3 introduction (3.1); wet moisture regime
corresponds to the combined moist and wet zones in the tropics and wet zone temperate region (see Figure 3.1.3); dry zone is the sameas defined Figure 3.1.3.

2+ two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis adefault, based on expert judgement, of + 50% is used. NA denotes ‘Not Applicable’, where factor values constitute defined reference
values.

3This error range does not include potential systematic error due to small sample sizes that may not be representative of the true impact
for all regions of the world.

4

The second value applies to the Aquic soil class as defined in the IPCC Guidelines. No significant differences were found for different
soil types in the updated estimates produced here for the Good Practice Guidance.

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Tier 1: For Tier 1, default reference carbon stocks and stock change factors are used (as shown in Equation
3.3.3) for major cropland systems in a country, stratified by the default climate and soil types (Equation 3.3.4).
For the aggregate area of cropland remaining cropland, stock changes can be calculated either by tracking
management changes and calculating stock changes on individual parcels of land (Equation 3.3.4B) or by
calculating aggregate soil carbon stocks at the start and end of the inventory period from more general data on
the area distribution of cropland systems (Equation 3.3.4A). Aggregate results will be the same with either
approach, the main difference being that attribution of the effects of specific changes in management require
activity data that tracks management changes on specific areas of land. Default values for this calculation are
described in Section 3.3.1.2.1.2.
EQUATION 3.3.4
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILS IN CROPLAND REMAINING CROPLAND∆CCCMineral = ∑c∑s∑i [(SOC0 – SOC(0-T)) ● A ] c,s,i / T(A)∆CCCMineral = [∑c∑s∑i (SOC0 ● A) c,s,i – ∑c∑s∑i (SOC(0-T) ● A) c,s,i ] / T(B)

Where:∆CCCMineral = annual change in carbon stocks in mineral soils, tonnes C yr-1
SOC0 = soil organic carbon stock in the inventory year, tonnes C ha-1
SOC(0-T) = soil organic carbon stock T years prior to the inventory, tonnes C ha-1
T = inventory time period, yr (default is 20 yr)
A = land area of each parcel, hac represents the climate zones, s the soil types, and i the set of major cropland systems that are present in acountry.
Example: The following example shows calculations for aggregate areas of cropland soil carbon
stock change using Equation 3.3.4B. In a warm temperate moist climate on Mollisol soils, there
are 1Mha of permanent annual cropland. The native reference carbon stock (SOCREF) for the regionis 88 tonnes C ha-1. At the beginning of the inventory calculation period (i.e. 20 yrs earlier) the
distribution of cropland systems were 400,000 ha of annual cropland with low carbon input levels
and full tillage and 600,000 ha of annual cropland with medium input levels and full tillage. Thus
initial soil carbon stocks for the area were: 400,000 ha ● (88 tonnes C ha-1 ● 0.71 ● 1 ● 0.91) +600,000 ha ● (88 tonnes C ha-1 ● 0.71 ● 1 ● 1) = 60.231 million tonnes C. In the (current)
inventory year, there are: 200,000 ha of annual cropping with full tillage and low C input, 700,000
ha of annual cropping with reduced tillage and medium C input, and 100,000 ha of annual
cropping with no-till and medium C input. Thus total soil carbon stocks in the inventory year are:200,000 ha ● (88 tonnes C ha-1 ● 0.71 ● 1 ● 0.91) + 700,000 ha ● (88 tonnes C ha-1 ● 0.71 ● 1.09
● 1) + 100,000 ha ● (88 tonnes C ha-1 ● 0.71 ● 1.16 ● 1) = 66.291 million tonnes C. Thus theaverage annual stock change over the period for the entire area is: (66.291 – 60.231) million tonnes
C / 20 yr = 6.060 million tonnes / 20 yr = 303,028 tonnes per year soil C stock increase.
Tier 2: For Tier 2, the same basic equations as in Tier 1 are used but country-specific values for reference
carbon stocks and/or stock change factors are used. In addition, Tier 2 approaches will likely involve a moredetailed stratification of management systems if sufficient data are available.
Tier 3: Tier 3 approaches, using a combination of dynamic models along with detailed soil C emission/stockchange inventory measurements, will likely not employ simple stock change or emission factors per se.
Estimates of emissions using model-based approaches derive from the interaction of multiple equations that
estimate the net change of soil C stocks within the models. A variety of models designed to simulate soil carbon
dynamics exist (for example, see reviews by McGill et al., 1996; Smith et al., 1997).
Key criteria in selecting an appropriate model are that the model is capable of representing all of the
management practices that are represented and that model inputs (i.e. driving variables) are compatible with theavailability of country-wide input data. It is critical that the model be validated with independent observations
from country or region-specific field locations that are representatives of the variability of climate, soil andmanagement systems in the country. Examples of appropriate validation data sets include long-term replicatedfield experiments (e.g. SOMNET, 1996; Paul et al., 1997) or long-term measurements of ecosystem carbon flux
for agricultural systems, using techniques such as eddy covariance (Baldocchi et al., 2001). Ideally, an inventory
system of permanent, statistically representative “on-farm” plots, that include major climatic regions, soil types,

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and management systems and system changes, would be established where repeated measures of soil carbon
stocks could be made over time. Recommended re-sampling frequencies in most cases should not be less than 3
to 5 years (IPCC, 2000b). Where possible, measurements of soil carbon stocks should be made on an equivalentmass basis (e.g. Ellert et al., 2001). Procedures should be implemented to minimize the influence of spatial
variability with repeated sampling over time (e.g. Conant and Paustian 2002). Such inventory measurements
could be integrated with a model-based methodology.

Organic Soils
The basic methodology for estimating carbon stock change in organic (e.g. peat-derived) soils is to assign anannual loss rate of C due to the drainage and other perturbations such as tillage of the land for agricultural
production. Drainage and tillage stimulate the oxidation of organic matter previously built up under a largely
anoxic environment. The area of cropland organic soils under each climate type is multiplied by the emission
factor to derive an estimate of annual C emissions, as shown in Equation 3.3.5 below:
EQUATION 3.3.5
CO2 EMISSIONS FROM CULTIVATED ORGANIC SOILS IN CROPLAND REMAINING CROPLAND∆CCCOrganic= ∑c (A ● EF) c
Where:∆CCCOrganic = CO2 emissions from cultivated organic soils in cropland remaining cropland, tonnes C yr-1
A = land area of organic soils in climate type c, ha
EF = emission factor for climate type c (see Table 3.3.5), tonnes C ha-1 yr-1
Tier 1: For Tier 1, default emission factors (Table 3.3.5) are used along with area estimates for cultivatedorganic soils within each climate region present in the country (Equation 3.3.5). Area estimates can be developed
using the guidance in Chapter 2.
Tier 2: The Tier 2 approach uses Equation 3.3.5 where emission factors are estimated from country-specificdata, stratified by climate region, as described in Section 3.3.2.1.3. Area estimates should be developed
following the guidance of Chapter 2.
Tier 3: Tier 3 approaches for organic soils will include more detailed systems integrating dynamic models andmeasurement networks as described above for mineral soils.

TABLE 3.3.5
ANNUAL EMISSION FACTORS (EF) FOR CULTIVATED ORGANIC SOILS
Climatic temperature regime

IPCC Guidelines default

Error #

(tonnes C ha-1 yr-1)#

Cold Temperate

1.0

+ 90%

Warm Temperate

10.0

+ 90%

Tropical/sub-tropical

20.0

+ 90%

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

Lim ing
The IPCC Guidelines include application of carbonate containing lime (e.g. calcic limestone (CaCO3), or
dolomite (CaMg(CO3)2) to agricultural soils as a source of CO2 emissions. A simplified explanation of theprocess is that when carbonate lime is dissolved in soil, the base cations (Ca++, Mg++) exchange with hydrogenions (H+) on soil colloids (thereby reducing soil acidity) and the bicarbonate formed (2HCO3) can react further to
evolve CO2 and water (H2O). Although the liming effect generally has a duration of a few years (after whichlime is again added), depending on climate, soil and cropping practices, the IPCC Guidelines account foremission as CO2 of all the added carbonate carbon in the year of application. Thus the basic methodology issimply the amount of agricultural lime applied times an emission factor that varies slightly depending on thecomposition of the material added.

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EQUATION 3.3.6
ANNUAL CARBON EMISSIONS FROM AGRICULTURAL LIME APPLICATION∆CCCLime = MLimestone ● EFLimestone + MDolomite ● EFDolomite
Where:∆CCCLime = annual C emissions from agricultural lime application, tonnes C yr-1
M = annual amount of calcic limestone (CaCO3) or dolomite (CaMg(CO3)2), tonnes yr-1
EF = emission factor, tonnes C (tonne limestone or dolomite)-1 (These are equivalent to carbonate carbon
contents of the materials (12% for CaCO3, 13% for CaMg(CO3)2 )).
Tier 1: For Tier 1, the total amount of carbonate containing lime applied annually to cropland soil and an
overall emission factor of 0.12 can be used to estimate CO2 emissions, without differentiating between variable
compositions of lime material. Note that while carbonate limes are the dominant liming material used, oxides
and hydroxides of lime, which do not contain inorganic carbon, are used to a limited extent for agriculturalliming and should not be included here (CO2 is produced in their manufacture but not following soil application).
Tier 2: A Tier 2 approach could entail differentiation of different forms of lime and specific emission factors if
data are available, since different carbonate liming materials (limestone as well as other sources such as marl andshell deposits) can vary somewhat in their carbon content and overall purity.
Tier 3: A Tier 3 approach could entail a more detailed accounting of emissions stemming from lime
applications than is assumed under Tiers 1 and 2. Depending on climate and soil conditions, biocarbonate
derived from lime application may not all be released as CO2 in the soil or from drainage water – some can beleached and precipitated deeper in the soil profile or be transported to deep groundwater, lakes and oceans andsequestered. If sufficient data and understanding of inorganic carbon transformation for specific climate-soil
conditions are available, specific emission factors could be derived. However, such an analysis would likely
necessitate including carbon fluxes associated with primary and secondary carbonate minerals in soil and their
response to agricultural management practices.

3.3.1.2.1.2 Choice of Emission/Removal Factors
Mineral soils
When using either the Tier 1 or Tier 2 method, the following emission/removal factors are needed for mineralsoils: reference carbon stock (SOCREF); stock change factor for land-use change (FLU); stock change factor for
management regime (FMG); stock change factor for input of organic matter (FI).
Reference carbon stocks (SOCREF)
Soils under native vegetation that have not been subject to significant land use and management impacts are used
as a baseline or reference to which management-induced changes in soil carbon can be related.
Tier 1: Under Tier 1, it is good practice to use the default reference carbon stocks (SOCREF) provided in Table
3.3.3. These are updated from those provided in the IPCC Guidelines with the following improvements: i)estimates are statistically-derived from recent compilations of soil profiles under native vegetation, ii) ‘Spodic’soils (defined as boreal and temperate zone podzols in WRB classification, Spodosols in USDA classification)
are included as a separate category, iii) soils within the boreal climate region have been included.
Tier 2: For Tier 2, reference soil C stocks can be determined from measurements of soils, for example, as partof a country’s soil survey and mapping activities. Advantages include more representative values for anindividual country and the ability to better estimate probability distribution functions that can be used in a formal
uncertainty analysis. Accepted standards for sampling and analysis of soil organic carbon and bulk density
should be used and documented.
Stock change factors (FLU, FMG, FI)
Tier 1: Under Tier 1, it is good practice to use default stock change factors (FLU, FMG, FI) provided in Table
3.3.4. These are updated from the IPCC Guidelines, based on a statistical analysis of published research.
Definitions guiding the selection of appropriate factor values are provided in the table.

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Tier 2: For the Tier 2 method, stock change factors can be estimated from long-term experiments (e.g. Smith etal., 1996; Paul et al., 1997) or other field measurements (e.g. field chronosequences2) for a particular country or
region. To estimate stock change factors, information compiled from published studies and other sources should
include organic C stock (i.e. mass per unit area to a specified depth) or all information needed to calculate SOC
stocks, i.e. percent organic matter together with bulk density. If the percent organic matter and not the percent
organic carbon are reported, a conversion factor of 0.58 for the carbon content of soil organic matter can be used.
Other information that must be included is depth of measurement and time frame over which the management
difference has been expressed. In the absence of specific information upon which to select an alternative depth
interval, it is good practice to compare stock change factors at a depth of at least 30 cm (i.e. the depth used forTier 1 calculations). Stock changes over a deeper depth may be desirable if a sufficient number of studies areavailable and if statistically significant differences in stocks due to land management are demonstrated at deeper
depths. However, it is critical that the reference soil carbon stocks (SOCRef) and stock change factors be
determined to a common depth. Factor values should be compiled for major climate and/or soils types, at least to
the level of detail used in the Tier 1 method.

Organic soils
When estimating emissions from organic soils, an emission factor (EF) is required for different climatic regimes
where organic soils have been drained for cropland use.
Tier 1: For Tier 1, default emission factors, unchanged from the IPCC Guidelines, are provided in Table 3.3.5.
These factors are differentiated by major climate (temperature) regimes and assume that soils have been drained
prior to use as cropland. Organic soils used for paddy rice or minor crops grown under flooded conditions (e.g.
cranberry bogs, wild rice) are excluded.
Tier 2: For Tier 2, it is possible to derive emission factors from literature data on carbon losses from organicsoils. Estimates of carbon losses from cultivated organic soils are usually based on measurements of subsidencewith fewer studies based on direct measurements of CO2 fluxes (Klemedtsson et al., 1997; Ogle et al., 2003).
Processes that contribute to subsidence include erosion, compaction, burning, and decomposition. Only
decomposition losses should be included in the emission factor estimate. If using subsidence data, appropriate
regional conversion factors to determine the proportion of subsidence attributable to oxidation should be used,based on studies measuring both subsidence and CO2 flux. In the absence of such information, a default factor of
0.5 for oxidation-to-subsidence, on a gram-per-gram equivalent basis, is recommended based on a review byArmentano and Menges (1986). If available, direct measurements of carbon fluxes are recommended asproviding the best means of estimating emission rates from organic soils.

Lim ing
See Section 3.3.1.2.1.1.

3.3.1.2.1.3 Choice of Activity Data
Mineral Soils
The area of cropland under different management practices (A) is required for estimating mineral soil
emissions/removals.
For existing cropland, activity data should record changes or trends in management practices that affect soil
carbon storage, such as crop types and crop rotations, tillage practices, irrigation, manure application, residue
management, etc. Two main types of management activity data exist: 1) aggregate statistics compiled by country
or for administrative areas within countries (e.g. provinces, counties) or 2) point-based land use and management
inventories making up a statistically-based sample of a country’s land area. Either type of activity data could be
used for any of the three tiers, depending on their spatial and temporal resolution. For Tier 1 and Tier 2inventories, activity data should be stratified by major climatic regions and soil types, since reference soil C
stocks vary significantly according to these factors. For the broadly defined soil categories used in Tier 1,
national or even global soil maps can be used to delineate soil divisions within the cropland land area. For
application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed
knowledge of the combinations of climate, soil, topographic and management data are needed, but the exact
requirements will be in part dependent on the model used.
2

Chronosequences consist of measurements taken from similar but separate locations that represent a temporal sequence inland use or management, for example, years since deforestation. Efforts are made to control all other between-site
differences (e.g. by selecting areas with similar soil type, topography, previous vegetation). Chronosequences are often
used as a surrogate for experimental studies or measurements repeated over time at the same location.

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Globally available land use and crop production statistics such as FAO databases (http://apps.fao.org) provide
annual compilations of total land area by major land-use types, with some differentiation of management
systems, (e.g., irrigated vs. non-irrigated cropland), area in ‘permanent’ crops (i.e. vineyards, orchards), and landarea and production for major crops (e.g. wheat, rice, maize, sorghum, etc.). Thus FAO or similar country-total
data would require additional in-country information to stratify areas by climate and soil types. If suchinformation has not already been compiled, an initial approach would be to overlay available land cover/land use
maps (of national origin or from global datasets such as IGBP_DIS) with soil maps of national origin or global
sources such as the FAO Soils Map of the World. Where possible, land areas associated with cropping systems(e.g. rotations and tillage practice), rather than simply area by crop, should be delineated and associated with theappropriate management factor values. [Note: This is applicable to the cropland biomass section as well since themethodology uses area-based estimates for specific crop types such as FAO classified “permanent crops”.] Refer
to Chapter 2 of this report.
National land-use and resource inventories, comprised of a collection of permanent sample points where data arecollected at regular intervals, have some advantages over aggregate agricultural and land-use statistics. Inventory
points can more readily be associated with a particular cropping system and the soil type associated with theparticular location can be determined by sampling or by referencing the location to a suitable soil map. Inventory
points selected based on an appropriate statistical design also enable estimates of the variability associated withactivity data, which can be used as part of a formal uncertainty analysis. An example of a point-based resource
inventory that includes cropland is the National Resource Inventory in the U.S. (Nusser and Goebel, 1997).

Organic Soils
The area of cultivated organic soils by climate regime (A) is required to estimate organic soil emissions. Similar
databases and approaches as those outlined above can be used for deriving area estimates. An overlay of soils
maps showing the spatial distribution of histosols (i.e. organic soils) with land use maps showing cropland area
can provide initial information on areas with organic soils under agricultural use. In addition, because organicsoils usually require extensive artificial drainage to be used for agricultural purposes, country-specific data on
drainage projects combined with soil maps and surveys can be used to get a more refined estimate of relevant
areas.

3.3.1.2.1.4 Uncertai nty Assessment
A formal assessment of uncertainty requires that uncertainty in per area emission/sequestration rates as well as
uncertainty in the activity data (i.e. the land areas involved in land-use and management changes), and theirinteraction be estimated. Where available, estimates of the uncertainty of the revised global default values
developed in this report are provided in the tables; these can be used with the appropriate estimates of variability
in activity data to estimate uncertainty, using the guidance provided in Chapter 5 of this report. Inventory
agencies should be aware that simple global defaults have a relatively high level of uncertainty associated withthem when applied to specific countries. In addition, because the field studies available to derive the global
defaults are not evenly distributed across climate regions, soil types and management systems, some areas –particularly in tropical regions – are underrepresented. For the Tier 2 methods, probability density functions (i.e.
providing mean and variance estimates) can be derived for stock change factors, organic soil emission factors
and reference C stocks as part of the process of deriving region- or country-specific data. For example, Ogle et al.
(2003) applied linear mixed-effect models to derive probability density functions for US specific factor values
and reference carbon stocks for agricultural soils. Activity data from a statistically-derived land use andmanagement inventory system should provide a basis to assign estimates of uncertainty to areas associated withland-use and management changes. Combining emission and activity data and their associated uncertainties canbe done using Monte-Carlo procedures to estimate means and confidence intervals for the overall inventory(Ogle et al., 2003; Smith and Heath, 2001) – see Chapter 5 of this report.

3.3.1.3

N ON -CO 2 GREENH OUSE GAS EMISSIO NSNITROUS OXIDEThe IPCC Guidelines and GPG2000 already address the following non-CO2 emission sources:•

N2O emissions from application of mineral and organic fertilisers, organic residues and biological nitrogen
fixation (IPCC Guidelines, Chapter 4 Agriculture);•

N2O, NOx, CH4 and CO emissions from on-site and off-site biomass burning (IPCC Guidelines, Chapter 4
Agriculture); and•N2O emissions from cultivation of organic soils.

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It is good practice to follow the existing IPCC Guidelines and GPG2000 and continue to report these emissions
under the Agriculture sector.

METHANE
Methane emissions from rice paddies are addressed in the IPCC Guidelines and GPG2000 and should bereported under the Agriculture sector.
Changes in the rate of methane oxidation in aerobic soils are not addressed at this time. The limited current
information indicates that the CH4 sink is small as compared to the CH4 sources from flooded soils such as ricepaddies. As more research is done and additional information becomes available, a fuller consideration of the
impact of various activities on methane oxidation should be possible.

3.3.2

Land Converted to Cropland

The conversion of land from other uses and from natural states to cropland will, in most cases, result inemissions of CO2 from both biomass and soils, at least for some years following conversion, as well as N2O andCH4 emissions from the soil. Possible exceptions are the irrigation of formerly arid lands, which can result in net
carbon gains in soils and biomass, and conversion of degraded lands to cropland. The calculation of carbon
emissions from conversion of forest land and grassland to cropland is found in the IPCC Guidelines in Section
5.2.3 (Forest and Grassland Conversion) and Section 5.3 (CO2 Emissions and Uptake from Soils). Whenestimating emissions and removals from land-use conversions to cropland, it is good practice to consider threesubcategories: change in carbon stocks in biomass (Section 3.3.2.1), change in carbon stocks in soil (Section
3.3.2.2), and emissions of nitrous oxide (Section 3.3.2.3). Methodological guidance is provided below for eachof these subcategories.
It is good practice to estimate emissions/removals from ‘land converted to cropland’ using the methods
described in this subsection for a period sufficient for the carbon stock changes to occur following land-use
conversion. However, biomass and soil pools respond differently to land-use conversions and therefore, time
periods are different for equilibrium carbon stocks to be reached. Changes in carbon in biomass pools are
estimated using the method in Section 3.3.2.1 below for the first time period following the land-use conversion
to cropland.3 After this time period, countries should estimate carbon stock changes in biomass using methods
described under Section 3.3.1.1 Cropland Remaining Cropland, Change in carbon stocks in biomass. Since thedefault inventory period for changes in soil carbon is 20 years, this period of time should be used in area
accounting for conversions to cropland.
The summary equation for carbon stock change in Land Converted to Cropland is shown below in Equation
3.3.7. In addition, methodologies based on emissions coefficients are discussed for N2O. Table 3.3.6 summarises
the tiers for each of the carbon subcategories, as well as for the N2O subcategory.
EQUATION 3.3.7
TOTAL CHANGE IN CARBON STOCKS IN LAND CONVERTED TO CROPLAND∆CLC = ∆CLCLB + ∆CLCSoils
Where:∆CLC = total change in carbon stocks in land converted to cropland, tonnes C yr-1
∆CLCLB = change in carbon stocks in living biomass in land converted to cropland, tonnes C yr-1
∆CLCSoils = change in carbon stocks in soil in land converted to cropland, tonnes C yr-1

3

The time period will depend on the frequency with which countries collect data. For example, if land use surveys arecollected on a five-year cycle, e.g., 1990, 1995, 2000, then a land conversion that takes place in 1992 will be captured by
the 1995 data collection and thus recorded using the methods below in the inventory report that employs survey data for
1995.

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3.3.2.1

C HANGE IN CARB ON STOCKS LI VING BIOMASSThis section provides good practice guidance for calculating carbon stock change in biomass due to theconversion of land from natural conditions and other uses to cropland, including deforestation and conversion of
pasture and grazing lands to cropland. The methods require estimates of carbon in living biomass stocks prior toand following conversion, based on estimates of the areas of lands converted during the period between land use
surveys. As a result of conversion to cropland, it is assumed (in Tier 1) that the dominant vegetation is removed
entirely, resulting in near zero amounts of carbon remaining in biomass. Some type of cropping system isplanted soon thereafter, increasing the amount of carbon stored in biomass. The difference between initial andfinal biomass carbon pools is used to calculate carbon stock change from land-use conversion and in subsequent
years accumulations and losses in perennial woody biomass in cropland are counted using methods in section
3.3.1 Croplands remaining Croplands.

3.3.2.1.1 METHODOLOGICAL ISSUES
The methodology estimates carbon stock change in living biomass. Currently, there is not sufficient information
to provide a basic approach with default parameters to estimate carbon stock change in dead organic matter poolsin land converted to cropland4. In addition, the methodology below considers carbon stock change inaboveground biomass only because limited data are available on belowground carbon stocks in perennial
cropland.
TABLE 3.3.6
TIER DESCRIPTIONS FOR SUBCATEGORIES UNDER LAND CONVERTED TO CROPLAND (LC)Tier
Sub
-categories

4

Tier 1

Tier 2Tier 3

Biomass

Use default coefficients to estimate
carbon stock change in biomass
resulting from land use conversions
and for carbon in biomass that
replaces cleared vegetation during
the year of land use transition.

Use at least some country-specificcarbon stock parameters to estimate
carbon stock changes from land use
conversion to cropland. Apportion
carbon from biomass removal toburning, decay, and other nationally
important conversion processes.
Estimate non-CO2 trace gas emissions
from the portion of biomass burned
both on-site and off-site. Use area
estimates that are disaggregated tonationally relevant climate zones andother boundaries to match countryspecific carbon stock parameters.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement).

Carbon stocks
in Soil

For change in soil carbon frommineral soils use default
coefficients. The areas must be
stratified by climate and soil type.
For change in soil carbon fromorganic soils use default
coefficients and stratify the areas byclimatic region. For emissions fromliming, use default emission factors.

For both mineral and organic soils use
some combination of default and or
country-specific coefficients and area
estimates of increasingly finer spatial
resolution. For emissions from liming,
use emission factors differentiated byforms of lime.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement)

Nitrous Oxide
from soiloxidation
duringconversion

Use default parameters and coarse
spatial disaggregation

Use of country-specific parameters andincreased spatial disaggregation

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement) andreport under
LULUCF croplandremaining cropland

Any litter and dead wood pools (estimated using the methods described in Section 3.2.2.2) should be assumed oxidized
following land conversion.

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3.3.2.1.1.1 Choice of Method
The IPCC Guidelines describe increasingly sophisticated alternatives that incorporate greater detail on the areasof land converted, carbon stocks on lands, and removal of carbon resulting from land conversions. Good practiceguidance reflects this in a tiered methodology with the choice of tier depending on data availability and national
circumstances. All countries should strive for improving inventory and reporting approaches by advancing to thehighest tier possible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3
approach if carbon emissions and removals in land converted to cropland is a key category and if thesubcategory of living biomass is considered significant based on principles outlined in Chapter 5. Countries
should use the decision tree in Figure 3.1.2 to help with the choice of method.
Tier 1: The Tier 1 method follows the approach in IPCC Guidelines Section 5.2.3. Forest and Grassland
Conversion where the amount of biomass that is cleared for cropland is estimated by multiplying the forest area
converted in one year by the average carbon stock in biomass in the forest prior to conversion. It is good practiceto account completely for all land conversions to cropland. Thus, this section elaborates on the method such thatit includes each initial land use, including but not limited to forests.
Equation 3.3.8 summarises the major elements of a first order approximation of carbon stock change from landuse conversion to cropland. Average carbon stock change on a per area basis is estimated for each type of
conversion. The average carbon stock change is equal to the carbon stock change due to the removal of biomass
from the initial land use (i.e., carbon in biomass immediately after conversion minus the carbon in biomass prior
to conversion), plus carbon stocks from one year of growth in cropland following conversion. As stated in the
IPCC Guidelines, it is necessary to account for any vegetation that replaces the vegetation that was cleared
during land use conversion. The IPCC Guidelines combine carbon in biomass after conversion and carbon inbiomass that grows on the land following conversion into a single term. In this method, they are separated into
two terms, CAfter and ∆CGrowth to increase transparency. At Tier 1, carbon stocks in biomass immediately afterconversion (CAfter) are assumed to be zero, i.e., the land is cleared of all vegetation before planting crops.
Average carbon stock change per area for a given land use conversion is multiplied by the estimated area oflands undergoing such a conversion in a given year. In subsequent years, change in biomass of annual crops isconsidered zero because carbon gains in biomass from annual growth are offset by losses from harvesting andchange in biomass of perennial woody crops are counted following the methodology in Section 3.3.1.1 (Change
in carbon stocks in biomass, in: Cropland Remaining Cropland).
The basic steps in estimating carbon stock change in biomass from land conversion to cropland are as follows:(i)

Estimate the average area of land undergoing a transition from non-cropland to cropland during ayear (Aconversion), separately for each initial land use (i.e., forest land, grasslands, etc.) and final crop
type (i.e., annual or perennial woody).

(ii)

For each type of land use transition to cropland, use Equation 3.3.8 to estimate the resulting change
in carbon stocks. Default data in Section 3.3.2.1.1.2 for CAfter, CBefore, and ∆CGrowth can be used toestimate the total stock change on a per area basis for each type of land use transition. The estimate
for stock change on a per area basis can then be multiplied by the appropriate area estimates fromstep 1.

(iii)

Estimate the total carbon stock change from all land-use conversions to cropland by summing theindividual estimates for each transition.

The default assumption for Tier 1 is that all carbon in biomass is lost to the atmosphere through decay processes
either on- or off-site. As such, Tier 1 calculations do not differentiate immediate emissions from burning andother conversion activities.
EQUATION 3.3.8
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN LAND CONVERTED TO CROPLAND∆CLCLB = AConversion ● (LConversion + ∆CGrowth)
LConversion = CAfter – CBefore
Where:∆CLCLB = annual change in carbon stocks in living biomass in land converted to cropland, tonnes C yr-1
AConversion = annual area of land converted to cropland, ha yr-1
LConversion = carbon stock change per area for that type of conversion when land is converted to cropland,
tonnes C ha-1

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Chapter 3: LUCF Sector Good Practice Guidance∆CGrowth = changes in carbon stocks from one year of cropland growth, tonnes C ha-1
CAfter= carbon stocks in biomass immediately after conversion to cropland, tonnes C ha-1
CBefore= carbon stocks in biomass immediately before conversion to cropland, tonnes C ha-1

Tier 2: The Tier 2 calculations are structurally similar to Tier 1, with these distinctions. First, Tier 2 relies on atleast some country-specific estimates of the carbon stocks in initial and final land uses rather than the defaults
provided in Section 3.3.2.1.1.2. Area estimates for land converted to cropland are disaggregated at finer spatial
scales to capture regional and crop systems variations in country-specific carbon stocks values.
Second, Tier 2 may modify the assumption that carbon stocks immediately following conversion are zero. This
enables countries to take into account land use transitions where some, but not all, vegetation from the originalland use is removed.
Third, under Tier 2, it is good practice to apportion carbon losses to burning and decay processes if applicable.
Emissions of carbon dioxide occur as a result of burning and decay in land-use conversions. In addition, nonCO2 trace gas emissions occur as a result of burning. By partitioning losses to burning and decay, countries canalso calculate non-CO2 trace gas emissions from burning. The IPCC Guidelines Workbook provides step-by-step
instructions for estimating carbon removals from burning and decay of biomass on-site and off-site and for
estimating non-CO2 trace gas emissions from burning (pages 5.7-5.17). Below is guidance on estimating carbon
removals from burning and decay and Section 3.2.1.4 of this chapter provides further guidance on estimating
non-CO2 trace gas emissions from burning.
The basic equations for estimating the amount of carbon burned or left to decay are provided in Equations 3.3.10
and 3.3.11 below. This methodology addresses burning for the purposes of land clearing. Non-CO2 emissions
from burning for management of cropland remaining cropland are covered in the Agriculture chapter of
GPG2000. The default assumption in Equations 3.3.10 and 3.3.11 is that only aboveground biomass, is burned
or decays. Countries are encouraged to use additional information to assess this assumption, particularly for
decaying belowground biomass. Equations 3.3.10 and 3.3.11 estimate the amount of carbon in biomass removed
during a land use conversion to cropland that is burned (on-site and off-site) or that decays, respectively. The
basic approach can be modified to address other conversion activities as well to meet the needs of national
circumstances. Both equations use as an input the total amount of carbon in biomass removed during land
clearing (∆Cconversion) (Equation 3.3.9), which is equivalent to area of land converted (AConversion) multiplied by the
carbon stock change per area for that type of conversion (LConversion in Equation 3.3.8).
The portion of biomass removed is sometimes used as wood products. In the case of wood products, countries
may use the default assumption that carbon in wood products is oxidized in the year of removal. Alternatively,
countries may refer to Appendix 3a.1 for estimation techniques for carbon storage in harvested wood products,
which may be accounted for provided carbon in the product pool is increasing.
EQUATION 3.3.9
CHANGE IN CARBON STOCKS AS A RESULT OF CLEARING BIOMASS IN A LAND USE CONVERSION∆Cconversion = Aconversion ● Lconversion
Where:∆Cconversion = change in carbon stocks as a result of clearing biomass in a land use conversion, tonnes C
AConversion = area of land converted to croplands from some initial use, ha
LConversion = carbon stocks removed when land is converted from some initial use to cropland, tonnes C ha-1
(from Equation 3.3.8)
EQUATION 3.3.10
CARBON LOSSES FROM BIOMASS BURNING, ON-SITE AND OFF-SITE
Lburn onsite = ∆Cconversion ● ρburned on site ● ρoxid
Lburn offsite = ∆Cconversion ● ρburned off site ● ρoxid
Where:
Lburn = carbon losses from biomass burned, tonnes C∆Cconversion = change in carbon stocks as a result of a clearing biomass in a land use conversion, tonnes Cρburned on site = fraction of biomass that is burned on-site, dimensionless

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Croplandρoxid = fraction of biomass that oxidizes when burned, dimensionlessρburned off site = fraction of biomass that is burned off-site, dimensionless

EQUATION 3.3.11
CARBON LOSSES FROM BIOMASS DECAY
Ldecay = ∆Cconversion ● ρdecay

ρdecay = 1 – (ρburned on site + ρburned off site )Where:
Ldecay = carbon losses from biomass decay, tonnes C∆Cconversion = change in carbon stocks as a result of a clearing biomass in a land use conversion, tonnes Cρdecay = fraction of biomass that is left on-site to decay, dimensionlessρburned on site = fraction of biomass that is burned on-site, dimensionlessρburned off site = fraction of biomass that is burned off-site, dimensionless
It is good practice for countries to use the terms Lburn on site and Lburn off site as inputs to estimate non-CO2 trace gas
emissions from burning following guidance provided in Section 3.2.1.4.
Tier 3: The Tier 3 method is similar to Tier 2, with the following distinctions: rather than relying on averageannual rates of conversion, countries can use direct estimates of spatially disaggregated areas converted annuallyfor each initial and final land use; carbon densities and soil carbon stock change are based on locally specificinformation, which makes possible a dynamic link between biomass and soil; and biomass volumes are based on
actual inventories.

3.3.2.1.1.2 Choice of Emission/Removal Factors
Tier 1: Default parameters are provided in both the IPCC Guidelines and in this report to enable countries withlimited data resources to estimate emissions and removals from this source. The first step in this methodology
requires parameters for carbon stocks before conversion for each initial land use (CBefore) and after conversion(CAfter). It is assumed that all biomass is cleared when preparing a site for cropland use, thus, the default for CAfteris 0 tonnes C ha-1. Table 3.3.7 provides default carbon stock values for CBefore in either forest or grassland land
uses prior to clearing.
In addition, a value is needed for carbon stocks after one year of growth in crops planted after conversion(∆CGrowth). Table 3.3.8 provides defaults for ∆CGrowth. Separate defaults are provided for annual non-woody crops
and perennial woody crops. For lands planted in annual crops, the default value of ∆CGrowth is 5 tonnes of C per
hectare, based on the original IPCC Guidelines recommendation of 10 tonnes of dry biomass per hectare (drybiomass has been converted to tonnes carbon in Table 3.3.8). Default carbon stocks from one year of growth inperennial woody crops the same as those in Table 3.3.2. The total accumulation of carbon in perennial woody
biomass will, over time, exceed that of the default carbon stock for annual cropland. However, default values
provide in this section are for one year of growth immediately following conversion, which usually give lower
carbon stocks for perennial woody crops compared to annual crops.
TABLE 3.3.7
DEFAULT BIOMASS CARBON STOCKS REMOVED DUE TO LAND CONVERSION TO CROPLAND
Carbon stock in biomass before conversion (CBefore)(tonnes C ha-1)

Error range #Forest land

See Tables 3A.2 and 3A.3 in Annex 3A.1 for carbon stocks in a range offorest types by climate regions. Stocks are in terms of dry matter. Multiply
values by a carbon fraction (CF) 0.5 to convert dry matter to carbon.

See Section 3.2.2(Land Converted toForest land)

Grassland

See Table 3.4.2 for carbon stocks in a range of grassland types by climate
regions.

+ 75%

Land-use category#

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

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TABLE 3.3.8
DEFAULT BIOMASS CARBON STOCKS PRESENT ON LAND CONVERTED TO CROPLAND
IN THE YEAR FOLLOWING CONVERSION

Carbon stock in biomass after
one year (∆CGrowth)(tonnes C ha-1)

Error range#

5

+ 75%

Temperate (all moisture regimes)

2.1

+ 75%

Tropical, dry

1.8

+ 75%

Tropical, moist

2.6

+ 75%

Tropical, wet

10.0

+ 75%

Crop type by climate region
Annual cropland
Perennial cropland#Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

Tier 2: Tier 2 methods should include some country-specific estimates for biomass stocks and removals due toland conversion, and also include estimates of on- and off-site losses due to burning and decay following land
conversion to cropland. These improvements can take the form of systematic studies of carbon content andemissions and removals associated with land uses and land-use conversions within the country and a reexamination of default assumptions in light of country-specific conditions.
Default parameters for emissions from burning and decay are provided, however countries are encouraged todevelop country-specific coefficients to improve the accuracy of estimates. The IPCC Guidelines use a general
default of 0.5 for the proportion of biomass burned on-site for both forest and grassland conversions. Researchstudies suggest that the fraction is highly variable and could be as low as 0.2 (Fearnside 2000, Barbosa andFearnside, 1996, and Fearnside, 1990). Updated default proportions of biomass burned on site are provided inTable 3A.13 for a range of forest vegetation classes. These defaults should be used for transitions from forest
land to cropland. For non-forest initial land uses, the default proportion of biomass left on-site and burned is 0.35.
This default takes into consideration research, which suggests the fraction should fall within the range 0.2 to 0.5(e.g. Fearnside, 2000; Barbosa and Fearnside, 1996; and Fearnside, 1990). It is good practice for countries to use
0.35, or another value within this range provided the rationale for the choice is documented. There is no default
value for the amount of biomass taken off-site and burned; countries will need to develop a proportion based onnational data sources. In Equation 3.3.10., the default proportion of biomass oxidized as a result of burning is 0.9,
as originally stated in the IPCC Guidelines.
The method for estimating emissions from decay assumes that all biomass decays over a period of 10 years. For
reporting purposes countries have two options: to report all emissions from decay in one year, recognizing that inreality they occur over a 10 year period, or report all emission from decay on an annual basis, estimating the rate
as one tenth of the totals in Equation 3.3.11. If countries choose the latter option, they should add a
multiplication factor of 0.10 to Equations 3.3.11.
Tier 3: Under Tier 3, all parameters should be country-defined using more accurate values rather than thedefaults.

3.3.2.1.1.3 Choice of Activity Data
All tiers require estimates of land areas converted to cropland. The same area estimates should be used for bothbiomass and soil calculations on land converted to cropland. Higher tiers require greater specificity of areas. To
be consistent with IPCC Guidelines, at a minimum, the area of forest and natural grassland converted to croplandshould be identified separately for all tiers. This implies at least some knowledge of the land uses prior toconversion; this may require expert judgment if Approach 1 in Chapter 2 is used for land area identification.
Tier 1: One type of activity data is needed for a Tier 1 approach: separate estimates of areas converted tocropland from initial land uses (i.e., forest land, grassland, settlement, etc.) to final crop type (i.e., annual or
perennial) (Aconversion). For example, countries should estimate separately the area of tropical moist forest
converted to annual cropland, tropical moist forest converted to perennial cropland, tropical moist grassland
converted to perennial cropland, etc. The methodology assumes that area estimates are based on a one-year timeframe. If area estimates are assessed over longer time frames, they should be converted to average annual areas
to match the default carbon stock values provided above. If countries do not have these data, partial samples may
be extrapolated to the entire land base or historic estimates of conversions may be extrapolated over time basedon the judgement of country experts. Under Tier 1 calculations, international statistics such as FAO databases,

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Cropland

IPCC Guidelines and other sources, supplemented with sound assumptions, can be used to estimate the area ofland converted to cropland from each initial land use. For higher tier calculations, country-specific data sources
are used to estimate all possible transitions from initial land use to final crop type.
Tier 2: Countries should strive to use actual area estimates for all possible transitions from initial land use to
final crop type. Full coverage of land areas can be accomplished either though analysis of periodic remotelysensed images of land use and land cover patterns, through periodic ground-based sampling of land use patterns,
or hybrid inventory systems. If finer resolution country-specific data are partially available, countries areencouraged to use sound assumptions from best available knowledge to extrapolate to the entire land base.
Historic estimates of conversions may be extrapolated over time based on the judgment of country experts.
Tier 3: Activity data used in Tier 3 calculations should be a full accounting of all land use transitions tocropland and be disaggregated to account for different conditions within a country. Disaggregation can occur
along political (county, province, etc.), biome, climate, or on a combination of these parameters. In many cases
countries may have information on multi-year trends in land conversion (from periodic sample-based or
remotely sensed inventories of land use and land cover).

3.3.2.1.1.4. Uncertainty Assessment
Tier 1: The sources of uncertainty in this method are from the use of global or national average rates of
conversion and coarse estimates of land areas converted to cropland. In addition, reliance on default parameters
for carbon stocks in initial and final conditions contributes to relatively high degrees of uncertainty. The default
values in this method have corresponding error ranges associated with them. A published compilation ofresearch on carbon stocks in agroforestry systems was used to derive the default data provided in Section
3.3.2.1.1.2 (Schroeder, 1994). While defaults were derived from multiple studies, their associated uncertainty
ranges were not included in the publication. Therefore, a default uncertainty level of +/- 75% of the carbon stock
has been assumed based on expert judgement.
Tier 2: Actual area estimates for different land use transitions will enable more transparent accounting andallow experts to identify gaps and double counting of land areas. The Tier 2 method uses at least some countrydefined defaults, which will improve the accuracy of estimates, because they better represent conditions relevantto the country. Use of country-specific values should entail sufficient sample sizes and or use of expert judgment
to estimate uncertainties, which, together with uncertainty estimates on activity data derived using the advice in
Chapter 2 should be used in the approaches to uncertainty analysis described in Chapter 5 of this report.
Tier 3: Activity data from a land use and management inventory system should provide a basis to assign
estimates of uncertainty to areas associated with land-use changes. Combining emission and activity data andtheir associated uncertainties can be done using Monte-Carlo procedures to estimate means and confidence
intervals for the overall inventory.

3.3.2.2

C HANGE IN CARB ON STOCKS IN SOILS

3.3.2.2.1 METHODOLOGICAL ISSUESLand conversion to cropland can occur from unmanaged land, including native, relatively undisturbed
ecosystems (e.g. forest land, grassland, savanna, wetland) and from land managed for other uses (e.g. managed
forest, managed grazing land). The more intensive management entailed in cropland use (i.e. high removal of
harvested biomass, often frequent soil disturbance by tillage) will usually result in losses of C in soil organic
matter and dead organic matter (surface litter and coarse woody debris). Any litter and dead wood pools(estimated using the methods described in Section 3.2.2.2) should be assumed oxidized following land
conversion and changes in soil organic matter C stocks should be estimated as described below.
The total change in carbon stocks in soils on Lands Converted to Cropland is shown in Equation 3.3.12 below:
EQUATION 3.3.12
ANNUAL CHANGE IN CARBON STOCKS IN SOILS IN LAND CONVERTED TO CROPLAND∆CLCSoils = ∆CLCMineral – ∆CLCOrganic – ∆CLCLiming
Where:∆CLCSoils = annual change in carbon stocks in soils in land converted to cropland, tonnes C yr-1
∆CLCMineral = change in carbon stocks in mineral soils in land converted to cropland, tonnes C yr-1

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Chapter 3: LUCF Sector Good Practice Guidance∆CLCOrganic = annual C emissions from cultivated organic soils converted to cropland (estimated as net
annual flux), tonnes C yr-1
∆CLCLiming = annual C emissions from agricultural lime application on land converted to cropland, tonnes
C yr-1
Criteria for selecting the most suitable estimation method are similar to that outlined for permanent croplandsoils. Key factors include type of land conversion and the longevity of the conversion, and availability of suitablecountry-specific information to estimate reference soil C stocks and stock change and emission factors.
All countries should strive for improving inventory and reporting approaches by advancing to the highest tierpossible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if
carbon emissions and removals in land converted to cropland is a key category and if the subcategory of soilorganic matter is considered significant based on principles outlined in Chapter 5. Countries should use thedecision tree in Figure 3.1.2 to help with the choice of method.

3.3.2.2.1.1 Choice of Method
Mineral Soils
The Tier 1 method is based on the IPCC Guidelines (CO2 Emissions and Uptake by Soils from Land-Use andManagement, Section 5.3), using Equation 3.3.3, following land conversion. Tier 1 methods rely on default
values for reference C stocks and stock change factors and relatively aggregated data on the location and rates ofland-use conversion.
For Tier 1, the initial (pre-conversion) soil C stock (SOC(0-T)) is determined from the same reference soil C
stocks (SOCREF) used for all land uses (Table 3.3.3), together with stock change factors (FLU, FMG, FI) appropriate
for the previous land use as shown in Table 3.3.9 (also see Sections 3.2.1.3 (Forest soils) and 3.4.1.2 (Grassland
soils)). For unmanaged land, as well as for managed forest and grazing land with low disturbance regimes, soil C
stocks are assumed equal to the reference values (i.e. land use, management and input factors equal 1). Current(SOC0) soil C stocks on land converted to cropland are estimated exactly as for permanent cropland, i.e., using
the reference carbon stocks (Table 3.3.3) and stock change factors (Table 3.3.9). Thus, annual rates of emissions(source) or removals (sink) are calculated as the difference in stocks (over time) divided by the inventory time
period (default is 20 years).
The calculation steps for determining SOC0 and SOC(0-T) and net soil C stock change per ha of land area are as
follows:
Step 1: Select the reference carbon stock value (SOCREF), based on climate and soil type, for each area of land
being inventoried.
Step 2: Calculate the pre-conversion C stock (SOC(0-T)) of land being converted into cropland, based on thereference carbon stock and previous land use and management, which determine land use (FLU),
management (FMG) and input (FI ) factors. Note that where the land being converted is forest or native
grassland, the pre-conversion stocks will be equal to the native soil carbon reference stocks.
Step 3: Calculate SOC0 by repeating step 2 using the same reference carbon stock (SOCREF), but with land use,
tillage and input factors that represent conditions in the land converted to cropland.
Step 4: Calculate the average annual change in soil C stock for the area over the inventory period (∆CCCMineral ).
Example: For a forest on volcanic soil in a tropical moist environment: SOCRef = 70 tonnes C ha-1.
For all forest soils (and for native grasslands) default values for stock change factors (FLU , FMG ,FI) are all 1; thus SOC(0-T) is 70 tonnes C ha-1. If the land is converted into annual cropland, withintensive tillage and low residue C inputs then SOC0 = 70 tonnes C ha-1 ● 0.58 ● 1 ● 0.91 = 36.9
tonnes C ha-1. Thus the average annual change in soil C stock for the area over the inventory
period is calculated as (36.9 tonnes C ha-1 – 70 tonnes C ha-1) / 20 yrs = -1.7 tonnes C ha-1 yr-1.
The IPCC Guidelines also provide estimates for C stock change associated with the transient land-use
conversion to cropland represented by shifting cultivation. In this case, the stock change factors are different
from those used if the conversion is to permanent cropland, and change in soil C stocks will depend on thelength of the fallow (vegetation recovery) cycle. The soil carbon stocks calculated for shifting cultivation
represent an average over the crop-fallow cycle. Mature fallow denotes situations where the non-croplandvegetation (e.g. forest, savanna) recovers to a mature or near mature state prior to being cleared again forcropland use, whereas in shortened fallow vegetation recovery is not attained prior to re-clearing. If land already

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Croplandin shifting-cultivation is converted to permanent cropland (or other land uses) the stock factors representing
shifting cultivation would provide the ‘initial’ C stocks in the calculations of changes following conversion.
The Tier 2 method for mineral soils also uses Equation 3.3.3, but involves country or region-specific reference C
stocks and/or stock change factors and more disaggregated land use activity data.

Organic Soils
Tier 1 and Tier 2 approaches for organic soils that are converted from other land uses to cropland within theinventory period are treated the same as long-term cropped organic soils, i.e., they have a constant emission
factor applied to them, based on climate regime (see Equation 3.3.5 and Table 3.3.5). In Tier 2, emission factors
are derived from country or region-specific data.

Mineral and organic soils
For both mineral and organic soils, Tier 3 methods will involve more detailed and country-specific models
and/or measurement-based approaches along with highly disaggregated land use and management data. Tier 3
approaches for estimating soil C change from land-use conversions to cropland should employ models and data
sets that are capable of representing transitions over time between different land use and vegetation types,
including forest, savanna, grasslands, cropland. The Tier 3 method needs to be integrated with estimates ofbiomass removal and the post-clearance treatment of plant residues (including woody debris and litter), as
variation in the removal and treatment of residues (e.g. burning, site preparation) will affect C inputs to soilorganic matter formation and C losses through decomposition and combustion. It is critical that models bevalidated with independent observations from country or region-specific field locations that are representative of
the interactions of climate, soil and vegetation type on post-conversion change in soil C stocks.

Lim ing
If agricultural lime is applied to cropland converted from other land uses then the methods for estimating CO2
emissions from liming are the same as described for Cropland Remaining Cropland, in Section 3.3.1.2.1.1.

3.3.2.2.1.2 Choice of Emission/Removal Factors
Mineral soils
The following variables are needed when using either the Tier 1 or Tier 2 method:
Reference carbon stocks (SOCREF)
Tier 1: Under Tier 1, it is good practice to use the default reference carbon stocks (SOCREF) provided in Table
3.3.3. These are updated from those provided in the IPCC Guidelines with the following improvements: i)estimates are statistically-derived from recent compilations of soil profiles under native vegetation, ii) ‘Spodic’soils (defined as boreal and temperate zone podzols in WRB classification, Spodosols in USDA classification)
are included as a separate category, iii) soils within the boreal climate region have been included.
Tier 2: For the Tier 2 method, reference soil C stocks can be determined from measurements of soils, forexample, as part of a country’s soil survey and mapping activities. It is important that reliable taxonomic
descriptions of measured soils be used to group soils into the classes defined in Table 3.3.3 or if a finer
subdivision of reference soil C stocks is used definitions of soil groupings need to be consistently and well
documented. Advantages to using country-specific data for estimating reference soil C stocks include moreaccurate and representative values for an individual country and the ability to better estimate probability
distribution functions that can be used in a formal uncertainty analysis.
Stock change factors (FLU, FMG, FI)
Tier 1: Under Tier 1, it is good practice to use default stock change factors (FLU, FMG, FI) provided in Table
3.3.9. These are updated from the IPCC Guidelines, based on a statistical analysis of published research.
Definitions guiding the selection of appropriate factor values are provided in the table. Stock change factors areused in estimating both post- (SOC0) and pre-conversion (SOC(0-T)) stocks; values will vary according to land
use and management conditions before and after the conversion. Note that where forest land or native grasslands
are converted to cropland use, the stock change factors all have the value of one, such that the pre-conversion
soil carbon stocks are equal to the native vegetation reference values (SOCREF).
Tier 2: For the Tier 2 method, estimation of country-specific stock change factors for land-use conversion tocropland will typically be based on paired-plot comparisons representing converted and unconverted lands,
where all factors other than land-use history are as similar as possible (e.g. Davidson and Ackermann, 1993).
Ideally several sample locations can be found that represent a given land use at different times since conversion– referred to as a chronosequence (e.g. Neill et al., 1997). There are few replicated long-term experiments ofland- use conversions and thus stock change factors and emission factors for land-use conversions will have

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greater uncertainty than for permanent cropland. In evaluating existing studies or conducting new measurements
it is critical that the plots being compared have similar pre-conversion histories and management as well as
similar topographic position, soil physical properties and be located in close proximity. As for permanent
cropland, required information includes C stock (i.e. mass per unit area to a specified depth) for each land use(and time point if a chronosequence). As previously described under Cropland Remaining Cropland, in theabsence of specific information upon which to select an alternative depth interval, it is good practice to compare
stock change factors at a depth of at least 30 cm (i.e. the depth used for Tier 1 calculations). Stock changes overa deeper depth may be desirable if a sufficient number of studies are available and if statistically significant
differences in stocks due to land management are demonstrated at deeper depths. However, it is critical that thereference soil carbon stocks (SOCRef) and stock change factors (FLU, FMG, FI) be determined to a common depth.

Organic soils
Tier 1 and Tier 2 choice of C emission factors from organic soils recently converted to cropland should observe
the same procedures for deriving emission factors as described earlier under the Cropland Remaining Croplandsection.
TABLE 3.3.9
RELATIVE SOIL STOCK CHANGE FACTORS (FLU, FMG, FI) FOR LAND-USE CONVERSIONS TO CROPLAND
Factorvalue typeLand use

Land use
Land use,
Management,& Input
Land use,
Management,& Input
Land use,
Management,& Input#

Level
Native forest or
grassland(non-degraded)
Shifting cultivation– Shortened fallow
Shifting cultivation– Mature fallow

Climateregime

IPCC
Guidelines
default

Error#

Definition

Temperate

1NA

Tropical1NA

Tropical

0.64

+ 50%

Tropical

0.8

+ 50%

Represents native or long-term, nondegraded and sustainably managed forestand grasslands.
Permanent shifting cultivation, where
tropical forest or woodland is cleared forplanting of annual crops for a short time(e.g. 3-5 yr) period and then abandoned toManaged forest

See Equation 3.2.14 and accompanying text

Managed grassland

See default values in Table 3.4.5

CroplandSee default values in Table 3.3.4

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean. NA denotes ‘Not
Applicable’, where factor values constitute defined reference values.

3.3.2.2.1.3 Choice of Activity Data
Mineral and Organic Soils
At a minimum, countries should have estimates of the areas of land converted to cropland during the inventory
period. If land use and management data are limited, aggregate data, such as FAO statistics on land conversions,can be used as a starting point, along with knowledge of country experts of the approximate distribution of land
use types (e.g. forest land and grassland areas and their respective soil types) being converted and knowledge ofthe types of cropland practices being used on land converted to cropland. More detailed accounting can beaccomplished either through analysis of periodic remotely sensed images of land use and land cover patterns,
through periodic ground-based sampling of land use patterns, and/or hybrid inventory systems. Estimates ofland-use conversions to cropland should be stratified according to major soil types, as defined for Tier 1, orbased on country-specific stratifications if employed in Tier 2 or 3 approaches. This can be based on overlays
with suitable soil maps and spatially-explicit data of the location of land conversions.

3.3.2.2.1.4 Uncertai nty Assessment
Because most conversions to cropland uses entail losses from soil carbon stocks, the most critical data from thestandpoint of reducing overall uncertainty is accurate estimates of the land area being converted to cropland. Dueto their high native soil carbon stocks and potential for large losses, conversions to cropland occurring onorganic soils, as well as wetland mineral soils and volcanic soils, are of particular importance. Reducing
uncertainty in the estimates of stock change and emission factors for lands recently (<20 yrs) converted tocropland can best be accomplished from direct monitoring of C stocks (and emissions) before and after (for a

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period of several year) conversion to cropland, at the same location. However, data based on indirect estimates,
so-called chronosequences, in which land converted to cropland at different times in the past and at different
locations, are more common. Use of estimates based on chronosequences will have a higher uncertainty thandirect monitoring over time. In constructing and evaluating chronosequences it is important to select areas which
are as similar as possible with respect to original vegetation, soil type and landscape position – i.e. the maindifference being time since conversion. Estimates should be based on more than one chronosequence. Overall
uncertainty assessment will require combining uncertainties associated with stock change and emission factors
and activity data concerning land areas converted to cropland.

3.3.2.3

N ON -CO 2 GREENH OUSE GAS EMISSIO NSThis section deals with the increase in N2O emissions arising from the conversion of forest land, grassland, andother land to cropland. An increase in N2O emissions can be expected following the conversion of forest land,grassland and other land to cropland. This is a consequence of the enhanced mineralisation (conversion to
inorganic form) of soil organic matter (SOM) that normally takes place as a result of that conversion. The
mineralisation results not only in a net loss of soil C and hence a net CO2 emission (Section 3.3.2.2.1.2) but also
in associated conversion of nitrogen previously in the SOM to ammonium and nitrate. Microbial activity in thesoil converts some of the ammonium and nitrate present to N2O. Thus an increase in this microbial substratecaused by a net decrease in SOM can be expected to give an increase in net N2O emissions. The approach here is
to use the same emission factor (EF1) as that used for direct emissions from agricultural land which has been incultivation for a long time (see Agriculture, GPG2000), and has the same logical basis, i.e. that N converted into
inorganic form in the soil, as a result of mineralisation, is all of equal value as a substrate for the organisms
producing N2O by nitrification and denitrification, no matter what the organic source is, soil organic matter inthis case of land-use conversion to cropland, or plant roots and crop residues from cultivation after harvest, oradded organic manures as in the case of the N2O emissions addressed in the IPCC Guidelines, Chapter 4
Agriculture and GPG2000.
Guidance on estimating trace gas emissions (N2O, NOx, CH4 and CO) from on-site and off-site biomass burning
is provided in Section 3.2.1.4.
The rate of methane oxidation in aerated topsoils can change due to conversion to cropland. The reduction inoxidation is not addressed in this report, however, due to limited information. In the future, as more data become
available, it may be possible to provide a fuller consideration of the impact of various activities on methane
oxidation rates.

3.3.2.3.1 METHODOLOGICAL ISSUESNITROUS OXIDE FROM MINERAL SOILS
3.3.2.3.1.1 Choice of Method
The total emissions of N2O are equivalent to the sum of all N2O emissions from land use conversions as shownin Equation 3.3.13 and 3.3.14. These are emissions from mineralisation of soil organic matter resulting fromconversion of forest land, grassland, settlements or other land to cropland.
EQUATION 3.3.13
TOTAL ANNUAL EMISSIONS OF N2O FROM MINERAL SOILS IN LAND CONVERTED TO CROPLAND
Total N2O-Nconv = ∑i N2O-Nconv,iWhere:
Total N2O-Nconv = total annual emissions of N2O from mineral soils in land converted to cropland, kg
N2O-N yr-1
N2O-Nconv,i = N2O emissions from land conversion type i, kg N2O-N yr-1
Emissions from fertilisation: N2O emissions from nitrogen application in the preceding land use (managed forest
or grassland) and new land use (cropland) are calculated elsewhere in the inventory (GPG 2000) and should not
be reported here, to avoid double counting.

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EQUATION 3.3.14
N2O EMISSIONS AS A RESULT OF THE DISTURBANCE ASSOCIATED WITH LAND-USE CONVERSIONOF FOREST LAND, GRASSLAND, OR OTHER LAND TO CROPLAND
N2O-Nconv = N2Onet-min-N
N2Onet-min-N = EF1 ● Nnet-min
Where:
N2O-Nconv = N2O emissions as a result of the disturbance associated with land-use conversion of forest
land, grassland, or other land to cropland, kg N2O-N yr-1
N2Onet-min-N = additional emissions arising from the land-use change, kg N2O-N yr-1
Nnet-min = N released annually by net soil organic matter mineralisation as a result of the disturbance, kg N yr-1
EF1 = IPCC default emission factor used to calculate emissions from agricultural land caused by added N,
whether in the form of mineral fertilisers, manures, or crop residues, kg N2O-N/kg N. (The default
value is 0.0125 kg N2O-N/kg N)
Note: Multiply N2O-Nconv by 44/28 and10-6 to obtain N2O emissions in Gg N2O yr-1

The N released by net mineralisation, Nnet-min, can be calculated following the calculation of the soil C
mineralised over the same period (20 years). The default method assumes a constant C:N ratio in the soil organic
matter over the period, thus:
EQUATION 3.3.15
ANNUAL NITROGEN RELEASED BY NET SOIL ORGANIC MINERALISATION AS A RESULT OF THE
DISTURBANCE (BASED ON SOIL C MINERALISED)
Nnet-min = ∆CLCMineral ● 1 / C:N ratio
Where:
Nnet-min = annual N released by net soil organic matter mineralisation as a result of the disturbance, kg N yr-1
∆CLCMineral = values obtained from Equation 3.3.12 (see also Section 3.3.2.2.1.1)), where applied to anarea of land converted to cropland (see Section 3.3.2.2.1.), kg C yr-1
C:N ratio = the ratio by mass of C to N in the soil organic matter (SOM), kg C (kg N)-1

Tier 1: Use default values and minimal spatial disaggregation with Equations 3.3.13 and 3.3.14
Tier 2: Actual measurements of locally specific C:N ratios in SOM will improve the calculations of N2Oemissions after conversion.
Tier 3: Tier 3 comprises a more dynamic way of simulating emissions using process models, based on locallyspecific data, possibly spatially explicit, taking into account local characteristics of the land use conversion tocropland.

3.3.2.3.1.2 Choice of Emission Factor
The following factors are needed:•

EF1: The emission factor for calculating emissions of N2O from N in the soil. The global default value is0.0125 kg N2O-N/kg N, based on the general default emission factor used for N2O emissions in Chapter 4(Agriculture) of the IPCC Guidelines.

•

C released is calculated using Equation 3.3.3.

•

C:N ratio: The ratio of C to N in soil organic matter is by default 15. This reflects the somewhat greater
C:N ratio found in forest or grassland soils compared to most cropland soils where C:N ratios typicallyaround 8-12.

The box below highlights ways in which further refinement of emissions estimates may be made, by analogy
with the equivalent text in GPG2000.

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BOX 3.3.1
GOOD PRACTICE IN DERIVATION OF COUNTRY-SPECIFIC EMISSION FACTORSIn situations where higher-tier methods may be possible, the following points apply:
Good practice requires the measurement of N2O emissions by individual sub-source category (e.g.
synthetic fertiliser (FSN), animal manure (FAM), crop residue mineralisation (FCR) and (in thepresent context of land-use conversion to cropland), mineralisation of soil organic N (FOM-min).
For N2O emission factors to be representative of environmental and management conditions within
the country, measurements should be made in the major crop growing regions within a country, inall seasons, and if relevant, in different geographic and soil regions and under different
management regimes. Soil factors such as texture and drainage condition, temperature andmoisture will affect EFs (Firestone and Davidson, 1989; Dobbie et al., 1999).
Validated, calibrated, and well-documented simulation models may be a useful tool to develop
area-average N2O emission factors on the basis of measurement data.
Regarding measurement period and frequency, N2O emission measurements should be taken overan entire year (including fallow periods), and preferably over a series of years, in order to reflect
differences in weather conditions and inter-annual climatic variability. Measurements should befrequent during the initial period after land conversion.

3.3.2.3.1.3 Choice of Activity Data
Aconv: The area of land being converted is required. For Tier 1 the Aconv is a single value, but for Tier 2 it isdisaggregated by the types of conversions.

3.3.3

CompletenessA complete data series for land area estimates contains, at a minimum, the area of land within country
boundaries that is considered cropland during the time period covered by land use surveys or other data sources
and for which greenhouse gas emission and removals are estimated in the LULUCF sector. The total area
covered by the cropland inventory methodology is the sum of land remaining in cropland and land converted tocropland during the time period. This inventory methodology may not include some cropland areas where
greenhouse gas emissions and removals are believed to be insignificant or constant through time, such as nonwoody cropland where there are no management or land-use changes. Therefore, it is possible for the total
cropland area for which estimates are prepared to be less than the total area of cropland within country
boundaries. In this case, it is good practice for countries to document and explain the difference in cropland areain the inventory and total cropland within their boundaries. Countries are encouraged to track through time thetotal area of land in cropland within country boundaries, keeping transparent records on which portions are used
to estimate carbon dioxide emissions and removals. As addressed in Chapter 2, all cropland areas, including
those not covered by the emissions inventory, should be part of the consistency checks to help avoid double
counting or omission. When summed with area estimates for other land uses, the cropland area data series will
enable a complete assessment of the land base included in a countries’ LULUCF sector inventory report.
Countries that use Tier 2 or 3 methods for cropland biomass and soil pools should include more detail in theirinventory on the cropland area data series. For example, countries may need to stratify the cropland area bymajor climate and soil types, including both the inventoried and non-inventoried cropland areas. When stratified
land areas are used in the inventory, it is good practice for countries to use the same area classifications for boththe biomass and soils pools. This will ensure consistency and transparency, allow for efficient use of land
surveys and other data collection tools, and enable the explicit linking between carbon dioxide emissions andremovals in biomass and soil pools.

3.3.4

Developing a Consistent Time Series

To maintain a consistent time series, it is good practice for countries to maintain records on the cropland areasused in inventory reports over time. These records should track the total cropland area included in the inventory,
subdivided by land remaining in cropland and land converted to cropland. Countries are encouraged to include
an estimate of the total cropland area within country boundaries. To ensure that area estimates are treatedconsistently through time, land use definitions should be clearly defined and kept constant. If changes are madeto land use definitions, it is good practice to keep transparent records of how the definition changed. Consistent

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definitions should also be used for each of the cropland types and management systems included in the inventory.
In addition, to facilitate the proper accounting of carbon emissions and removals over several periods,
information on historic land conversions can be utilized. Even if a country cannot rely on historic data for current
inventories, improvements to current inventory practices to provide the ability to track land conversions acrosstime will have benefits in future inventories.

3.3.5

Reporting and Documentation

The categories described in Section 3.3 can be reported using the reporting tables in Annex 3A.2. The estimates
under the cropland category can be compared with the reporting categories in the IPCC Guidelines as follows:•

Carbon dioxide emissions and removals in biomass in cropland remaining cropland to IPCC Reporting
Category 5A, Changes in woody biomass;•

Carbon dioxide emissions and removals in soils in cropland remaining cropland to IPCC Reporting
Category 5D, Changes in soil carbon; and•Carbon dioxide emissions and removals resulting from land-use conversions to cropland to IPCC
Reporting Category 5B for biomass, IPCC Reporting category 5D for soils, and IPCC Reporting Category
5E for non-CO2 gases.

It is good practice to maintain and archive all information used to produce national inventory estimates.
Metadata and data sources for information used to estimate country-specific factors should be documented andboth mean and variance estimates provided. Actual databases and procedures used to process the data (e.g.
statistical programs) to estimate country-specific factors should be archived. Activity data and definitions used tocategorise or aggregate the activity data must be documented and archived. Procedures used to categorise
activity data by climate and soil types (for Tier 1 and Tier 2) must be clearly documented. For Tier 3 approaches
that use modelling, model version and identification must be documented. Use of dynamic models requires that
copies of all model input files as well as copies of model source code and executable programs be permanently
archived.

3.3.6

Inventory Quality Assurance/Quality Control(QA/QC)

It is good practice to implement quality control checks and external expert review of inventory estimates anddata. Specific attention should be paid to country-specific estimates of stock change and emission factors toensure that they are based on high quality data and verifiable expert opinion.
Specific QA/QC checks across the cropland methodology include:
Cropland remaining cropland: Cropland soil estimates may be based on area data that includes both perennial
woody crops and annual crops, while biomass estimates are based on area data for perennial woody crops only.
Therefore, the area estimates underlying biomass and soils estimates in cropland remaining cropland may differ,with biomass estimates based on a smaller land area than soil estimates. This will be true in most cases, except incountries where cropland is comprised entirely of perennial woody crops or management and land use isconstant on annual crops.
Lands converted to cropland: Aggregate area totals for land converted to cropland should be the same in thebiomass and soils estimations. While biomass and soil pools may be disaggregated to different levels of detail,the same general categories should be used to disaggregate the area data.
For all soil carbon stock change estimates using Tier 1 or Tier 2 methods, total areas for each climate-soil typecombination must be the same for the start (year(0-T)) and the end (year(0)) of the inventory period (see Equation
3.3.4).

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3.3.7

Estimation of Revised GPG Tier 1 Defaults for
Mineral Soil C Emissions/Removals for Cropland(see Table 3.3.4)

Cropland management factors were computed for tillage, input, set-aside, and land use conversion fromgrassland or forest land. The land use conversion factor represents the loss of carbon that occurs after 20 years ofcontinuous cultivation. Tillage factors represent the impact of changing management from a conventional tillage
system, in which the soil is completely inverted, to conservation practices, including no-till and reduced till. Notillage is direct seeding without tillage of the soil. Reduced tillage involves some tillage, but does not involve full
inversion of the soil and typically leaves more than 60% of the soil surface covered by residue, including
practices such as chisel, mulch, and ridge tillage. The input factors represent the effect changing carbon input tothe soil by planting more productive crops, cropping intensification, or applying amendments; input factors
include cropping systems categorised as low, medium, high, and high w/manure amendments. Low input factors
represent low residue crops, rotations with bare-fallow, or cropping systems in which the residue is burned or
removed from the field. Medium input cropping systems represent cereals in which the residue is returned to thefield or rotations receiving organic amendments that otherwise would be considered low input due to residue
removal. High input rotations have high residue-yielding crops, cover crops, improved vegetated fallow, or years
with grass cover, such as hay or pasture in the rotation. Tillage and input factors represent the effect on C stocks
after 20 years since the management change. Set-aside factors represent the effect of temporary removal ofcropland from production and placing it into grass vegetation for a period of time that may extend to 20 years.
The data were synthesized in linear mixed-effects models, accounting for both fixed and random effects. Fixed
effects included depth, number of years since the management change, and the type of management change (e.g.,
reduced tillage vs. no-till). For depth, data were not aggregated but included C stocks measured for each depth
increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) as a separate point in the dataset. Similarly, time series data
were not aggregated, even though those measurements were conducted on the same plots. Consequently, random
effects were used to account for the interdependence in times series data and the interdependence among data
points representing different depths from the same study. Data were transformed with a natural log
transformation if model assumptions were not met for normality and homogeneity of variance (back-transformed
values are given in the tables). Factors represent the effect of the management practice at 20 years for the top 30
cm of the soil, with the exception of the land use conversion factor, which represents the average loss of carbon
at 20 years or longer time period following cultivation. Users of this carbon accounting method can approximate
the annual change in carbon storage by the dividing the inventory estimate by 20. Variance was calculated foreach of the factor values, and can be used to construct probability distribution functions with a normal density.

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REFERENCES USED FOR THE ANALYSIS IN SECTION 3.3.7
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Campbell CA, Bowren KE, Schnitzer M, Zentner RP, Townley-Smith L (1991) Effect of crop rotations andfertilization on soil organic matter and some biochemical properties of a thick black Chernozem. Canadian
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Dalal, R.C., P.A. Henderson, and J.M. Glasby. (1991). Organic matter and microbial biomass in a vertisol after 20
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Dalal, R.C., and R.J. Mayer. (1986). Long-term trends in fertility of soils under continuous cultivation and cereal
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REFERENCES USED FOR THE ANALYSIS IN SECTION 3.3.7 (CONTINUED)
Dick WA, Edwards WM, McCoy EL. (1997). Continuous application of no-tillage to Ohio soils: Changes in crop
yields and organic matter-related soil properties. Pages 171-182 in: E.A. Paul, E.T. Elliott, K. Paustian, andC.V. Cole. Soil organic matter in temperate agroecosystems: Long-term experiments in North America. CRC
Press, Boca Raton, FL.
Dick, W.A., and J.T. Durkalski. (1997). No-tillage production agriculture and carbon sequestration in a Typic
Fragiudalf soil of Northeastern Ohio. Pages 59-71 in Lal, R., J.M. Kimble, R.F. Follett, and B.A. Stewart,
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Karlen, D.L., A. Kumar, R.S. Kanwar, C.A. Cambardella, and T.S. Colvin. (1998). Tillage system effects on 15year carbon-based and simulated N budgets in a tile-drained Iowa field. Soil and Tillage Research 48:155-165.
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Soil and Water Conservation 54:439-444.
Karlen, D.L., N.C. Wollenhaupt, D.C. Erbach, E.C. Berry, J.B. Swan, N.S. Eash, and J.L. Jordahl. (1994). Longterm tillage effects on soil quality. Soil and Tillage Research 32:313-327.
Kushwaha, C.P., S.K. Tripathi, and K.P. Singh. (2000). Variations in soil microbial biomass and n availability due
to residue and tillage management in a dryland rice agroecosystem. Soil and tillage Research 56:153-166.
Lal, R., A.A. Mahboubi, and N.R. Fausey. (1994). Long-term tillage and rotation effects on properties of a central
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Lal, R. (1998). Soil quality changes under continuous cropping for seventeen seasons of an alfisol in western
nigeria. Land Degradation and Development 9:259-274.
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McCarty, G.W., N.N. Lyssenko, and J.L. Starr. (1998). Short-term changes in soil carbon and nitrogen pools during
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Mielke, L.N., J.W. Doran, and K.A. Richards. (1986). Physical environment near the surface of plowed and no-tilledsoils. Soil and Tillage Research 7:355-366.

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Chapter 3: LUCF Sector Good Practice Guidance

REFERENCES USED FOR THE ANALYSIS IN SECTION 3.3.7 (CONTINUED)
Mikhailova, E.A., R.B. Bryant, I.I. Vassenev, S.J. Schwager, and C.J. Post. (2000). Cultivation effects on soil
carbon and nitrogen contents at depth in the Russian Chernozem. Soil Science Society of America Journal
64:738-745.
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stability of a Calcixeroll soil under different wheat rotations and tillage systems in a semiarid area of Morocco.
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and N content in a decade. Pages 93-99 in Lal, R., J. Kimble, E. Levine, and B.A. Stewart, editors. Advances inSoil Science: Soil Management and Greenhouse effect. CRC Press. Boca Raton, FL.
Parfitt, R.L., B.K.G. Theng, J.S. Whitton, and T.G. Shepherd. (1997). Effects of clay minerals and land use onorganic matter pools. Geoderma 75:1-12.
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Saggar, S., G.W. Yeates, and T.G. Shepherd. (2001). Cultivation effects on soil biological properties, microfauna
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Sainju, U.M., B.P. Singh, and W.F. Whitehead. (2002). Long-term effects of tillage, cover crops, and nitrogen
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Schiffman, P.M., and W.C. Johnson. (1989). Phytomass and detrital carbon storage during forest regrowth in thesoutheastern United States Piedmont. Canadian Journal of Forest Research 19:69-78.

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Six, J., E.T. Elliot, K. Paustian, and J.W. Doran. (1998). Aggregation and soil organic matter accumulation incultivated and native grassland soils. Soil Science Society of America Journal 62:1367-1377.
Six, J., K. Paustian, E.T. Elliott, and C. Combrink. (2000). Soil structure and organic matter: I. Distribution of
aggregate-size classes and aggregate-associated carbon. Soil Science Society of America Journal 64:681-689
Slobodian, N., K. Van Rees, and D. Pennock. (2002). Cultivation-induced effects on belowground biomass andorganic carbon. Soil Science Society of America Journal 66:924-930.
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fractionation. Soil Science Society of America Journal 66: 969-978.
Sparling, G.P., L.A. Schipper, A.E. Hewitt, and B.P. Degens. (2000). Resistance to cropping pressure of two New
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Stenberg, M., B. Stenberg, and T. Rydberg. (2000). Effects of reduced tillage and liming on microbial activity andsoil properties in a weakly-structured soil. Applied Soil Ecology 14:135-145.
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Unger PW. (2001). Total carbon, aggregation, bulk density, and penetration resistance of cropland and nearby
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52:216-222.

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Grassland

3.4

GRASSLAND

Grassland as defined in Chapter 2 covers about one-quarter of the earth’s land surface (Ojima et al., 1993) andspan a range of climate conditions from arid to humid. Grasslands can vary greatly in their degree and intensityof management, from extensively managed rangelands and savannahs – where animal stocking rates and fire
regimes are the main management variables – to intensively managed (e.g. with fertilization, irrigation, specieschanges) continuous pasture and hay land. Grasslands generally have a vegetation dominated by perennial
grasses, with grazing as the predominant land use, and are distinguished from “forest” by having a tree canopy
cover of less than the threshold used in the forest definition.
Belowground carbon dominates in grassland, mainly in roots and soil organic matter. For a given climate regime,
grassland often has higher soil carbon contents than other vegetation types. Grazing and fire are common
perturbations that grassland has evolved with; consequently both the vegetation and soil carbon are relatively
resistant to moderate disturbances from grazing and fire regimes (Milchunas and Lauenroth, 1993). In many
grasslands, the presence of fire is a key factor in preventing the invasion of woody species which cansignificantly affect ecosystem carbon stores (Jackson et al., 2002).
The Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC Guidelines) deal withbiomass and soil carbon stock changes for land-use conversions between grassland and other land uses (e.g.,
cropland), soil carbon stock changes due to management changes between improved and unimproved pasture,
and CO2 emissions for wetlands that are drained and from liming of pasture.
This report complements the IPCC Guidelines by:•

Elaborating on the methodologies needed to address C stock changes in the two main pools in grassland:
living biomass and soils;•

Explicitly including impacts of natural disturbances and vegetation fires on managed grassland; and•Covering comprehensively the estimation of land use conversion to grassland.

In this section, guidance on the use of basic and advanced methodologies for inventorying and reporting
emissions and removals for grassland remaining grassland and land converted to grassland is provided forbiomass and soil carbon pools. Methods for non-CO2 emissions are also covered. Methodologies follow ahierarchical tier structure where Tier 1 methods use default values, typically with limited disaggregation of area
data. Tier 2 corresponds to use of country-specific coefficients and/or finer scale area disaggregation, which will
reduce uncertainty in emission/removal estimates. Tier 3 methods refer to the use of more complex countryspecific approaches. Where possible, default values from the IPCC Guidelines are updated and new default
values are provided based on the most up-to-date research findings.

3.4.1

Grassland Remaining Grassland

Carbon stocks in permanent grassland are influenced by human activities and natural disturbances, including
harvesting of woody biomass, rangeland degradation, grazing, fires, rehabilitation, pasture management, etc.
Annual production of biomass in grassland can be large, but due to rapid turnover and removals through grazing
and fire, standing stock of aboveground biomass rarely exceeds a few tonnes per hectare. Larger amounts canaccumulate in the woody component of vegetation, in root biomass and in soils. The extent to which carbon stocks
increase or decrease in each of these pools is affected by management practices such as those described above.
This section provides guidance on estimating carbon stock changes in grassland remaining grassland (GG) for
two carbon pools: living biomass and soils. At this time, not enough information is available to develop default
coefficients for estimating the dead organic matter pool. The total annual carbon stock change in grassland
remaining grassland is therefore the sum of annual estimates of carbon stock changes in each carbon pool—living biomass and soils—as shown in Equation 3.4.1. Estimation techniques for each pool are described
separately below.
EQUATION 3.4.1
ANNUAL CHANGE IN CARBON STOCKS IN GRASSLAND REMAINING GRASSLAND∆CGG = ∆CGGLB + ∆CGGSoils
Where:∆CGG = annual change in carbon stocks in grassland remaining grassland, tonnes C yr-1

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Chapter 3: LUCF Sector Good Practice Guidance∆CGGLB = annual change in carbon stocks in living biomass in grassland remaining grassland, tonnes C yr-1
∆CGGSoils = annual change in carbon stocks in soils in grassland remaining grassland, tonnes C yr-1
To convert tonnes C to Gg CO2, multiply the value by 44/12 and by 10-3. For the convention (signs), refer to
Section 3.1.7 or Annex 3A.2 (Reporting Tables and Worksheets).
TABLE 3.4.1
TIER DESCRIPTIONS FOR SUBCATEGORIES UNDER GRASSLAND REMAINING GRASSLAND
Tier
Sub-categories

Tier 1

Tier 2Tier 3

LivingBiomass

Assume there is no change incarbon stocks.

Use country-specific values for carbon
accumulation and removal rates andannual or periodic surveys to estimate
the areas under different classes ofgrassland by climate region.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement)

Soils

For changes in soil carbon frommineral soils use default
coefficients. The areas must be
stratified by climate and soil type.
For changes in soil carbon fromorganic soils use default
coefficients and stratify the areas byclimatic region. For emissions fromliming, use default emission factors.

For both mineral and organic soils use
some combination of default and/or
country-specific coefficients and area
estimates of increasingly finer spatial
resolution. For emissions from liming,
use emission factors differentiated byforms of lime.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement)

3.4.1.1

C HANGE IN CARB ON STOCKS IN LIVI NG BIOMASS

Although the methods used for estimating biomass changes are conceptually similar between grassland, cropland,
and forest (described in detail in Section 3.2.1.1), grasslands are unique in a number of ways. Grasslands are
subject to frequent vegetation fires that can influence savannah thickening1, mortality and regrowth, and root toshoot ratio. Other management activities, such as tree and brush removal, pasture improvement, tree planting(silvopastoralism), as well as overgrazing and degradation can influence biomass stocks. For woody species insavannahs (grassland with trees), the allometric relationships differ from those used in forests because of large
numbers of multi-stem trees, large number of shrubs, hollow trees, high proportion of standing dead trees, highroot-to-shoot ratios and coppicing regeneration.

3.4.1.1.1 METHODOLOGICAL ISSUES
Equation 3.4.2 shows the summary equation for estimating changes in carbon stocks in living biomass ingrassland remaining grassland. Depending on the methodological tier being used and data availability, grassland
can be disaggregated by type, region or climate zone.
EQUATION 3.4.2
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN GRASSLAND REMAINING GRASSLAND∆CGGLB = ∑c∑i ∑m ∆CGGLB(c,i,m)Where:∆CGGLB = annual change in carbon stocks in living biomass in grassland remaining grassland summed
across all grassland types i, climate zones c, and management regimes m, tonnes C yr-1
∆CGGLB(c,i,m) = change in carbon stocks in living biomass for a specific grassland type i, climate zone c andmanagement regime m, tonnes C yr-1
1

Savannah thickening is a general term referring to an increase in the density and biomass of woody species in grassland
ecosystems over time due to changes in fire and/or grazing regimes as well as climate changes. For example, in the southcentral US woody biomass encroachment/thickening on grasslands is estimated to have increased biomass stocks by around
0.7 tonnes d.m. ha-1 yr-1 over a several year period (Pacala et. al. 2001)

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Grassland

The living biomass pool in grassland includes above- and belowground carbon stocks in woody and herbaceous(grasses and forbs) vegetation. However, carbon stocks in the aboveground herbaceous component are usuallysmall and relatively insensitive to management; thus aboveground grass biomass is only considered for
estimating non-CO2 emissions from burning. Carbon stocks in belowground biomass of grasses are larger andmore sensitive to management changes and are therefore included in estimates of carbon stock changes in living
biomass of grassland.

3.4.1.1.1.1 Choice of Method
All countries should strive for improving inventory and reporting approaches by advancing to the highest tierpossible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if
carbon emissions and removals in grassland remaining grassland is a key category and if the sub-category ofliving biomass is considered significant based on principles outlined in Chapter 5. Countries should use thedecision tree in Figure 3.1.1 to help with the choice of method.
Tier 1: In grassland where management practices are static, biomass carbon stocks will be in an approximate
steady-state (i.e. carbon accumulation through plant growth is roughly balanced by losses through decomposition
and fire). In grassland where management changes are occurring over time (e.g. through savannah thickening,tree/brush removal for grazing management, improved pasture management or other practices), the stockchanges can be significant. However, information is not available to develop broadly applicable default rates ofchange in living biomass carbon stocks in grassland for these different management regimes. Therefore, the Tier
1 assumption is no change in living biomass carbon stocks.
Tier 2: At Tier 2, carbon stock changes are estimated for above- and belowground biomass in perennial woody
vegetation and for belowground biomass of grasses, as summarised in Equation 3.4.3.
EQUATION 3.4.3
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN GRASSLAND REMAINING GRASSLAND∆CGGLB(c,i,m) = (∆Bperennial + ∆Bgrasses) ● CF
Where:∆CGGLB(c,i,m) = change in carbon stocks in living biomass for a specific grassland type i, climate zone c andmanagement regime m tonnes C yr-1
∆Bperennial = change in above- and belowground perennial woody biomass, tonnes d. m. yr-1
∆Bgrasses = change in belowground biomass of grasses, tonnes d. m. yr-1
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonnes d.m.)-1
Changes in living biomass (∆B) can be estimated in one of two ways: using annual rates of growth and loss
(Equation 3.4.4) or (b) with biomass stocks at two points in time (Equation 3.4.5).
EQUATION 3.4.4
ANNUAL CHANGE IN LIVING BIOMASS ( RATE APPROACH)∆Bi = Ai ● (G – L)
Where:∆Bi = annual change in living biomass in grassland of type i, tonnes d. m. yr-1
Ai = area of grassland of type i, ha
G = average annual biomass growth, tonnes d. m. ha-1 yr-1
L = average annual biomass loss, tonnes d. m. ha-1 yr-1
The biomass difference approach (Equation 3.4.5) can be applied where data on biomass stocks are estimated atregular time intervals through some types of national inventory system. The difference between total biomass
stocks at two points in time is calculated. This value is divided by the number of years between measurements togenerate an annual rate of change in biomass stocks.

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EQUATION 3.4.5
ANNUAL CHANGE IN LIVING BIOMASS (DIFFERENCE APPROACH)∆B = (B t2 – B t1) / (t2 – t1)
Where:∆B = annual change in living biomass, tonnes d. m. yr-1
B t2 = biomass at time t2, tonnes d. m.
B t1 = biomass at time t1, tonnes d. m.
Tier 2 methods involve country- or region-specific estimates of biomass stocks by major grassland types andmanagement activity and estimates of stock change as a function of major management activity (i.e. grazing andfire regimes, productivity management).
Either of the approaches described above can be used to estimate changes in above- and belowground biomass.
In long-established grassland, changes in biomass are likely only in response to relatively recent changes (e.g.
within the past 20 yrs) in management practices. Therefore, it is good practice to associate estimates of biomasschange with specific management conditions, categorized if possible by climate and grassland type. For
example, when using the rate approach, the area of semi-arid grassland under intensive grazing should be
multiplied by coefficients (G and L) that are specific to semi-arid intensively grazed grassland. If the difference
approach is used, then biomass stocks should be measured or estimated separately for different grassland types
under specific management regimes. A stratification of management regimes/grassland conditions could include
categories such as: native, extensively managed grassland, grassland subject to woody encroachment, moderately
and severely degrading grassland, intensively managed, improved pastures (see broadly defined management
conditions in Section 3.4.1.2. on Changes in Carbon Stocks in Soils).
While Equations 3.4.4 and 3.4.5 can be used to estimate changes in belowground biomass stocks directly,
belowground biomass stocks are often approximated using expansion factors applied to aboveground biomass
stocks. Such expansion factors are ratios of belowground to aboveground biomass, otherwise known as root toshoot ratios. The ratios may vary by grassland type, climate region, and management activity. Equation 3.4.6
demonstrates how to estimate total (above- and belowground) biomass stocks. Note that aboveground biomass(BAG) must be estimated first and then applied in Equation 3.4.6. Total biomass stock (BTotal), belowgroundbiomass stock (BBG), or aboveground biomass stock (BAG) from Equation 3.4.6 can be used in Equations 3.4.5 toestimate changes in biomass stocks over time.
EQUATION 3.4.6
TOTAL BIOMASS
BTotal = BAG + BBG
andBBG = BAG ● R
Where:
BTotal = total biomass, including above- and belowground, tonnes d. m.
BAG = aboveground biomass, tonnes d. m.
BBG = belowground biomass, tonnes d. m.
R = root-to-shoot ratio, dimensionless
Tier 3: Tier 3 involves inventory systems using statistically-based sampling of carbon stocks over time and/or
process models, stratified by climate, grassland type and management regime. For example, validated speciesspecific growth models that incorporate management effects such as grazing intensity, fire, and fertilization, withcorresponding data on management activities, could be used to estimate net changes in grassland biomass carbon
stocks over time. Models can be used together with periodic sampling-based stock estimates similar to thoseused in detailed forest inventories could be applied to estimate stock changes as in Equation 3.4.5 to make spatial
extrapolations for grassland areas

3.4.1.1.1.2 Choice of Emission/Removal Factors
Tier 1: At Tier 1, the default assumption is no change in biomass stocks. Therefore, no default
emission/removal factors are provided.

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Grassland

Tier 2: Some data are available to assist in making estimates at Tier 2. The factors needed for a Tier 2 estimate
are: biomass growth (G) and loss (L) or biomass stocks at multiple points in time (Bt, Bt-1), and expansion factors
for belowground biomass.
The rate-based approach (Equation 3.4.4) requires derivation of loss rates (i.e. L in Equation 3.4.4), for woody
biomass (e.g. losses from harvest or bush removal) and belowground biomass of herbaceous species (e.g. due topasture degradation) , and net growth rates (e.g. from savannah thickening or pasture improvements) of woody
and belowground biomass (G in Equation 3.4.4). To develop carbon growth and loss coefficients from reported
carbon stock values, estimates for at least two points in time are needed. The change in carbon stocks between
two time periods are then calculated and this amount is divided by the number of years during the time period todevelop an annual rate. Rates of change should be estimated in response to changes in specific management/land
use activities (e.g. pasture fertilization, shrub removal, savannah thickening). Results from field research should
be compared to estimates of carbon growth and losses from other sources to verify that they are withindocumented ranges. Reported carbon growth and loss rates may be modified based on additional data and expert
opinion, provided clear rationale and documentation are included in the inventory report. (Note: It is important,in deriving estimates of biomass accumulation rates, to recognize that net changes in biomass stocks will occur
primarily during the first years (e.g. 20 years) following changes in management. After which time biomass
stocks will tend towards a new steady-state level with little or no change in biomass stocks occurring unlessfurther changes in management conditions occur).
Region- or country-specific data on biomass stocks over time are needed for use in Equation 3.4.5. These can beobtained through a variety of methods, including estimating density (crown coverage) of woody vegetation fromair photos (or high resolution satellite imagery) and ground-based measurement plots. Species composition,
density and above- vs. below-ground biomass can vary widely for different grassland types and conditions andthus it may be most efficient to stratify sampling and survey activities by grassland types. General guidance on
survey and sampling techniques for biomass inventories is given in Chapter 5 (Section 5.3).
Default estimates of above-ground biomass stocks and annual above-ground productivity are provided in Table
3.4.2. These are globally-averaged valued, by major climate zones, and are not intended as a basis for Tier 2
estimates of biomass stock change but can serve as defaults for estimating non-CO2 emissions from burning (seeSection 3.4.1.3) and for a first-order comparison with country-derived biomass stock estimates.
TABLE 3.4.2
DEFAULT ESTIMATES FOR STANDING BIOMASS GRASSLAND (AS DRY MATTER)
AND ABOVEGROUND NET PRIMARY PRODUCTION, CLASSIFIED BY IPCC CLIMATE ZONESIPCC Climate zone

Peak aboveground live biomass(tonnes d.m. ha-1)

Aboveground net primary production(ANPP)(tonnes d.m. ha-1 yr-1)

AverageNo. of studies

Error1

AverageNo. of studies

Error1

2

1.7

3

+ 75%

1.8

5

+ 75%

Cold Temperate - Dry

1.7

10

+ 75%

2.2

18

+ 75%

Boreal - Dry & Wet

Cold Temperate -Wet

2.4

6

+ 75%

5.6

17

+ 75%

Warm Temperate – Dry

1.6

8

+ 75%

2.4

21

+ 75%

Warm Temperate –Wet

2.7

5

+ 75%

5.8

13

+ 75%

Tropical - Dry

2.3

3

+ 75%

3.8

13

+ 75%

Tropical - Moist & Wet

6.2

4

+ 75%

8.2

10

+ 75%

Data for standing live biomass are compiled from multi-year averages reported at grassland sites registered in the ORNL DAAC NPP
database [http://www.daac.ornl.gov/NPP/html_docs/npp_site.html]. Estimates for above-ground primary production are from: Olson, R.
J., J. M. O. Scurlock, S. D. Prince, D. L. Zheng, and K. R. Johnson (eds.). 2001. NPP Multi-Biome: NPP and Driver Data for Ecosystem
Model-Data Intercomparison. Sources available on-line at [http://www.daac.ornl.gov/NPP/html_docs/EMDI_des.html]).
1

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

2

Due to limited data, dry and moist zones for the boreal temperature regime and moist and wet zones for the tropical temperature regime
were combined.

Estimating below-ground biomass can be an important component of biomass surveys of grassland but fieldmeasurements are laborious and difficult and thus expansion factors to estimate below-ground biomass fromabove-ground biomass are often used. Adaptations to fire and grazing have led to higher root-to-shoot ratios
compared to many other ecosystems; thus forest-based biomass expansion factors cannot be applied without
modification. Root-to-shoot ratios show wide ranges in values at both individual species (e.g. Anderson et al.,1972) and community scales (e.g. Jackson et al., 1996; Cairns et al., 1997). Thus it is recommended to use, as far
as possible, empirically-derived root-to-shoot ratios specific to a region or vegetation type. Table 3.4.3 provides

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default root-to-shoot ratios for major grassland ecosystems of the world; these data can be used as defaults whencountries do not have more regionally specific information to develop country-specific ratios. Ratios forwoodland/savannah and shrublands are also included for use by countries that include these lands in thegrassland section of their inventory.
Tier 3: Tier 3 approaches, e.g. using a combination of dynamic models along with inventory measurements ofbiomass stock changes, do not employ simple stock change or emission factors per se. Estimates ofemissions/removals using model-based approaches derive from the interaction of multiple equations that
estimate the net change of biomass stocks within the models. Key criteria in selecting appropriate models arethat they are capable of representing all of the management practices that are represented in the activity data. It is
critical that the model be validated with independent observations from country or region-specific field locations
that are representatives of the variability of climate, soil and grassland management systems in the country.

Grassland

TABLE 3.4.3
DEFAULT EXPANSION FACTORS (ROOT-TO-SHOOT [R:S] RATIOS)FOR THE MAJOR SAVANNAH/RANGELAND ECOSYSTEMS OF THE WORLD

Other

Vegetation type

Approximate IPCC climate zone1

R:S
ratio

n

Error2

Steppe/tundra/prairie grassland

Boreal (Dry & Wet), Cold Temperate
Wet, Warm Temperate Wet

4.0

7+ 150%

Semi-arid grassland

Dry (Cold Temperate, Warm
Temperate and Tropical)

2.8

9

+ 95%

Sub-tropical/ tropical grassland

Tropical Moist & Wet

1.6

7

Woodland/savanna

0.5

19

Shrubland

2.8

9+ 130%+ 80%+ 144%

1

Classification of the source data was by grassland biome types and thus correspondence to the IPCC climate zones are approximations.

2

Error estimates are given as two times standard deviation, as a percentage of the mean.

3.4.1.1.1.3 Choice of Activity Data
Activity data in this section refer to estimates of land areas (Ai) of long-term grassland (i.e. not recently
converted from other land uses). In addition, countries will need to estimate area burned each year to estimate
non-CO2 emissions. Chapter 2 provides general guidance on approaches for obtaining and categorizing area bydifferent land use classes. For estimating emissions and removals from this source, countries need to obtain area
estimates for grassland, disaggregated as required to correspond to the available emission factors and otherparameters. Because Tier 1 assumes no net change in grassland biomass through growth and losses, there is noneed to develop activity data at Tier 1, except to estimate non-CO2 emissions associated with burning (Section
3.4.1.3). Guidance below is for developing activity data for Tiers 2 and 3 methods.
Annual or periodic surveys are used in conjunction with the approaches outlined in Chapter 2 to estimate theaverage annual area of land in grassland. The area estimates are further sub-divided into general climate regions
and management practices to match the G and L values. International statistics such as FAO databases, IPCC
Guidelines, and other sources can be used to estimate the area of land in grassland. Area of grassland burning
can be estimated from knowledge of the average fire frequency for different grassland types or from moreaccurate assessments, such as use of remote sensing to inventory burned areas.
To improve estimates, more detailed annual or periodic surveys are used to estimate the areas of grassland
stratified by grassland types, climatic regions and management regimes. If finer resolution country-specific data
are only partially available, countries are encouraged to extrapolate to the entire land base of grassland using
sound assumptions from best available knowledge.
Tier 3 requires high-resolution activity data disaggregated at sub-national to fine grid scales. Similar to Tier 2, landarea is classified into specific grassland types by major climate, and management categories. If possible, spatially
explicit area estimates are used to facilitate complete coverage of the grassland and ensure that areas are not overor underestimated. Furthermore, spatially explicit area estimates can be related to locally relevant carbon
accumulation and removal rates, and restocking and management impacts, improving the accuracy of estimates.

3.4.1.1.1.4 Uncertainty Assessm ent
Because Tier 1 assumes no change in grassland biomass, it is not relevant to develop uncertainty estimates forTier 1. Guidance below is for developing uncertainty estimates for Tiers 2 and 3 methods.

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Grassland

Sources of uncertainty include the degree of accuracy in land area estimates (Ai), fraction of land area burned(fburned,i), carbon increase and loss (G and L), carbon stock (B), and expansion factor (EF) terms. It is goodpractice to calculate error estimates (i.e., standard deviations, standard error, or ranges) for each of thesecountry-defined terms and to use these estimates in a basic uncertainty assessment. Default uncertainty estimatesprovided in Table 3.4.3 can be used for the biomass expansion factors.
Tier 2 approaches may also use finer resolution activity data, such as area estimates for different climatic regions
or for grassland management systems within national boundaries. The finer-resolution data will reduce
uncertainty levels when associated with carbon accumulation factors defined for those finer-scale land bases.
This information can be used with a measure of uncertainty in area estimates from Chapter 2 to assess theuncertainty in estimates of carbon emissions and removals in grassland biomass using the Tier 1 methodology
for uncertainty analysis in Chapter 5.2 (Identifying and quantifying uncertainties).

3.4.1.2

C HANGE IN CARB ON STOCKS IN SOILS

3.4.1.2.1 METHODOLOGICAL ISSUESThe IPCC Guidelines provide methods for estimating CO2 Emissions and Uptake by Soils from Land-Use andManagement (Section 5.3) that can be applied to all land uses, including grassland. The methodology considers
organic carbon stock changes (CO2 emissions or removals) for mineral soils, CO2 emissions from organic soils(i.e. peat or muck soils) converted to pastures and emissions of CO2 from liming of grassland soils.
For carbon stock changes in mineral soils, the IPCC Guidelines define soil carbon stocks as organic carbon
incorporated into mineral soil horizons to a depth of 30cm and do not include C in surface residue (i.e. deadorganic matter) or changes in inorganic carbon (i.e. carbonate minerals). In most grassland soils, surface residue
represents a minor stock compared with carbon within the soil.
The summary Equation 3.4.7 for estimating the change in carbon stocks in soils is shown below:
EQUATION 3.4.7
ANNUAL CHANGE IN CARBON STOCKS IN SOILS IN GRASSLAND REMAINING GRASSLAND∆CGGSoils = ∆CGGMineral – ∆CGGOrganic – ∆CGGLiming
Where:∆CGGSoils = annual change in carbon stocks in soils in grassland remaining grassland, tonnes C yr-1
∆CGGMineral = annual change in carbon stocks in mineral soils in grassland remaining grassland, tonnes C yr-1
∆CGGOrganic = annual change in carbon stocks in organic soils in grassland remaining grassland (estimatedas net annual flux), tonnes C yr-1
∆CGGLiming = annual C emissions from lime application to grassland, tonnes C yr-1
For Tier 1 and 2 methods, changes in dead organic matter and inorganic carbon stocks should be assumed to be
zero. If dead organic matter is included in a Tier 3 approach, measurements should be based on the lowest
amounts present during an annual cycle to avoid including newly senesced plant material that represents atransient organic matter pool. Selection of the most suitable tier will depend on: (i) availability and detail ofactivity data on grassland management and changes in management over time, (ii) availability of suitable
information to estimate base C stocks and stock change and emission factors, and (iii) availability of dedicatednational inventory systems designed for soils.
All countries should strive for improving inventory and reporting approaches by advancing to the highest tierpossible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if
carbon emissions and removals in grassland remaining grassland is a key category and if the sub-category of soilorganic matter is considered significant based on principles outlined in Chapter 5. Countries should use thedecision tree in Figure 3.1.1 to help with the choice of method.

3.4.1.2.1.1 Choice of Method
The method used to estimate carbon stock changes in mineral soils is different from the method used for organicsoils. It is also possible that countries will use different Tiers to prepare estimates of the separate components on

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this subcategory, given availability of resources. Thus, mineral soils, organic soils, and emissions from liming
are discussed separately below.

Mineral Soils
For mineral soils, the estimation method is based on changes in soil C stocks over a finite period followingchanges in management that impact soil C, as shown in Equation 3.4.8. Previous soil C stocks (SOC(0-T) ) andsoil C stocks in the inventory year (SOC0) for the area of a grassland system in the inventory are estimated fromreference carbon stocks (Table 3.4.4) and stock change factors (Table 3.4.5), applied for the respective time
points. Here a grassland system refers to a specific climate, soil and management combination. Annual rates ofemissions (source) or removals (sink) are calculated as the difference in stocks (over time) divided by theinventory time period. The default time period is 20 years.
EQUATION 3.4.8
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILS FOR A SINGLE GRASSLAND SYSTEM∆CGGMineral = [(SOC0 – SOC(0 –T)) ● A ] / T
SOC = SOCREF ● FLU ● FMG ● FI
Where:∆CGGMineral = annual change in carbon stocks in mineral soils, tonnes C yr-1
SOC0 = soil organic carbon stock in the inventory year, tonnes C ha-1
SOC(0-T) = soil organic carbon stock T years prior to the inventory, tonnes C ha-1
T = inventory time period, yr (default is 20 yr)
A = land area of each parcel, ha
SOCREF = the reference carbon stock, tonnes C ha-1; see Table 3.4.4
FLU = stock change factor for land use or land-use change type, dimensionless; see Table 3.4.5
FMG = stock change factor for management regime, dimensionless; see Table 3.4.5
FI = stock change factor for input of organic matter, dimensionless; see Table 3.4.5
The types of land use and management factors supplied are broadly defined and include: 1) a land use factor (FLU)
that reflects C stock levels relative to native ecosystems, 2) a management factor (FMG) that represents broad
categories of improved and degraded grassland and 3) an input factor (FI) representing different levels of C
inputs to soil, which is implemented for improved grassland only. If the area was in other land use (e.g. forest
land, cropland) at the beginning of the inventory period, then guidance provided under Section 3.4.2, Land
Converted to Grassland, should be followed.
The calculation steps for determining SOC0 and SOC(0-T) and net soil C stock change per ha of land area are as
follows:
Step 1: Select the reference carbon stock value (SOCREF), based on climate and soil type, for each area ofgrassland being inventoried.
Step 2: Select the management condition of the grassland (FMG) present at beginning of the inventory period(e.g. 20 years ago) and the C input level (FI). These factors, multiplied by the reference soil C stock,
provide the estimate of ‘initial’ soil C stock (SOC(0-T)) for the inventory period. Note for Grassland
Remaining Grassland the land use factor (FLU) always equals 1.
Step 3: Calculate SOC0 by repeating step 2 using the same reference carbon stock (SOCREF) and FLU=1, but withmanagement and input factors that represent conditions in (current) inventory year.
Step 4: Calculate the average annual change in soil C stock for the area over the inventory period (∆CGGMineral)

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Example: For an Ultisol soil in a tropical moist climate, SOCRef (0-30 cm) is 47 tonnes C ha-1.
Under management resulting in an unimproved, moderately overgrazed pasture, the soil carbon
stock at the beginning of the inventory period (default is 20 yr previous) is (SOCRef ● FLU ● FMG ●FI) = 47 tonnes C ha-1 ● 1 ● 0.97 ● 1 = 45.6 tonnes C ha-1. Improved pasture with fertiliser
addition (FMG = 1.17) is the management condition in the (current) inventory year, yielding a soil
carbon stock estimate of 47 tonnes C ha-1 ● 1 ● 1.17 ● 1 = 55 tonnes C ha-1. Thus the averageannual change in soil C stock for the area over the inventory period is calculated as (55 tonnes C
ha-1 – 45.6 tonnes C ha-1) / 20 yrs = 0.47 tonnes C ha-1 yr-1.
Tier 1: For Tier 1, default reference carbon stocks and stock change factors are used (as shown in Equation 3.4.8)
for major grassland systems in a country, stratified by the default climate and soil types (Equation 3.4.9). For theaggregate area of grassland remaining grassland, stock changes can be calculated either by tracking managementchanges and calculating stock changes on individual parcels of land (Equation 3.4.9A) or by calculating aggregate
soil carbon stocks at the start and end of the inventory period from more general data on the area distribution ofgrassland systems (Equation 3.4.9B). Aggregate results will be the same with either approach, the main difference
being that attribution of the effects of specific changes in management requires activity data that tracks
management changes on specific areas of land. Default values for this calculation are described in Section
3.4.1.2.1.2.
EQUATION 3.4.9
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILSIN TOTAL GRASSLAND REMAINING GRASSLAND∆CGGMineral = ∑c∑s∑i [(SOC0 – SOC(0 –T)) ● A ] c,s,i / T(A)∆CGGMineral = ∑ c ∑ s ∑ i (SOC0 ● A) c,s,i – ∑ c ∑ s ∑ i (SOC(0 –T) ● A) c,s,i / T(B)

Where:∆CGGMineral = annual change in carbon stocks in mineral soils, tonnes C yr-1
SOC0 = soil organic carbon stock in the inventory year, tonnes C ha-1
SOC(0 – T) = soil organic carbon stock T years prior to the inventory, tonnes C ha-1
T = inventory time period, yr (default is 20 yr)
A = land area of each parcel, hac represents the climate zones, s the soil types, and i the set of major grassland types that are present in acountry.
Example: The following example shows calculations for aggregate areas of grassland soil carbon
stock change using Equation 3.4.9B. In a tropical moist climate on Ultisol soils, there are 1Mha ofpermanent grassland. The native reference carbon stock (SOCRef) for the climate/soil type is 47
tonnes C ha-1. At the beginning of the inventory calculation period (i.e. 20 yrs earlier) the
distribution of grassland systems was 500,000 ha of unmanaged native grassland, 400,000 ha ofunimproved, moderately degraded grazing land and 100,000 ha of heavily degraded grassland.
Thus initial soil carbon stocks for the area were: 500,000 ha ● (47 tonnes C ha-1 ● 1 ● 1 ● 1) +400,000 ha ● (47 tonnes C ha-1 ● 1 ● 0.97 ● 1) + 100,000 ha ● (47 tonnes C ha-1 ● 1 ● 0.7 ● 1) =45.026 million tonnes C. In the (current) inventory year, there are: 300,000 ha of unmanaged
native grassland, 300,000 ha of unimproved, moderately degraded grazing land, 200,000 ha ofheavily degraded grassland, 100,000 ha of improved pasture receiving fertiliser, and 100,000 ha ofhighly improved pasture receiving fertiliser together with irrigation. Thus total soil carbon stocks
in the inventory year are: 300,000 ha ● (47 tonnes C ha-1 ● 1 ● 1 ● 1) + 300,000 ha ● (47 tonnes C
ha-1 ● 1 ● 0.97 ● 1) + 200,000 ha ● (47 tonnes C ha-1 ● 1 ● 0.7 ● 1) + 100,000 ha ● (47 tonnes C
ha-1 ● 1 ● 1.17 ● 1) + 100,000 ha ● (47 tonnes C ha-1 ● 1 ● 1.17 ● 1.11) = 45.960 million tonnes C.
The average annual stock change over the period for the entire area is: (45.960 – 45.026) million
tonnes C/20 yr = 0.934 million tonnes/20 yr = 46,695 tonnes per year soil C stock increase.
Tier 2: For Tier 2, the same basic equations as in Tier 1 are used but country-specific values for reference
carbon stocks and/or stock change factors are used. In addition, Tier 2 approaches will likely involve a moredetailed stratification of management systems if sufficient data are available.

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Tier 3: Tier 3 approaches, using a combination of dynamic models along with detailed soil C emission/stockchange inventory measurements, will likely not employ simple stock change or emission factors per se.
Estimates of emissions using model-based approaches derive from the interaction of multiple equations that
estimate the net change of soil C stocks within the models. A variety of models designed to simulate soil carbon
dynamics exist (for example, see reviews by McGill et al., 1996; Smith et al., 1997).
Key criteria in selecting an appropriate model are that the model is capable of representing all of the
management practices that are represented and that model inputs (i.e. driving variables) are compatible with theavailability of country-wide input data. It is critical that the model be validated with independent observations
from country or region-specific field locations that are representatives of the variability of climate, soil andmanagement systems in the country. Examples of appropriate validation data sets include long-term grassland
experiments (e.g. Conant et al., 2001) or long-term measurements of ecosystem carbon flux for grassland
systems, using techniques such as eddy covariance (Baldocchi et al., 2001). Ideally, an inventory system ofpermanent, statistically representative grassland plots, that include major climatic regions, soil types, andmanagement systems and system changes, would be established where repeated measures of soil carbon stocks
could be made over time. Recommended re-sampling frequencies in most cases should not be less than 3 to 5
years (IPCC, 2000b). Where possible, measurements of soil carbon stocks should be made on an equivalent mass
basis (e.g. Ellert et al., 2001). Procedures should be implemented to minimize the influence of spatial variability
with repeated sampling over time (e.g. Conant and Paustian, 2002a). Such inventory measurements could be
integrated with a process model-based methodology.
Organic Soils
The methodology for estimating carbon stock change in organic soils used for managed grassland is to assign anannual loss rate of C due to the drainage and other management perturbations in adapting these soils to managed
grassland 2. Drainage and pasture management practices stimulate the oxidation of organic matter previously
built up under a largely anoxic environment (although emission rates are lower than under annual cropland use
where repeated tillage further stimulates decomposition). The area of grassland organic soils under each climate
type is multiplied by the emission factor to derive an estimate of annual C emissions, as shown in Equation
3.4.10 below:
EQUATION 3.4.10
CO2 EMISSIONS FROM CULTIVATED ORGANIC SOILS IN GRASSLAND REMAINING GRASSLAND∆CGGOrganic= ∑c (A ● EF) c
Where:∆CGGOrganic= CO2 emissions from cultivated organic soils in grassland remaining grassland, tonnes C yr-1
A = land area of organic soils in climate type c, ha
EF = emission factor for climate type c (see Table 3.4.6), tonnes C ha-1 yr-1

Tier 1: For Tier 1, default emission factors (Table 3.4.6) are used along with area estimates for organic soils
under grassland management within each climate region present in the country (Equation 3.4.10). Area estimates
can be developed using the guidance in Chapter 2.
Tier 2: The Tier 2 approach uses Equation 3.4.10 where emission factors are estimated from country-specificdata, stratified by climate region, as described in Section 3.4.1.2.1.2. Area estimates should be developed
following the guidance Chapter 2.
Tier 3: Tier 3 approaches for organic soils will include more detailed systems integrating dynamic models andmeasurement networks as described above for mineral soils.

Lim ing
The IPCC Guidelines include application of carbonate containing lime (e.g. calcic limestone (CaCO3) or
dolomite CaMg(CO3)2) to soils as a source of CO2 emissions. In humid regions, intensively managed pastures
may be periodically limed to reduce soil acidity. A simplified explanation of the process is that when carbonate
lime is dissolved in soil, the base cations (Ca++, Mg++) exchange with hydrogen ions (H+) on soil colloids(thereby reducing soil acidity) and the bicarbonate formed (2HCO3) can react further to evolve CO2 and water

2

Natural, ‘wetland’ grasslands that may be used for seasonal grazing but have not been artificially drained should not be
included in this category.

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Grassland(H2O). Although the liming effect generally has a duration of a few years (after which lime is again added),depending on climate, soil and management practices, the IPCC Guidelines account for emission as CO2 of all
the added carbonate carbon in the year of application. Thus the basic methodology is simply the amount of lime
applied times an emission factor that varies slightly depending on the composition of the material added.
EQUATION 3.4.11
ANNUAL CARBON EMISSIONS FROM AGRICULTURAL LIME APPLICATION∆CGGLiming = MLimestone ● EFLimestone + MDolomite ● EFDolomite
Where:∆CGGLiming = annual C emissions from agricultural lime application, tonnes C yr-1
M = annual amount of calcic limestone (CaCO3) or dolomite (CaMg(CO3)2), tonnes yr-1
EF = emission factor, tonnes C (tonne limestone or dolomite)-1 (These are equivalent to carbonate carbon
contents of the materials (12% for CaCO3, 13% for CaMg(CO3)2 ))Tier 1: For Tier 1, the total amount of carbonate containing lime applied annually to grassland soil and an
overall emission factor of 0.12 can be used to estimate CO2 emissions, without differentiating between variable
compositions of lime material. Note that while carbonate limes are the dominant liming material used, oxides
and hydroxides of lime, which do not contain inorganic carbon, are used to a limited extent for agriculturalliming and should not be included here (CO2 is produced in their manufacture but not following soil application).
Tier 2: A Tier 2 approach could entail differentiation of different forms of lime and specific emission factors if
data are available, since different carbonate liming materials (limestone as well as other sources such as marl andshell deposits) can vary somewhat in their carbon content and overall purity.
Tier 3: A Tier 3 approach could entail a more detailed accounting of emissions stemming from lime
applications than is assumed under Tiers 1 and 2. Depending on climate and soil conditions, biocarbonate
derived from lime application may not all be released as CO2 in the soil or from drainage water – some can beleached and precipitated deeper in the soil profile or be transported to deep groundwater, lakes and oceans andsequestered. If sufficient data and understanding of inorganic carbon transformation for specific climate-soil
conditions are available, specific emission factors could be derived. However, such an analysis would likely
necessitate including carbon fluxes associated with primary and secondary carbonate minerals in soil and their
response to grassland management practices.

3.4.1.2.1.2 Choice of Emission/Removal Factors
Mineral soils
When using either the Tier 1 or Tier 2 method, the following emission/removal factors are needed for mineralsoils: reference carbon stock (SOCREF); stock change factor for land-use change (FLU); stock change factor for
management regime (FMG); and factor for input of organic matter (FI).
Reference carbon stocks (SOCREF)
Soils under native vegetation that have not been subject to significant land use and management impacts are used
as a baseline or reference to which management-induced changes in soil carbon can be related.
Tier 1: Under Tier 1, it is good practice to use the default reference carbon stocks (SOCREF) provided in Table
3.4.4. These are updated from those provided in the IPCC Guidelines with the following improvements: i)estimates are statistically-derived from recent compilations of soil profiles under native vegetation, ii) ‘Spodic’soils (defined as boreal and temperate zone podzols in WRB classification, Spodosols in USDA classification)
are included as a separate category, iii) soils within the boreal climate region have been included.
Tier 2: For Tier 2, reference soil C stocks can be determined from measurements of soils, for example, as part of acountry’s soil survey and mapping activities. Advantages include more representative values for an individual
country and the ability to better estimate probability distribution functions that can be used in a formal uncertainty
analysis. Accepted standards for sampling and analysis of soil organic carbon and bulk density should be used.
Stock change factors (FLU, FMG, FI)
Tier 1: Under Tier 1, it is good practice to use default stock change factors (FLU, FMG, FI) provided in Table
3.4.5.
These are updated from the IPCC Guidelines, based on statistical analysis of published research. Wheresufficient data exists, separate values were computed for temperate and tropical grassland. All grasslands

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Chapter 3: LUCF Sector Good Practice Guidance(excluding those on organic soils) are assigned a base or (land use) factor of 1. Four categories of grassland
management condition are defined (unimproved/non-degraded, moderately degraded, severely degraded andimproved – see definitions in Table 3.4.5). Improved grasslands are defined as sustainably (non-degraded)managed grassland that receive at least one type of external input (e.g. improved species, fertilization, or
irrigation) to increase productivity. For improved grasslands there are two levels for the input factor value,‘nominal’ (which denotes the base case (FI=1) where there is no additional management improvement, beyond
that required for classification as improved grassland) and ‘high’, in which at least one addition improvement has
been implemented (e.g. fertilization plus irrigation), representing highly intensive grassland management. Values
for the moderately degraded grassland category were based on studies reporting conditions or treatments
representative of overgrazing and/or degradation. However, in many cases, particularly in the tropics, pasturedegradation is associated with a loss of more palatable grass species and replacement by ‘weedy’ species (often
woody plants). Although this constitutes degradation from the standpoint of use for grazing, negative impacts on
soil C may be less severe (as indicated by the small reduction in FLU for moderately degraded grassland, relative
to the native condition). In the IPCC Guidelines there was only one category specified for degraded grasslandwith a much lower value for FMG (0.7), implying severe degradation and high soil C loss. There are insufficientstudies in the literature to re-estimate a factor value for this condition and thus the previous value has beenretained to represent this severely degraded condition.
Tier 2: For Tier 2 applications, stock change factor values can be estimated from long-term experiments orother field measurements (e.g., field chronosequences) for a particular country or region. Advantages include
more accurate and representative values for the country of interest and the ability to estimate probability
distribution functions for factor values that can be used in a scientific uncertainty analysis. There are few
replicated long-term experiments investigating the impacts of grassland management on soil C stocks, and thus
uncertainties of emission factors for grassland management are greater than those for permanent cropland. Many
studies evaluate stock differences in paired plots and it is important that the plots being compared have similar
land use/management histories prior to implementation of experimental management treatments. If sufficientsequestration rate and land management data are available, factor values may be calculated for specific grassland
management practices (e.g., fertilisation, sowing improved grass and legume species, grazing management, etc.).
Information compiled from published studies and other sources should include C stock (i.e., mass per unit area toa specified depth) or all information needed to calculate SOC stocks, i.e., percent organic matter together withbulk density. If the percent organic matter and not the percent organic carbon are reported, a conversion factor of
0.58 for the carbon content of soil organic matter can be used. Other information that must be included in theanalysis is the soil type (e.g., WRB or USDA Soil Taxonomy Reference), depth of measurement, and time frame
over which the management difference has been expressed. Stock change factors should encompass sufficientdepth to include the full influence of management changes on soil C stocks and correcting for possible changes
in bulk density (Ellert et al., 2001). It is good practice to include a minimum depth of at least 30 cm (i.e., thedepth used for Tier 1 calculations); stock changes over deeper depths may be desirable if a sufficient number ofstudies are available and if statistically significant differences in stocks due to land management aredemonstrated at those depths.

Organic soils
When estimating emissions from organic soils that have been modified through artificial drainage and otherpractices for use as managed grassland, an emission factor (EF) is required for different climatic regimes.
Tier 1: For Tier 1, default emission factors, unchanged from the IPCC Guidelines, are provided in Table 3.4.6.
Natural, ‘wetland’ grasslands that may be used for seasonal grazing but have not been artificially drained are
excluded.
Tier 2: For Tier 2, there are limited literature data on emissions from organic soils used for managed grassland;
published studies usually make estimates based on subsidence, with a limited number of direct measurements of
CO2 fluxes from grassland organic soils (Ogle et al., 2003). Processes that contribute to subsidence include
erosion, compaction, burning, and decomposition, only the latter of which should be included in the emission
factor estimate. If using subsidence data, appropriate regional conversion factors to determine the proportion ofsubsidence attributable to oxidation should be used, based on studies measuring both subsidence and CO2 flux.
In the absence of such information, a default factor of 0.5 for oxidation-to-subsidence, on a gram-per-gram
equivalent basis, is recommended based on reviews by Armentano and Menges (1986). If available, direct
measurements of carbon fluxes are recommended as providing the best means of estimating emission rates fromorganic soils.

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Grassland

TABLE 3.4.4
DEFAULT REFERENCE (UNDER NATIVE VEGETATION) SOIL ORGANIC C STOCKS (SOCREF)(TONNES C PER HA FOR 0-30 CM DEPTH)
RegionHAC soils1

LAC soils2

Sandy soils3

Spodic soils4

Volcanic soils5

Wetlandsoils6

Boreal

68

NA

10#

117

20#

146

Cold temperate, dry

50

33

34

NA

20#

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry
Warm temperate,
moist
Tropical, dry

38

24

19

NA

70#

88

63

34

NA

80

38

35

31

NA

50#

Tropical, moist

65

47

39

NA

70#

NA

#

Tropical, wet

44

60

66

130

87

88

86

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

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

2

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

3

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

4

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

5

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

6

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

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TABLE 3.4.5
RELATIVE STOCK CHANGE FACTORS FOR GRASSLAND MANAGEMENT[SEE SECTION 3.4.7 FOR METHODS USED TO ESTIMATE THE STOCK CHANGE FACTORS]
FactorLevel

Climateregime

IPCC
Guidelines
default

GPG
reviseddefault

Error 1,2

Land use
(FLU)

All

All

1.0

1.0

NA

Management(FMG)

Nominally
managed (non All–degraded)

1.0

1.0

NA

Management(FMG)

Moderately
degradedgrassland

Temperate/Boreal

NA

0.95

+ 12%

Tropical

NA

0.97

+ 10%

All

0.7

0.7

+ 50%

Temperate/Boreal

1.1

1.14

+ 10%

Tropical

1.1

1.17

+ 10%

All

NA

1.0

NA

Temperate/Boreal

NA

1.11

+ 8%

Tropical

NA

1.11

+ 8%

SeverelydegradedManagement(FMG)

Improved
grasslandManagement(FMG)

Input (applied
only to improved
grassland) (FI )Input (applied
only to improved
grassland) (FI )NominalHigh

Definition
All permanent grassland is assigned aland use factor of 1.
Represents, non-degraded andsustainably managed grassland, but
without significant management
improvements.
Represents overgrazed or moderately
degraded grassland, with somewhat
reduced productivity (relative to thenative or nominally managed grassland)
and receiving no management inputs.
Implies major long-term loss of
productivity and vegetation cover, due
to severe mechanical damage to thevegetation and/or severe soil erosion.
Represents grassland which issustainably managed with moderategrazing pressure and that receive atleast one improvement (e.g.
fertilization, species improvement,
irrigation).
Applies to improved grassland where
no additional management inputs havebeen used.
Applies to improved grassland where
one or more additional management
inputs/improvements have been used(beyond that required to be classified as
improved grassland).

1+ two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis adefault, based on expert judgement, of + 50% is used. NA denotes ‘Not Applicable’, for factor values that constitute reference values orwhere factor values were not previously estimated for the IPCC Guidelines.

2

This error range does not include potential systematic error due to small sample sizes that may not be representative of the true impact
for all regions of the world.

TABLE 3.4.6
ANNUAL EMISSION FACTORS (EF) FOR MANAGED GRASSLAND ORGANIC SOILS
Error #

Cold Temperate

IPCC Guidelines default(tonnes C ha-1 yr-1)
0.25

Warm Temperate

2.5

+ 90%

Tropical/sub-tropical

5.0

+ 90%

Climatic temperature regime

#

+ 90%

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean

Lim ing
See discussion under Section 3.4.1.2.1.1.

3.4.1.2.1.3 Choice of Activity Data
Mineral Soils
The area of grassland under different management practices (A) is required for estimating mineral soil
emissions/removals.

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Grassland

For existing grassland, activity data should record changes or trends in management practices or utilization of thegrassland that affect soil carbon storage by impacting production. Two main types of activity data exist: (i)aggregate statistics compiled at a national level or for administrative areas within countries (e.g., provinces,counties, districts), or (ii) point-based land use and management inventories making up a statistically-based
sample of a country’s land area. The use of both sorts of activity data is described in Chapter 2, and the use of
the methods set out there with the three tiers described here will depend on the spatial and temporal resolution
required. For Tier 1 and Tier 2 inventories, activity data need to be stratified by major climatic differences andsoil types, since reference soil C stocks vary significantly according to these factors. For application of dynamic
models and/or a direct measurement-based inventory in Tier 3, similar or more detailed knowledge of the
combinations of climate, soil, topographic and management data are needed, but the exact requirements will bein part dependent on the model used.
Globally available land use statistics such as FAO’s databases (http://www.fao.org/waicent/portal/glossary_en.asp)
provide annual compilations of total land area by major land use types, without any additional details forgrassland management, climate, or soil. Thus FAO or similar country-total data would require additional incountry information to stratify areas by management, climate, and soil types. If such information has not alreadybeen compiled, an initial approach would be to overlay available land cover/land use maps (of national origin orfrom global datasets such as IGBP_DIS) with soil maps of national origin or global sources such as the FAO
Soils Map of the World. Where possible land areas associated with a characteristic grassland management
should be delineated and associated with the appropriate general (i.e., degraded, native, or improved) or specific(e.g., fertilization or grazing intensity) management factor values. Soil degradation maps may be a useful sourceof information for stratifying grassland according to management (e.g. Conant and Paustian, 2002b).
National land use and resource inventories, comprised of a collection of permanent sample points where data iscollected at regular intervals, have some advantages over aggregate pastoral and land use statistics. Inventory
points can more readily be associated with a particular grassland management system and the soil typeassociated with the particular location can be determined by sampling or by referencing the location to a suitablesoil map. Inventory points selected based on an appropriate statistical design also enable estimates of thevariability associated with activity data, which can be used as part of a formal uncertainty analysis. The
principles of sampling are described in Chapter 2 and an example of a point-based resource inventory is the
National Resource Inventory in the U.S. (Nusser and Goebel, 1997).

Organic Soils
The area of cultivated organic soils by climate regime (A) is required to estimate organic soil emissions. Similar
databases and approaches as those outlined above can be used for deriving area estimates. An overlay of soils
maps showing the spatial distribution of histosols (i.e. organic soils) with land cover maps showing grasslandarea can provide initial information on areas with organic soils under grassland. Country-specific data on
drainage projects combined with soil maps and surveys can be used to get a more refined estimate of relevant
areas of managed grassland on organic soils.

3.4.1.2.1.4 Uncertai nty Assessment
An assessment of uncertainty requires that uncertainty in per area emission/removal rates as well as uncertainty
in the activity data (i.e. the land areas involved in land-use and management changes), and their interaction be
estimated.
Where available, estimates of the standard deviation (and sample size) for the revised global default values
developed in this report are provided in the tables; these can be used with the appropriate estimates of variability
in activity data to estimate uncertainty, using the guidance provided in Chapter 5 of this report. Inventory
agencies should be aware that simple global defaults have a relatively high level of uncertainty associated withthem when applied to specific countries. In addition, because the field studies available to derive the global
defaults are not evenly distributed across climate regions, soil types and management systems, some areas –particularly in tropical regions – are underrepresented. For the Tier 2 methods, probability density functions (i.e.
providing mean and variance estimates) can be derived for stock change factors, organic soil emission factors
and reference C stocks as part of the process of deriving region- or country-specific data. Uncertainty in soil
emission and removal rates can be reduced by field studies of management influences on soil C stocks for major
grassland types and management regimes. Where chronosequence data are used, uncertainty in the carbon stockchanges estimates can be relatively high and thus it is desirable to use the mean of several ‘replicate’ studies toderive more representative values.

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3.4.1.3

N ON -CO 2 GREENH OUSE GAS EMISSIO NS

Coverage of Non-CO 2 gases in IPCC Guidelines
The IPCC Guidelines and GPG2000 (Chapter 4, Agriculture) already address the following emissions:•

N2O emissions from application of mineral and organic fertilisers, organic residues and biological nitrogen
fixation in managed grassland;•

N2O, NOx, CH4 and CO emissions from grassland (savanna) burning in the tropics; and•CH4 emissions from grazing livestock.

It is good practice to follow the existing IPCC Guidelines (Chapter 4, Agriculture) and GPG2000 to estimate
and report these fluxes in the Agriculture section.
Additional sources of emissions and removals, not included in IPCC Guidelines (Chapter 4, Agriculture) andGPG2000, include N2O emissions from organic nitrogen mineralization in drained, organic grassland soils 3,changes reduced uptake of CH4 in managed grassland soils and emissions from burning in temperate grassland.
Insufficient data on N2O emissions from enhanced mineralization of organic nitrogen on organic grassland soils
and management-induced reductions in CH4 sinks in grassland soils preclude recommending specificmethodologies at this time. In most circumstances they are likely to represent minor fluxes and as more researchis done and additional information becomes available, a fuller consideration of these sources may be possible.
For grassland burning occurring in grassland outside the tropics (and hence not included in IPCC Guidelines(Chapter 4, Agriculture) and GPG2000), methods to estimate N2O, NOx, CH4 and CO released from grassland
burning are described in Section 3.2.1.4. Default estimates for standing biomass, used to estimate the quantity offuel consumed, can be obtained from Table 3.4.2. Note that the amount of biomass that can serve as fuel canvary considerably according to the time of year and grazing regime and thus country-specific biomass estimatesthat correspond to when and where grassland burning occurs are recommended.

3.4.2

Land Converted to Grassland

The carbon implications of the conversion from other land uses (mostly forest land, cropland, and to lesser
degree wetlands and seldom settlements) to grassland is less clearcut than the case of conversion to cropland.
Literature on the main conversion type (from forest land to grassland in the tropics) provides evidence for net
gains as well as net losses in soil carbon, and the effect of management on the soil carbon changes of grassland
after conversion is critical (see for example Veldkamp, 2001). Conversion of land from other uses and fromnatural states to grassland can result in net emissions (or net uptake) of CO2 from both, biomass and soil.
Emissions from biomass are addressed in Section 3.4.2.1 and those from soil in Section 3.4.2.2. The calculationof carbon stock changes in biomass as a result of land use conversions to grassland is found in the IPCC
Guidelines in Section 5.2.3. (Forest and Grassland Conversion).
Methods described in this section are designed to account for changes in biomass and soils stocks associatedwith the land use conversion and the establishment of new grassland. Subsequent stock changes should be
estimated under Grassland Remaining Grassland.
The summary equation for carbon stock changes in Lands Converted to Grassland is shown below in Equation
3.4.12. Two sub-categories are estimated for the category of Lands Converted to Grasslands: living biomass andsoil organic matter. Table 3.4.7 summarises the tiers for each of the carbon subcategories.
EQUATION 3.4.12
TOTAL CHANGE IN CARBON STOCKS IN LAND CONVERTED TO GRASSLAND∆CLG = ∆CLGLB + ∆CLGSoils
Where:∆CLG = total change in carbon stocks in land converted to grassland, tonnes C yr-1
∆CLGLB = change in carbon stocks in living biomass in land converted to grassland, tonnes C yr-1

3

Emissions from fertilization and manuring on these grasslands are included in IPCC Guidelines (Chapter 4, Agriculture)
and GPG2000.

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Grassland∆CLGSoils = change in carbon stocks in soils in land converted to grassland, tonnes C yr-1

3.4.2.1

C HANGE IN CARB ON STOCKS IN BIO MASS

3.4.2.1.1 METHODOLOGICAL ISSUESThis section provides good practice guidance for calculating CO2 emissions and removals in biomass due to theconversion of land from natural conditions and other uses to grassland, including deforestation and conversion ofcropland to pasture and grazing lands. The carbon emissions and removals in biomass in land use conversion to
grassland result from the removal of existing and replacement with different vegetation. This process may resultin increases or decreases in carbon stocks in biomass depending on the type of land use conversion. This isdifferent from the concepts underlying carbon stock changes in biomass of grassland remaining grassland wherechanges are tied to management practices.
Generically, the methods to quantify emissions and removals of carbon due to conversion of other land uses tograssland require estimates of the carbon stocks prior to and following conversion (depending on whether
previous land use was forest land, cropland, wetlands) and the estimates of the areas of land converted during theperiod over which conversion has an effect. As a result of conversion to grassland, it is assumed that thedominant vegetation is removed entirely, after which some type of grass is planted or otherwise established (e.g.
in establishment of pasture). Alternatively, grassland can result from the abandonment of the preceding land use
e.g. cropland, and the area is taken over by grassland. Vegetation that replaces that which was cleared during
conversion should be accounted for using this methodology in conjunction with the methods in Section 3.4.1.

3.4.2.1.1.1 Choice of Method
Tier 1: The Tier 1 method follows the approach in IPCC Guidelines Section 5.2.3. Forest and Grassland
Conversion where the amount of carbon removed is estimated by multiplying the area converted annually by thedifference between average carbon stocks in biomass prior to and following conversion, accounting for carbon inbiomass that replaces cleared vegetation. It is good practice to account completely for all land conversions tograssland. Thus, this section elaborates on the method such that it includes each initial land use, including but notlimited to forests. All countries should strive for improving inventory and reporting approaches by advancing to
the highest tier possible given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3
approach if carbon emissions and removals in land converted to grassland is a key category and if the subcategory of living biomass is considered significant based on principles outlined in Chapter 5. Countries should
use the decision tree in Figure 3.1.2 to help with the choice of method.
Equation 3.4.13 summarises the major elements of a first order approximation of carbon stock changes from land
use conversion to grassland. Average carbon stock change on a per area basis is estimated for each type of
conversion. The average carbon stock change is equal to the carbon stock change due to the removal of biomass
from the initial land use (i.e., carbon in biomass immediately after conversion minus the carbon in biomass prior
to conversion), plus carbon stocks from biomass growth following conversion. As stated in the IPCC Guidelines,
it is necessary to account for any vegetation that replaces the vegetation that was cleared during land use
conversion. The IPCC Guidelines combine carbon in biomass after conversion and carbon in biomass that grows
on the land following conversion into a single term. In this method, they are separated into two terms, CAfter andCGrowth to increase transparency. At Tier 1, carbon stocks in biomass immediately after conversion (CAfter) are
assumed to be zero, i.e., the land is cleared of all vegetation before grass or woody vegetation is seeded, planted
or naturally regenerated. Average carbon stock change per area for a given land use conversion is multiplied by
the estimated area of lands undergoing such a conversion in a given year. In subsequent years, carbon stockchanges in living biomass of grassland, resulting from management changes, are counted following themethodology in Section 3.4.1.1 (Change in Biomass in: Grassland Remaining Grassland).

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TABLE 3.4.7
TIER DESCRIPTIONS FOR SUBCATEGORIES UNDER LAND CONVERTED TO GRASSLAND
Tier
Subcategories

Tier 1

Tier 2Tier 3

Living biomass

Use default coefficients toestimate carbon stock change
in biomass resulting from land
use conversions and for carbon
in biomass that replaces
cleared vegetation.

Use at least some country-specific carbon
stock parameters to estimate carbon stockchanges from land use conversion to
grassland. Apportion carbon from biomass
removal to burning, decay, and othernationally important conversion processes.
Estimate non-CO2 trace gas emissions fromthe portion of biomass burned both on-siteand off-site. Use area estimates that are
disaggregated to nationally relevant climate
zones and other boundaries to match
country-specific carbon stock parameters.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement).

Carbon stocks
in soil

For changes in soil carbon
from mineral soils use default
coefficients. The areas must be
stratified by climate and soil
type. For changes in soil
carbon from organic soils use
default coefficients and stratify
the areas by climatic region.
For emissions from liming, use
default emission factors.

For both mineral and organic soils use somecombination of default and/or countryspecific coefficients and area estimates ofincreasingly finer spatial resolution. For
emissions from liming, use emission factors
differentiated by forms of lime.

Use country-specificapproach at finespatial scale (e.g.,modeling,
measurement).

The basic steps in estimating carbon stock changes in biomass from land conversion to grassland are as follows:1.

Estimate the average area of land undergoing a transition from non-grassland to grassland during a year
(Aconversion), separately for each initial land use (i.e., forest land, cropland, etc.) and final grassland type.

2.

For each type of land use transition to grassland, use Equation 3.4.13 to estimate the resulting change incarbon stocks. Default data in Section 3.4.2.1.1.2 for CAfter, CBefore, and CGrowth can be used to estimate thetotal stock change on a per area basis for each type of land use transition. The estimate for stock change on a
per area basis can then be multiplied by the appropriate area estimates from step 1.

3.

Estimate the total carbon stock change from all land use conversions to grassland by summing the individual
estimates for each transition.

The default assumption for Tier 1 is that all carbon in biomass is lost to the atmosphere through decay processes
either on- or off-site. As such, Tier 1 calculations do not differentiate immediate emissions from burning andother conversion activities.
EQUATION 3.4.13
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASS IN LAND CONVERTED TO GRASSLAND∆CLGLB = AConversion ● (LConversion + ∆CGrowth)
LConversion = CAfter – CBefore
Where:∆CLGLB = annual change in carbon stocks in living biomass in land converted to grassland, tonnes C yr-1
AConversion = annual area of land converted to grassland from some initial use, ha yr-1
LConversion = carbon stock change per area for that type of conversion when land is converted to grassland,
tonnes C ha-1
∆CGrowth = carbon stocks from one year of growth of grassland vegetation after conversion, tonnes C ha-1
CAfter= carbon stocks in biomass immediately after conversion to grassland, tonnes C ha-1
CBefore= carbon stocks in biomass immediately before conversion to grassland, tonnes C ha-1

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Biomass stocks in newly established grassland tend to level out within a few years following conversion (e.g. 1-2
years for above-ground herbaceous biomass, 3-5 years for below-ground biomass), varying depending on thetype of land conversion (for example, sown pastures can become quickly established whereas naturalregeneration on abandoned cropland may take several years), climate and management conditions. Since under
Tier 1 Grassland Remaining Grassland the default biomass stock change is zero, changes in biomass carbon
stocks for grassland established following land use conversion are accounted for in the year of the conversion.
Tier 2: The Tier 2 calculations are structurally similar to Tier 1, with these distinctions. First, Tier 2 relies on atleast some country-specific estimates of the carbon stocks in initial and final land uses rather than the defaults
provided in Section 3.4.2.1.1.2. Area estimates for land converted to grassland are disaggregated at finer spatial
scales to capture regional variations in country-specific carbon stocks values.
Second, Tier 2 may modify the assumption that carbon stocks immediately following conversion are zero. This
enables countries to take into account land use transitions where some, but not all, vegetation from the originalland use is removed. In addition, under Tier 2 it is possible to account for biomass accumulation following
grassland establishment over a several year period (rather than accounting all biomass stock change in the yearof conversion) if data are available to estimate the time to full biomass establishment and the annual stockchanges.
Third, under Tier 2, it is good practice to apportion carbon losses to burning and decay processes if applicable.
Emissions of carbon dioxide occur as a result of burning and decay in land-use conversions. In addition, nonCO2 trace gas emissions occur as a result of burning. By partitioning losses to burning and decay, countries cancalculate non-CO2 trace gas emissions from burning. The IPCC Guidelines Workbook provides step-by-step
instructions for estimating carbon removals from burning and decay of biomass on-site and off-site and for
estimating non-CO2 trace gas emissions from burning (pages 5.7-5.17). Below is guidance on estimating carbon
removals from burning and decay and Section 3.2.1.4 of this chapter provides further guidance on estimating
non-CO2 trace gas emissions from burning.
The basic equations for estimating the amount of carbon burned or left to decay are provided in Equations 3.4.15
and 3.4.16 below, respectively. This methodology addresses burning for the purposes of land clearing. Non-CO2
emissions from burning in Grassland Remaining Grassland are covered in Section 3.4.3 of this report. The
default assumption in Equations 3.4.15 and 3.4.16 is that only aboveground biomass is burned or decays.
Countries are encouraged to use additional information to assess this assumption, particularly for decaying
belowground biomass. The basic approach can be modified to address other conversion activities as well as tomeet the needs of national circumstances. Both equations use as an input the total amount of carbon in biomass
removed during land clearing (∆Cconversion) (Equation 3.4.14), which is equivalent to area of land converted(AConversion) multiplied by the carbon stock change per area for that type of conversion (LConversion) in Equation
3.4.13).
The portion of woody biomass removed is sometimes used as wood products. In the case of wood products,
countries may use the default assumption that carbon in wood products is oxidized in the year of removal.
Alternatively, countries may refer to Appendix 3a.1 for estimation techniques for carbon storage in harvested
wood products, which may be accounted provided carbon in the product pool is increasing.
EQUATION 3.4.14
CHANGE IN CARBON STOCKS AS A RESULT OF BIOMASS CLEARING DURING LAND USE
CONVERSION∆Cconversion = Aconversion ● (Lconversion)
Where:∆Cconversion = change in carbon stocks as a result of a clearing biomass in a land use conversion, tonnes C
AConversion = area of land converted to grassland, ha
LConversion = carbon stock change per area for that type of conversion, tonnes C ha-1 (from Equation 3.4.13)

EQUATION 3.4.15
CARBON LOSSES FROM BIOMASS BURNING, ON-SITE AND OFF-SITE
Lburn onsite = ∆Cconversion ● ρburned on site ● ρoxid
Lburn offsite = ∆Cconversion ● ρburned off site ● ρoxid
Where:

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Lburn = carbon losses from biomass burned, tonnes C∆Cconversion = change in carbon stocks as a result of a clearing biomass in a land use conversion, tonnes Cρburned on site = proportion of biomass that is burned on-site, dimensionlessρoxid = proportion of biomass that oxidizes when burned, dimensionlessρburned off site = proportion of biomass that is burned off-site, dimensionless
EQUATION 3.4.16
CARBON LOSSES FROM BIOMASS DECAY
Ldecay = ∆Cconversion ● ρdecay

ρdecay = 1 – (ρburned on site + ρburned off site )Where:
Ldecay = carbon losses from biomass decay, tonnes C∆Cconversion = change in carbon stocks as a result of a clearing biomass in a land use conversion, tonnes Cρdecay = proportion of biomass that is left on-site to decay, dimensionlessρburned on site = proportion of biomass that is burned on-site, dimensionlessρburned off site = proportion of biomass that is burned off-site, dimensionless
It is good practice for countries to use the terms Lburn on site and Lburn off site as inputs to estimate non-CO2 trace gas
emissions from burning following guidance provided in Section 3.2.1.4.
Tier 3: Tier 3 is similar to Tier 2, with the following distinctions: rather than relying on average annual rates of
conversion, countries use direct estimates of spatially disaggregated areas converted annually for each initial andfinal land use; carbon stock changes are based on locally specific information. In addition, countries may use
dynamic models, making it possible to spatially and temporally link biomass and soil carbon stock change
estimates.

3.4.2.1.1.2 Choice of Emission/Removal Factors
Tier 1: The first step in this methodology requires parameters for carbon stocks before conversion for each
initial land use (CBefore) and after conversion (CAfter). It is assumed that all biomass is cleared when preparing asite for grassland use, thus, the default for CAfter is 0 tonnes C ha-1. Table 3.4.8 provides users with directions on
where to find carbon stock values for CBefore in land uses prior to clearing. Table 3.4.9 provides default values forcarbon stocks in grassland after conversion (∆CGrowth). These values are based on the defaults abovegroundbiomass stocks (Table 3.4.2) and the root:shoot ratios (Table 3.4.3), provided in Section 3.4.1.1.1.2 under
Grassland Remaining Grassland, and apply to herbaceous (i.e. non-woody) biomass only.
TABLE 3.4.8
DEFAULT BIOMASS CARBON STOCKS REMOVED DUE TO LAND CONVERSION TO GRASSLAND
Land-use category
Forest land

Cropland:
Perennial Woody Crops
Cropland:Annual Crops
1

Carbon stock in biomass before conversion (CBefore)(tonnes C ha-1)
See Table 3A.1.2 for carbon stocks in a range of forest types by climate
regions. Stocks are in terms of dry matter of carbon. Multiply values by acarbon fraction (CF) 0.5 to convert dry matter to carbon.
See Table 3.3.2 for carbon stocks in a range of climate regions for genericperennial woody cropland. Use the term for aboveground biomass carbon
stocks at harvest. Values are in units of tonnes C ha-1.
Use IPCC Guidelines default of 5 tonnes carbon ha-1 (or 10 tonnes dry
matter ha-1)

Error
Range 1

+ 75%
+ 75%

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

Tier 2: Tier 2 methods should include some country-specific estimates for biomass stocks and removals due toland conversion, and also include estimates of on- and off-site losses due to burning and decay following land
conversion to grassland. These improvements can take the form of systematic studies of carbon content andemissions and removals associated with land uses and land-use conversions within the country and a reexamination of default assumptions in light of country-specific conditions.

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Default parameters for emissions from burning and decay are provided, however countries are encouraged todevelop country-specific coefficients to improve the accuracy of estimates. The IPCC Guidelines use a general
default of 0.5 for the proportion of biomass burned on-site for forest conversions. Research studies suggests thatthe fraction is highly variable and could be as low as 0.2 (e.g. Fearnside, 2000; Barbosa and Fearnside, 1996; andFearnside, 1990). Updated default proportions of biomass burned on site are provided here. Table 3A.1.12
provides defaults for proportion of biomass consumed in on-site burning by a range of forest vegetation classes.
These defaults should be used for transitions from forest land to grassland. For non-forest initial land uses, thedefault proportion of biomass left on-site and burned is 0.35. This default takes into consideration research,which suggests the fraction should fall within the range 0.2 to 0.5 (Fearnside, 2000; Barbosa and Fearnside, 1996;
and Fearnside, 1990). It is good practice for countries to use 0.35, or another value within this range provided
the rationale for the choice is documented. There is no default value for the amount of biomass taken off-site andburned; countries will need to develop a proportion based on national data sources. In Equation 3.4.15., thedefault proportion of biomass oxidized as a result of burning is 0.9, as originally stated in the IPCC Guidelines.
The method for estimating emissions from decay assumes that all biomass decays over a period of 10 years. For
reporting purposes countries have two options: to report all emissions from decay in one year, recognizing that inreality they occur over a 10 year period, or report all emission from decay on an annual basis, estimating the rate
as one tenth of the totals in Equation 3.4.16. If countries choose the latter option, they should add a
multiplication factor of 0.10 to Equations 3.4.16.
Tier 3: Under Tier 3, all parameters should be country-defined using more accurate values rather than thedefaults.
TABLE 3.4.9
DEFAULT BIOMASS CARBON STOCKS PRESENT ON LAND CONVERTED TO GRASSLAND
IPCC Climate zone

Total (above- and belowground) nonwoody biomass(tonnes d.m. ha-1)

Error 1

Boreal - Dry & Wet 2

8.5

+ 75%

Cold Temperate - Dry

6.5

+ 75%

Cold Temperate -Wet

13.6

+ 75%

Warm Temperate – Dry

6.1

+ 75%

Warm Temperate –Wet

13.5

+ 75%

Tropical - Dry

8.7

+ 75%

Tropical - Moist & Wet

16.1

+ 75%

1

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

2

Due to limited data, dry and moist zones for the boreal temperature regime and moist and wet zones for the tropicaltemperature regime were combined.

3.4.2.1.1.3 Choice of Activity Data
All tiers require estimates of land areas converted to grassland. The same area data should be used for biomass
calculations and the soil estimates described in Section 3.4.2.2. If necessary, area data used in the soils analysis
can be aggregated to match the spatial scale required for lower order estimates of biomass; however, at higher
tiers, stratification should take account of major soil types. Area data should be obtained using the methods
described in Chapter 2. Higher tiers require greater detail but the minimum requirement for inventories to beconsistent with the IPCC Guidelines is that the areas of forest conversion can be identified separately. This isbecause forest will usually have higher carbon density before conversion. This implies that at least partial
knowledge of the land-use change matrix and therefore, where Chapter 2 Approaches 1 and 2 are being used,supplementary surveys may be needed to identify how much of the land being converted to grassland came fromforest. As pointed out in Chapter 2, where surveys are being set up, it will often be more accurate to seek toestablish directly areas under conversion, than to estimate these from the differences in total land areas underparticular uses at different times.
Tier 1: At this level, one type of activity data is needed: estimates of areas converted to grassland from initialland uses (i.e., forest land, cropland, settlements, etc.) to final grassland type (Aconversion). The methodology
assumes that area estimates are based on a one-year time frame. If area estimates are assessed over longer timeframes, they should be converted to average annual areas to match the default carbon stock values provided. If
countries do not have these data, partial samples may be extrapolated to the entire land base or historic estimatesof conversions may be extrapolated over time based on the judgement of country experts. At a minimum,

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Chapter 3: LUCF Sector Good Practice Guidancecountries can rely on information on average deforestation rates and land-use conversions to grassland frominternational sources, including the FAO. Tier 1 approaches may use average annual rates of conversion andestimated areas in place of direct estimates.
Tier 2: Countries should strive to use actual area estimates for all possible transitions from initial land use to
final grassland type. Complete reporting can be accomplished either through analysis of periodic remotelysensed images of land use and land cover patterns, and/or periodic ground-based sampling of land use patterns,
or hybrid inventory systems.
Tier 3: Activity data used in Tier 3 calculations should be a full accounting of all land use transitions tograssland and be disaggregate to account for different conditions within a country. Disaggregation can occur
along political (county, province, etc.), biome, climate, or on a combination of these parameters. In many cases
countries may have information on multi-year trends in land conversion (from periodic sample-based or
remotely sensed inventories of land use and land cover).

3.4.2.1.1.4 Uncertai nty Assessment
Tier 1: The sources of uncertainty in this method are from the use of global or national average rates of
conversion and course estimates of land areas converted to grassland. In addition, reliance on default parameters
for carbon stocks in initial and final conditions contributes to relatively high degrees of uncertainty. The default
values in this method have corresponding error ranges associated with them and the values are included in
default tables.
Tier 2: The use of actual area estimates rather than average rates of conversion will improve the accuracy of
estimates. In addition, the tracking of each land area for all possible land-use transitions will enable moretransparent accounting and allow experts to identify gaps and areas where land areas are accounted for multiple
times. Finally, a Tier 2 method uses at least some country-defined defaults, which will improve the accuracy of
estimates, provided they better represent conditions relevant to the country. Probability density functions (i.e.
providing mean and variance estimates) can be derived for all country-defined parameters. Such data can be usedin advanced uncertainty analyses such as Monte Carlo simulations. Refer to Chapter 5 (Section 5.2) of this reportfor guidance on developing estimates of sample-based uncertainties. At a minimum, Tier 2 methods should
provide error ranges in the form of percent standard deviations for each country-defined parameter.
Tier 3: Activity data from a land use and management inventory system should provide a basis to assign
estimates of uncertainty to areas associated with land-use changes by use of various methods, including Monte
Carlo simulations.

3.4.2.2

C HANGE IN CARB ON STOCKS IN SOILS

3.4.2.2.1 METHODOLOGICAL ISSUESLand conversion to grassland can occur from unmanaged land, including native, relatively undisturbed
ecosystems (e.g. forest land, wetlands) and from intensively managed cropland. With conversion from forest
land, disturbance associated with land clearing will usually result in losses of C in dead organic matter (surface
litter and coarse woody debris). Any litter and coarse woody debris pools (estimated using the methods describedin Section 3.2.2.2) should be assumed oxidized following land conversion and changes in soil organic matter C
stocks should be estimated as described below.
The total change in carbon stocks in soils on Lands Converted to Grassland is shown in Equation 3.4.17 below:
EQUATION 3.4.17
ANNUAL CHANGE IN CARBON STOCKS IN SOILS IN LAND CONVERTED TO GRASSLAND (LG)
∆CLGSoils = ∆CLGMineral – ∆CLGOrganic – ∆CLGLime
Where:∆CLGSoils = annual change in stocks in soils in land converted to grassland, tonnes C yr-1
∆CLGMineral = change in carbon stocks in mineral soils in land converted to grassland, tonnes C yr-1
∆CLGOrganic = annual C emissions from organic soils converted to grassland (estimated as net annual flux),
tonnes C yr-1

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Grassland∆CLGLime = annual C emissions from agricultural lime application on land converted to grassland, tonnes
C yr-1
Criteria for selecting the most suitable estimation method depend on the type of land conversion and thelongevity of the conversion, and availability of suitable country-specific information to estimate reference soil C
stocks and stock change and emission factors. All countries should strive for improving inventory and reporting
approaches by advancing to the highest tier possible given national circumstances. It is good practice forcountries to use a Tier 2 or Tier 3 approach if carbon emissions and removals in land converted to grassland is a
key category and if the sub-category of soil organic matter is considered significant based on principles outlined
in Chapter 5. Countries should use the decision tree in Figure 3.1.2 to help with the choice of method.

3.4.2.2.1.1 Choice of Method
Mineral Soils
Tier 1: The Tier 1 method is fundamentally similar as for Grasslands Remaining Grasslands (Equation 3.4.8 in
Section 3.4.1.2.1.1) except pre-conversion carbon stocks are dependent of parameters for other land use. Tier 1
methods rely on default values for reference C stocks and stock change factors and relatively aggregated data on
the location and rates of land-use conversion.
For Tier 1, the initial (pre-conversion) soil C stock (SOC(0-T)) is determined from the same reference soil C
stocks (SOCREF) used for all land uses (Table 3.4.4), together with stock change factors (FLU, FMG, FI) appropriate
for the previous land use as well as for grassland use. For native unmanaged land, as well as for managed forest,
soil C stocks are assumed equal to the reference values (i.e. land use, management and input factors equal 1).
Current (SOC0) soil C stocks on land converted to grassland are estimated exactly as for permanent grassland,i.e., using the reference carbon stocks (Table 3.4.4) and stock change factors (Table 3.4.5). Thus, annual rates ofemissions (source) or removals (sink) are calculated as the difference in stocks (over time) divided by theinventory time period (default is 20 years).
The calculation steps for determining SOC0 and SOC(0-T) and net soil C stock change per ha of land area are as
follows:
Step 1: Select the reference carbon stock value (SOCREF), based on climate and soil type, for each area of land
being inventoried.
Step 2: Calculate the pre-conversion C stock (SOC(0-T)) of land being converted into grassland, based on thereference carbon stock and previous land use and management, which determine land use (FLU),
management (FMG) and input (FI ) factors. Note that where the land being converted is forest the preconversion stocks will be equal to the native soil carbon reference stocks.
Step 3: Calculate SOC0 by repeating step 2 using the same reference carbon stock (SOCREF)), but management
and input factors that represent conditions in the land converted to grassland.
Step 4: Calculate the average annual change in soil C stock for the area over the inventory period (∆CLGMineral ).
Example 1: For a forest on volcanic soil in a tropical moist environment: SOCREF = 70 tonnes C ha-1.
For all forest soils default values for stock change factors (FLU , FMG , FI) are all 1; thus SOC(0-T) is
70 tonnes C ha-1. If the land is converted into pasture that is moderately degraded/overgrazed then
SOC0 = 70 tonnes C ha-1 ● 1 ● 0.97 ● 1 = 67.9 tonnes C ha-1. Thus the average annual change insoil C stock for the area over the inventory period is calculated as (67.9 tonnes C ha-1 – 70 tonnes
C ha-1) / 20 yrs = -0.01 tonnes C ha-1 yr-1.
Example 2: For tropical moist, volcanic soil that has been under long-term annual cropland, withintensive tillage and where crop residues are removed from the field, carbon stocks at the
beginning of the inventory period SOC(0-T) are 70 tonnes C ha-1 ● 0.58 ● 1 ● 0.91 = 36.9 tonnes C
ha-1. Following conversion to improved (e.g. fertilised) pasture, carbon stocks (SOC0) are 70
tonnes C ha-1 ● 1● 1.17 ● 1 = 81.9 tonnes C ha-1. Thus the average annual change in soil C stock
for the area over the inventory period is calculated as (81.9 tonnes C ha-1 – 36.9 tonnes C ha-1) / 20
yrs =2.25 tonnes C ha-1 yr-1.
Tier 2: The Tier 2 method for mineral soils also uses Equation 3.4.8, but involves country or region-specificreference C stocks and/or stock change factors and more disaggregated land use activity data.

Organic Soils
Tier 1 and Tier 2 approaches for organic soils that are converted from other land uses to grassland within theinventory period are treated the same as long-term grassland on organic soils, i.e., they have a constant emission

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factor applied to them, based on climate regime (see Equation 3.4.10 and Table 3.4.6). In Tier 2, emission factors
are derived from country or region-specific data.

Mineral and Organic soils
For both mineral and organic soils, Tier 3 methods will involve more detailed and country-specific models
and/or measurement-based approaches along with highly disaggregated land use and management data. Tier 3
approaches for estimating soil C changes from land-use conversions to grassland should employ models and data
sets that are capable of representing transitions over time between different land use and vegetation types,
including forest, savanna, grassland and cropland. The Tier 3 method needs to be integrated with estimates ofbiomass removal and the post-clearance treatment of plant residues (including woody debris and litter), as
variation in the removal and treatment of residues (e.g. burning, site preparation) will affect C inputs to soilorganic matter formation and C losses through decomposition and combustion. It is critical that models bevalidated with independent observations from country or region-specific field locations that are representative of
the interactions of climate, soil and vegetation type on post-conversion changes in soil C stocks.

Lim ing
If lime is applied to grassland converted from other land uses then the methods for estimating CO2 emissions
from liming are the same as described for Grassland Remaining Grassland, in Section 3.4.1.2.1.1.

3.4.2.2.1.2 Choice of Emission/Removal Factors
Mineral soils
The following variables are needed when using either the Tier 1 or Tier 2 method:
Reference carbon stocks (SOCREF)
Tier 1: Under Tier 1, it is good practice to use the default reference carbon stocks (SOCREF) provided in Table
3.4.4. These are updated from those provided in the IPCC Guidelines with the following improvements: i)estimates are statistically-derived from recent compilations of soil profiles under native vegetation, ii) ‘Spodic’soils (defined as boreal and temperate zone podzols in WRB classification, Spodosols in USDA classification)
are included as a separate category, iii) soils within the boreal climate region have been included.
Tier 2: For the Tier 2 method, reference soil C stocks can be determined from measurements of soils, forexample, as part of a country’s soil survey and mapping activities. It is important that reliable taxonomic
descriptions of measured soils be used to group soils into the classes defined in Table 3.4.4 or if a finer
subdivision of reference soil C stocks is used definitions of soil groupings need to be consistently and well
documented. Advantages to using country-specific data for estimating reference soil C stocks include moreaccurate and representative values for an individual country and the ability to better estimate probability
distribution functions that can be used in a formal uncertainty analysis.
Stock change factors (FLU, FMG, FI)
Tier 1: Under Tier 1, it is good practice to use default stock change factors (FLU, FMG, FI) as referred to in Table
3.4.10. These are updated from the IPCC Guidelines, based on a statistical analysis of published research.
Definitions guiding the selection of appropriate factor values are provided in the table. Stock change factors areused in estimating both post- (SOC0) and pre-conversion (SOC(0-T)) stocks; values will vary according to land
use and management conditions before and after the conversion. Note that where forest is converted to grasslanduse, the stock change factors all have the value of one, such that the pre-conversion soil carbon stocks are equal
to the native vegetation reference values (SOCREF).
TABLE 3.4.10
RELATIVE SOIL STOCK CHANGE FACTORS FOR LAND-USE CONVERSIONS TO GRASSLAND
Factor value typeLevel

GPG default

Land use, Management, & Input

Managed grassland

See default values in Table 3.4.5

Land use, Management, & Input

CroplandSee default values in Table 3.3.4

Land use, Management, & Input

Forest land

Default values for FLU, FMG, FI = 1

Tier 2: For the Tier 2 method, estimation of country-specific stock change factors for land-use conversion to
grassland will typically be based on paired-plot comparisons representing converted and unconverted lands,
where all factors other than land-use history are as similar as possible (e.g. Davidson and Ackermann, 1993).
Ideally several sample locations can be found that represent a given land use at different times since conversion– referred to as a chronosequence (e.g. Neill et al., 1997). There are few replicated long-term experiments of

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Grassland

land- use conversions and thus stock change factors and emission factors for land-use conversions will have arelatively high uncertainty. In evaluating existing studies or conducting new measurements it is critical that theplots being compared have similar pre-conversion histories and management as well as similar topographic
position, soil physical properties, and be located in close proximity. As for permanent grassland, required
information includes C stock (i.e. mass per unit area to a specified depth) for each land use (and time point if achronosequence). As previously described under Grassland Remaining Grassland, in the absence of specificinformation upon which to select an alternative depth interval, it is good practice to compare stock change
factors at a depth of at least 30 cm (i.e. the depth used for Tier 1 calculations). Stock changes over a deeper depth
may be desirable if a sufficient number of studies are available and if statistically significant differences instocks due to land management are demonstrated at deeper depths. However, it is critical that the reference soil
carbon stocks (SOCRef) and stock change factors (FLU, FMG, FI) be determined to a common depth.

Organic soils
Tier 1 and Tier 2 choice of C emission factors from organic soils recently converted to managed grassland
should observe the same procedures for deriving emission factors as described earlier under the Grassland
Remaining Grassland section.

3.4.2.2.1.3 Choice of Activity Data
All tiers require estimates of land areas converted to grassland. The same area estimates should be used for bothbiomass and soil calculations on land converted to grassland. Higher tiers require greater specificity of areas. To
be consistent with IPCC Guidelines, at a minimum, the area of land converted to grassland should be identified
separately for all tiers. This implies at least some knowledge of the land uses prior to conversion; this may
require expert judgment if Approach 1 in Chapter 2 is used for land area identification.
Tier 1: One type of activity data is needed for a Tier 1 approach: separate estimates of areas converted tograssland from initial land uses (i.e., forest land, cropland), by climate region. Distribution of land use
conversion by soil type (i.e. within a climate region) needs to be estimated, either by spatially explicit methods(e.g. overlays between maps of land use conversion and soils maps) or by knowledge of the distribution of major
soil types within areas subject to land use conversion by country experts. The determination of the area of land
converted to grassland needs to be consistent with the time period (T in Equation 3.4.8) used in the stock change
calculations. If countries do not have these data, partial samples may be extrapolated to the entire land base or
historic estimates of conversions may be extrapolated in time based on the judgement of country experts. Under
Tier 1 calculations, international statistics such as FAO databases, IPCC Guidelines, and other sources,
supplemented with sound assumptions by country experts, can be used to estimate the area of land converted tograssland from each initial land use. For higher tier calculations, country-specific data sources are used toestimate all transitions from initial land use to grassland.
Tier 2: Countries should strive to use actual area estimates for all possible transitions from initial land use to
grassland, stratified by management condition. Full coverage of land areas can be accomplished through analysis
of periodic remotely sensed images of land use and land cover patterns, through periodic ground-based samplingof land use patterns, or hybrid inventory systems. If such finer resolution country-specific data are partiallyavailable, countries are encouraged to use sound assumptions from best available knowledge to extrapolate to theentire land base. Historical estimates of conversions may be extrapolated in time based on the judgment ofcountry experts.
Tier 3: Activity data used in Tier 3 calculations should be a full accounting of all land use transitions tograssland and be disaggregated to account for different conditions within a country. Disaggregation can occur
along political (county, province, etc.), biome, climate, or on a combination of these parameters. In many cases
countries may have information on multi-year trends in land conversion (from periodic sample-based or
remotely sensed inventories of land use and land cover).

3.4.2.2.1.4 Uncertai nty Assessment
Tier 1: The sources of uncertainty in this method are from the use of global or national average rates of
conversion and course estimates of land areas converted to grassland. In addition, reliance on default parameters
for carbon stocks in initial and final conditions contributes to relatively high degrees of uncertainty. The default
values in this method have corresponding error ranges associated with them.
Tier 2: Actual area estimates for different land use transitions will enable more transparent accounting andallow experts to identify gaps and double counting of land areas. The Tier 2 method uses at least some countrydefined defaults, which will improve the accuracy of estimates, because they better represent conditions relevantto the country. Use of country-specific values should entail sufficient sample sizes and or use of expert judgment
to estimate uncertainties, which, together with uncertainty estimates on activity data derived using the advice in
Chapter 2 should be used in the approaches to uncertainty analysis described in Chapter 5 of this report.

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Tier 3: Activity data from a land use and management inventory system should provide a basis to assign
estimates of uncertainty to areas associated with land-use changes. Combining emission and activity data andtheir associated uncertainties can be done using Monte-Carlo procedures to estimate means and confidence
intervals for the overall inventory.

3.4.2.3

N ON -CO 2 GREENH OUSE GASES

As for all grasslands, sources of CH4 and N2O emissions associated with grassland that have recently undergonea change in land use are likely to be:•

Emissions from vegetation fires;•

N2O emissions from mineralisation of soil organic matter;•N2O from fertiliser use;•

Increase in N2O emissions and reduction in CH4 emissions from drainage of organic soils; and•Reduced CH4 sink in aerobic soils due to fertiliser use.

Emissions of methane from grazing livestock (enteric fermentation) and nitrous oxide from fertiliser use andanimal waste should be calculated and reported using the methods set out in Chapter 4 (the Agriculture chapter)of the IPCC Guidelines and the corresponding parts (Section 4.2 and 4.7) of GPG2000.
Fire related emissions should be calculated using the methods set out in Section 3.2.1.4, taking account, wheredata are available to do so, of the fact that the fuel load will often be higher during the transition period if theprevious land use was forest.
Land-use conversion may lead to mineralisation of soil organic matter nitrogen, which can increase N2O
emissions. However, depending on the previous land use, climate and soil type, land-use conversion to grassland
can also increase soil organic matter (Guo and Gifford, 2002).
Fertilization of grassland will tend to reduce the soil methane uptake, and, where wetland soils have been drained
nitrous oxide emissions may increase and countries reporting Agricultural emissions at Tier 3 may wish to takethese effects into account as described in Section 3.4.1.3. Additional effects of the transition to grassland that
may influence non-CO2 emissions, for example soil disturbance due to ploughing, or compaction where
mechanical equipment is used for clearance, but the effects are unlikely to be large, and no default methods exist
to account for them. Changes in the rate of removal CH4 from the atmosphere by aerated topsoil arising from theconversion is not addressed in this guidance, though a fuller consideration of various activities on methane
oxidation may be possible in future.

3.4.3

CompletenessA complete data series for land area estimates contains, at a minimum, the area of land within country
boundaries that is considered grassland during the time period covered by land use surveys or other data sources
and for which greenhouse gas emission and removals are estimated in the LULUCF sector. The total area
covered by the grassland inventory methodology is the sum of land remaining in grassland and land converted tograssland during the time period. This inventory methodology may not include some grassland areas where
greenhouse gas emissions and removals are believed to be insignificant or constant through time, such as native
grassland with moderate grazing and no significant management inputs. Therefore, it is possible for the total
grassland area for which estimates are prepared to be less than the total area of grassland within country
boundaries. In this case, it is good practice for countries to document and explain the difference in grassland areain the inventory and total grassland within their boundaries. Countries are encouraged to track through time thetotal area of land in grassland within country boundaries, keeping transparent records on which portions are used
to estimate carbon dioxide emissions and removals. As addressed in Chapter 2, all grassland areas, including
those not covered by the emissions inventory, should be part of the consistency checks to help avoid double
counting or omission. When summed with area estimates for other land uses, the grassland area data series will
enable a complete assessment of the land base included in a countries’ LULUCF sector inventory report.
Countries that use Tier 2 or 3 methods for grassland biomass and soil pools should include more detail in theirinventory on the grassland area data series. For example, countries may need to stratify the grassland area bymajor climate and soil types, including both the inventoried and non-inventoried grassland areas. When stratified
land areas are used in the inventory, it is good practice for countries to use the same area classifications for boththe biomass and soils pools. This will ensure consistency and transparency, allow for efficient use of land

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Grassland

surveys and other data collection tools, and enable the explicit linking between carbon dioxide emissions andremovals in biomass and soil pools.

3.4.4

Developing a Consistent Time Series

To maintain a consistent time series, it is good practice for countries to maintain records on the grassland areasused in inventory reports over time. These records should track the total grassland area included in the inventory,
subdivided by lands remaining in grassland and land converted to grassland. Countries are encouraged to include
an estimate of the total grassland area within country boundaries. To ensure that area estimates are treatedconsistently through time, land use definitions should be clearly defined and kept constant. If changes are madeto land use definitions, it is good practice to keep transparent records of how the definition changed. Consistentdefinitions should also be used for each of the grassland types and management systems included in theinventory. In addition, to facilitate the proper accounting of carbon emissions and removals over several periods,
information on historic land conversions can be utilized. Even if a country cannot rely on historic data for current
inventories, improvements to current inventory practices to provide the ability to track land conversions acrosstime will have benefits in future inventories.
Consistent estimation and reporting requires common definitions of activities, climate and soil types during theperiod of the inventory, which may require work to relate definitions used by national agencies involved in datacollection, as set out in Chapter 2.

3.4.5

Reporting and Documentation

The categories described in Section 3.4 can be reported using the reporting tables in Annex 3A.2. The estimates
under the grassland category can be compared with the reporting categories in the IPCC Guidelines as follows:•

Carbon dioxide emissions and removals in woody biomass in grassland remaining grassland to IPCC
Reporting Category 5A, Changes in woody biomass;•

Carbon dioxide emissions and removals in soils in grassland remaining grassland to IPCC Reporting
Category 5D, Changes in soil carbon; and•Carbon dioxide emissions and removals resulting from land-use conversions to grassland to IPCC Reporting
Category 5B for biomass, IPCC Reporting category 5D for soils, and IPCC Reporting Category 5E for nonCO2 gases.
It is good practice to maintain and archive all information used to produce national inventory estimates.
Metadata and data sources for information used to estimate country-specific factors should be documented andboth mean and variance estimates provided. Actual databases and procedures used to process the data (e.g.
statistical programs) to estimate country-specific factors should be archived. Activity data and definitions used tocategorize or aggregate the activity data must be documented and archived. Procedures used to categorize
activity data by climate and soil types (for Tier 1 and Tier 2) must be clearly documented. For Tier 3 approaches
that use modelling, the model version and identification must be documented. Use of dynamic models requiresthat copies of all model input files as well as copies of model source code and executable programs bepermanently archived.

3.4.6

Inventory Quality Assurance/Quality Control(QA/QC)

It is good practice to implement quality control checks and external expert review of inventory estimates anddata. Specific attention should be paid to country-specific estimates of stock change and emission factors toensure that they are based on high quality data and verifiable expert opinion.
Specific QA/QC checks across the grassland methodology include:
Grassland remaining grassland: Areas reporting of grassland biomass stock changes and grassland soil stockchanges should be the same. Grassland may include areas where soil stock changes are accounted for but
biomass changes are assumed to be zero (e.g. where non-woody biomass is largely absent), areas where bothbiomass and soil stocks are changing (e.g. areas with woody biomass encroachment), and areas where neither
biomass nor soil stocks are changing (e.g. extensively managed native grassland). To increase transparency andeliminate errors, the total grassland area where any stock changes are estimated should be reported, and where

IPCC Good Practice Guidance for LULUCF

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Chapter 3: LUCF Sector Good Practice Guidancebiomass stock changes equal zero these should still be reported if soil carbon stock changes are reported for thesame area.
Lands converted to grassland: Aggregate area totals for land converted to grassland should be the same in thebiomass and soils estimations. While biomass and soil pools may be disaggregated to different levels of detail,the same general categories should be used to disaggregate the area data.
For all soil carbon stock change estimates using Tier 1 or Tier 2 methods, total areas for each climate-soil typecombination must be the same for the start (year(0-T)) and the end (year(0)) of the inventory period (see Equation
3.4.9).

3.4.7

Estimation of Revised GPG Tier 1 Defaults for
Grassland Management (see Table 3.4.5)

Grassland C stock change factors were calculated for three general types of grassland condition: degraded,nominally managed, and improved grassland. An additional input factor was included for application to
improved grassland. The management improvements considered here were limited to fertilization (organic or
inorganic), sowing legumes or more grass species, and irrigation. Overgrazed grassland and poorly managed (i.e.,
none of the management improvements were applied) tropical pastures were classified as degraded grassland.
Native or introduced grasslands that were unimproved were grouped into the nominal grassland classification.
Grasslands with any single type of management improvement were classified as improved grassland withmedium C input rates. For improved grassland in which multiple management improvements were implemented,
C input rates were considered high. The data were synthesized in linear mixed-effects models, accounting forboth fixed and random effects. Fixed effects included depth, number of years since the management change, andthe type of management change (e.g., reduced tillage vs. no-till). For depth, we did not aggregate data but
included C stocks measured for each depth increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) as a separate pointin the dataset. Similarly, we did not aggregate data collected at different points in time from the same study.
Consequently, random effects were used to account for the interdependence in times series data and theinterdependence among data points representing different depths from the same study. We estimated factors forthe effect of the management practice at 20 years for the top 30 cm of the soil. Variance was calculated for eachof the factor values, and used to construct probability distribution functions with a normal density.

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Grassland

REFERENCES USED FOR ANALYSIS IN SECTION 3.4.7
Abril, A., and E. H. Bucher. (1999). The effects of overgrazing on soil microbial community and fertility in the
Chaco dry savannas of Argentina. Applied Soil Ecology 12:159-167.
Aina, P. O. (1979). Soil changes resulting from long-term management practices in Western Nigeria. Soil Science
Society of America Journal 43:173-177.
Arnold, P. W., F. Hunter, and P. Gonzalez Fernandez. (1976). Long-term grassland experiments at Cockle Park.
Annales Agronomiques 27:1027-1042.
Banerjee, M. R., D. L. Burton, W. P. McCaughey, and C. A. Grant. (2000). Influence of pasture management on soil
biological quality. Journal of Range Management 53:127-133.
Bardgett, R. D., C. Frankland Juliet, and J. B. Whittaker. (1993). The effects of agricultural practices on the soil
biota of some upland grasslands. Agriculture, Ecosystems and Environment 45:25-45.
Barrow, N. J. (1969). The accumulation of soil organic matter under pasture and its effect on soil properties.
Australian Journal of Experimental Agriculture and Animal Husbandry 9:437-445.
Biondini, M. E., B. D. Patton, and P. E. Nyren. (1998). Grazing intensity and ecosystem processes in a northern
mixed-grass prairie, USA. Ecological Applications 8:469-479.
Cantarutti, R. B., J. M. Brage, R. M. Boddey, and S. d. P. Resende. (1995). Caracterizacao do status de nitrogenio
em solosob pastagm de Brachiaria humidicola pura e consorciada com Desmodium ovalifolium cv. Itabela.
Pages 733-735 in Proceedings of the XXV Congresso Brasileiro do Ciencia do Solo, Micosa, MG, Brazil.
Carr, S. C. M., and J. S. Turner. (1959). The ecology of the Bogong high plains II. Fencing experiments in grassland
C. Australian Journal of Botany 7:34-83.
Carter, M. R., D. A. Angers, and H. T. Kunelius. (1994). Soil structural for and stability, and organic matter undercool-season perennial grasses. Soil Science Society of America Journal 58:1194-1199.
Cerri, C. C., B. Volkoff, and F. Andreaux. (1991). Nature and behavior of organic matter in soils under naturalforest, and after deforestation, burning and cultivation, near Manaus. Forest Ecology and Management 38:247257.
Chone, T., F. Andreuz, J. C. Correa, B. Volkhoff, and C. C. Cerri. (1991). Changes in organic matter in an Oxisol
from the central Amazonian forest during eight years as pasture determined by 13C isotopic composition.
Pages 397-405 in J. Berthelin, editor. Diversity of Environmental Biogeochemistry. Elsiver, Amsterdam.
Chuluun, T., L. L. Tieszen, and D. Ojima. (1999). Land use impact on C4 plant cover of temperate east Asian
grasslands. Pages 103-109 in K. Otsubo, editor. NIES Workshop on Information Bases and Modeling for Landuse and Land-cover Changes Studies in East Asia. Center for Global Environmental Research.
Desjardins, T., F. Andreauz, B. Volkoff, and C. C. Cerri. (1994). Organic carbon and 13C content in soils and soil
size-fractions, and their changes due to deforestation and pasture installation in eastern Amazonia. Geoderma
61:103-118.
Eden, M. J., D. F. M. McGregor, and N. A. Q. Viera. (1990). Pasture development on cleared forest land in northern
Amazonia. The Geographical Journal 156:283-296.
Escobar, C. J., and J. L. Toriatti Dematte. (1991). Distribution of organic matter and natural carbon-13 in an Ultisolin the Amazon piedmont. Pasturas Tropicales 13:27-30.
Feigl, B. J., J. Melillo, and C. C. Cerri. (1995). Changes in the origin and quality of soil organic matter after pasture
introduction in Rondonia (Brazil). Plant and Soil 175:21-29.
Fisher, M. J., I. M. Tao, M. A. Ayarza, C. E. Lascano, J. I. Sanz, R. J. Thomas, and R. R. Vera. (1994). Carbon
storage by introduced deep-rooted grasses in the South American savannas. Nature 371:236-238.
Frank, A. B., D. L. Tanaka, L. Hofmann, and R. F. Follett. (1995). Soil carbon and nitrogen of Northern Great
Plains grasslands as influenced by long-term grazing. Journal of Range Management 48:470-474.
Franzluebbers, A. J., N. Nazih, J. A. Stuedmann, J. J. Fuhrmann, H. H. Schomberg, and P. G. Hartel. (1999). Soil
carbon and nitrogen pools under low- and high-endophyte-infected tall fescue. Soil Science Society of America
Journal 63:1687-1694.
Franzluebbers, A. J., J. A. Stuedmann, H. H. Schomberg, and S. R. Wilkinson. (2000). Soil organic C and N pools
under long-term pasture management in the Southern Piedmont USA. Soil Biology and Biochemistry 32:469-478.
Garcia-Oliva, F., I. Casar, P. Morales, and J. M. Maass. (1994). Forest-to-pasture conversion influences on soilorganic carbon dynamics in a tropical deciduous forest. Oecologia 99:392-396.
Goh, K. M., J. D. Stout, and T. A. Rafter. (1977). Radiocarbon enrichment of soil organic matter fractions in New
Zealand soils. Soil Science 123:385-391.
Jackman, R. H. (1964). Accumulation of organic matter in some New Zealand soils under permanent pasture I.
Patterns of change of organic carbon, nitrogen, sulphur, and phosphorous. New Zealand Journal of Agricultural
Research 7:445-471.
Kohn, G. D., G. J. Osborne, G. D. Batten, A. N. Smith, and W. J. Lill. (1977). The effect of topdresedsuperphosphate on changes in Nitrogen : Carbon : Sulphur : Phosphorous and pH on a red earth soil during along term grazing experiment. Australian Journal of Soil Research 15:147-158.

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Chapter 3: LUCF Sector Good Practice Guidance

REFERENCES USED FOR ANALYSIS IN SECTION 3.4.7 (CONTINUED)
Koutika, L. S., F. Bartoli, F. Andreux, C. C. Cerri, G. Burtin, T. Chone, and R. Philippy. (1997). Organic matter
dynamics and aggregation in soils under rain forest and pastures of increasing age in the eastern Amazon
Basin. Geoderma 76.
Loiseau, P., and C. Grignani. (1991). Status of organic nitrogen and fate of mineral nitrogen in mid-mountainpastures. Agronomie 11:143-150.
Lovell, R. D., S. C. Jarvis, and R. D. Bardgett. (1995). Soil microbial biomass and activity in long-term grassland:
effects of management changes. Soil Biology and Biochemistry 27:969-975.
Lytton Hitchins, J. A., A. J. Koppi, and A. B. McBratney. (1994). The soil condition of adjacent bio-dynamic andconventionally managed dairy pasture in Victoria, Australia. Soil Use and Management 10:79-87.
Malhi, S. S., J. T. Harapiak, M. Nyborg, K. S. Gill, and N. A. Flore. (2002). Autumn and spring applications ofammonium nitrate and urea to bromegrass influence total and light fraction organic C and N in a thin Black
Chernozem. Canadian Journal of Soil Science 82:211-217.
Malhi, S. S., M. Nyborg, J. T. Harapiak, K. Heier, and N. A. Flore. (1997). Increasing organic C and N in soil underbromegrass with long-term N fertilization. Nutrient Cycling in Agroecosystems 49:255-260.
Manley, J. T., G. E. Schuman, J. D. Reeder, and R. H. Hart. (1995). Rangeland soil carbon and nitrogen responses to
grazing. Journal of Soil and Water Conservation 50:294-298.
Moulin, A. P., D. H. McCartney, S. Bittman, and W. F. Nuttall. Long-term effects of fertilizer on soil carbon in a
pasture soil.
Naeth, M. A., A. W. Bailey, D. J. Pluth, D. S. Chanasyk, and R. T. Hardin. (1991). Grazing impacts on litter and soilorganic matter in mixed prairie and fescue grassland ecosystems of Alberta. Journal of Range Management
44:7-12.
Neill, C., J. M. Melillo, P. A. Steudler, C. C. Cerri, J. F. L. d. Moraes, M. C. Piccolo, and M. Brito. (1997). Soil
carbon and nitrogen stocks following forest clearing for pasture in the Southwestern Brazilian Amazon.
Ecological Applications 7:1216-1225.
Nyborg, M., S. S. Malhi, E. D. Solberg, and R. C. Izaurralde. (1999). Carbon storage and light fraction C in a
grassland dark gray chernozem soil as influenced by N and S fertilization. Canadian Journal of Soil Science
79:317-320.
Oberson, A., D. K. Friesen, H. Tiessen, C. Morel, and W. Stahel. (1999). Phosphorus status and cycling in nativesavanna and improved pastures on an acid low-P Colombian oxisol. Nutrient Cycling in Agroecosystems
55:77-88.
Reiners, W. A., A. F. Bouwman, W. F. J. Parsons, and M. Keller. (1994). Tropical rain forest conversion to pasture:
Changes in vegetation and soil properties. Ecological Applications 4:363-377.
Ridley, A. M., W. J. Slattery, K. R. Halyar, and A. Cowling. (1990). The importance of the carbon cycle toacidification of grazed animal pasture. Australian Journal of Experimental Agriculture 30:529-537.
Rixon, A. J. (1966). Soil fertility changes in a redbrown earth under irrigated pastures. Australian Journal of
Agricultural Research 17:303-316.
Russell, J. S. (1960). Soil fertility changes in the long term experimental plots at Kybybolite, South Australia. I.
Changes in pH, total nitrogen, organic carbon and bulk density. Australian Journal of Agricultural Research
11:902-926.
Schuman, G. E., J. D. Reeder, J. T. Manley, R. H. Hart, and W. A. Manley. (1999). Impact of grazing managementon the carbon and nitrogen balance of a mixed-grass rangeland. Ecological Applications 9:65-71.
Shiel, R. S. (1986). Variation in amounts of carbon and nitrogen associated with particle size fractions of soils fromthe Palace Leas meadow hay plots. Journal of Soil Science 37:249-257.
Skjemstad, J. O., V. R. Catchpoole, R. P. l. Feuvre, and R. P. Le Feuvre. (1994). Carbon dynamics in Vertisols
under several crops as assessed by natural abundance 13C. Australian Journal of Soil Research 32:311-321.
Smoliak, S., J. F. Dormaar, and A. Johnston. (1972). Long-term grazing effects on Stipa-Bouteloua prairie soils.
Journal of Range Management 25:246-250.
Trumbore, S. E., E. A. Davidson, P. Barbosa De Camargo, D. C. Nepstad, and L. A. Martinelli. (1995).
Belowground cycling of carbon in forests and pastures of Eastern Amazonia. Global Biogeochemical Cycles
9:515-528.
Veldkamp, E. (1994). Organic carbon turnover in three tropical soils under pasture after deforestation. Soil Science
Society of America Journal 58:175-180.
Walker, T. W., B. K. Thapa, and A. F. R. Adams. (1959). Studies on soil organic matter. 3. Accumulation of carbon,
nitrogen, sulphur, organic and total phosphorous in improved grassland soils. Soil Science 87:135-140.
Wang, Y., and Z. Chen. (1998). Distribution of soil organic carbon in the major grasslands of Xilinguole, Inner
Mongolia, China. Acta Phytoecologica Sinica 22:545-551.
Wood, K. M., and W. H. Blackburn. (1984). Vegetation and soil responses to cattle grazing systems in the Texas
rolling plains. Journal of Range Management

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Wetlands

3.5

WETLANDS

Wetlands include land that is covered or saturated by water for all or part of the year (e.g. peatland) and that does
not fall into the forest land, cropland, grassland or settlements categories defined in Chapter 2 of this report(Section 2.2, Land Categories)1. This category can be subdivided into managed and unmanaged according tonational definitions. It includes reservoirs as a managed subdivision and natural rivers and lakes as unmanaged
subdivisions. Forest land, cropland, and grassland that are established on peaty or wet soils are addressed in
Sections 3.2, 3.3, and 3.4, respectively, of this chapter. Rice paddies are addressed in the Agriculture chapter of
the IPCC Guidelines and GPG2000. Flooding and wetland drainage are included in the IPCC Guidelines in
Section 5.4.3 Other Possible Categories of Activity.
For purposes of estimating greenhouse gas emissions, it is necessary to distinguish between managed andunmanaged wetlands. In this report, managed wetlands are those in which the water table is artificially changed(e.g. drained peatlands) or those that are created through human activity (e.g., damming a river). Major
greenhouse gas emissions from managed wetlands, and the sections of this report in which they are estimated,
are summarised in Table 3.5.1.
TABLE 3.5.1
SECTIONS AND APPENDICESADDRESSING MAJOR GREENHOUSE GAS EMISSIONS FROM MANAGED WETLANDS IN THIS REPORT

PeatlandFlooded Land2

CO2

Appendix 3a.3

Appendix 3a.3

CH4

Not addressed

Appendix 3a.3

N2O

Appendix 3a.3

Appendix 3a.3

Section 3.5

Section 3.5

Not addressed(drainage and rewetting of forest soils
is discussed in Appendix 3a.2)
Appendix 3a.3
(drainage and rewetting of forest soils
is discussed in Appendix 3a.2)

Covered in Appendix 3a.3
(no distinction is made based on theage of the reservoir)
Covered in Appendix 3a.3
(no distinction is made based on theage of the reservoir)

Wetlands Remaining Wetlands

Land Converted to Wetlands
CO2
CH4

N2O

3.5.1

Wetlands Remaining Wetlands

This category is addressed in Appendix 3a.3 Wetlands Remaining Wetlands: Basis for future methodologicaldevelopment.

3.5.2

Land Converted to Wetlands

In this section, CO2 emissions associated with either peat extraction or flooding are addressed. The conversion oflands to wetlands may be an important component of national estimates of deforestation (or other nationally
important land use conversions). For conversions related to peat extraction, carbon stock changes associated withliving biomass and soil are addressed below. For conversions related to flooding, only the carbon stock change
associated with the loss of living biomass is addressed.
Lands converted to wetlands include conversions from forest land, cropland, grassland and settlements to thiscategory. The most likely conversions are conversions from forest land to wetlands (e.g. rewetting of peatlands

1

The definition used in this report agrees with common definitions used in the Ramsar Convention on Wetlands and theConvention on Biological Diversity (CBD).

2

Flooded lands are defined as water bodies regulated by human activities for energy production, irrigation, navigation,
recreation, etc. and where substantial changes in water area due to water regulation occur. Regulated lakes and rivers,
where the main pre-flooded ecosystem was a natural lake or river, are not considered as flooded lands. Rice paddies are
addressed in the Agriculture Chapter of the IPCC Guidelines and GPG2000.

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Chapter 3: LUCF Sector Good Practice Guidancedrained for forestry purposes), conversions related to peat extraction (conversion of natural peatlands to managed
lands), or conversions to flooded land (for hydroelectric or other purposes). Methodologies for rewetting are notincluded due to the scarcity of available data (Appendix 3a.2 addresses emissions of non-CO2 greenhouse gases
from drainage and rewetting, with emphasis on drainage). As shown in Equation 3.5.1, guidance on estimating
carbon stock change in land converted to wetlands covers conversion to two possible land uses: peat extraction
and flooding.
EQUATION 3.5.1
CHANGE IN CARBON STOCKS IN LAND CONVERTED TO WETLANDS∆CLW = ∆CLW peat + ∆CLW flood
Where:∆CLW = change in carbon stocks in land converted to wetlands, tonnes C yr-1
∆CLW peat = change in carbon stocks in land converted to peat extraction (Section 3.5.1), tonnes C yr-1
∆CLW flood = change in carbon stocks in land converted to flooded land (Section 3.5.2), tonnes C yr-1
The carbon stock change in tonnes C is converted to Gg CO2 emissions by multiplying the value with 44/12 and10-3 to correspond to the reporting requirements. Emissions are reported as positive values and removals as
negative values (Equation 3.5.1 is expected to result in a loss of carbon). For more details on reporting and therule on the signs, see Section 3.1.7 and Annex 3A.2 (Reporting Tables and Worksheets).
Figure 3.1.2 provides a general decision tree to select the appropriate tier for land conversion and is applicable forland converted to wetlands. If data are available, the choice of tier should be performed separately for each land
conversion type (forest land to wetlands, grassland to wetlands, cropland to wetlands, other land to wetlands).

3.5.2.1

C HANGE IN CARBON STOCKS IN LAND CONVERTED TO
PEAT EXTRACTION

3.5.2.1.1 METHODOLOGICAL ISSUES
A method to estimate emissions from land converted to peat extraction is given below. Neither emissions fromorganic soils managed for peat extraction nor land-use changes associated with organic soils managed for peat
extraction are dealt with explicitly in the IPCC Guidelines. Emissions from peat combustion are dealt with in the
Energy section of the IPCC Guidelines. Therefore, the method below addresses only emissions from removal ofvegetation from land prepared for peat extraction and changes in soil organic matter due to oxidation of peat in
the aerobic layer on the land during the extraction. The removal of peat is covered by the estimates from peat
combustion in the energy section and is not considered in this section. This method, and the associated default
values used for Tier 1 estimates, can be applied for both lands with ongoing peat extraction (to be reported under
Wetlands remaining wetlands subcategory) and land converted to peat extraction.

3.5.2.1.1.1

Choice of Method

The estimate of carbon stock changes from land converted to peat extraction has two basic elements, as shown inEquation 3.5.2. Equation 3.5.2 calculates a loss of carbon.
EQUATION 3.5.2
ANNUAL CHANGE IN CARBON STOCKS IN LAND CONVERTED TO PEAT EXTRACTION∆CLW peat = ∆CLW peatLB + ∆CLW peatSoils
Where:∆CLW peat = annual change in carbon stocks in land converted to peat extraction, tonnes C yr-1
∆CLW peatLB = annual change in carbon stocks in living biomass, tonnes C yr-1
∆CLW peatSoils = annual change in carbon stocks in soils, tonnes C yr-1
It is assumed that the dead organic matter pool is not significant. If a country has data on dead organic matter, it
can be included in the estimate under Tier 2 or 3 methods.
Carbon stock changes in living biomass associated with the conversion of land to peat extraction are estimatedby Equation 3.5.3.

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Wetlands

EQUATION 3.5.3
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN LAND CONVERTED TO PEAT EXTRACTION∆CLW peatLB = Σ Ai ● (BAfter – BBefore) i ● CF
Where:∆CLW peatLB = annual change in carbon stocks in living biomass in land converted to peat extraction,
tonnes C yr-1
Ai = area of land converted annually to peat extraction from original land use i, ha yr-1
BBefore = aboveground biomass immediately before conversion to peat extraction, tonnes d.m. ha-1
BAfter = aboveground biomass immediately following conversion to peat extraction, tonnes d.m. ha-1
(default = 0)
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonnes d.m.)-1
The method follows the approach in IPCC Guidelines Section 5.2.3 (Forest and Grassland Conversion) and isconsistent with the tiered approaches for estimating carbon stock changes in living biomass outlined in Sections
3.2.2, 3.3.2, and 3.4.2. As the equation shows, the amount of living aboveground biomass that is cleared for peat
extraction is estimated by multiplying the land area converted annually to peat extraction by the difference incarbon stocks between biomass in the original land use prior to conversion and in the peatland after conversion.
Where forests are converted to peatlands and the timber cleared is reflected in harvesting statistics, the lattershould be adjusted by the amount of timber harvested from BBefore to avoid double-counting.
The default assumption for a Tier 1 estimate of carbon stock changes in living biomass on land converted to peat
extraction are that all aboveground biomass present before conversion to peat extraction will be lost in the same
year as the conversion takes place and that carbon stocks in living biomass following conversion (BAfter) areequal to zero. It is good practice for countries to estimate the area of land converted to peat extraction fromforest, by major forest categories and to use default carbon stock values from Annex 3A.1, Tables of default
values for Section 3.2 (Forest land), to develop estimates of BBefore for each initial forest category, and each
initial other land-use category including unmanaged peatland. Where grassland is the previous land use, default
values for aboveground biomass should be taken from Table 3.4.2.
In cases where fires are used to clear vegetation, emissions of non-CO2 gases, i.e., CH4 and N2O will also occur.
These emissions can be estimated under Tiers 2 and 3 following guidance provided in Section 3.2.1.4. Drainageof peatland also increases N2O emissions. These emissions can be estimated following guidance provided inAppendix 3a.3, N2O emissions from organic soils managed for peat extraction.
CO2 emissions from soils occur at several stages in the peat process, as shown in Equation 3.5.4.
EQUATION 3.5.4
ANNUAL CHANGE IN CARBON STOCKS IN SOILS IN LAND CONVERTED TO PEAT EXTRACTION∆CLW peatSoils = ∆C drainage + ∆C extraction + ∆C stockpiling + ∆C restoration
Where:∆CLW peatSoils = annual change in carbon stocks in soils in land converted to peat extraction, tonnes C yr-1
∆C drainage = annual change in carbon stocks in soils during drainage, tonnes C yr-1
∆C extraction = annual change in carbon stocks in soils during peat extraction (excluding the amount ofcarbon in the extracted peat), tonnes C yr-1
∆C stockpiling = annual change in carbon stocks in soils during stockpiling of peat prior to removal for
combustion, tonnes C yr-1
∆C restoration = annual change in carbon stocks in soils due to practices undertaken to restore previously
cultivated lands, tonnes C yr-1
Tier 1: In the case of land converted to peat extraction, only the effect of peat drainage (∆C drainage) is considered
under Tier 1. The Tier 1 method relies on basic area identification and default emission factors and the basic
method for estimating carbon emissions from organic soils converted to peat extraction is shown in Equation
3.5.5. This equation is applied at an aggregate level to a country’s entire area of organic soils converted to peat

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extraction, divided into nutrient-rich and nutrient-poor, using default emission factors. At this time, it is only
possible to provide a method and data for estimating the average changes in carbon stocks associated with peatdrainage over longer periods, although the emissions will be higher in the first year of drainage than in later
years.
EQUATION 3.5.5
ANNUAL CHANGE IN CARBON STOCKS IN SOILSDUE TO DRAINAGE OF ORGANIC SOILS CONVERTED TO PEAT EXTRACTION∆C drainage = ANrich ● EFNrich + ANpoor ● EFNpoor
Where:∆C drainage = annual change in carbon stocks in soils due to drainage of organic soils converted to peat
extraction, tonnes C yr-1
ANrich = area of nutrient rich organic soils converted to peat extraction, ha
ANpoor = area of nutrient poor organic soils converted to peat extraction, ha
EFNrich = emission factor for changes in carbon stocks in nutrient rich organic soils converted to peat
extraction, tonnes C ha-1 yr-1
EFNpoor = emission factor for changes in carbon stocks in nutrient poor organic soils converted to peat
extraction, tonnes C ha-1 yr-1
Tier 2: The Tier 2 method can extend the Tier 1 method, if area data and country-specific emission factors areavailable. In this case, countries may be able to subdivide activity data and emission factors according to peat
fertility, peat type and drainage intensity, and/or previous land use or land cover.
Tier 3: Tier 3 methods require statistics on the area of organic soils managed for peat extraction according tosite type, fertility, time since drainage, and/or time since restoration, which could be combined with appropriate
emission factors, and/or process-based models. Studies utilising information on changes in soil bulk density,
carbon content and peat depth could also be used to detect changes in soil C stocks provided the sampling
intensity was sufficient and covered the entire peat layer. Such data should be corrected for carbon losses due todissolved organic carbon leaching, losses of dead organic matter through runoff, or as CH4 emissions.

3.5.2.1.1.2

Choice of Emission/Removal Factors

Tier 1: When estimating the carbon stock change for organic soils converted to peat extraction under Tier 1, it
is good practice to use the default emission factors presented in Table 3.5.2.
TABLE 3.5.2
EMISSION FACTORS AND ASSOCIATED UNCERTAINTY FOR ORGANIC SOILS AFTER DRAINAGE
Emission Factor
tonne C ha yr-1

Uncertainty atonne C ha yr-1

Boreal and Temperate
Nutrient Poor (EFNpoor )0.2

0 to 0.63

Laine and Minkkinen, 1996; Alm et al., 1999;
Laine et al., 1996; Minkkinen et al., 2002

Nutrient Rich (EFNrich )1.1

0.03 to 2.9

Laine et al., 1996; LUSTRA, 2002;
Minkkinen et al., 2002; Sundh et al., 2000

2.0

0.06 to 6.0

Calculated from the relative difference
between temperate (nutrient poor) andtropical in Table 3.3.5.

Region/Peat Type

Tropical

Reference/Comment baRange of underlying datab

The boreal and temperate values have been developed as the log-normal mean from a review of paired plot measurements, assumingthat conditions on organic soils converted to peat extraction are lightly drained only. Most of the data are from Europe.

Boreal countries that do not have information on areas of nutrient-rich and nutrient-poor peatland areas should
use the emission factor for nutrient-poor peatlands. Temperate countries that do not have such data should use
the emission factor for nutrient-rich peatland. For tropical countries, only a single default can be provided at thistime.
Tier 2: Tier 2 requires country-specific data that takes into account management practices such as drainage ofdifferent peat types, and drainage intensity.

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Wetlands

Tier 3: Under Tier 3, all parameters should be country-defined using more accurate values rather than thedefaults. The literature is sparse and results are sometimes contradictory, so it is good practice to derive countryspecific emission factors by measurements against appropriate reference virgin sites. Data should be shared
between countries with similar environmental conditions.

3.5.2.1.1.3

Choice of Activity Data

Tier 1: The activity data required for all tiers is the area of organic soil converted to peat extraction. For theestimation of carbon stock change from living biomass, this overall area value is used, while for the estimate ofcarbon stock change from organic soil, a distinction between nutrient-rich and nutrient poor organic soils isneeded. Ideally, under Tier 1, countries will obtain national data on the areas converted to peat extraction andtheir original land uses. Possible sources of such data are national statistics, peat mining companies andgovernment ministries responsible for land use. It can be assumed that the proportion of nutrient-rich versus
nutrient-poor soils is similar to the relative importance of these peatland types at national level.
Tier 2: Under Tier 2, countries can incorporate information based on the original land use, peat type andfertility, and intensity of peat disturbance and drainage of the areas of organic soils converted to peat extraction.
This information could be gathered from regular updates of the national peatland inventory.
Tier 3: Under Tier 3, detailed information on the original land use, peat type and fertility, and intensity of peat
disturbance and drainage of the areas of organic soils converted to peat extraction may be needed. The modelingapproach used will determine specific data needs and level of disaggregation.

3.5.2.1.1.4

Uncertainty Assessm ent

For the estimation of emissions from land conversions to peat, the principal uncertainties are related to area
estimates and emission factors.
Tier 1: The sources of uncertainty in the Tier 1 method are from the use of global or national averages forcarbon stocks in forests before conversion and coarse estimates of land areas and their original use converted topeat extraction, although most of the converted area is likely to be more or less densely treed peatland. Most
default values in this method do not have corresponding error ranges associated with them. The default emission
factors provided for Tier 1 have been developed from only a few (less than 10) data points only, which may not
be representative for large areas or climate zones. Therefore, a default uncertainty level of +/- 75% of theestimated carbon emission or removal has been assumed based on expert judgement. The uncertainty probability
distribution of the emissions is likely to be non-normal, so the 95% interval of a log-normal distribution isassumed here as default uncertainty (Table 3.5.2). It is good practice to use this range rather than a symmetricalstandard deviation.
The area of drained peatlands is estimated to have an uncertainty of 50% in Europe and North America, but may
be a factor of 2 in the rest of the world. Uncertainty in Southeast Asia is extremely high since peatlands areunder particular pressure, mainly because of urbanisation and intensification of agriculture and forestry, andmaybe also for peat extraction. It is assumed that the data of land conversion to peatland has the same
uncertainty although countries with a predominance of commercial peat extraction will have better data.
Tier 2: Under Tier 2, actual area estimates for land conversion will enable more transparent accounting andallow experts to identify gaps and avoid double counting of land areas. The Tier 2 method uses at least somecountry-defined defaults, which will improve the accuracy of estimates, provided they better represent conditions
relevant to the country. When country-specific defaults are developed, countries should use sufficient sample
sizes and techniques to minimize standard errors. Probability density functions (i.e. providing mean and varianceestimates) should be derived for all country-defined parameters. Such data can be used in advanced uncertainty
analyses such as Monte Carlo simulations. Refer to Chapter 5 of this report for guidance on developing suchanalyses. At a minimum, Tier 2 approaches should provide error ranges for each country-defined parameter.
Tier 3: Under Tier 3, activity data from a land use and management inventory system should provide a basis toassign estimates of uncertainty to areas associated with land conversion. Combining emission and activity data
and their associated uncertainties can be done using Monte-Carlo procedures to estimate means and confidence
intervals for the overall inventory. Process-based models will probably provide more realistic estimates but need
to be calibrated and validated against measurements. Generic guidance on uncertainty assessment for advanced
methods is given in Chapter 5 (Section 5.2, Identifying and Quantifying Uncertainties) of this report. Sincedrainage of peatlands leads to peat compaction and oxidation and carbon losses other than as CO2 the stockchange approach to monitor CO2 fluxes can be imprecise. If used, it should be calibrated with appropriate flux
measurements.

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3.5.2.2

C HANGE IN CARBON STOCKS IN LAND CONVERTED TO
FLOODED LAND (R ESERVOIRS )The method for estimating carbon stock change due to land conversion to flooded land is shown in Equation
3.5.6. As with the method described in the previous section for peatland, this method assumes that the carbon
stock of land prior to conversion is lost in the first year following conversion. The carbon stock of the land prior
to conversion can be estimated following the method for living biomass described for various land-use categories
in other sections of this chapter. In Tier 1, it is assumed that the carbon stock after conversion is zero.
EQUATION 3.5.6
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN LAND CONVERTED TO FLOODED LAND

∆CLW floodLB = [Σ Ai ● (BAfter – BBefore) i] ● CF
Where:∆CLWfloodLB = annual change in carbon stocks in living biomass in land converted to flooded land, tonnes
C yr-1
Ai = area of land converted annually to flooded land from original land use i, ha yr-1
BBefore = living biomass in land immediately before conversion to flooded land, tonnes d.m. ha-1
BAfter = living biomass immediately following conversion to flooded land, tonnes d.m. ha-1 (default = 0)
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonnes d.m.)-1

In actuality, it is possible that the carbon remaining on the converted land prior to flooding may be emitted over
several years after flooding. Under Tier 2, this emission process can be modelled. Countries will need to develop
country-specific emission factors and can refer to the discussion of ongoing emissions from flooded landremaining flooded land in Appendix 3a.3 for general guidance on how to implement such a method.
No guidance is provided on carbon stock changes from soils due to land conversion to flooded land at this time.
Emissions of non-CO2 gases from land converted to flooded land are covered in Appendix 3a.3.

3.5.3

CompletenessA complete estimate of emissions from land converted to wetlands should include all land converted to either
peat extraction or flooded land. For organic soils managed for peat extraction, a complete inventory should cover
all land converted to industrial peatlands. It should be consistent with a complete inventory of all industrial
peatlands including abandoned peat mining areas in which drainage is still active, and areas drained for future
peat extraction, but omitting areas reverting to wetland status.

3.5.4

Developing a Consistent Time Series

General guidance on consistency in time series can be found in Section 5.6 (Time Series Consistency and
Recalculation). The emission estimation method should be applied consistently to every year in the time series,
at the same level of disaggregation. Moreover, when country-specific data are used, national inventories agency
should use same measurements protocol (sampling strategy, method, etc.) over time, following the guidance inSection 5.3, Sampling. If it is not possible to use the same method or measurement protocol throughout the timeseries, the guidance on recalculation in Chapter 5 should be followed.
The area of organic soils converted to peat extraction may need to be interpolated for longer time series or trends.
If this is required, consistency checks should be made (i.e., by contacting peat-mining companies), to gather
temporal information about areas affected by former or future peat extraction. Differences in greenhouse gas
emissions between inventory years should be explained, e.g. by demonstrating changes in areas of industrial
peatlands or by updated emission factors.

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Wetlands

3.5.5

Reporting and Documentation

It is appropriate to document and archive all information required to produce the national emissions / removals
inventory estimates as outlined in Chapter 5 of this report subject to the following specific considerations.
Emissions from land converted to peat extraction or flooding have not been explicitly mentioned in the IPCC
Guidelines. They can be reported in using the reporting tables in Annex 3A.2.
Emission factors: Since the literature data are so sparse, the scientific basis of new determinations of emission
factors, parameters and models should be completely described and documented. This includes defining the
input parameters and describing the process by which the emission factors, parameters and models were derived,
as well as describing sources of uncertainties.
Activity data: Sources of all activity data used in the calculations (data sources, databases and soil map
references) should be recorded, plus (subject to any confidentiality considerations) the communication withcompanies dealing with peat extraction. This documentation should cover the frequency of data collection andestimation, and estimates of accuracy and precision, and reasons for significant changes in emission levels.
Emission results: Significant fluctuations in emissions between years should be explained. A distinction should
be made between changes in activity levels and changes in emission factors, parameters and methods from year
to year, and the reasons for these changes documented. If different emission factors, parameters and methods areused for different years, the reasons for this should be explained and documented.

3.5.6

Inventory Quality Assurance/Quality Control(QA/QC)

It is appropriate to implement quality assurance/quality control (QA/QC) checks as outlined in Chapter 5(Section 5.5) of this report, and to conduct expert review of the emission estimates. Given the shortage of data,
these reviews should be conducted regularly to take account of new research findings. Additional quality control
checks, as outlined in Tier 2 procedures in Chapter 8, QA/QC, of GPG2000, and quality assurance procedures
may also be applicable, particularly if higher tier methods are used to quantify emissions from this source
category. Where country-specific emission factors are used, they should be based on high quality experimental
data, developed using a good practice measurement programme, and be adequately documented.
It is, at present, not possible to cross-check emissions estimates from organic soils managed for peat extraction
with other measurement methods. However, the inventory agency should ensure that emission estimates undergo
quality control by:•

Cross-referencing reported country-specific emissions factors with default values and data from othercountries; and•Check plausibility by cross-referencing areas of organic soils managed for peat extraction with data of peatindustries and peat production.

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Settlements

3.6

SETTLEMENTS

This land-use category is described in Chapter 2 as including all developed land, including transportation
infrastructure and human settlements of any size, unless they are already included under other land-use
categories. In this chapter, the focus of settlements is on the terrestrial components of developed land that aremanaged and may influence CO2 fluxes between the atmosphere and terrestrial carbon pools. In this context, theland-use category “Settlements” includes all classes of urban tree formations, namely: trees grown along streets,in public and private gardens, and in different kinds of parks, provided such trees are functionally or
administratively associated to cities, villages, etc. While dead organic matter and soil carbon pools may also be
sources or sinks of CO2 in settlements and CH4 and N2O emissions may result from urban land management
practices, little is known about the role and magnitude of these pools in overall greenhouse gas fluxes. Therefore,
the focus of the methodological discussions is on the subcategory of change in carbon stocks in living biomass,
where some research has been conducted (Nowak 1996, 2002).
Change in carbon stocks in living biomass in “Settlements” can be estimated in two parts: “Settlements
Remaining Settlements (SS)” and “Land Converted to Settlements (LS)”. The latter part may be an importantcomponent of national estimates of deforestation (or other nationally important land-use conversions). Therefore,
brief guidance is provided below on estimating change in carbon stocks due to conversion of forest land tosettlements. Only living biomass is addressed in this section.

3.6.1

Settlements Remaining Settlements

A basic method for estimating CO2 emissions and removals in settlements remaining settlements is provided inAppendix 3a.4 because the methods and available default data for this land-use conversion are preliminary.
Countries with data on dead wood, soil carbon, and non-CO2 gases in settlements are encouraged to report thisinformation as well.

3.6.2

Land Converted to Settlements

The fundamental equation for estimating change in carbon stocks associated with land-use conversions has been
explained in other sections of this chapter, namely Sections 3.2.2, 3.3.2 and 3.4.2 with regard to land converted
to forest land, cropland and grassland, respectively. The same decision tree (see Figure 3.1.2) and the same basic
method can be applied to estimate change in carbon stocks in forest land converted to settlements, following
Equation 3.6.1.
EQUATION 3.6.1
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN FOREST LAND CONVERTED TO SETTLEMENTS (FS)∆CFSLB = A ● (CAfter – CBefore)
Where:∆CFSLB = annual change in carbon stocks in living biomass due to conversion of forest land to settlements,
tonnes C yr-1
A = area of land converted annually from forest land to settlements, ha yr-1
CAfter = carbon stocks in living biomass immediately following conversion to settlements, tonnes C ha-1
CBefore = carbon stocks in living biomass in forest land immediately before conversion to settlements,
tonnes C ha-1

This method follows the approach in the IPCC Guidelines (Section 5.2.3, Forest and Grassland Conversion) where
the amount of living aboveground biomass that is cleared for expanding settlements is estimated by multiplying theforest area converted annually to settlements by the difference in carbon stocks between biomass in the forest prior
to conversion (CBefore) and that in the settlements after conversion (CAfter). The tiered approaches for estimatingchange in carbon stocks in living biomass outlined in Sections 3.2.2, 3.3.2 and 3.4.2 apply here as well. A Tier 1
estimate is developed using default assumptions and default values for carbon stocks. At Tier 2, country-specific

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Chapter 3: LUCF Sector Good Practice Guidancecarbon stocks are applied to activity data disaggregated to appropriate scales. At Tier 3, countries use advanced
estimation methods that may involve complex models and highly disaggregated activity data.
The default assumptions for a Tier 1 estimate of change in carbon stocks in living biomass in land converted tosettlements are that all living biomass present before conversion to settlements will be lost in the same year as
the conversion takes place, and that carbon stocks in living biomass following conversion (CAfter) are equal tozero. Countries should estimate the area of forest land converted to settlements, by major forest types, and use
default carbon stock values in Tables 3A.1.2 and 3A.1.3 to develop estimates of carbon stocks in living biomass
before conversion (CBefore) for each initial forest type.
In cases where fires are used to clear vegetation, emissions of non-CO2 gases, i.e. CH4 and N2O, will also occur.
Countries may choose to estimate non-CO2 emissions from burning when fires are used to clear vegetation fordevelopment of settlements. The basic method for estimating non-CO2 emissions from fires can be found in
Section 3.2.1.4.

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Other Land

3.7

OTHER LAND

“Other Land” is defined in Chapter 2 of this report as including bare soil, rock, ice, and all unmanaged land areasthat do not fall into any of the other five land-use categories treated in Sections 3.2 to 3.6. This land-use category
is included to allow the total of identified land areas to match the national area, where data are available.
Consistent with the IPCC Guidelines, change in carbon stocks and non-CO2 emissions and removals would notneed to be assessed for the category of “Other Land Remaining Other Land (OO)” assuming that it is typicallyunmanaged. At present, no guidance can be given for “Other Land” that is managed. “Other Land” is included,
however, for checking overall consistency of land area and tracking conversions to and from other land since
many methods require knowledge of associated carbon stocks. It is of particular importance to include complete
information on forest land converted to other types of land uses, including “Other Land”, in order to ensure
consistency with the requirements in Chapters 4 and 5.

3.7.1

Other Land Remaining Other Land

Change in carbon stocks and non-CO2 emissions and removals are not considered for this category as mentioned
above.

3.7.2

Land Converted to Other Land

Although unlikely, lands may be converted to “Other Land”, e.g. as a result of deforestation with subsequentdegradation. This conversion of land use, either starting with a human activity or a natural driving force affecting
managed land, requires the calculation of emissions of CO2 because the act of conversion releases the carbon
previously held on the land, and emissions and/or removals due to management activities cease. Emissions fromland converted to bare soil as a result of development of settlements should be included in the “Settlements”
land-use category (See Section 3.6.2, Land Converted to Settlements.).
It is good practice to estimate the change in carbon stocks associated with the conversion of all types of managed
land to other land. Figure 3.1.2 provides the decision tree which can be used to identify the appropriate tier-level
for land converted to “Other Land”.
The summary equation for change in carbon stocks in land converted to “Other Land” (LO) is shown in Equation
3.7.1.
EQUATION 3.7.1
ANNUAL CHANGE IN CARBON STOCKS IN LAND CONVERTED TO “OTHER LAND”∆CLO = ∆CLOLB + ∆CLOSoils
Where:∆CLO = annual change in carbon stocks in land converted to ”Other Land”, tonnes C yr-1
∆CLOLB = annual change in carbon stocks in living biomass in land converted to “Other land”, tonnes C yr-1
∆CLOSoils = annual change in carbon stocks in soils in land converted to “Other Land”, tonnes C yr-1

3.7.2.1

C HANGE IN CARB ON STOCKS IN LIVI NG BIOMASSThis section provides good practice guidance for calculating change in carbon stocks in living biomass due tothe conversion of land from natural conditions and other uses to “Other Land”. The method requires estimates ofcarbon in living biomass stocks prior to conversion, based on estimates of the areas of land converted during theperiod between land-use surveys. As a result of conversion to “Other Land”, it is assumed that the dominant
vegetation is removed entirely, resulting in no carbon remaining in living biomass after conversion. The
difference between initial and final living biomass carbon pools is used to calculate change in carbon stocks due
to land-use conversion. In subsequent years accumulations and losses in living biomass in “Other Land” are notconsidered (see Section 3.7.1).

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3.7.2.1.1

METHODOLOGI CAL ISSUES

3.7.2.1.1.1

Choice of Method

Equation 3.7.2 summarises how to estimate the change in carbon stocks in living biomass on land converted to“Other Land”. Average change in carbon stocks on a per area basis are estimated to be equal to the change incarbon stocks due to the removal of living biomass from the initial land uses. Given the definition of the “OtherLand”, the default assumption is that carbon stock after conversion is zero.

EQUATION 3.7.2
ANNUAL CHANGE IN CARBON STOCKS IN LIVING BIOMASSIN LAND CONVERTED TO “OTHER LAND”∆CLOLB = AConversion ● (BAfter – BBefore) ● CF
Where:∆CLOLB = annual change in carbon stocks in living biomass in land converted to “Other Land”, tonnes C
yr-1
AConversion = area of land converted annually to “Other Land” from some initial land uses, ha yr-1
BAfter= amount of living biomass immediately after conversion to “Other Land”, tonnes d.m. ha-1
BBefore= amount of living biomass immediately before conversion to “Other Land”, tonnes d.m. ha-1
CF = carbon fraction of dry matter (default = 0.5), tonnes C (tonnes d.m.)-1

Tier 1: A Tier 1 method follows the approach in the IPCC Guidelines, Section 5.2.3 (Forest and Grassland
Conversion) where the amount of aboveground biomass that is removed is estimated by multiplying the forest
area converted annually to other land by the average annual carbon content of biomass in the land prior toconversion. It is assumed that the entire biomass is removed in the year of conversion. The recommended default
assumption for the Tier 1 calculation is that all carbon in biomass is released to the atmosphere through decay
processes either on- or off-site.
Tier 2: A Tier 2 method can be used if country-specific data on carbon stocks in initial land uses are obtainable.
In addition, under Tier 2, carbon losses can be apportioned to specific conversion processes, such as burning or
harvesting. This allows more accurate estimation of non-CO2 greenhouse gas emissions. (See Section 3.2.1.4 forthe basic method for estimating non-CO2 greenhouse gas emissions from biomass burning.) The portion of
biomass removed is sometimes used as wood products or as fuel wood. In the case of wood products, countries
may use the default assumption that carbon in wood products is oxidized in the year of removal. Alternatively,
countries may refer to Appendix 3a.1 for estimation techniques for carbon storage in harvested wood products.
Tier 3: A Tier 3 method is similar to the Tier 2 method but requires more detailed data/information than the
Tier 2 approach, e.g.:•••

Actual areas converted annually are used for each forest land converted to “Other Land”;
Carbon densities and change in soil carbon stocks are based on locally specific information, possibly with adynamic link between biomass and soil; andBiomass volumes removed are based on actual inventories and/or the model estimations.

3.7.2.1.1.2

Choice of Emission/Removal Factors

Tier 1: Default parameters are provided in both the IPCC Guidelines and in this report to enable countries withlimited data resources to estimate emissions and removals from this source. The method requires the estimationof carbon stocks before conversion for the initial land use (CBefore) and assumes that the carbon stock afterconversion (CAfter) is zero. Tables 5-4 to 5-6 of the IPCC Guidelines, Table 3A.1.7 (Annual average
aboveground volume increment in plantation by species) and Table 3A.1.8 (Average belowground toaboveground biomass ratio in natural regeneration by broad category) of this report, can be used to estimate
carbon stocks before conversion in case the initial land-use category was forest land. If the initial land-use
category is cropland or grassland, guidance is given in Section 3.3.2 or 3.4.2, respectively.
Tier 2: The default carbon stock values provided above can be applied to some parameters in a Tier 2 approach.
However, the Tier 2 method requires at least some country-specific information, which may be obtained, forexample, through systematic studies of carbon stock of initial forests and other land-use categories. Default
parameters for emissions from biomass burning are provided in Section 3.2.1.4. However, inventory compilers

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Other Landare encouraged to develop country-specific coefficients to improve the accuracy of estimates. The default value
for the proportion of biomass oxidized as a result of burning is 0.9, as originally stated in the IPCC Guidelines.
Tier 3: Under Tier 3, all parameters should be country-specific and more accurate than the default values.

3.7.2.1.1.3

Choice of Activity Data

All tiers require some estimate of the area of land converted to “Other land” over a time period that is consistent
with land-use surveys. The same aggregate area estimates should be used for both biomass and soil in thecalculations of change in carbon stocks on land converted to “Other Land”. As described below, higher tiersrequire greater specificity of areas.
Tier 1: For a Tier 1 approach, activity data on areas of different land-use categories converted to”Other Land”
are needed. If countries do not have these data, partial samples may be extrapolated to the entire land base or
historic estimates of conversions may be extrapolated over time based on expert judgement.
Tier 2: Under Tier 2, inventory compilers should strive to use actual area estimates for transitions from various
land-use categories to “Other Land”. Full coverage of land areas can be accomplished either through analysis ofperiodic remotely sensed images of land-use and land cover patterns, through periodic ground-based sampling ofland-use patterns, or hybrid inventory systems.
Tier 3: The activity data used in Tier 3 calculations should be a full accounting of all land-use categorytransitions to other land and should be disaggregated to account for different conditions within a country.
Disaggregation can occur along political (county, province, etc.), biome, climate, or on a combination of theseparameters. In many cases, information on multi-year trends in land conversion may be available (from periodic
sample-based or remotely sensed inventories of land use and land cover).

3.7.2.1.1.4

Uncertainty Assessm ent

Tier 1: Under Tier 1, the sources of uncertainty are the use of global or national averages for carbon stocks inforest land or other land uses before conversion and coarse estimates of areas converted to “Other Land”. Most
default values in this method do not have corresponding error ranges associated with them. Therefore, a default
uncertainty level of +/- 75% of the estimated CO2 emission or removal has been assumed based on expert
judgement.
Tier 2: Actual area estimates for land converted to “Other Land” will enable more transparent accounting andallow experts to identify gaps and double counting of land areas. The Tier 2 method uses at least some countryspecific values, which will improve the accuracy of estimates, provided they better represent conditions relevantto the country. When country-specific values are developed, inventory compilers should use sufficient sample
sizes and techniques to minimize standard errors. Probability density functions (i.e. providing mean and varianceestimates) can be derived for all country-parameters. Such data can be used in advanced uncertainty analyses
such as Monte Carlo simulations. Chapter 5 of this report can be referred for guidance on developing suchanalyses. At a minimum, Tier 2 approaches should provide error ranges for each country-specific parameter.
Tier 3: Activity data should provide a basis to assign estimates of uncertainty to areas associated with land
conversion. Combining emission/removal factors and activity data and their associated uncertainties can be doneusing Monte Carlo procedures to estimate means and confidence intervals for the overall inventory.

3.7.2.2

C HANGE IN CARB ON STOCKS IN SOILS

The conversion of land to “Other Land”, especially to bare soils, could result in the release of carbon previously
held in soil on the land. On land converted to “Other Land” inventory compilers should estimate the change incarbon stocks in mineral soils under the initial land uses. The resulting carbon stocks in mineral soils for “OtherLand” can be assumed as zero for many situations. It is also assumed that the change in carbon stocks in organicsoils are not relevant in this section.

3.7.2.2.1

METHODOLOGI CAL ISSUES

3.7.2.2.1.1

Choice of Method

The estimation method for mineral soil is based on change in soil carbon stocks over a finite period followingchange in management that impacts soil carbon stocks, as shown in Equation 3.7.3. Previous soil carbon stocks(SOC(0-T)) and soil carbon stocks in the inventory year (SOC0) are estimated from reference carbon stocks(Sections 3.3, Table 3.3.3) and stock change factors (Section 3.4, Table 3.3.4), applied for the respective time
points. The default time period between these two time points is 20 years. This approach is similar to that

IPCC Good Practice Guidance for LULUCF

3.147

Chapter 3: LUCF Sector Good Practice Guidancedescribed in Section 3.2.2.3 (forest soil carbon section) except that it is assumed that the soil carbon stocks in theinventory year are zero for land converted to ”Other Land”.
EQUATION 3.7.3
ANNUAL CHANGE IN CARBON STOCKS IN MINERAL SOILSIN LAND CONVERTED TO “OTHER LAND”∆CLOMineral = [(SOC0 – SOC(0-T)) ● A ] / T
SOC = SOCREF ● FLU ● FMG ● FI
Where:∆CLOMineral = annual change in carbon stocks in mineral soils in land converted to “Other Land”, tonnes C
yr-1
SOC0 = soil organic carbon stocks in the inventory year, tonnes C ha-1
SOC(0-T) = soil organic carbon stocks T years prior to the inventory, tonnes C ha-1
T = time period for the conversion, yr (default is 20 yr)
A = land area of each parcel, ha
SOCREF = the reference carbon stocks, tonnes C ha-1; see Table 3.3.3
FLU = stock change factor for land use or land-use change type, dimensionless; see Table 3.3.4
FMG = stock change factor for management regime, dimensionless; see Table 3.3.4
FI = stock change factor for input of organic matter, dimensionless; see Table 3.3.4

Tier 1: Tier 1 methods rely on default values for reference carbon stocks in mineral soils under native
vegetation (see Table 3.3.3) and coarse estimates of areas converted to ”Other Land”. Soil carbon stocks afterconversion are assumed to be zero for “Other Land” such as bare or degraded soils or deserts.
Tier 2: Tier 2 methods involve country or region-specific reference carbon stocks and more disaggregated landuse activity data.
Tier 3: Tier 3 methods can involve a variety of more detailed and country-specific data and use model and/or
measurement-based approaches along with data on highly disaggregated land use and management. For all tiers,
it is assumed that soil carbon stock in the inventory year is zero due to conversion to the “Other Land” category.

3.7.2.2.1.2

Choice of Emission/Removal Factors

Mineral soils
The following variables are needed when using either the Tier 1 or Tier 2 method:
Reference carbon stocks (SOCREF)
Tier 1: Under Tier 1, it is good practice to use the default reference carbon stocks (SOCREF) provided in Table
3.3.3.
Tier 2: For a Tier 2 method, reference soil carbon stocks can be determined from measurements of soils, forexample, as part of a country’s soil survey and mapping activities.
Stock change factors (FLU, FMG, FI)
Tier 1: Under Tier 1, it is good practice to use default stock change factors (FLU, FMG, FI) provided in Table
3.3.4. These are updated from the IPCC Guidelines, based on a statistical analysis of published research. Notethat where lands are converted to ”Other Land”, all the stock change factors have the value of one, such that the
pre-conversion soil carbon stocks are equal to the native vegetation reference values (SOCRef).
Tier 2: For the Tier 2 method, estimation of country-specific stock change factors for land-use conversion tocropland will typically be based on paired-plot comparisons representing converted and unconverted lands,
where all factors other than land-use history are as similar as possible (e.g. Davidson and Ackermann, 1992).

3.7.2.2.1.3

Choice of Activity Data

It is good practice for inventory compilers to use the same area estimates for land converted to “Other Land” forestimating change in carbon stocks in living biomass and soils. Some general issues regarding activity data are

3.148

IPCC Good Practice Guidance for LULUCF

Other Land

described in Section 3.7.2.1.1.3. For purposes of estimating soil carbon stock change, area estimates of land-use
conversions to “Other Land” should be stratified according to major soil types, as defined for Tier 1, or based on
country-specific stratifications if employed in Tier 2 or 3 approaches. This can be based on overlays withsuitable soil maps and spatially-explicit data of the location of land conversions.

3.7.2.2.1.4

Uncertainty Assessm ent

The sources of uncertainty are from the use of global or national average rates of conversion and course
estimates of land areas converted to “Other Land”. In addition, reliance on default parameters for carbon stocks
in initial and final conditions contributes to relatively high degrees of uncertainty. The default values in thismethod have corresponding error ranges associated with them and the values are included in default tables.
The use of actual area estimates rather than average rates of conversion will improve the accuracy of estimates.
In addition, the tracking of each land area for all possible land-use transitions will enable more transparent
accounting and allow experts to identify gaps and areas where land areas are accounted for multiple times.

3.7.3

Completeness

The total area of ”Other Land” covered by the inventory methodology is the sum of “Other Land”
remaining ”Other Land” and land converted to “Other Land” during the time period. Inventory compilers areencouraged to track through time the total area of land classified as “Other Land” within country boundaries,
keeping transparent records on which portions are used to estimate change in carbon stocks. As addressed in
Chapter 2, all areas including those not covered by the greenhouse gas inventory, should be part of theconsistency checks to help avoid double counting or omission. Areas under the “Other Land”, when summed
with area estimates for “Other Land” will enable a complete assessment of the land base included in a countries’
LULUCF sector inventory report.

3.7.4

Developing a Consistent Time Series

It is good practice for inventory compilers to maintain records on the “Other Land” areas used in inventory
reports over time. These records should track the total area classified as “Other Land” as included in theinventory, subdivided by “Other Land” remaining in “Other Land” and land converted to “Other Land”.

3.7.5

Reporting and Documentation

The categories described in this section can be reported using the reporting tables in Annex 3A.2. It is goodpractice to maintain and archive all information used to produce national inventory estimates. Metadata and data
sources for information used to estimate country-specific parameters should be documented, and both mean andvariance estimates provided. Actual databases and procedures used to process the data (e.g. statistical programs)
to estimate country-specific factors should be archived. Activity data and definitions used to categorise or
aggregate the activity data should be documented and archived.

3.7.6

Inventory Quality Assurance/Quality Control(QA/QC)

It is good practice to implement quality control checks and external expert review of inventory estimates anddata. Specific attention should be paid to country-specific estimates of stock change factors and emission factors
to ensure that they are based on high quality data and verifiable expert opinion.

IPCC Good Practice Guidance for LULUCF

3.149

Annex 3A.1

Annex 3A.1 Biomass Default Tables for Section 3.2 Forest Land

Contents
Where to use the tables ......................................................................................................................... 3.152
Table 3A.1.1 Forest area change ....................................................................................................... 3.153
Table 3A.1.2 Aboveground biomass stock in naturally regenerated forests by broad category......... 3.157
Table 3A.1.3 Aboveground biomass stock in plantation forests by broad category .......................... 3.158
Table 3A.1.4 Average growing stock volume (1) and aboveground biomass (2)content (dry matter) in forest in 2000 .......................................................................... 3.159
Table 3A.1.5 Average annual increment in aboveground biomass in natural regeneration
by broad category ....................................................................................................... 3.163
Table 3A.1.6 Average annual increment in aboveground biomass in plantation
by broad category ....................................................................................................... 3.164
Table 3A.1.7 Average Annual above ground net increment in volume in plantations by species ..... 3.167
Table 3A.1.8 Average belowground to aboveground biomass ratio (root-to-shoot ratio, R)
in natural regeneration by broad category .................................................................... 3.168
Table 3A.1.9-1 Basic wood densities of stemwood for boreal and temperate species .......................... 3.171
Table 3A.1.9-2 Basic wood densities (D) of stemwood for tropical tree species .................................. 3.172
Table 3A.1.10 Default values of biomass expansion factors (BEFs) ................................................... 3.178
Table 3A.1.11 Default values for fraction out of total harvest left to decay in the forest, fBL .............. 3.178
Table 3A.1.12 Combustion factor values (proportion of prefire biomass consumed) for
fires in a range of vegetation types.............................................................................. 3.179
Table 3A.1.13 Biomass consumption values for fires in a range of vegetation types .......................... 3.180
Table 3A.1.14 Combustion efficiency (proportion of available fuel actually burnt)
relevant to land-clearing burns, and burns in heavy logging slash for a range
of vegetation types and burning conditions .................................................................. 3.184
Table 3A.1.15 Emission ratios for open burning of cleared forests ..................................................... 3.185
Table 3A.1.16 Emission factors applicable to fuels combusted
in various types of vegetation fires............................................................................... 3.185

IPCC Good Practice Guidance for LULUCF

3.151

Chapter 3: LUCF Sector Good Practice GuidanceWhere to Use the Tables
Table
Table 3A.1.1 Forest Area Change
Table 3A.1.2 Aboveground Biomass Stock innaturally regenerated forests by broad category

Application
To be used for verification of ‘A’ in Equation 3.2.4
To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in Cropland section and for L conversion in
Equation 3.4.13 in Grassland section, etc. Not to be
applied for Ct2 or Ct1 in Forest section Equation 3.2.3

Table 3A.1.3 Aboveground Biomass Stock inplantation forests by broad category

To be used for Bw in Equation 3.2.9, for Lconversion inequation in Equation 3.3.8 in Cropland section and for
Lconversion in Equation 3.4.13 in Grassland section, etc. Not
to be applied for Ct2 or Ct1 in Forest section Equation 3.2.3

Table 3A.1.4 Average Growing stock volume(1) and aboveground biomass (2) content (drymatter) in forest in 2000

(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13 in grassland section, etc. Not to be applied
for Ct2 or Ct1 in Forest section Equation 3.2.3.

Table 3A.1.5 Average Annual Increment inAboveground Biomass in Natural Regeneration
by broad category
Table 3A.1.6 Annual Average Aboveground
Biomass Increment in plantations by broad
category
Table 3A.1.7 Annual Average Above groundvolume Increment in plantations by species
Table 3A.1.8 Average Belowground to
Aboveground Biomass ratio in Natural
Regeneration by broad category
Table 3A.1.9 –1 Basic wood densities ofstemwood for boreal and temperate species
Table 3A.1.9-2 Basic wood densities (D) of
stemwood for Tropical tree species
Table 3A.1.10 default values of Biomass
Expansion Factors (BEFs)
Table 3A.1.11 default values for fraction out of
total harvest left to decay in the forest
Table 3A.1.12 Combustion factor values(proportion of prefire biomass consumed) for
fires in a range of vegetation types
Table 3A.1.13 Biomass consumption values for
fires in a range of vegetation types
Table 3A.14 Combustion Efficiency(proportionof available fuel actually burnt) relevant toland-clearing burns, and burns in heavy logging
slash for a range of vegetation types andburning conditions.
Table 3A.1.15 Emission ratios for open
burning of cleared forests
Table 3A.1.16 Emission Factors applicable tofuels combusted in various types of vegetation
fires

3.152

To be used for Gw in Equation 3.2.5
To be used for Gw in Equation 3.2.5. In case of missing
values it is preferred to use stemwood volume increment
data Iv from Table 3A.1.7
To be used for Iv in Equation 3.2.5
To be used for R in Equation 3.2.5
To be used for D in Equations 3.2.3., 3.25, 3.2.7, 3.2.8
To be used for D in Equations 3.2.3., 3.25, 3.2.7, 3.2.8
BEF2 to be used in connection with growing stock biomass
data in Equation 3.2.3; and BEF1 to be used in connection
with increment data in Equation 3.2.5
To be used only for fBL in Equation 3.2.7
Values in column ‘mean’ are to be used for (1-fBL) in
Equation 3.2.9. and for ρburned on site in Equation 3.3.10
To be used in Equation 3.2.9. for the part of the equation:‘BW • (1- fBL)’ , i.e. an absolute amountTo be used in sections ‘forest lands converted to cropland’,‘converted to grassland’, or ‘converted to settlements orother lands’
To be applied to Equation 3.2.19
To be used in connection with Equation 3.2.20

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.1
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)
a. AFRICACountry

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)
a. AFRICA (Continued)

Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area
1990

2000

000 ha000 ha000 ha /yrCountry% / yrForest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area
1990

2000

000 ha000 ha000 ha /yr% / yrAlgeria

1 879

2 145

27

1.3

Madagascar

12 901

11 727-117-0.9

Angola70 99869 756-124-0.2

Malawi

3 269

2 562

-71

-2.4

14 17913 186

-99

-0.7

415

317

-10

-2.7

Benin

3 349

2 650

-70

-2.3

Mali

Botswana

13 611

12 427-118-0.9

Mauritania

Burkina Faso

7 241

7 089

-15

-0.2

Mauritius

17

16

n.s.

-0.6

Burundi

241

94

-15

-9.0

Morocco

3 037

3 025

-1

n.s.

Cameroon

26 07623 858-222-0.9

Mozambique

31 23830 601-64-0.2Cape Verde

35

85

5

9.3

Namibia

8 774

8 040

-73

-0.9

Central African
Republic

23 20722 907

-30

-0.1

Niger

1 945

1 328

-62

-3.7

Chad

13 50912 692-82-0.6

Nigeria

17 50113 517

-398

-2.6

12

8

n.s.

-4.3

Réunion

76

71

-1

-0.8

Congo

22 23522 060-17-0.1

Rwanda

457

307

-15

-3.9

Côte d'Ivoire

9 766

7 117

-265

-3.1

Saint Helena

2

2

n.s.

n.s.

Dem. Rep. of
the Congo

140 531

135 207

-532

-0.4

Sao Tome andPrincipe

27

27

n.s.

n.s.

Comoros

Djibouti

6

6

n.s.

n.s.

Senegal

6 655

6 205

-45

-0.7

Egypt

52

72

2

3.3

Seychelles

30

30

n.s.

n.s.

Equatorial
Guinea

1 858

1 752

-11

-0.6

Sierra Leone

1 416

1 055

-36

-2.9

Eritrea

1 639

1 585

-5

-0.3

Somalia

8 284

7 515

-77

-1.0

Ethiopia

4 996

4 593

-40

-0.8

South Africa

8 997

8 917

-8

-0.1

Gabon

21 92721 826

-10

n.s.

Sudan

71 21661 627-959-1.4

Gambia

436

481

4

1.0

Swaziland

464

522

6

1.2

Ghana

7 535

6 335

-120

-1.7

Togo

719

510

-21

-3.4

Guinea

7 276

6 929

-35

-0.5

Tunisia

499

510

1

0.2

Guinea-Bissau

2 403

2 187

-22

-0.9

Uganda

5 103

4 190

-91

-2.0

Kenya

18 027

17 096-93-0.5

39 72438 811-91-0.2

Lesotho

14

14

n.s.

n.s.

UnitedRepublic of
Tanzania
Western Sahara

152

152

n.s.

n.s.

Liberia

4 241

3 481

-76

-2.0

Zambia39 75531 246-851-2.4

Zimbabwe

22 23919 040-320-1.5

Libyan Arab
311
358
5
1.4
Jamahiriya
n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

IPCC Good Practice Guidance for LULUCF

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

3.153

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)

b. ASIACountryb. ASIA (Continued)
Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest area
1990

2000

000 ha000 ha000 ha /yrCountry

Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area

% / yr1990

2000

000 ha000 ha

6 299

6 248

-5

-0.1

1 504

1 504

n.s.

n.s.

000 ha /yr% / yrAfghanistan

1 351

1 351

n.s.

n.s.

Armenia

309

351

4

1.3

Republic of
Korea
Saudi Arabia

Azerbaijan

964

1 094

13

1.3

Singapore

2

2

n.s.

n.s.

Bahrain

n.s.

n.s.

n.s.

14.9

2 288

1 940

-35

-1.6

Bangladesh

1 169

1 334

17

1.3

461

461

n.s.

n.s.

Bhutan

3 016

3 016

n.s.

n.s.

Sri Lanka
Syrian Arab
Republic
Tajikistan

380

400

2

0.5

452

442

-1

-0.2

Thailand15 886

14 762-112-0.7Brunei
Darussalam
Cambodia

9 896

9 335

-56

-0.6

Turkey10 005

10 225

22

0.2

China

145 417163 480

1 806

1.2

3 755

3 755

n.s.

n.s.

Cyprus

119

172

5

3.7

Turkmenistan
United ArabEmirates

243

321

8

2.8

8 210

8 210

n.s.

n.s.

Uzbekistan

1 923

1 969

5

0.2

541

507

-3

-0.6

Viet Nam

9 303

9 819

52

0.5

-

-

-

-

West Bank

-

-

-

-

Georgia

2 988

2 988

n.s.

n.s.

Yemen

541

449

-9

-1.9

India

63 73264 11338

0.1

c. OCEANIA

Indonesia

118 110

104 986-1 312-1.2

American
Samoa

12

12

n.s.

n.s.

Iran, Islamic
Rep.

7 299

7 299

n.s.

n.s.

Australia

157 359

154 539

-282

-0.2

Iraq

799

799

n.s.

n.s.

Cook Islands

22

22

n.s.

n.s.

Israel

82

132

5

4.9

832

815

-2

-0.2

Japan

24 04724 081

3

n.s.

Fiji
FrenchPolynesia
Guam

105

105

n.s.

n.s.

Dem People's
Rep. of Korea
East Timor
Gaza Strip

JordanKazakhstan

86

86

n.s.

n.s.

9 758

12 148

239

2.2

3

5

n.s.

3.5

21

21

n.s.

n.s.

28

28

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

24

15

-1

-4.5

n.s.

n.s.

n.s.

n.s.
n.s.

Kyrgyzstan
Lao People'sDem. Rep
Lebanon

775

1 003

23

2.6

Kiribati
MarshallIslands
Micronesia

13 08812 561

-53

-0.4

Nauru

37

36

n.s.

-0.4

New Caledonia

372

372

n.s.

Malaysia

21 66119 292-237-1.2

New Zealand

7 556

7 946

39

0.5

Maldives

1

1

n.s.

n.s.

6

6

n.s.

n.s.

Mongolia

11 245

10 645

-60

-0.5

14

14

n.s.

n.s.

Myanmar39 58834 419

-517

-1.4

35

35

n.s.

n.s.

Nepal

4 683

3 900

-78

-1.8

31 73030 601

-113

-0.4

Oman

1

1

n.s.

5.3

130

105

-3

-2.1

Pakistan

2 755

2 361

-39

-1.5

2 580

2 536

-4

-0.2

Philippines

6 676

5 789

-89

-1.4

Niue
Northern
Mariana Isl.
Palau
Papua New
GuineaSamoa
SolomonIslands
Tonga

4

4

n.s.

n.s.

n.s.

1

n.s.

9.6

Vanuatu

441

447

1

0.1

KuwaitQatar

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

3.154

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)

d. EUROPECountry

Albania

d. EUROPE
Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area
1990

2000

000 ha000 ha1 069

991

000 ha /yr% / yr-8-0.8Country

1990

2000

000 haLiechtenstein
Lithuania

Forest Area Change
1990-2000
Annual Change
ChangeRate

Total Forest Area

000 ha6

7000 ha /yr% / yrn.s.

1.2

Andorra

-

-

-

-

1 946

1 994

5

0.2

Austria

3 809

3 886

8

0.2

Malta

n.s.

n.s.

n.s.

n.s.

Belarus

365

375

1

0.3

6 840

9 402

256

3.2

Netherlands

Belgium &Luxembourg

741

728

-1

-0.2

Norway

8 558

8 868

31

0.4

Bosnia &Herzegovina

2 273

2 273

n.s.

n.s.

Poland

8 872

9 047

18

0.2

Bulgaria

3 486

3 690

20

0.6

Portugal

3 096

3 666

57

1.7

318

325

1

0.2

Croatia

1 763

1 783

2

0.1

Republic ofMoldova

Czech Republic

2 627

2 632

1

n.s.

Romania

6 301

6 448

15

0.2

Denmark

445

455

1

0.2

RussianFederation

850 039851 392

135

n.s

Estonia

1 935

2 060

13

0.6

San Marino

-

-

-

-

Finland

21 855

21 9358n.s.

Slovakia

1 997

2 177

18

0.9

France14 72515 34162

0.4

Slovenia

1 085

1 107

2

0.2

Germany

10 74010 740

n.s.

n.s.

Spain

13 51014 37086

0.6

Greece

3 299

3 599

30

0.9

Sweden27 12827 1341n.s.

Hungary

1 768

1 840

7

0.4

Switzerland

1 156

1 199

4

0.4

Iceland

25

31

1

2.2

The FYR of
Macedonia

906

906

n.s.

n.s.

Ireland

489

659

17

3.0

Ukraine

9 274

9 584

31

0.3

Italy

8 737 1

10 00330

0.3

UnitedKingdom

2 624

2 794

17

0.6

Latvia

2 796

2 923

13

0.4

Yugoslavia

2 901

2 887

-1

-0.1

1

The value for Italy was provided by Italy and is referred to in their
Third National Communication to the UNFCCC.

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

IPCC Good Practice Guidance for LULUCF

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

3.155

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)
e. NORTH AND CENTRAL AMERICACountry

1990

2000

000 ha000 haAntigua andBarbuda9

Bahamas842

Barbados

f. SOUTH AMERICA

Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area

TABLE 3A.1.1 (CONTINUED)
FOREST AREA CHANGE(To be used for verification of ‘A’ in Equation 3.2.4)

Country

Forest Area Change
1990-2000
AnnualChangeChangeRate

Total Forest Area
1990

2000

000 ha000 ha000 ha /yr% / yr000 ha /yr% / yr

9

n.s.

n.s.

Argentina

37 49934 648

-285

-0.8

842

n.s.

n.s.

Bolivia

54 67953 068

-161

-0.3

2

2

n.s.

n.s.

Brazil566 998

543 905-2 309

-0.4

1 704

1 348

-36

-2.3

Chile

15 73915 536-20-0.1

Bermuda

-

-

-

-

Colombia

51 50649 601-190-0.4

British Virgin
Is.

3

3

n.s.

n.s.

Ecuador11 92910 557-137-1.2

Canada244 571

244 571

n.s.

n.s.

Falkland Islands

-

-

-

-

CaymanIslands

13

13

n.s.

n.s.

French Guiana

7 926

7 926

n.s.

n.s.

Costa Rica

2 126

1 968

-16

-0.8

Guyana17 36516 879

-49

-0.3

Cuba

Belize

2 071

2 348

28

1.3

Paraguay

24 60223 372-123-0.5

Dominica

50

46

n.s.

-0.7

Peru

67 90365 215-269-0.4

Dominican
Republic

1 376

1 376

n.s.

n.s.

Suriname

14 11314 113n.s.

n.s.

El Salvador

193

121

-7

-4.6

Uruguay

791

1 292

50

5.0

Greenland

-

-

-

-

Venezuela

51 68149 506-218-0.4

Grenada

5

5

n.s.

0.9

Guadeloupe

67

82

2

2.1

Guatemala

3 387

2 850

-54

-1.7

158

88

-7

-5.7

Honduras

5 972

5 383

-59

-1.0

Jamaica

379

325

-5

-1.5

Haiti

Martinique

47

47

n.s.

n.s.

61 51155 205-631-1.1

Montserrat

3

3

n.s.

n.s.

Netherlands
Antilles

1

1

n.s.

n.s.

Nicaragua

4 450

3 278

-117

-3.0

PanamaMexico

3 395

2 876

-52

-1.6

Puerto Rico

234

229

-1

-0.2

Saint Kitts and
Nevis

4

4

n.s.

-0.6

Santa Lucia

14

9

-1

-4.9

Saint Pierre &Miquelon

-

-

-

-

Saint Vincent& Grenadines

7

6

n.s.

-1.4

Trinidad andTobago

281

259

-2

-0.8

United States

222 113225 993

388

0.2

14

14

n.s.

n.s.

US Virgin
Islands

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

n.s. - not specified
Source: FRA 2000 and Working Paper 59, FRA Programme,
Forestry Department of FAO, Rome 2001, 69p(www.fao.org/forestry/fo/fra/index.jsp)

3.156

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.2
ABOVEGROUND BIOMASS STOCK IN NATURALLY REGENERATED FORESTS BY BROAD CATEGORY (tonnes dry matter/ha)(To be used for Bw in Equation 3.2.9, for Lconversion in Equation 3.3.8 in Cropland section and for Lconversion in
Equation 3.4.13. in Grassland section, etc. Not to be applied for Ct or Ct in Forest section Equation 3.2.3)
2

Tropical Forests

1

1

Wet

Moist with
Short Dry
SeasonMoist withLong Dry Season

Dry

Montane Moist

MontaneDry

310(131 - 513)

260
(159 – 433)

123
(120 - 130)72
(16 - 195)

191

40

Continental

275(123 - 683)

182
(10 – 562)

127(100 - 155)

60

222
(81 - 310)

50

Insular348(280 - 520)

290

160

70

362
(330 - 505)

50America

347(118 - 860)

217
(212 - 278)212
(202- 406)78
(45 - 90)234
(48 - 348)

60

AfricaAsia & Oceania:

Temperate Forests
Age Class

Coniferous

Broadleaf

Mixed Broadleaf-Coniferous≤20 years

100(17 - 183)1740

>20 years

134(20 - 600)

122
(18 -320)128(20-330)≤20 years

52(17-106)58(7-126)49(19-89)>20 years

126(41-275)132(53-205)140(68-218)

Eurasia & Oceania

America

Boreal Forests
Age Class

Mixed Broadleaf-Coniferous

Coniferous

Forest-Tundra

Eurasia≤20 years>20 years

12

10

4

50

60
(12.3-131)

20(21- 81)

America≤20 years

15

7

3

>20 years

40

46

15

Note: Data are given in mean value and as range of possible values (in parentheses).
1

The definition of forest types and examples by region are illustrated in Box 2 and Tables 5-1, p 5.7-5.8 of the IPCC Guidelines (1996).

IPCC Good Practice Guidance for LULUCF

3.157

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.3
ABOVEGROUND BIOMASS STOCK IN PLANTATION FORESTS BY BROAD CATEGORY (tonnes dry matter/ha)(To be used for Bw in Equation 3.2.9, for Lconversion in equation in Equation 3.3.8 in Cropland section and for Lconversion in
Equation 3.4.13. in Grassland section, etc. Not to be applied for C t or C t in Forest section Equation 3.2.3)
2

1

Tropical and sub-tropical Forests
Age ClassWet

R > 2000

Moist with Moist with
Short DryLong Dry
SeasonSeason2000>R>1000

Dry

MontaneMoist

MontaneDry

R<1000R>1000

R<1000AfricaBroadleaf spp≤20 years

100

80

30

20

100

40

>20 years

300

150

70

20

150

60

≤20 years

60

40

20

15

40

10

>20 years

200

120

60

20

100

30

Broadleaf

All

220

180

90

40

150

40

other species

All

130

100

60

30

80

25

Pinus

All

300

270

110

60

170

60

Eucalyptus

All

200

140

110

60

120

30

Tectona

All

170

120

90

50

130

30

other broadleaved

All

150

100

60

30

80

30

Pinus sp
Asia:

AmericaTemperate Forests
Age class

Pine

Other coniferous

Broadleaf

Eurasia
Maritime
Continental≤20 years

40

40

30

>20 years

150

250

200

≤20 years

25

30

15

>20 years

150

200

200

≤20 years

17

20

10

>20 years

100

120

80

S. America

All

120

90

N America

All

100
175
(50−275)

300−

Mediterranean & steppe

Boreal Forests
Eurasia
N. America

3.158

Age class

Pine

Other coniferous

Broadleaf≤20 years

55>20 years

40

40

All

50

40

5
25
25

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.4
AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.

(1) To be used for V in Equation 3.2.3.

(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

2

1

a. AFRICACountry
Algeria2

1

a. AFRICA (Continued)
VolumeBiomass(aboveground) (aboveground)

Information

m3 / hat / haSource44

75

NICountry

VolumeBiomass(aboveground) (aboveground)

Information

m3 / hat / haSourceMadagascar

114

194

NI

Angola39

54

NI

Malawi

103

143

NI

Benin

140

195

PI

Mali

22

31

PI

Botswana

45

63

NI

Mauritania

4

6

ES

Burkina Faso

10

16

NI

Mauritius

88

95

ES

Burundi

110

187

ES

Morocco

27

41

NI

Cameroon

135

131

PI

Mozambique

25

55

NI

Cape Verde

83

127

ES

Namibia

7

12

PI

Central African
Republic

85

113

PI/EX

Niger

3

4

PI

Chad

11

16

ES

Nigeria

82

184

ES

Comoros

60

65

ES

Réunion

115

160

ES

Congo

132

213

EX

Rwanda

110

187

ES

Côte d'Ivoire

133

130

PI

Saint Helena

Dem. Rep. of the
Congo

133

225

NI

Sao Tome andPrincipe

108

116

NI

Djibouti

21

46

ES

Senegal

31

30

NI

Egypt

108

106

ES

Seychelles

29

49

ES

Equatorial Guinea

93

158

PI

Sierra Leone

143

139

ES

Eritrea

23

32

NI

Somalia

18

26

ES

Ethiopia

56

79

PI

South Africa

49

81

EX

Gabon

128

137

ES

Sudan

9

12

ES

Gambia13

22

NI

Swaziland

39

115

NI

Ghana

49

88

ES

Togo

92

155

PI

Guinea

117

114

PI

Tunisia

18

27

NI

Guinea-Bissau

19

20

NI

Uganda

133

163

NI

Kenya

35

48

ES

United Republic of
Tanzania

43

60

NI

Lesotho

34

34

ES

Western Sahara

18

59

NI

Liberia

201

196

ES

Zambia

43

104

ES

Libyan Arab
Jamahiriya

14

20

ES

Zimbabwe

40

56

NI

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

IPCC Good Practice Guidance for LULUCF

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)3.159

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.4
AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.

(1) To be used for V in Equation 3.2.3.

(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

2

1

b. ASIACountry2

1

b. ASIA (Continued)
VolumeBiomass(aboveground) (aboveground)

Information

m3 / hat / haSourceAfghanistan

22

27

FAO

Armenia

128

66

Azerbaijan

136

105

VolumeBiomass(aboveground) (aboveground)Country

Information

m3 / hat / haSource

Qatar

13

12

FAO

FAO

Republic of Korea

58

36

NI

FAO

Saudi Arabia

12

12

FAO
FAO

Bahrain

14

14

FAO

Singapore

119

205

Bangladesh

23

39

FAO

Sri Lanka

34

59

FAO

Bhutan

163

178

FAO

Syrian Arab Rep.

29

28

FAO

Brunei
Darussalam

119

205

FAO

Tajikistan

14

10

FAO

Cambodia

40

69

FAO

Thailand

17

29

NI

China

52

61

NI

Turkey

136

74

FAO

Cyprus

43

21

FAO

Turkmenistan

4

3

FAO

Dem People's Rep.
of Korea

41

25

ES

United ArabEmirates---

East Timor

79

136

FAO

Uzbekistan

6

Viet Nam

38

66

ES

Gaza Strip

FAO

Georgia

145

97

FAO

West Bank

-

-

-

India

43

73

NI

Yemen

14

19

FAO

Indonesia

79

136

FAO

Iran, Islamic Rep.

86

149

FAO

Iraq

29

28

FAO

Israel

49

-

FAO

Japan

145

88

FAO

Jordan

38

37

FAO

Kazakhstan

35

18

FAO

Kuwait

21

21

FAO

Kyrgyzstan

32

-

FAO

29

31

NI

Lao People's Dem.
Rep
LebanonTABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.
21

c. OCEANIA

23

22

FAO

Malaysia

119

205

ES

Maldives---

Mongolia

128

80

NI

American Samoa

Country

Biomass
Volume(aboveground) (aboveground)t / ham3 / haInformation
SourceMyanmar

33

57

NI

Australia

55

57

FAO

Nepal

100

109

PI

Cook Islands

-

-

-

Oman

17

17

FAO

Fiji

-

-

-

Pakistan

22

27

FAO

French Polynesia

-

-

-

Philippines

66

114

NI

Guam

-

-

-

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

3.160

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.
21

c.OCEANIA (Continued)
Country

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.
2

1

d. EUROPE (Continued)

VolumeBiomass(aboveground) (aboveground)

Information

m3 / hat / haSourceKiribati---

Marshall Islands

-

-

-

Country

VolumeBiomass(aboveground) (aboveground)

Information

m3 / hat / haSourceCroatia

201

107

FAO

Czech Republic

260

125

FAO

Micronesia

-

-

-

Denmark

124

58

FAO

Nauru

-

-

-

Estonia

156

85

FAO

New Caledonia

-

-

-

Finland

89

50

NI

New Zealand

321

217

FAO

France

191

92

FAO

Niue

-

-

-

Germany

268

134

FAO

Northern Mariana
Isl.

-

-

-

Greece

45

25

FAO

Palau

-

-

-

Hungary

174

112

FAO

Papua New Guinea

34

58

NI

Iceland

27

17

FAO

Samoa

-

-

-

Ireland

74

25

FAO

Solomon Islands

-

-

-

Italy

145

74

FAO

Tonga

-

-

-

Latvia

174

93

FAO

Liechtenstein

254

119

FAO

Lithuania

183

99

FAO

Malta

232

Netherlands

160

107

FAO

Norway

89

49

FAO

Poland

213

94

FAO

Portugal

82

33

FAO

Republic ofMoldova

128

64

FAO

Romania

213

124

FAO

Russian Federation

105

56

FAO

San Marino

0

0

FAO

Slovakia

253

142

FAO

Slovenia

283

178

FAO

Spain

44

24

FAO

Vanuatu
Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.
2

1

d. EUROPECountry

Biomass
Volume(aboveground) (aboveground)t / ham3 / haInformation
SourceFAO

Albania

81

58

FAO

Sweden

107

63

NI

Andorra0

0

FAO

Switzerland

337

165

FAO

Austria

286

250

FAO

The FYR of
Macedonia

70

-

FAO

Belarus

153

80

FAO

Ukraine

179

-

FAO

218

101

FAO

United Kingdom

128

76

FAO

110

-

FAO

Yugoslavia

111

23

FAO

130

76

FAO

Belgium &Luxembourg
Bosnia &Herzegovina
Bulgaria

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

IPCC Good Practice Guidance for LULUCF

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)3.161

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

(1) To be used for V in Equation 3.2.3.
(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

2

1

e. NORTH AND CENTRAL AMERICA

1e. NORTH AND CENTRAL AMERICA (Continued)

Volume(aboveground)

Biomass(aboveground)

Information

m3 / hat / haSourceAntigua andBarbuda

116

210

ES

Bahamas---Country2

Volume(aboveground)

Biomass(aboveground)

Information

m3 / hat / haSourceSaint Vincent andGrenadines

166

173

NI

Trinidad andTobago

71

129

ES

Country

Barbados---

United States

136

108

FAO

Belize

202

211

ES

US Virgin Islands

-

-

-

Bermuda---British Virgin
Islands---

Canada

120

83

FAO

Cayman Islands

-

-

-

Costa Rica

211

220

ES

(1) To be used for V in Equation 3.2.3.

Cuba

71

114

NI

Dominica
Dominican
Republic

91

166

ES

29

53

ES

(2) To be used for Bw in Equation 3.2.9, for Lconversion in
Equation 3.3.8 in cropland section and for Lconversion in
Equation 3.4.13. in grassland section, etc. Not to be
applied for C t or C t in Forest section Equation 3.2.3.

El Salvador

223

202

FAO

Greenland---

Grenada

83

150

PI

Guadeloupe--

Guatemala

355

371

TABLE 3A.1.4 (CONTINUED)AVERAGE GROWING STOCK VOLUME (1) ANDABOVEGROUND BIOMASS CONTENT (2) (DRY MATTER) IN
FOREST IN 2000. (SOURCE FRA 2000)

2

1

f. SOUTH AMERICACountry

Volume(aboveground)

Biomass(aboveground)

Information-

Argentina

m3 / ha25t / ha68

SourceES

ES

Bolivia

114

183

PI

Haiti

28

101

ES

Brazil

131

209

ES

Honduras

58

105

ES

Chile

160

268

ES

Jamaica

82

171

ES

Colombia

108

196

NI

Martinique

5

5

ES

Ecuador

121

151

ES

Mexico52

54

NI

Falkland Islands

-

-

-

Montserrat

-

-

-

French Guiana

145

253

ES

Netherlands
Antilles---Guyana

145

253

ES

Nicaragua

154

161

ES

Paraguay

34

59

ES

Panama

308

322

ES

Peru

158

245

NI

Puerto Rico

-

-

-

Suriname

145

253

ES

Saint Kitts and
Nevis

-

-

-

Uruguay---Saint Lucia

190

198

ES

Venezuela

134

233

ES

Saint Pierre &Miquelon
Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

3.162

Information source: NI = National inventory; PI = Partial inventory;
ES = Estimate; EX = External data (from other regions)

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.5
AVERAGE ANNUAL INCREMENT IN ABOVEGROUND BIOMASS IN NATURAL REGENERATION BY BROAD CATEGORY(tonnes dry matter/ha/year)(To be used for GW in Equation 3.2.5)
Tropical and Sub-Tropical Forests
Moist with
Short Dry
SeasonWet

Age ClassR > 2000

Moist withLong Dry
Season2000>R>1000

Dry

Montane Moist Montane Dry

R<1000R>1000

R<1000Africa≤20 years

10.0

5.3

2.4
(2.3 – 2.5)

1.2
(0.8 – 1.5)

5.0

2.0
(1.0 – 3.0)

>20 years

3.1 (2.3 -3.8)

1.3

1.8
(0.6 – 3.0)

0.9
(0.2 – 1.6)

1.0

1. 5
(0.5 – 4.5)

≤20 years

7.0(3.0 – 11.0)

9.0

6.0

5.0

5.0

1.0

>20 years

2.2(1.3 – 3.0)

2.0

1.5

1.3
(1.0 – 2.2)

1.0

0.5

≤20 years

13.0

11.0

7.0

2.0

12.0

3.0

>20 years

3.4

3.0

2.0

1.0

3.0

1.0

≤20 years

10.0

7.0

4.0

4.0

5.0

1.8

>20 years

1.9(1.2 – 2.6)

2.0

1.0

1.0

1.4
(1.0 – 2.0)

0.4

Asia & Oceania
Continental

InsularAmericaTemperate Forests
Age Class

Coniferous

Broadleaf≤20 years

3.0(0.5 – 6.0)

4.0
(0.5 – 8.0)

>20 years

3.0(0.5 – 6.0)

4.0
(0.5 – 7.5)

Boreal forests
Mixed BroadleafConiferous

Coniferous

Forest-Tundra

Broadleaf≤20 years

1.0

1.5

0.4
(0.2 – 0.5)

1.5
(1.0 – 2.0)

>20 years

1.5

2.5

0.4
(0.2 – 0.5)

1.5

≤20 years

1.1(0.7 – 1.5)

0.8
(0.5 – 1.0)

0.4
(0.2 – 0.5)

1.5
(1.0 – 2.0)

>20 years

1.1(0.7 –– 1.5)

1.5
(0.5 – 2.5)

0.4
(0.2 – 0.5)

1.3
(1.0 – 1.5)

Age ClassEurasia

America

Note: R= annual rainfall in mm/yr
Note: Data are given as mean value and as the range of possible values.

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Chapter 3: LUCF Sector Good Practice Guidance

Table 3A.1.6
ANNUAL AVERAGE ABOVEGROUND BIOMASS INCREMENT IN PLANTATIONS BY BROAD CATEGORY(tonnes dry matter/ha/year )(To be used for GW in Equation 3.2.5.
In case of missing values it is preferred to use stemwood volume increment data IV from Table 3A.1.7)
Tropical and sub-tropical Forests

Age Class

Wet

Moist with
Short Dry
Season

R >2000

Moist withLong Dry
Season2000>R>1000

Dry

MontaneMoist

MontaneDry

R<1000R>1000

R<1000Africa
Eucalyptus spp

Pinus sp

≤20 years

-

20.0

12.6

5.1
(3.0-7.0)-->20 years

-

25.0

-

8.0
(4.9-13.6)--≤20 years

18.0

12.0

8.0

3.3 (0.5-6.0)

-

-

15.0

11.0

2.5

-

-

>20 years≤20 years

6.5(5.0-8.0)

9.0(3.0-15.0)

10.0(4.0-16.0)

15.0

11.0

-

>20 years

-

-

-

11.0

-

-

Eucalyptus spp

All

5.0
(3.6-8.0)

8.0

15.0
(5.0-25.0)

-

3.1

-

other species-

5.2(2.4-8.0)

7.8(2.0-13.5)

7.1(1.6-12.6)

6.45(1.2-11.7)

5.0(1.3-10.0)-

America

-

-

-

-

-

-

-

7.0
(4.0 - 10.3)

5.0

14.0

-

others

Asia

Pinus

-

18.0

14.5
(5.0 – 19.0)

Eucalyptus-

21.0(6.4 - 38.4)

16.0(6.4 - 32.0)

16.0(6.4 - 32.0)

16.0

13.0
(8.5 - 17.5)-

Tectona

-

15.0

8.0
(3.8 - 11.5)

8.0(3.8 - 11.5)

-

2.2

-

other broadleaved-

17.0(5.0 - 35.0)

18.0(8.0 – 40.0)

10.5(3.2 - 11.8)

-

4.0

-

Note 1 : R= annual rainfall in mm/yr
Note 2 : Data are given as mean value and as the range of possible values.
Note 3 : Some Boreal data were calculated from original values in Zakharov et al. (1962), Zagreev et al. (1993), Isaev et al. (1993) using
0.23 as belowground/aboveground biomass ratio and assuming a linear increase in annual increment from 0 to 20 years.
Note 4 : For plantations in temperate and boreal zones, it is good practice to use stemwood volume increment data (Iv in Equation 3.2.5)
instead of above ground biomass increment as given in above table.

References for Tables 3A.1.2, 3A.1.3, 3A.1.4, 3A.1.5, and 3A.1.6
Tropical and subtropical
Brown, S. (1996). A primer for estimating biomass and biomass change of tropical forest. FAO, Rome, Italy. 55 pp.
Budowski, G. (1985). The place of Agroforestry in managing tropical forest. In La conservación como instrumento para el desarrollo.
Antología. San José, Costa Rica. EUNED. 19 pp.
Burrows, W. H.; Henry, B. K.; Back, P. V., et al. (2002) Growth and carbon stock change in eucalypt woodlands in northeast Australia:
ecological and greenhouse sink implications. Global Change Biology 8 (8): 769-784 2002
Chudnoff, M. (1980). Tropical Timbers of the World. US Department of Agriculture, Forest Service, Forest Products Laboratory. Madison,W1. 831 pp.
Clarke et al. (2001) NPP in tropical forests: an evaluation and synthesis of existing field data. Ecol. Applic. 11:371-384
Evans, J. (1982). Plantation forestry in the tropics. Oxford.
Favrichon, V. (1997). Réaction de peuplements forestiers tropicaux a des interventions sylvicoles. Bois et des forets des tropiques 254: 5-24

3.164

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

FBDS: FUNDACAO BRASILERA PARA O DESEMVOLVIMENTO SUSTENTAVEL. (1997). Avaliacao das emissoes de gases de efeito
estufa devido as mudancas no estoques de florestas plantadas. Rio de Janeiro (Brasil). 44 pp.
Fearnside, P.M. (1997). Wood density for estimating forest biomass in Brazilian Amazonia. Forest Ecology and Management 90(1): 59-87.
FIA: Fundación para la Innovación Agraria. (2001). Potencial de proyectos forestales en el Mecansimo de Desarrollo Limpio en Chile. In
IV Seminario Regional forestal del Cono Sur, elaboración de proyectos forestales en el Mecanismo de Desarrollo Limpio, realizado 06-07
de diciembre de 2001. Santiago de Chile. 26 pp.
GASTON G., BROWN S., LORENZINI M. & SING. (1998). State and change in carbon pools in the forests of tropical Africa.Global
Change Biology 4 (1), 97-114.
Gower S.T., Gholz H.L, Nakane K.,Baldwin V.C. (1994). Production and carbon allocation patterns of pine forests Ecological bulletins
43:115-135 (data converted from aNPP values assuming litterfall =2 x L(-38)C foliage annual production)
Grace J., Malhi Y., Higuchi N., Meir P. (2001). Productivity of tropical Rain Forests in "Terrestrial Global productivity" Roy J, Saugier B.,& Mooney H.Eds, Physiological Ecology Series, Academic Press, San Diego , 401-426
Hofmann-Schielle, C., A. Jug, et al. (1999). Short-rotation plantations of balsam poplars, aspen and willows on former arable land in theFederal Republic of Germany. I. Site-growth relationships. Forest Ecology and Management 121(1/2): 41-55.
IBDF. (1983). Potencial madereira do Grande Carajás. Instituto Brasileiro de Desenvolvimento Florestal. Brasilia, DF, Brazil. 134 pp.
IPCC Guidelines (1996). Workbook p 5.22. from Houghton etal. 1983, 1987.
Klinge, H.; Rodrigues, W.A. (1973). Biomass estimation in a central Amazonian rain forest. Acta Científica Venezolana 24:225-237
Laclau, J. P., J. P. Bouillet, et al. (2000). Dynamics of biomass and nutrient accumulation in a clonal plantation of Eucalyptus in Congo.
Forest Ecology and Management 128(3): 181-196
Lamprecht, H. (1990). Silviculture in the tropics. GTZ. Rossdorf, Deutsche. 333 pp.
Mandouri T. et al. in "Annales de la recherche forestière (1951-1999); and Thesis from National High School of Forestry (ENFI); andHassan II Agronomic Institut(IAVHII)MDSP/PNCC: MINISTERIO DE DESARROLLO SOSTENIBLE Y PLANIFICACION; PROGRAMA NACIONAL DE CAMBIOS
CLIMATICOS. (2002). Inventariación de Emisiones de Gases de Efecto Invernadero, Bolivia, 1990, 1994, 1998 y 2000. La Paz (Bolivia).
443 pp.
MINISTERIO DE MEDIOAMBIENTE Y RECURSOS NATURALES. (2000). Taller Regional Centro Americano sobre el Cambio
Climático, 24-26 de junio de 2000. Ciudad de Panamá, Panamá.
Montagnini, F. (2000). Accumulation in above-ground biomass and soil storage of mineral nutrients in pure and mixed plantations in ahumid tropical lowland. Forest Ecology and Management 134(1/3): 257-270.
Moreno, H. (2001). Estado de la Investigación sobre dinámica del carbono en proyectos Forestales de Colombia. Universidad Nacional de
Colombia, Sede Medellín, Departamento de Ciencias Forestales. Medellín, Colombia.
Norgrove, L. and S. Hauser (2002). Measured growth and tree biomass estimates of Terminalia ivorensis in the 3 years after thinning todifferent stand densities in an agrisilvicultural system in southern Cameroon. Forest Ecology and Management 166(1/3): 261-270.
PAC-NK: NOEL KEMPFF CLIMATE ACTION PROJECT. (2000). Noel Kempff Climate Action Project: project case carbon inventory
and offset benefits. Winrock Drive. Arlington, U.S.A. 45 pp.
Pandey, D (1982).
Parrotta, J. A. (1999). Productivity, nutrient cycling, and succession in single- and mixed-species plantations of Casuarina equisetifolia,
Eucalyptus robusta, and Leucaena leucocephala in Puerto Rico. Forest Ecology and Management 124(1): 45-77
Peters, R. (1977). Fortalecimiento al sector forestal Guatemala inventarios y estudios dendrométricos en bosques de coniferas.
FO:DP/GUA/72/006, Informe Técnico 2, FAO, Rome, Italy.
Ramírez, P.; Chacón, R. (1996). National Inventory of Sources and Sinks of Greenhouse Gases in Costa Rica. U.S. Contry Studies Program.
Kluwer Academic Publishers. Boston, U.K. 357-365.
Russell, C.E. (1983). Nutrient cycling and productivity of native and plantation forest at Jari Florestal, Pará, Brazil. Ph.D. dissertation in
ecology, University of Georgia, Athens, Georgia, U.S.A. 133 pp.
Saldarriaga, C.A.; Escobar, J.G.; Orrego, S. A.; Del Valle, I. (2001). Proyectos de Reforestación como parte del Mecanismo de Desarrollo
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Wadsworth, F.H. (1997). Forest production for tropical America. USDA Forest Service Agriculture Handbook 710. Washington, DC, USDA
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Webb, D.B., Wood, P.J., Smith, J.P. & Henman, G.S. (1984). A guide to species selection for tropical and subtropical plantations. Tropical
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Temperate
Data includes values compiled by DR. JIM SMITH, USDA FOREST SERVICE, DURHAM NH USA 03824. jsmith11@fs.fed.us,
Lheath@fs.fed.us
Botkin D.B., Simpson L.G. (1990) Biomass of North American Boreal Forest. Biogeochemistry, 9: 161-174.
Brown S., Schroeder P., Kern J.S. (1999) Spatial distribution of biomass in forests of the eastern USA. Forest Ecology and Management,
123:81-90
Burrows, W. H.; Henry, B. K.; Back, P. V., et al. (2002) Growth and carbon stock change in eucalypt woodlands in northeast Australia:
ecological and greenhouse sink implications. Global Change Biology 8 (8): 769-784 2002
Fang, S., X. Xu, et al. (1999). Growth dynamics and biomass production in short-rotation poplar plantations: 6-year results for three clones atfour spacings. Biomass and Bioenergy 17(5): 415-425.
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Chapter 3: LUCF Sector Good Practice Guidance

Gower S.T., Gholz H.L, Nakane K.,Baldwin V.C. (1994) Production and carbon allocation patterns of pine forests Ecological bulletins
43:115-135 (data converted from aNPP values assuming litterfall =2 x foliage annual production)
Grierson, P. F., M. A. Adams, et al. (1992). Estimates of carbon storage in the above-ground biomass of Victoria's forests. Australian Journalof Botany 40(4/5): 631-640.
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the 1990 New Zealand baseline.Global Change Biology,7:389-403.
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Mitchell, C. P., E. A. Stevens, et al. (1999). Short-rotation forestry - operations, productivity and costs based on experience gained in the
UK. Forest Ecology and Management 121(1/2): 123-136.
Santa Regina, I. and T. Tarazona (2001). Nutrient cycling in a natural beech forest and adjacent planted pine in northern Spain. Forestry(Oxford) 74(1): 11-28
Schroeder, P., S. Brown, et al. (1997). Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest
Science 43(3): 424-434.
Shan, J Morris L A. & Hendrick, R L. (2001) The effects of management on soil and plant carbon sequestration in slash pine plantations.
Journal of Applied Ecology 38 (5), 932-941.
Smith and Heath. Data includes values compiled by DR. JIM SMITH, USDA FOREST SERVICE, DURHAM NH USA 03824.
jsmith11@fs.fed.us, Lheath@fs.fed.us
Son YH; Hwang JW; Kim ZS; Lee WK; Kim JS (2001) Allometry and biomass of Korean pine (Pinus koraiensis) in central Korea.
Bioresource Technology 78 (3): 251-255 2001
Turnbull, C.R.A., McLeod, D.E., Beadle, C.L., Ratkowsky, D.A., Mummery, D.C. and Bird, T. (1993). Comparative growth of Eucalyptus
species of the subgenera Monocalyptus and Symphyomyrtus in intensively managed plantations in southern Tasmania. Aust. For. 56, pp.
276–286.
UN-ECE/FAO (2000). Forest Resources of Europe, CIS, North America, Australia, Japan and new Zealand (industrialized temperate / boreal
countries.UN-ECE/FAO contribution to th Global Forest Resources Assessment 2000, united nations, New-Ypork and Geneva, geneva
Timber and Forest Study papers, No 17.446 p.
U'soltsev and Van Clay. (1995). Stand Biomass Dynamics of Pine plantations and natural forests on dry steppe in Kazakhstan Scan J For
Res, 10, 305-312
Vogt K (1991). Carbon budgets of temperate forest ecosystems. Tree Physiology, 9:69-86.
Zhou, G., Y. Wang, et al. (2002). Estimating biomass and net primary production from forest inventory data: a case study of China's Larix
forests. Forest Ecology and Management 169(1/2): 149-157.

BorealFinnish Forest Research Institute (2002). Finnish Statistical Yearbook of Forestry. SVT Agriculture and Forestry, Helsinki, Finland.
Isaev, A.S., Korovin, G.N., Utkin A.I., Pryazhnikov A.A., and D.G. Zamolodchikov (1993) Estimation of Carbon Pool and Its Annual
Deposition in Phytomass of Forest Ecosystems in Russia, Forestry (Lesovedenie), 5: 3-10 (In Russian).
Kajimoto, T., Y. Matsuura, et al. (1999). Above- and belowground biomass and net primary productivity of a Larix gmelinii stand near Tura,
central Siberia. Tree Physiology 19(12): 815-822.
Koivisto, 1959; Koivisto, P., (1959) Growth and Yield Tables. Commun. Inst. For. Fenn. Vol 51 no. 51.8: 1-49 (In Finnish with headings in
English).
Kurz, W.A. and M.J. Apps. (1993): Contribution of northern forests to the global C cycle: Canada as a case study. Water, Air, and Soil
Pollution, 70, 163-176.
Nilsson S., Shvidenko A., Stolbovoi V., Glick M., Jonas M., Obersteiner M. (2000). Full carbon account for Russia. Interim Report IR -00021 Int Inst Appl Anal, 181 pages.
UN-ECE/FAO (2000). Forest Resources of Europe, CIS, North America, Australia, Japan and new Zealand (industrialized temperate / boreal
countries.UN-ECE/FAO contribution to th Global Forest Resources Assessment 2000, United Nations, New-Ypork and Geneva, geneva
Timber and Forest Study papers, No 17.446 p.
Vuokila, Y. and Väliaho, H. (1980). Growth and yield models for conifers cultures in Finland. Commun. Inst. For. Fenn. 99(2):1-271
Wirth C. , E.-D. Schulze, W. Schulze, D. von Stünzner-Karbe, W. Ziegler, I. M. Miljukova, A. Sogatchev, A. B. Varlagin, M. Panvyorov, S.
Grigoriev, W. Kusnetzova, M. Siry, G. Hardes, R. Zimmermann, N. N. Vygodskaya (1999). Above-ground biomass and structure ofpristine Siberian Scots pine forests as controlled by competition and fire. Oecologia 121 : 66-80
Zakharov, V.K., Trull, O.A., Miroshnikov, V.S., and V.E. Ermakov (1962) The Reference Book on Forest Inventory. Belarus State
Publishing, Minsk, p. 368. (In Russian).
Zagreev, V.V., Sukhikh, B.I., Shvidenko, A.Z., Gusev, N.N., and A.G. Moshkalev (1993) The All-Union Standards for Forest Inventory.
Kolos, Moscow, p. 495. (In Russian).

3.166

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.7
AVERAGE ANNUAL ABOVE GROUND NET INCREMENT IN VOLUME IN PLANTATIONS BY SPECIES(m3/ha/yr)(To be used for Iv in Equation 3.2.5)IV(m³ ha-¹ yr-¹)

Species
Range

Mean*

E. deglupta

14 - 50

32

E. globulus

10 - 4025

E. grandis

15 - 50

32.5

E. saligna

10 - 55

32.5

E. camaldulensis

15 - 30

22.5

E. urophylla

20 - 60

40

E. robusta

10 - 40

25

Pinus caribaea var. caribaea

10 - 28

19

Pinus caribaea var. hondurensis

20 - 50

35

Pinus patula

8 - 4024

Pinus radiata

12 - 35

23.5

Pinus oocarpa

10 - 40

25

Araucaria angustifolia

8 - 2416

A. cunninghamii

10 - 18

14

Gmelina arborea

12 - 50

31

Swietenia macrophylla

7 - 3018.5

Tectona grandis

6 - 18

12

Casuarina equisetifolia

6 - 2013

C. junghuhniana

7 - 11

9

Cupressus lusitanica

8 - 4024

Cordia alliadora

10 - 20

15

Leucaena leucocephala

30 - 55

42.5

Acacia auriculiformis

6 - 2013

Acacia mearnsii

14 - 25

19.5

Terminalia superba

10 - 14

12

Terminalia ivorensis

8 - 17

12.5

Dalbergia sissoo
5-8
6.5* For those parties that have reason to believe that their plantations are located on more than
average fertile sites it is suggested to use the mean value + 50%, for those Parties that havereason to believe their plantations are located on poor sites, it is suggested to use the meanvalue -50%
Source: Ugalde,L. and Prez,O. Mean annual volume increment of selected industrial forest planatation
species. Forest Plantation Thematic Papers, Working paper 1. FAO (2001)
Available at http://www.fao.org/DOCREP/004/AC121E/AC121E00.HTM

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Chapter 3: LUCF Sector Good Practice Guidance

Other

Grassland

Temperate broadleaf forest/
plantation

Conifer
forest/plantation

Tropical/subtropical forest

TABLE 3A.1.8
AVERAGE BELOWGROUND TO ABOVEGROUND BIOMASS RATIO (ROOT-SHOOT RATIO, R) IN NATURAL REGENERATION BY
BROAD CATEGORY (tonnes dry matter/tonne dry matter)(To be used for R in Equation 3.2.5)
Vegetation type

Abovegroundbiomass (t/ha)

Mean

SD

lower
range

upper
range

References

Secondary tropical/sub-tropicalforest

<125

0.42

0.22

0.14

0.83

5, 7, 13, 25, 28, 31, 48,
71

Primary tropical/sub-tropicalmoist forest

NS

0.24

0.03

0.22

0.33

33, 57, 63, 67, 69

Tropical/sub-tropical dry forest

NS

0.27

0.01

0.27

0.28

65

Conifer forest/plantation

<50

0.46

0.21

0.21

1.06

2, 8, 43, 44, 54, 61, 75

Conifer forest/plantation

50-150

0.32

0.08

0.24

0.50

6, 36, 54, 55, 58, 61

Conifer forest/plantation

>150

0.23

0.09

0.12

0.49

1, 6, 20, 40, 53, 61, 67,
77, 79

Oak forest

>70

0.35

0.25

0.20

1.16

15, 60, 64, 67

Eucalypt plantation

<50

0.45

0.15

0.29

0.81

9, 51, 59

Eucalypt plantation

50-150

0.35

0.23

0.15

0.81

4, 9, 59, 66, 76

Eucalypt forest/plantation

>150

0.20

0.08

0.10

0.33

4, 9, 16, 66

Other broadleaf forest

<75

0.43

0.24

0.12

0.93

30, 45, 46, 62

Other broadleaf forest

75-150

0.26

0.10

0.13

0.52

30, 36, 45, 46, 62, 77,
78, 81Other broadleaf forest

>150

0.24

0.05

0.17

0.30

3, 26, 30, 37, 67, 78, 81

Steppe/tundra/prairie grassland

NS

3.95

2.97

1.92

10.51

50, 56, 70, 72

Temperate/sub-tropical/ tropicalgrassland

NS

1.58

1.02

0.59

3.11

22, 23, 32, 52

Semi-arid grassland

NS

2.80

1.33

1.43

4.92

17-19, 34

Woodland/savanna

NS

0.48

0.19

0.26

1.01

10-12, 21, 27, 49, 65,
73, 74

Shrubland

NS

2.83

2.04

0.34

6.49

14, 29, 35, 38, 41, 42,
47, 67

Tidal marsh

NS

1.04

0.21

0.74

1.23

24, 39, 68, 80

NS = Not specified

References for Table 3A.1.8
1.

Alban, D., D. Perala, and B. Schlaegel (1978) Biomass and nutrient distribution in aspen, pine, and spruce stands on the same soil typein Minnesota. Canadian Journal of Forest Research 8: 290-299.

2.

Albaugh, T., H. Allen, P. Dougherty, L. Kress, and J. King (1998) Leaf area and above- and below-ground growth responses of loblolly
pine to nutrient and water additions. Forest Science 44(2): 317-328.

3.

Anderson, F. (1971) Methods and Preliminary results of estimation of biomass and primary production in a south Sweedish mixed
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4.

Applegate, G. (1982) Biomass of Blackbutt (Eucalyptus pilularis Sm.) Forests on Fraser Island. Masters Thesis. University of New
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5.

Bartholomew, W., J. Meyer, and H. Laudelout (1953) Mineral nutrient immobilization under forest and grass fallow in the Yangambi(Belgian Congo) region. Publications de l'Institut National Pour l'Etude Agronomique du Congo Belge Serie scientifique 57: 27pp total.

6.

Baskerville, G. (1966) Dry-matter production in immature balsam fir stands: roots, lesser vegetation, and total stand. Forest Science 12: 49-53.

7.

Berish, C. (1982) Root biomass and surface area in three successional tropicl forests. Canadian Journal of Forest Research 12: 699-704.

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Annex 3A.1

13. Buschbacher, R., C. Uhl, and E. Serrao (1988) Abandoned pastures in eastern Amazonia. II. Nutrient stocks in the soil and vegetation.
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Chapter 3: LUCF Sector Good Practice Guidance

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Annex 3A.1

TABLE 3A.1.9-1BASIC WOOD DENSITIES OF STEMWOOD (tonnes dry matter/m3 fresh volume)
FOR BOREAL AND TEMPERATE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
Species or genus
Abies

Basic wood density
m0/VwetSource

0.40

1

Acer

0.52

1

Alnus

0.45

1

Betula0.51

1

Carpinus betulus

0.63

3

Castanea sativa

0.48

3

Fagus sylvatica

0.58

1

Fraxinus

0.57

1

Juglans

0.53

3

Larix decidua

0.46

1

Larix kaempferi

0.49

3

Picea abies

0.40

1

Picea sitchensis

0.40

2

Pinus pinaster

0.44

5

Pinus strobus

0.32

1

Pinus sylvestris

0.42

1

Populus

0.35

1

Prunus0.49

1

Pseudotsuga menziesii

0.45

1

Quercus

0.58

1

Salix

0.45

1

Thuja plicata

0.31

4

Tilia

0.43

1

Tsuga

0.42

4

Source:1. Dietz, P. 1975: Dichte und Rindengehalt von Industrieholz. Holz Roh- Werkstoff 33: 135-141
2. Knigge, W.; Schulz, H. 1966: Grundriss der Forstbenutzung. Verlag Paul Parey, Hamburg, Berlin
3. EN 350-2 (1994): Durability of wood and wood products - Natural durability of solid wood - Part 2:
Guide to the natural durability and treatability of selected wood species of importance in Europe
4. Forest Products Laboratory: Handbook of wood and wood-based materials. Hemisphere Publishing
Corporation, New York, London
5. Rijsdijk, J.F.; Laming, P.B. 1994: Physical and related properties of 145 timbers. Kluwer Academic
Publishers, Dordrecht, Boston, London
6. Kollmann, F.F.P.; Coté, W.A. 1968: Principles of wood science and technology. Springer Verlag,
Berlin, New YorkIPCC Good Practice Guidance for LULUCF

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.9-2
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
D

TROPICAL AMERICA

D

Acacia leucophloea

TROPICAL ASIA

0.76

Albizia spp.

0.52

Afzelia spp.

TROPICAL AFRICA

0.67

D

Adina cordifolia

0.58, 0.59+

Alcornea spp.

0.34

Aidia ochroleuca

0.78*

Aegle marmelo

0.75

Alexa grandiflora

0.6

Albizia spp.

0.52

Agathis spp.

0.44

Alnus ferruginea

0.38

0.63*

Aglaia llanosiana

0.89

Anacardium excelsum

0.41

Alangium longiflorum

0.65

Anadenanthera macrocarpa

0.86

Allanblackia floribunda
Allophyllus africanus f.
acuminatus
Alstonia congensis

Albizzia amara

0.70*

Andira retusa

0.67

Amphimas pterocarpoides

0.63*

Albizzia falcataria

0.25

Aniba riparia lduckei

0.62

Anisophyllea obtusifolia

0.63*
0.29*

0.45
0.33

Aleurites trisperma

0.43

Antiaris africana

0.38

Annonidium mannii

Alnus japonica

0.43

Apeiba echinata

0.36

Anopyxis klaineana

0.74*

Alphitonia zizyphoides

0.5

0.7

Anthocleista keniensis

0.50*

Alphonsea arborea

0.69

0.75

Anthonotha macrophylla

0.78*

Alseodaphne longipes

0.49

Artocarpus comunis
Aspidosperma spp.
(araracanga group)
Astronium lecointei

0.73

Anthostemma aubryanum

0.32*

Alstonia spp.

0.37

Bagassa guianensis

0.68,0.69+

Amoora spp.

0.6

Banara guianensis

Anisophyllea zeylanica

0.46*

Anisoptera spp,

0.54

Anogeissus latifolia

Antiaris spp.

0.38

0.61

Antrocaryon klaineanum

0.50*

Basiloxylon exelsum

0.58

Aucoumea klaineana

0.37

Beilschmiedia sp.

0.61

Autranella congolensis

0.78

0.78, 0.79+

Berthollettia excelsa

0.59, 0.63+

Baillonella toxisperma

0.71

Anthocephalus chinensis

0.36,0.33+

Bixa arborea

0.32

Balanites aegyptiaca

0.63*

Antidesma pleuricum

0.59

Bombacopsis sepium

0.39

Baphia kirkii

0.93*

Aphanamiris perrottetiana

0.52

Borojoa patinoi

0.52

Beilschmiedia louisii

0.70*

Araucaria bidwillii

0.43

0.74

Beilschmiedia nitida

0.50*

Artocarpus spp.

0.58

0.64, 0.66+

Berlinia spp.

0.58

Azadirachta spp.

0.52

Bowdichia spp.
Brosimum spp. (alicastrum
group)
Brosimum utile

0.41, 0.46+

Blighia welwitschii

0.74*

Balanocarpus spp.

0.76

Brysenia adenophylla

0.54

Barringtonia edulis *0.48

Buchenauia capitata

Bauhinia spp.

0.67

Beilschmiedia tawa

0.58

Berrya cordifolia

0.78*

Bischofia javanica

Bombax spp.

0.4

0.61, 0.63+

Brachystegia spp.

0.52

Bucida buceras

0.93

Bridelia micrantha

0.47*

Bulnesia arborea

1

Calpocalyx klainei

0.63*

Bursera simaruba

0.29, 0.34+

Canarium schweinfurthii

0.40*

0.64

Canthium rubrocostratum

0.63*

Bleasdalea vitiensis

0.54,0.58,0.62+ Byrsonima coriacea
0.43

Cabralea cangerana

0.55

Carapa procera

0.59

Bombax ceiba
Bombycidendron
vidalianum

0.33

Caesalpinia spp.

1.05

Casearia battiscombei

0.5

0.53

Calophyllum sp.

0.65

Cassipourea euryoides

0.70*

0.33,0.50+

Cassipourea malosana

0.59*

Bridelia squamosa

0.5

Campnosperma
panamensis
Carapa sp.

Buchanania latifolia

0.45

Caryocar spp.

Boswellia serrata

0.5

0.47
0.69, 0.72+

Ceiba pentandra

0.26

Celtis spp.

0.59

Bursera serrata

0.59

Casearia sp.

0.62

Chlorophora ercelsa

0.55

Butea monosperma

0.48

Cassia moschata

0.71

Chrysophyllum albidum

0.56*

Calophyllum spp.

0.53

Casuarina equisetifolia

0.81

Cleistanthus mildbraedii

0.87*

Calycarpa arborea

0.53

Catostemma spp.

0.55

Cleistopholis patens

0.36*

Cananga odorata

0.29

Cecropia spp.

0.36

Coelocaryon preussii

0.56”

Canarium spp.

0.44

Cedrela spp.

0.40, 0.46+

0.70”

Canthium monstrosum

0.42

Cedrelinga catenaeformis

0.41, 0.53+

Cola sp.
Combretodendron
macrocarpum

0.7

0.23,0.24,0.25,
Conopharyngia holstii
0.50*
0.29+
+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.
Carallia calycina

3.172

0.66*

Ceiba pentandra

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIAD

TROPICAL AMERICAD

TROPICAL AFRICA

D

Cassia javanica

0.69

Centrolobium spp.

0.65

Copaifera religiosa .

0.50”

Castanopsis philippensis

0.51

0.63

Cordia millenii

0.34

Casuarina equisetifolia

0.83

0.8

Cordia platythyrsa

0.36”

Casuarina nodiflora

0.85

Cespedesia macrophylla
Chaetocarpus
schomburgkianus
Chlorophora tinctoria

0.71,0.75+

Corynanthe pachyceras

0.63”

Cedrela odorata

0.38

Clarisia racemosa

0.53,0.57+

Coda edulis

0.78*

Cedrela spp.

0.42

0.67

Croton megalocarpus

0.57

0.26

Cryptosepalum staudtii

0.70*

0.46, 0.55+

Ctenolophon englerianus

0.78*

0.74

Cylicodiscus gabonensis

0.8

0.48

Cynometra alexandri

0.74

Cedrela toona
Ceiba pentandra
Celtis luzonica
Chisocheton pentandrus
Chloroxylon swietenia

Clusia rosea
Cochlospermum
0.43
orinocensis
0.23
Copaifera spp.
Cordia spp. (gerascanthus
0.49
group)
Cordia spp. (alliodora
0.52
group)
0.76, 0.79, 0.80+ Couepia sp.

0.7

Dacryodes spp.

0.61

0.50,0.53+

Daniellia ogea

0.40*

Chukrassia tabularis

0.57

Couma macrocarpa

Citrus grandis

0.59

Couratari spp.

0.5

Desbordesia pierreana

0.87”

Cleidion speciflorum

0.5

Croton xanthochloros

0.48

Detarium senegalensis

0.63*

Cleistanthus eollinus

0.88

Cupressus lusitanica

0.43, 0.44+

Dialium excelsum

0.78*

Cleistocalyx spp.
Cochlospermum
gossypium+religiosum
Cocos nucifera

0.76

Cyrilla racemiflora

0.53

Didelotia africana

0.78”

0.27

Dactyodes colombiana

0.51

Didelotia letouzeyi

0.5

0.5

Dacryodes excelsa

0.52, 0.53+

Colona serratifolia
Combretodendron
quadrialatum
Cordia spp.

0.33

Dalbergia retusa.

0.89

Diospyros spp.

0.82
0.32*

0.47

Discoglypremna caloneura
Distemonanthus
benthamianus
Drypetes sp.

0.57

Dalbergia stevensonii

0.82

0.53

Declinanona calycina

Cotylelobium spp.

0.69

Dialium guianensis

0.63*

0.87

Ehretia acuminata

Crataeva religiosa

0.53*

0.51*

Dialyanthera spp.

0.36, 0.48+

Enantia chlorantha

Cratoxylon arborescens

0.42”

0.4

Dicorynia paraensis

0.6

Endodesmia calophylloides

0.66”

Cryptocarya spp.

0.59

Didymopanax sp.

0.74

Entandrophragma utile

0.53

Cubilia cubili

0.49

Dimorphandra mora

0.99*

Eribroma oblongum

0.60*

Cullenia excelsa

0.53

Diplotropis purpurea

0.76, 0.77, 0.78+ Eriocoelum microspermum

0.50”

Cynometra spp.

0.8

Dipterix odorata

0.81,0.86,0.89+ Erismadelphus ensul

0.56*

0.45, 0.47+

Drypetes variabilis

0.69

Erythrina vogelii

0.25”

0.46

Dussia lehmannii

0.59

Erythrophleum ivorense

0.72

Dacrycarpus imbricatus
Dacrydium spp.

0.58

Dacryodes spp.

0.61

Ecclinusa guianensis

0.63

Erythroxylum mannii

0.5

Dalbergia paniculata

0.64

0.39

Fagara macrophylla

0.69

Decussocarpus vitiensis

0.37

0.82

Ficus iteophylla

0.40”

0.78

Fumtumia latifolia

0.45*

0.4

Gambeya spp.

0.56*

Degeneria vitiensis

0.35

Endlicheria cocvirey
Enterolobium
schomburgkii
Eperua spp.

Dehaasia triandra

0.64

Eriotheca sp.

Dialium spp.

0.8

Erisma uncinatum

Dillenia spp.

0.59

Erythrina sp.

Diospyros spp.

0.7

Eschweilera spp.

Diplodiscus paniculatus

0.63

Eucalyptus robusta

0.42, 0.48+

Garcinia punctata
Gilletiodendron
0.23
mildbraedii
Gossweilerodendron
0.71,0.79,0.95+
balsamiferum
0.51
Guarea thompsonii

Dipterocarpus caudatus

0.61

Eugenia stahlii

Dipterocarpus eurynchus

0.56

Euxylophora paraensis

0.73

Dipterocarpus gracilis

0.61

Fagara spp.

0.69

Dipterocarpus grandiflorus

0.62

Ficus sp.

0.32

0.68,0.70+

0.78”
0.87”
0.4
0.55”

Guibourtia spp.

0.72

Hannoa klaineana
Harunganamadagascariensis
Hexalobus crispiflorus

0.28”
0.45”
0.48”

Dipterocarpus kerrii
0.56
Genipa spp.
0.75
Holoptelea grandis
0.59”+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIAD

TROPICAL AMERICA

Dipterocarpus kunstlerii

0.57

Goupia glabra

0.67, 0.72+

TROPICAL AFRICAD

Homalium spp.

0.7

Dipterocarpus spp.

0.61

Guarea chalde

0.52

Hylodendron gabonense.

0.78”

Dipterocarpus warburgii

0.52

Dracontomelon spp.

0.5

Guarea spp.

0.52

Hymenostegia pellegrini

0.78”

Guatteria spp.

0.36

Irvingia grandifolia

Dryobalanops spp.

0.61

0.78”

Guazuma ulmifolia

0.52, 0.50+

Julbernardia globiflora

Dtypetes bordenii

0.78

0.75

Guettarda scabra

0.65

Khaya ivorensis

Durio spp.

0.44

0.53

Guillielma gasipae

0.95, 1.25+

Klainedoxa gabonensis

0.87

Dyera costulata

0.36

Gwtavia sp.

Dysoxylum quercifolium

0.49

Helicostylis tomentosa

Elaeocarpus serratus

0.40*

Hernandia Sonora

Emblica officinalis

0.8

Hevea brasiliense

Endiandra laxiflora

0.54

Himatanthus articulata

Endospermum spp.

0.38

Hirtella davisii

Enterolobium
cyclocarpum

0.35

Humiria balsamifera

Epicharis cumingiana

0.73

Humiriastrum procera

Erythrina subumbrans
Erythrophloeum
densiflorum
Eucalyptus citriodora

0.24

Hura crepitans

0.65

Hyeronima alchorneoides

0.64

Hyeronima laxiflora

Eucalyptus deglupta

0.34

Hymenaea davisii

Eugenia spp.

0.65

Hymenolobium sp.
Inga sp.

D

0.56

Lannea welwitschii

0.45”’

Lecomtedoxa klainenna

0.78”

0.29

Letestua durissima

0.87”

0.49

Lophira alata

0.87”

Lovoa trichilioides
Macarangakilimandscharica

0.45”

0.68, 0.72+

0.40,0.54+
0.74
0.66,0.67+

Maesopsis eminii

Malacantha sp. aff.
alnifolia
0.36, 0.37, 0.38+ Mammea africana
0.7

0.60,0.64+

0.40*
0.41
0.45”
0.62

Manilkara lacera

0.78”

0.59

Markhamia platycalyx

0.45*

0.67

Memecylon capitellatum
Microberlinia
brazzavillensis

0.77”

0.64

0.49,0.52,0.58,
Microcos coriaceus
0.64+
0.46
Milletia spp.

Fagraea spp.

0.73

Ficus benjamina

0.65

Iryanthera spp.

Ficus spp.

0.39

Jacaranda sp.

0.55

Ganua obovatifolia

0.59

Joannesia heveoides

0.39

0.7
0.42”
0.72

Garcinia myrtifolia

0.65

Lachmellea speciosa

0.73

Mitragyna stipulosa
Monopetalanthus
pellegrinii
Musanga cecropioides

Garcinia spp.

0.75

Laetia procera

0.68

Nauclea diderrichii

0.63

Gardenia turgida

0.64

Lecythis spp.

0.77

0.32”

Garuga pinnata

0.51

Licania spp.

0.78

0.63

Licaria spp.

0.82

Neopoutonia macrocalyx
Nesogordonia
papaverifera
Ochtocosmus africanus

Lindackeria sp.

0.41

Odyendea spp.

0.32

Linociera domingensis

0.81

Oldfieldia africana

0.78*
0.72

Gluta spp.
Gmelina arborea

0.41,0.45+

Gmelina vitiensis

0.54

0.47
0.47”
0.23

0.65
0.78’

Gonocaryum calleryanum

0.64

Lonchocarpus spp.

0.69

Ongokea gore

Gonystylus punctatus

0.57

Loxopterygium sagotii

0.56

Oxystigma oxyphyllum

0.53

Grewia tiliaefolia

0.68

Lucuma spp.

0.79

Pachyelasma tessmannii

0.70”

Hardwickia binata

0.73

Luehea spp.

0.5

Pachypodanthium staudtii

0.58”

Harpullia arborea

0.62

Lueheopsis duckeana

0.64

Paraberlinia bifoliolata

0.56”

Heritiera spp.

0.56

Mabea piriri

0.59

Parinari glabra

0.87”

Hevea brasiliensis

0.53

Machaerium spp.

0.7

Parkia bicolor

0.36”

Hibiscus tiliaceus

0.57

Macoubea guianensis

0.40*

Pausinystalia brachythyrsa

0.56”

Homalanthus populneus

0.38

Magnolia spp.

0.52

Pausinystalia cf. talbotii

0.56”

Homalium spp.

0.76

Maguira sclerophylla

0.57

Pentaclethra macrophylla

0.78”

Hopea acuminata

0.62

Mammea americana

0.62

Pentadesma butyracea

0.78”

Hopea spp.

0.64

Mangifera indica

0.55

Phyllanthus discoideus

0.76”

Intsia palembanica

0.68

Manilkara sp.

0.89

Pierreodendron africanum

0.70;”

Kayea garciae
0.53
Marila sp.
0.63
Piptadeniastrum africanum
0.56+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

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Annex 3A.1

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIA
Kingiodendron
alternifolium
Kleinhovia hospita
Knema spp.
Koompassia excelsa
Koordersiodendron
pinnatum
Kydia calycina
Lagerstroemia spp.
Lannea grandis
Leucaena leucocephala
Litchi chinensis ssp.
philippinensis
Lithocarpus soleriana

D

TROPICAL AMERICAD

TROPICAL AFRICA

D

0.48

Marmaroxylon racemosum

0.78*

Plagiostyles africana

0.70”

0.36
0.53
0.63

Matayba domingensis
Matisia hirta
Maytenus spp.

0.7
0.61
0.71

Poga oleosa
Polyalthia suaveolens
Premna angolensis

0.36
0.66”
0.63”

0.65, 0.69+

Mezilaurus lindaviana

0.68

Pteleopsis hylodendron

0.63*

0.72
0.55
0.5
0.64

Michropholis spp.
Minquartia guianensis
Mora sp.
Mouriria sideroxylon

0.610.76,0.79+
0.71
0.88

Pterocarpus soyauxii
Pterygota spp.
Pycnanthus angolensis
Randia cladantha

0.61
0.52
0.4
0.78*

0.88

Myrciaria floribunda

0.73

Rauwolfia macrophylla

0.47*

0.46

Ricinodendron heudelotii

0.2

0.74, 0.76, 0.78+ Saccoglottis gabonensis

0.74”

0.63

Myristica spp.

Litsea spp.

0.4

Myroxylon balsamum

Lophopetalum spp.

0.46

Nectandra spp.

0.52

Santiria trimera

0.53*

Macaranga denticulata

0.53

0.51

Sapium ellipticum

0.50*

Madhuca oblongifolia

0.53

0.64

Schrebera arborea

0.63*

Mallotus philippensis

0.64

O c o t e a spp.
Onychopetalum
amazonicum
Ormosia spp.

0.59

Sclorodophloeus zenkeri

0.68*
0.56

Mangifera spp.

0.52

Ouratea sp.

0.66

Scottellia coriacea

Maniltoa minor

0.76

Pachira acuatica

0.43

Scyphocephalium ochocoa

0.48

Mastixia philippinensis

0.47

Paratecoma peroba

0.6

Scytopetalum tieghemii

0.56”

Melanorrhea spp.

0.63

Parinari spp.

0.68

Sindoropsis letestui

0.56*

Melia dubia

0.4

Parkia spp.

0.39

Staudtia stipitata

0.75

Melicope triphylla

0.37

Peltogyne spp.

0.79

Stemonocoleus micranthus

0.56”

Meliosma macrophylla

0.27

Pentaclethra macroloba

Melochia umbellata

0.25Peru glabrata

Me&a ferrea

0.83,0.85+

Peru schomburgkiana

Metrosideros collina

0.70,0.76+

Persea spp.

Michelia spp.

0.43

Petitia domingensis

0.65,0.68+

Sterculia rhinopetala

0.64

0.65

Strephonema pseudocola

0.56*

0.59

Strombosiopsis tetrandra

0.63”

0.40, 0.47,0.52+ Swartzia fistuloides

0.82

0.66

Symphonia globulifera

0.58”
0.59*

Microcos stylocarpa

0.4

Pinus caribaea

0.51

Syzygium cordatum

Micromelum compressum

0.64

Pinus oocarpa

0.55

Terminalia superba

0.45

Milliusa velutina

0.63

Pinus patula

0.45

Tessmania africana

0.85”

Mimusops elengi

0.72*

Piptadenia sp.

0.58

Testulea gabonensis

0.6

Mitragyna parviflora

0.56

Piranhea longepedunculata

0.9

Tetraberlinia tubmaniana

0.60”

Myristica spp.

0.53

Tetrapleura tetraptera

0.50”

0.53

Piratinera guianensis
Pithecellobium guachapele(syn. Pseudosamea)
Platonia insignis
Platymiscium spp.
Podocarpus spp.
Pourouma aff. melinonii
Pouteria spp.
Prioria copaifera
Protium spp.
Pseudolmedia laevigata
Pterocarpus spp.
Pterogyne nitens
Qualea albiflora
Qualea cf. lancifolia
Qualea dinizii

0.96

Neesia spp.

0.56

Tieghemella heckelii

0.55”

Trema sp.
Trichilia prieureana
Trichoscypha arborea
Triplochiton scleroxylon.
Uapaca spp.
Vepris undulata
Vitex doniana
Xylopia staudtii

0.40*
0.63”
0.59”
0.32
0.6
0.70”
0.4
0.36*

Neonauclea bernardoi
Neotrewia cumingii
Ochna foxworthyi
Ochroma pyramidale
Octomeles sumatrana
Oroxylon indicum
Ougenia dalbergiodes
Palaquium spp.
Pangium edule
Parashorea malaanonan
Parashorea stellata
Paratrophis glabra
Parinari spp.

0.62
0.55
0.86
0.3
0.27, 0.32+
0.32
0.7
0.55
0.5
0.51
0.59
0.77
0.68

0.70’
0.71, 0.84+
0.46
0.32
0.64, 0.67+
0.40,0.41+
0.53,0.64+
0.64
0.44
0.66
0.5
0.58
0.58

+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIAD

TROPICAL AMERICAD

TROPICAL AFRICA

Parkia roxburghii
Payena spp.

0.34
0.55

Qualea spp.
Quararibaea guianensis

0.55
0.54

Peltophorum pterocarpum

0.62

Quercus alata

0.71

Pentace spp.

0.56

Quercus costaricensis

0.61

Phaeanthus ebracteolatus

0.56

Quercus eugeniaefolia

0.67

Phyllocladus hypophyllus

0.53

Quercus spp.

0.7

Pinus caribaea

0.48

Raputia sp.

0.55

Pinus insularis

0.47,0.48+

Rheedia spp.

0.72

Pinus merkusii

0.54

Rollinia spp.

0.36

Pisonia umbellifera

0.21

Saccoglottis cydonioides

Pittosporum pentandrum

0.51

Sapium ssp.

Planchonia spp.

0.59

Schinopsis spp.

1

Podocarpus spp.

0.43

Sclerobium spp.

0.47
0.52

D

0.72
0.47,0.72+

Polyalthia flava

0.51

Sickingia spp.

Polyscias nodosa

0.38

Simaba multiflora

0.51

Pometia spp.

0.54

Simarouba amara

0.32, 0.34,0.38+

Pouteria villamilii

0.47

Sloanea guianensis

0.79

Premna tomentosa

0.96

Spondias mombin

0.30, 0.40,0.41+

Pterocarpus marsupium

0.67

Sterculia spp.

0.55

Pterocymbium tinctorium

0.28

Stylogyne spp.

0.69

Pyge’um vulgare

0.57

Swartzia spp.

Quercus spp.

0.7

Swietenia macrophylla

Radermachera pinnata

0.51

Salmalia malabarica

0.32,0.33+

Samanea saman

0.45, 0.46+

Symphonia globulifera
Tabebuia spp. (lapachogroup)
Tabebuia spp. (roble)

0.95
0.42,0.45,0.46,
0.54+
0.68

Sandoricum vidalii

0.43

Tabebuia spp. (white cedar)

Sapindus saponaria

0.58

Tabebuia stenocalyx

Sapium luzontcum

0.4

Tachigalia myrmecophylla

0.56

Schleichera oleosa

0.96

Talisia sp.

0.84

Schrebera swietenoides

0.82

Tapirira guianensis

Semicarpus anacardium

0.64

Terminalia sp.

Serialbizia acle

0.57

Tetragastris altisima

Serianthes melanesica

0.48

Toluifera balsamum

0.74

Sesbania grandiflora
Shorea assamica forma
philippinensis
Shorea astylosa

0.4

Torrubia sp.

0.52

0.41

Toulicia pulvinata

0.63

0.73

Tovomita guianensis

0.6

0.91
0.52
0.57
0.55,0.57+

0.47*
0.50, 0.51,
0.58+
0.61

Shorea ciliata

0.75

Trattinickia sp.

0.38

Shorea contorta

0.44

Trichilia propingua

0.58

Shorea gisok

0.76

Trichosperma mexicanum

0.41

Shorea guiso

0.68

Triplaris spp.

0.56

Shorea hopeifolia

0.44

Trophis sp.

0.54

Shorea malibato

0.78

Vatairea spp.

Shorea negrosensis

0.44

Virola spp.

Shorea palosapis

0.39

Vismia spp.

Shorea plagata

0.7

Vitex spp.

0.6
0.40, 0.44,
0.48+
0.41
0.52,0.56,
0.57++ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

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Annex 3A.1

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIAD

TROPICAL AMERICA

D

Shorea polita

0.47

Vitex stahelii

Shorea polysperma

0.47

Vochysia spp.

Shorea robusta

0.72

Vouacapoua americana

0.6
0.40,0.47,
0.79+
0.79

Shorea spp. balau group
Shorea spp. dark red
meranti
Shorea spp. light red
meranti
Shorea spp. white meranti
Shorea spp. yellow
meranti
Shorea virescens

0.7

Warszewicsia coccinea

0.56

0.55

Xanthoxylum martinicensis

0.46

0.4

Xanthoxylum spp.

0.44

0.48

Xylopia frutescens

0 64”

TROPICAL AFRICA

D

0.46
0.42

Sloanea javanica

0.53

Soymida febrifuga

0.97

Spathodea campanulata

0.25

Stemonurus luzoniensis

0.37

Sterculia vitiensis
Stereospermum
suaveolens
Strombosia philippinensis

0.31

0.71

Strychnos potatorum

0.88

Swietenia macrophylla

0.49,0.53+

0.62

Swintonia foxworthyi

0.62

Swintonia spp.

0.61

Sycopsis dunni

0.63

Syzygium spp.

0.69, 0.76+

Tamarindus indica

0.75

Tectona grandis
Teijsmanniodendron
ahernianum
Terminalia citrina

0.50,0.55+

Terminalia copelandii

0.46

Terminalia foetidissima

0.55

Terminalia microcarpa

0.53

Terminalia nitens

0.58

0.9
0.71

Terminalia pterocarpa

0.48

Terminalia tomentosa

0.73,0.76, 0.77+

Ternstroemia megacarpa

0.53

Tetrameles nudiflora

0.3

Tetramerista glabra

0.61

Thespesia populnea

0.52

Toona calantas

0.29

Trema orientalis
0.31+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.9-2 (CONTINUED)
BASIC WOOD DENSITIES (D) OF STEMWOOD (tonnes dry matter/m3 fresh volume) FOR TROPICAL TREE SPECIES(To be used for D in Equations 3.2.3., 3.2.5, 3.2.7, 3.2.8)
TROPICAL ASIA

D

Trichospermum richii

0.32

Tristania spp.

0.80

Turpinia ovalifolia

0.36

Vateria indica

0.47*

Vatica spp.

0.69

Vitex spp.
Wallaceodendron
celebicum
Weinmannia luzoniensis

0.65

TROPICAL AMERICAD

TROPICAL AFRICA

D

0.55, 0.57+
0.49

Wrightia tinctorea

0.75

Xanthophyllum excelsum
Xanthostemon
verdugonianus
Xylia xylocarpa

0.63
1.04
0.73,0.81+

Zanthoxylum rhetsa

0.33

Zizyphus spp.

0.76

+ The wood densities specified pertain to more than one bibliographic source.
* Wood density value is derived from the regression equation in Reyes et al. (1992).
Source: Reyes, Gisel; Brown, Sandra; Chapman, Jonathan; Lugo, Ariel E. 1992. Wood densities of tropical tree species. Gen. Tech. Rep.
SO-88 New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station. 15pp.

TABLE 3A.1.10
DEFAULT VALUES OF BIOMASS EXPANSION FACTORS (BEFS)(BEF 2 to be used in connection with growing stock biomass data in Equation 3.2.3;
and BEF 1 to be used in connection with increment data in Equation 3.2.5)

Climatic zone
BorealTemperate

TropicalMinimum dbh(cm)

BEF2 (overbark)

BEF1 (overbark)

Forest type

to be used in connection to growing
stock biomass data (Equation 3.2.3)

to be used in connection toincrement data (Equation 3.2.5)

Conifers

0-8.0

1.35 (1.15-3.8)

1.15 (1-1.3)

Broadleaf
Conifers:
Spruce-fir
Pines

0-8.0

1.3 (1.15-4.2)

1.1 (1-1.3)

0-12.5

1.3 (1.15-4.2)

1.15 (1-1.3)

0-12.5

1.3 (1.15-3.4)

1.05 (1-1.2)

Broadleaf

0-12.5

1.4 (1.15-3.2)

1.2 (1.1-1.3)

Pines

10.0

1.3 (1.2-4.0)

1.2 (1.1-1.3)

Broadleaf

10.0

3.4 (2.0-9.0)

1.5 (1.3-1.7)

Note: BEF2s given here represent averages for average growing stock or age, the upper limit of the range represents young forests orforests with low growing stock; lower limits of the range approximate mature forests or those with high growing stock. The values apply
to growing stock biomass (dry weight) including bark and for given minimum diameter at breast height; Minimum top diameters andtreatment of branches is unspecified. Result is above-ground tree biomass.
Sources: Isaev et al., 1993; Brown, 1997; Brown and Schroeder, 1999; Schoene, 1999; ECE/FAO TBFRA, 2000; Lowe et al., 2000;
please also refer to FRA Working Paper 68 and 69 for average values for developing countries ( http://www.fao.org/forestry/index.jsp)

TABLE 3A.1.11
DEFAULT VALUES FOR FRACTION OUT OF TOTAL HARVEST LEFT TO DECAY IN THE FOREST, fBL(To be used only for fBL in Equation 3.2.7)
Region3.178

fBL

Boreal intensively managed

0.07

Temperate intensively managed

0.1

Temperate semi natural forests

0.15

Tropical plantation

0.25

Tropical selective logging in primary forests

0.4

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Annex 3A.1

TABLE 3A.1.12
COMBUSTION FACTOR VALUES (PROPORTION OF PREFIRE BIOMASS CONSUMED) FOR FIRESIN A RANGE OF VEGETATION TYPES.
(Values in column ‘mean’ are to be used for (1-fBL) in Equation 3.2.9 and for ρburned on site in Equation 3.3.10)
Vegetation TypePrimary Tropical
Forest (slash andburn)

Sub-category

Mean

SD

No. m1Range

No. r2

References

Primary tropical forest

0.32

0.12

14

0.20 – 0.62

17

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

Primary open tropical forest

0.45

0.09

3

0.36 – 0.54

3

21

Primary tropical moist forest

0.50

0.03

2

0.39 – 0.54

237, 73

Primary tropical dry forest

-

-

0

0.78 – 0.95

166

All primary tropical forests

0.36

0.13

19

0.19 – 0.95

23Young secondary tropicalforest (3-5 yrs)

0.46

-

1

0.43 – 0.52

161Intermediate secondary
tropical forest (6-10 yrs)

0.67

0.21

2

0.46 – 0.90

261, 35

Advanced secondary tropicalforest (14-17 yrs)

0.50

0.10

2

0.36 – 0.79

261, 73All secondary tropical forests

0.55

0.06

8

0.36 – 0.90

956, 66, 34, 30

All Tertiary tropical forest

0.59

-

1

0.47 – 0.88

266, 30

Secondary tropicalforest (slash andburn)

Boreal Forest

Wildfire (general)

0.40

0.06

2

0.36 – 0.45

233

Crown fire

0.43

0..21

3

0.18 – 0.76

666, 41, 64, 63

surface fire

0.15

0.08

3

0.05 – 0.73

364, 63

Post logging slash burn

0.33

0.13

4

0.20 – 0.58

4

49, 40, 18

Land clearing fire

0.59

-

1

0.50 – 0.70

1

67

0.34

0.17

15

0.05 – 0.76

1645, 47All Boreal Forest
Wildfire
Eucalyptus forests

-

-

0

-

0

Prescribed fire – (surface)

0.61

0.11

6

0.50 – 0.77*

672, 54, 60, 9

Post logging slash burn

0.68

0.14

5

0.49 – 0.82

5

25, 58, 46

Felled and burned (landclearing fire)

0.49

-

1

-

1

62

0.63

0.13

12

0.49 – 0.82

12

Post logging slash burn

0.62

0.12

7

0.48 – 0.84

755, 19, 27, 14

Felled and burned (landclearing fire)

0.51

-

1

0.16 – 0.58

353, 24, 71

0.45

0.16

19

0.16 – 0.84

1753, 56All Eucalyptus Forests
Other temperate
forests

All “other” temperate forests
Shrubland (general)

0.95

-

1

-

1

44

Calluna heath

0.71

0.30

4

0.27 – 0.98

426, 56, 39

Fynbos

0.61

0.16

2

0.50 – 0.87

270, 44

0.72

0.25

7

0.27 – 0.98

7Savanna woodland@

0.22

-

1

0.01 – 0.47

128

Savanna parkland

0.73

-

1

0.44 – 0.87

157

Other savanna woodlands

0.37

0.19

4

0.14 – 0.63

4

22, 29All savanna woodlands (early dry season burns)

0.40

0.22

6

0.01 – 0.87

6

0.72

-

1

0.71 – 0.88

2

Shrublands

All Shrublands
Savanna Woodlands(early dry season
burns)*

Savanna woodland @Savanna Woodlands(mid/late dry season
burns)*

66, 57

Savanna parkland

0.82

0.07

6

0.49 – 0.96

6

57, 6, 51

Tropical savanna#

0.73

0.04

3

0.63 – 0.94

552, 73, 66, 12

Other savanna woodlands

0.68

0.19

7

0.38 – 0.96

722, 29, 44, 31, 57

0.74

0.14

17

0.29 – 0.96

20

All savanna woodlands (mid/late dry season burns)*
1

No. m = the number of observations for the mean
No. r = the number of observations for the range* Surface layer combustion only, # campo cerrado, cerrado sensu stricto, $ campo sujo, campo limpo, dambo, @ miombo~
derived from slashed tropical forest (includes unburned woody material)
2

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3.179

Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.1.12 (CONTINUED)COMBUSTION FACTOR VALUES (PROPORTION OF PREFIRE BIOMASS CONSUMED) FOR FIRESIN A RANGE OF VEGETATION TYPES.
(Values in column ‘mean’ are to be used for (1-fBL) in Equation 3.2.9 and for ρburned on site in Equation 3.3.10)
Vegetation Type

Sub-category

Mean

SD

No.m1

Range

No.r2

Savanna Grasslands /Pastures (early dry
season burns)*

Tropical/sub-tropicalgrassland$

0.74

-

1

0.44 – 0.98

1

28

-

-

0

0.18 – 0.78

148

Grassland

All savanna grasslands (early dry season burns)*

Savanna Grasslands /Pastures (mid/latedry season burns)*

0.74

-

1

0.18 – 0.98

2

Tropical/sub-tropicalgrassland$

0.92

0.11

7

0.71 – 1.00

8

44, 73, 66, 12, 57

Tropical pasture~

0.35

0.21

6

0.19 – 0.81

74, 23, 38, 66

Savanna

0.86

0.12

16

0.44 – 1.00

23

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

0.77

0.26

29

0.19 – 1.00

38

Peatland

0.50

-

1

0.50 – 0.68

2

20, 44Tropical Wetlands

0.70

-

1

-

1

44

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

References

1

No. m = the number of observations for the mean
2
No. r = the number of observations for the range* Surface layer combustion only, # campo cerrado, cerrado sensu stricto, $ campo sujo, campo limpo, dambo, @ miombo~
derived from slashed tropical forest (includes unburned woody material)

TABLE 3A.1.13
BIOMASS CONSUMPTION (t/ha) VALUES FOR FIRES IN A RANGE OF VEGETATION TYPES(To be used in Equation 3.2.9. for the part of the equation: ‘BW • (1- fBL)’ , i.e., an absolute amount)
Sub-category

Mean

SE

No. m1Range

No. r 2

References

Primary tropical forest

83.9

25.8

6

10 – 22897, 15, 66, 3, 16,
17, 45

Primary open tropical forest

163.6

52.1

3

109.9 – 214

3

21,

Primary tropical moist forest

160.4

11.8

2

115.7 – 216.6

237, 73
66

Vegetation TypePrimary Tropical
Forest (slash andburn)Primary tropical dry forest

-

-

0

57 – 70

1

119.6

50.7

11

10 – 22815

Young secondary tropicalforest (3-5 yrs)

8.1

-

1

7.2 – 9.4

1

61

Intermediate
secondarytropical forest (6-10 yrs)

41.1

27.4

2

18.8 – 66

2

61, 35

Advanced secondary tropicalforest (14-17 yrs)

46.4

8.0

2

29.1 – 63.2

2

61, 73All secondary tropical forests

42.2

23.6

5

7.2 – 93.6

566, 30All Tertiary tropical forest

54.1

-

1

4.5 – 532

66, 30Wildfire (general)

52.8

48.4

6

18 – 149

6

2, 33, 66

Crown fire

25.1

7.9

10

15 – 43

10

11, 43, 66, 41, 63,
64

Surface fire

21.6

25.1

12

1.0 – 148

13

43, 69, 66, 63, 64,1

Post logging slash burn

69.6

44.8

7

7 – 202949, 40, 66, 18

Land clearing fire

87.5

35.0

3

48 – 136

3

10, 67

41.0

36.5

44

1.0 – 20249

43, 45, 69, 47

Wildfire

53.0

53.6

8

20 – 1798

66, 32, 9

Prescribed fire – (surface)

16.0

13.7

8

4.2 – 178

66, 72, 54, 60, 9

Post logging slash burn

168.4

168.8

5

34 – 453

5

25, 58, 46

Felled and burned (landclearing fire)

132.6

-

1

50 – 133

2

62, 9

69.4

100.8

22

4.2 – 453

23

All primary tropical forests

Secondary tropicalforest (slash andburn)

Boreal Forest

All Boreal Forest

Eucalypt forests

All Eucalypt Forests

3.180

IPCC Good Practice Guidance for LULUCF

Annex 3A.1

TABLE 3A.1.13 (CONTINUED)
BIOMASS CONSUMPTION (t/ha) VALUES FOR FIRES IN A RANGE OF VEGETATION TYPES(To be used in Equation 3.2.9. for the part of the equation: ‘BW • (1- fBL)’ , i.e., an absolute amount)
Mean

SE

No. m1Range

No. r 2

References

Wildfire

19.8

6.3

4

11 - 25

4

32, 66

Post logging slash burn

77.5

65.0

7

15 – 220855, 19, 14, 27, 66

Felled and burned (landclearing fire)

48.4

62.7

2

3 – 1303

53, 24, 71

Vegetation Type

Other temperate
forests

Sub-category

50.4

53.7

15

3 – 22018

43, 56

Shrubland (general)

26.7

4.2

3

22 – 30

3

43

Calluna heath

11.5

4.3

3

6.5 – 213

26, 39Sagebrush

5.7

3.8

3

1.1 – 18

4

66

Fynbos

12.9

0.1

2

5.9 – 23

2

70, 66

14.3

9.0

11

1.1 – 30

12Savanna woodland@

2.5

-

1

0.1 – 5.3

1

Savanna parkland

2.7

-

1

1.4 – 3.9

1

All “other” temperate forests

Shrublands

All Shrublands
Savanna Woodlands(early dry season
burns)*All savanna woodlands (early dry season burns)
Savanna Woodlands(mid/late dry season
burns)*

2.6

0.1

2

0.07 – 3.9

2

3.3

-

1

3.2 – 3.3

1

Savanna parkland

4.0

1.1

6

1 – 10.6

6

57, 6, 51

Tropical savanna#

6

1.8

2

3.7 – 8.4

252, 73

Other savanna woodlands

5.3

1.7

3

3.7 – 7.6

359, 57, 31

4.6

1.5

12

1.0 – 10.6

12

Tropical/sub-tropicalgrassland$

2.1

-

1

1.4 – 3.1

1

Grassland---

1.2 – 111

All savanna grasslands (early dry season burns)*

Savanna Grasslands /Pastures (mid/latedry season burns)*

57

28
48

2.1

-

1

1.2 – 112

Tropical/sub-tropicalgrassland$

5.2

1.7

6

2.5 – 7.1

6

Grassland

4.1

3.1

6

1.5 – 10

6

43, 9

Tropical pasture

23.7

11.8

6

4.7 – 4574, 23, 38, 66

Savanna

7.0

2.7

6

0.5 – 1810

42, 50, 6, 45, 13,
65

10.0

10.1

24

0.5 – 45

29

Peatland

41

1.4

2

40 – 422

68, 33Tundra

10

-

1

-

-

33

~

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

57

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

28

1

No. m = the number of observations for the mean

2

No. r = the number of observations for the range

9, 73, 12, 57* Surface layer combustion only, # campo cerrado, cerrado sensu stricto, $ campo sujo, campo limpo, dambo,@

miombo~ derived from slashed tropical forest (includes unburned woody material)

References to Tables 3A.1.12 and 3A.1.13
1.

Alexander, M., Calculating and interpreting forest fire intensities. CANADIAN JOURNAL OF BOTANY, 1978. 60: p. 349-357.

2.

Amiro, B., J. Todd, and B. Wotton, Direct carbon emissions from Canadian forest fires, 1959-1999. CANADIAN JOURNAL OF
FOREST RESEARCH, 2001. 31: p. 512-525.

3.

Araújo, T., J. Carvalho, N. Higuchi, A. Brasil, and A. Mesquita, A tropical rainforest clearing experiment by biomass burning in thestate of Pará, Brazil. ATMOSPHERIC ENVIRONMENT, 1999. 33: p. 1991-1998.

4.

Barbosa, R. and P. Fearnside, Pasture burning in Amazonia: Dynamics of residual biomass and the storage and release ofaboveground carbon. JOURNAL OF GEOPHYSICAL RESEARCH, 1996. 101(D20): p. 25847-25857.

5.

Bilbao, B. and E. Medina, Types of grassland fires and nitrogen volatilization in tropical savannas of calabozo, in Biomass Burning
and Global Change: Volume 2. Biomass burning in South America, Southeast Asia, and temperate and boreal ecosystems, and the oilfires of Kuwait, J. Levine, Editor. 1996, MIT Press: Cambridge. p. 569-574.

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Chapter 3: LUCF Sector Good Practice Guidance

6.

Cachier, H., C. Liousse, M. Pertusiot, A. Gaudichet, F. Echalar, and J. Lacaux, African fire Particulate emissions and atmosphericinfluence, in Biomass Burning and Global Change: Volume 1. Remote Sensing, Modeling and Inventory Development, and Biomass
Burning in Africa, J. Levine, Editor. 1996, MIT Press: Cambridge. p. 428-440.

7.

Carvalho, J., N. Higuchi, T. Araujo, and J. Santos, Combustion completeness in a rainforest clearing experiment in Manaus, Brazil.
JOURNAL OF GEOPHYSICAL RESEARCH, 1998. 103(D11): p. 13195.

8.

Carvalho, J., F. Costa, C. Veras, et al., Biomass fire consumption and carbon release rates of rainforest-clearing experiments
conducted in northern Mato Grosso, Brazil. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2001. 106(D16): p.
17877-17887.

9.

Cheyney, N., R. Raison, and P. Khana, Release of carbon to the atmosphere in Australian vegetation fires, in Carbon Dioxide andClimate: Australian Research, G. Pearman, Editor. 1980, Australian Academy of Science: Canberra. p. 153-158.

10. Cofer, W., J. Levine, E. Winstead, and B. Stocks, Gaseous emissions from Canadian boreal forest fires. ATMOSPHERICENVIRONMENT, 1990. 24A(7): p. 1653-1659.
11. Cofer, W., E. Winstead, B. Stocks, J. Goldammer, and D. Cahoon, Crown fire emissions of CO2, CO, H2, CH4, and TNMHC from adense jack pine boreal forest fire. GEOPHYSICAL RESEARCH LETTERS, 1998. 25(21): p. 3919-3922.
12. De Castro, E.A. and J.B. Kauffman, Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root
mass and consumption by fire. Journal of Tropical Ecology, 1998. 14(3): p. 263-283.
13. Delmas, R., On the emission of carbon, nitrogen and sulfur in the atmosphere during bushfires in intertropical savannah zones.
GEOPHYSICAL RESEARCH LETTERS, 1982. 9(7): p. 761-764.
14. Einfeld, W., D. Ward, and C. Hardy, Effects of fire behaviour on prescribed fire smoke characteristics: A case study, in Global
Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, J. Levine, Editor. 1991, MIT Press: Massechusetts. p. 412-419.
15. Fearnside, P., N. Filho, and F. Fernandes, Rainforest burning and the global carbon budget: biomass, combustion efficiency andcharcoal formation in the Brazilian Amazon. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 1993. 98(D9): p.
16733-16743.
16. Fearnside, P., P. Graca, N. Filho, J. Rodrigues, and J. Robinson, Tropical forest burning in Brazilian Amazonia: measurement ofbiomass loading, burning efficiency and charcoal formation at Altamira, Para. FOREST ECOLOGY AND MANAGEMENT, 1999.
123: p. 65-79.
17. Fearnside, P., P. Graca, and J. Rodrigues, Burning of Amazonian rainforests: burning efficiency and charcoal formation in forest
cleared for cattle pasture near Manaus, Brazil. FOREST ECOLOGY AND MANAGEMENT, 2001. 146: p. 115-128.
18. Feller, M. The influence of fire severity, not fire intensity, on understory vegetation biomass in British Columbia. in 13th Fire andForest Meteorology Conference. 1998. Lorne, Australia: IAWF.
19. Flinn, D., P. Hopmans, P. Farell, and J. James, Nutrient loss from the burning of Pinus radiata logging residue. AUSTRALIANFOREST RESEARCH, 1979. 9: p. 17-23.
20. Garnett, M., P. Ineson, and A. Stevenson, Effects of burning and grazing on carbon sequestration in a Pennine blanket bog, UK.
HOLOCENE, 2000. 10(6): p. 729-736.
21. Graca, P., P. Fearnside, and C. Cerri, Burning of Amazonian forest in Ariquemes, Rondonia, Brazil: biomass, charcoal formation andburning efficiency. FOREST ECOLOGY AND MANAGEMENT, 1999. 120: p. 179-191.
22. Griffin, G. and M. Friedel, Effects of fire on central Australian rangelands. I Fire and fuel characteristics and changes in herbage andnutrients. AUSTRALIAN JOURNAL OF ECOLOGY, 1984. 9: p. 381-393.
23. Guild, L., J. Kauffman, L. Ellingson, and D. Cummings, Dynamics associated with total aboveground biomass, C, nutrient pools, andbiomass burning of primary forest and pasture in Rondonia, Brazil during SCAR-B. JOURNAL OF GEOPHYSICAL RESEARCHATMOSPHERES, 1998. 103(D24): p. 32091-32100.
24. Gupta, P., V. Prasad, C. Sharma, A. Sarkar, Y. Kant, K. Badarinath, and A. Mitra, CH4 emissions from biomass burning of shifting
cultivation areas of tropical deciduous forests - experimental results from ground - based measurements. CHEMOSPHERE GLOBAL CHANGE SCIENCE, 2001. 3: p. 133-143.
25. Harwood, C. and W. Jackson, Atmospheric losses of four plant nutrients during a forest fire. AUSTRALIAN FORESTRY, 1975. 38(2):p. 92-99.
26. Hobbs, P. and C. Gimingham, Studies on fire in Scottish heathland communities. JOURNAL OF ECOLOGY, 1984. 72: p. 223-240.
27. Hobbs, P., J. Reid, J. Herring, et al., Particle and trace-gas measurements from prescribed burns of forest products in the PacificNorthwest, in Biomass Burning and Global Change: Volume 2. Biomass burning in South America, Southeast Asia, and temperate andboreal ecosystems, and the oil fires of Kuwait, J. Levine, Editor. 1996, MIT Press: Cambridge. p. 697-715.
28. Hoffa, E., D. Ward, W. Hao, R. Susott, and R. Wakimoto, Seasonality of carbon emissions from biomass burning in a Zambiansavanna. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 1999. 104(D11): p. 13841-13853.
29. Hopkins, B., Observations on savanna burning in the Olokemeji forest reserve, Nigeria. JOURNAL OF APPLIED ECOLOGY, 1965.
2(2): p. 367-381.
30. Hughes, R., J. Kauffman, and D. Cummings, Fire in the Brazilian Amazon 3. Dynamics of biomass, C, and nutrient pools inregenerating forests. OECOLOGIA, 2000. 124(4): p. 574-588.
31. Hurst, D., W. Griffith, and G. Cook, Trace gas emissions from biomass burning in tropical Australian savannas. JOURNAL OF
GEOPHYSICAL RESEARCH, 1994. 99(D8): p. 16441-16456.
32. Jackson, W., Nutrient stocks in Tasmanian vegetation and approximate losses due to fire. Papers and proceedings of the Royal Societyof Tasmania, 2000. 134: p. 1-18.

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Annex 3A.1

33. Kasischke, E., N. French, L. Bourgeau-Chavez, and N. Christensen, Estimating release of carbon from 1990 and 1991 forest fires in
Alaska. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 1995. 100(D2): p. 2941-2951.
34. Kauffman, J. and C. Uhl, 8 interactions of anthropogenic activities, fire, and rain forests in the Amazon Basin, in Fire in the Tropical
Biota: Ecosystem Processes and Global Changes, J. Goldammer, Editor. 1990, Springer-Verlag: Berlin. p. 117-134.
35. Kauffman, J., R. Sanford, D. Cummings, I. Salcedo, and E. Sampaio, Biomass and nutrient dynamics associated with slash fires inneotropical dry forests. ECOLOGY, 1993. 74(1): p. 140-151.
36. Kauffman, J., D. Cummings, and D. Ward, Relationships of fire, biomass and nutrient dynamics along a vegetation gradient in theBrazilian cerrado. JOURNAL OF ECOLOGY, 1994. 82: p. 519-531.
37. Kauffman, J., D. Cummings, D. Ward, and R. Babbitt, Fire in the Brazilian Amazon: 1. Biomass, nutrient pools, and losses in slashedprimary forests. OECOLOGIA, 1995. 104: p. 397-408.
38. Kauffman, J., D. Cummings, and D. Ward, Fire in the Brazilian Amazon: 2. Biomass, nutrient pools and losses in cattle pastures.
OECOLOGIA, 1998. 113: p. 415-427.
39. Kayll, A., Some characteristics of heath fires in north-east Scotland. JOURNAL OF APPLIED ECOLOGY, 1966. 3(1): p. 29-40.
40. Kiil, A., Fuel consumption by a prescribed burn in spruce-fir logging slash in Alberta. THE FORESTRY CHRONICLE, 1969: p. 100102.
41. Kiil, A., Fire spread in a black spruce stand. CANADIAN FORESTRY SERVICE BI-MONTHLY RESEARCH NOTES, 1975. 31(1):p. 2-3.
42. Lacaux, J., H. Cachier, and R. Delmas, Biomass burning in Africa: an overview of its impact on atmospheric chemistry, in Fire in theEnvironment: The Ecological, Atmospheric, and Climatic Importance of Vegetation Fires, P. Crutzen and J. Goldammer, Editors. 1993,
John Wiley & Sons: Chichester. p. 159-191.
43. Lavoue, D., C. Liousse, H. Cachier, B. Stocks, and J. Goldammer, Modeling of carbonaceous particles emitted by boreal andtemperate wildfires at northern latitudes. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2000. 105(D22): p. 2687126890.
44. Levine, J., Global biomass burning: a case study of the gaseous and particulate emissions released to the atmosphere during the 1997
fires in Kalimantan and Sumatra, Indonesia, in Biomass Burning and its Inter-relationships with the Climate System, J. Innes, M.
Beniston, and M. Verstraete, Editors. 2000, Kluwer Academic Publishers: Dordrecht. p. 15-31.
45. Levine, J. and W. Cofer, Boreal forest fire emissions and the chemistry of the atmosphere, in Fire, Climate Change and Carbon
Cycling in the Boreal Forest, E. Kasischke and B. Stocks, Editors. 2000, Springer-Verlag: New York. p. 31-48.
46. Marsdon-Smedley, J. and A. Slijepcevic, Fuel characteristics and low intensity burning inEucalyptus obliqua wet forest at the Warra
LTER site. TASFORESTS, 2001. 13(2): p. 261-279.
47. Mazurek, M., W. Cofer, and J. Levine, Carbonaceous aerosols from prescribed burning of a boreal forest ecosystem, in Global
Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, J. Levine, Editor. 1991, MIT Press: Massechusetts. p. 258-263.
48. McNaughton, S., N. Stronach, and N. Georgiadis, Combustion in natural fires and global emissions budgets. ECOLOGICALAPPLICATIONS, 1998. 8(2): p. 464-468.
49. McRae, D. and B. Stocks. Large-scale convection burning in Ontario. in Ninth Conference on Fire and Forest Metearology. 1987. San
Diego, California: American Meterological Society.
50. Moula, M., J. Brustet, H. Eva, J. Lacaux, J. Gregoire, and J. Fontan, Contribution of the Spread-Fire Model in the study of savanna
fires, in Biomass Burning and Global Change: Volume 1. Remote Sensing, Modeling and Inventory Development, and Biomass
Burning in Africa, J. Levine, Editor. 1996, MIT Press: Cambridge. p. 270-277.
51. Neil, R., N. Stronach, and S. McNaughton, Grassland fire dynamics in the Serengeti ecosystem, and a potential method ofretrospectively estimating fire energy. JOURNAL OF APPLIED ECOLOGY, 1989. 26: p. 1025-1033.
52. Pivello, V. and L. Coutinho, Transfer of macro-nutrients to the atmosphere during experimental burnings in an open cerrado(Brazilian savanna). JOURNAL OF TROPICAL ECOLOGY, 1992. 8: p. 487-497.
53. Prasad, V., Y. Kant, P. Gupta, C. Sharma, A. Mitra, and K. Badarinath, Biomass and combustion characteristics of secondary mixed
deciduous forests in Eastern Ghats of India. ATMOSPHERIC ENVIRONMENT, 2001. 35(18): p. 3085-3095.
54. Raison, R., P. Khana, and P. Woods, Transfer of elements to the atmosphere during low intensity prescribed fires in three Australiansubalpine eucalypt forests. CANADIAN JOURNAL OF FOREST RESEARCH, 1985. 15: p. 657-664.
55. Robertson, K., Loss of organic matter and carbon during slash burns in New Zealand exotic forests. NEW ZEALAND JOURNAL OF
FORESTRY SCIENCE, 1998. 28(2): p. 221-241.
56. Robinson, J., On uncertainty in the computation of global emissions from biomass burning. CLIMATIC CHANGE, 1989. 14: p. 243262.
57. Shea, R., B. Shea, J. Kauffman, D. Ward, C. Haskins, and M. Scholes, Fuel biomass and combustion factors associated with fires insavanna ecosystems of South Africa and Zambia. JOURNAL OF GEOPHYSICAL RESEARCH, 1996. 101(D19): p. 23551-23568.
58. Slijepcevic, A., Loss of carbon during controlled regeneration burns in Eucalyptus obliqua forest. TASFORESTS, 2001. 13(2): p. 281289.
59. Smith, D. and T. James, Characteristics of prescribed burns andresultant short-term environmental changes in Populus tremuloides
woodland in southern Ontario. CANADIAN JOURNAL OF BOTANY, 1978. 56: p. 1782-1791.
60. Soares, R. and G. Ribeiro. Fire behaviour and tree stumps sprouting in Eucalyptus prescribed burnings in southern Brazil. in III
International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology. 1998. Luso.

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61. Sorrensen, C., Linking smallholder land use and fire activity: examining biomass burning in the Brazilian Lower Amazon. FOREST
ECOLOGY AND MANAGEMENT, 2000. 128(1-2): p. 11-25.
62. Stewart, H. and D. Flinn, Nutrient losses from broadcast burning of Eucalyptus debris in north-east Victoria. AUSTRALIAN FORESTRESEARCH, 1985. 15: p. 321-332.
63. Stocks, B., Fire behaviour in immature jack pine. CANADIAN JOURNAL OF FOREST RESEARCH, 1987. 17: p. 80-86.
64. Stocks, B., Fire behaviour in mature jack pine. CANADIAN JOURNAL OF FOREST RESEARCH, 1989. 19: p. 783-790.
65. Stocks, B., B. van Wilgen, W. Trollope, D. McRae, J. Mason, F. Weirich, and A. Potgieter, Fuels and fire behaviour dynamics onlarge-scale savanna fires in Kruger National Park, South Africa. JOURNAL OF GEOPHYSICAL RESEARCH, 1996. 101(D19): p.
23541-23550.
66. Stocks, B. and J. Kauffman, Biomass consumption and behaviour of wildland fires in boreal, temperate, and tropical ecosystems:
parameters necessary to interpret historic fire regimes and future fire scenarios, in Sediment Records of Biomass Burning and Global
Change, J. Clark, et al., Editors. 1997, Springer-Verlag: Berlin. p. 169-188.
67. Susott, R., D. Ward, R. Babbitt, and D. Latham, The measurement of trace emissions and combustion characteristics for a mass fire, in
Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications, J. Levine, Editor. 1991, MIT Press: Massechusetts. p.
245-257.
68. Turetsky, M. and R. Wieder, A direct approach to quantifying organic matter lost as a result of peatland wildfire. CANADIANJOURNAL OF FOREST RESEARCH, 2001. 31(2): p. 363-366.
69. Van Wagner, C., Duff consumption by fire in eastern pine stands. CANADIAN JOURNAL OF FOREST RESEARCH, 1972. 2: p. 3439.
70. van Wilgen, B., D. Le Maitre, and F. Kruger, Fire behaviour in South African fynbos (macchia) vegetation and predictions from
Rothermel's fire model. JOURNAL OF APPLIED ECOLOGY, 1985. 22: p. 207-216.
71. Vose, J. and W. Swank, Site preparation burning to improve southern Appalachian pine-hardwood stands: aboveground biomass,forest floor mass, and nitrogen and carbon pools. CANADIAN JOURNAL OF FOREST RESEARCH, 1993. 23: p. 2255-2262.
72. Walker, J., Fuel dynamics in Australian vegetation, in Fire and the Australian Biota, A. Gill, R. Groves, and I. Noble, Editors. 1981,
Australian Academy of Science: Canberra. p. 101-127.
73. Ward, D., R. Susott, J. Kauffman, et al., Smoke and fire characteristics for Cerrado and deforestation burns in Brazil: BASE-B
Experiment. JOURNAL OF GEOPHYSICAL RESEARCH, 1992. 97(D13): p. 14601-14619.

TABLE 3A.1.14
COMBUSTION EFFICIENCY (PROPORTION OF AVAILABLE FUEL ACTUALLY BURNT) RELEVANT TO LAND-CLEARING BURNS,
AND BURNS IN HEAVY LOGGING SLASH FOR A RANGE OF VEGETATION TYPES AND BURNING CONDITIONS(To be used in sections ‘forest lands converted to cropland’, ‘converted to grassland’, or ‘converted to settlements or other lands’)
Burn type and drying time (Months)
Forest Types
BroadcastWindrow
Windrow+Stoking<6>60.15-0.3

~0.30

<6

>6

<6

>6

-

0.8

-

~0.95

Tropical moist- primary a- secondary

b

0.40

Tropical dry- Mixed species c- Acacia>0.9dTemperate Eucalyptus e

Boreal forest f

0.3

0.5-0.6

0.25

Note: The combustion efficiency or fraction of biomass combusted, is a critical number in the calculation of emissions, that is highlyvariable depending on fuel arrangement (e.g. broadcast v heaped), vegetation type affecting the (size of fuel components andflammability) and burning conditions (especially fuel moisture).
Sources: aFearnside (1990), Wei Min Hao et. al (1990); bWei Min Hao et. al (1990); cKauffmann and Uhl; et. al (1990); dWilliams et. al(1970), Cheney (pers. comm. 2002); e McArthur (1969), Harwood & Jackson (1975), Slijepcevic (2001), Stewart & Flinn (1985); andfFrench et. al (2000)

3.184

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Annex 3A.1

TABLE 3A.1.15
EMISSION RATIOS FOR OPEN BURNING OF CLEARED FORESTS(To be applied to Equation 3.2.19)
CompoundEmission Ratios

CH40.012 (0.009-0.015)aCO

0.06 (0.04-0.08)b

N2O

0.007 (0.005-0.009)c
0.121 (0.094-0.148)c

NOx
a

b

Source: Delmas, 1993, Lacaux et al., 1993, and Crutzen and Andreae, 1990. Note: Ratios for carbon
compounds, i.e. CH4 and CO, are mass of carbon compound released (in units of C) relative to mass of
total carbon released from burning. Those for the nitrogen compounds are expressed as the ratios ofemission (in units of N) relative to total nitrogen released from the fuel.

TABLE 3A.1.16
EMISSION FACTORS (G/KG DRY MATTER COMBUSTED)APPLICABLE TO FUELS COMBUSTED IN VARIOUS TYPES OF VEGETATION FIRES(To be used in connection with Equation 3.2.20)
Moist/infertile broadleaved savanna
Arid fertile fineleaved savanna
Moist- infertile
grassland
Arid-fertile grassland
Wetland
All vegetation types l

CO2

CO

CH4

NOx

N2O*

NMHC 2

1 523

92

3

6

0.11

-

Scholes (1995)

1 524

73

2

5

0.11

-

Scholes (1995)

1 498

59

2

4

0.10

-

Scholes (1995)

1 540

97

3

7

0.11

-

Scholes (1995)

Source

1 554

58

2

4

0.11

-

Scholes (1995)

1 403 -1 503

67-1204-7

0.5-0.8

0.10-

IPCC (1994)

Forest fires

1 531

112

7.1

0.6-0.8

0.11

8-12

Savanna fires

1 612

152

10.8

-

0.11

-

Ward et al. (1992)

Kaufman et al. (1992)

Forest fires

1 580

130

9

0.7

0.11

10

Delmas et al. (1995)

Savanna fires

1 640

65

2.4

3.1

0.15

3.1

Delmas et al. (1995)lAssuming 41-45% C content, 85-100% combustion completeness.
NMHC non methane hydrocarbons.
* Calculated from data of Crutzen and Andreae (1990) assuming an N/C ratio of 0.01, except for savanna fires.

2

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Reporting Tables and Worksheets

Annex 3A.2 Reporting Tables and Worksheets
All users should report their inventory information in the format prescribed by the Reporting Tables. Users are,of course, required to fill in only those cells in the tables that relate to the gases and source/sink categories they
have estimated and included in their inventory.
Equations to estimate emissions and removals of CO2 and non-CO2 greenhouse gases, from different land-use
categories in Chapter 3 (LUCF Sector Good Practice Guidance), are translated into different Worksheets.
Resulting estimates of emissions and removals in the Worksheets are compiled into the Compilation Worksheets
and finally to the Reporting Tables. The Reporting Tables were designed, using the same format as in the IPCC
Guidelines wherever possible.
Worksheets are presented in modules, and each module corresponds to specific land-use category (see Box
3A.2.1). A module is divided into two sub-modules to distinguish between those lands that remain in the sameland-use category and those lands converted to other land-use categories. Each sub-module consists ofworksheets which are mainly grouped into four: living biomass worksheets; dead organic matter worksheets;
soils worksheets (which are further sub-grouped into mineral soils and organic soils); and non-CO2 greenhousegas emissions worksheets. The worksheets are largely based on Tier 1 methods, but they are supplemented withhigher tier methods where appropriate. Symbols of the variables or parameters used in the equations in the main
text are included in the Worksheets to facilitate their use. Note that the Worksheets also cover sources and landuse categories for which reporting is optional.
BOX 3A.2.1 STRUCTURE OF THE WORKSHEETS (EXAMPLE GIVEN FOR FOREST LAND)

Module: Forest Land
Sub-module: Forest Land Remaining Forest Land
Worksheets:- FL-1a (FL for Forest Land; 1 for Forest Land Remaining Forest Land; 2 will mean Land
Converted to Forest Land; and “a” for biomass)- FL-1b (“b” for dead organic matter (DOM))- FL-1c1 (“c” for soils (SOM) which is further subdivided into c1 for mineral soil, c2 for organicsoil, etc)
- FL-1d (”d” for non-CO2 greenhouse gases)
Two sets of Compilation Worksheets are provided to compile separately the CO2 emissions and removals andthe non-CO2 greenhouse gas emissions. The tables are designed to compile emissions and removals by land-use
category and by carbon pool (i.e. living biomass, dead organic matter, and soils). In case of non-CO2 gasemissions, carbon pools are grouped into biomass and soils.
The Reporting Tables come in two types. The first type of table is used for reporting emissions and removals of
CO2 and non-CO2 greenhouse gases from all land-use categories, including emissions and removals from land
converted to any other land-use category. The second type of table is a subset of this, and designed to report,
using the information from first table, emissions and removals of CO2 and non-CO2 greenhouse gases due toconversion of Forest Land and Grassland to any other land-use categories.
When compiling emissions and sinks estimates from land use, land-use change and forestry with other elementsof national greenhouse gas inventories, signs (+/-) must be used consistently. In Reporting Tables, emissions(decrease in the carbon stock, non-CO2 greenhouse gas emissions) are always positive (+) and removals(increase in the carbon stock) are negative (-). For calculating initial estimates, the convention used in Chapter 5of the IPCC Guidelines in which net increases of carbon stocks are positive (+) and net decreases are negative (-)
is used also here. As in the IPCC Guidelines, the signs of these values need to be converted in the final reportingtables in order to maintain consistency with other sections of national inventory reports.
Units - CO2 emissions/removals and emissions of non-CO2 greenhouse gases are reported in gigagrams (Gg) in
the Reporting Tables. To convert tonnes C to Gg CO2, multiply the value by 44/12 and then by 10-3. To convert
kg N2O-N to Gg N2O, multiply the value by 44/28 and then by 10-6.
Convention - For the purpose of reporting, which is consistent with the IPCC Guidelines, the signs are always(+) for emissions and (-) for removals (uptake).

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Annex 3A.2
TABLE 3A.2.1A
REPORTING TABLE FOR EMISSIONS AND REMOVALS OF CO2 AND NON-CO2 GASES FROM LULUCF IN THE REPORTING YEAR
Land-use
Category
InitialLand-use
Land-useduringreportingYear

Living Biomass

Forest Land

5A

Cropland

Forest Land5A, 5C, 5D

Grassland
Wetlands
Settlements
Other Land

Forest Land5A, 5C, 5D
Forest Land5A, 5C, 5D
Forest Land5A, 5C, 5D
Forest Land5A, 5C, 5D
Sub-Total for Forest Land
Cropland5A, 5D
Cropland5B, 5D
Cropland5B, 5D
Cropland5D
Cropland5D
Cropland
5D
Sub-Total for Cropland
Grassland
5A, 5D
Grassland
5B, 5D
Grassland
5C, 5D
Grassland
5C, 5D
Grassland
5C, 5D
Grassland
5C, 5D
Sub-Total for Grassland
Wetlands
5A, 5E
Wetlands
5B
Wetlands
5E
Wetlands
5B
Wetlands
5E
Wetlands
5E
Sub-Total for Wetlands

Grassland
Forest Land
CroplandWetlands
Settlements
Other Land
Wetlands
Forest Land
Cropland
Grassland
Settlements
Other Land

Dead Organic
MatterSoils

CO2 Emissions/Removals 2

CH4

N2O

NOx 3

CO 3

(Gg)

(Gg)

(Gg)

(Gg)

D = (A+B+C) • (-1)

Forest Land

Cropland
Forest Land
Grassland
Wetlands
Settlements
Other Land

Annual change in carbon stocks, Gg CO2

IPCC
Guidelines 1

A

B

C

∆CLFLB 5∆CLFDOM∆CLFSOMD(SEE CONTINUATION OF ROWS FOR OTHER CATEGORIES ON BACK PAGE)

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.2.1A (CONTINUED)REPORTING TABLE FOR EMISSIONS AND REMOVALS OF CO2 AND NON-CO2 GASES FROM LULUCF IN THE REPORTING YEAR
Land-use
Category
InitialLand-use
Land-useduringreportingYear

Annual change in carbon stocks, Gg CO2

IPCC
Guidelines 1
Living Biomass

Other Land
Forest Land
Cropland
Grassland
Wetlands
Settlements

Soils

CO2 Emissions/Removals 2

N2O

NOx 3

CO 3

(Gg)

(Gg)

(Gg)

(Gg)

D = (A+B+C) • (-1)
A

Settlements
Forest Land
Cropland
Grassland
Wetlands
Other Land

Dead Organic
Matter

CH4

B

C

D

Settlements
5A
Settlements
5B
Settlements
5E
Settlements
5B
Settlements
5E
Settlements
5E
Sub-Total for Settlements
Other Land
5A
Other Land
5B
Other Land
5E
Other Land
5B
Other Land
5E
Other Land
5E
Sub-Total for Other Land

Other 4(pls.
specify)
Sub-Total for Other
Total
1

Headings from the IPCC Guidelines Reporting Instructions p.1.14 - 1.16: 5A - Changes in Forest and Other Woody Biomass Stocks; 5B - Forest and Grassland Conversion; 5C - Abandonment of Managed Lands; 5D Emissions and Removals from Soils, and 5E - Other.
2
For the purpose of reporting, it is necessary to reverse the sign so that the resulting value is expressed as (-) for removal or uptake and (+) for emission. Thus, negative 1 is multiplied to the resulting CO2 emission or
removal.
3

The IPCC Guidelines and this report provide methodology to estimate NOx and CO emissions for Land Use, Land-Use Change and Forestry for emissions from fires only. If you have reported additional data, you should
provide additional information (method, activity data, and emission factors) used to make these estimates.

4

This may include other non-specified sources or sinks such as HWP, etc.

5

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one landuse category as an example.

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Annex 3A.2

TABLE 3A.2.1B
REPORTING TABLE FOR EMISSIONS AND REMOVALS OF CO2 AND NON-CO2 GASES DUE TO CONVERSION OF FOREST LANDAND GRASSLAND TO OTHER LAND CATEGORIES IN THE REPORTING YEAR
Land-use
Category
InitialLand-use
Land-useduringreportingYear

Living Biomass

Dead Organic
MatterSoils

CO2 Emissions/Removals 2

CH4

N2O

NOx 3

CO 3

(Gg)

(Gg)

(Gg)

(Gg)

D = (A+B+C) • (-1)

Forest Land
Forest Land

Cropland
Grassland

Forest Land
Forest Land
Forest Land

Wetlands
5B
Settlements
5B
Other Land
5B
Sub-Total for Forest Land
Forest Land5A, 5C, 5D
Cropland5B, 5D
Wetlands
5B
Settlements
5B
Other Land
5B
Sub-Total from Grassland
Total

Grassland
Grassland
Grassland
Grassland
Grassland

Annual change in carbon stocks, Gg CO2

IPCC
Guidelines 1

5B, 5D5B, 5DA

B

C∆CLGLB 4∆CLGDOM∆CLGSOM

D

1

Headings from the IPCC Guidelines Reporting Instructions p.1.14 - 1.16: 5A - Changes in Forest and Other Woody Biomass Stocks; 5B - Forest and Grassland Conversion; 5C - Abandonment of Managed Lands; 5D Emissions and Removals from Soils, and 5E - Other.
2
For the purpose of reporting, it is necessary to reverse the sign so that the resulting value is expressed as (-) for removal or uptake and (+) for emission. Thus, negative 1 is multiplied to the resulting CO2 emission or
removal.
3

The IPCC Guidelines and this report provide methodology to estimate NOx and CO emissions for Land Use, Land-Use Change and Forestry for emissions from fires only. If you have reported additional data, you should
provide additional information (method, activity data, and emission factors) used to make these estimates.

4

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one landuse category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.2.2A
COMPILATION WORKSHEETS FOR REPORTING CO2 EMISSIONS AND REMOVALS 1
Land-use Category 2
InitialLand-use

Land-useduringreporting Year

Land
Area
(ha)

Living Biomass
Annual increasein carbon
stocks(tonnes C
yr-1)

Annual decrease Annual change inin carbon stocks
carbon stocks(tonnes C(Gg CO2
-1
yr )
yr-1)

Carbon stockchange in deadwood(tonnes C
yr-1)

Carbon stockchange in litter(tonnes C
yr-1)

C = (A-B) •10-3 • 44/12
Forest Land
Cropland
Grassland
Wetlands
Settlements
Other Land
Cropland
Forest Land
Grassland
Wetlands
Settlements
Other Land
Grassland
Forest Land
CroplandWetlands
Settlements
Other Land
Wetlands
Forest Land
Cropland
Grassland
Settlements
Other Land

Forest Land
Forest Land
Forest Land
Forest Land
Forest Land
Forest Land
Sub-total for Forest Land
CroplandCroplandCroplandCroplandCroplandCroplandSub-total for Cropland
Grassland
Grassland
Grassland
Grassland
Grassland
Grassland
Sub-total for Grassland
Wetlands
Wetlands
Wetlands
Wetlands
Wetlands
Wetlands
Sub-total for Wetlands

Soils3

Dead Organic Matter
Annual change incarbon stock(Gg CO2
yr-1)

Carbon stockchange inmineral soils(tonnes C
yr-1)

F = (D+E) •10-3 • 44/12

Carbon stock
Annual change inchange in organiccarbon stocksoils(Gg CO2(tonnes C
yr-1)
yr-1)
I = (G+H) •10-3 • 44/12

A

B

C

D

E

F

G

H

I

∆CLFG 4

∆CLFL

∆CLFLB∆CLFDW∆CLFLT∆CLFDOM∆CLFMineral∆CLFOrganic∆CLFSoils(SEE CONTINUATION OF ROWS FOR OTHER CATEGORIES ON BACK PAGE)
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Annex 3A.2
TABLE 3A.2.2A (CONTINUED)COMPILATION WORKSHEETS FOR REPORTING CO2 EMISSIONS AND REMOVALS 1
Land-use Category
InitialLand-use

Land-useduringreporting Year

Land
Area
(ha)

Living Biomass
Annualincrease incarbon stocks(tonnes C
yr-1)

Annual decrease Annual change inin carbon stocks
carbon stocks(tonnes C(Gg CO2
yr-1)
yr-1)

Carbon stockchange in deadwood(tonnes C
yr-1)

Carbon stockchange in litter(tonnes C
yr-1)

C = (A-B) •10-3 • 44/12
A
Settlements
Forest Land
Cropland
Grassland
Wetlands
Other Land
Other Land
Forest Land
Cropland
Grassland
Wetlands
Settlements

B

Soils 3

Dead Organic MatterC

Annual change
in carbon stock(Gg CO2
yr-1)

Carbon stockchange inmineral soils(tonnes C
yr-1)

Carbon stockchange inorganic soils(tonnes C
yr-1)

F = (D+E) •10-3 • 44/12
DEF

Annual change incarbon stock(Gg CO2
yr-1)
I = (G+H) •10-3 • 44/12

G

H

I

Settlements
Settlements
Settlements
Settlements
Settlements
Settlements
Sub-total for Settlements
Other Land
Other Land
Other Land
Other Land
Other Land
Other Land
Sub-total for Other Land

Other (pls.
specify) 2
Sub-total for Other
Total
1
The sign convention for net carbon changes in columns C, F, and I are: net gain (+) and net loss (-).
2

May include other non-specified sources or sinks such as HWP, etc.

3

An additional column can be added to include the change in carbon stock in soils due to liming.

4

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one landuse category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3A.2.2B
COMPILATION WORKSHEETS FOR REPORTING NON-CO2 EMISSIONS 1
Land-useLand
Category
Area
Initial LandLand-use;(ha)usereportingYear
Forest Land
Forest Land
Cropland
Forest Land
Grassland
Forest Land
Wetlands
Forest Land
Settlements
Forest Land
Other Land
Forest Land
Sub-total for Forest Land
CroplandCropland
Forest Land
Cropland
Grassland
CroplandWetlands
CroplandSettlements
Cropland
Other Land
CroplandSub-total for Cropland
Grassland
Grassland
Forest Land
Grassland
Cropland
Grassland
Wetlands
Grassland
Settlements
Grassland
Other Land
Grassland
Sub-total for Grassland
Wetlands
Wetlands
Forest Land
Wetlands
CroplandWetlands
Grassland
Wetlands
Settlements
Wetlands
Other Land
Wetlands
Sub-total for Wetlands

CH4
(Gg)
Biomass2

IPCC Good Practice Guidance for LULUCF

Soils

N2O
(Gg)
Total

Biomass2

Soils3

NOx
(Gg)
Total

Biomass2

Soils

CO
(Gg)
Total

Biomass2

Soils

Total

3.194

Annex 3A.2
TABLE 3A.2.2B (CONTINUED)COMPILATION WORKSHEETS FOR REPORTING NON-CO2 EMISSIONS 1
Land-use
Category
Initial Landuse
Settlements
Forest Land
Cropland
Grassland
Wetlands
Other Land
Other Land
Forest Land
Cropland
Grassland
Wetlands
Settlements

Land
Area

Land-use;(ha)reportingYear
Settlements
Settlements
Settlements
Settlements
Settlements
Settlements
Sub-total for Settlements
Other Land
Other Land
Other Land
Other Land
Other Land
Other Land
Sub-total for Other Land

CH4
(Gg)
Biomass2

Soils

N2O
(Gg)
Total

Biomass2

Soils3

NOx
(Gg)
Total

Biomass2

Soils

CO
(Gg)
Total

Biomass2

Soils

Total

Other (pls. specify)
Sub-total for Other
Total
1

All units should be reported in gigagram (Gg). To convert unit from “kg N2O-N” to Gg N2O, multiply the value (from the worksheets) by 44/28 and 10-6. Similar to the convention used in the worksheets, the sign forremoval (uptake) is positive (+) and for emission is negative (-).

2

Disturbances to woody biomass growth may occur only in forest land and grassland. Non-CO2 emissions from prescribed burning of savanna (grassland) are reported in Chapter 4 of the IPCC Guidelines.

3

Fertilisation is practiced in forest land, cropland, and grassland. N2O emissions from the use of N-fertilisers in cropland are reported in Chapter 4 of the IPCC Guidelines.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet
Land-use
Category 2
InitialLand useFL

Land-useduringreportingYear

FL

Forest Land
Forest Land Remaining Forest LandFL-1a: Annual change in carbon stocks in living biomass (includes above and below ground biomass) 1
1 of 4
Subcategories for
ReportingYear 3

Area of forest landremaining forest
land
(ha)

Average annual
net increment involume suitablefor industrial
processing

Basic wood density(tonnes d.m.
per m-3 fresh volume)(m3 ha-1 yr-1)

Biomass Expansion factor
for conversion of annual
net increment (includingbark) to above ground tree
biomass increment

Average annual
aboveground biomass
increment

Root-shoot ratio
appropriate toincrements

Average annual biomass
increment above andbelow ground(tonnes d.m. ha-1 yr-1)(dimensionless)(tonnes d.m ha-1 yr-1)(dimensionless)

E=B•C•D

G = E • (1+F)

A

B

C

D

E

F

G

A

IV

D

BEF1

GW

R

GTOTAL(a)(b)(c)
Sub-total

Total
1

Calculations are based on default method (see Section 3.2.1.1)

2

FL stands for forest land. See Chapter 2 for approaches in representing land areas.

3

Land use should be further divided according to forest type and climatic zones in the country.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest LandFL-1a: Annual change in carbon stocks in living biomass (includes above and below ground biomass)
2 of 4Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Carbon fraction of dry
matterAnnual increase in carbon
due to biomass increment

Annually extracted
volume of roundwood(default is 0.5)

(tonnes C yr-1)(m3 yr-1)-1(tonnes C tonne d.m. )Biomass density(tonnes d.m.
m-3 fresh volume)

Biomass expansion factor
for converting volumes ofextracted roundwood to total
aboveground biomass(including bark)

Fraction of biomass left
to decay in forest(dimensionless)

I=A•G•H(dimensionless)

FL

FL

H

I

J

K

L

M

CF

∆CFFG

H

D

BEF2

fBL

(a)
(b)
(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest LandFL-1a: Annual change in carbon stocks in living biomass (includes above and below ground biomass)
3 of 4Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Annual carbon loss due tocommercial fellings

Annual volume offuelwood
gathering(tonnes C yr-1)

Biomass density(tonnes d.m.
m-3 fresh volume)(m3 yr-1)

Biomass expansion factor
for converting volumes ofextracted roundwood tototal aboveground biomass(including bark)

N = J • K • L • (1-M) • H

Annual carbon loss due tofuelwood gathering

Forest areas affectedby disturbances

Average biomass
stock of forest areas(tonnes C yr-1)(ha yr-1)(tonnes d.m. ha-1)

R=O•P•Q•H(dimensionless)

FL

FL

N

O

P

Q

R

S

T

Lfellings

FG

D

BEF2

Lfuelwood

Adisturbance

BW

(a)
(b)
(c)
Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest LandFL-1a: Annual change in carbon stocks in living biomass (includes above and below ground biomass)
4 of 4Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

FL

Fraction of biomass left to decay inforestAnnual other losses of carbon

Annualdecrease in carbon due to biomass
loss

Annual change in carbon stocks in living
biomass(tonnes C yr-1)(tonnes C yr-1)

W = N+R+V

X = I-W-1(tonnes C yr )(dimensionless)(b)

V = S • T • (1-U) • H
U

V

W

X

fBL

Lother losses∆CFFL∆CFFLB(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1b: Annual change in carbon stocks in dead organic matter (dead wood and litter) 1
1 of 3Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

FL

Area of managed forest landremaining forest land(b)

Annual transfer into deadwood(tonnes d.m. ha-1 yr-1)(ha)Annual transfer out ofdead wood(tonnes d.m.
ha-1 yr-1)

Carbon fraction of dry
matterAnnual change of carbon indead wood(default is 0.5)

(tonnes C yr-1)

Reference stock of litter
under native, unmanaged
forest corresponding tostate i(tonnes C (tonne d.m.)-1)

E = A • (B-C) • D(tonnes C ha-1)

A

B

C

D

E

F

A

Binto

Bout

CF

∆CFFDW

LTref(i)(c)
Sub-total

Total
1

The calculation is based on Tier 2 since Tier 1 assumes that the net change in carbon in dead wood and litter is zero.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1b: Annual change in carbon stocks in dead organic matter (dead wood and litter)2 of 3Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

FL

Adjustment factor reflecting
the effect of management
intensity or practices on
LTref(i) in state i

Adjustment factor reflecting
a change in the disturbance
regime on LTref (i) in state i

Stable litter stock
under previous state i

Reference stock of litter
under previous state j(tonnes C ha-1)(tonnes C ha-1)(dimensionless)

Adjustment factor reflecting
the effect of management
intensity or practices on
LTref (j)in state j

Adjustment factor
reflecting a change in the
disturbance regime on
LTref (j)in state j(dimensionless)(dimensionless)

I=F•G•H(dimensionless)

G

H

I

J

K

L

fmgt_intensity ifdist_regime i

Ci

LTref (j)fmgt_intensity jfdist_regime j

(a)
(b)
(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1b: Annual change in carbon stocks in dead organic matter (dead wood and litter)3 of 3Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Stable litter stock underprevious state j

Forest area undergoing atransition from state i to j

(tonnes C ha-1)(ha)Time period of thetransition from state ito jAnnual litter carbon stockchangeAnnual change in carbon
stocks in dead organic matter(tonnes C yr-1)(tonnes C yr-1)

P = (M-I) • N / O

Q = E+P

Default is 20 yrs
M=J•K•L
(yr)

FL

FL

M

N

O

P

Q

Cj

Aij

Tij

∆CFFLT∆CFFDOM

(a)
(b)
(c)
Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1c1: Annual change in carbon stocks in mineral soils 1
1 of 2Land-use
Category 2
InitialLand use
Land useduringreportingYear

Subcategories for
ReportingYear 3

Forest area
undergoing atransition fromstate i to j(ha)Time period of thetransition from
SOCi to SOCj
(default is
20 yr)

Reference carbon
stock under native,
unmanaged forest on
a given soil

Adjustment factor
reflecting the effect of achange from the native
forest to the forest type instate i

Adjustment factor
reflecting the effectof management
intensity or practices
on forest in state i(dimensionless)(dimensionless)(tonnes C ha-1)

(yr)

FL

FL

Adjustment factor
reflecting the effect of achange in the
disturbance regime tostate i with respect tothe native forest

Stable soil organiccarbon stock underprevious state i(tonnes C ha-1)
G=C•D•E•F(dimensionless)

A

B

C

D

E

F

G

Aij

Tij

SOCREF

fforest typei

fman intensityi

fdist regimei

SOC i

(a)
(b)
(c)
Sub-total

Total
1

The calculation is based on Tier 2 since Tier 1 assumes that the net change in carbon in mineral soil, for forest land remaining forest land is zero.

2

FL stands for forest land. See Chapter 2 for approaches in representing land areas.

3

Land use may be further divided according to forest type or tree species, national land classification system, or ecological zones.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1c1: Annual change in carbon stocks in mineral soils
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

FL

Reference carbon stock
under native, unmanaged
forest on a given soil
(tonnes C ha-1)

Adjustment factor reflecting
the effect of a change fromthe native forest to the forest
type in state j

Adjustment factor
reflecting the effect ofmanagement intensity
or practices on forest in
state jAdjustment factor
reflecting the effect of achange in the disturbance
regime to state j withrespect to the native forest(dimensionless)(dimensionless)(dimensionless)Stable soil organic carbon
stock under current state j

Annual soil carbon stockchange(tonnes C ha-1)(tonnes C yr-1)

L=H•I•J•K

M = (L-G) • A /B

H (= C)

I

J

K

L

M

SOCREF

fforest typej

fman intensityj

fdist regimej

SOC j∆CFFMineral

(a)
(b)
(c)
Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1c2: Annual change in carbon stocks in organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

FL

(b)

Area of drained organic forest soils(ha)Emission factor for CO2 from drained organic forest soils-1-1(tonnes C ha yr )CO2 emissions from drained organic forest soils(tonnes C yr-1)
C=A•B

A

B

C

ADrained

EFDrainage∆CFFOrganic(c)Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1c3: Annual change in carbon stocks in soils (summary worksheet)
1 of 1

Annual change in carbon stock change in mineral soils-1(tonnes C yr )CO2 emissions from drained organic soils-1(tonnes C yr )Annual change in carbon stock in soils(tonnes C yr-1)
C = A+B

A

B

C∆CFFMineral∆CFFOrganic∆CFFSoils

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Forest Land Remaining Forest Land
FL-1d: Non-CO2 emissions from vegetation fires
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

FL

Area burnt(ha)

Mass of available
fuel(kg d.m. ha-1)

Combustion
efficiency or
fraction of
biomass
combusted(dimension-less)

Emission factor
for each GHG(g /kg d.m.)CH4 Emissions
from fires

CO Emissions fromfires

N2O Emissions
from fires

NOx Emissions
from fires(tonnes CH4)

(tonnes CO)(tonnes N2O)

(tonnes NOx)

E=A•B•C•D• 10-6

F=A•B•C•D• 10-6

G=A•B•C•D• 10-6

H=A•B•C•
D • 10-6

F

G

H

A

B

C

D

E

A

B

C

DCH4

CH4

FL
(a)

DCO
DN2O
DNOx

CO
N2O
NOx

(b)

(c)

Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest LandFL-2a: Annual change in carbon stocks in living biomass (includes above and below ground biomass)
1 of 1

Method follows Worksheet
FL-1a: Annual change in carbon stocks in living
biomass (includes above and below ground biomass) in
Forest Land Remaining Forest Land

Method follows Worksheet
FL-1a: Annual change in carbon stocks in living biomass(includes above and below ground biomass) in Forest
Land Remaining Forest Land

Annual change in carbon stocks in biomass from landuse conversion to forest land
(tonnes C yr-1)
C = A+B

A

B

C

∆CLFG

∆CLFL

∆CLFLB

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3.208

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest LandFL-2b: Annual change in carbon stocks in dead organic matter (dead wood and litter) 1
1 of 2Land-use
Category 2
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear 3

Area of land
converted toforest landthrough naturalregeneration(ha)Standing
biomass stockin terms ofcarbon in
naturally
regenerated
forestMortality ratein naturally
regenerated
forest(dimensionless)(tonnes d.m.
ha-1)

CL

FL

Annual transfer
into dead woodfor naturally
regenerated
forest area

Annual transfer
out of dead woodfor naturally
regenerated forest
area(tonnes d.m.
ha-1 yr-1)(tonnes d.m.
ha-1 yr-1)

Area of land
converted into
forest landthroughestablishment ofplantations

Standing biomass
stock in terms ofcarbon in
artificiallyregenerated forest

Mortality rate in
artificiallyregenerated
forestAnnual transfer into
dead wood for
artificiallyregenerated forest
area(dimensionless)(tonnes d.m. ha-1)(tonnes d.m.
ha-1 yr-1)

(ha)

D=B•C

I=G•H

A

B

C

D

E

F

G

H

I

ANatR

BstandingNatR

MNatR

BintoNatRBoutNatR

AArtR

BstandingArtR

MArtR

BintoArtR

(a)
(b)
(c)
Sub-total

GL

FL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

FL

(a)
(b)
(c)
Sub-total

Total
1

The calculation is based on Tier 2 since Tier 1 assumes that the net change in carbon in dead wood and litter is zero.

2

FL stands for forest land; CL for cropland; GL for grassland; WL for wetlands, SL settlements, and OL for other lands. See Chapter 2 for approaches in representing land areas.

3

Land use may be further divided according to forest type or tree species, national land classification system, or ecological zones.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

1B - Land Converted to Forest Land
Land Converted to Forest LandFL-2b: Annual change in carbon stocks in dead organic matter (dead wood and litter)2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

FL

Annual transfer outof dead wood for
artificiallyregenerated forest
area(tonnes d.m. ha-1
yr-1)

Carbon fractionof dry matter

Annual change incarbon stocks in deadwood

Annual change in litter
carbon for naturally
regenerated forest

Annual change in litter
carbon for artificiallyregenerated forest

Annual change incarbon stocks in litter

Annual change incarbon stocks in deadorganic matter(tonnes C yr-1)(tonnes C ha-1 yr-1)(tonnes C ha-1 yr-1)(tonnes C yr-1)(tonnes C yr-1)

O = (A • M)+(F • N)

P= L+O(default is 0.5)
(tonnes C(tonne d.m.)-1 )L = [A • (D-E) + F •(I-J)] • K

J

K

L

M

N

O

P

Bout ArtR

CF∆CLFDW 1∆CNatR∆CArtR∆CLFLT∆CLFDOM

(a)
(b)
(c)
Sub-total

GL

FL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

FL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.210

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest Land
FL-2c1: Annual change in carbon stocks in mineral soils 1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

FL

Total afforested land
derived from former
cropland or grassland

Reference carbon stock
under native, unmanaged
forest on a given soil,
SOCrefStable soil organic carbon on
previous land use, either croplandor grassland, SOCNon-forest Land

Duration of the transition from
SOCNon-forest Land to SOCref

Change in carbon stock in mineral
soils

(yr)

(tonnes C yr-1)(tonnes C ha-1)(ha)E = (B-C) • A / D(tonnes C ha-1)

(b)

A

B

C

D

E

AAFF,xSOCref

SOCNon-forest_land

TAFF∆CLFMineral 2(c)
Sub-total
GL

FL

(a)
(b)
(c)
Sub-total

Total
1This LULUCF Good Practice Guidance provides default values only for cropland and grassland converted into forest land.

2

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.211

Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest Land
FL-2c2: Annual change in carbon stocks in organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

FL

Area of drained organic soils in land converted to forest land(b)

Emission factor for CO2 from drainedorganic forest soils

CO2 emissions from drained organic soils(tonnes C yr-1)

(ha)
-1

-1

(tonnes C ha yr )
C=A•B
A

B

C

ADrained

EFDrainage∆CLFOrganic 1(c)Sub-total
GL

FL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

FL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.212

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest Land
FL-2c3: Annual change in carbon stocks in soils (summary worksheet)
1 of 1

Annual soil carbon stock change in mineral soils-1(tonnes C yr )CO2 emissions from drained organic soils-1(tonnes C yr )Annual change in carbon stocks in soils(tonnes C yr-1)
C = A+B

A

B

C∆CLFMineral∆CLFOrganic∆CLFSoils

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Forest Land
Land Converted to Forest Land
FL-2d: Non-CO2 emissions from vegetation fires
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

FL

Area burnt

Mass of available
fuel present(ha)(kg d.m. ha-1)

Combustion
efficiency or
fraction of
biomass
combusted(dimension-less)

Emission factor
for each GHG

CH4 Emissions
from fires

CO Emissions fromfires

N2O Emissions
from fires

NOx Emissions
from fires(g /kg d.m.)(tonnes CH4)

(tonnes CO)

tonnes (N2O)(tonnes NOx)

E=A•B•C•D• 10-6

F=A•B•C•D• 10-6

G=A•B•C•D• 10-6

H=A•B•C•D• 10-6

D
DCH4

E

F

G

H

A

B

C

A

B

C

DCO
DN2O
DNOx

CH4
CO
N2O
NOx

(b)

Sub-total
GL

FL

(a)

(b)

Subtotal
Total

IPCC Good Practice Guidance for LULUCF

3.214

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1a: Annual change in carbon stocks in living biomass 1
1 of 1Land-use
Category 2
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear 3

CL

(a)

CL

Annual area of croplandwith perennial woody
biomass

Annual growth rate of perennial
woody biomass

Annual carbon stock in biomass
removed(removal or harvest)(tonnes C ha-1 yr-1)

Annual change in carbon stocks inbiomass(tonnes C yr-1)(tonnes C ha-1 yr-1)(ha)

D = A • (B-C)

(b)

A

B

C

D

A

G

L

∆CCCLB(c)
Sub-total

Total
1

The change in biomass is only estimated for perennial woody crops. For annual crops, increase in biomass stocks in a single year is assumed equal to biomass losses from harvest and mortality inthat same year – thus there is no net accumulation of biomass carbon stocks.

2

CL stands for cropland. See Chapter 2 for approaches in representing land areas.

3

Land use should be further divided according to type of perennial woody vegetation and climate zones.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1c1: Annual change in carbon stocks in mineral soils
1 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Land area of each parcel1

Inventory time period

Reference
carbon stock(default is 20 yr)(tonnes C ha-1)(ha)Stock change factor forland use or land-use
change type in the
beginning of inventory
year

Stock change factor for
management regime in
the beginning of
inventory year

Stock change factor for input
of organic matter in the
beginning of inventory year

(dimensionless)(dimensionless)(dimensionless)

CL

CL

A

B

C

D

E

F

A

T

SOCrefFLU(0-T)

FMG(0-T)

FI(0-T)

(a)
(b)
(c)
Sub-total

Total
1

Major cropland system in the country should be covered.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1c1: Annual change in carbon stocks in mineral soils
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

CL

Soil organic carbon stock at
T years (beginning of
inventory year)

Stock change factor forland use or land-use
change type in current
inventory year

(tonnes C ha-1)

Stock change factor for
management regime incurrent inventory year

Stock change factor for
input of organic matter incurrent inventory year

Soil organic carbon
stock in current
inventory year

(dimensionless)(dimensionless)(tonnes C ha-1)

Annual change in carbon
stocks in mineral soils(tonnes C yr-1)(dimensionless)
G=C•D•E•F(b)

K=C•H•I•J

L = [(K-G) • A] / B

G

H

I

J

K

L

SOC(0-T)

FLU(0)

FMG(0)

FI(0)

SOC0

∆CCCMineral(c)
Sub-total

Total

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ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1c2: Annual change in carbon stocks in organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

CL

Land area of organic soils in climate type c

(b)

(ha)

Emission factor for climate type c-1-1(tonnes C ha yr )CO2 emissions from cultivated organic soils(tonnes C yr-1)
C=A•B

A

B

C

A

EF

∆CCCOrganic(c)Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1c3: Carbon emissions from agricultural lime application
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

CL

(a)

CL

(b)

Type of lime

Total Annual amount of lime applied

Emission Factor (carbonate carbon contentsof the materials)-1(tonnes lime yr )Annual CO2 emissions from agricultural
lime application(tonnes C yr-1)(tonnes C/tonne lime)
D=B•C
A

B

C

D

type

AmountEF∆CCC Liming(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

CroplandCropland Remaining CroplandCL-1c4: Annual soil carbon stock change in croplands
1 of 1

Annual soil carbon stock change inmineral soils

CO2 emissions from cultivated organic
soils

CO2 Emissions from liming

Annual change in carbon stocks in soils(tonnes C yr-1)-1(tonnes C yr )-1(tonnes C yr )-1(tonnes C yr )C = A-B-C

A

B

C

D

∆CCCMineral∆CCCOrganic∆CCCLiming∆CCCSoils

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2a: Annual change in carbon stocks in living biomass
1 of 1Land-use
Category 1
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear 2

Annual area of land
converted to cropland

Carbon stocks in biomass
immediately afterconversion to cropland(ha yr-1)(tonnes C ha-1)

Carbon stocks inbiomass immediately
before conversion tocropland

Carbon stock change per
area for that type of
conversion when land isconverted to cropland

Change in carbon stockfrom one year of
cropland growth(tonnes C ha-1)(tonnes C ha-1)(tonnes C ha-1)

Annual change in carbon
stocks in living biomass inland converted to cropland(tonnes C yr-1)
F = A • (D+E)

D = B-C

FL

CL

A

B

C

D

E

F

AConversion

CAfterCBeforeLConversion∆CGrowth∆CLCLB 3

(a)
(b)
(c)
Sub-total

GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

FL stands for forest land; CL for cropland; GL for grassland; WL for wetlands, SL settlements, and OL for other land. See Chapter 2 for approaches in representing land areas.

2

Land use should be further divided according to type of perennial woody vegetation and climate zones.

3

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2c1: Annual change in carbon stocks in mineral soils
1 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

CL

Area of land converted to a
cropland system1
(ha)

(b)

Inventory time period(default is 20 yr)

Reference carbon stock-1(tonnes C ha )Stock change factor forland use or land-use
change type in the initial
year (pre-conversion)

Stock change factor for
management regime in
the initial year (preconversion)(dimensionless)(dimensionless)

Stock change factor for input
of organic matter in the initial
year (pre-conversion)(dimensionless)

A

B

C

D

E

F

A

T

SOCrefFLU(0-T)

FMG(0-T)

FI(0-T)(c)
Sub-total
GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

Major cropland system in the country should be covered.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2c1: Annual change in carbon stocks in mineral soils
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

CL

Soil organic carbon stock in
the initial year (preconversion)

Stock change factor forland use or land-use
change type in current
inventory year

(tonnes C ha-1)

Stock change factor for
management regime incurrent inventory year

Stock change factor for
input of organic matter incurrent inventory year

Soil organic carbon
stock in current
inventory year

(dimensionless)(dimensionless)(tonnes C ha-1)

Annual change in carbon
stocks in mineral soils(tonnes C yr-1)(dimensionless)
G=C•D•E•F(b)

K=C•H•I•J

L = [(K-G) • A] / B

G

H

I

J

K

L

SOC(0-T)

FLU(0)

FMG(0)

FI(0)

SOC0

∆CLCMineral 1(c)
Sub-total
GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2c2: Annual change in carbon stocks in organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

CL

Land area of organic soils in climate type c which areconverted to cropland(b)

Emission factor for climate type c-1-1(tonnes C ha yr )CO2 emissions from cultivated organic soils(tonnes C yr-1)

(ha)
C=A•B
A

B

C

A

EF

∆CLCOrganic 1(c)Sub-total
GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

IPCC Good Practice Guidance for LULUCF

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2c3: Carbon emissions from agricultural lime application
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

CL

Type of lime

Total Annual amount of lime applied

Emission Factor (carbonate carbon contentsof the materials)

Annual CO2 emissions from agricultural
lime application(tonnes C/tonne lime)(tonnes C yr-1)-1(tonnes lime yr )
D=B•C

(b)

A

B

C

D

type

AmountEF∆CLCLiming 1(c)
Sub-total
GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2c4: Annual soil carbon stock change in croplands
1 of 1

Annual soil carbon stock change inmineral soils

Carbon emissions from cultivatedorganic soils

CO2 Emissions from liming-1(tonnes C yr )-1(tonnes C yr )Annual change in carbon stocks in soils(tonnes C yr-1)-1(tonnes C yr )C = A-B-C

A

B

C

D

∆CLCMineral∆CLCOrganic∆CLCLiming∆CLCSoil

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

CroplandLand Converted to CroplandCL-2d: Annual emissions of N2O from mineral soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

IPCC default emission factor
used to calculate emissions fromagricultural land caused by added
N, whether in the form of mineral
fertilisers, manures, or crop
residues

N released annually by net soilorganic matter mineralisation as aresult of the disturbance

Additional emissions arising from the land-use
changeN2O emissions as a result of the disturbanceassociated with land-use conversion of forest,
grassland or other land to cropland(kg N2O-N yr-1)(kg N2O-N yr-1)(See Note 1 below)(kg N yr-1)

C=A•B

D=C

A

B

C

D

EF1

Nnet-minN2Onet-min-N

N2O EmissionLC 2(kg N2O-N/ kg N)

FL

CL

(a)
(b)
(c)
Sub-total

GL

CL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

CL

(a)
(b)
(c)
Sub-total

Total
1

Column C = value of Column A in Worksheet CL-2c4 divided by the C:N ratio (see Equation 3.3.15). The default value for the C:N ratio is 15.

2

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1a: Annual change in carbon stocks in living biomass 1
1 of 2Land-use
Category 2
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear 3

GL

(a)

GL

Area of grassland covered
with perennial woody
biomass

Average annual biomass
growth of perennial
woody biomass(ha)(tonnes d.m. ha-1 yr-1)

Average annual biomass loss of
perennial woody biomass

Change in above- and belowgroundliving perennial woody biomass

Area of grassland covered withgrasses(tonnes d.m. ha-1 yr-1)(tonnes d.m. yr-1)(ha)

D = A • (B-C)

(b)

A

B

C

D

E

Aperennial

Gperennial

Lperennial∆Bperennial

Agrasses(c)
Sub-total

Total
1

The worksheet is based on Tier 2 method. The Tier 1 assumption is no change in living biomass carbon stocks.

2

GL stands for grassland. See Chapter 2 for approaches in representing land areas.

3

Land-use should be further divided according to grassland type and climate zone.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1a: Annual change in carbon stocks in living biomass
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

GL

(a)

GL

Average annual biomass
growth of grasses

Average annual biomass
loss of grasses

Change in belowground biomass ofgrasses

Carbon fraction of dry matter(default is 0.5)

Change in carbon stocks in living
biomass(tonnes d.m. ha-1 yr-1)(tonnes d.m. ha-1 yr-1)(tonnes d.m. yr-1)(tonnes C tonne d.m.-1 )(tonnes C yr-1)

H = E • (F-G)(b)

J = (D+H) • I

F

G

H

I

J

Ggrasses

Lgrasses∆Bgrasses

CF∆CGGLB(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1c1: Annual change in carbon stocks in mineral soils
1 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Land area of each parcel(ha)

Inventory time period(default is 20 yr)

Reference carbon stock-1(tonnes C ha )Stock change factor forland use or land-use
change type in the
beginning of inventory
year

Stock change factor for
management regime in
the beginning of
inventory year

Stock change factor for input
of organic matter in the
beginning of inventory year
(dimensionless)(dimensionless)(dimensionless)

GL

GL

A

B

C

D

E

F

A

T

SOCrefFLU(0-T)

FMG(0-T)

FI(0-T)

(a)
(b)
(c)
Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1c1: Annual change in carbon stocks in mineral soils
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

GL

(a)

GL

Soil organic carbon stock at
T years (beginning of
inventory year)

Stock change factor forland use or land-use
change type in current
inventory year

(tonnes C ha-1)

Stock change factor for
management regime incurrent inventory year

Stock change factor for
input of organic matter incurrent inventory year

(dimensionless)(dimensionless)

Annual change in carbon
stocks in mineral soils(tonnes C yr-1)(tonnes C ha-1)

L = [(K-G) • A] / B(dimensionless)

G=C•D•E•F(b)

Soil organic carbon
stock in current
inventory year

K=C•H•I•J

G

H

I

J

K

L

SOC(0-T)

FLU(0)

FMG(0)

FI(0)

SOC0

∆CGGMineral(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1c2: Annual change in carbon stocks in cultivated organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

GL

(a)

GL

Land area of organic soils in climate type c

(b)

(ha)

Emission factor for climate type c-1-1(tonnes C ha yr )CO2 emissions from cultivated organic soils(tonnes C yr-1)
C=A•B

A

B

C

A

EF

∆C GGOrganic(c)Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1c3: Annual carbon emissions from agricultural lime application
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

GL

(a)

GL

Type of lime

Total Annual amount of lime applied

Emission Factor (carbonate carbon contentsof the materials)

Annual carbon emissions from agricultural
lime application(tonnes C/tonne lime)(tonnes C yr-1)-1(tonnes lime yr )
D=B•C

(b)

A

B

C

D

type

AmountEF∆C GGLiming(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1c4: Annual soil carbon stock change in grassland
1 of 1

Annual soil carbon stock change inmineral soils

CO2 emissions from cultivated organic
soils

Annual carbon emissions fromagricultural lime application

Annual change in carbon stocks in soils(tonnes C yr-1)-1(tonnes C yr )-1(tonnes C yr )-1(tonnes C yr )C = A-B-C

A

B

C

D

∆CGGMineral∆C GGOrganic∆C GGLiming∆CGGSoils

IPCC Good Practice Guidance for LULUCF

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Grassland Remaining Grassland
GL-1d: Non-CO2 emissions from vegetation fires
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Area ofgrasslandburnedMass of available
fuel(kg d.m. ha-1)(ha)Combustion
efficiency or
fraction of
biomass
combusted

Emission factor
for each GHG(g /kg d.m.)(dimension-less)

GL

GL

(a)

A

B

C

A

B

C

D
DCH4
DCO
DN2O
DNOx

CH4 Emissions
from fires

CO Emissions fromfires

N2O Emissions
from fires

NOx Emissions
from fires(tonnes CH4)

(tonnes CO)

tonnes (N2O)(tonnes NOx)

E=A•B•C•D• 10-6

F=A•B•C•D• 10-6

G=A•B•C•D• 10-6

H=A•B•C•D• 10-6

E

F

G

H

CH4
CO
N2O
NOx

(b)

(c)

Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2a: Annual change in carbon stocks in living and dead biomass
1 of 1Land-use
Category 1
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear 2

Area of land converted tograssland from some initialuse

Carbon stocks in biomass
immediately afterconversion to grassland(ha yr-1)(tonnes C ha-1)

Carbon stocks inbiomass immediately
before conversion to
grassland

Carbon stock change per
area for that type of
conversion

Carbon stocks from one
year of growth ofgrassland vegetation
after conversion

Annual change in carbon
stocks in living biomass(tonnes C ha-1)

F = A • (D+E)(tonnes C yr-1)(tonnes C ha-1)(tonnes C ha-1)
D = B-C

FL

GL

A

B

C

D

E

F

AConversion

CAfterCBeforeLConversion∆CGrowth∆CLGLB 3

(a)
(b)
(c)
Sub-total

CL

GL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

GL

(a)
(b)
(c)
Sub-total

Total
1

FL stands for forest land; CL for cropland; GL for grassland; WL for wetlands, SL settlements, and OL for other lands. See Chapter 2 for approaches in representing land areas.

2

Land use should be further divided according to grassland type and climate zone.

3

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.236

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2c1: Annual change in carbon stocks in mineral soils
1 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

GL

Area of land converted tograssland from some initial
use

(b)

Inventory time period(default is 20 yr)

Reference carbon stock-1(tonnes C ha )Stock change factor forland use or land-use
change type in the initial
year (pre-conversion)

Stock change factor for
management regime in
the initial year (preconversion)(dimensionless)(dimensionless)(ha)

Stock change factor for input
of organic matter in the initial
year (pre-conversion)(dimensionless)

A

B

C

D

E

F

A

T

SOCrefFLU(0-T)

FMG(0-T)

FI(0-T)(c)
Sub-total
CL

GL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

GL

(a)
(b)
(c)
Sub-total

Total

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2c1: Annual change in carbon stocks in mineral soils
2 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

GL

Soil organic carbon stock in
the initial year (preconversion)

Stock change factor forland use or land-use
change type in current
inventory year

(tonnes C ha-1)

Stock change factor for
management regime incurrent inventory year

Stock change factor for
input of organic matter incurrent inventory year

Annual change in carbon
stocks in mineral soils(tonnes C yr-1)(tonnes C ha-1)(dimensionless)(dimensionless)(dimensionless)

G=C•D•E•F(b)

Soil organic carbon
stock in current
inventory year

K=C•H•I•J

L = [(K-G) •A] / B

G

H

I

J

K

L

SOC(0-T)

FLU(0)

FMG(0)

FI(0)

SOC0

∆CLGMineral 1(c)
Sub-total
CL

GL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

GL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.238

Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2c2: Annual change in carbon stocks in cultivated organic soils
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

GL

Land area of organic soils in climate type c which areconverted to grassland(b)

Emission factor for climate type c-1-1(tonnes C ha yr )CO2 emissions from cultivated organic soils(tonnes C yr-1)

(ha)
C=A•B
A

B

C

A

EF

∆C LGOrganic 1(c)Sub-total
CL

GL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

GL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

IPCC Good Practice Guidance for LULUCF

3.239

Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2c3: Annual carbon emissions from agricultural lime application
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

GL

(b)

Type of lime

Total annual amount of lime applied

Emission Factor(carbonate carbon contents of the materials)-1(tonnes lime yr )Annual carbon emissions from agricultural
lime application(tonnes C yr-1)(tonnes C/tonnes lime)
D=B•C
A

B

C

D

type

AmountEF∆C LGLiming 1(c)
Sub-total
CL

GL

(a)
(b)
(c)
Sub-total

WL, SL,
OL

GL

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2c4: Annual soil carbon stock change in grassland
1 of 1

Annual change in carbon stocks inmineral soils

CO2 emissions from cultivated organic
soils

Annual carbon emissions fromagricultural lime application

Annual change in carbon stocks in soils(tonnes C yr-1)-1(tonnes C yr )-1(tonnes C yr )-1(tonnes C yr )C = A-B-C

A

B

C

D

∆CLGMineral∆C LGOrganic∆C LGLiming∆CLGSoils

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Grassland
Land Converted to Grassland
GL-2d: Non-CO2 emissions from vegetation fires
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

GL

(a)

Area ofgrasslandburnedBiomass of available
fuel present(kg d.m. ha-1)(ha)Combustion
efficiency or
fraction of
biomass
combusted(dimension-less)

Emission factor
for each GHG

CH4 Emissions
from fires

CO Emissions fromfires

N2O Emissions
from fires

NOx Emissions
from fires(g /kg d.m.)(tonnes CH4)

(tonnes CO)

tonnes (N2O)(tonnes NOx)

E=A•B•C•D• 10-6

F=A•B•C•D• 10-6

G=A•B•C•D• 10-6

H=A•B•C•D• 10-6

D
DCH4

E

F

G

H

A

B

C

A

B

C

DCO
DN2O
DNOx

CH4
CO
N2O
NOx

(b)

Sub-total
CL

GL

(a)

(b)

Subtotal
Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Wetlands
Wetlands Remaining Wetlands (Organic soils managed for peat extraction)
WL-1c: Annual carbon stock change in soil 1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Area of nutrient rich organicsoils managed for peat
extraction, including
abandoned areas in which
drainage is still present

Emission factor for CO2 fromnutrient rich organic soils
managed for peat extraction(tonnes C ha-1 yr-1)

WL

Emission factor for CO2 from nutrient
poor organic soils managed for peat
extraction

CO2 emissions from organic soils
managed for peat extraction(tonnes C yr-1)(tonnes C ha-1 yr-1)
E = (A • B) + (C • D)

(ha)

WL

Area of nutrient poor organicsoils managed for peat
extraction, including
abandoned areas in which
drainage is still present
(ha)

A

B

C

D

E

ApeatNrich

EFpeatNrich

ApeatNpoor

EFpeatNpoor∆CWW peatSoils = ∆CWW peatSoils extraction

(a)
(b)
(c)
Sub-total

Total
1CO2 emissions occurring from peat stockpiles and restoration operations are not well understood. Hence, only method and data for estimating the change in soil carbon stock associated with peat extraction (essentiallyemissions due to enhanced oxidation at the production fields) are given.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Wetlands
Wetlands Remaining Wetlands (Organic soils managed for peat extraction)
WL-1d1: N2O emissions from peatland drainage
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

WL

(a)

WL

Area of nutrient rich drainedorganic soils

Emission factor for N2O for
nutrient rich organic soils

Area of nutrient poor drainedorganic soils

Emission factor for N2O for nutrient
poor organic soils

N2O emissions from drainedorganic soils(ha)(kg N2O-N ha-1 yr-1)(ha)(kg N2O-N ha-1 yr-1)(Gg N2O yr-1)
E = [(A • B) + (C • D)] •44/28 • 10-6

(b)

A

B

C

D

E

A peat Nrich

EF2peat Nrich

A peat Npoor

EF2peat Npoor

N2O EmissionsWW peat(c)Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Wetlands
Wetlands Remaining Wetlands (Flooded Land Remaining Flooded Land)
WL-1d2: CO2 Emissions from flooded lands1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

WL

(a)

WL

Total flooded surface area,
including flooded land,
flooded lake and flooded
river surface area

Flooding period(days per year)

Gg CO2 ha-1 day-1)

(ha)

(b)

Average daily diffusive
emissions2

Total CO2 emissions from flooded
lands(Gg CO2 yr-1)
D=A•B•C

A

B

C

D

Aflood, total surface

P

E(CO2)diff

CO2 Emissions WW flood(c)Sub-total

Total
1

The default assumption is that the CO2 emission would be limited to approximately 10 years and land flooded > 10 years ago need not be included.

2

Usually 365 days for annual inventory estimates.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Wetlands
Wetlands Remaining Wetlands (Flooded Land Remaining Flooded Land)
WL-1d3: CH4 emissions from flooded lands
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

WL

(a)

WL

Total flooded surface area,
including flooded land,
flooded lake and flooded
river surface area

Flooding period

Average daily diffusive
emissionsAverage daily bubble emissions

Total CH4 emissions from flooded
lands(Gg CH4 ha-1 day-1)(Gg CH4 yr-1)

1(days per year)(Gg CH4 ha-1 day-1)
E = A •B • (C + D)

(ha)

(b)

A

B

C

D

E

Aflood, total surface

P

E(CH4)diff

E(CH4)bubble

CH4 Emissions WW flood(c)Sub-total

Total
1

Usually 365 days for annual inventory estimates.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Wetlands
Wetlands Remaining Wetlands (Flooded Land Remaining Flooded Land)
WL-1d4: N2O emissions from flooded lands
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

WL

(a)

WL

Total flooded surface area,
including flooded land, flooded
lake and flooded river surface area(b)

Flooding period

Average daily diffusive emissions

1

Total N2O emissions from flooded lands(Gg N2O yr-1)(days per year)-1-1(Gg N2O ha day )D=A•B•C

(ha)
A

B

C

D

Aflood, total surface

P

E(N2O)diff

N2O EmissionsWW flood(c)Sub-total

Total
1

Usually 365 days for annual inventory estimates.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Wetlands
Land converted to peat extraction
WL-2a1: Annual change in carbon stocks in living biomass
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Area of land converted
annually to peat extraction
from original land use iAboveground biomass
immediately following
conversion to peat extraction

Aboveground biomass
immediately before conversion
to peat extraction

Carbon fraction of dry matter(ha yr-1)(tonnes d.m. ha-1)(tonnes d.m. ha-1)[tonnes C (tonnes d.m.)-1](default = 0.5)

Annual change in carbon
stocks in living biomass inland converted to peat
extraction(tonnes C yr-1)
E = A • (B-C) • D

FL

WL

A

B

C

D

E

Ai

BAfter

BBeforeCF∆CLW peatLB 1

(a)
(b)
(c)
Sub-total

CL

WL

GL

WL

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land
use category as an example.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Wetlands
Land converted to peat extraction
WL-2c: Annual carbon stock change in soil 1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

WL

Area of nutrient rich organicsoils converted to peat
extraction

Emission factor for changes incarbon stocks in nutrient richorganic soils converted to peat
extraction(ha)Area of nutrient poor organicsoils converted to peat
extraction

Emission factor for carbon stocks innutrient poor organic soils converted
to peat extraction

Annual change in carbon stocks insoil due to drainage of organic soils
converted to peat extraction(ha)(tonnes C ha-1 yr-1)(tonnes C yr-1)(tonnes C ha-1 yr-1)
E = (A • B) + (C • D)

(b)

A

B

C

D

E

ANrichEFNrichANpoor

EFNpoor∆CLW peatSoils 2 = ∆Cdrainage(c)
Sub-total
CL

WL

GL

WL

Total
1

In the case of land converted to peat extraction, only the effect of peat drainage is considered.

2

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Wetlands
Land converted to flooded land (Reservoirs)
WL-2a2: Annual change in carbon stock in living biomass1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

WL

Area of land converted
annually to flooded land
from land use i

Living biomass immediately
following conversion toflooded land

Living biomass in land
immediately before conversion
to flooded land

Carbon fraction of dry matter(default = 0.5)

Annual change in carbon stocks inliving biomass in land converted toflooded land(default = 0)

(tonnes d.m. ha-1)[tonnes C (tonnes d.m.)-1](tonnes C yr-1)(ha yr-1)
E = A • (B-C) • D(tonnes d.m. ha-1)

(b)

A

B

C

D

E

Ai

BAfter

BBeforeCF∆CLW floodLB 2(c)Sub-total
CL

WL

GL

WL

Total
1

Only carbon stock changes in living above-ground biomass due to conversion to flooded land are considered assuming the carbon stock prior to the conversion is lost the first year after the conversion (Tier 1).

2

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land use
category as an example.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Settlements
Settlements Remaining Settlements
SL-1a: Annual carbon stock change in living biomass 1
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

SL

(a)

SL

Total crown cover area

Crown cover area-based
growth rate

Annual biomass growth

Annual biomass loss 2

Changes in carbon stocks in living
biomass(tonnes C yr-1)(tonnes C yr-1)

-1

(ha)

(tonnes C yr )[tonnes C (ha crown cover)-1
yr-1]

C=A•B
E = C-D

(b)

A

B

C

D

E

ACROWN

CRW

∆BSSG

∆BSSL

∆CSSLB(c)
Sub-total

Total
1

There are two options for a Tier 1 estimation of changes in carbon stock in living biomass: a) crown cover area method; and b) tree growth rate method. This worksheet is based on crown cover area method.

2

Carbon stock change in biomass loss set to zero if the average age of the tree population is less than or equal to 20 years; otherwise assume that carbon stock change in biomass growth is equal to loss.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Settlements
Land Converted to Settlements (Forest Land Converted to Settlements)
SL-2a: Annual carbon stock change in living biomass
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReporting
Year

FL

(a)

SL

Area of land converted annuallyfrom forest land to settlements

Carbon stock in living biomass immediately
following conversion to settlements(ha yr-1)(tonnes C ha-1)

Carbon stock in living biomass in forest
immediately before conversion tosettlements

Annual changes in carbon stocks in living
biomass due to conversion of forest land tosettlements(tonnes C ha-1)(tonnes C yr-1)
D = A • (B-C)

(b)

A

B

C

D

A

CAfterCBefore∆CFSLB 1(c)
Sub-total

Total
1

The subscript FS means “forest land converted to settlements”.

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Other Land
Land Converted to Other Land
OL-2a: Annual change in living biomass
1 of 1Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

FL,CL,GL,WL

OL

Area of land converted
annually to “Other Land”
from some initial land usesin the reporting year

Amount of living biomass
immediately afterconversion to “OtherLand”(ha yr-1)(tonnes d.m. ha-1)

Amount of living biomass
immediately before conversion to“Other Land”

Carbon fraction of dry matter(default is 0.5)

Annual change in carbon stocks inliving biomass in land converted to“Other Land”(tonnes d.m. ha-1)[tonnes C (tonnes d.m.)-1](tonnes C yr-1)
E = A • (B-C) •D

A

B

C

D

E

AConversion

BAfter

BBeforeCF∆CLOLB 1

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Chapter 3: LUCF Sector Good Practice Guidance

ModuleSub-module
Worksheet
Sheet

Other Land
Land Converted to Other Land
OL-2c1: Annual change in carbon stocks in mineral soil
1 of 2Land-use
Category
InitialLand use
Land useduringreportingYear

Subcategories
forReportingYear

Reference carbon stock (seeTable 3.3.3)(tonnes C ha-1)

Stock change factor forland use or land-use
change type in theinventory year (see Table
3.3.4)

Stock change factor for
management regime in theinventory year (see Table 3.3.4)

Stock change factor for input
of organic matter in the inventory year
(see Table 3.3.4)(dimensionless)(dimensionless)Soil organic carbon stocks in theinventory year
(tonnes C ha-1)
E=A • B • C • D(dimensionless)

FL,CL,GL
,WL

OL

A

B

C

D

E

SOCRefFLU(0)

FMG(0)

FI(0)

SOC0

(a)
(b)
(c)
Sub-total

Total

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Annex 3A.2
ModuleSub-module
Worksheet
Sheet

Other Land
Land Converted to Other Land
OL-2c1: Annual change in carbon stocks in mineral soil
2 of 2Land-use
Category
Initial Land use
Land useduringreportingYear

Subcategories
forReportingYear

Time period
for theconversion(default is
20)

Land area
converted
to ”Other
Land”

Reference carbon
Stock(see Table 3.3.3)(tonnes C ha-1)

(ha)
(yrs)

Stock change
factor for land use
or land-use change
type T years prior
to the inventory
year, (see Table
3.3.4)

Stock change factor
for management
regime T years
prior to theinventory year
(see Table 3.3.4)

Stock change
factor for input
of organic matter T
years prior to theinventory year
(see Table 3.3.4)

Soil organiccarbon stocks Tyears prior to theinventory year

(dimensionless)(dimensionless)

L = H • I • J •K

(tonnes C ha-1)(tonnes C yr-1)(dimensionless)

FL, CL, GL, WL

OL

Annual change incarbon stocks insoil organic
matter in mineralsoils

M = [(E-L) • G]
/F

F

G

H (= A)

I

J

K

L

M

T

A

SOCRefFLU(0-T)

FMG(0-T)

FI(0-T)

SOC(0-T)∆CLOMineral 1

(a)
(b)
(c)
Sub-total

Total
1

Symbols are provided to show the relationship among the worksheets, compilation worksheets, reporting table, and the equations in the main body of the report. Please note that symbols are provided for only one land-use
category as an example.

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Appendix 3a.1

Appendix 3a.1 Harvested wood products:
Basis for future methodological development
3a.1.13a.1.1.1

Methodological Issues
R ELATIONSHIP TO THE IPCC GUIDELINES 1

The IPCC Guidelines (IPCC, 1997) provide an outline of how harvested wood could be treated in national
greenhouse gas (GHG) inventories. This section shows the relation of that outline to the approaches andestimation methods to be presented in this Appendix. Wood and paper products are referred to as harvested wood
products (HWP). It does not include carbon in harvested trees that are left at harvest sites. The issue of harvested
wood is discussed in Box 5 (IPCC Guidelines, Reference Manual, p. 5.17) as follows:“For the purposes of the basic calculations, the recommended default assumption is that all carbon
removed in wood and other biomass from forests is oxidised in the year of removal. This is clearly notstrictly accurate in the case of some forest products, but is considered a legitimate, conservative
assumption for initial calculations.”
and“...the recommended default assumption is that all carbon in biomass harvested is oxidised in theremoval year. This is based on the perception that stocks of forest products in most countries are notincreasing significantly on an annual basis.” The Guidelines go on to say “The proposed method
recommends that storage of carbon in forest products be included in a national inventory only in thecase where a country can document that existing stocks of long term forest products are in factincreasing. If data permit, one could add a pool to Equation (1) in the changes in forest and otherwoody biomass stocks calculation to account for increases in the pool of forest products. This
information would, of course, require careful documentation, including accounting for imports andexports of forest products during the inventory period.”
A note on the relationship between this discussion and this report: The IPCC Guidelines recommend that storage
estimates only be included in inventories if a country can document a method indicating that stocks are
increasing. This Appendix is intended to further the discussion as to when such methods may be available forcountries to determine and document increases in HWP stocks. This Appendix is based on the presumption thatan effort should be made to enable countries to determine if they may meet the “only if” condition of the IPCC
Guidelines.
The above outline in the IPCC Guidelines provides the starting point in the development of good practiceguidance for HWP estimation and reporting. The recommended default assumption – basically that harvested
wood is oxidised in the removal year – has the same effect as the case where there are no significant changes in
product stocks. In this case carbon flux of harvesting equals the decay flux of HWP into the atmosphere, butthere could still be a delay in emissions (and substantial but constant HWP stocks). This assumption is called the
IPCC default approach in the remainder of this section. The outline says that if data permit, positive stockchanges in HWP can be reported in national greenhouse gas inventories. There are two alternatives ways to do
this:Approach 1: Estimation of annual carbon stock changes of HWP in a country regardless of wood origin. This
would mean that:•

Wood carbon sources are not spatially specific – that is, product carbon comes from a number of land areas
including foreign forests but the carbon ends up in the reporting country.

•

Estimates of stock changes would be based on data for what happens to products in uses and waste disposal
within the borders of a country – it could include movements of products into and out of the country. Data
on uses and disposition would be found in one country.

1

The Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 1997) is abbreviated as IPCC
Guidelines in this report.

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Chapter 3: LUCF Sector Good Practice Guidance•

Wood is from many sources and management activities – possibly outside the country. The change in stockcannot be linked to activities on one land area.

•

The approach may be used as part of an evaluation of the effect of factors on the accumulation and loss of
HWP carbon stored in a country.

•

There are several types of removals (or transfers to HWP) and emissions associated with the estimate of thechange in HWP stock in a country. These include the transfer of domestic harvest to products, the transfer ofimports to products, and the transfer of products to other countries, and the emissions from products to theatmosphere (see Figure 3a.1.1).

•

The positive carbon stock changes would be interpreted as removals or equivalently as negative emissions,expressed in Gg CO2/year in national greenhouse gas inventories.

Approach 1 is named as the Stock Change Approach.
Figure 3a.1.1 Carbon flows and stocks associated with forests and harvested wood products(HWP) to illustrate the Stock Change and Atmospheric Flow Accounting 2
Approaches.

ATMOSPHERE
NEE

E

Own country
PEX

Forestecosystems

H

HWP

PIM

Variable definitions:NEE= net ecosystem exchangeH= harvested wood transported from forestsE= emissions from HWP within country borders= exports of HWP including roundwood, wood-based waste and refined products
PEX= imports of HWP including roundwood, wood-based waste and refined products
PIM
Approach 2: Estimation of annual carbon stock changes of HWP where the carbon is from trees harvested in thereporting country. This would mean:•

Estimates of stock changes would be based on what happens to wood carbon that originated from one landarea – it could include movement of products out of the country and its disposition in other countries. Data
on uses and disposition would potentially be needed from different countries or assumptions may be needed
about disposition in other countries.

•

Consequently, the reporting boundary would not coincide with national borders.

•

Wood is from one land source and carbon stock change would be associated with management activities onthat land.

•This approach might be used as part of an evaluation of carbon storage changes associated with management
on certain land areas.

•

This approach could follow the life cycle of all wood carbon harvested from a specific land area.

2

Atmospheric Flow Approach is the Approach 3 in this section.

3.258

IPCC Good Practice Guidance for LULUCF

Appendix 3a.1•

The positive carbon stock changes would be interpreted as removals or equivalently as negative emissions,expressed in Gg CO2/year in national greenhouse gas inventories.

•

There are several removals (or transfers to HWP) and emissions associated with the estimate of changes in
HWP stocks that came from timber in a country. These include the transfer of domestic harvest to productsin the country and to other countries, the emissions from HWP in the country that came from domestic
harvest and emissions from HWP in other countries that came from domestic harvest (see Figure 3a.1.2).

Approach 2 is named as the Production Approach.

Figure 3a.1.2 Carbon flows and stocks associated with forests and harvested wood products(HWP) to illustrate the Production Accounting Approach.

ATMOSPHERE
E

NEE

DOM

Own CountryH

EX OTH

Abroad
HWP

Forestecosystems

E

E EX DOM

EIM

HWP

in usein useor in waste sites

or in waste

sites

PEX
PIM

Variable definitions:NEE= net ecosystem exchangeH= harvested wood transported from forests= emissions from HWP in own country made from wood harvested from domestic forestsEDOM
EEX DOM = emissions from HWP in other countries made from wood exported abroad that were madefrom wood harvested from own country’s forests= emission from imported HWP in own countryEIM
EEX OTH = emissions from HWP in other countries made from wood harvested in other countries= exports of HWP including roundwood, wood-based waste and refined products
PEX= imports of HWP including roundwood, wood-based waste and refined products
PIM
Approaches 1 and 2 above were elaborated at an IPCC Expert Meeting on Harvested Wood Products (IPCC,
1998). If either approach was used by an inventory agency, the estimated annual change of HWP stocks would
be added to the estimated annual biomass change in Equation 1 in the IPCC Guidelines (Reference Manual, p.
5.19). Equation 1 in the IPCC Guidelines corresponds to the sum of Equations 3.2.1 and 3.2.21 in Chapter 3 ofthis report. Equation 3.2.1 indicates carbon change on forest land that remains forest land and Equation 3.2.21
indicates carbon change on non-forest land that is converted to forest land. The Production Approach would addchange in HWP carbon where the carbon came from trees in domestic forests (the land sources cited in
Equations 3.2.1 and 3.2.21). The Stock Change Approach would add the change in HWP carbon that is resident
in the country (includes imports, excludes exports).
A third approach, having no explicit reference in the IPCC Guidelines, was also elaborated at the abovementioned IPCC Expert Meeting.
Approach 3: Estimation of annual atmospheric fluxes between the atmosphere and forests/HWP within nationalboundaries. This would mean that:•

The viewpoint of the approach deviates from the previous ones. Instead of focusing on stock changes(Approaches 1 and 2), the focus is directly on carbon fluxes from and to the atmosphere. It considers theannual carbon removal by forests and emissions from the HWP.

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Chapter 3: LUCF Sector Good Practice Guidance•Instead of reporting the annual HWP stock change as in Approach 1, the annual emissions are reported in
Approach 3 (see Figure 3.a.1.1).

•

This approach could require modification of the existing reporting practice concerning forests. Instead ofreporting only the net annual forest biomass change as growth minus harvest (and the changes in carbon of
the other stocks in forest ecosystems), the annual net carbon flux into forest ecosystems (net ecosystem
exchange) would be reported along with the estimates of emissions from HWP (see Figure 3.a.1.1).

•

Estimates of emissions would be based on data for what happens to products in use and waste disposal
within the boarders of a country – it could include movements of products into and out of the country. Data
on uses and disposition would be found in the reporting country. In this sense it is similar to Approach 1 (seeFigures 3a.1.1 and 3a.1.3.)•

Wood is from many sources and management activities – possibly outside the country. The emissions arelinked to the location of emissions but not to the land the wood carbon came from. The latter is analogous toApproach 1.

•

This approach may be used to evaluate the effect of all the factors that influence the emissions from woodcarbon in a country.

•

There are several removals (or transfers to HWP) and emissions associated with the estimate of theemissions from HWP stock in a country. These include the transfer of harvest to products, the emissions
from HWP remaining in the country, and the emissions from products imported to the country (see Figure
3a.1.1).

•

The carbon flux E in Figure 3a.1.1 would be interpreted as an emission, expressed in Gg CO2/year innational greenhouse gas inventories.
Approach 3 is named as the Atmospheric Flow Approach.

Figure 3a.1.3 Carbon flows and stocks when products both in use and in solid waste disposalsites (SWDS) are considered (Stock change and Atmospheric flow accounting
approaches).

ATMOSPHERE
E

NEE

EW

Own country

Forestecosystems

HWP
in use

H

PIM

W

HWP
in SWDS

PEX

Variable definitions:
HWP= harvested wood products
NEE= net ecosystem exchangeH= harvested wood transported from forestsE= emissions from HWP in use within country borders= exports of HWP including roundwood, wood-based waste and refined products
PEX= imports of HWP including roundwood, wood-based waste and refined products
PIM
W= HWP carbon disposed into SWDS
= emissions from HWP in SWDS within country borders
EW

Objective of this Appendi xThis appendix provides information on possible methods to estimate stock changes consistent with the advice in
the IPCC Guidelines, if data are available. In addition, it would be relevant for any of the three approaches just

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outlined, or potentially for other approaches, depending on decisions from the COP and/or COP/MOP on thismatter.3

The issue of how to account f or carbon in w ood-based w aste
One additional issue to be resolved when deciding on methods is whether or not changes in HWP stocks in solid
waste disposal sites (SWDS) should be included in emission/removal estimation and reporting. And if so, howshould such stock changes be included? There are several questions to consider:•First, should assumptions about decay of wood in SWDS be consistent between the Waste Sector and theForest Sector? That is, if the Waste Sector estimates that a portion of wood carbon stocks in SWDS is notdecaying, should the Forest Sector assume the same?

•

Second, should the Waste Sector keep track of HWP stored in SWDS sites? If so, how would that bereflected in accounting for HWP in the Forest Sector? The Waste Sector currently accounts for andestimates methane emissions from Solid Waste Disposal Sites (SWDS) (including emissions from wood andpaper) but does not estimate corresponding changes in the carbon stock in SWDS.

The above mentioned questions are not resolved in this section but suggestions are presented on methods for
estimating changes in HWP carbon stored in SWDS.

The issue of how to account f or harvested w ood use f or energy production
Currently wood energy emissions are noted but not included in emissions accounting for the Energy Sector orother sectors that produce wood energy. These emissions are assumed to be accounted for in the Land Use
Change and Forestry (LUCF) Sector. That is, they are part of the emissions from harvested wood. A
consideration for an accounting approach for HWP may be that it properly accounts for emissions from woodenergy in a country. The Stock Change and Atmospheric Flow Approaches both account for all emissions fromwood burned for energy in a country but the Production Approach may not account for all wood burned forenergy if some wood is imported and later burned for energy. Such emissions are not accounted for because
imported wood (including amounts burned after being imported) is not included in the Production Approach.

Proposed Tier struct ure
Three tiers of estimation methods are suggested:
Ti er 1
IPCC Guidelines Default estimation method is the way of making the Tier 1 estimate. This tier or method
assumes that all carbon in biomass harvested is oxidised in the removal year. This would correspond to an
estimate of no change in HWP carbon stocks for both the Stock Change Approach and the Production Approach.
Ti e r 2 : F i r st o r de r de c a y ( a f l u x met ho d)
Estimates are made of stock changes of HWP carbon in products in use and – in the case where waste is included
in reporting – HWP carbon in SWDS. The estimates are made by tracking inputs to and outputs from these poolsof carbon (also called input and output fluxes). Data beginning a number of decades in the past up to the present
time are used to estimate 1) additions to HWP in use, 2) removals from use, 3) additions to HWP in SWDS, and
4) decay from SWDS. This procedure is needed to obtain an estimate of the existing HWP stock accumulated
from historical wood use and current year emissions from those stocks as they go out of use (also termed“inherited emissions”).
If HWP in SWDS is included, data used for Tier 2 is intended to be consistent with data used for the Tier 2
method used for the Waste Sector (Chapter 5, Waste, GPG20004). The numerical factors a country uses to3

Decisions about how to treat harvested wood products have been deferred. The Conference of the Parties decides that any
changes to the treatment of harvested wood products shall be in accordance with future decisions of the COP(FCCC/CP/2001/13/Add/1, page 55, paragraph 4). The SBSTA, in FCCC/SBSTA/2003/L.3, recalled decision 11/CP.7,
paragraph 4, and noted the possible inclusion of methods to estimate the change in carbon stored in harvested wood
products as an annex or appendix to the IPCC report on good practice guidance for LULUCF. The purpose of the Appendix
is to support the decisions of the Subsidiary Body for Scientific and Technological Advice. Because SBSTA has requested
that the UNFCC Secretariat “…prepare a technical paper on harvested wood products accounting…” this section focuses
on methods that the authors suggest may be used whatever is developed concerning accounting (FCC/SBSTA/2001/8, 4
Feb 2002).

4

IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC 2000) is
abbreviated as GPG2000 in this report.

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Chapter 3: LUCF Sector Good Practice Guidancecompute the methane emissions from SWDS should be consistent with the numerical factors used to computeamounts of HWP carbon retained in SWDS.
Ti er 3: Co unt ry- Sp ecif ic M etho d s
Change in HWP carbon in products in use and HWP carbon in SWDS (if agreed for inclusion) can each becomputed by separate methods. These methods may apply to some but not all of the accounting approaches(Flugsrud et al., 2001).
Method A – Estimate Change in inventories (stock methods)
Use inventories of HWP in use or HWP in waste disposal sites at two or more points in time and calculate thechange in carbon stored. The HWP pool of products in use in building structures is normally a major part of thetotal HWP pool. The amount of HWP carbon can be estimated, for example, by multiplying the average HWP
content per square metre of floor space times the total floor space for several types of buildings. Change incarbon may be estimated by noting the change between inventories estimated at different points in time.
Examples of such inventories are reported in Gjesdal et al., 1996 (for Norway) and in Pingoud et al., 1996, 2001
(for Finland). In this case no procedure for integration of the existing HWP stock from historical wood use datais needed, which is an advantage compared to the flux methods (Tier 2 and Tier 3/Method B). Similarly, it has
been suggested that the change in HWP carbon in SWDS could be estimated using information about the area,
average depth, and average wood and paper carbon content per cubic metre in these sites, although no examplesof this method have been reported in the literature.
Method B – Track input and output flows using detailed country data (flux methods)
Use detailed country data beginning a number of decades in the past and estimate each year, up to the present
time, (i) additions to pools of HWP in use, (ii) removals from use, (iii) additions to pools of HWP in SWDS, and(iv) decay from SWDS. Estimates for SWDS may use survey estimates of the amount of HWP placed in SWDS
each year rather than the amount of HWP going out of use and the portion going to SWDS.
Method C – Combine Method A and Method B estimates
An example of combining methods is 1) to use changes in inventory to estimate carbon changes in buildings andfurniture and 2) to use input and output flows to estimate changes of carbon in paper products (see example for
Norway, Flugsrud et al., 2001).

3a.1.1.2

C HOICE OF METH OD

With default data and country-specific estimates for some parameters, countries can use Tier 2 to makepreliminary estimates to evaluate changes in HWP stocks and whether counted increases in stocks would be a
key category. If country information is available it is suggested to use Tier 3 custom country methods, such aschange between actual inventories of wood products stored in long-lived pools for these purposes. If HWP is a
key category it is suggested that work would be done to develop national data for Tier 2 or Tier 3 estimates. If
HWP is not a key category, the Tier 1 method may be applied.

3a.1.1.3

C HOICE OF ACTIVITY DATA AND FACTORS IN
CALCULATIONS

Ti er 1: IP C C Gu id e li ne s D ef a u lt
Under Tier 1 the recommended default assumption is that all carbon in biomass harvested is oxidised in theremoval year. This is based on the perception that stocks of forest products in most countries are not increasingor decreasing significantly on an annual basis.
Ti e r 2 : F ir st o r de r de c a y me t h o d ( F O D)This method is termed the first order decay method because carbon in each of the carbon pools (products in use
and products in SWDS) is estimated to leave the pool at a constant percentage rate. The Tier 2 method for the
Waste Sector uses this technique to estimate methane emissions from SWDS (see Chapter 6, Waste, of the IPCC
Guidelines; and Chapter 5, Waste, of GPG2000).
The Tier 2 method is divided into two parts – Tier 2a to estimate HWP carbon changes for products in use andTier 2b to estimate HWP carbon changes in SWDS (see Figure 3a.1.3). Tier 2b is omitted if carbon changes in
SWDS are not included in reporting.

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The proposed method for estimating changes in carbon stored in HWP utilizes data on production andinternational trade of primary HWP (saw wood, panels and paper). Only primary products are used because data
are available for virtually all countries. Data on secondary products such as furniture may also be used if
available but care is needed to avoid double counting of HWP carbon5. Data on input flows and output flows
over several decades is used to calculate current year change in the pool of HWP carbon. The input flow to thepool in a country is calculated by adding imports to the national production of primary products, and subtracting
the exports. The output of the pool or decay is assumed to be of first order. That is, a constant fraction of eachpool is lost each year. The pool of primary products will include wood used in all its final uses. Wood-basedmaterial that is not accumulated into the stocks of HWP in use (or HWP in SWDS) in a country is assumed toform emissions. These calculations are valid for Stock Change Approach and may also be used to computecarbon flows for the Atmospheric Flow Approach. Stock Change and Atmospheric Flow Approaches in the case,where both products in use and in SWDS are included, are illustrated in Figure 3a.1.3. The Production Approachrequires additional approximations, as typically only a part of the HWP in a country are of domestic origin and,in addition, HWP of domestic origin might be exported (see Figure 3a.1.2).
The Tier 2 equations for the three approaches are as follows:
Tier 2a: Change in HWP carbon in products in use
EQUATION 3a.1.1
ANNUAL CHANGE IN HWP CARBON IN PRODUCTS IN USE AND THE ASSOCIATED CO2 EMISSIONS(1A) ∆CHWP IU SCA = PA – PL
CO2 emissions/removals SCA = ∆CHWP IU SCA ● 10-3 ● 44/12 ● (-1)(Stock Change Approach)(1B) ∆CHWP IU PA = PHA – PHL
CO2 emissions/removals PA = ∆CHWP IU PA ● 10-3 ● 44/12 ● (-1)(Production Approach)(1C) E = – ∆CHWP IU SCA + H – PEX + PIM – W
CO2 emissions/removals AFA = E ● 10-3 ● 44/12(Atmospheric Flow Approach)

Note 1: The quantity E estimated is the real flux of C from HWP stock into the atmosphere within
the borders of the reporting country (see Figures 3a.1.1 and 3a.1.3). The forest sector
should then report the real flux of carbon from atmosphere into the forest ecosystems(NEE) or the sum of stock changes in forest ecosystems + H, which is a deviation fromthe existing reporting practice in which only stock changes are reported (NEE – H).
Note 2: Each term has a year subscript t – omitted to simplify the format; each term on the righthand side of the equations has at least two parts: at least one for solidwood products andat least one for paper products.
Note 3: The changes in carbon in HWP are as a rule estimated as tonnes C yr-1 and converted to
Gg CO2 for reporting by multiplying with 10-3 ● 44/12. The emissions are reported as
positive and removals as negative – hence the multiplication with -1 (see also Section
3.7.1 and Annex 3A.2 Reporting Tables and Worksheets).
Where:∆CHWP IU SCA = annual change in carbon stored in HWP in use in the country, tonnes C yr-1
∆CHWP IU PA = annual change in carbon in HWP in use from wood harvested in the country (includes
carbon in exports and excludes carbon in imports, tonnes C yr-1
E = carbon flux from HWP into the atmosphere within the borders of the reporting country, tonnes C yr-1
H = current year wood carbon harvested and removed from sites to be processed into forest products(including fuelwood), tonnes C yr-1
W = current year HWP carbon disposed into SWDS (in case HWP in SWDS is included in reporting,otherwise W = 0), tonnes C yr-1

5

Use of wood products forms a chain and flow of carbon from round wood through primary and secondary products to finaluse. Double counting in estimation of the C input flow to the HWP pool is possible if, for instance, the consumption ofround wood and primary products or primary products and secondary products are summed up. In the proposed Tier 2amethod the consumption of primary products is assumed to form the input to the HWP pool.

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Chapter 3: LUCF Sector Good Practice GuidanceEach variable below has at least two parts – at least one for solidwood products, and at least one for paper products.
PA = current year additions to HWP carbon in use from domestic consumption calculated on the basis of
the primary products carbon flux, tonnes C yr-1
See Table 3a.1.1 for information on data for these values, tonnes C yr-1
PL = current year loss of HWP carbon from uses (placed in use in current or prior years) , tonnes C yr-1
PHA = current year additions to HWP carbon from wood harvested in the country calculated on the basisof the primary products carbon flux, tonnes C yr-1
See Table 3a.1.1 for information on data and calculating PHA, tonnes C yr-1
PHL = current year loss of HWP carbon in use (place in use in current or prior years) from wood harvested
in the country, tonnes C yr-1
PEX = exports of wood and paper products including roundwood, chips, residue, pulp, and recovered(recycled) paper, tonnes C yr-1
PIM = imports of wood and paper products including roundwood, chips, residue, pulp, and recovered(recycled) paper, tonnes C yr-1.
The procedure to calculate ∆CHWP IU SCA and ∆CHWP IU PA uses a recursive process shown below rather thancalculating losses from HWP use, PL or PHL, for the current year directly.
Beginning in, say j = year 1900, compute the following equation recursively6 for each year up to the current year t.
CHWP IUSCA (j) = (1 / (1 + fD )) ● (PAj + CHWP IUSCA(j – 1))(Stock Change Approach)

CHWP IUPA (j) = (1 / (1 + fHD )) ● (PAj + CHWP IUPA(j – 1))(Production Approach)Or
For the initial year, e.g. j = 1900, the value of C HWP IUSCA = 0 or C HWP IUPA = 0
For the current year calculate∆CHWP IUSCA (t) = CHWP IUSCA (t) – CHWP IUSCA (t – 1)

(Stock Change Approach)∆CHWP IUPA (t)(Production Approach)Or= CHWP IUPA (t) – CHWP IUPA (t – 1)

Where:∆CHWP IUSCA = annual change in carbon stored in HWP in use in the country, tonnes C yr-1
∆CHWP IUPA = annual change in carbon in HWP in use from wood harvested in the country (includes
carbon in exports and excludes carbon in imports), tonnes C yr-1
PA = current year additions to HWP carbon in use from domestic consumption calculated on the basis of
the primary products carbon flux, tonnes C yr-1
t = current year
j = year of data, starting in, for example, 1900, which is long enough in the past so that current decay isvery small from HWP placed in use in the early years
fD = fraction of HWP carbon in use in a country in a given year that is discarded in that year (discarded
products include those that are recycled)
fHD = fraction of HWP carbon in use in a country in a given year (includes exports) that is discarded inthat year (discarded products include those that are recycled).

6

The recursive formula above for stock change approach is equivalent to the equation:(CHWP IU
– CHWP IU (j – 1)) / ∆t = PA – fD ● CHWP IU, where ∆t is 1 yr.
SCA(j)SCAj

SCA(j)

This implicit Euler method (see Burden and Faires, 2001), is used as an approximation of a constant rate of decay from a
HWP pool specified by the differential equation dCHWP IU /dt = PA – fD ● CHWP IU .
SCA

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TABLE 3a.1.1
FAO DATA, AND FACTORS TO ESTIMATE PA AND PHA FOR TIER 2 EQUATION 3a.1.1
FAO Product Data
(Solidwood products data are in m3;
pulp and paper products are in Gg )Roundwood harvest (Coniferous)
Roundwood harvest (Non-Coniferous)
Saw wood (Coniferous)
Saw wood (Non Coniferous)
Veneer sheets
Plywood
Particle board
Fibreboard Compressed
Hardboard
MDF

Default conversion factors(Gg of oven dry product per m3
or Gg of product)

Time period
for data

Equation variables(see footnotes)

1961-2000

H

1961-2000

PDP (solidwood)
PIM (solidwood)
PEX (solidwood)

Roundwood harvest data
0.45 (Gg/ m3)
0.56 (Gg/ m3)
Solidwood products data
0.45 (Gg/ m3)
0.56 (Gg/ m3)
0.59 (Gg/ m3)
0.48 (Gg/ m3)
0.26 (Gg/ m3)
1.02 (Gg/ m3)
1.02 (Gg/ m3)
0.50 (Gg/ m3)
Pulp, paper and paperboard data

1961-1994
1995-2000

PDP (paper)
Paper and paperboard

0.9 (Gg/ Gg)

1961-2000

Recovered paper(Values set to zero from 1900 to 1969)

0.9 (Gg/ Gg)

1970-2000

Wood pulp

0.9 (Gg/ Gg)

1961-2000

Recovered fibre pulp

0.9 (Gg/ Gg)

1998-2000

Other fiber pulp

0.9 (Gg/ Gg)

1961-2000

PIM (paper)
PEX (paper)
RP
IM (RP)
EX (RP)WP
IM (WP)
EX (WP)
IM (RFP)EX (RFP)OFPIM (OFP)EX (OFP)

Industrial roundwood data
Industrial roundwood (Coniferous)

0.49 Gg/ m3)

Industrial roundwood (Non-Coniferous)

0.56 Gg/ m3)

Industrial roundwood (Coniferous)

0.49 Gg/ m3)

Industrial roundwood (Non-Coniferous)

0.56 Gg/ m3)

1961-2000

IRW
IM (IRW)

1990-2000

EX (IRW)

Sources: For FAO data see: http://apps.fao.org/page/collections?subset=forestry
Source of Conversion factors: Solidwood factors (Haynes et al. 1990, Tables B-7 and B-6)
NOTES:
Paper and pulp factors – One tonne of paper or pulp air dry are assumed to have 0.9 tonne of paper or pulp oven dry.
The equations below indicate how to compute PA and PHA for Equation 3a.1.1 using FAO data.
PA (solidwood) is the sum of solidwood products production; PA (paper) is the sum of paper products production.
PA (solidwood) = PDP (solidwood) + PIM (solidwood) – PEX (solidwood)
PA (paper) = [PDP (paper) + PIM (paper) – PEX (paper)] ● WPratio
Where WPratio is the fraction of all pulp that is wood pulp (excludes other fiber pulp).
WPratio = [(WP + IM (WP) – EX (WP))/ ((WP + IM (WP) – EX (WP)) + (OFP + IM (OFP) – EX (OFP))]PHA (solidwood) = PA (solidwood) ● IRW / (IRW + IM (IRW) – EX (IRW))PHA (paper) = [(PA (paper) + EX (WP) – IM (WP) ● WPratio + EX (RP) – IM (RP) + EX (RFP) – IM (RFP)] ● IRW / (IRW + IM (IRW)– EX (IRW))
Convert tonnes of dry products PA and PHA to tonnes of carbon by multiplying by 0.5 (tonnes carbon / tonnes product).

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Chapter 3: LUCF Sector Good Practice Guidance

Tier 2b: Change in HWP carbon in Solid Waste Disposal Sites (SWDS)
If included in reporting, stock changes of HWP in SWDS could be calculated in similar manner as HWP in use:
EQUATION 3a.1.2
ANNUAL CHANGE IN HWP CARBON IN SWDS AND THE ASSOCIATED CO2 EMISSIONS(2A) ∆CHWP WSCA = WAP + WAD – WL
CO2 emissions/removals SCA = ∆CHWP W SCA ● 10-3 ● 44/12 ● (-1)(2B) ∆CHWP WPA(Stock Change Approach)= WHAP + WHAD – WHL

CO2 emissions/removals PA = ∆CHWP W PA ● 10-3 ● 44/12 ● (-1)(Production Approach)(2C) ∆CHWP W AFA = WAP + WAD – ∆CHWP WSCA = WL
CO2 emissions/removals AFA = ∆CHWP W AFA ● 10-3 ● 44/12(Atmospheric Flow Approach)

Note 1: Each term has a year subscript t – omitted to simplify the format.
Note 2: Each term on the right hand side of the equations has at least two parts – at least one forsolidwood products and at least one for paper products)
Where:∆ C HWP WSCA = annual change in carbon stored in HWP in SWDS in the country, tonnes C yr-1
∆C HWP WPA= annual change in carbon in HWP in SWDS from wood harvested in the country (includes
carbon in exports and excludes carbon in imports), tonnes C yr-1

∆CHWP WAFA = carbon emissions from HWP in SWDS, tonnes C yr-1

Each variable below has at least two parts – at least one for solidwood products, and at least one for paper
products
WAP = amount of current year additions of HWP carbon to SWDS which are permanent (no decay)7,
tonnes C yr-1
WAD = amount of current year additions of HWP carbon to SWDS which decay over time (note that WAP+ WAD = W in Tier 2a), tonnes C yr-1
WL = loss of HWP carbon from SWDS (placed in sites in current or prior years)
WHAP = amount of current year additions of HWP carbon to SWDS that are permanent (no decay) (fromwood harvested in the country), tonnes C yr-1
WHAD = amount of current year additions of HWP carbon to SWDS which decay over time (from wood
harvested in the country), tonnes C yr-1
WHL = loss of HWP carbon from SWDS (placed in sites in current or prior years) (from wood harvested
in the country), tonnes C yr-1
We do not provide detailed equations and data to estimate storage in SWDS because more development isneeded on default data and methods and this development needs to be coordinated with guidance provided for
the Waste Sector on how to calculate emissions from SWDS.
In general terms, estimation of the HWP carbon storage in SWDS requires data on:

7(i)

The fraction of discarded HWP carbon that goes to SWDS each year;(ii)

The fraction of HWP carbon going to SWDS that goes to anaerobic conditions (versus aerobic
conditions);(iii)

The fraction of HWP carbon going to anaerobic conditions in SWDS that decays (a portion does
not decay as indicated by the good practice guidance for the Waste Sector (GPG2000);(iv)

The rate of decay for the portion of HWP carbon (in anaerobic conditions) that does decay; and(v)

The rate of decay for HWP carbon in aerobic conditions.

Only a portion of degradable organic carbon in SWDS decays as indicated in the IPCC Guidelines for the Waste Sector(see variable DOCF in IPCC Guidelines, Reference Manual p 6.5).

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Information on default data for items 2 through 5 above are indicated in the good practice guidance for the Waste
Sector (GPG2000). Country-specific data are needed for item 1 above – the fraction of discarded HWP carbon
that goes to SWDS each year.
Ti er 3: Cu st o m co u ntry met ho ds
EQUATION 3a.1.3
ANNUAL CHANGE IN CARBON IN HWP (EXAMPLE OF A CUSTOM COUNTRY METHOD)(3A) ∆CHWP BLDG SCA = (ABLDG t ● fC BLDG t) – (ABLDG t -1 ● fC BLDG t-1)

(Stock Change Approach)(3B) ∆CHWP W SCA = (VHWP SWDS t ● fC SWDS t) – (VHWP SWDS t-1 ● fC SWDS t -1)(Stock Change
Approach)

Where:∆CHWP BLDG SCA= annual change in HWP carbon contained in buildings, tonnes C yr-1
∆CHWP W SCA = annual change in HWP carbon contained in SWDS, tonnes C yr-1
ABLDG = floor area of buildings, m2
fC BLDG = HWP carbon in buildings per unit of floor area, tonnes C m-2
VHWP SWDS = Volume of HWP waste in disposal sites, m3
fC SWDS = HWP carbon in SWDS per unit volume of SWDS, tonnes C m-3
Data so u rce s fo r th e Ti er 2 met ho d
The following bullet points summarise how to obtain the data needed for Tier 2 calculations, identifying defaults
which are available in many cases.
Data for variables PA (carbon in HWP consumed in a country) and PHA (carbon in HWP products produced by acountry) are as follows:•

Default data for HWP production, imports and exports are available in the United Nations FAOSTATForestry database since 19618 (see Table 3a.1.1). Separate PA values need to be computed for solidwood andpaper products as indicated in the notes to Table 3a.1.1 to allow for different lifetimes in use and disposal
patterns.

•

Data to convert units of solidwood products to carbon content are shown in Table 3a.1.1.

•

Data prior to 1961 can be estimated using a trend in growth back to 1900.

For each forest product in Table 3a.1.1, the values before 1961 can be estimated by:
EQUATION 3a.1.4
EQUATION TO ESTIMATE PRODUCTION AND TRADE FOR YEARS BEFORE 1961
Vt = V1961 ● e ( r ● ( t – 1961))

Where V is value of the forest product in question, t is a year before 1961 and r is the estimated growth rate prior
to 1961. Default r values for growth between 1900 and 1961 are indicated in columns 7 and 8 in Table 3a.1.2.
•

See Table 3a.1.1 for factors to convert product amounts from volume or weight measure to tonnes of carbon.

Data for parameters fD and fHD (the fraction of HWP carbon put in use in year t that goes out of use in each year)•

Separate fD and fHD values are needed for solidwood products and paper products.

•

The average fD and fHD values for solidwood products could be the weighted average of fD and fHD for
lumber, panels and other industrial roundwood.

•

The average for fHD would be a weighted average of fD (for the home country) and for countries where
exports are used and later discarded. Weights would be the portion of PHAt that is from domestic use and the
portion of PHAt that is exported. A starting point would be to assume fD equals fHD.

8

See http://apps.fao.org/page/collections?subset=forestry

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Chapter 3: LUCF Sector Good Practice Guidance•fD and fHD values may also be converted from estimates of the half life for products in use or from theaverage life of a product. The half-life is the number of years until one-half of the products have gone out ofuse. The average life is the average number of years a product is in use.
fD = ln 2 / (half life in years) = 0.693 / (half life in years)
fD = 1 / (average life in years)
average life in years = 1 / fD•

Half life values of various products used in recent studies, including suggested default values, are shown in
Table 3a.1.3. Each country needs to determine values appropriate for their country.

3a.1.2Completeness

The Tier 2 methods include all primary wood and paper products. By doing so they include the carbon in any
secondary wood products made from those primary products. But the methods do not include the effect on stockchanges of carbon in imports and exports of secondary wood products, such as furniture or wooden crafts.
Methods may need to be adapted to include imports and exports of secondary wood products if HWP is a key
category and amounts of secondary wood product traded are notable in comparison to amounts of primary
products produced or consumed. The Tier 2 method also omits any estimates of the amount of waste wood that
goes from primary or secondary wood and from paper mills directly to SWDS. If these amounts are significant
then separate direct estimates may be needed for these wood waste flows to SWDS.

3a.1.3

Uncertainty Assessment

Uncertainty estimates for variables and parameters for the Tier 2 method are shown in Table 3a.1.4. The
estimates are based on published studies and expert judgement. If national values are used for variables andparameters, uncertainties should be evaluated consistent with the guidance in Section 5.2, (Identifying andQuantifying Uncertainties) of this report.
The only firm estimates of uncertainty likely to be available are those associated with national surveys of woodand paper products production and trade. For these, the error can be relatively low.
For the Tier 2 method, the effect of uncertainty in production and trade several decades in the past is relatively
less if the half-life of products in use and the half-life in SWDS is relatively short. This means that with longer
use life, it becomes more important to use country-specific data on production and trade before 1961.
Uncertainty in Tier 2 estimates could be quite large particularly if country-specific uncertainty is large inestimates over time in 1) fraction of discarded wood and paper going to SWDS, and 2) proportion of products in
SWDS undergoing anaerobic decay. Because of these uncertainties it is desirable to use Tier 3 national level
inventory surveys of wood stored in stocks such as housing, if possible. Such surveys may have relatively low
uncertainties. Estimating uncertainties associated specifically with the Production Approach would include
estimating the uncertainty of decay of products exported to other countries. Overall, uncertainties for Tier 2 orTier 3 methods may be estimated using Tier 3 (Monte Carlo) methods discussed in Section 5.2, (Identifying andQuantifying Uncertainties). Further work is needed to specify a simpler method to evaluate uncertainties – that is
equations that could use uncertainties from Table 3a.1.4 directly to estimate the overall uncertainty rather than to
use the Monte Carlo simulation method. Use of the Tier 2 methods with default data, that is, without countryspecific data, is unlikely to produce estimates with uncertainly less than ± 50%.

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0.0226
0.01930.03590.05480.04920.0412Latin America

AfricaAsia

Oceania

0.01730.00740.01550.02550.00540.00160.00870.012

0.00490.01550.00780.01020.01630.01480.00610.00590.0085(1900-1950)(4)

Population

0.01930.01930.0226

0.02680.01700.01730.00800.0182(1900-1950)(5)=(2)IPCC Good Practice Guidance for LULUCF

Column 4 – 1900-1950: (UN Pop Div 1999)

Population – 1950-1960: (UN Pop Div. 1998), 1961-1975: (UNFAO 2002b)

Column 3 – Industrial roundwood - 1950-53: (UNFAO 1957), 1954-1960: (UNFAO 1965), 1961-1975: (UNFAO 2002a)

Column 2 – 1950-1960: (UN Pop Div 1998), 1961: (UNFAO 2002b)

Industrial
roundwood

Industrial
roundwood

Industrial
roundwood

Industrial
roundwood

0.02290.02330.03570.02170.01640.01480.01790.0134(1900-1950)(6)=(3)+(4)0.02330.02470.02840.02850.01550.02160.01190.0208(1900-1961)(7) see note

0.02620.02800.03910.02430.01500.01960.02000.0169(1900-1961)(8) see note

3.269

Production with
Production with
Production with
Production withproduction per
production per
production per
production percapita deceasing at
capita fixed at 1950 capita deceasing at capita fixed at 1950
1950 to 1975 ratelevel
1950 to 1975 ratelevel before 1950
before 1950

Data Sources: Column 1 -- 1950-53: (UNFAO 1957), 1954-1960: (UNFAO 1965), 1961: (UNFAO 2002a)

Note:Column 8 is ln ( EXP(col 6*50)*EXP(col 1*11) )/ 61

Note:Column 7 is ln ( EXP(col 5*50)*EXP(col 1*11) )/ 61

0.01930.02680.01700.04120.0085USSR

North America

0.01820.00800.03260.0296(1950-1975)(3)(1950-1961)(1)

EuropeProduction percapitaIndustrial
roundwood

Production(1950-1961)(2)

Population

World total

World region

Industrial
roundwood

TABLE 3a.1.2
ESTIMATED ANNUAL RATES OF GROWTH FOR INDUSTRIAL ROUNDWOOD PRODUCTION (HARVEST) BY WORLD REGION, FOR SELECTED PERIODS 1900 TO 1961.
(Columns 7 and 8 are rates that may be used to project wood and paper products production and trade data backward in time from 1961 using equation 3A.1.4)

Appendix 3a.1

Chapter 3: LUCF Sector Good Practice Guidance

Table 3a.1.3
HALF LIFE OF HARVESTED WOOD PRODUCTS IN USE – EXAMPLES FROM STUDIESCountry/
regionDefaults

FinlandFinlandFinlandNetherlands

UnitedStatesHalf life in use(years)Fraction loss each year (f D j )(ln(2) / Half life in years)

Saw wood
Veneer, plywood and structural
panelsNon structural panels

35

0.019830

0.023120

0.0347

Paper
Saw wood and plywood (based onchange in inventory of products)
Saw wood and plywood average

20.346630

0.023150

0.0139Paper from mechanical pulp average

70.0990Paper from chemical pulp average

5.3

0.1308Average for paper

1.80.3851

Newsprint, household, sanitary paper
Linerboard fluting and folding
boxboard
80 % of printing and writing paper

0.51.38631

0.6931

10.6931

Reference

Pingoud
et al. 2001
Karjalainen
et al. 1994

Pingoud
et al. 1996

Nabuurs
1996

Skog and
Nicholson
2000

HWP category

20% of printing and writing paper

100.0693Paper

20.3466

Packing wood30.2310Particleboard

20

0.0347

Saw wood average

35

0.0198Saw wood – spruce & poplar

18

0.0385

Saw wood – oak & beech

45

0.0154

Saw wood

40

0.0173Structural panels

45

0.0154

Non structural panels

23

0.0301

Paper (free sheet)

60.1155

Other paper

10.6931

Note: It is recommended that use of these estimated half lives be accompanied with verification of the resulting stock change estimatesas indicated, for example, in Section 3a.1.5. Adjustments in half lives may be needed as a result.

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WCr (in Equation 3a.1.4)

fD (solidwood)fHD (solidwood)fD (paper)fHD (paper)

HWP production, imports and exports – FAO data

Product volume to product weight

Oven dry product weight to carbon weight

Growth rate of production, imports and exports prior
to first year of FAO data

Fraction of solid wood products discarded from use
each year

Fraction of paper products discarded from use eachyear

IPCC Good Practice Guidance for LULUCF

Table 3a.1.1

PDP ,PIM , PEX WP, IM(WP), EX (WP)
OFP, IM(OFP), EX(OFP)
RP, IM(RP), EX(RP)
IM(RFP), EX(RFP)
Amount of products produced, imported andexported

Table 3a.1.3

Table 3a.1.3

Table 3a.1.2, columns 7
and 8

0.5 (Table 3a.1.1)

Table 3a.1.1

Table 3a.1.1

H

Roundwood harvest (wood harvested and removed
from sites for products including fuelwood)

ValuesParameter

Description of Parameter

Half life in years =( 0.693 / fD (paper))
Uncertainty in half life = ±50%
Uncertainty is greater for fHD depending on thesize and destination of exports

Half life in years =( 0.693 / fD (solidwood))
Uncertainty in half life = ±50%
Uncertainty is greater for fHD depending on thesize and destination of exports

3.271

Rate of increase in production prior to 1961 ±15%
for a region, larger for country within a region.
Rate of increase in trade prior to 1961 ±50% for aregion, larger for country within a region.

±10%

±15%

Country-specific for FAO data
Production and trade – for countries withsystematic census or surveys - ±15% since 1961
Production and trade – for countries without
systematic census or survey ±50% since 1961

Country-specific for FAO data

Uncertainty Range

TABLE 3A.1.4
PARAMETERS, AND ESTIMATES OF UNCERTAINTIES ASSOCIATED WITH DEFAULT VALUES FOR THE TIER 2 METHOD FOR ESTIMATING CHANGE IN CARBON STORAGE IN HWP IN USE

Chapter 3: LUCF Sector Good Practice GuidanceChapter 3: LUCF Sector Good Practice Guidance

3a.1.4

Reporting and Documentation

It is suggested to document and archive all information used to produce national estimates of stock change. This
includes wood and paper production and trade data, parameters used. Changes in parameters to make estimatesof stock change from one year to the next should be documented. The national inventory report should containsummaries of methods used and references to source data so that the steps used in making the estimates could be
retraced.

3a.1.5Inventory Quality Assurance/Quality Control

Regardless of whether or not HWP are a key category it is suggested to conduct quality control checks asoutlined in Section 5.5 (Quality Assurance and Quality Control), for data and parameters used for the method
selected. If HWP is a key category it is suggested to use additional Tier 2 quality control checks from Section 5.5,
(Quality Assurance and Quality Control), particularly development and expert review of data and parameters,
and develop, as necessary, national level estimates of data and parameters using national data sources and using
expert judgement as outlined in Section 6.2.5, Expert Judgement (GPG2000 ).
One suggestion to aid in quality control (to verify stock or stock change estimates) if Tier 2 is used, is to makeseparate estimates of total carbon storage or annual change in specific product groups, e.g., lumber or panels inbuildings. The lumber and panels in buildings would be a portion of all lumber stored. The Tier 2 method could
be used to estimate the total amount of lumber and panels in buildings, or the change in lumber and panels stored
in a recent year. One would need to have an estimate of the portion of wood and panels going to buildings overtime. These estimates could be compared to separate estimates of wood in buildings, or change in wood inbuildings as follows. The current total of wood and panels in buildings could be calculated as square metres offloor area in buildings times the lumber content per square metre. The change in lumber in buildings could be
calculated as square metres of buildings built in a given year times the lumber content per square metre.
Another suggestion, if Tier 2 is used, to aid in checking half life of buildings is to use historical information on
the number and age of buildings over time. Data would be needed on the number of buildings of a given age (orage range) at a certain time in the past and the number of those buildings that are standing at more recent points
in time. These figures could be used to estimate the fraction loss of buildings per year. The percentage loss per
year could be used to estimate a half life. See Table 3a.1.3 for the relation between half life and fraction loss per
year under the assumption that a constant fraction is lost each year).

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Appendix 3a.2

Appendix 3a.2

3a.2.1

Non-CO2 Emissions from Drainage andRewetting of Forest Soils: Basis for Future
Methodological Development

Introduction

The drainage and rewetting of organic soils and wet mineral soils with high contents of soil organic carbon affectemissions and removals of greenhouse gases. CO2 is significantly affected, and methods for estimating changes
in emissions/removals of CO2 from these lands are discussed in the sections dealing with organic soils in
Sections 3.2 to 3.5.
In addition, intensively drained soils have large N2O emissions because drainage increases the aerated layer andenhances the mineralisation of soil organic matter. In contrast, unmanaged organic soils are very small natural
sources or sinks of N2O (Regina et al., 1996). The effect of drainage on N2O emissions depends upon soil
characteristics; higher emissions are associated with minerotrophic (nutrient rich) and lower emissions withombrotrophic (nutrient poor) peat types (Regina et al., 1996). Data on N2O emissions from drained organic soils
and wet mineral soils are relatively sparse and variable, so the uncertainty in the methods presented here is high.
In the following, the methodologies for N2O emissions focus on forest land not addressed in the IPCC
Guidelines. N2O emission from drained cropland and grassland soils are covered in the Agriculture Chapter of
the IPCC Guidelines and GPG2000. Given data availability and the current state of understanding, the same
method can be used for forest land remaining forest land and lands converted to forest land.
Rewetting organic soils will reduce the N2O emissions down to the original level around zero.
The CH4 emitted from undrained organic soils is a natural process and the emissions are highly variable.
Drainage of organic soils reduces these emissions and may even turn the area into a small CH4 sink (see IPCC
Guidelines, Reference Manual, Section 5.4.3, Wetland drainage). Methods for estimating the effect of drainage
or rewetting of forests and wetlands on CH4 emissions are not provided in the IPCC Guidelines nor in this reportdue to paucity of data although the magnitude of the effect, in terms of CO2-equivalent, may be large in cases inwhich high CH4 emitting areas are intensively drained. However, the effect of drainage on CH4 may be small incases a) with low natural CH4 emissions, b) in which still a shallow water table is maintained, or c) in which theCH4 sink in drained areas is compensated by CH4 emissions from drainage ditches. A default value of zero
emissions of CH4 after drainage is used in this appendix (Laine et al., 1996; Roulet and Moore, 1995).
CH4 emissions can increase in rewetted organic soils. “Rewetting” means the return of the water table to predrainage levels. If a country is rewetting organic soils, these soils are considered as managed. In this case, it isthese drainage/rewetting effects that can be reported based on country-specific data. According to literature, theCH4 source by rewetting organic soil covered by forest is estimated in a first approximation in a range of 0 to 60
kg CH4 ha-1 yr-1 in temperate and boreal climate, and 280 to 1260 kg CH4 ha-1 yr-1 in tropical conditions (Bartlett
and Harriss 1993). There is some evidence that CH4 emissions may still be smaller in rewetted peatlands than in
the virgin state (Komulainen et al. 1998, Tuittila et al. 2000). At present, no good practice guidance can be given
for CH4 emissions from rewetting of organic soils.

3a.2.2

Methodological Issues

3a.2.2.1 C HOICE OF METHOD
The same method is applied for forest land remaining forest land (FF) and lands converted to forest land (LF).
The decision trees presented in Section 3.1 (Figure 3.1.1 Decision tree for identification of appropriate tier-level
for land remaining in the same land-use category and Figure 3.1.2 (Decision tree for identification of appropriate
tier-level for land converted to another land-use category) can be used to identify the appropriate tier for the N2O
estimate, by considering the availability data. N2O emissions from drainage and rewetting of forest soils
contribute to the subcategory “soils” in the decision trees.
The basic method for estimating direct N2O emissions from drained forest organic soils is shown in Equation
3a.2.1. N2O emissions from rewetted forest organic soils are estimated to be at natural level, and the default is set

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Chapter 3: LUCF Sector Good Practice Guidanceas zero. The equation can be applied at various levels of disaggregation depending upon data availability,
particularly with respect to the availability of country-specific emission factors.
EQUATION 3a.2.1
DIRECT N2O EMISSIONS FROM DRAINED FOREST SOILS (TIER 1)
N2O emissionsFF = ∑(AFForganic IJK • EFFFdrainage, organic IJK) + AFFmineral • EFFFdrainage, mineral • 44/28 • 10-6
Where:
N2O emissionsFF=

emission of N2O in units of nitrogen, kg N

AFForganic=area of drained forest organic soils, ha

AFFmineral=area of drained forest mineral soils, ha

EFFFdrainage, organic=

emission factor for drained forest organic soils, kg N2O-N ha-1 yr-1

EFFFdrainage, mineral=emission factor for drained forest mineral soils, kg N2O-N ha-1 yr-1

ijk=

soil type, climate zone, intensity of drainage, etc. (depends on level ofdisaggregation)

The same method is applied to calculate N2O emissions from drained organic soils of lands converted to forest.
Tier 1: In Tier 1 Equation 3a.2.1 is applied with a simple disaggregation of drained forest soils into “nutrientrich” and “nutrient poor” areas and default emission factors are used. Default data are presented in Section
3a.2.2.2 and guidance for obtaining activity data is described in Section 3a.2.2.3.
Tier 2: Tier 2 can be used if country-specific emission factors and corresponding area data are available.
Typically, these data will enable the estimate to be disaggregated to account for management practices such as
drainage of different peatland types, fertility (e.g., bog versus fen, nitrogen status), and tree type (broadleaved
versus coniferous), with specific emission factors developed for each sub-class. Adequately disaggregated area
data could be obtained from soil information in the national forest inventory.
Tier 3: If more complex models or detailed surveys are available, a national Tier 3 approach can be used toestimate N2O emissions. Given the spatial and temporal variability and uncertainty in N2O emissions, this typeof approach is most warranted in a country in which direct N2O emissions from managed forest are a key
category because applying advanced methods could more accurately represent the management practices and themost relevant driving variables.

3a.2.2.2 C HOICE OF EMISSION / REMOVAL FACTORS
Where Tiers 1 and 2 are used, emission factors for N2O emissions per unit area per year are needed.
Tier 1: Default emission factors derived from the literature are used in Tier 1, and these values are shown in
Table 3a.2.1.
Due to the paucity of data, the default emission factors for the respective nutrient levels and climatic zones canbe taken as indicative only and may not properly reflect the real magnitude of emissions in a given country.
Emissions from drained forest mineral soils should be calculated by using separate and lower emission factors
than for drained forest organic soils. Emissions from drained forest mineral soils can be assumed as about a tenthof EFdrainage for organic soils (Klemedtsson et al., 2002). More measurements, especially in tropical climate, areneeded to improve the indicative emission factors of Table 3a.2.1. If drained forest is rewetted (i.e., the watertable returns to pre-drainage levels) it is assumed that N2O emissions return to the natural level close to zero.

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Appendix 3a.2

Climate Zone
and Soil TypeTABLE 3a.2.1
DEFAULT EMISSION FACTORS N2O EMISSIONS FROM DRAINAGE OF FOREST SOILS
Emission Factor
Uncertainty
EFFF
range*
Reference/ Comments
drainage-1-1
kg N2O-N ha-1 yr-1 kg N2O-N ha yr

Temperate and Boreal Climate
Nutrient Poor Organic Soil

0.1

0.02 to 0.3

Alm et al., 1999; Laine et al., 1996
Martikainen et al., 1995; Minkkinen
et al., 2002: Regina et al., 1996

Nutrient Rich Organic Soil

0.6

0.16 to 2.4

Klemedtsson et al., 2002; Laine etal., 1996; Martikainen et al., 1995;
Minkkinen et al., 2002: Regina et al.,
1996

Mineral Soil

0.06

0.02 to 0.24

Klemedtsson et al., 2002

Tropical Climate

8

0 to 24

Estimated as half the factor ofdrained organic croplands* 95% confidence interval of log-normal distribution

Tier 2: When country-specific data are available, in particular for different management regimes, specificemission factors can be defined in Tier 2. These country-specific emissions should be derived from surveys
performed in the country or in comparable neighbouring countries and, if possible, disaggregated by drainage
level, vegetation (broadleaved versus coniferous) and fertility of peat. Since literature is sparse and results
sometimes contrasting, country-specific emission factors should be derived through a rigorous measurement
programme. Good practice guidance on how to derive country-specific emission factors for N2O emissions fromsoils is given in Box 4.1, Good Practice in Derivation of Country-Specific Emission Factors, Page 4.62, of
GPG2000.
Tier 3: Under Tier 3, all parameters should be country-defined using more accurate values rather than thedefaults. The literature is scarce and results sometimes contradictory and countries are therefore encouraged toderive country-specific emission factors by measurements against appropriate undrained forest sites as areference. Data should be shared between countries with similar environmental conditions.

3a.2.2.3 C HOICE OF ACTIVITY DATA
The activity data needed to estimate this source is the area of drained and rewetted forest lands. In Tier 1, thenational estimate of drained forest soils is stratified by soil fertility, since the default values are provided for
nutrient rich and nutrient poor soils. National data will be available at soil services and from wetland surveys,
e.g., for international conventions. In case no stratification by peat fertility is possible, countries may rely on
expert judgement. Boreal climates tend to promote nutrient-poor raised bogs, while temperate and oceanic
climates tend to promote the formation of nutrient-richer peatlands. Further stratification may be possible under
Tier 2. For example, area could also be distinguished by management practices such as drainage of different peattypes, and tree types. Chapter 2 provides guidance on the approaches available to classify land area.

3a.2.2.4 U NCERTAINTY ASSESSMENT
Estimates of anthropogenic emissions of N2O emissions from forests are highly uncertain because of: a) highspatial and temporal variability of the emissions, b) scarcity of long-term measurements and their likely nonrepresentativeness over larger regions, and c) uncertainty in spatial aggregation and uncertainty inherent to theemission factors and activity data.
Tier 1: The uncertainty associated with the Tier 1 default emission factors are shown in Table 3a.2.1.
The uncertainty in the area of forest peatlands and its division between nutrient-poor (ombrotrophic, bogs) andnutrient-rich (minerotrophic, fens) peat types is best calculated by a country-specific assessment of uncertainties.
Present estimates of areas of drained and rewetted forest peatlands within a country vary in a wide range
between different data sources and may have an uncertainty of 50% or more.
Tier 2: Good practice in derivation of country-specific emission factors is described in Box 4.1, Good Practicein Derivation of Country-Specific Emission Factors, Page 4.62, of GPG2000.

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Chapter 3: LUCF Sector Good Practice Guidance

The area of forest peatlands and its division between nutrient-poor and nutrient-rich peat types needs a countryspecific assessment of uncertainties, preferably by comparing various sources of data and applying different area
statistics, e.g., in sensitivity or Monte Carlo analyses (Section 5.2, Identifying and Quantifying Uncertainties).
Tier 3: Process-based models will probably provide a more realistic estimate but need to be calibrated andvalidated against measurements. Sufficient representative measurements are needed for validation purposes.
Generic guidance on uncertainty assessment for advanced methods is given in Section 5.2, Identifying andQuantifying Uncertainties.

3a.2.3CompletenessIn order to ensure consistency with reporting on CO2 emissions from drained forest soils, please refer to Section
3.2.3 on completeness in the main text.

3a.2.3.1 D EVELOPING A CONSISTENT TIME SERIESIn order to ensure consistency with reporting on CO2 emissions from drained forest soils, please refer to Section
3.2.4 on developing a consistent time series in the main text.

3a.2.4

Reporting and Documentation

In order to ensure consistency with reporting on CO2 emissions from drained forest soils, please refer to Section
3.2.5 on reporting and documentation in the main text.

3a.2.5

Quality Assurance/ Quality Control (QA/QC)

In order to ensure consistency with reporting on CO2 emissions from drained forest soils, please refer to Section
3.2.6 on inventory quality assurance/quality control (QA/QC) in the main text.

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Appendix 3a.3

Appendix 3a.3 Wetlands Remaining Wetlands:
Basis for future methodological development
3a.3.1Introduction

This section develops the coverage of Section 5.4.3 (Other Possible Categories of Activity) of the IPCC
Guidelines by describing methodologies for estimating carbon stock changes, as well as CH4 and N2O emissions(which can be as significant as CO2 emissions) from wetlands remaining wetlands. Land conversion to wetlands
is described in Section 3.5 of this report.
The estimate of CO2 emissions in wetland has two basic elements, as shown in Equation 3a.3.1.
EQUATION 3a.3.1
CO2 EMISSIONS IN WETLAND REMAINING WETLAND
CO2 emissionsWW = CO2 emissionsWW peat+ CO2 emissionsWW flood
Where:
CO2 emissionsWW = CO2 emissions in wetland remaining wetland, Gg CO2 yr-1
CO2 emissionsWW peat = CO2 emissions from organic soils managed for peat extraction (Section 3a.3.1),
Gg CO2 yr-1
CO2 emissionsWW flood = CO2 emissions from flooded land (Section 3a.3.2), Gg CO2 yr-1

The estimate of N2O emissions has the same two basic elements, as shown in Equation 3a.3.2.
EQUATION 3a.3.2
N2O EMISSIONS FROM WETLAND REMAINING WETLAND
N2O emissions WW = N2O emissionsWW peat + N2O emissionsWW flood
Where:
N2O emissionsWW = N2O emissions from wetland remaining wetland, Gg N2O yr-1
N2O emissionsWW peat = N2O emissions from organic soils managed for peat extraction (Section 3a3.2),
Gg N2O yr-1
N2O emissionsWW flood = N2O emissions from flooded land (Section 3a.3.3), Gg N2O yr-1

At present, a default methodology for CH4 can be provided only for flooded land (Equation 3a.3.3):
EQUATION 3a.3.3
METHANE EMISSIONS FROM WETLANDS REMAINING WETLANDS
CH4 emissionsWW = CH4 emissionsWW flood
Where:
CH4 emissionsWW = CH4 emissions from wetlands remaining wetlands, Gg CH4 yr-1
CH4 emissionsWW flood = CH4 emissions from flooded land (Section 3a.3.3), Gg CH4 yr-1

3a.3.2

Organic Soils Managed for Peat Extraction

As shown in Table 3a.3.1 and Equations 3a.3.1 and 3a.3.2, methods for estimating emissions from organic soils
managed for peat extraction currently are provided only for CO2 and N2O.

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Chapter 3: LUCF Sector Good Practice Guidance

TABLE 3a.3.1
SUMMARY OF TIERS FOR ORGANIC SOILS MANAGED FOR PEAT EXTRACTION
Tier 1

Tier 2Tier 3

Change in Living
Biomass(∆CWW peat )Not estimated (or assumedzero).

Unlikely to be significant (seebelow), but may be estimatedif country-specific data are
available, following guidancein Section 3.4.1.1 (Grassland,
Changes in Carbon Stock in
Living Biomass).

Unlikely to be significant (seebelow), but may be estimatedif detailed country-specificdata or advanced methods areavailable, following guidancein Section 3.4.1.1 (Grassland,
Changes in Carbon Stock in
Living Biomass).

Change in Soil
Organic Matter)(∆ CWW peat

Emissions from peat extraction
can be estimated using default
emission factors and area data.

Estimated using moredisaggregated, countryspecific factors. If data are
available, emissions fromrestoration of peatlands andstockpiles may be estimated.

May be estimated if detailed
country-specific data oradvanced methods areavailable.

N2O

Emissions from peat extraction
can be estimated using default
emission factors and area data.

Estimated using moredisaggregated, countryspecific factors. If data are
available, emissions fromrestoration of peatlands may
be estimated.

May be estimated if detailed
country-specific data oradvanced methods areavailable.

CH4

Not estimated at present.

Estimated using countryspecific factors. If data are
available, emissions fromrestoration of peatlands may
be estimated.

May be estimated if detailed
country-specific data oradvanced methods areavailable.

LB

SOM

3a.3.2.1

CO 2 EMISSIONS FROM ORGANIC SOILS MANAGED FOR
PEAT EXTRACTION

The estimate of CO2 emissions from lands managed for peat extraction has two basic elements, as shown inEquation 3a.3.4.
EQUATION 3a.3.4
CO2 EMISSIONS IN LAND MANAGED FOR PEAT EXTRACTION
CO2 emissionsWW peat = (∆CWW peatLB + ∆CWW peatSoils ) ● 10-3 ● 44/12
Where:
CO2 emissionWWpeat = CO2 emissions from land managed for peat, Gg CO2 yr-1
∆CWW peat LB = change in carbon stock in living biomass, tonnes C yr-1
∆CWW peat Soils = change in carbon stock in soils, tonnes C yr-1
The carbon stock changes are converted to CO2 emissions (Equation 3a.3.4 is expected to result in a loss ofcarbon). Emissions are reported as positive values and removals as negative values (for more details on reporting
and the rule on the signs, see Section 3.1.7 and Annex 3A.2 Reporting Tables and Worksheets).

3a.3.2.1.1 CHANGE I N CARBON STOCKS LIVI NG BIOMASS
In general, the portion of emissions coming from the change in carbon stock in living biomass will be small
compared to the carbon emissions associated with soil organic matter. This is because vegetation is typicallyremoved on organic soils managed for peat extraction, although there may be some vegetation in drainage
ditches or along boundaries. Nevertheless substantial amounts of vegetation may be removed when the peatland
comes under management, which is addressed in Section 3.5 of this report. Due to scarcity of data and the likely
small relevance of changes in biomass on lands managed for peat extraction, no default guidance is provided
here, and it can be assumed in Tier 1 that the change in carbon stocks in living biomass on managed peatland is

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Appendix 3a.3

zero. However, countries in which wetlands are a key category may develop data to support the estimation ofemissions from vegetation using higher tier methods based on national expertise.

3a.3.2.1.2 CHANGE IN CARBON STOCKS IN SOILS
3a.3.2.1.2.1 Methodol ogical Issues
CO2 emissions from soil occur at several stages in the peat process, as shown in Equation 3a.3.5.
EQUATION 3a.3.5
CHANGE IN SOIL CARBON ON LANDS MANAGED FOR PEAT EXTRACTION∆CWW peatSoils = (∆CWW peatSoils, drainage + ∆CWW peatSoils, extraction + ∆CWW peatSoils, stockpiling+ ∆CWW peatSoils, restoration)
Where:∆CWW peat Soils = change in carbon stock in soils, tonnes C yr-1
∆CWW peatSoils, drainage =change in soil carbon during drainage, tonnes C yr-1

∆CWW peatSoils, extraction =change in soil carbon during peat extraction, tonnes C yr-1

∆CWW peatSoils, stockpiling = change in soil carbon during stockpiling of peat prior to removal for combustion,
tonnes C yr-1
∆CWW peatSoils, restoration =change in soils carbon due to practices undertaken to restore previously
cultivated lands, tonnes C yr-1

Currently a default method can only be provided for estimating the changes in carbon stock associated with peat
extraction (∆CWWSoils, extraction), that are essentially emissions caused by enhanced oxidation of soil organic matterat the production fields. Emissions from peat stockpiles and restoration operations are much less well understood.
Higher temperatures may cause stockpiles to release more CO2 than the excavation field, but data are not at present
sufficient to provide guidance. Countries may develop national methods for estimating the other terms in equation
3a.3.5 at higher tiers, which could also account for the effect of peatlands restoration and the dynamics which leadto higher emissions immediately after drainage compared with the period during which peat is being removed.

Choice of m ethod
The Tier 1 method relies on basic area identification and default emission factors, while the Tier 2 method is
disaggregated to smaller spatial scales and uses country-specific emission factors where available. Given thecurrent state of the science, few countries will use Tier 3 methods, and so only the main elements for a Tier 3
method are described.
Tier 1: Tier 1 estimates only emissions directly associated with the change in soil carbon during peat extraction(fugitive emissions from the production fields). The emissions from the peat extracted are covered by theemissions from peat combustion which are reported in the Energy Sector. In Tier 1, Equation 3a.3.6 is applied at
an aggregate level to a country’s area of organic soils managed for peat extraction, using default emission factors.
EQUATION 3a.3.6
CO2 EMISSIONS FROM ORGANIC SOILS MANAGED FOR PEAT EXTRACTION∆CWW peatSoils, extraction = Apeat Nrich ● EFpeat Nrich + Apeat Npoor ● EFpeat Npoor
Where:∆CWW peat Soils, extraction =CO2 emission from organic soils managed for peat extraction expressed ascarbon, tonnes C yr-1

Apeat Nrich =area of nutrient rich organic soils managed for peat extraction, including abandoned areas inwhich drainage is still present, ha

Apeat Npoor =area of nutrient poor organic soils managed for peat extraction, including abandoned areasin which drainage is still present, ha

EFpeat Nrich = emission factors for CO2 from nutrient rich organic soils managed for peat extraction,
tonnes C ha-1 yr-1

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EFpeatNpoor = emission factors for CO2 from nutrient poor organic soils managed for peat extraction,
tonnes C ha-1 yr-1
Tier 2: The Tier 2 method can be applied if area data and country-specific emission factors are available. It may
be possible to subdivide activity data and emission factors according to soil fertility, site type and drainage level,and previous land use such as forest or cropland. Emission factors for sub-categories such as peat stockpiles,
drained and restored peatlands could also be included. In addition, it may be possible to develop emission factorsthat reflect differences in emission levels between the period directly after drainage and the period of ongoingpeat extraction.
Tier 3: Tier 3 methods would require statistics on the area of organic soils managed for peat extraction according
to site type, fertility, time since drainage, time since restoration, which could be combined with appropriate
emission factors, and or/process based models. Studies utilising information on changes in soil bulk density andcarbon content could also be used to detect changes in soil carbon stocks provided the sampling was of sufficient
intensity. Such data could also be used to develop appropriate emission factors for CO2, correcting for carbon losses
as dissolved organic carbon leaching, losses of dead organic matter through runoff or as CH4 emissions.

Choice of em ission factors
Tier 1: Implementation of the Tier 1 method requires default emission factors for EFpeat. Default emission
factors for Tier 1 are presented in Table 3a.3.2. These factors are identical to those provided in Table 3.5.2(Emission factors and associated uncertainty for organic soils after drainage) to estimate CO2 emissions
associated with the drainage of land for peat extraction (a land conversion described in Section 3.5). Although it
is recognised that emissions in the period immediately following drainage will be higher than those duringongoing peat extraction, there are currently no sufficient data to develop specific default emission factors for
those activities. As noted above, under Tier 2, countries may be able to develop more disaggregated countryspecific emission factors and differentiate between emission rates during land conversion to peat land and theongoing fugitive emissions during peat extraction.
TABLE 3A.3.2
EMISSION FACTORS FOR CO2-C AND ASSOCIATED UNCERTAINTY FOR ORGANIC SOILS AFTER DRAINAGE
Region/Peat Type

Emission Factor
tonnes C ha-1 yr-1

Boreal and Temperate
Nutrient Poor
EFNpoorNutrient Rich
EFNrich0.2
1.1

Uncertainty atonnes C ha-1 yr-1
0 to 0.63
0.03 to 2.9

Reference/Comment b

Laine and Minkkinen, 1996; Alm et al., 1999;
Laine et al., 1996; Minkkinen et al., 2002
Laine et al., 1996; LUSTRA, 2002;
Minkkinen et al., 2002; Sundh et al., 2000

TropicalEFa2.0

0.06 to 6.0

Calculated from the relative difference
between temperate (nutrient poor) andtropical in Table 3.3.5.

Range of underlying datab

The boreal and temperate values have been developed as the mean from a review of paired plot measurements, assuming thatconditions on organic soils converted to peat extraction are lightly drained only. Most of the data are from Europe.

Nutrient-poor bogs predominate in boreal regions, whilst in temperate regions, nutrient-rich fens and mires are
more common. Boreal countries that do not have information on areas of nutrient-rich and nutrient-poor
peatlands should use the emission factor for nutrient-poor peatlands. Temperate countries that do not have suchdata should use the emission factor for nutrient-rich peatlands. Only one default factor is provided for tropicalregions, so disaggregating peatland area by soil fertility is not necessary for tropical countries using the Tier 1
method. The uncertainty values come from a lognormal distribution and represent a 95% confidence interval.
Tiers 2 and Tier 3: Tiers 2 and 3 require country-specific data that accounts for management practices such as
drainage of different peat types. The literature is sparse and results sometimes contrasting. Countries areencouraged to derive country-specific emission factors by measurements against appropriate reference virginsites. Data should be shared between countries with similar environmental conditions.

Choice of activity data
Tier 1: The activity data required for all tiers is the area of organic soil managed for peat extraction. Ideally,
under Tier 1 countries will obtain national data on the area of peat extraction. In boreal and temperate regions,these area data need to be disaggregated by soil fertility to correspond to the default emission factors presented
in Table 3a.3.2. Possible sources of such data are national statistics, peat mining companies and government

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ministries responsible for land use. Peat extraction area can also be estimated using statistics on peat production
for fuel and horticultural use if the national average extraction rate is known. If this rate is unavailable it can beroughly assumed that the extraction rate is 0.04 million m³/km² or 0.016 million t/km².
If neither of these approaches is possible, default data on areas in peat can be obtained from estimates in theliterature. Data on organic soils areas for other countries and an estimate of the proportion of tropical versus
temperate and boreal peatlands are available from Table 1 in Andriesse (1988). Table 3a.3.3 provides roughestimates of the drainage of wetlands at the continental scale. These data do not necessarily apply to organic soils
and do not distinguish site type. However they can be regarded as a first crude estimate of land use on peatlands
where more detailed data are unavailable. Additional data on peatland areas can be obtained from the following:
Andriesse (1988), Lappalainen (1996), OECD/IUCN (1996), Tarnocai, et al.. (2000), Umeda and Inoue (1996),
Xuehui and Yan (1996). Other sources of data are http://www.worldenergy.org/wec-geis/publications/reports/ser/peat/peat.asp and http://www.wetlands.org.
Tiers 2 and 3: Countries should assess the total area of organic soils managed for peat extraction including
abandoned areas on which drainage or the effects of former peat extraction are still present to the level ofdisaggregation required by the tier calculation or the modelling approach being used. If possible, countries areencouraged to collect data on the areas of fens versus bogs and drainage level to enable the use of moredisaggregated default emission factors or country-specific factors. If restoration is underway, countries areencouraged to report separately the areas of restored organic soils formerly managed for peat extraction andestimate emissions from peat extraction lands.

TABLE 3a.3.3
ESTIMATES OF PEATLAND AREAS AND USE FOR TIER 1 IN 1000 HECTARESCountry or
regionPeatland area
total
(Unmanaged +managed)Agriculture,drained(Cropland +grassland)

1000 ha1000 haEurope95695

Belarus2939

Managed forest,
drained1000 ha

Peat
extraction(Industrial
peatlands)% in tropicsb

Reference

0

1, 9

0

1, 2

1000 ha a(56-65% of wetlands drained for
agriculture and forestry)
900(small)109

Denmark

142

140(small)

1.2

0

1, 2

Estonia

1009

130

320

258

0

1, 2

Finland

8920

350

3540

53

0

1, 2, 3

France10055(small)(small)0

1, 2Germany1420

210(small)

32

0

1, 2

Great Britain

1754

500

500

5.4

0

1, 2

0.2

Hungary

100

80

0

Iceland1000

120(small)Ireland

1176

90

45

Italy

120

30

Latvia

669

160

50

82

0

1, 2

0

1, 2

0

1, 2

(small)

0

1, 2

27

0

1, 2

Lithuania

352

25

190

36

0

1, 2

Netherlands

279

250

(small)

3.6

0

1, 2

Norway

2370

190

280

2.5

0

1, 2

Poland

1255

760

370

2.5

0

1, 2

Slovenia

100

30

0

(small)0

1, 2

Sweden

10379

300

524

12

0

1, 2

Ukraine

1008

19

0

1, 2

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TABLE 3A.3.3 (CONTINUED)
ESTIMATES OF PEATLAND AREAS AND USE FOR TIER 1 IN 1000 HECTARESCountry or
regionAsia

Peatland area
total
(Unmanaged +managed)1000 ha24446

Agriculture, Managed forest,
draineddrained(Cropland +grassland)
1000 ha1000 ha
(27% of wetlands drained for
agriculture and forestry,increasing)

Peat
extraction(Industrial
peatlands)
1000 ha a% in tropicsb

Reference

4b, 9

Burma

965

China

1044-3480

135

104

Indonesia

17000-27000

400

3.6 (fuel only)

100

4

30

4b, 5

100

4

Iraq

1790

100

4

Japan

201

0

4b, 6

100

4b

685

100

4b

104-240

100

4b

0

1, 2

0

4b

30

8

Malaysia
Papua New
GuineaPhillipines

2250-2730

Russia39000-76000

South Korea

630

New Zealand

165

500

700

2500

9120

(2% of wetlands drained for
agriculture and forestry)

Africa5840

Guinea

525

100

4a

Nigeria

700

100

4a

South Africa

950

100

4a

Uganda

1420

100

4a

Zambia

1106

100

4a

North AmericacCanadaUSA
Alaska:S of 49°N:
Central andSouth America
BrazilChile

173500111328(56-65% of wetlands drained for
agriculture and forestry)
25
100

4a, 11

4c, 9
16

49400
10240
11222

0

7

0
2.5

8

(6% of wetlands drained for
agriculture and forestry)4c, 91500-3500

100

4c

1047

10

4c

Cuba

658

100

4c

Guyana

814

100

4c

Honduras

453

100

4c

Mexico

1000

100

4c

Nicaragua

371

100

4c

Venezuela
1000
100
4c
References: 1 Lappalainen (1996), 2 European wetlands inventory review, draft national reports (http://www.wetlands.org),
3 national inventory, 4a-c Lappalainen and Zurek (1996), 5 Xuehui and Yan (1996), 6 Umeda and Inoue (1996), 7
Tarnocai, et al. (2000), 8 Andriesse (1988), 9 OECD/IUCN (1996)aPeat extraction for fuel: http://www.worldenergy.org/wec-geis/publications/reports/ser/peat/peat.aspbAndriesse (1988); The definition for tropics used by Andriesse (1988) is broader than the commonly used area between the between the
Tropic of Cancer (25o N) and the Tropic of Capricorn (25o S). Using this definition, e.g. land areas of New Zealand and Iraq would not beclassified as tropical.
c

Total area affected by hydroelectric reservoir construction estimated to exceed 9000km2.

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3 A .3.2.1.2.2 Uncertai nty Assessment
Tier 1: The key uncertainties in Tier 1 are the default emission factors and area estimates. Emission factors andparameters have been developed from only a few (less than 10) data points, and may not be representative forlarge areas or climate zones. The standard deviation of the emission factors easily exceeds 100% of the mean,
but underlying probability functions are likely to be non-normal. Countries are encouraged to use the range
rather than the standard deviation.
The area of drained peatlands may have an uncertainty of 50% in Europe and North America, but may be afactor of 2 in the rest of the world. Uncertainty in Southeast Asia is extremely high and the peatlands are underparticular pressure, mainly because of urbanisation and intensification of agriculture and forestry, and possibly
also for peat extraction.
Tier 2: Countries with significant areas of organic soils managed for peat extraction that use a Tier 2 method
are encouraged to provide an assessment of total uncertainty (see Chapter 5, Section 5.2, Identifying andQuantifying Uncertainties of this report) for all significant contributions to the emissions (drainage / rewetting,area, country-specific parameters).
Tier 3: Process-based models will in principle provide more realistic estimates but need to be calibrated andvalidated against measurements. Generic guidance on uncertainty assessment for advanced methods is given in
Chapter 5 (Section 5.2 Identifying and Quantifying Uncertainties) of this report. Since drainage of peatlands
leads to peat compaction and oxidation the stock change approach to monitor CO2 fluxes can be imprecise. If
used, it should be calibrated with appropriate flux measurements.

3a.3.2.2

N 2 O EMISSIONS FROM DRAI NED PEATLAND

3a.3.2.2.1 Methodological Issues
The method for estimating N2O emissions from drained peatlands is shown in the equation below.
EQUATION 3a.3.7
N2O EMISSIONS FROM DRAINED WETLANDS
Direct N2O emissions WW peat = (A peatNrich ● EF peat Nrich + A peat Npoor ● EF peat Npoor)● 44/28 ● 10-6
Where:
N2O emissions WW peat = emissions of N2O, Gg N2O yr-1
ApeatNrich = area of drained nutrient rich organic soils, ha
ApeatNpoor = area of drained nutrient poor organic soils, ha
EFpeat Nrich = emission factor for drained nutrient rich wetlands organic soils, kg N2O-N ha-1 yr-1
EFpeat Npoor = emission factor for drained nutrient poor organic soils, kg N2O-N ha-1 yr-1

Choice of m ethod
Tier 1: The Tier 1 method for estimating N2O emissions from drained wetlands is similar to that described fordrained agricultural soils in the IPCC Guidelines, and for drained forest soils (Appendix 3a.2 Non-CO2
emissions from drainage and rewetting of forest soils: Basis for future methodological development) and isshown in Equation 3a.3.7. The area of drainage (disaggregated as appropriate) is multiplied by a corresponding
emission factor. As with drained forest lands, under the Tier 1 method, the default factors for temperate andboreal lands are provided for nutrient poor and nutrient rich soils. As only a single emission factor is provided
for tropical regions, it is not necessary to disaggregate by soil fertility in this case.
Tier 2: Under Tier 2, land area is disaggregated by additional factors such as fertility, site type and drainage
level and disaggregated country-specific emission factors are used.
Tier 3: Process-based models will in principle provide a more realistic estimate but need to be calibrated andvalidated against measurements. Sufficient representative measurements are needed for validation purposes.
Generic guidance on uncertainty assessment for advanced methods is given in Section 5.2, Identifying andQuantifying Uncertainties.

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Choice of em ission/rem oval factors
Tier 1: Default emission factors for the Tier 1 method are provided in Table 3a.3.4.
TABLE 3a.3.4
DEFAULT EMISSION FACTORS FOR N2O EMISSIONS FROM WETLANDS
Climate Zone
and Soil Type

Emission Factor
EF2 peatkg N2O-N ha-1 yr-1

Uncertainty range*
kg N2O-N ha-1 yr-1

0.1

0 to 0.3

1.8

0.2 to 2.5

Boreal andTemperate Climate
Nutrient Poor
Organic Soil
Nutrient Rich
Organic Soil

Tropical Climate

18

2 to 25

Reference/ Comments

Alm et al., 1999; Laine et al., 1996;
Martikainen et al., 1995; Minkkinen et al.,
2002; Regina et al., 1996
The value for tropical areas is calculated fromthe relative difference between temperate andtropical in Chapter 4 of the IPCC Guidelines
and GPG2000. The same approach was used inTable 3.2.2 and the orders of magnitude aresimilar.

* The uncertainty values come from a lognormal distribution and represent a 95% confidence interval.

Tier 2: Tier 2 integrates country-specific data, if available, especially data that accounts for management
practices such as drainage of different peat types. Since literature is sparse and results sometimes contrasting,countries are encouraged to derive country-specific emission factors by measurements against appropriate
reference virgin sites. Specific guidance on how to derive country-specific emission factors for N2O is given inBox 4.1.of GPG2000 (page 4.62).
Tier 3: Tier 3 incorporates models that should be validated against measurements. The suitability to countryspecific conditions should be proven.

Choice of activity data
The same activity data should be used for estimating CO2 and N2O emissions from organic soils managed forpeat extraction, and information on obtaining these data is provided in Section 3a.3.3.3.1 above. For countries inboreal and temperate regions using the Tier 1 method, area data should be stratified by soil fertility, since thedefault values are provided for nutrient rich and nutrient poor soils. National data should be available from soil
services and from wetland surveys, e.g., for international conventions. If it is not possible to stratify by peat
fertility, countries may rely on expert judgement. Boreal climates tend to promote nutrient-poor raised bogs,
while temperate and oceanic climates tend to promote the formation of nutrient-richer peatlands.
Further stratification may be possible under Tier 2. For example, area could also be distinguished bymanagement practices such as drainage of different peat types, fertility (e.g., bog versus fen, nitrogen status), andtree type. Chapter 2 provides guidance on the approaches available to classify land area.
Tier 3 may require additional, possibly geo-referenced, information about soil properties, management andclimate conditions depending on the input to models or other sophisticated methodologies.

3a.3.2.2.2 Uncertaint y assessment
Tier 1: The default emission factors of Tier 1 are based on fewer than 20 paired data sets from a limited numberof studies with geographical focus on Europe. For these reasons, they should be considered highly uncertain. The
standard deviation of the emission factors easily exceeds 100% of the mean, but underlying probability functions
are likely to be non-normal. Therefore, both the standard deviation of the mean and the range of the underlying
data are given below. Given the preliminary nature of the underlying data, countries are encouraged to use therange rather than the standard deviation. Uncertainties for the default emission factors for EF2WW in Tier 1 aregiven in Table 3a.3.4.
The uncertainty in the area of peatlands and its division between nutrient-poor (ombrotrophic, bogs) andnutrient-rich (minerotrophic, fens) peat types is best calculated by a country-specific assessment of uncertainties.
Present estimates of areas of drained and rewetted forest peatlands within a country vary in a wide range
between different data sources and may have an uncertainty of 50% or more.
Tier 2: Where country-specific emission factors are used, the uncertainty should be calculated as part of theprocess of developing the factors. Guidance in derivation of country-specific emission factors is described inBox 4.1, Good Practice in Derivation of Country-Specific Emission Factors, GPG2000.

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The area of peatlands and its division between nutrient-poor and nutrient-rich peat types needs a country-specificassessment of uncertainties, which can be conducted by comparing various sources of data and applying different
area statistics, e.g., in sensitivity or Monte Carlo analyses (Section 5.2, Identifying and Quantifying Uncertainties).
Tier 3: Process-based models will probably provide a more accurate estimate of emissions but they need to becalibrated and validated against measurements. Sufficiently representative measurements are needed forvalidation purposes. Generic guidance on uncertainty assessment is given in Section 5.2, Identifying andQuantifying Uncertainties.

3a.3.2.3

C OMPLETENESS

A complete inventory should estimate emissions from all industrial peatlands including abandoned peat mining
areas in which drainage is still active, and areas drained for future peat extraction.

3a.3.2.4

D EVELOPING A CO NSISTENT TIME SERIES

General guidance on consistency in time series can be found in Section 5.6 (Time Series Consistency and
Recalculations). The emission estimation method should be applied consistently to every year in the time series,
at the same level of disaggregation. Moreover, when country-specific data are used, national inventories agency
should use same measurements protocol (sampling strategy, method, etc.). If it is not possible to use the same
method or measurement protocol throughout the time series, the guidance on recalculation in Chapter 5 should
be followed. The area of organic soils managed for peat extraction may need to be interpolated for longer timeseries or trends. Consistency checks should be made (i.e., by contacting peat-mining companies), to gather
temporal information about areas affected by former or future peat extraction, and differences in emissions
between inventory years should be explained, e.g., by demonstrating changes in areas of industrial peatlands orby updated emission factors. Differences in emissions between inventory years should be explained, e.g., bydemonstrating changes in areas of peatlands or by updated emission factors.

3a.3.2.5

R EPORTING AND DOCUMENTATION

It is appropriate to document and archive all information required to produce the national emissions/removals
inventory estimates as outlined in Chapter 5 of this report subject to the following specific considerations.
Emissions from land managed for peat extraction are not explicitly mentioned in the IPCC Guidelines butcorrespond in aggregate to the IPCC category 5E “Other”.
Emission factors: Since the literature data are so sparse, the scientific basis of new country-specific emission
factors, parameters and models should be fully described and documented. This includes defining the input
parameters and describing the process by which the emission factors, parameters and models were derived, as
well as describing sources of uncertainties.
Activity data: Sources of all activity data used in the calculations (data sources, databases and soil map
references) should be recorded, plus (subject to any confidentiality considerations) communication withcompanies dealing with peat extraction. This documentation should cover the frequency of data collection andestimation, and estimates of accuracy and precision, and reasons for significant changes in emission levels.
Emission results: Significant fluctuations in emissions between years should be explained. A distinction should
be made between changes in activity levels and changes in emission factors, parameters and methods from year
to year, and the reasons for these changes documented. If different emission factors, parameters and methods areused for different years, the reasons for this should be explained and documented.

3a.3.2.6

I NVENTORY QA/QC

Quality assurance/quality control (QA/QC) checks should be implemented as outlined in Chapter 5 (Section 5.5)of this report. Additional quality control checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, of
GPG2000, and quality assurance procedures may also be applicable, particularly if higher tier methods are used
to quantify emissions from this source category. Where country-specific emission factors are being used theyshould be based on high quality experimental data, developed using a rigorous measurement programme, and beadequately documented.

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It is, at present, not possible to cross-check emissions estimates from organic soils managed for peat extraction
with other measurement methods. However, the inventory agency should ensure that emission estimates undergo
quality control by:•

Cross-referencing reported country-specific emissions factors with default values and data from othercountries;•

Checking the plausibility of estimates by cross-referencing areas of organic soils managed for peat
extraction with data of peat industries and peat production.

3a.3.3

Flooded Land Remaining Flooded Land

Flooded lands are defined as water bodies regulated by human activities for energy production, irrigation,navigation, recreation, etc., and where substantial changes in water area due to water level regulation occur.
Regulated lakes and rivers, where the main pre-flooded ecosystem was a natural lake or river, are not considered
as flooded lands. Rice paddies are addressed in the Agriculture chapter of the IPCC Guidelines and GPG 2000.
There is little statistical evidence to suggest that greenhouse gas emissions from flooded lands vary with time
(Duchemin et al., 1999; Duchemin, 2000; Duchemin et al., 2000 and 2002a; Keller and Stallard, 1994), although
recent studies suggest that CO2 emissions for the first ten years after flooding are as a result of decay of organic
matter on the land prior to flooding, whereas subsequent CO2 emissions are from material transferred into theflooded area (S. Houel, 2002; Hélie, 2003). If this is true, then the CO2 emissions attributed to flooding alone
would be limited to approximately 10 years.
This section provides preliminary information on how to estimate emissions of CO2, CH4 and N2O from flooded
lands. This information is drawn from available literature and may be useful to countries that want to begin
estimating emissions from this source. Due to the close linkage between CO2, CH4 and N2O emissions andmethodologies, all three gas species are addressed in this section and no distinction for emissions from flooded
land is made based on the age of the reservoir. The emissions from changes in living aboveground biomass due
the conversion to flooded land are addressed in Section 3.5.2.2.

3a.3.3.1

M ETHODOLO GICAL I SSUESGreenhouse gas emissions from flooded lands can occur via the following pathways after flooding has occurred:•

Molecular diffusion across the air-water interface for CO2, CH4 and N2O (diffusive emissions);•

Bubbles of CH4 from the sediment through the water column (bubble emissions);•

Emissions resulting from the water passing through a turbine and/or through the spillway and turbulence
downstream (degassing emissions); and•Emissions from decay of above-water biomass1.

The first two pathways – diffusive emissions and bubble emissions – are estimated in the Tier 1 method. For
hydroelectric reservoirs, degassing emissions, which are caused by an increase in dissolved CO2 and CH4 in thewater due to flooding and are released to the atmosphere when water is passing through the turbine or over the
spillway (Galy-Lacaux and al., 1997), can be included in Tier 2 if data are available.. In tropical regions,emissions from the decay of above-water biomass can be an important pathway (Fearnside, 2002) and related
emissions can be estimated at Tier 3. CO2 and CH4 emissions from reservoirs are affected by season. In boreal
and temperate regions CO2 and CH4 will be accumulated under ice and release at break-up (Duchemin, 2000).

CHOICE OF METHOD
The following discussion describes how to estimate emissions from reservoirs under various tiers, withincreasing level of accuracy associated with higher tier methods. Within the discussion of particular tiers,
specific issues related to estimating emissions of CO2, CH4 and N2O are covered.

1

Above-water biomass is the biomass in trees not submerged by the flooding, especially located in shallow flooded zones(Fearnside, 2002)

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Tier 1The Tier 1 approach provides a simplified approach to estimating greenhouse gas emissions from reservoirs
using default emission data and highly aggregated area data. Unless otherwise indicated the area used in Tier 1
calculations is the flooded total surface area, which includes any areas covered with water before the flooding,because area data minus these previously flooded areas are generally not available.
CO2 emissions
The method in Section 3.5.2.2 to estimate the carbon stock change in aboveground living biomass due to land
conversion to flooded land assumes that all aboveground biomass is converted into CO2 in the first year
following the conversion. In actuality, the part of the above-ground biomass that is left on site before flooding
will decompose more slowly. Decay of soil carbon will also contribute to the emissions and a Tier 1 method forthese CO2 emissions is shown in Equation 3a.3.8:
EQUATION 3a.3.8
CO2 EMISSIONS FROM FLOODED LANDS (TIER 1)
CO2 emissionsWW flood = P ● E(CO2)diff ● Aflood, total surface
Where:
CO2 emissionsWW flood= total CO2 emissions from flooded lands, Gg CO2 yr-1

P = period, days (usually 365 for annual inventory estimates)
E(CO2)diff = averaged daily diffusive emissions, Gg CO2 ha-1 day-1
Aflood, total surface = total flooded surface area, including flooded land, flooded lake and flooded river surface
area, haThe CO2 estimation method is simple – the only emission pathway that is estimated under Tier 1 is diffusion
emission during ice-free and ice-cover periods. CO2 bubble emissions are not significant. The default assumption
is that the CO2 emissions would be limited to approximately 10 years after the flooding took place.
The CO2 emissions estimated with the equation 3a.3.8 are highly uncertain and will depend on the site-specificconditions (soil type in particular). The use of the Equation 3a.3.8 may also lead to overestimation of theemissions when used together with the Equation 3.5.6 in Section 3.5.2.2. If countries use a Tier 2 method they
can more accurately represent the proper time profile of the CO2 emissions following flooding. Guidance on Tier
2 methods is given below.
CH4 emissions
The Tier 1 method for estimating CH4 emissions from flooded lands includes the diffusion and bubble pathways(Equation 3a.3.9):
EQUATION 3a.3.9
CH4 EMISSIONS FROM FLOODED LANDS (TIER 1)
CH4 emissionsWW flood = P ● E(CH4)diff ● Aflood, total surface + P ● E(CH4)bubble ● Aflood, total surface
Where:
CH4 emissionsWW flood= total CH4 emissions from flooded land, Gg CH4 yr-1

P = period, days (usually 365 for annual inventory estimates)
E(CH4)diff = averaged daily diffusive emissions, Gg CH4 ha-1 day-1
E(CH4)bubble = averaged bubbles emissions, Gg CH4 ha-1 day-1
Aflood, total surface = total flooded surface area, including flooded land, flooded lake and flooded river surface
area, haN2O emissions
The Tier 1 method for estimating N2O emissions from flooded lands includes the diffusion pathway only. N2Oemissions via the bubble pathway are not significant (Equation 3a.3.10):

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EQUATION 3a.3.10
N2O EMISSIONS FROM FLOODED LANDS (TIER 1)
N2O emissionsww flood = P ● E(N2O)diff ● Aflood, total surface
Where:= total N2O emissions from flooded land, Gg N2O yr-1

N2O emissionsWW flood

P = period, days (usually 365 for annual inventory estimates)
Ef(N2O)diff = averaged daily diffusive emissions, Gg N2O ha-1 day-1
Aflood, surface =total flooded surface area, including flooded land, flooded lake and flooded river surface
area, haTier 2CO2 emissions
In Tier 2, CO2 emissions can be estimated from reservoirs following the approach shown in Equation 3a.3.11.
The CO2 emissions from flooded lands should be estimated only for ten years after flooding when using Tier 2 or
3 methods unless country-specific research indicates otherwise.
Depending on the amount of data available, both diffusive and degassing emissions can be estimated when usinga Tier 2 approach. For the estimation of diffusive emissions, default emission factors can be used or countryspecific factors can be developed. For estimation of degassing emissions, country-specific factors are necessary.
The estimation of diffusion emissions can also be extended to distinguish between periods in which thereservoirs are ice-free and those in which they are ice-covered. This may be a significant improvement inaccuracy for countries in colder climates. Flooded land surface area rather than total flooded area data can beused, depending on data availability. The flooded land area may be further disaggregated by climatic zone.
EQUATION 3a.3.11
CO2 EMISSIONS FROM FLOODED LANDS (TIER 2)
CO2 emissionsWW flood = (Pf ● Ef(CO2)diff ● Aflood,land) + (Pi ● Ei(CO2)diff ● Aflood, land) + (([CO2]diss –[CO2]equ) ● Outflow ● 10-6) + (([CO2]spillway – [CO2]equ) ● Spillway ● 10-6)

Where:
CO2 emissionsWW flood = total CO2 emissions from flooded land, Gg CO2 yr-1
Pf = ice-free period, days
Pi = period with ice cover, days
Ef(CO2)diff = averaged daily diffusive emissions from air water-interface during the ice-free period,
Gg CO2 ha-1 day-1
Ei(CO2)i = diffusive emissions related to the ice-cover period, Gg CO2 ha-1 day-1
Aflood, land = flooded land area, ha[CO2]diss = averaged concentrations of CO2 before the turbines (water intake depth), kg l-1[CO2]equ = averaged concentrations of CO2 dissolved gases downstream of the dam or at equilibriumwith the atmosphere, kg l-1[CO2]spillway = averaged concentrations of CO2 before the spillway (water intake depth), kg l-1
Outflow = the averaged annual outflow rate in litres at the turbines, per hydroelectric reservoir, l yr-1
Spillway = the averaged annual outflow rate in litres at the spillway, per hydroelectric reservoir, l yr-1

CH4 emissions
Tier 2 can extend the Tier 1 method by replacing default values with country-specific emission factors, byaccounting for differences in diffusion and bubble emissions during periods when reservoirs are ice-free or ice-

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Appendix 3a.3

covered (for countries in the “boreal, wet” climate zone), by including (if data are available) degassing emissions
from outflows and spillways (mostly hydroelectric reservoirs), and by correcting area estimates to flooded landarea. Flooded land area may also be disaggregated by climatic zone. Tier 2 is described in Equation 3a.3.12:

EQUATION 3a.3.12
CH4 EMISSIONS FROM FLOODED LANDS (TIER 2)
CH4 emissionsWW flood = (Pf ● E(CH4)diff ● Aflood, land) + (Pf ● E(CH4)b ● Aflood, land) + Pi ● (Ei(CH4)diff+ Ei(CH4)bubble) ● Aflood, land + (([CH4]diss – [CH4]equ.) ● Outflow● 10-6) + (([CH4]spillway – [CH4]equ)● Spillway ● 10-6)
Where:
CH4 emissionsWW flood = total CH4 emissions from flooded lands per year, Gg CH4 yr-1
Pf = ice-free period, days
Pi = period with ice cover, days
E(CH4)diff = averaged daily diffusive emissions from air water-interface, Gg CH4 ha-1 day-1
E(CH4)bubble = averaged bubbles emissions from air water-interface, Gg CH4 ha-1 day-1
Aflood, land = flooded land area, ha[CH4]diss = averaged concentrations of CH4 before the turbines (water intake depth), kg l-1[CH4 ]equ. = averaged concentrations of CH4 dissolved gases downstream of the dam or at equilibriumwith the atmosphere, kg l-1[CH4]spillway = averaged concentrations of CH4 before the spillway (water intake depth), kg l-1
Outflow = the averaged annual outflow rate in litre at the turbines, per hydroelectric reservoir, l yr-1
Spillway = the averaged annual outflow rate in litre at the spillway, per hydroelectric reservoir, l yr-1
N2O emissions
The Tier 2 method for estimating N2O emissions from flooded lands is the same as shown in Equation 3a.3.10,
except that country-specific emission factors can be used, and (where data are available) flooded land surface
area should be used rather than total flooded surface area.

Tier 3
The Tier 3 methods for estimating emissions of all gases are more comprehensive and can include additionalcountry-specific data, such as emissions from above-water biomass. Tier 3 requires partitioning between emissions
from the degradation of flooded organic matter and from the decay of organic matter that comes from the watershed.

CHOICE OF EMISSI ON FACTORSThe key default values needed to implement Tier 1 method are emission factors for CO2, CH4 and N2O via thediffusion pathways, and an emission factor for CH4 via the bubbles pathways. Table 3a.3.5 provides default
emission factors for various climate zones that can be used under Tier 1. These default emission factors integrate
some spatial and temporal variations in the emissions from reservoirs, as well as fluxes at the water-air interfaceof reservoirs. All default data have been obtained from measurements in hydroelectric or flood control reservoirs.
The emissions factors for the ice-free period should be used in Tier 1 for the entire year.
For Tier 2, in addition to the above factors, data on CH4 concentrations at various points upstream anddownstream of the dam are needed to estimates degassing emissions. Country-specific emissions should be usedinstead of default factors to the extent possible. It is anticipated that a mix of default values and country-specificemission factors will be used when the latter do not cover the full range of environmental and management
conditions. The development of country-specific emission factors is discussed in Box 3a.3.1. The derivation ofcountry-specific factors should be clearly documented, and ideally published in peer reviewed literature.
Guidance in Box 3a.3.1 is applicable also for derivation of emission factors for Tier 3.

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ClimateTABLE 3A.3.5
DEFAULT EMISSIONS FACTORS FOR RESERVOIRS
Diffusive emissions (ice-free period)
Ef (GHG)diff (kg ha-1 d-1)
CH4
CO2
N2O

References

Boreal, wet

0.11
± 88%

15.5
±56%

0.008
±300%

Cold temperate, wet

0.2
±55%

9.3
±55%

nm

Duchemin, 2000; Duchemin 2002a, St-Louiset al., 2000; Smith and Lewis, 1992

0.063
± 0.032
0.096±0.074

-3.1
±3.6

nm

Duchemin 2002b

13.2
±6.9

nm

Duchemin 2002b

Tropical, wet

0.64
±330%

60.4
±145%

0.05
±100%

Tropical,moist-long dry season

0.31
±190%

11.65
±260%

nm

Tropical,moist-short dry season

0.44
±465%

35.1
±290%

nm

Pinguelli Rosa et al., 2002; Dos Santos,
2000

Tropical, dry

0.3
±115%

58.7
±270%

nm

Pinguelli Rosa et al., 2002; Dos Santos,
2000

Warm temperate, dry
Warm Temperate, wet

Duchemin, 2000; Huttunen et al., 2002;
Schlellhase, 1994, Duchemin et al., 1999

Keller et Stallard, 1994; Galy-Lacaux et al.,
1997; Duchemin et al., 2000; Pinguelli Rosa
et al., 2002
Pinguelli Rosa et al., 2002; Dos Santos,
2000

Bubbles emissions (ice-free period)
Ef (GHG)bubble (kg ha-1 d-1)
Boreal, wet

0.29
±160%

ns

ns

Duchemin, 2000, Huttunen et al., 2002;
Schlellhase, 1994

Cold temperate, wet

0.14
±70%

ns

ns

Duchemin, 2002a; St-Louis et al., 2000;
Smith and Lewis, 1992

Tropical, wet

2.83
±45%

ns

ns

Galy-Lacaux et al., 1997; Duchemin et al.,
2000; Pinguelli Rosa et al., 2002

Tropical,moist-long dry season

1.9
±155%

ns

ns

Pinguelli Rosa et al., 2002

Tropical,moist-short dry season

0.13
±135%

ns

ns

Pinguelli Rosa et al., 2002

Tropical, dry

Boreal, wet

0.3
ns
ns
±324%
Emissions associated with the ice coverperiodEi (GHG)diff + Ei (GHG)bubble(kg ha-1 d-1)
0.05
0.45
nm
±60%
±55%

Pinguelli Rosa et al., 2002

Duchemin, 2000; Duchemin et al., 2002ans : not significant, nm: not measured

CHOICE OF ACTIVI TY DATA
Several different types of activity data may be needed to estimate flooded land emissions, depending on the tierbeing implemented and the climatic zone. For Tier 1, total flooded area is required in all cases. For Tier 2,
additional activity data includes the period during which reservoirs are ice-covered or ice-free in boreal wet
regions as well as flow rates through hydroelectric outflow and spillways and flooded land area.
Flooded land area
Ideally, data on flooded area should be collected from national agencies. If such data are unavailable, however,Table 3a.3.6 contains information on total flooded surface area that can be used to estimate the emissions under
Tier 1. This table only includes surface area of flooded land that existed before 1990.
For Tier 2, flooded land area is required to estimate diffusive and bubble emissions. These data can frequently beobtained from hydro utility companies. Alternatively, countries can obtain the flooded land area by a drainage
basin cover analysis or by national dams database.

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Appendix 3a.3

TABLE 3a.3.6
DEFAULT RESERVOIR SURFACE AREA DATA
ICOLD
Surface area
(Mha)

Specific-country data
Surface area
(Mha)

Russia

7.32

7.96

USA

---

6.98

Canada

0

6.5

China

---

5.8

India

4.57

---

Brazil

0.69

3.98

Finland0.73---

Thailand

0.71

---

Egypt

0.70

---

Australia

0.66

---

Mexico0.60---

Zimbabwe

0.59

---

Country

Venezuela

0.58

---

Turkey0.56---

Argentina

0.50

---

Ivory coast

0.29

---

New-Zealand

0.21

---

Malik et al., 2000; US Army Corps Dams Database 1996; WCD, 2001; ICOLD 1998. Environment
Canada Reservoir Database (Duchemin, 2002a); Dos Santos, 2000.

Period of ice-free cover/Period of ice-cover
Under Tiers 2, and 3, the periods during which the reservoirs are ice-free or ice-covered are required to estimate
diffusive and bubbles emissions of CH4. These data can be obtained from national meteorological services orhydro utilitity companies.
Outflow/Spillway Volume
Under Tier 2, flooded land outflow and spillway volume are required to estimate degassing emissions of CH4.
These data can be obtained from hydro utility companies. Degassing fluxes are, mainly, a particularity ofhydroelectric reservoirs.
Tier 3 has much more extensive data requirements which can support more complex modelling of emissions
over time. Generally, this data can be compiled in a national reservoir inventory. The national reservoir
inventory should cover all types of reservoirs and include data and/or information on reservoir names, types,
surface area, depth, outflow rates, gas concentration before and after the turbines, climate conditions, water pH,
geological basement, eco-region type, and geographical coordinates (Duchemin, 2000; Duchemin et al., 1995;
Tavares de lima, 2002; Duchemin et al., 1999; Duchemin, 2002a).
CO2 and CH4 concentrations upstream and downstream of dams
Under Tiers 2 and 3, CH4 concentrations upstream and downstream of dams would be needed for estimation of
the degassing emissions. These data can be obtained as described by Fearnside (2002), Galy-Lacaux et al. (1997)
and Duchemin (2002b).

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BOX 3a.3.1
DERIVATION OF COUNTRY-SPECIFIC EMISSION FACTORSIn general, derivation of country-specific emission factors requires the measurement of emissions
by individual sub-source category (i.e., flooded land surface area, flooded land age, management
types, such as hydroelectric, agriculture, and water regulation). Emission levels vary widely
between reservoirs depending upon factors such as: area, type of ecosystems flooded, reservoir
depth and shape, local climate, geological basement the way in which the dam is operated, andecological and physical characteristics of the dammed river basin. Emissions can also vary widely
between different parts of the same reservoir (largely due to changes in depth, exposure to windand sun, and growth of water plants), and from year to year, season to season, and even between
night and day (Duchemin, 2000; Duchemin et al., 1995; Tavares de lima, 2002; Duchemin et al.,
1999; Duchemin, 2002a).
For emission factors to be representative of environmental and management conditions within the
country, measurements should be made in different flooded lands regions within a country, in allseasons, and if relevant, in different geographic regions and under different management regimes(Duchemin et al., 1999, Duchemin et al., 2002a). Appropriate selection of regions or regimes may
enable a reduction in the number of sites that must be sampled to derive a reliable flux estimate.
Maps, remote sensing data, or a dams database can provide a useful basis for delineation by
utilising the variability of a system or landscape. Aggregation errors may occur if available
measurements do not cover the actual range of environmental and flooded lands management
conditions, and inter-annual climatic variability. Validated, calibrated, and well-documented
simulation models may be a useful tool to develop area-average emission factors on the basis ofmeasurement data (Duchemin, 2000).
Regarding measurement period and frequency, emission measurements should be taken over anentire year, and preferably over a series of years, in order to reflect differences in weather
conditions, inter-annual climatic variability and flooded land evolution (Scott et al., 1999;
Duchemin, 2000; Tavares de Lima, 2002). A good description of the measurement techniques thatare available can be found in Duchemin et al. (1995), Galy-Lacaux et al. (1997), Duchemin(2000), Fearnside (2002) and Duchemin et al (2002b).
To ensure accurate emission factors of diffusive and bubble emissions, representative sites for factorsthat may influence annual and inter-annual variability of the emissions, would need to be monitored.
Such factors include depth and water level variation, water temperature, wind speed. Degassing
emission factors may vary with water temperature, which should be measured upstream of turbinesand downstream of dams so that the correlation can be established for higher tier methods.
The frequency of measurement should be consistent with the frequency of the factors that influenceannual and inter-annual variability. Emissions are likely to be variable among geographic regions,especially among different eco-regions, climatic zones and geological basements.
In general, emission factors are determined by taking the mean of the emissions of representative
sites. This averaging needs to consider the importance of each geographic zone and seasonal
period for the country.

3a.3.3.2

U NCERTAINTY ASSESSMENT

The two largest sources of uncertainty in the estimation of greenhouse gas emissions from reservoirs areassociated with the emission factors from the various pathways (diffusive, bubble and degassing) and to thereservoir surface area estimates.
Emission factors: Daily average diffusive emissions, derived from field measurements, vary by an order ofmagnitude for CH4 and by a factor of 5 for CO2 and N2O (Table 3a.3.4). Furthermore, daily average bubble
emissions of CH4 vary by more than an order of magnitude. Use of default measurements for different reservoir
types and in other regions will also result in uncertainty. Furthermore, most of the greenhouse gas flux
measurements have been undertaken on hydroelectric reservoirs, so that other types of reservoirs are notincluded in the default emissions estimates.
Flooded land surface area: Information on the flooded area retained behind larger dams should be available
and will probably be uncertain by no more than a few percent. However, information on the flooded land surface
area may be more difficult to obtain and will probably be uncertain to more than a few percent, especially in

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Appendix 3a.3

countries without large dams or with only a few hydroelectric reservoirs. Detailed information on the location,
type and function of smaller dams may be also difficult to obtain, though statistical inference may be possible
based on the size distribution of reservoirs for which data are available. In addition, reservoirs are created forvariety of reasons that influence the availability of data.

3a.3.3.3

C OMPLETENESS

A complete inventory should include all flooded lands. Maintaining a full area accounting, stratified by major
climate and ecosystem zones and by purposes is encouraged.

3a.3.3.4

D EVELOPING A CO NSISTENT TIME SERIES

General guidance on consistency in time series can be found in Section 5.6 (Time Series Consistency and
Recalculation). The emission estimation method should be applied consistently to every year in the time series,
at the same level of disaggregation. Moreover, when country-specific data are used, national inventories agency
should use same measurements protocol (sampling strategy, method, etc.). If it is not possible to use the same
method or measurement protocol throughout the time series, the guidance on recalculation presented in Chapter
5 should be followed. Differences in greenhouse gas emissions between inventory years should be explained,e.g., by demonstrating changes in areas of flooded lands or by updated emission factors. Consistency checks
should be made (i.e., by contacting hydro utility companies) to gather temporal information about areas affectedby former or future flooding.

3a.3.3.5

R EPORTING AND DOCUMENTATION

It is appropriate to document and archive all information required to produce the national inventory estimates. It issuggested that the following additional information is particularly important to document for this source category:Emission factors: The sources of the emission factors and parameters that were used (i.e., specific IPCC default
values or otherwise) should be given. If country- or region-specific emission factors and parameters were used,
and if new methods (other than IPCC default methods) were used, the scientific basis of these emission factors,
parameters and models should be well- documented. This includes defining the input parameters and describing
the process by which the emission factors, parameters and models were derived, as well as describing sources
and magnitudes of uncertainties.
Activity data: Sources of all activity data used in the calculations should be documented (i.e. complete citations
for the statistical databases from which the data were collected, communication with companies dealing withreservoirs). In cases where activity data were not available directly from databases or multiple data sets werecombined, the information, assumptions and procedures that were used to derive the activity data should be
described. This documentation should include the frequency of data collection and estimation, and estimates ofaccuracy and precision.
Emission results: Significant fluctuations in emissions between years should be explained. A distinction should
be made between changes in activity levels and changes in emission factors, parameters and methods from year
to year, and the reasons for these changes documented. If different emission factors, parameters and methods areused for different years, the reasons for this should be explained and documented.

3a.3.3.6

I NVENTORY QA/QC

It is appropriate to implement quality assurance/quality control (QA/QC) checks as outlined in Chapter 5(Section 5.5) of this report, and to conduct expert review of the emission estimates. Given the shortage of data
these reviews should be conducted regularly to take account of new research findings. Additional quality control
checks as outlined in Tier 2 procedures in Chapter 8, QA/QC, of GPG2000, and quality assurance procedures
may also be applicable, particularly if higher tier methods are used to quantify emissions from this source
category. Where country-specific emission factors are being used they should be based on high quality
experimental data, developed using a rigorous measurement programme, and be adequately documented.
It is, at present, not possible to cross-check emissions estimates from flooded lands through externalmeasurements. However, the inventory agency should ensure that emission estimates undergo quality control by:•

Cross-referencing reported country-specific emissions factors with default values and data from othercountries;

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Cross-referencing areas of flooded land with data of hydro utility companies, with the database of theInternational Commission on Large Dams, and with data submitted to national dams safety inventories.

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Appendix 3a.4

Appendix 3a.4 Settlements:
Basis for Future Methodological Development
Appendix 3a.4 presents a basic method for estimating emissions and removals of carbon by trees in settlements.
This land-use category was addressed in the Reference Manual of the IPCC Guidelines in Section 5.2 (Changes
in Forest and Other Woody Biomass Stocks. The methodology covers the subcategory of changes in carbon
stocks in living biomass. At this point, sufficient information is not available to develop a basic methodology
with default data to estimate the contribution of dead organic matter and soils to CO2 emissions and removals insettlements.

3a.4.1

Settlements Remaining Settlements

The category of settlements remaining settlements refers to all classes of urban tree formations, focusing
primarily on urban trees grown along streets, in gardens, and parks, in lands that have been in use as settlements(e.g., areas that are functionally or administratively associated with cities, villages, etc.) since the last datacollection period. Emissions and removals of CO2 in this category are estimated by a single subcategory ofchanges in carbon stocks in biomass, as summarised in Equation 3a.4.1.

EQUATION 3a.4.1
SUMMARY EQUATION FOR CHANGES IN CARBON STOCKSIN SETTLEMENTS REMAINING SETTLEMENTS∆CSS = ∆CSSLBWhere:∆CSS=∆CSSLB =changes in carbon stocks in settlements remaining settlements, tonnes C yr-1
changes in carbon stocks in living biomass in settlements remaining settlements,
tonnes C yr-1

3a.4.1.1

C HANGES IN CARB ON STOCKS IN LIVING BIOMASS

3A.4.1.1.1

METHODOLOGI CAL ISSUES

When estimating emissions for settlements, it is assumed that changes in carbon stocks occur only in tree
biomass. Changes in carbon stocks in bush biomass are not considered because data on bush growth are scarce.
However, if there are activity data and parameter values for bush species, their effect on CO2 emissions andremovals can be estimated with either a Tier 2 or Tier 3 method. Also meadow and ornamental plants in parks
and gardens are not addressed because sufficient information is not available.
Few data are available to estimate carbon removal by trees in settlements. Novak and Crane (2002) estimated thecarbon removal by trees in settlements in the conterminous USA as 23 million tonnes C yr-1. Besides anevaluation of the sink capacity of urban trees in Sydney (Brack, 2002), there are no similar studies from otherregions of the world. The methods described in this section are based on research carried out mainly in US cities.
They are useful as a first approximation to assess the net CO2 emissions and removals by urban trees. However,it should be recognised that additional data are needed for other regions to develop a fully generalised method.
The general method estimates changes in biomass carbon stocks as a result of tree growth, subtracting out losses
in biomass carbon stocks as a result of pruning and mortality. Depending on the magnitude of growth and losses,
the resulting average annual changes in living biomass carbon stocks may be positive or negative.
This method is shown in Equation 3a.4.2.
EQUATION 3a.4.2
CHANGES IN CARBON STOCKS IN BIOMASS IN SETTLEMENTS REMAINING SETTLEMENTS∆CSSLB = ∆CSSG – ∆CSSL

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Chapter 3: LUCF Sector Good Practice GuidanceWhere:∆CSSLB = changes in carbon stocks in living biomass in settlements remaining settlements, tonnes C yr-1
∆CSSG = changes in carbon stocks due to growth in living biomass in settlements remaining settlements,
tonnes C yr-1
∆CSSL = changes in carbon stocks due to losses in living biomass in settlements remaining settlements,
tonnes C yr-1

3a.4.1.1.1.1 Choice of Method
Depending on the availability of relevant data, either of the methodological tiers described in what follows canbe used. Both are based on the same methodology (growth minus losses) as in Section 3.2.1.1 and shown inEquation 3a.4.2.
Tier 1: There are two options for a Tier 1 estimation of changes in living biomass in settlements remaining
settlements. Tier 1a uses changes in carbon stocks per tree crown cover area as a removal factor, and Tier 1b
uses changes in carbon stocks per number of trees as a removal factor. The choice of method will depend on
availability of activity data.
Tier 1a: Crown cover area method
This method is represented by Equation 3a.4.3A and should be used when data are available on total area of tree
crown cover in settlements remaining settlements.
EQUATION 3a.4.3A
ANNUAL BIOMASS GROWTH BASED ON TOTAL CROWN COVER AREA
∆BSSG = (ACROWN ● CRW)
Where:∆BSSG = annual biomass growth in settlements remaining settlements, tonnes C yr-1
ACROWN = total crown cover area, ha
CRW = crown cover area-based growth rate, tonnes C (ha crown cover)-1 yr-1

This method can be implemented in three steps:
Step 1: Estimate the total tree crown area of trees in all settlements remaining settlements.
Step 2: Multiply the total tree crown area by the appropriate default removal factor for CRW(see Sec. 3a.4.1.1.1.2) to obtain ∆BSSG.
Step 3: Use the estimate for ∆BSSG in Equation 3a.4.2. In addition, set ∆BSSL = 0 if the average age of the tree

population is less than or equal to 20 years; otherwise assume ∆BSSG = ∆BSSL (see Section 3a.4.1.1.1.2).

Tier 1b: Tree growth rate method
The method is represented by Equation 3a.4.3B and should be used where data on the number of trees by broad
species class in settlements remaining settlements are available.
EQUATION 3a.4.3B
ANNUAL AMOUNT OF BIOMASS GROWTH BASED ON NUMBER OF INDIVIDUAL TREESIN BROAD SPECIES CLASSESn∆BSSG = ∑ (NTi ● CRatei )i =1
Where:∆BSSG = annual biomass growth in settlements remaining settlements, tonnes C yr-1
NTi = number of trees in broad species class i, tree #;3.296

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Appendix 3a.4

CRatei = annual average carbon accumulation per tree of broad species class i, tonnes C yr-1 tree #-1

TABLE 3A.4.1
TIER 1B DEFAULT AVERAGE ANNUAL CARBON ACCUMULATION PER TREE (TONNES C YR-1)
IN URBAN TREES BY SPECIES CLASSES

Broad species classDefault annual carbon accumulation per tree(tonnes C yr-1)

Aspen

0.0096

Soft Maple

0.0118Mixed Hardwood

0.0100Hardwood Maple

0.0142Juniper

0.0033

Cedar/larch

0.0072

Douglas fir

0.0122

True fir/Hemlock

0.0104Pine

0.0087

Spruce0.0092

Source: D. Nowak (2002; personal communication)

This method can be implemented in four steps:
Step 1: Estimate the number of trees in settlements remaining settlements for each broad species class.
Step 2: Multiply each estimate by the appropriate rate of changes in carbon per tree to obtain the amount ofcarbon removed.
Step 3: Sum the amount of carbon removed by each broad species class over all classes present in settlements
remaining settlements.
Step 4: Use the estimate for ∆BSSG in Equation 3a.4.2. In addition, set ∆BSSL = 0 if the average age of the tree

population is less than or equal to 20 years; otherwise assume ∆BSSG = ∆BSSL (see Section 3a.4.1.1.1.2).

Tier 2: Under Tier 2, the basic equations laid out in Tiers 1a and 1b can be used with country-specific removal
factors (CRW or CRatei). In addition to relying on country-specific data, Tier 2 methods may disaggregate

settlements by climate regions in order to apply more detailed removal factors to the data. Biomass loss (∆BSSL)
should be estimated explicitly rather than relying on default assumptions. Higher-level estimates of changes incarbon stocks in settlements may also include additional subcategories in the estimation, such as belowgroundbiomass, dead organic matter, and soil organic matter.
Given the preliminary nature of this methodology, an explicit Tier 3 method is not provided. However, countries
may choose to develop higher order estimation approaches, provided they yield more certain estimates ofgreenhouse gas emissions and removals in settlements.

3a.4.1.1.1.2 Choice of Emission/Removal Factors
In Tier 1a, the removal factor is CRW in Equation 3a.4.3A. If using Tier 1a, use a default CRW of 2.9 tonnes C(ha crown cover)-1 yr-1. This estimate is based on a sample of eight US cities, with values that ranged from 1.8 to
3.4 tonnes C (ha crown cover)-1 yr-1 (Nowak, 2002).
In Tier 1b, the removal factor is CRatei in Equation 3a.4.3B. If using Tier 1b, use defaults in Table 3a.4.1 forcarbon accumulation rates for each broad species class. These estimates are based on various allometricequations and limited field data from urban areas in the USA.
Under higher tiers, countries should develop removal factors that are appropriate for national circumstances.
Either area- or individual-based rates may be used. Country-specific removal rates should be based on thedominant climate zones and tree species of settlements areas in a country. If country-specific removal rates aredeveloped from estimates of biomass dry matter, they must be converted to units of carbon using either a default
carbon fraction (CF) of 0.5 tonnes carbon per tonne dry matter, or a carbon fraction that is determined to be more
appropriate for country-specific data.

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Chapter 3: LUCF Sector Good Practice Guidance

The default that ∆BSSL = 0 is based on the assumption that urban trees are net sinks for carbon when they are
actively growing and that the active growing period is roughly 20 years, depending on tree species, planting
density, and location (e.g., trees along thoroughfares or in parks, in shaded or sunny places, etc.). While growing
conditions in parks and gardens may be good, the growth and health condition of older trees are assumed toprogressively deteriorate with time because of the harshness of urban conditions (e.g., relatively low radiationlevels, air pollution). Therefore, the method assumes that the accumulation of carbon in biomass slows with age,and thus for trees greater than 20 years of age, increases in biomass carbon are assumed offset by losses frompruning and mortality. This is conservatively accounted for by setting ∆BSSG = ∆BSSL.
Under higher tier levels, the assumptions for ∆BSSLshould be evaluated and modified to better address national
circumstances. For instance, countries may have information on age-dependent and or species-specific carbon
losses in settlements trees. In this case, countries should develop a loss term and document the resources andrationale used in its development.

3a.4.1.1.1.3 Choice of Activity Data
The activity data needed to implement a Tier 1 method are either ACROWN, areas of tree crown cover, or NTi,
number of individual trees in broad species classes. For Tier 1a, crown cover area data (ACROWN) can be obtained
from aerial photographs of urban areas with the help of personnel skilled in photo interpretation, image sampling
and area measurement (Nowak et al., 1996). Crown cover is typically defined as the percent of ground coveredby a vertical projection of the outermost perimeter of the natural spread of the foliage of plants. It is important tonote that Equation 3a.4.3A uses a term for area and not percent. Values in percent crown cover should be
converted to total crown cover area for use in Equation 3a.4.3A by multiplying the percent crown cover by the
total area of trees.
For Tier 1b, records of tree populations, disaggregated into species or broad species classes may be obtained
from municipal agencies caring for urban vegetation or from sampling methods.
Under Tier 2, tree population numbers, disaggregated into species or broad species classes, can be obtained by
an appropriate sampling design. The area sampling methods described in Chapter 5, Section 5.3 (Sampling) canbe adapted to that purpose.

3a.4.1.1.1.4 Uncertainty Assessm ent
There are two primary sources of uncertainty in the basic methods: uncertainty in removal factors anduncertainty in activity data. The default Tier 1a removal factor, CRW, has an uncertainty of ±50% of the mean.
The default values provided for Tier 1b removal factors have a general uncertainty of ±30% of the mean, basedon expert judgement. Countries will need to assess the uncertainty of area estimates or tree numbers used ineither the Tier 1a or 1b approach. Common to the activity data of each of the tiers is the uncertainty in thedelineation of settlements boundaries. These influence the relative sizes of urban land-use types (e.g.,commercial, residential, parks, etc.) differing in tree population and extent of paved and built surfaces.
Uncertainties in activity data depend on the method used to estimate tree crown cover area. Most methods are
based on the interpretation of aerial photographs, but differ in the methods used for sampling those photographs.
The relative uncertainty of crown cover area estimates may conservatively range from ±5% to ±20% of the mean
estimate. Uncertainties in activity data (number of trees in each broad species class) are mainly derived from thesampling methods used for estimating the size of the tree population. Conservative uncertainty estimates range
from ±15% to ±25% of the tree number value.
For general guidance to identifying, quantifying, and combining uncertainties refer to Chapter 5, Section 5.2(Identifying and Quantifying Uncertainties) of this report.

3a.4.2Completeness

Ensuring the completeness of emission and removal estimates from settlements requires the inclusion of all
settlements in a country or at least those above some definite threshold size, and estimates of all greenhousegases and sources and sinks relevant to settlements.
At present, developing a complete estimate of changes in carbon stocks for this land-use category is constrainedby the lack of worldwide studies providing both quantification methods and default parameter data. With data
available at most municipal agencies, however, the methods and methodological approaches presented above
should allow for a fairly complete accounting of the changes in the carbon pools of settlements.

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Appendix 3a.4

3a.4.3Developing a consistent time series

Guidance for developing consistent time series is given in Chapter 5, Section 5.6 (Time Series Consistency and
Recalculations). To develop a consistent time series for the category of settlements remaining settlements, efforts
should be made to develop a regular inventory of settlements trees. The inventory may occur annually or oversome other regular time period, and include the number of individual species, and a measure of tree size, such as
diameter at breast height (dbh) such that growth can be estimated over multiple sampling periods. In addition,
biomass losses through pruning and mortality should also be tracked, ideally through the regular settlements tree
inventory.

3a.4.4

Reporting and Documentation

Countries should document estimates of emissions and removals in biomass of settlements remaining settlements
in reporting tables. Changes in carbon stocks (tonnes C yr-1) as well as emissions / removals of CO2 (Gg CO2 yr-1)
should be included in the reporting tables. It is critical to note that, by convention, changes in carbon stocks are
positive when carbon stocks in terrestrial pools are increasing and negative when carbon stocks in terrestrial
pools are decreasing. In contrast, CO2 emissions / removals follow an opposing convention. More guidance on
the sign convention is given in Section 3.1.7 Reporting and in Annex 3A.2 Reporting tables.
For the purposes of transparent reporting and to facilitate further refinement of inventory estimates, countries
should carefully document decisions made and approaches used to estimate CO2 emissions and removals fromsettlements. To meet this end, countries should consider the following items when developing documentation:•

Name and geographical location of each settlements;•

Name of the source (or sources) of activity data, or of data the latter were derived from;•

Methods used to obtain activity data;•

Criteria used for including tree species into the broad species classes indicated in Table 3a.4.1;•

Factors and/or ratios used to adjust average annual carbon accumulation per tree to growth in urban
conditions, if applicable;•

Source (or sources) of growth equations and methods used for combining them, and for obtaining parametervalues different from those presented in this appendix;•

Sampling methods and models used for developing country-specific carbon accumulation rates;•

Description of the methods used for settlements area delimitation; and•The results of time-trend analysis of previous emission records, the justification of their recalculation, andthe procedures used to that end. Large oscillations in the series values should be explained. For generalguidance see Chapter 5 of this report.

The foregoing documentation should be properly archived for future reference.

3a.4.5Inventory Quality Assurance/Quality Control

It is advisable to implement quality control checks as outlined in Chapter 5, Section 5.5 (Quality Assurance andQuality Control) of this report, and supplement the general QA/QC related to data processing, handling, andreporting as outlined in Chapter 5 of this report, with source-specific procedures, particularly the review of theparameters, equations, and calculations used to estimate emission values. External specialists (particularly
experts on urban forestry) as well as concerned stakeholders should peer-review the inventory estimates and thevalues of all important parameters and emission factors.

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References

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McGill W. B. (1996). Review and classification of ten soil organic matter models. In: Powlson D.S., Smith P.,
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Neill C., Melillo J.M., Steudler P.A., Cerri C.C., de Moraes J.F.L., Piccolo M.C., and Brito M. (1997). Soil
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Paul E.A., Paustian K., Elliott E.T. and Cole C.V. (eds) (1997) Soil organic matter in temperate agroecosystems:
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Barbosa R.I. and Fearnside P.M. (1996). Pasture burning in Amazonia: Dynamics of residual biomass and thestorage and release of aboveground carbon. Journal of Geophysical Research (Atmospheres) 101(D20):
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Conant R.T., Paustian K., and Elliott E.T. (2001). Grassland management and conversion into grassland: Effects
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Davidson E. A. and Ackerman I.L. (1993). Changes in soil carbon inventories following cultivation ofpreviously untilled soils. Biogeochemistry 20: pp. 161–164.
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Ellert B.H., Janzen H.H., and McConkey B.G. (2001). Measuring and comparing soil carbon storage. In: R.
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Guo L.B. and Gifford R.M. (2002). Soil carbon stocks and land use change: a meta analysis. Global Change
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3.5 WETLANDS
Alm J., Saarnio S., Nykanen H., Silvola J., and Martikainen P. J. (1999). Winter CO2, CH4 and N2O fluxes onsome natural and drained boreal peatlands. Biogeochemistry 44: pp. 163-186.
Intergovernmental Panel on Climate Change (IPCC). (1997). Houghton J.T., Meira Filho L.G., Lim B., TreantonK., Mamaty I., Bonduki Y., Griggs D.J. and Callander B.A. (Eds). Revised 1996 IPCC Guidelines for
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IPCC/OECD/IEA/IGES, Hayama, Japan.
Laine J. and Minkkinen K. (1996). Effect of forest drainage on the carbon balance of a mire - a case study.
Scandinavian Journal of Forest Research 11: pp. 307-312.
Laine J., Silvola J., Tolonen K., Alm J., Nykanen H., Vasander H., Sallantaus T., Savolainen I., Sinisalo J., andMartikainen P. J. (1996). Effect of water-level drawdown on global climatic warming - northernpeatlands. Ambio 25: pp. 179-184.
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Sundh I., Nilsson M., Mikkela C., Granberg G., and Svensson B. H. (2000). Fluxes of methane and carbon
dioxide on peat-mining areas in Sweden. Ambio 29: pp. 499-503.

3.6 SETTLEMENTS
Nowak D.J. and Crane D.E. (2002). Carbon storage and sequestration by urban trees in the United States.
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Nowak D.J., Rowntree R.A., McPherson E.G., Sisinni S.M., Kerkmann E.R., and Stevens J.C. (1996).
Measuring and analyzing urban tree cover. Landscape and Urban Planning 36: pp. 49-57.

APPENDIX 3A.1 HARVESTED WOOD PRODUCTS: BASIS FOR FUTURE
METHODOLOGICAL DEVELOPMENT
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Heath L. S., Birdsey R.A., Row C., and Plantinga A.J. (1996). Carbon pools and fluxes in U.S. forest products.
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APPENDIX 3A.2 NON-CO2 FROM DRAINAGE AND REWETTING OF FOREST SOILS:BASIS FOR FUTURE METHODOLOGY DEVELOPMENT
Alm J., Saarnio S., Nykanen H., Silvola J. and Martikainen P.J. (1999). Winter CO2, CH4 and N2O fluxes onsome natural and drained boreal peatlands. Biogeochemistry 44: pp. 163 – 186.
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dynamics of a restored cut-away peatland. Global Change Biology 6: 569-581.

APPENDIX 3A.3 WETLANDS REMAINING WETLANDS: BASIS FOR FUTURE
METHODOLOGICAL DEVELOPMENTORGANIC SOILS MANAGED FOR PEAT EXTRACTION
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Virtanen, O.V. Lindqvist, O. S. Nenonen, and P.J. Martikanen, (2002), Fluxes of CH4, CO2, and N2O inhydroelectric reservoir Lokka and Porttipahata in the northern boreal zone in Finland, Global
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Laine, J., and K. Minkkinen. (1996). Effect of forest drainage on the carbon balance of a mire - a case study.
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Laine, J., J. Silvola, K. Tolonen, J. Alm, H. Nykanen, H. Vasander, T. Sallantaus, I. Savolainen, J. Sinisalo, and
P. J. Martikainen. (1996). Effect of water-level drawdown on global climatic warming - northernpeatlands. Ambio 25:179-184.
Lappalainen, E. (1996) Global Peat Resources. Saarijärvi, Finland, Saarijärven Offset Oy.
Lappalainen, E. and Zurek, S. (1996a). Peatlands in other African countries. In: Lappalainen (Ed.), Global Peat
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Lappalainen, E. and Zurek, S. (1996b). Peatlands in other Asian countries. In: Lappalainen (Ed.), Global Peat
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Lappalainen, E. and Zurek, S. (1996c). Peatlands in central and south America. In: Lappalainen (Ed.), Global
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Minkkinen, K., R. Korhonen, I. Savolainen, and J. Laine.( 2002). Carbon balance and radiative forcing of
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International Peat Society, Finland, pp 163-168
FLOODED LAND REMAINING FLOODED LAND
Dos Santos, M.A., (2000), Inventário emissões de gases de efeito estufa derivadas de Hidréletricas, PhD.
Dissertation, University of Rio de Janeiro, Rio de Janeiro, Brazil, 154p.
Duchemin, É, (2000), Hydroelectricity and greenhouse gases: Emission evaluation and identification ofbiogeochemical processes responsible for their production, PhD. Disseration, Université du Québec àMontréal, Montréal (Québec), Canada, 321 p (available on CD-ROM)
Duchemin, É. (2002a), Greenhouse gases emissions from US reservoirs: Spot sampling in the Columbia River
Basin and in the Sierra Nevada region, Report for Environmental Fund Defense, DREXenvironment, 47p.
(available on CD-ROM)
Duchemin, É., (2002b), Canadian Reservoir Database, Environment Canada/DREXenvironment, CD-ROM.
Duchemin, É., M. Lucotte, R. Canuel and A. Chamberland, (1995), Production of the greenhouse gases CH4 andCO2 by hydroelectric reservoirs of the boreal region, Global Biogeochemical Cycles, 9, 4, 529-540.
Duchemin, É., R. Canuel, P. Ferland, and M. Lucotte, (1999), Étude sur la production et l’émission de gaz à effetde serre par les réservoirs hydroélectriques d’Hydro-Québec et des lacs naturels (Volet 2), Scientificreport, Direction principal Planification Stratégique - Hydro-Québec, 21046-99027c, 48p.
Duchemin, É., M. Lucotte, R. Canuel, D. Almeida Cruz, H. C. Pereira, J. Dezincourt and A. G. Queiroz, (2000),
Comparison of Greenhouse Gas Emissions from an Old Tropical Reservoir and from other Reservoirs
Worldwide, Verh. International Verein. Limnol., 27, 3, 1391-1395.
Duchemin, É., M. Lucotte, R. Canuel, (2002a), CH4 and CO2 emissions from boreal reservoirs upon ice breakup, submitted to Global Biogeochemical Cycles.
Duchemin, É, M. Lucotte, V. St-Louis,and R. Canuel, (2002b), Hydroelectric reservoirs as anthropogenic sourceof greenhouse gases, World Resources Review, 27, 3,
Fearnside, P.M. (2002). Greenhouse gas emissions from a hydroelectric reservoir (Brazil’s Tucuruí Dam) andthe energy policy implications. Water, Air and Soil Pollution 133(1-4): 69-96.
Galy-Lacaux, C., R. Delmas, C. Jambert, J.-F. Dumestre, L. Labroue, S. Richard and P. Gosse, (1997), Gaseousemissions and oxygen consumption in hydroelectric dams: a case study in French Guyana, Global
Biogeochemical Cycle, 11,4, 471-483.
Hélie, (2003), Approche isotopique des flux et de la géochimie du carbone dans les milieux aquatiques de l’est
du Canada : exemple du Saint-Laurent et du réservoir Robert-Bourassa, PhD. Dissertation, Université duQuébec à Montréal, Montréal (Québec), Canada.
Houel, (2002), Dynamique de la matière organique terrigène dans les réservoirs boréaux, PhD. Dissertation,
Université du Québec à Montréal, Montréal (Québec), Canada, 111p.

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Chapter 3: LUCF Sector Good Practice Guidance

Huttunen, J.T., T.S. Vaisanen, S. K. Hellsten, M. Heikkinen, H. Nykanen, H. Jungner, A. Niskanen, M. O.
Virtanen, O.V. Lindqvist, O. S. Nenonen, and P.J. Martikanen, (2002), Fluxes of CH4, CO2, and N2O inhydroelectric reservoir Lokka and Porttipahata in the northern boreal zone in Finland, Global
Biogeochemical Cycles, 16,1.
International Commission on Large Dams (ICOLD). 1998. World register of Dams (1998). Paris. International
Comittee on large Dams (Ed.). Metadatabase..
Junk, W.J., J.A.S.N. Mello, (1990), Impactos ecologico das represas hidrelectricas na bacia amazonica brasileira,
Estudo Avançado, 4 (8), 126-143
Keller, M. and R.F. Stallard. (1994). Methane emission by bubbling from Gatun lake, Panama, J. Geophys. Res.,99, D4, 8307-8319.
Malik, L.K., Koronkevich,N.I., Zaitseva, I.S., Barabanova, E.A. (2000). Development of Dams in the RussianFederation and NIS Countries, A WCD briefing paper prepared as an input to the World Commission on
Dams, Cape Town, http://www.dams.org
Pinguelli Rosa, L., B. Matvienko Sikar, M.A. dos Santos, E. Matvienko Sikar, (2002), Emissoes de dioxido decarbono e de metano pelos reservatorios hydroelectricos brasileiros, Relatorio de referencia – Inventorio
brasileiro de emissoes antropicas de gase de efeito de estufa, Ministerio da Ciencia e tecnologia, Brazil,
199p.
Schlellhase, H.U. (1994). B.C. Hydro Strategic R&D; Carbon project - Reservoir case study, Powertech Labs
inc., Final Report, 1-57.
Scott, K.J., C.A. Kelly, J.W.M. Rudd, (1999), The importance of floating peat to methane fluxes from flooded
peatlands, Biogeochemistry, 47, 187-202.
Smith, L.K., and W.M. Lewis, (1992), Seasonality of methane emissions from five lakes and associated wetlandsof the Colorado Rockies, Global Biogeochemical Cycles, 6, 4, 323-338
St-Louis, V., C. A. Kelly, É. Duchemin, J. W. M. Rudd and D.M. Rosenberg. (2000). Reservoir surfaces assources of greenhouse gases: a global estimate, Bioscience, 50,9, 766-775.
Tavares de Lima I. (2002). Emissoa de metano em reservatorio hidreletricos amazonicos atraves de leis depotencia (Methane emission from Amazonian hydroelectric reservoirs through power laws), PhD
Dissertation, Universidade de Sao Paulo, Sao Paulo, Brazil, 119 p.
US Army Corps. (1996). United States Army Corps of Engineers’ national Inventory of Dams. Metadatabase.US
Army Corps (Ed.). USA.
WCD, (2000), Dams and Development a new framework for Decision-Making, The report of the World
Commission on Dams, Earthscan Publications Ltd, London and Sterling, VA, 356 p.
WCD, (2001), Dams and Development a new framework for Decision-Making, The report of the World
Commission on Dams, Earthscan Publications Ltd, London and Sterling, VA, 356 p.Xue, Y., D.A.
Kovacic, M.B. David, L.E. Gentry, R.L. Mulvaney and C.W. Lindau (1999). In situ measurements ofdenitrification in constructed wetlands. J. Environ. Qual. 28, 263-269.Xuehui, M, and Yan, H. 1996. Peatand peatlands in China. . In: Lappalainen (Ed.), Global Peat Resources, International Peat Society,
Finland, pp 163-168.

APPENDIX 3A.4 SETTLEMENTS: BASIS FOR FUTURE METHDOLOGICALDEVELOPMENT
Brack, C.L. (2002). Pollution mitigation and carbon sequestration by a urban forest. Environmental Pollution
116(Suppl. 1): S195-S200.
Nowak, D.J.; Crane, D.E. (2002). Carbon storage and sequestration by urban trees in the United States.
Environmental Pollution 116(3): 381-389.
Nowak, D.J.; Rowntree, R.A.; McPherson, E.G.; Sisinni, S.M.; Kerkmann, E.R. and Stevens, J.C. (1996).
Measuring and analyzing urban tree cover. Landscape and Urban Planning 36: 49-57.

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4

SUPPLEMENTARY METHODS AND
GOOD PRACTICE GUIDANCE ARISINGFROM THE KYOTO PROTOCOLIPCC Good Practice Guidance for LULUCF

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

AUTHORS AND REVIEW EDITORS
Sections 4.1 – 4.2
Coordinating Lead Authors
Bernhard Schlamadinger (Austria)
Kansri Boonpragob (Thailand), Henry Janzen (Canada), Werner Kurz (Canada), Rodel Lasco (Philippines), andPete Smith (UK)

Lead Authors
Pascale Collas (Canada), El Nur Abdalla El Siddig (Sudan), Andreas Fischlin (Switzerland), Mitsuo Matsumoto(Japan), Alexander Nakhutin (Russia), Ian Noble (Australia), Gérôme Pignard (France), Zoltán Somogyi(Hungary), and Xiao-Quan Zhang (China)

Contributing Authors
Mark Easter (USA), Wojciech Galinski (Poland), Geneviève Patenaude (Canada), Keith Paustian (USA), andYoshiki Yamagata (Japan)

Review Editors
Masahiro Amano (Japan) and Eveline Trines (Netherlands)

Section 4.3
Coordinating Lead Authors
Sandra Brown (USA) and Omar Masera (Mexico)

Lead Authors
Vitus Ambia (New Papua Guinea), Barbara Braatz (USA), Markku Kanninen (Finland), Thelma Krug (Brazil),
Daniel Martino (Uruguay), Phanuel Oballa (Kenya), Richard Tipper (UK), and Jenny L. P. Wong (Malaysia)

Contributing Authors
Ben de Jong (Mexico) and David Shoch (USA)

Review Editor
Soobaraj N. Sok Appadu (Mauritius)

4.2

IPCC Good Practice Guidance for LULUCF

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto ProtocolContents
4.1

INTRODUCTION

4.9

4.1.1

Overview of Steps to Estimating and Reporting Supplementary Information for Activities
Under Articles 3.3, 3.4, 6 and 12 .................................................................................................. 4.10

4.1.2

General Rules for Categorisation of Land Areas under Articles 3.3 and 3.4 ................................ 4.13

4.1.3

Relationship between Annex I Parties’ National Inventories and Article 6 LULUCF Projects.... 4.19

4.2

METHODS FOR ESTIMATION, MEASUREMENT, MONITORING AND REPORTING
OF LULUCF ACTIVITIES UNDER ARTICLES 3.3 AND 3.4
4.20
4.2.1

Relationship between UNFCCC Land-Use Categories and Kyoto Protocol (Articles 3.3
and 3.4) Land-Use Categories ....................................................................................................... 4.20

4.2.2

Generic Methodologies for Area Identification, Stratification and Reporting .............................. 4.23
4.2.2.1 Reporting requirements ........................................................................................................ 4.23
4.2.2.2 Reporting Methods for lands subject to Article 3.3 and Article 3.4 activities...................... 4.24
4.2.2.3 Relationship between Approaches in Chapter 2 and Reporting Methods in Chapter 4 ........ 4.25
4.2.2.4 Choice of Reporting Method ................................................................................................ 4.26
4.2.2.5 How to identify lands (units of land) in general ................................................................... 4.27

4.2.3

Generic Methodological Issues For Estimating Carbon Stock Changes And Non-CO2
Greenhouse Gas Emissions ........................................................................................................... 4.29
4.2.3.1 Pools to be reported .............................................................................................................. 4.30
4.2.3.2 Years for which to estimate carbon stock changes and non-CO2 greenhouse gas emissions 4.31
4.2.3.3 Reporting and measurement intervals .................................................................................. 4.32
4.2.3.4 Choice of method ................................................................................................................. 4.32
4.2.3.5 Factoring out indirect, natural and pre-1990 effects............................................................. 4.32
4.2.3.6 Disturbances ......................................................................................................................... 4.33
4.2.3.7 Interannual variability .......................................................................................................... 4.33

4.2.4

Other Generic Methodological Issues ........................................................................................... 4.34
4.2.4.1 Developing a consistent time series...................................................................................... 4.34
4.2.4.2 Uncertainty assessment ........................................................................................................ 4.36
4.2.4.3 Reporting and documentation............................................................................................... 4.39
4.2.4.4 Quality assurance and quality control................................................................................... 4.50
4.2.4.5 Verification........................................................................................................................... 4.50

4.2.5

Afforestation and Reforestation .................................................................................................... 4.51
4.2.5.1 Definitional issues and reporting requirements .................................................................... 4.51
4.2.5.2 Choice of methods for identifying units of land subject to direct human-induced
afforestation/ reforestation.................................................................................................... 4.51
4.2.5.3 Choice of methods for estimating carbon stock changes and non-CO2 emissions ............... 4.54

4.2.6

Deforestation ................................................................................................................................. 4.56
4.2.6.1 Definitional issues and reporting requirements .................................................................... 4.56

IPCC Good Practice Guidance for LULUCF

4.3

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.2.6.2 Choice of methods for identifying units of land subject to direct human-induced
deforestation ......................................................................................................................... 4.57
4.2.6.3 Choice of methods for estimating carbon stock-changes and non-CO2 greenhouse gas
emissions .............................................................................................................................. 4.59
4.2.7

Forest Management ....................................................................................................................... 4.61
4.2.7.1 Definitional issues and reporting requirements .................................................................... 4.61
4.2.7.2 Choice of methods for identifying lands subject to forest management............................... 4.61
4.2.7.3 Choice of methods for estimating carbon stock changes and non-CO2 greenhouse gas
emissions .............................................................................................................................. 4.64

4.2.8

Cropland Management .................................................................................................................. 4.66
4.2.8.1 Definitional issues and reporting requirements .................................................................... 4.66
4.2.8.2 Choice of methods for identifying lands .............................................................................. 4.68
4.2.8.3 Choice of methods for estimating carbon stock changes and non-CO2 greenhouse gas
emissions .............................................................................................................................. 4.69

4.2.9

Grazing Land management ........................................................................................................... 4.81
4.2.9.1 Definitional issues and reporting requirements .................................................................... 4.81
4.2.9.2 Choice of methods for identifying lands .............................................................................. 4.82
4.2.9.3 Choice of methods for estimating carbon stock changes and non-CO2 greenhouse gas
emissions .............................................................................................................................. 4.83

4.2.10

Revegetation.................................................................................................................................. 4.85

4.2.10.1 Definitional issues and reporting requirements .................................................................... 4.85
4.2.10.2 Choice of methods for identifying lands .............................................................................. 4.86
4.2.10.3 Choice of methods for estimating carbon stock changes and non-CO2 greenhouse gas
emissions .............................................................................................................................. 4.86
4.3

LULUCF PROJECTS
4.3.1

4.89

Introduction ................................................................................................................................... 4.89
4.3.1.1 Definition of Projects and relevance to Articles 6 and 12 .................................................... 4.90

4.3.2

Project Boundaries ........................................................................................................................ 4.90
4.3.2.1 Geographic area.................................................................................................................... 4.90
4.3.2.2 Temporal boundaries ............................................................................................................ 4.91
4.3.2.3 Activities and practices ........................................................................................................ 4.91

4.3.3

Measuring, Monitoring, and Estimating Changes in Carbon Stocks and Non-CO2 Greenhouse
Gas Emissions ............................................................................................................................... 4.93
4.3.3.1 Baseline ................................................................................................................................ 4.94
4.3.3.2 Stratification of the project area ........................................................................................... 4.95
4.3.3.3 Selection of carbon pools and non-CO2 greenhouse gases ................................................... 4.95
4.3.3.4 Sampling design ................................................................................................................... 4.96
4.3.3.5 Field measurements and data analysis for estimating carbon stocks .................................. 4.100
4.3.3.6 Estimating changes in non-CO2 greenhouse gas emissions and removals ......................... 4.106
4.3.3.7 Monitoring changes in greenhouse gas emissions and removals from project operation
practices.............................................................................................................................. 4.109
4.3.3.8 Considerations for the monitoring plan .............................................................................. 4.109

4.3.4

4.4

Quality Assurance and Quality Control Plan .............................................................................. 4.111

IPCC Good Practice Guidance for LULUCF

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.3.4.1 Procedures to ensure reliable field measurements .............................................................. 4.111
4.3.4.2 Procedures to verify field data collection ........................................................................... 4.111
4.3.4.3 Procedures to verify data entry and analysis ...................................................................... 4.111
4.3.4.4 Data maintenance and storage ............................................................................................ 4.112
Annex 4A.1

Annex 4A.2

Tool for estimation of changes in soil carbon stocks associated with managementchanges in croplands and grazing lands based on IPCC default data

4.113

Examples of allometric equations for estimating aboveground biomass andbelowground biomass of trees

4.114

References

4.117

Equations
Equation 4.2.1 Annual soil carbon emissions/removals from cropland management ........................... 4.72
Equation 4.3.1 Estimation of aboveground biomass of forests............................................................ 4.102
Equation 4.3.2 Volume of lying dead wood ........................................................................................ 4.105
Equation 4.3.3 Soil organic carbon content ......................................................................................... 4.106

IPCC Good Practice Guidance for LULUCF

4.5

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

FiguresFigure 4.1.1

Decision tree for classifying a unit of land under Article 3.3 (ARD) or land under
Article 3.4 (FM, CM, GM and RV) as of year X of the commitment
period (2008, 2009, …, 2012) ...................................................................................... 4.14

Figure 4.2.1

Land classification in the national inventories under the UNFCCC of a hypotheticalcountry in year X of the commitment period................................................................. 4.22

Figure 4.2.2

Land classification for Kyoto Protocol reporting for a hypothetical country inYear X of the commitment period ................................................................................ 4.22

Figure 4.2.3

Two Reporting Methods for land subject to Articles 3.3 and 3.4 activities .................. 4.24

Figure 4.2.4

Decision tree for choosing a reporting method for land subject to activities under
Articles 3.3 and 3.4........................................................................................................ 4.26

Figure 4.2.5

Decision tree for determining whether a unit of land qualifies for direct
human-induced (dhi) Afforestation/Reforestation (AR) or Revegetation (RV). ........... 4.53

Figure 4.2.6

Decision tree for determining whether a unit of land is subject to direct
human-induced (dhi) Deforestation (D) ........................................................................ 4.59

Figure 4.2.7

Relationship between different forest categories........................................................... 4.62

Figure 4.2.8

Decision tree for determining whether land qualifies as being subject to Forest
Management ................................................................................................................. 4.63

Figure 4.2.9

Decision tree for selecting the appropriate tier for estimating carbon stock changes
in mineral soils under cropland for Kyoto Protocol reporting....................................... 4.71

Figure 4.2.10 Conceptual illustration of the matrix of carbon stock change factors derived for
different land-use, land-management transitions for each set of biophysicalcombinations (Tier 1) ..................................................................................................... 4.72
Figure 4.2.11 Schematic representation of a change in soil carbon stocks after acarbon-sequestering management change is imposed. .................................................. 4.74
Figure 4.2.12 Conceptual illustration of the matrix of carbon stock change factors derived for
different land-use, land-management transitions for each set of biophysicalcombinations (Tier 2) ................................................................................................... 4.75
Figure 4.2.13 Decision tree for selecting the tier at which to report carbon stock changes inorganic soils under the Kyoto Protocol ......................................................................... 4.77

4.6

Figure 4.3.1

An example of the relationship between number of plots and precision level ............. 4.97

Figure 4.3.2

Illustration of the relationship between the magnitude of the Reliable Minimum
Estimate (RME) between Time 1 and Time 2 sampling periods and the 95%
confidence interval around the mean soil carbon content ............................................. 4.99

Figure 4.3.3

An example of how the percent absolute change in mean soil carbon (with 95%
confidence) for an afforestation project varies in relation to the sampling intervaland sample size ........................................................................................................... 4.100

IPCC Good Practice Guidance for LULUCF

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TablesTable 4.1.1

Summary of the LULUCF Activities under the Kyoto Protocol and the associatedaccounting rules............................................................................................................. 4.13

Table 4.2.1

Relationship between activities under Articles 3.3 and 3.4 of the Kyoto Protocol andthe basic land-use categories of Section 2.2 .................................................................. 4.21

Table 4.2.2

Relationship between Approaches in Chapter 2 and Reporting Methods in Chapter 4. 4.26

Table 4.2.3

Calendar Years for which carbon stock changes are to be reported (for each activity
and each of the five pools described above), as a function of the time when theactivity started .............................................................................................................. 4.31

Table 4.2.4a

Supplementary Inventory Information to be reported prior to 1 January 2007 or one
year after the entry into force of the Kyoto Protocol for the Party, whichever is later .. 4.40

Table 4.2.4b

Supplementary Information to be reported for the annual greenhouse gas inventory
during the first commitment period according to the Marrakesh Accords. ................... 4.41

Table 4.2.5

Land transition matrix ................................................................................................... 4.44

Table 4.2.6a

Table for Reporting (AR/D/FM) ................................................................................... 4.45

Table 4.2.6b

Table for Reporting (CM/GM/RV) ............................................................................... 4.46

Table 4.2.6c

Table for Reporting (Projects)....................................................................................... 4.47

Table 4.2.7

Summary table of greenhouse gas emissions by sources and removals by sinks by
Articles 3.3, 3.4 and 6 activities for the inventory year................................................. 4.48

Table 4.2.8

Sections where methodologies can be found for estimating different carbon poolson cropland .................................................................................................................... 4.69

Table 4.3.1

A decision matrix to illustrate possible selection criteria of pools to measure andmonitor in LULUCF projects ........................................................................................ 4.96

Table 4.3.2

Possible LULUCF project practices that may result in emissions or removals of
non-CO2 greenhouse gases .......................................................................................... 4.107

Table 4.3.3

Location of IPCC default methods and data for estimation of non-CO2 greenhousegas emissions and removals......................................................................................... 4.108

Table 4.A.1

Allometric equations for estimating aboveground biomass of tropical andtemperate hardwood and pine species ......................................................................... 4.114

Table 4.A.2

Allometric equations for estimating aboveground biomass of palm trees common
in tropical humid forests of Latin America.................................................................. 4.114

Table 4.A.3

Examples of allometric equations for estimating aboveground biomass of someindividual species commonly used in the Tropics....................................................... 4.115

Table 4.A.4

Allometric equations for estimating belowground or root biomass of forests............. 4.115

IPCC Good Practice Guidance for LULUCF

4.7

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Boxes

4.8

Box 4.1.1

Examples for the assignment of units of land to Article 3.3 activities and lands to
Article 3.4 activities over time ...................................................................................... 4.17

Box 4.2.1

An example of consistency for management practices.................................................. 4.35

Box 4.2.2

Links.............................................................................................................................. 4.54

Box 4.2.3

Links.............................................................................................................................. 4.55

Box 4.2.4

Links.............................................................................................................................. 4.57

Box 4.2.5

Links.............................................................................................................................. 4.60

Box 4.2.6

Links.............................................................................................................................. 4.64

Box 4.2.7

Links.............................................................................................................................. 4.65

Box 4.2.8

An example of cropland management areas in 1990 and the commitment period(net-net accounting)....................................................................................................... 4.67

Box 4.2.9

Links.............................................................................................................................. 4.69

Box 4.2.10

Links.............................................................................................................................. 4.70

Box 4.2.11

Examples of possible influences of carbon stock changes on emissions of
non-CO2 gases ............................................................................................................... 4.80

Box 4.2.12

Links.............................................................................................................................. 4.82

Box 4.2.13

Links.............................................................................................................................. 4.83

Box 4.2.14

Links.............................................................................................................................. 4.86

Box 4.2.15

Links.............................................................................................................................. 4.87

Box 4.3.1

Afforestation or Reforestation projects ........................................................................ 4.91

Box 4.3.2

Cropland management projects: conversion from conventional to zero tillage in
agriculture ..................................................................................................................... 4.92

Box 4.3.3

Forest management projects: reduced impact logging ................................................. 4.92

Box 4.3.4

Forest improvement projects: enrichment planting on logged-over forest or
secondary growth forest ................................................................................................ 4.92

Box 4.3.5

Guidance on estimating greenhouse gas emissions from mobile sources ................... 4.109

Box 4.3.6

Monitoring projects involving multiple small-scale landholders ................................ 4.110

IPCC Good Practice Guidance for LULUCF

Introduction

4.1

INTRODUCTION

This chapter describes the supplementary methods and good practice guidance specifically linked to the land use,land-use change and forestry (LULUCF) activities in the Kyoto Protocol and gives full consideration to therequirements and methodologies for measuring, estimating and reporting of activities under Article 3.3, andunder Article 3.4 (if elected by a Party). The supplementary methods and good practice guidance of this chapter
apply generally to those Parties listed in Annex B of the Kyoto Protocol that have ratified the Protocol. This
chapter also provides good practice guidance for LULUCF projects hosted by Parties listed in Annex B (Article
6 projects) and afforestation / reforestation projects hosted by Parties not listed in Annex B of the Kyoto Protocol(Article 12, Clean Development Mechanism or CDM projects), see Section 4.3.1
Under the Kyoto Protocol, Parties are to report emissions by sources and removals by sinks of CO2 and othergreenhouse gases resulting from LULUCF activities under Article 3.3, namely afforestation (A), reforestation (R)and deforestation (D) that occurred since 1990. They are also to report any elected human-induced activities
under Article 3.4, which can be: forest management, revegetation, cropland management and grazing land
management. 2 In the commitment period Parties have to report annually, along with their annual reports ofgreenhouse gas emissions by sources and removals by sinks, supplementary information related to LULUCF
under the provisions of the Kyoto Protocol and the Marrakesh Accords to ensure compliance with theiremission-limitation and reduction commitments.3 The annual reporting requirement does not imply a need forannual measurements; however, Parties are expected to develop systems that combine measurements, models
and other tools that enable them to report on an annual basis.

1

It is assumed that the reader is familiar with Articles 3.3, 3.4, 3.7, 6 and 12 of the Kyoto Protocol(http://unfccc.int/resource/docs/convkp/kpeng.pdf).

2

LULUCF related requirements are outlined in paragraph 1 of the Annex to draft decision -/CMP.1 (Land use, land-use
change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.58:“Afforestation” is the direct human-induced conversion of land that has not been forested for a period of at least 50 years toforested land through planting, seeding and/or the human-induced promotion of natural seed sources.
“Reforestation” is the direct human-induced conversion of non-forested land to forested land through planting, seeding
and/or the human-induced promotion of natural seed sources, on land that was forested but that has been converted to nonforested land. For the first commitment period, reforestation activities will be limited to reforestation occurring on thoselands that did not contain forest 31 December 1989.
“Deforestation” is the direct human-induced conversion of forested land to non-forested land.
“Revegetation” is a direct human-induced activity to increase carbon stocks on sites through the establishment ofvegetation that covers a minimum area of 0.05 hectares and does not meet the definitions of afforestation and reforestation
contained here.
“Forest management” is a system of practices for stewardship and use of forest land aimed at fulfilling relevant ecological(including biological diversity), economic and social functions of the forest in a sustainable manner.
“Cropland management” is the system of practices on land on which agricultural crops are grown and on land that is set
aside or temporarily not being used for crop production.
“Grazing land management” is the system of practices on land used for livestock production aimed at manipulating theamount and type of vegetation and livestock produced.

3

Paragraph 5 of the Annex to draft decision -/CMP.1 (Article 7) contained in document FCCC/CP/2001/13/Add.3, p.22:
Each Party included in Annex I shall include in its annual greenhouse gas inventory information on anthropogenic
greenhouse gas emissions by sources and removals by sinks from land use, land-use change and forestry activities under
Article 3, paragraph 3 and, if any, elected activities under Article 3, paragraph 4, in accordance with Article 5, paragraph2, as elaborated by any good practice guidance in accordance with relevant decisions of the COP/MOP on land use, landuse change and forestry. Estimates for Article 3, paragraphs 3 and 4, shall be clearly distinguished from anthropogenic
emissions from the sources listed in Annex A to the Kyoto Protocol. In reporting the information requested above, eachParty included in Annex I shall include the reporting requirements specified in the paragraphs 6 to 9 below, taking into
consideration the selected values in accordance with paragraph 16 of the annex to decision -/CMP.1 (Land use, land-use
change and forestry). The footnote to the word “annual” in the first sentence says: It is recognised in the IPCC 1996
Revised Guidelines that the current practice on land use, land-use change and forestry does not in every situation request
annual data collection for the purpose of preparing annual inventories based on sound scientific basis.
Article 7, paragraph 3 of the Kyoto Protocol: Each Party included in Annex I shall submit the information required underparagraph 1 above annually, beginning with the first inventory due under the Convention for the first year of thecommitment period after this Protocol has entered into force for that Party[…].

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Relationship between UNFCCC and Kyoto reporting:
The information to be reported under the Kyoto Protocol is supplementary to the information reported under theConvention. Countries do not have to submit two separate inventories but should provide information under the
Kyoto Protocol as supplementary, within the inventory report.4
In practice, national circumstances, and specifically the technical details of the carbon accounting systems put
into place by each country, will determine the sequence in which the reporting information is compiled. For
example, it is possible to start with the UNFCCC inventory (with the additional spatial information required for
Kyoto Protocol reporting) and expand it to the Kyoto Protocol inventory, or it is possible to use a system thatgenerates the information for both UNFCCC and Kyoto Protocol reporting.
Example: when a Party that has elected cropland management under Article 3.4 prepares its UNFCCC inventory
for croplands according to Section 3.3 of this report, it is efficient to use the stratification into geographicalboundaries (Section 4.2.2) in doing so. Then, in preparing the supplementary information to be reported under
the Kyoto Protocol, the Party would delineate those UNFCCC cropland areas that were forests before (Section
3.3.2, Land converted to cropland), report these under deforestation according to Article 3.3, and report theremaining croplands under Article 3.4.
This chapter covers supplementary estimation and inventory reporting requirements needed for accounting under
the Kyoto Protocol. However, it does not address the implementation of accounting rules as agreed in the Kyoto
Protocol and Marrakesh Accords (such as caps, net-net accounting 5 and other specific provisions related toaccounting). This is because accounting is a policy matter and is not covered in the request to the IPCC.
Estimation refers to the way in which inventory estimates are calculated, reporting in the tables or other standard
formats used to transmit inventory information. Accounting refers to the way the information is used to assess
compliance with commitments under the Protocol.
The Marrakesh Accords refer to land in two ways, and these terms are adopted here:•

Units of land refers to those areas subject to the activities defined under Article 3.3, namely afforestation,
reforestation and deforestation, and•Land refers to those areas subject to the activities defined under Article 3.4, namely forest management,
cropland management, grazing land management, and revegetation.

4.1.1

Overview of steps to estimating and reporting
supplementary information for activities under
Articles 3.3, 3.4, 6 and 12

This section gives an overview of the steps required to estimate, measure, monitor and report changes in carbon
stocks and emissions and removals of non-CO2 greenhouse gases for Articles 3.3, 3.4, 6 and 12 under the Kyoto
Protocol. Detailed methods and good practice guidance for each individual activity are provided in Sections 4.2
and 4.3.
STEP 1: Define “forest”, apply definitions to national circumstances, establishing precedence conditions
and/or a hierarchy among selected Article 3.4 activities.
STEP 1.1: Select the numerical values in the definition of “forest”.6

4

Article 7, paragraph 1 of the Kyoto Protocol: Each Party included in Annex I shall incorporate in its annual inventory […]the necessary supplementary information for the purposes of ensuring compliance with Article 3 […].
Article 7, paragraph 2 of the Kyoto Protocol: Each Party included in Annex I shall incorporate in its national
communication, submitted under Article 12 of the Convention, the supplementary information necessary to demonstratecompliance with its commitments under this Protocol.

5Net-net accounting refers to the provisions of paragraph 9 of the Annex to draft decision -/CMP.1 (Land use, land-use
change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.59-60.

6According to the Marrakesh Accords, “forest” is a minimum area of land of 0.05 – 1.0 hectares with tree crown cover (orequivalent stocking level) of more than 10 – 30 per cent with trees with the potential to reach a minimum height of 2 – 5
metres at maturity in situ. A forest may consist either of closed forest formations where trees of various storeys andundergrowth cover a high proportion of the ground, or open forest. Young natural stands and all plantations which have yet
to reach a crown density of 10 – 30 per cent or tree height of 2 – 5 metres are included under forest, as are areas normallyforming part of the forest area which are temporarily unstocked as a result of human intervention such as harvesting or

4.10

IPCC Good Practice Guidance for LULUCF

Introduction

Parties must, by the end of 2006, decide on their choice of parameters to define forest, i.e., they must choose aminimum area (0.05 – 1 ha), the minimum crown closure at maturity (10 – 30%), and the minimum tree height atmaturity (2 – 5 m). Areas that meet these minimum criteria are considered forest, as are recently disturbed forests oryoung forests that are expected to reach these parameter thresholds. The numerical values of those parameters
cannot be changed for the commitment period. Each Party has to justify in its reporting that such values are
consistent with the information that has historically been reported to the Food and Agriculture Organization of theUnited Nations or other international bodies, and if they differ, explain why and how differing values were chosen.
In addition to the minimum area of forest, it is good practice that countries specify the minimum width that they
will apply to define forest and units of land subject to ARD activities, as explained in Section 4.2.2.5.1.
STEP 1.2: Apply definitions to national circumstances.
Parties must, by the end of 2006, decide and report which, if any, activities under Article 3.4 they elect (forest
management, cropland management, grazing land management and/or revegetation). It is good practice that
Parties document, for each elected activity, how the definitions will be applied to national circumstances and thatthey list the criteria that determine under which activity a land would be assigned. These criteria should bechosen in such a way as to minimize or avoid overlap and should be consistent with the guidance provided in thedecision tree in Figure 4.1.1 in Section 4.1.2.
STEP 1.3: Establish precedence conditions and/or a hierarchy among selected Article 3.4 activities.
For cases where overlaps may occur, it is good practice that the country specifies its precedence conditions
and/or a hierarchy among Article 3.4 activities prior to the commitment period, rather than on a case-by-casebasis. For example, if land could fall into both cropland management and forest management (such as inagroforestry systems), then it is good practice to consistently apply the specified scheme of precedence
conditions and/or hierarchy7 in determining under which activity the land is to be reported.
STEP 2: Identify lands subject to activities under Article 3.3 and any elected activities under Article 3.4.
The second step of the inventory assessment is to determine the areas on which the activities have taken placesince 1990 (and for which emissions and removals must be calculated). This step builds on the approaches
described in Chapter 2.
STEP 2.1: Compile land-use and land-cover information in 1990 for the relevant activities.
Using the selected definition of forest, develop means for determining forest and non-forest areas in 1990. Thiscan be accomplished with a map that identifies all areas considered forest on 1 January 1990. All forest-related
land-use change activities since 1990 can then be determined with reference to this base map (see Section 4.2.2.2
Reporting methods for lands subject to Article 3.3 and Article 3.4 activities).
STEP 2.2: Stratify the country into areas of land for which the geographic boundaries will be reported, as wellas the area of the units of land subject to Article 3.3 and/or the areas of lands subject to Article 3.4 within thesegeographic boundaries (see Section 4.2.2.4). This step can be omitted if Reporting Method 2 (see Section 4.2.2.2)
is used.
STEP 2.3: Identify units of land that, since 1990, are subject to activities defined in Article 3.3, and estimate thetotal area of these units of land within each geographic boundary. Under Reporting Method 2 (Section 4.2.2.2)
the estimation of the area of the units of land will be carried out individually for each unit of land.
Article 3.3 of the Kyoto Protocol requires that net carbon stock changes and non-CO2 greenhouse gas emissions
during the commitment period on land areas subject to afforestation (see Footnote 1 above), reforestation (R) anddeforestation (D) since 1990 are used to meet the commitments under Article 3. The Marrakesh Accords requireParties to estimate the area of the units of lands that have been subject to afforestation, reforestation and/or
deforestation within the boundaries mentioned in STEP 2.2 above (for details see Sections 4.2.2.2, 4.2.5 and 4.2.6).
STEP 2.4: Identify land areas subject to elected activities under Article 3.4, and estimate the total size of theseland areas within each geographic boundary. Under Reporting Method 2 (Section 4.2.2.2) the estimation of land
will be carried out individually for each land area subject to elected Article 3.4 activities.
For forest management (FM), if elected, each Party must identify the land area subject to forest management ineach inventory year of the commitment period. A Party could interpret the definition of forest management interms of specified forest management practices, such as fire suppression, harvesting or thinning, undertaken
since 1990. Alternatively, a country could interpret the definition of forest management in terms of a broad
natural causes but which are expected to revert to forest. See paragraph 1(a) in the Annex to draft decision -/CMP.1 (Landuse, land-use change and forestry), contained in document FCCC/CP/2001/13/Add.1, p.58.
7

Such as, e.g., “precedence is given to the dominant activity”, or “precedence is given to cropland management”.

IPCC Good Practice Guidance for LULUCF

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

classification of land subject to a system of forest management practices, without the requirement that a specified
forest management practice has occurred on each land. (For details see Sections 4.2.2.2 and 4.2.7).8
For cropland management (CM), grazing land management (GM), or revegetation (RV), the area subject to eachof these activities in any inventory year during the commitment period needs to be determined. As is discussed
in more depth in Sections 4.2.8 – 4.2.10, the area under the same activity in 1990 (or the applicable base year)
will also have to be determined, because carbon stock changes and non-CO2 greenhouse gas emissions on thisarea in 1990 have to be known in order to implement net-net accounting rules of the Marrakesh Accords (seeSection 4.2.8.1.1).
STEP 2.5: Identify the areas subject to projects under Article 6.
Some units of land subject to Article 3.3 or lands subject to Article 3.4 can also be projects under Article 6 of the
Kyoto Protocol. These have to be reported under Article 3.3 or Article 3.4 (if the relevant activity was elected).
In addition, these units of land or lands need to be delineated and the carbon stock changes and non-CO2
greenhouse gas emissions reported separately as part of the project reporting (see Section 4.3). The relationshipbetween estimation and reporting of activities under Articles 3.3 and 3.4, and projects under Article 6, isdiscussed in Section 4.1.3.
STEP 3: Estimate carbon stock changes and non-CO2 greenhouse gas emissions on the lands identified
under Step 2 above.
This step builds on the methodologies provided by Chapter 3 of this report (LUCF sector good practice guidance)and shows supplementary methodologies relevant to reporting of carbon stock changes and non-CO2 greenhousegas emissions under the Kyoto Protocol.
STEP 3.1: Estimate carbon stock changes and non-CO2 greenhouse gas emissions for each year of thecommitment period, on all areas subject to afforestation, reforestation or deforestation (as identified in STEP 2.3)
and all areas subject to elected activities covered under Article 3.4 (as identified in STEP 2.4), while ensuringthat there are no gaps and no double counting.
The estimation of carbon stock changes and non-CO2 greenhouse gas emissions for an activity begins with theonset of the activity or the beginning of the commitment period, whichever comes later. For further details
regarding the beginning of an activity see Section 4.2.3.2 (Years for which to estimate stock changes and nonCO2 greenhouse gas emissions).
STEP 3.2: Estimate carbon stock changes and non-CO2 greenhouse gas emissions in projects under Article 6 (seeSection 4.3.3 Measuring, monitoring, and estimating changes in carbon stocks and non-CO2 greenhouse gas
emissions).
For Article 12 Projects:
STEP 1: Identify areas. (Details can be found in Section 4.3.2 Project boundaries)
STEP 2: Estimate carbon stock changes and non-CO2 greenhouse gas emissions. (Details can be found in
Section 4.3.3 Measuring, monitoring, and estimating changes in carbon stocks and non-CO2 greenhouse gas
emissions).
Table 4.1.1 provides an overview of the LULUCF activities in the Kyoto Protocol, and the accounting rules thatare prescribed by the Marrakesh Accords. This information is summarized here because it has implications forthe supplementary estimation and inventory reporting requirements under the Kyoto Protocol.

8

Possible issues related to unbalanced accounting resulting from selective inclusion of forest management and revegetation
are addressed in the IPCC Report on Definitions and Methodological Options to Inventory and Report Emissions from
Direct Human-Induced Degradation of Forests and Devegetation of Other Vegetation Types.

4.12

IPCC Good Practice Guidance for LULUCF

Introduction

TABLE 4.1.1
SUMMARY OF THE LULUCF ACTIVITIES UNDER THE KYOTO PROTOCOL AND THE ASSOCIATED ACCOUNTING RULES
Net-net accounting9

Baseline scenario

Cap on Credits10

Article 3.3 (Afforestation, Reforestation,
Deforestation)

No

No

No

Article 3.4 (Forest Management)

No

No

Yes

Article 3.4 (all other)

Yes

No

No

Article 6No

Yes

Yes for Forest
Management

Article 12(Clean Development Mechanism)

No

Yes

Yes

Activities

4.1.2

General rules for categorisation of land areas under
Articles 3.3 and 3.4

Chapter 2 (Basis for consistent representation of land areas) describes approaches to classifying and representing
land areas associated with LULUCF activities. This is the basis for the good practice guidance in Chapter 4 for
identifying all relevant lands, for Kyoto reporting and for avoiding double counting of lands. It is good practice
to follow the decision tree in Figure 4.1.1 for each year of the commitment period in order to•Distinguish between afforestation and reforestation, deforestation, forest management, cropland
management, grazing land management and revegetation activities under Articles 3.3 and 3.4, as well as toremove potential overlaps and gaps between them; and to•

Assign lands to a single activity at any given point in time (i.e., for each year of the commitment period
2008-2012). This is required because of the possible land-use changes which can lead to double counting ofunits of lands / lands subject to Articles 3.3 and/or 3.4. Additional guidance on how to deal with shifts inland use over time is given in the examples of Box 4.1.1 at the end of this section.

The decision tree in Figure 4.1.1 is based on Marrakesh Accords (MA) definitions and it identifies a single
activity for a given year X of the commitment period under which the land should be reported. The decision tree
recognises that a given piece of land could be reported under different activities over time, subject to certain
conditions explained below. The decision tree is to be applied annually during the commitment period in order toupdate the allocation of lands to activities, thus taking into account shifts in land use that may have occurred.
This may be achieved by annual tracking of land or by interpolation.
There are two main branches in the decision tree in Figure 4.1.1. If a unit of land was subject to an afforestation,
reforestation or deforestation activity since 1990, then in addition, if a Party has elected one or more Article 3.4
activities, then the questions in the right branch should be answered to determine whether the land was also
subject to an elected Article 3.4 activity (secondary classification). This is needed to fulfil the reporting needs ofthe Marrakesh Accords 11 and to demonstrate that there is no double counting (which could occur if fullenumeration was not applied). More detailed decision trees to determine whether or not land or a unit of land issubject to specific activities are presented in Sections 4.2.5 through 4.2.10.

9Net-net accounting refers to the provisions of paragraph 9 of the Annex to draft decision -/CMP.1 (Land use, land-use
change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.59-60.

10See paragraphs 10 to 12 and 14 of the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry) containedin document FCCC/CP/2001/13/Add.1, p.60-61.

11

Paragraph 6 (b), bullet (ii) in the Annex to draft decision -/CMP.1 (Article 7), contained in document
FCCC/CP/2001/13/Add.3, p.22:
6. General information to be reported for activities under Article 3, paragraph 3, and any elected activities under Article 3,
paragraph 4, shall include:[…](b) The geographical boundaries of the areas that encompass:(i) Units of land subject to activities under Article 3, paragraph 3;(ii) Units of land subject to activities under Article 3, paragraph 3, which would otherwise be included in land subject
to elected activities under Article 3, paragraph 4, under the provisions of paragraph 8 of the annex to decision /CMP.1 (Land use, land-use change and forestry); and(iii) Land subject to elected activities under Article 3, paragraph 4.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Figure 4.1.1

No

Decision tree for classifying a unit of land under Article 3.3 (ARD) or land
under Article 3.4 (FM, CM, GM and RV) as of year X of the commitment
period (2008, 2009, …, 2012)

Has
forest ever beenpresent on this land or unit of landsince 31 Dec 1989?
Yes
Has theunit of land been subject to a
D activity since 31 Dec 1989?
(Note 1)

Yes

Classify as unit ofland subject to D
Yes

No
Has the unitof land been subject to an AR
activity since 31 Dec 1989?

Has theunit of land been
non-forested for at least 50 years
before the start of theconversion?
(Note 2)Yes

No

Did yourcountry elect FM, CM, GM
or RV?

Has theunit of land inany year of the commitment
period been subject to
FM, CM, GM or RV?
(Note 3)

No

Yes

No

Is
the landsubject to FM, CM,
GM or RV in
year X?

Keep classification of
the previous year in
the commitment
periodClassify as unit ofland subject to R

No

Yes

No reporting of theland in year X under
Art. 3.3 or 3.4

Has
the land inany year of the commitment
period been subject to
FM, CM, GM or RV?
(Note 3)No

Did yourcountry elect FM, CM, GM
or RV?

No

YesClassify as unit ofland subject to A

No

Yes

Yes

Yes

Classify as land
subject to FM,
CM, GM or RV
activity for
year XClassify as unit of land
subject to AR or D withsecondary classification
under the FM, CM, GM or
RV activity for year X

Is theunit of land
subject to FM, CM,
GM or RV in
year X?
No
Keep classification of theprevious year in thecommitment period,
including the secondaryclassification

Classify asunit of land
subject to
AR or D

Note 1: No matter whether it had been subject to an AR activity before.
Note 2: The distinction between A and R is often irrelevant, in particular if the same methodologies apply. But sometimes
they might differ in the rate and direction of soil and litter C stock change.
Note 3: Apply this test only to those activities that your country has elected.

Abbreviations used in the Figure:
AR Afforestation / Reforestation
CM Cropland Management

4.14

D
GM

Deforestation
Grazing Land Management

FM
RV

Forest Management
Revegetation

IPCC Good Practice Guidance for LULUCF

Introduction

The left branch is for lands that are reported under Article 3.4, and needs to be checked by Parties that haveelected one or more Article 3.4 activities. This is necessary to know whether a land was subject to an Article 3.4
activity, and also to determine which Article 3.4 activity (if elected) applied on the land most recently. If a landis subject to more than one Article 3.4 activity over the course of time, it is good practice to classify that land
under only one Article 3.4 category. Therefore, it is good practice for countries to set up a hierarchy among theactivities forest management, cropland management, grazing land management and revegetation, and – within
the scope of the definitions in the Marrakesh Accords – to set up criteria by which lands will be assigned to asingle category (see Section 4.1.1, Overview, STEP 1.3). For example, where agriculture and forestry arepracticed on the same land, the land may qualify under forest management and under cropland management or
grazing land management. It is good practice to assign land according to specific, pre-determined rules, rather
than on a case-by-case basis. The definitions in the Marrakesh Accords imply that•••

Forest management can only take place on lands that meet the definition of a forest;
Revegetation can only take place when the land is forest neither before nor after the transition (otherwise it
would be afforestation, reforestation or forest management); andGrazing land and cropland management can take place on either forest or non-forest lands, but will be
predominantly on non-forest lands in practice. Any forest land under grazing land or cropland management
can be subject to a deforestation activity.

Regarding the relationship between forest management on the one hand, and cropland/grazing land managementon the other hand, countries have two options: 1) It is good practice to interpret the definition of forest
management such that all managed forests are included, including those where also cropland and grazing land
management takes place. With this, all lands subject to grazing or cropland management would necessarily haveto be non-forest. 2) Alternatively, it is also good practice to use pre-defined criteria other than "forest / nonforest" to determine whether a land area is subject to forest management or grazing land management / cropland
management. In that case it is possible that some forest lands are included under cropland or grazing land
management.
Special attention should be given to avoid overlap or gaps between lands subject to revegetation (if elected) that
could qualify under cropland management, grazing land management or potentially forest management (if elected).
In addition note that:•

The decision tree in Fig. 4.1.1 is not sufficient to identify all lands that fall under each activity. For thereporting of these lands, it is good practice to follow the methodological guidance provided under“Identification of lands” in the generic Section 4.2.2, and in the activity-specific sections on land
identification (Sections 4.2.5.1 / 4.2.6.1 / 4.2.7.1 / 4.2.8.1 / 4.2.9.1 and 4.2.10.1).

•

For the first commitment period, Article 3.3 applies to land that is subject to an afforestation, reforestation
or deforestation activity at any time between 1 January 1990 and 31 December 2012.

•

For reporting during the commitment period Article 3.4 applies to land that is subject to an elected forest
management, cropland management, and grazing land management activity during the commitment
period12,13 . Article 3.4 also applies to land subject to revegetation resulting from direct human-induced
activities since 1 January 1990.14•

Once a land is reported under Article 3.3 or Article 3.4, all anthropogenic greenhouse gas emissions bysources and removals by sinks on this land must be reported during the first and throughout subsequent and12

Conversely, for base year reporting, Article 3.4 applies to land that was subject to an elected cropland management, grazing
land management or revegetation activity in the base year.

13

The reason is that if a land was subject to an Article 3.4 activity between 1 January 1990 and 31 December 2007, but is nolonger in the years 2008-2012, it could not be accounted for under the Kyoto Protocol. Carbon reporting of this land during
the commitment period would be highly complicated because the land would be under a different land use. Land that left
the FM category as a result of deforestation would, of course, be reported under Article 3.3.

14

As stated in STEP 1.2 above, it is good practice to apply the definitions of Article 3.4 activities to national circumstances.
In doing so, there may be Article 3.4 activities where an individual practice triggers the land to be reported (“narrowly
defined activities”). This is likely to apply to revegetation, also possibly to forest management, and requires to report alllands that are subject to the activity since 1990 (as for AR and D). On the other hand, there will be Article 3.4 activities
where the mere classification of the land, without a concrete practice, will suffice for the land to be reported (“broadly
defined activities”). This is most likely for cropland and grazing land management – also because there the practices aremost likely to occur on an annual basis anyway. Here it is sufficient to report the lands subject to the activity in thereporting year of the commitment period.

IPCC Good Practice Guidance for LULUCF

4.15

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

contiguous commitment periods15, except the Party chooses not to report a pool that has been shown not to
be a source as explained in Section 4.2.3.1. That is, the total land area included in the reporting of Article
3.3 and 3.4 activities can never decrease.
•

If certain activities occur during the commitment period, it is possible that a unit of land or land can bereported under different activities in Article 3.3 and/or Article 3.4 over time during the commitment period.
However, for each year it can only be reported under a single activity.

•

In order to avoid the reporting of lands or units of land in more than one activity in any year during thecommitment period, the following should be applied:(i)

Units of land subject to activities under Article 3.3 which would otherwise be included in land subject
to an Article 3.4 activity (see item (ii) in footnote 11) must be reported separately as lands that are bothsubject to Article 3.3 and 3.4 activities (referred to as AR or D land with a secondary classification in
the decision tree). The decision tree implies that afforestation, reforestation and deforestation haveprecedence over the other activities for land classification and reporting purposes not only in a given
year, but for the entire period between 1990 and 2012.16(ii)

For lands that are subject to several activities under Article 3.4 it is good practice to apply the national
criteria that establish the hierarchy among Article 3.4 activities (in the Marrakesh Accords noprecedence is implied among Article 3.4 activities, see STEP 1.3 above).

•

A land subject to land-use changes (LUCs) can move between categories in the following cases:− Afforestation/reforestation land that is subsequently deforested is reclassified as deforestation land
(Section 4.2.4.3.2 describes specific provisions for units of land subject to afforestation and reforestation
activities since 1990).
− Land under one elected Article 3.4 activity is converted into land under another elected Article 3.4
activity and must be reclassified accordingly.
− Land under an elected Article 3.4 activity becomes subject to an Article 3.3 activity and must
subsequently be reported under the latter.

•On the other hand, the following transitions are not possible. Note that these restrictions apply to reporting
under the Kyoto Protocol (but do of course not affect the actual management that a country applies to its
lands):− Land cannot shift from an elected Article 3.4 activity to another Article 3.4 activity that was not elected.
− Land cannot leave the Article 3.3 reporting.
− Deforestation land cannot become afforestation/reforestation land in the first commitment period. That is,
if a forest is established on land deforested since 1990, the carbon removals cannot be reported as areforestation activity during the first commitment period because of the time limits in the definition forreforestation agreed in the Marrakesh Accords, designed not to credit reforestation on lands that were
forest land in 1990.17 However, because there is the need for continuous full reporting of lands subject to
Article 3.3 and 3.4 activities, any carbon stock increases later in the commitment period on deforestation
lands will be reported under the deforestation category.

•

Boundaries between forest management and cropland or grazing land systems can be difficult to define
where these activities are practiced on the same land area. The decision tree in Figure 4.1.1 suggests thatplanting of shelterbelt trees or orchards after 1990 that meet the criteria for a forest would be reported under
the afforestation and reforestation category, even if they occur on lands whose use is mainly agricultural.
For shelterbelts and orchards which already existed in 1990, however, the decision tree implies that thecountry can prioritise the Article 3.4 reporting category as either cropland management or grazing land
management, or as forest management – provided that the land meets the definition of the category chosen,
and the prioritisation is consistent with the hierarchy of Article 3.4 activities set up at the beginning. For
example, if shelterbelts or farm woodlots do not appear to be part of forest management as such, and are

15

Paragraph 19 of the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.61.

16This is implied in the text of the Marrakesh Accords cited in footnote 11 above, item b (ii).

17Paragraph 1(c) of the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p 58.

4.16

IPCC Good Practice Guidance for LULUCF

Introduction

clearly associated with cropping or grazing land systems, the hierarchical system set up by a country might
determine this to be reported under cropland management or grazing land management.
In summary, this means that the area under Article 3.3 (afforestation, reforestation and deforestation lands) willgrow from 0 hectares on 1 January 1990 up to a certain value in 2012. At any given point in time, theafforestation, reforestation and deforestation categories should contain all areas of land that have been afforested,reforested or deforested since 1990. The area under Article 3.3 (deforestation) will stay constant or increase insize during the commitment period. The land area in the afforestation and reforestation category will typicallyincrease, but can also decrease if afforestation and reforestation lands are subject to deforestation activities.
The amount of lands in the forest management, cropland management, grazing land management, andrevegetation categories can fluctuate because of various land-use changes. It is unlikely that those areas will stay
constant over time for the purpose of reporting because, for example:••••

Afforestation and reforestation, and deforestation land areas are allowed to grow;Grazing lands can become croplands and vice versa;
Revegetated lands can become croplands or grazing lands or vice versa; andForest management land areas can increase, for example, as countries expand the road infrastructure toareas previously unmanaged.

Box 4.1.1 provides several examples that summarise the Marrakesh Accords and the considerations that apply
for lands subject to activities under Articles 3.3 and 3.4 of the Kyoto Protocol. The preceding sections of Chapter
4 have provided merely an overview of the Marrekech Accords. For more detailed explanations of the rationale
behind the examples in Box 4.1.1, the reader is referred to the detailed explanations in the remaining sections of
Chapter 4.
BOX 4.1.1
EXAMPLES FOR THE ASSIGNMENT OF UNITS OF LAND TO ARTICLE 3.3 ACTIVITIESAND LANDS TO ARTICLE 3.4 ACTIVITIES OVER TIME

The following examples are intended to show, conceptually, how different land-use transitions
would be categorised in different inventory years under the Kyoto Protocol. This does notnecessarily imply that the land-use transition can be directly measured on an annual basis. Notethat for croplands and grazing lands only carbon stock changes are discussed in the examples
below. Non-CO2 greenhouse gas emissions for such lands are reported under the Agriculture
Sector of the IPCC Guidelines (Section 4.5.2 in the Reference Manual), independently of which
Article 3.4 activities were elected by the Party.
Example 1: A land under forest management is deforested in 1995 and turned into a cropland.
2008-2012: Carbon stock changes and non-CO2 greenhouse gas emissions on this land are reported
under deforestation. The methodology for croplands that were previously forest (Section 3.3.2) is
to be used.
Carbon stock changes on this land will not be reported under cropland management, even if
cropland management was elected, because deforestation takes precedence over cropland
management. The decision tree in Figure 4.1.1 therefore assigns this land to deforestation, withcropland management as a secondary classification.
Should trees be re-established on this land again, for example in 2011, the land remains in the
deforestation category, because reforestation is not admissible on lands that were forest in 1990. The
methodology to be used to estimate for carbon stock changes, however, is the one for reforestation.
Example 2: A land under forest management is deforested on 1 January 2010 and turned
into a cropland.
2008-2009: Carbon stock changes and non-CO2 greenhouse gas emissions on this land for theyears 2008 and 2009 are reported under forest management (if forest management is elected,
otherwise they are not reported at all under the Kyoto Protocol, only as part of the regular annual
LUCF inventory under the UNFCCC).
2010-2012: Carbon stock changes and non-CO2 greenhouse gas emissions on this land in the years
2010-2012 are reported under deforestation. The methodology for croplands that were previously
forest (Section 3.3.2) should be used. Non-CO2 greenhouse gas emissions directly resulting fromthe deforestation should be reported under the Deforestation category. Non-CO2 greenhouse gas
emissions resulting from the agricultural practices should be reported in the Agriculture sector of
the national inventory as per the IPCC Guidelines. Double counting should be avoided.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

BOX 4.1.1 EXAMPLES (CONTINUED)

Carbon stock changes on this land will not be reported under cropland management, even if
cropland management has been elected, because deforestation takes precedence over cropland
management. The decision tree in Figure 4.1.1 therefore assigns this land to deforestation withcropland as a secondary classification.
Example 3: A cropland is turned into a grazing land in 2010.
2008-2009: Carbon stock changes and non-CO2 GHG emissions on this land are reported undercropland management (if elected, otherwise not reported at all under the Kyoto Protocol, only as
part of the annual LUCF inventory).
2010-2012: If grazing land management is elected, carbon stock changes and non-CO2 greenhousegas emissions from this land are reported under grazing land management (Sections 3.4.2 and4.2.9). If grazing land management is not elected, carbon stock changes and non-CO2 greenhousegas emissions on this land will still have to be reported under cropland management for those years(if cropland management is elected), because of the requirement to continue to report on future
stock changes once land has entered the Kyoto reporting system.
Example 4: A grazing land is turned into a settlement in 2005.
2008-2012: Carbon stock changes and non-CO2 greenhouse gas emissions from this land are notreported under the Kyoto Protocol, since it was not subject to an elected activity during thecommitment period.
Example 5: A grazing land is turned into a settlement land in 2010.
The land needs to be reported as being subject to grazing land management (if elected) in all five
years of the commitment period (because it was under grazing land management at least in one
year during the commitment period). Pre-2010, the grazing land methods need to be used whereas,
starting in 2010, the methodologies for conversion to settlements need to be used.
Example 6: Forest management land is turned into a settlement in 2010.
2008-2009: Carbon stock changes and non-CO2 greenhouse gas emissions from this land arereported under forest management (if elected, otherwise not reported at all under the Kyoto
Protocol, only under the managed forest of the regular LUCF inventory).
2010-2012: Land reported as “deforested”, using the methodologies of Chapter 3, Section 3.6, forlands converted to settlements.
Example 6 shows that land which is converted from an elected land use during the commitment
period should continue to be reported. This does not apply to Example 4 because no removal units
will have been generated.
Example 7: Forest management land is turned into a settlement18 in 1995.
2008-2012 carbon stock changes are reported under Article 3.3, deforestation.
Example 8: Other land is turned into grazing land (and reported as revegetation) in 2005.
In each year of the commitment period the carbon stock changes and non-CO2 greenhouse gas
emissions from this land are reported under revegetation (if elected).

18

which, by definition, is non-forest, see Chapter 2.

4.18

IPCC Good Practice Guidance for LULUCF

Introduction

4.1.3

Relationship between Annex I Parties’ national
inventories and Article 6 LULUCF projects

Emissions or removals resulting from Article 6 projects will be part of the host country’s annual inventory under
the UNFCCC and Kyoto Protocol reporting. The methods for estimating, measuring, monitoring and reporting
greenhouse gas emissions and removals resulting from LULUCF project activities are addressed in Section 4.3(LULUCF Projects).
When estimating the greenhouse gas emissions and removals of Article 3.3 and 3.4 activities, it is possible to use
the information that is reported for, or is meeting the standards of, Article 6 LULUCF projects on these lands(but not vice versa). Two options exist for Article 3.3 and Article 3.4 estimation, both of which are consideredgood practice:
Option 1: Carry out Article 3.3 and Article 3.4 assessment without consideration of information reported for
Article 6 projects (which are reported separately according to Section 4.3). This assumes that a properly
designed national system will also automatically include the effects of Article 6 projects. This approach is also
taken in the other emission sectors. For example, an Article 6 project that reduces emissions from fossil fuels is
not individually considered in the national emissions inventory, but will implicitly be included due to theproject’s impacts in the national statistics for fossil fuels.
Option 2: Consider all changes of carbon stocks as well as greenhouse gas emissions and removals at the projectlevel as a primary data source for Article 3.3 and/or Article 3.4 estimation and reporting, for example byconsidering projects as a separate stratum. Any Article 3.3 and 3.4 activities that are not projects need to bemonitored separately. In this case, the design of the monitoring must ensure that projects are explicitly excluded
from the remaining lands under Articles 3.3 and 3.4, to avoid double counting.
One important difference between project and national (Articles 3.3 and 3.4) accounting is that projects have abaseline scenario (i.e., only additional carbon stock changes and non-CO2 greenhouse gas emissions due to theproject are accounted), while afforestation, reforestation, deforestation, forest management, cropland
management, grazing land management and revegetation do not have a baseline scenario. Therefore, when using
project-level information for reporting under Articles 3.3 and 3.4, one must take account of the overall carbon
stock changes and non-CO2 greenhouse gas emissions associated with the projects, and not just the change
relative to the baseline scenario.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.2

METHODS FOR ESTIMATION,
MEASUREMENT, MONITORING ANDREPORTING OF LULUCF ACTIVITIES UNDERARTICLES 3.3 AND 3.4

Section 4.2 provides a discussion of generic methodological issues that concern all possible land use, land-use
change and forestry (LULUCF) activities under Kyoto Protocol Articles 3.3 and 3.4 (Section 4.2.1 on therelationship between land-use categories in reporting under the UNFCCC and the Kyoto Protocol, 4.2.2 on land
areas, Section 4.2.3 on estimating carbon stock changes and non-CO2 greenhouse gas emissions, and Section
4.2.4 on other generic methodological issues). This is followed by specific methodologies for monitoring
afforestation and reforestation (treated together), deforestation, forest management, cropland management,
grazing land management and revegetation (Sections 4.2.5 – 4.2.10), and projects (Section 4.3). Readers shouldrefer to both the generic and the specific issues for any one of the activities.

4.2.1

Relationship between UNFCCC land-use categories
and Kyoto Protocol (Articles 3.3 and 3.4) land-use
categories

This subsection provides an overview of how the activities under Articles 3.3 and 3.4 relate to the land-use
categories introduced in Chapter 2 and elaborated/utilized for the purposes of reporting on national greenhousegas emissions and removals under the UNFCCC in Chapter 3 (LUCF sector good practice guidance).
Land-use systems are classified in Chapters 2 and 3 into:(i)

Forest land (managed and unmanaged) (Section 3.2)

(ii)

Cropland (Section 3.3)

(iii)

Grassland (managed and unmanaged) (Section 3.4)

(iv)

Wetlands (Section 3.5 and Appendix 3a.3)(v)

Settlements (Section 3.6 and Appendix 3a.4)(vi)

Other land (Section 3.7)

Relationships exist between the basic land-use categories (i) to (vi) described in Section 2.2 and the activities of
the Kyoto Protocol and Marrakesh Accords (Table 4.2.1). Land subject to Kyoto Protocol activities should be
identified as a subcategory of one of these six main types.
Using categories (i) to (vi) as a basis for estimating the effects of Articles 3.3 and 3.4 activities helps meet goodpractice requirements and will be consistent with the national land categorization used for preparing LUCF
greenhouse gas inventories under the Convention. For example: Forest Land could be partitioned into: a) Forest
Land under Article 3.3; b) Forest Land under Article 3.4, c) Other managed Forest Land (this would be the caseif the definition of “managed forests” differs from the definition of “lands subject to forest management”); and d)
Unmanaged Forest Land. More information on the relationship between “managed forests” and “forest
management” can be found in Section 4.2.7, Figure 4.2.7.
Many of the methods described in subsequent sections of Chapter 4 build on methodologies that appear in
Chapters 2 and 3 of this report or in the IPCC Guidelines. For continuity and clarity, cross-references back tothese preceding descriptions appear periodically in Boxes, as they become pertinent. Direct references to theresults in Chapter 3 reporting tables is not possible because for Kyoto Protocol reporting additional spatial
stratification is required that cannot be inferred from Chapter 3 Reporting Tables.

4.20

IPCC Good Practice Guidance for LULUCF

Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

TABLE 4.2.1
RELATIONSHIP BETWEEN ACTIVITIES UNDER ARTICLES 3.3 AND 3.4 OF THE KYOTO PROTOCOLAND THE BASIC LAND-USE CATEGORIES OF SECTION 2.2
Read this table as follows: For example, if a land is initially cropland and then managed forest, then this event must
constitute either afforestation or reforestation. Such mandatory Article 3.3-related classifications are highlighted in bold.
On the other hand, if a land is first cropland and then managed grassland, then this may constitute GM or RV. The latterchoice depends on the election of Article 3.4 activities by a country and on how national circumstances are applied to thedefinitions related to Article 3.4. Such Article 3.4-related, election-dependent classifications are printed in normal font.
Final
InitialManaged Unmanaged
Cropland
Forest land Forest landManaged Unmanaged
WetlandGrassland Grassland

Settlements Other landManagedForest land

FM or GM
or CM

D*

D*

D*

D*

D*

Unmanaged
Forest land

FM

D*

D*

D*

D*

D*

CroplandA/R*

CM, RV

GM or RVRVRVManagedGrassland

A/R*

CM

GM or RVRV

RV

Unmanaged
Grassland

A/R*

CM

GM

Wetland

A/R*

CM

GM

RV

RV

Settlements

A/R*

CM

GM or RVRV

RVOther land

A/R*

CM, RV

GM or RV

RV

RV

RV

* Transitions involving Article 3.3 activities have to be the result of direct human-induced activities.
Notes
1.

“Initial” and “Final” refer to the categories before and after a land-use change. A – Afforestation (land has not been forested for atleast 50 years), R – Reforestation (land has not been forested at the end of the year 1989), D – Deforestation, FM – Forest
management, CM – Cropland management, GM – Grazing land management, RV – Revegetation (activities other than A or R that
increase carbon stocks by establishment of vegetation).

2.

If the “initial” categorization was done for a year of the commitment period, then the land must be classified under the same activity
for all subsequent years, even if the land use changes once more.

3.

All units of land subject to direct human-induced A/R activities are considered to be managed forests, and therefore unmanaged forest
land cannot result from an A/R event in the table. Similarly, it is assumed that all units of land subject to direct human-induced D
activities are managed lands. This includes natural D followed by a change to a managed land use.

Figures 4.2.1 and 4.2.2 graphically show the relationship between these land-use categories reported in national
inventories under the UNFCCC and those under Articles 3.3 and 3.4 of the Kyoto Protocol in any single
reporting year. The outer rectangle represents the boundaries of a hypothetical country. The top diagram shows
the reporting categories for the UNFCCC national inventory according to Chapter 3, and the bottom diagram
includes an additional layer with the Article 3.3 and Article 3.4 categories under the Kyoto Protocol.

IPCC Good Practice Guidance for LULUCF

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Figure 4.2.1

Land classification in the national inventories under the UNFCCC of ahypothetical country in year X of the commitment period 19
Wetland, Settlements, Other Lands
Managed forest

Managed grassland

Unmanaged
grassland

Unmanaged
forestCropland/arable/tillage

Figure 4.2.2

Land classification for Kyoto Protocol reporting for a hypothetical country
in year X of the commitment period. This classification corresponds to the“final” status in Table 4.2.1.
##

##

RV

Wetland, Settlements, Other Lands

D

Managed forest

Managed grassland
* ##

FM

AR

CM

GM

RV# ##

D** ##GM

Unmanaged
forest

# ##

RV

Note

*

** ##

D

Unmanaged
grassland
Cropland/arable/tillageCM

A/R takes precedence over FM, and therefore the land is subject to FM, but not reported in the FM category.

**

D takes precedence over cropland/grassland categories.

#

Land can only count either in RV or in cropland/grassland management (choice according to hierarchy by country)##

For A/R, D and RV the units of land are shown after the land-use transition has occurred. Therefore, A/R is in forest land, andRV and D are in non-forest lands in the Figure.

A/R : Afforestation / Reforestation, D : Deforestation, FM : Forest Management, CM : Cropland Management
GM : Grazing Land Management, RV : Revegetation

Some further observations relating to Figure 4.2.2:•

19

The areas surrounded by dashed lines are areas subject to the additional activities under Article 3.4, i.e.,
forest management, cropland management and grazing land management activities.

Unmanaged forests and unmanaged grasslands are not reported in UNFCCC inventories.

4.22

IPCC Good Practice Guidance for LULUCF

Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4•

Forest, as defined by the Marrakesh Accords, relates to the physical characteristics of forests. An area
subject to forest management is subsequently determined as an area upon which particular management
practices are undertaken, consistent with Article 3.4 and the Marrakesh Accords. Forest management lands
can include all managed forests according to the IPCC Guidelines. However, this situation may not always
apply, because (i) countries could use different thresholds for defining forests for Kyoto Protocol as opposed
to UNFCCC reporting, (ii) Article 3.4 as well as the Marrakesh Accords require that the activity took placesince 1990, and (iii) the Marrakesh Accords’ definition of forest management20 contains additional criteria
on stewardship. For further discussion of this possible definitional difference see Figure 4.2.8 andaccompanying text in Section 4.2.7.2 (Choice of Methods for identifying lands subject to forest
management). Unmanaged forests that remain unmanaged are included neither in the UNFCCC nor in the
Kyoto Protocol reporting.

•

For Kyoto reporting lands subject to cropland management as described in the Marrakesh Accords are
identical to Cropland/arable/tillage lands in UNFCCC reporting.

•

Grazing land management usually occurs on lands classified as grasslands in the UNFCCC inventory.
However, grazing land management can also occur in managed forests, and not all grasslands are necessarily
grazing lands. Unmanaged grasslands will be excluded from both the UNFCCC and the Kyoto Protocol
reporting.

•

Afforested and reforested (A/R) lands are always managed forests. Yet, carbon stock changes and non-CO2
greenhouse gas emissions are to be reported under Article 3.3 only.

•

Deforested lands are usually managed (thus, there is no “D” box in the unmanaged grasslands). An
exception is a wetland created from alterations of a hydrological regime, e.g., through the construction of a
road.

4.2.2
4.2.2.1

Generic methodologies for area identification,
stratification and reportingR EPORTI NG REQ UIREMENTS

The Marrakesh Accords state that areas of land subject to Article 3.3 and Article 3.4 activities must be
identifiable21, adequately reported22 and tracked in the future.23 Section 4.2.2.2 discusses two land reporting
20

Paragraph 1 (f) of the Annex to the draft decision -/CMP.1 (Land use, land-use change and forestry), contained in
document FCCC/CP/2001/13/Add.1, p. 58: “Forest management” is a system of practices for stewardship and use of forest
land aimed at fulfilling relevant ecological (including biological diversity), economic and social functions of the forest in asustainable manner.

21

Paragraph 20 of the Annex to the draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.61: National inventory systems under Article 5.1 shall ensure that areas of land subject toland use, land-use change and forestry activities under Article 3, paragraphs 3 and 4 are identifiable, and information
about these areas should be provided by each Party included in Annex I in their national inventories in accordance with
Article 7. Such information will be reviewed in accordance with Article 8.

22

Paragraph 6 of the Annex of the draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3, p.22:
General information to be reported for activities under Article 3, paragraph 3, and any elected activities under Article 3,
paragraph 4, shall include: […](b)The geographical location of the boundaries of the areas that encompass:(i) Units of land subject to activities under Article 3, paragraph 3;(ii) Units of land subject to activities under Article 3, paragraph 3, which would otherwise be included in land
subject to elected activities under Article 3, paragraph 4, under the provisions of paragraph 8 of the annex todecision -/CMP.1 (Land use, land-use change and forestry); and(iii) Land subject to elected activities under Article 3, paragraph 4. […](c)The spatial assessment unit used for determining the area of accounting for afforestation, reforestation anddeforestation.

23

Paragraph 19 of the Annex to the draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.61: Once land is accounted for under Article 3, paragraphs 3 and 4, all anthropogenic
greenhouse gas emissions by sources from and removals by sinks on this land must be accounted for throughout
subsequent and contiguous commitment periods.

IPCC Good Practice Guidance for LULUCF

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

methods that can be applied to all Article 3.3 and Article 3.4 activities. Section 4.2.2.3 discusses how thesereporting methods can draw on the three approaches presented in Chapter 2. Section 4.2.2.4 provides a decision
tree for selecting one of the two reporting methods, and Section 4.2.2.5 includes a more detailed discussion of
how lands subject to Articles 3.3 and 3.4 can be identified, so that the requirements of either reporting method
can be satisfied.

4.2.2.2

R EPORTI NG M ETH ODS FOR L ANDS SUBJECT TO A RTICLE
3.3 AND ARTI CLE 3.4 ACTIVITIESTo meet the reporting requirements of the Marrakesh Accords, general information to be reported on activities
under Articles 3.3 and 3.4 must include the geographical boundaries of areas encompassing units of land subject
to afforestation and reforestation, deforestation, and lands subject to elected activities among forest management,
cropland management, grazing land management and revegetation activities. To achieve this, a Party may choose
one of two methods (Figure 4.2.3):
Reporting Method 1 entails delineating areas that include multiple land units subject to Article 3.3 and 3.4
activities by using legal, administrative, or ecosystem boundaries. This stratification is based on samplingtechniques, administrative data, or grids on images produced by remote sensing techniques. The identified
geographic boundaries must be georeferenced.
Reporting Method 2 is based on the spatially explicit and complete geographical identification of all units ofland subject to Article 3.3 activities and all lands subject to Article 3.4 activities.
To implement Reporting Method 1, it is good practice to stratify the entire country and to define and report thegeographic boundaries of these areas of land. Criteria for stratification of the country could include statisticalconsiderations for the sampling intensity or sampling approaches, considerations of the type and amount of landuse change activities (Article 3.3) and elected activities (Articles 3.4), as well as ecological or administrative
considerations. Within each resulting geographic boundary the units of land subject to Article 3.3 activities andthe lands subject to any Article 3.4 activities (if elected) must then be quantified using the approaches describedin Chapter 2 (Section 2.3 Representing land areas), in accordance with the guidance in Section 4.2.2.3, as well as
the methods in Sections 4.2.2.5 (generic methods) and 4.2.5 to 4.2.10 (activity specific methods).
To implement Reporting Method 2, a Party should identify and report the spatial location of all lands and unitsof land based on a complete mapping of all areas within its national boundaries. This is described in Chapter 2 as
the wall-to-wall mapping version of Approach 3 (Section 2.3.2.3). This reporting method uniquely identifies
lands and units of land and enables activities to be reported without the risk of double counting. To put thisreporting method fully into practice requires large-scale data collection and analysis, and the preparation ofsummary statistics to ensure that reporting is transparent yet concise.
Figure 4.2.3

4.24

Two reporting methods for land subject to Articles 3.3 and 3.4 activities

IPCC Good Practice Guidance for LULUCF

Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

With either reporting method, once land is reported as being subject to activities specified under the Marrakesh
Accords, it should be traceable for the first and subsequent commitment periods. Therefore, if a Party chooses
Reporting Method 1, it is good practice to record the information needed to identify the sample locations and theunits of land or lands identified in the samples, and to use the same sample locations for any future monitoring.
This ensures that changes in the status of land covered by sample plots (Reporting Method 1) or in the entire
country (Reporting Method 2) can be tracked and monitored from 1990 to the end of the commitment period.
The geographic boundaries resulting from the stratification of the country should be reported using printed maps
or digital maps, as described in Section 4.2.4.3.1 (Reporting).

4.2.2.3

R ELATIONSHIP BETWEEN A PPROACHES IN C H APTER 2
AND R EPORTING METHODS I N C HAPTER 4

Chapter 2 (Basis for consistent representation of land areas) describes three approaches to representing land area.
The detailed reporting requirements of Articles 3.3 and 3.4 of the Kyoto Protocol as elaborated in the Marrakesh
Accords are met by the two reporting methods given in this chapter, and underpinned by the approaches
described in Chapter 2. This section, summarised in Table 4.2.2, discusses which of the three Chapter 2
approaches are suitable for identifying units of land subject to Article 3.3 activities or lands subject to selected
activities under Article 3.4. Note that even the most data-intensive Approach 3 outlined in Chapter 2 can only
meet the requirements of the Marrakesh Accords without supplemental information if the spatial resolution atwhich land-use changes are tracked is consistent with the size parameter selected by a country to define forest,i.e., polygon sizes of 0.05 to 1 ha or grids of 20 to 100 m (see STEP 1.1 in Section 4.1.1). Land cover and landuse mapping using, for example, 1 km2 (100 ha) pixel resolution does not meet the Protocol’s requirements andsupplemental information will be required.

4.2.2.3.1

APPROACH 1

Approach 1 in Chapter 2 provides information that is not spatially explicit and it only reports the net changes in
the areas of different land-use categories. Hence, this approach does not meet the land identification
requirements of the Marrakesh Accords. National inventory databases are often compiled from detailed spatial
inventories that can be based, for example, on sampling approaches that involve a grid or sample plot system. In
countries where this is the case, it may be possible to re-compile the detailed inventory information for thegeographical boundaries, which have resulted from the stratification of the country, to meet the reporting
requirements of the Marrakesh Accords. This means that Approach 1 can only be applied to Reporting Method 1
if additional spatial data at the required spatial resolution are available as a result of re-compiling the inventory
information, and if the gross land-use transitions (rather than the net changes in land-use categories) arequantified.

4.2.2.3.2

APPROACH 2

Approach 2 focuses on land-use transitions. Although it provides useful information on land-use changes,
especially regarding afforestation, reforestation and deforestation under Article 3.3, it is not spatially explicit.
Hence, additional spatial information at the required spatial resolution is necessary to meet the reporting
requirements of the Marrakesh Accords. This approach can therefore only be used to identify units of land or
land subject to activities under Articles 3.3 and 3.4 if additional spatial data are available. As with Approach 1, it
may be possible to apply Approach 2 to Reporting Method 1 if additional spatial data at the required spatial
resolution become available from re-compiling the inventory information.

4.2.2.3.3

APPROACH 3

Approach 3 explicitly tracks land based on sample approaches, a grid system, or a polygon system within thegeographic boundaries, which have resulted from the stratification of the country. This approach is applicable toReporting Methods 1 and 2 above, as long as the spatial resolution is fine enough to represent the minimum
forest area as defined by the Party under the Marrakesh Accords.

IPCC Good Practice Guidance for LULUCF

4.25

Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.2
RELATIONSHIP BETWEEN APPROACHES IN CHAPTER 2 AND REPORTING METHODS IN CHAPTER 4
Chapter 2 Approaches

Reporting Method 1(Broad area identification)

Reporting Method 2(Complete identification)

Approach 1

Can only be used if additional spatial information isavailable by re-compiling inventories.

Not applicable

Approach 2

Can only be used if additional spatial information isavailable by re-compiling inventories.

Not applicable

Good practiceGood practice

if resolution is fine enough to represent minimum
forest area. Involves aggregating data within thereported geographic boundaries.

if resolution is fine enough torepresent minimum forest area.

Approach 3

4.2.2.4

C HOICE OF R EPORTING M ETHOD

It is good practice to choose an appropriate reporting method using the decision tree given in Figure 4.2.4.
National circumstances may allow a Party to use a combination of both reporting methods. In such a case, it isgood practice to first stratify the entire country and then to quantify and report the area of units of land and land
using Reporting Method 1. Within those geographical boundaries where complete spatial identification of lands
and units of land is possible, Reporting Method 2 can then be applied.
Figure 4.2.4

Decision tree for choosing a reporting method for land subject to activities
under Articles 3.3 and 3.4

Does yourcountry use Approach 3 fornational UNFCCC
reporting?

Yes

No
Develop additional spatial
information by re-compilingdetailed inventory database

Develop spatial
information of
the boundaries

No

Is spatial
information of boundaries
encompassing units of land or
land under Articles 3.3 and 3.4
available?

Yes
Use Reporting Method 1

Is fine-scalespatial information ofunits of land or land under
Articles 3.3 and 3.4
Noavailable?

Yes

Use Reporting Method 2

When using Method 1 it is usually good practice to use the same geographical boundaries for all activities. This
will greatly facilitate the identification, quantification, and reporting of land-use changes. However, national
circumstances may provide justification for different choices of geographic boundaries for different activities.
For example, different geographic boundaries may be chosen so as to reduce the variance of estimates for one
activity within a given boundary. When a Party uses more than one set of geographic boundaries (i.e., more than
one stratification system is used), lands or units of land subject to Article 3.3 or 3.4 activities that moved from

4.26

IPCC Good Practice Guidance for LULUCF

Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

one category to another must be appropriately assigned to the correct geographical boundary. This might require
proportional allocation of the units of land to each stratification system in use.

4.2.2.5

H OW TO IDENTIFY LANDS ( UNI TS OF LAND ) IN GENERAL

4.2.2.5.1

SPATIAL CO NFIGURATION OF FORESTS AND AFFORESTATION,
REFORESTATION O R DEFORESTATION EVENTSThe Marrakesh Accords specify that each Annex I Party to the Kyoto Protocol must choose country-specificparameters within the definition of forest as an integral part of their Kyoto Protocol reporting. The latest possible
date to do that is 31 December 2006, or one year after the entry into force of the Kyoto Protocol for that Party,
whichever is later24. This requires selecting values for the following three parameters: the size of the minimum
area of land that can constitute a forest, ranging between 0.05 and 1 ha, and parameters for crown cover (10 –30%) and tree height at maturity (2 – 5 m). The parameter for the minimum area of land that constitutes a forest
does also specify the minimum area on which afforestation/reforestation or deforestation events occur. Thus acountry that selects, say 0.5 ha as the minimum area of forest land, must also identify all deforestation events
that occur on lands that are 0.5 ha or larger. The identification of units of land on which land-use changes occur,
such as deforestation, requires the detection of a reduction in forest cover from above to below the countryspecific threshold of forest, accompanied by a change in land-use.
The Marrakesh Accords do not specify the shape of areas, neither for forest, nor for those areas on which
afforestation, reforestation or deforestation events occur. Square areas that meet the size range of the Marrakesh
Accords would be 22.36 m (0.05 ha) to 100 m (1 ha) on each side. But a rectangle that is 10 m wide and 1,000 mlong is also 1 ha in area, as is a 5 m wide and 2,000 m long rectangle. Therefore, a treed shelterbelt or any otherstrip of trees that exceeds these sizes could be considered a forest. But if such “linear forests” are included in a
Party’s definition of forest, it is good practice to also consider as non-forest any areas being cleared from trees
by "linear deforestation events", such as roads, transmission right-of-ways, or pipeline corridors. When suchcorridors have resulted from cuts since 1990, they should be treated as deforestation events under Article 3.3.
For example, if a country selects 1 ha as the minimum area of forests and afforestation, reforestation ordeforestation events, and further specifies that these areas are square, then a 20 m wide corridor cut through aforest with 100% canopy closure, will reduce canopy closure to 80%. This is higher than the range of canopy
closures (10 – 30%) that could be selected by a Party. Therefore the residual area is defined as forest, and evenwhen this corridor through the forest is cut since 1990, it would not constitute a deforestation event. If this
"only" 20 m wide corridor is part of a long corridor, which streches for many kilometers, such as a transmission
right-of-way or a pipeline corridor, the total corridor area is much greater than 1 ha. Therefore the definitional
criteria applied to specify the shape of the forests and of the areas subject to afforestation, reforestation ordeforestation events can have a large impact on the amount of land reported under Article 3.3.
It is therefore good practice for countries to include, with their report on the choice of forest definitions, adescription of the definitional criteria which are used to identify forests and areas on which afforestation,
reforestation or deforestation events occur. It is also good practice to apply these criteria consistently to theidentification of both deforestation and afforestation or reforestation events that have occurred since 1990. For
instance, these criteria can simply be defined as the minimum width that will be accepted for a forest and an area
subject to an afforestation, reforestation or deforestation event. Then the minimum length of the area follows
from the combination of width and the chosen parameter for minimum area which can constitute a forest. For
example, if the size were defined as 1 ha, with a minimum width of 20 m, then a rectangle of minimum width
has to be at least 500 m long to meet the 1 ha size requirement.
"Linear deforestation events" narrower than the selected minimum width criteria can contribute to reported
carbon stock changes if they occur within lands subject to FM activities, given the Party has elected FM as
Article 3.4 activity. Similarly, shelterbelts that are narrower than the selected minimum width criteria can also
contribute to reported carbon stock changes, if these shelterbelts are within lands subject to cropland
management, grazing land management, or revegetation activities, given the Party has elected the respective
Article 3.4 activity.

24

See paragraph 16 of the Annex of Draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p. 61, and paragraph 8 (b) of the Annex to Draft decision -/CMP.1 (Modalities for theaccounting of assigned amounts), contained in document FCCC/CP/2001/13/Add.2, p. 59, and also Table 4.2.4a.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.2.2.5.2

SOURCES OF DATA FOR IDENTIFYING LANDS

The needs for the reporting of lands subject to activities under Articles 3.3 and 3.4 have been outlined in theprevious sections. The data and information available to a country to meet these needs will depend largely on
national circumstances. These include the land and forest inventory systems already in place and the additional
measures a country chooses to implement in order to meet the reporting requirements.
In very general terms there are three major options that can be taken to meet the information needs:• To use information from existing land-use and forest inventory systems.
• To implement a monitoring and measurement system.
• To implement an activity reporting system that includes verification and auditing procedures.
It is likely that in most countries the existing land use and inventory systems are inadequate to meet all the land
reporting requirements of the Kyoto Protocol, and that, with varying degrees of incremental efforts, additional
information must be obtained through monitoring or in-country reporting systems. A country’s choices of theappropriate systems will depend on national circumstances. For example, a country could determine that it would
be most efficient to combine an activity reporting system to identify units of land subject toafforestation/reforestation, and a monitoring system to identify units of land subject to deforestation.

Use of existing invent ories
Countries that maintain detailed forest and other land-use inventories or collect annual or periodic spatial land
statistics may be able to identify lands affected by Article 3.3 and 3.4 activities since 1990 from their inventories.
This, however, will only be possible if the national inventory and data collection systems meet stringent
technical requirements. The systems must be able to define the land use and forest area in 1990, have an update
cycle that is sufficiently short to capture land-use change events between 1990 and 2008, and between 2008 and2012, and be of sufficient spatial resolution to identify events of the size of the minimum forest area chosen by
the country, i.e., 1 ha or smaller. Also, the sample plots within a “boundary” need to be georeferenced and used
repeatedly during future monitoring. If the latter is not possible, e.g., because monitoring procedures werechanged, it is good practice to develop computational procedures, which allow to convert the data between theused sampling schemes or which, at least to have a method, allow to map the data from a previous to a successor
sampling scheme (see also Sections 4.2.4.1. Developing a consistent time series and 4.2.4.1.1 Recalculation).
Forest inventories in large countries often do not record polygons less than, for example, 3 ha in size. The
requirement to identify afforestation, reforestation or deforestation activities at a resolution of 0.05 to 1 hectares
can be met, however, with additional statistical analyses to establish the area subject to afforestation,
reforestation or deforestation events that occurred in units less than 3 ha in size. One possible approach could beto determine the size-class distributions of afforestation/reforestation and of deforestation events in the country,
using a statistical sampling approach. The proportion of the area of afforestation/reforestation and of
deforestation events that is between 0.05 – 1 ha and the minimum mapping unit in the inventory (in this example
3 ha) can then be applied to estimate the area of afforestation/reforestation and deforestation events from the 3ha resolution inventory. For example, if the 3-ha resolution inventory shows that there have been 1,000 ha ofafforestation/reforestation events in units of 3 ha or larger, and the sample-based size-class distribution ofafforestation/reforestation events shows that on average 5% of the afforestation/reforestation events is in areas ofsize between 0.05 – 1 ha and 3 ha, then the 1,000 ha represent 95% of the total afforestation/reforestation area
(and the total is estimated to be 1,000 • 100/95 = 1,052.6 ha). It is good practice to document the statistical
validity of the sample-based size-class distribution, and its regional and temporal variation. Note that thisapproach to augmenting existing inventory information also has implications for the determination of carbon
stock changes: since these 5% of the area are not geographically referenced, only statistical methods such as
regional averages can be used to determine their carbon stock changes and trace their fate, once they are included
under Article 3.3 or 3.4, over time.
Countries that choose an inventory-based approach for the identification of units of land subject toafforestation/reforestation activities can face the challenge that non-forest areas are not normally included in theforest inventory. In this case, countries must ensure that their inventory system detects land-use transitions fromnon-forest to forest and expands the forest inventory into the newly created forest area. Some countries monitorchanges from non-forest to forest by means of remote sensing of lands not previously covered by the forest
inventory or by maintaining inventory plots on non-forest land.

4.28

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Monitoring and meas urement of activities
In order to meet the reporting requirements of Articles 3.3 and 3.4, countries may have to develop andimplement a monitoring system for the identification and recording of land use and land-use change. Such amonitoring system could combine a base map (or other sources of spatial information) on forest area and land
use on 31 December 1989 with spatial data on land-use and forest area in subsequent years. Changes in land-use
and forest area can then be inferred from a time series of spatial data. This may require interpolation, forexample where a base map has been derived from composite satellite images obtained over several years, as isoften the case where cloud cover, sensor failures, or other technical reasons make it impossible to obtaincomplete national coverage for a single point in time.
In many countries repeated complete (wall-to-wall) coverage of the entire country is not feasible on an annual
basis. When implementing temporal and spatial sampling strategies, it is good practice to ensure that the
sampling methods are statistically sound, well-documented and transparent, and that estimates of uncertainty areprovided (see Sections 2.4.2 Sampling methods; 4.2.4.3 Uncertainty assessment; 5.2 Identifying and quantifying
uncertainties; and 5.3 Sampling). Appropriate pre-stratification of the country (see Section 4.1.1, STEP 1.3) for
which sample estimates will be developed may reduce the uncertainty.

Activity reporting
Identification of lands that are subject to activities under Articles 3.3 and 3.4 can be achieved through theimplementation of an activity reporting system. For example, since afforestation events are often difficult to
detect through remote sensing and often occur outside the area of existing forest inventories, a country may
choose to identify these lands through an activity reporting system. Instead of trying to detect afforestation
events from inventory or monitoring systems, countries can request that those individuals or agencies thatafforest or reforest areas report on their activities. Activity reporting may also be most efficient where
information about land use is required that may not be readily determined from remote sensing, such as cropland
management, or grazing land management.
Reporting systems can usefully include spatial databases that facilitate the compilation of the pertinent activity
information. It is good practice to include the location and the area of the activity, and information relevant to
the estimation of carbon stock changes, such as site preparation methods, tree species planted, and the actual aswell as the expected volume growth function for the land.
It is good practice for Parties that rely on activity reporting systems, which put into place methods for internal
auditing and verification to ensure that activities are neither over- nor underreported. Administrative information
on programmes or subsidies for afforestation activities alone may not include information on plantation
establishment success. Spatially explicit information, i.e., either the delineation of the units of land, or references
to a country’s national map grid coordinates (e.g., UTM, Universal Transverse Mercator) or legal description of
the units of land subject to an activity, are required for the domestic audit and verification procedures applied toa reporting system.
Further details on the identification of lands are provided in the activity-specific sections of this chapter(Sections 4.2.5 to 4.2.10).

4.2.3

Generic Methodological Issues for Estimating Carbon
Stock Changes and Non-CO2 Greenhouse Gas Emissions

Once the areas subject to activities under Articles 3.3 and 3.4 have been determined, the Marrekesh Accords
specify that the carbon stock changes and non-CO2 greenhouse gas emissions on these areas must be estimated.
The generic methods of estimating the carbon stock changes, for all pools to be reported (see below), aredescribed in Chapter 3 (LUCF sector good practice guidance). This section provides supplementary guidance
applicable to all activities under Articles 3.3 and 3.4. Guidance for specific activities can be found in Sections
4.2.5 to 4.2.10.
Coverage of activities under Articles 3.3 and 3.4 requires an estimation of all carbon stock changes, andemissions and removals of non-CO2 greenhouse gases (regardless of cause, such as growth, harvest, natural
disturbance, decomposition etc.) from all lands subject to the included activities and for all pools withdiscretionary omission of those that are not a source of carbon, with higher-tier methods used for key categories.
The methodology used to estimate greenhouse gas emissions and removals for a particular year (1990, 2008,2009,…, or 2012) depends on the land use in the current and in prior years, because shifts in categories or land

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

uses can occur over time (see Section 4.1.2). Therefore the methodologies may vary between units of land or
land within one Article 3.3 or Article 3.4 category. 25 The methodology used to calculate greenhouse gas
emissions or removals associated with a unit of land or land at a given year should correspond to the actual land
use on that land in that year, supplemented by additional methodologies to account for past land uses andchanges in land use, where appropriate. If the land use in the current year does not correspond to an Article 3.3
activity or an elected Article 3.4 activity, and if a reporting requirement was not established through land use orland-use change in prior years, then the land is not reported at all under the Kyoto Protocol.

4.2.3.1

P OOLS TO BE REPORTEDThe IPCC Guidelines provide methodologies for the estimation of the carbon stock changes in two major carbon
pools: biomass and soil organic carbon; they mention dead organic matter as an area that should be considered infuture work on inventory methods. The Marrakesh Accords specify that carbon stock changes in five pools must
be reported: aboveground biomass, belowground biomass, dead wood, litter, and soil organic carbon (Table
3.1.2). Decreases in one pool may be offset by increases in another pool, e.g., biomass pools decline after adisturbance but litter and dead wood pools can increase. Thus the change in a single pool can be greater than the
net change in the sum of the pools.
Once the individual pools have been estimated and reported for a specific area, the sum of the carbon stockincreases or decreases in the five pools is calculated. Any net decrease in carbon stocks is converted to theequivalent CO2 emission in the reporting tables (see Section 4.2.4.3) and any net increase is reported as theequivalent CO2 removal. Carbon stock changes are converted to CO2 emissions and removals by multiplying thenet carbon stock change by 44/12 (the stochiometric ratio of CO2 and C) and by converting the sign: a decreasein carbon stocks (negative sign) leads to an emission to the atmosphere (positive sign) and vice versa. The
storage of carbon in harvested wood products is not included in the reporting since it is not listed as a poolcovered by the Marrakesh Accords. Chapter 3 provides clear definitions of carbon pools (Table 3.1.2). If
national circumstances require modifications to those definitions, rationale and documentation should be
provided for these modifications and on the criteria used to distinguish between carbon pools. It is good practiceto provide such information on both the individual pools included in the reporting, and on the total carbon stockchange of the five pools.
The Marrakesh Accords specify that a Party may choose not to account for a given pool in a commitment period,
if transparent and verifiable information is provided that the pool is not a source.26 Good practice in providing
verifiable information, which demonstrates that excluded pools, if any, are not a net source of greenhouse gases,
can be achieved by:•Representative and verifiable sampling and analysis to show that the pool has not decreased. It is goodpractice under this approach to measure the pool at enough sites, within regions, to provide statisticalconfidence, and to document the sampling and research methods;•

Reasoning based on sound knowledge of likely system responses. For instance, if cropland is converted toforest land by afforestation or reforestation, the dead wood pool cannot decrease, because there is typicallyno deadwood in a cropland (if it does not contain trees, e.g., if it does not contain any shelterbelts, was no
orchard, and was no other agroforestry system);•

Surveys of peer-reviewed literature for the activity, ecosystem type, region and pool in question (forexample, showing that in the climatic situation and with the soil types of the region, afforestation or
reforestation of cropland leads to increases in soil organic carbon stocks); or•

Combined methods.

It is good practice to report, wherever it is applicable, levels of confidence in estimates that led to the exclusionof a pool, and how this level of confidence was established (see also Section 4.2.4.2 Uncertainty Assessment).

25

For example, two units of land may both be in the cropland management category. However, one of them may haveresulted from grassland conversion into cropland, the other from continuing cropland management, so that the greenhousegas assessment methods need to take account of differing values of soil carbon resulting from their different management
histories.

26

See paragraph 21 in the Annex to the draft decision -/CMP.1 (Land use, lan-use change and forestry), contained in
document FCCC/CP/2001/13/Add.1, p.62.

4.30

IPCC Good Practice Guidance for LULUCF

Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.3.2

Y EARS FOR WHICH TO ESTIMATE CARBON STOCKCHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONSThe Marrakesh Accords specify that the carbon stock changes for each unit of land subject to an Article 3.3
activity, and for lands subject to elected activities under Article 3.4 be reported for each year of the commitment
period27, beginning with the start of the commitment period, or with the start of the activity, whichever is later.
To ensure that actual carbon stock changes are reported, and not artefacts resulting from changes in area overtime, the calculations of carbon stock changes should be implemented in the following sequence: For each unitof land or land, the carbon stock change should first be calculated for the year of interest, and these stockchanges should then be summed for all areas. The inverse sequence, i.e., first summing up the carbon stocks
across all areas at times t1 and t2 and then calculating the difference in carbon stocks, can result in errors if thearea at times t1 and t2 is not the same, and is therefore not recommended.28
It is therefore good practice to conduct all calculations of carbon stock changes and greenhouse gas emissions
for the area at the end of the inventory year, and to use this approach consistently through time.
This means that if the activity started on 1 July 2009, then the carbon stock changes and greenhouse gas
emissions should be reported for each of the last four years of the commitment period, 2009-2012. If the activity
started after 1990 but before 1 January 2008, then reporting of the carbon stock changes and greenhouse gas
emissions for the commitment period should cover each of the five years of the commitment period, 1 January
2008 to 31 December 2012. These reporting requirements as a function of time are summarized in Table 4.2.3.
Where differences occur between the sum of the five annual reports and the report for the entire commitment
period, these should be addressed and reconciled at the end of the commitment period (see Sections 4.2.3.3,
4.2.4.1.1 and Chapter 5).

TABLE 4.2.3
CALENDAR YEARS FOR WHICH CARBON STOCK CHANGES ARE TO BE REPORTED (FOR EACH ACTIVITY
AND EACH OF THE FIVE POOLS DESCRIBED ABOVE), AS A FUNCTION OF THE TIME WHEN THE ACTIVITYSTARTED. “R” DENOTES YEARS FOR WHICH REPORTING IS NECESSARY
Activity started

Calendar year for which reporting is necessary
2008

2009

2010

2011

2012

Before 2008

R

R

R

R

R

In 2008

R

R

R

R

R

R

R

R

R

In 2009
In 2010
In 2011
In 2012

R

R

R

R

R
R

Each activity (afforestation, reforestation, deforestation, forest management, cropland management, grazing land
management and revegetation) may consist of a suite of practices and may begin with one or several of these.
For instance, an afforestation programme may begin with planning, land purchase, producing propagationmaterial etc. Operations like site preparation can also precede the planting or seeding (as a result of which theland actually becomes a “forest”). Some of these operations are carbon-neutral, while others like site preparation
may result in significant carbon, nitrous oxide or methane emissions. It is good practice to interpret the
beginning of an activity as the start of in situ carbon stock change and/or non-CO2 emissions due to any of thesuite of the operations. For example, if an afforestation activity includes site preparation, then it is good practice
to include carbon stock changes caused by site preparation. In order to do that, one can either a) measure the27

See paragraph 5 in the Annex to the draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3, p.
22.

28

For example, if the area of an Article 3.4 activity is 100 ha at the beginning of an inventory year and 200 ha at the end of
the same inventory year, then the difference in carbon stocks on the 200 ha over the inventory year must be calculated –otherwise the carbon stock at the beginning of the year (X tonnes of C / ha • 100 ha) is almost always smaller than the
carbon stock at the end of the year (Y tonnes of C / ha • 200 ha), and an apparent increase would merely result from thepresence of carbon stocks as the area increases.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

carbon stocks on the site prior to the start of any operations related to the activity (in case carbon stock changes
are estimated using multiple stock measurements), or b) make sure that the estimate of the stock change includes
an estimate of the emissions resulting from these initial operations.

4.2.3.3

R EPORTI NG AND MEASUREMENT INTERVALS

The Marrakesh Accords specify that all emissions from sources and removal by sinks caused by Article 3.3 andelected Article 3.4 activities be reported annually. 29 A number of methods are available to obtain annual
estimates and the annual reporting requirement does not imply that annual field measurements are necessary.
This would be neither feasible nor cost-effective. In fact, although more frequent measurement will generally
decrease uncertainties, the opposite can also happen because of short-term variability, as discussed in Section
4.2.3.7 (Interannual variability). Carbon stock changes for pools with high uncertainties, e.g., soil organic carbon,
are usually not detectable on an annual or short-term basis. Broadly speaking, when countries are developing andselecting methods to meet their reporting requirements, they should seek a balance which is affordable, makebest use of data that are already available, allow stock changes to be verified consistently with the approaches set
out in Chapter 5 (Section 5.7 Verification), and not make inventories susceptible to the impacts of annual
fluctuations in weather. Although Section 4.2.3.7 suggests that field data collection on a five-year cycle may
represent a reasonable compromise, the re-measurement interval also depends on the pool and the magnitude ofthe expected changes relative to the spatial variability in the pool and the uncertainties involved in pool size
assessments. For example, changes in soil carbon can often only be detected over longer time periods. Dataalready available annually, such as planting or harvest statistics, may be combined with measurements conducted
over longer time periods – which are less affected by annual fluctuations – or with data based on a five-year
running mean.

4.2.3.4

C HOICE OF METH OD

Estimation of carbon stock changes and non-CO2 greenhouse gas emissions from Articles 3.3 and elected Article
3.4 activities should be consistent with the methods set out in Chapter 3. For each unit of land under Article 3.3
or land under Article 3.4, it is good practice to use the same tier or a higher tier for estimating stock changes andgreenhouse gas emissions as the one that was used for the same land in the UNFCCC inventory, following
Chapter 3 of this report. The only exception to this rule is revegetation: if the lands on which revegetation occurs
are not a key category, then revegetation is also not a key category. If the lands on which the revegetation occurs
are a key category in the UNFCCC inventory30, then revegetation can either be treated as a key category, or aseparate test to identify the “key category” can be applied (see Chapter 5, Section 5.4.4 Identifying key
categories under Kyoto Protocol Articles 3.3 and 3.4).
Tier 1 as elaborated in Chapter 3 assumes that the net change in the carbon stock for litter (forest floor), deadwood and soil organic carbon (SOC) pools is zero, but the Marrakesh Accords specify that above- andbelowground biomass, litter, dead wood and SOC should all be counted unless the country chooses not to counta pool that can be shown not to be a source. Therefore Tier 1 can only be applied if the litter, dead wood and
SOC pools can be shown not to be a source using the methods outlined in the Section 4.2.3.1. Tier 1 can also
only be applied if forest management is not considered a key category, which can only be the case if “forests
remaining forests” in Chapter 3 are not a key category.

4.2.3.5

F ACTORI NG OUT INDIRECT , NATURAL AND PRE -1990
EFFECTS

The Marrakesh Accords specify that information be provided whether or not anthropogenic greenhouse gas
emissions by sources and removals by sinks from activities under Articles 3.3 and 3.4 factor out removals fromelevated carbon dioxide concentrations above pre-industrial levels, indirect nitrogen deposition, and the dynamic

29

Note that although annual reporting is required, countries have the option to account either annually or over the entire
commitment period (cf. paragraph 8(d) in the Annex to draft decision -/CMP.1 (Modalities for the accounting of assigned
amounts), contained in document FCCC/CP/2001/13/Add.2, p.59).

30

This is possible where the croplands or grasslands on which the revegetation takes place are key categories with respect tothe UNFCCC inventory, whereas the area on which the revegetation takes place may be very small compared to thoseunder cropland or grassland management.

4.32

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

effects of age structure resulting from activities prior to 1 January 1990.31 In addition to the requirement to report
whether or not these effects are factored out, those Parties that choose factoring out should also report themethods they used. For the purpose of accounting under the Kyoto Protocol for the first commitment period,“factoring out” has been addressed through the cap for carbon credits for forest management under Articles 3.4
and 6. The "factoring out" issue is currently under consideration by the IPCC and will therefore not be addressed
further here.

4.2.3.6

D ISTURBANCESDisturbances include processes that reduce or redistribute carbon pools in terrestrial ecosystems. Examples
include fire, windthrow, insects, droughts, flooding, ice storms, etc. Although disturbances can be either naturalor human-induced, or of unknown causes, they affect the carbon cycle of managed forests and other managed
lands, and therefore, they are to be included in the carbon stock change and greenhouse gas assessments for
those landsthat are subject to activities under Articles 3.3, 3.4 or 6. These disturbances are also considered in theinventories under the UNFCCC (see Chapter 3, e.g., the Introduction to Section 3.2 Forest land).
Since unmanaged forests and other unmanaged lands are included neither in the UNFCCC nor in the Kyoto
Protocol reporting requirements disturbances in areas which remain unmanaged are not to be considered.
Four major impacts of disturbances on managed ecosystems can be identified. First, disturbances can cause
direct releases of carbon and non-CO2 greenhouse gases to the atmosphere (e.g., during fires) or transfers ofcarbon out of the ecosystem (e.g., during harvest). Second, they redistribute carbon between ecosystem carbon
pools, e.g., live biomass is transferred to dead wood and litter. Third, they result in post-disturbance emissions,e.g., through the decay of residual biomass after a disturbance. Forth, they re-set stand dynamics to an earlier age
class of the same or a new growth trajectory. Tier 3 models that estimate carbon stock changes in forested
landscapes simulate each of these processes and integrate the impacts of disturbances on stand and landscapelevel carbon stocks (e.g., Kurz et al., 1992; Kurz and Apps 1999).
Taking this into account, the following can be said:•

Carbon stock changes and non-CO2 greenhouse gas emissions resulting from disturbances on land subject toan Article 3.3 activity (afforestation, reforestation, and deforestation) or an elected Article 3.4 activity (e.g.,forest management) have to be included in the reported numbers. See for example, Section 3.2.1.1 for
guidance on how to estimate and report carbon stock changes and Section 3.2.1.4 for greenhouse gas
emissions from fires. If the carbon stock changes resulting from disturbances were not included in the
UNFCCC reporting, they have to be added for the Kyoto reporting.

•

Carbon stock changes and non-CO2 greenhouse gas emissions resulting from disturbances during thecommitment period on land subject to projects (Article 6) have to be included in the reported numbers.

•

If project-related management activities (e.g., Article 6) result in a reduction or avoidance of disturbances(e.g., fire or insect control), a change in carbon stocks relative to a baseline (with disturbances) can occur. It
is good practice to estimate and include in the reporting the actual carbon stock changes occurring in the
project area.

4.2.3.7

I NTERANNUAL VARIABILITY

The annual rate of net carbon emissions or removals in an ecosystem is strongly influenced by local weather
patterns, climate variability, management actions, natural disturbance variations and other factors that alter
growth and decomposition rates (e.g., in Griffis et al.,2000 ; Tian et al., 1998; Flanagan et al., 2002).
Consequently, the rate of net carbon emissions or removals in a given area may vary from year to year, and canshift between a net source and a net sink in successive years.
There are two aspects to interannual variability, and they need to be addressed independently. First, the national
statistics on the between-years variation in harvest rates, land-use change, or natural disturbances such as thearea burned, are usually available, and it is good practice to include these in the calculation of carbon stockchanges. Second, the variations in growth and decomposition rates due to seasonal and annual variations inenvironmental conditions, such as moisture regimes, temperature, or growing season length are much moredifficult to quantify.

31

See paragraph 7 in the Annex to draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3, p. 23.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

The impacts of interannual variability in environmental conditions on the estimates of annual rates of net
emissions and removals of carbon may result in incorrect conclusions about long-term trends where estimatesfrom a single year are extrapolated. Conversely, interpolation of long-term trends in, for example, forest growth
rates may result in under- or overestimation of the actual growth in a single year. The forest growth functions
and yield tables used in countries with forest management planning systems are based on measurements ofperiodic growth (e.g., over 5 or 10-year re-measurement intervals) and thus are incorporating and averaging theimpacts of past interannual variability of environmental conditions. One approach that would meet good practiceis to use such growth functions to estimate biomass growth rates, because they represent the average growth
rates and are therefore influenced little by short-term fluctuations in environmental conditions.
Where empirical growth and yield functions are used to estimate stand growth, it is good practice to evaluate thepotential influences of interannual variability in environmental conditions, for example through comparisons ofpredicted and actual growth on a set of regionally distributed permanent sample plots. Where the periodic (e.g.,5-year) increment is consistently under- or over-predicted, the growth estimates should be adjusted accordingly.
Countries that use process-based models to simulate annual variability in stand growth and other stock changes
need to also evaluate these predictions against measurements of periodic stock changes on permanent sample
plots and adjust the predictions where necessary.
In addition to the carbon stock changes and non-CO2 greenhouse gas emissions during the commitment period,
the Kyoto Protocol also requires an estimate of carbon stock changes during the base year (1990 in most cases)
for those elected activities for which net-net accounting applies (Table 4.1.1). The impact of this estimate for asingle year could be large because it will be compared against the estimates for each year in the commitment
period in which this activity occurred. The effects of interannual variability in the base year could therefore belarge. The direction of the impact depends on how the year 1990 deviated from the long-term climatic averages.
Moreover, it may be difficult to confirm the estimate for the base year using direct measurements, unless thesewere already taken in 1990. Where environmental conditions in the base year (e.g., 1990) caused major
deviations in the carbon stock changes and non-CO2 greenhouse gas emissions from their longer-term (e.g., 5year) averages, it is good practice to consistently report emissions using longer-term averages of environmental
conditions or actual annual estimates of emissions when estimating stock changes and non-CO2 greenhouse gas
emissions.
The effect of interannual variability may decrease as the geographical area considered increases. For example,
the effects of local weather patterns may partially offset each other across a large country, but may be very
pronounced in a small country or within a small region of a country. There are, however, climatic processes thatcan synchronize variations in weather over large regions, such as El Niño Southern Oscillation (ENSO) events
which typically occur on time scales of 3 to 7 years, or global climate change. Within limits, the longer themeasurement or estimation interval the more likely it is that the results will capture the true long-term averagevalue. Where non-linear processes are involved, e.g., the sigmoidal accumulation of forest biomass over age,
simple linear interpolation for intermediate years will become increasingly unreliable with longer time periods.
In general, an averaging period of about five years is likely to reduce the impacts of interannual variation.
It is good practice to document whether the methods selected for the estimation of carbon stock changes andnon-CO2 greenhouse gas emissions are sensitive to interannual variability of environmental conditions during thecommitment period, and to report how interannual variation was addressed in the inventory calculations.

4.2.4
4.2.4.1

Other generic methodological issues
D EVELOPING A CO NSISTENT TIME SERIESThe lands subject to Article 3.3 or elected Article 3.4 activities and the management thereon need to be tracked
continuously through time, to ensure that all emissions and removals are reported. Moreover, the continuity ofmanagement greatly influences carbon emissions and removals, and changes in management or land use areoften the periods associated with the greatest changes in carbon stocks. For example, it is not sufficient merely tostate that 10% of a cropland management area has been under no-till for a specified period. The rate of carbon
stock change for the total area depends on whether the same 10% of land has remained under no-till or whether
the 10% of no-till occurred on a different portion of the area in different years. It is therefore good practice tofollow continuously the management of land subject to Article 3.3 and elected 3.4 activities. (See also Box 4.2.1)
Assessment of the continuity of management on land could be achieved either by continuously tracking lands
subject to an Article 3.3 or elected Article 3.4 activity from 1990 until the end of the commitment period (cf.
Section 4.2.7.2 Choice of methods for identification of forest management lands), or by developing statisticalsampling techniques that allow the transition of different types of management on land subject to Article 3.3 or

4.34

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

elected 3.4 activities to be determined (see Section 5.3 Sampling). An example of how such a scheme could
operate is given in Box 4.2.1.
A supplementary condition for developing a consistent time series is to use the same methods for estimating
carbon stock change and non-CO2 greenhouse gas emissions during the whole period.
Time series consistency is discussed further in Section 5.6 (Time series consistency and recalculations) of thisreport.
BOX 4.2.1
AN EXAMPLE OF CONSISTENCY FOR MANAGEMENT PRACTICES

To estimate changes in soil carbon stocks, whether by Tier 1, 2 or 3 methods, management
practices on applicable lands need to be followed continuously over time. Ideally, the managementof each land would be tracked explicitly. But such data may not always be available. An
alternative approach may be to estimate the average history of lands now under a given
management. Consider the following example.
Example: Cropland management
Suppose there was a cropland region of 10,000 ha, of which 5,000 are in no-till (NT) in the year
2000, up from 2,000 ha in 1990. The remainder, in each year, is under conventional tillage (CT).
In order to simplify this example, suppose also that the land management in the year 1990 was
unchanged for a long period before (more than 20 years). The estimated soil carbon change isbased on a matrix of coefficients; say 0.3 Mg C/ha/yr for land shifting from CT to NT, -0.3 Mg
C/ha/yr for a shift from NT to CT. (The carbon stock change is calculated by the amount of soil
carbon, the relative carbon stock change32 factor, over 20 years, for the management activity, andthe length of the period, one year. See Chapter 3.3.1.2, and Tables 3.3.3 and 3.3.4.) Unfortunately,
there has been no tracking of management on individual land. However, based on a statisticalanalysis (e.g., a survey), it is possible to estimate, with reasonable confidence, the following shifts:
CT



NT

3,500 ha

CT



CT

4,500 ha

NT



CT

500 haNTNT

1,500 haThe total carbon gain is therefore:(3,500 • 0.3 + 4,500 • 0 + 500 • (-0.3) + 1,500 • 0) Mg C/yr = 900 Mg C/yr.

4.2.4.1.1

RECALCULATIONAs inventory capacity and data availability improve, the methods and data used to calculate estimates are
updated and refined. Recalculation of historic emissions and removals is good practice when new methods are
introduced or existing ones refined, when new sources and sinks categories are included, or when data are
updated (for example through new measurements during the commitment period or the availability of new
information on verification). Recalculations may also be needed if lands are reclassified at a later time (e.g., forlands that have lost forest cover but where a classification as deforested lands was pending and has been resolved,
see Section 4.2.6.2.1).
The Marrakesh Accords make provisions for recalculation33, consistent with the UNFCCC reporting guidelines,
and mention that previous estimates should be recalculated using the new methods for all years in the time series.
Annual greenhouse gas emissions and removals reported for a given year during the commitment period can berecalculated in subsequent reporting years (up to reporting for 2012). Special attention must be given to those
activities under Article 3.4 to which the net-net accounting rule applies, i.e., all activities except Forest
Management. For these activities, the use of refined or updated data or changed methods should be peerreviewed or validated in another way before being implemented, especially if data in the base year will change asa result (see Chapter 7, Section 7.3 Recalculations, in GPG2000 and Chapter 5, Section 5.6.3 Recalculation and32

While Chapter 3 uses the language of emission/removal factors, in Chapter 4 also the term “carbon stock change factor” isin use to refer to carbon emission/removal factors.

33

See paragraphs 4, 12 (notably 12(d) and12(e)), 13 and 14(e) in the Annex to draft decision -/CMP.1 (Article 5.1),
contained in document FCCC/CP/2001/13/Add.3, pp. 5-8.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

periodic data, in this report for additional guidance). When recalculating emissions and/or removals, time seriesconsistency must be checked and ensured. It is also good practice to report why the new estimates are regarded
as more accurate or less uncertain.
One potential problem in recalculating previous estimates is that certain data sets may not be available for theearlier years. There are several ways of overcoming this limitation and they are explained in detail in Chapter 5(Cross-cutting issues) of this report and Section 7.3 (Recalculations) of the GPG2000.

4.2.4.2

U NCERTAINTY ASSESSMENT

According to the Marrakesh Accords, uncertainties should be quantified and all information on anthropogenic
greenhouse gas emissions by sources and removals by sinks which result from activities under Articles 3.3 and3.4 have to be within levels of confidence as elaborated by any IPCC good practice guidance adopted by the
COP/MOP. 34 Generally, the approaches provided in Chapters 2 and 3 and Sections 5.2 Identifying andquantifying uncertainties, and 5.3 Sampling, can be used for assessing uncertainties associated with estimatesreported under the UNFCCC and under the Kyoto Protocol LULUCF activities. However, some issues and terms
which are specific to the Kyoto Protocol require additional uncertainty assessment, for example the identificationof the areas subject to Article 3.3 and 3.4 activities or the need to track activities since 1990. For Kyoto Protocol
reporting, uncertainty assessment is particularly important in order to support verification in accordance with the
Quality Assurance and Quality Control requirements as specified in Chapter 5.35 In addition, to be consistent
with good practice, the uncertainties in inventory estimates should be reduced as far as practicable. Moreover,while selecting a particular tier to estimate changes in carbon stocks and non-CO2 greenhouse gas emissions, it isgood practice to consider the implications of this choice for the management of uncertainties.

4.2.4.2.1

IDENTIFYING UNCERTAINTIESFor a complete enumeration and explanation of each possible source of uncertainty relevant in the inventory
under the UNFCCC, the reader is referred to Chapters 2 and 3. In the context of the Kyoto Protocol thefollowing sources of uncertainties are likely to be significant:• Definitional errors, such as bias and inconsistencies resulting from the interpretation and implementation of
the various definitions in the Kyoto Protocol and the Marrakesh Accords (including the potential mismatch
between data available to Parties and their interpretation of the definitions);• Classification errors, such as land use and land transition classification errors (e.g., forest vs. non-forestclassification with possible errors regarding temporarily unstocked forest lands);• Activity data errors (e.g., distinction between the harvesting-regeneration cycle (Article 3.4) vs. deforestation(Article 3.3) or human-inducement of afforestation and reforestation);• Estimation errors, such as errors in area estimates (e.g., due to incorrect classification of change events i.e.,
both omission and commission errors in remote sensing (see below for details), or due to differing scales used
to identify lands subject to the various activities, e.g., afforestation/reforestation vs. deforestation, or
modifications made to the sampling procedures and/or densities during the course of time);• Identification errors arising while defining the geographical boundaries of areas encompassing lands and unitsof lands subject to the activities in Articles 3.3 and 3.4 (although this may not have a direct impact on theuncertainty of the carbon stock change estimates for a given activity);• Model errors occur whenever models or allometric equations are used to estimate carbon stock changes or
non-CO2 greenhouse gas emissions and removals, which is likely to be the case at higher tiers. It can be very
34

This refers to paragraph 6 (d) including footnote 5, and paragraph 9 including footnote 7 in the Annex to draft decision-/CMP.1 (Article 7) contained in FCCC/CP/2001/13/Add.3, p.23 and p.24, respectively.

35

For instance activities under Article 3.3 shall be "...measured as verifiable changes in carbon stocks in each commitment
period..." and "...The greenhouse gas emissions by sources and removals by sinks associated with those activities shall bereported in a transparent and verifiable manner…". Article 3.4 explicitly mentions uncertainties, i.e., "...human-induced
activities related to changes in greenhouse gas emissions by sources and removals by sinks in the agricultural soils and theland-use change and forestry categories shall be added to, or subtracted from, the assigned amounts for Parties includedin Annex I, taking into account uncertainties, transparency in reporting, [and] verifiability...". (Kyoto Protocol Articles 3.3
and 3.4). See also paragraphs 3(a), 3(b) and 3(c) in the Annex to draft decision -/CMP.1 (Article 5.1), contained in
FCCC/CP/2001/13/Add.3, pp.4-5.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

cumbersome to trace the propagation of errors through complex models chained to each other. In general, thismay introduce additional uncertainties, except for those cases where simpler models can be used to estimate
typical uncertainty ranges that can be combined with central estimates from complex models.
• Sampling errors associated with the number of samples (number and location) within a “geographicalboundary”. In this case samples do not sufficiently cover the temporal and spatial variability of the estimatedparameters. This is particularly critical when using Reporting Method 1 (as described in Section 4.2.2.2).
Sampling issues are described in detail in Section 5.3 (Sampling).

Some notes on f actors aff ecting uncertainty
Natural Variability
Natural variability is a result of variations in natural controlling variables, such as annual climate variability, andvariability within units of land that are assumed to be homogenous, e.g., the spatial variability of e.g., forest soils
within a given unit of land. When sufficient experimental data are available, good practice should permit
determination of the resulting combined plot-level and upscaling uncertainties using standard statistical methods(e.g., Tate et al., 2003). In some cases, especially for inter-annual or inter-decadal variability, considerable
effects may result that can change the sign of the reported net emissions and removals of an entire country or
region. In inventory calculations uncertainty due to natural variability can be reduced by using time averagecoefficients and by averaging direct measurements over a time period sufficiently long to assess the variability,
as discussed in Section 4.2.3.7 above.

Lack of activity data and docum entation in tim e-series consistency
In addition to uncertainties in default carbon emission and removal factors, there are known inaccuracies in thecase of missing activity data (cf. Section 4.2.8.1.1). Determining retrospectively the inventory for the base year,
i.e., for most Parties the year 1990, may pose a particular challenge for cropland management, grazing land
management and revegetation. Where the 1990 base year net carbon emission and removals cannot beestablished using the default carbon emission and removal factors, they may be estimated by extrapolating aconsistent time series. This requires data on the land management history for the past 20 years, because thedefault method for estimation of the greenhouse gas emissions/removals assumes that it takes 20 years for thesoil carbon pool to reach a new equilibrium after a land-use change to agriculture. Options to address the lack ofreliable data for the period 1970 to 1990 can be found in Section 4.2.8.1.1 (Base year, Cropland Management).

Resolution of rem ote sensing and ground truth
The objective of using satellite imagery for land cover assessments is to obtain, for an inventory region, total
area estimates, percentages of land-cover classes, or geographical boundaries. Remote sensing is particularly
well suited to produce a complete identification of lands and units of land when using Reporting Method 2 (seeSection 4.2.2.2). A primary source of uncertainty is the selection of imagery of inadequate resolution. In order tocapture changes in areas as small as one hectare, the resolution of the imagery must be finer than one hectare. In
addition, improper or insufficient ground truthing can result in classification errors.
Positional errors occur where (a) the geometric correction is not done, incomplete or false, (b) the pixel locationand location of ground truth plot do not coincide, and (c) there is insufficient accuracy in the definition of theborderlines. For example, when detecting land-use changes by a time series of remotely sensed images, thespatial displacement of pixels from one sampled image to the next will introduce errors. In the case of detectionof a transition from forest to non-forest or vice versa, the associated uncertainties will be larger when forests are
fragmented. Classification errors arise from an incorrect identification of the real land cover class. They
comprise omission errors, i.e., a population element from a given category is omitted and put erroneously intoanother class, and commission errors, i.e., classifying wrong categories into a given ground truth category.

4.2.4.2.2

QUANTIFYING UNCERTAINTIES

Uncertainties are to be quantified according to methods as described in this report: Chapters 2 and 3 provide thenecessary data and methodological advice on estimating uncertainties associated with carbon stock changes andemissions estimation. Chapter 5 (see equations in Section 5.2) shows how to combine these estimates into
overall uncertainties.
It is good practice to derive confidence intervals by applying a quantitative method to existing data. Confidence
intervals at given confidence levels provide a minimum basis for a simple quantitative estimate of uncertainty.
To remain consistent with GPG2000, uncertainties should be estimated at the 95% confidence limits, using
component uncertainties assessed by expert judgement aiming at 95% confidence where quantification is nototherwise possible (see Section 5.2 for guidance on expert judgement).

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Uncertainties for the Kyoto activities can be treated in the same way as other uncertainty estimates taking into
account that:•

The “since 1990” clause and the use of definitions specific to the Kyoto Protocol and the Marrakesh
Accords are likely to cause systematic errors related to the estimation of the required activity data. The
potential for differences between the managed forest area and the area subject to forest management, andalso between grassland area and area subject to grazing land management implies that the areas whose
uncertainties are being assessed may differ between the Kyoto Protocol activities and the corresponding
categories of the IPCC Guidelines.

•

Activity data can also relate to individual practices or ownership structures, e.g., the fraction of cropland
farmers using a given amendment on a particular soil. If the fraction is estimated by survey, the survey
design should incorporate an uncertainty estimate depending on the level of inventory data disaggregation,otherwise the uncertainty will have to come from expert judgement.

•

For cropland management, grazing land management and/or revegetation (if elected) uncertainty estimates
are needed also for the base year. These are likely to be higher than for estimates in the commitment period,
because this information may often be derived only by backward extrapolations or models, rather than by
actual inventories in or near the base year. In addition, determination of activities in the base year, whererequired, may pose difficulties if pre-base year surveys of land use are not available. Section 4.2.8(Cropland Management) discusses a default approach to this problem. The associated uncertainties could, inprinciple, be assessed by formal statistical methods, but more likely by expert judgement which is based on
the feasible ranges of backward extrapolation of time trends. Further advice on providing missing data inthis way is given in Section 5.6.

•

When remote sensing is employed for classification of land use and detection of land-use change including
units of land subject to Article 3.3, the uncertainties could be quantified by verifying classified lands withadequate actual ground truth data or higher resolution imagery (see Sections 5.7.2 and 2.4.4). A confusion
matrix as described in Section 2.4.4 can be used to assess accuracy.

Separate annual uncertainty estimates need to be made for each activity under Articles 3.3 and 3.4, for eachreported carbon pool, each greenhouse gas and geographical location. Estimates should be reported using tables
generated following the model of Tables 4.2.6a, 4.2.6b and 4.2.6c as found in Section 4.2.4.3 (Reporting andDocumentation). Separate tables should be reported for the base year in case CM, GM and/or RV are elected.
Estimates should be expressed as percent of the area and of the emissions by sources or removals by sinks (orchanges in stocks) reported in Tables 4.2.6 a, b and c.
Uncertainty associated with areas of lands and units of land need to be estimated. When using Reporting Method
1, it is good practice to report a separate estimate of uncertainty for each of the Article 3.3 activities, and each of
the elected Article 3.4 activities within a given geographical boundary. Under Reporting Method 2, eachgeographical boundary is subject to a single activity. Therefore there will only be one uncertainty estimate
needed for each geographical boundary.
Where uncertainties are difficult to derive, default values for uncertainties are to be used. Guidance on selecting
default carbon emission or removal factors for cropland management can be found in Annex 4A.1, Tool for
Estimation of Changes in Soil Carbon Stocks associated with Management Changes in Croplands and Grazing
Lands based on IPCC Default Data. Since these factors are taken from the IPCC Guidelines, no true uncertainty
ranges can be assigned. However, using expert judgement, default uncertainty ranges corresponding to acoefficient of variation (the ratio of the standard deviation and the mean) of 50% can be assigned, based on an
analysis of no-till long-term experiments in Europe in which the 95% confidence interval of the mean annual
emission or removal estimate was found to be around ±50% of that mean (Smith et al., 1998). For revegetation,
default uncertainty ranges cannot be specified. It is good practice for a Party electing revegetation to provide its
own estimates of the uncertainty associated with emissions and removals from all pools for the affected lands.
These could be derived from using Tier 2 and 3 methods to assess emissions and removals of carbon due torevegetation (see Section 5.2 Identifying and quantifying uncertainties).
Problems may arise when activity data are lacking or are not well-documented. Activity data necessary to apply
scaling factors (i.e., data on agricultural practices and organic amendments) may not be available in current
databases/statistics. Estimates of the fraction of farmers using a particular practice or amendment should then be
based on expert judgement, and so should the range in the estimated fraction. As a default value for theuncertainty in the fraction estimate, ±0.2 is proposed (e.g., the fraction of farmers using organic amendment
estimated at 0.4, the uncertainty range being 0.2–0.6). Chapter 6 in GPG2000 (Quantifying Uncertainties inPractice) and Chapter 5 of this report (Cross-cutting ssues) provide advice on quantifying uncertainties inpractice, including combining expert judgements and empirical data into overall uncertainty estimates.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.4.2.3

REDUCING UNCERTAINTIES

Estimating uncertainties in a quantitative manner helps to identify major sources of uncertainties and to pin-point
areas of potential improvements in order to reduce uncertainties in future assessments. In particular, for reporting
under the Kyoto Protocol it is recommended to make efforts to convey the overall uncertainty estimates to allagencies and/or firms involved in order to encourage improvement, i.e., reduced uncertainties in estimates of
future reports. It is also good practice to establish institutional means and procedures that are likely to contributetowards reducing uncertainties. For instance, a country may choose on purpose to estimate uncertainties by morethan one procedure. This will produce complementary results for the same country and data category, prompting
further research in potential sources of inconsistency and ultimately enhancing the robustness of estimates.
Often, uncertainties can be reduced if areas subject to land-use change are estimated directly as a class bythemselves within a stratification scheme, rather than as a difference between two overall estimates of land-use
areas.
The extra effort required for area identification should help to reduce uncertainties in the assessment of areas
subject to Kyoto Protocol activities.
Uncertainties are likely to be reduced by implementing means to make the design, procedure and frequency ofdata collection more systematic, for example by establishing – whenever possible – long-term, statistically soundmonitoring programmes.

4.2.4.3
4.2.4.3.1

R EPORTI NG AND DOCUMENTATIONREPORTI NG

The anthropogenic greenhouse gas emissions by sources and removals by sinks from land use, land-use change
and forestry activities, estimated using the methods described before and in the activity-specific Sections 4.2.5 –4.2.10, must be reported as outlined in the Marrakesh Accords.36 Some information on definitions and elected
activities must be reported prior to the first commitment period (by the end of 2006), whereas muchsupplementary information must be reported annually during the first commitment period. The information to bereported is summarised in Tables 4.2.4a and 4.2.4b, respectively, but excludes information associated withremoval unit (RMU) accounting. It is good practice to report all information requested in these tables.
Annual reports under the Kyoto Protocol must include estimates of areas of land subject to activities under
Article 3.3 and 3.4 (where elected), of emissions by sources and removals by sinks on these areas of land, andthe associated uncertainties, using Tables 4.2.5 through 4.2.7. It is good practice to include in these reports
additional information on methods and approaches used to identify lands and to estimate the emissions andremovals.

36

See paragraphs 4 to 9 of the Annex to draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3,
pp.22-24.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.4a
SUPPLEMENTARY INVENTORY INFORMATION TO BE REPORTED PRIOR TO 1 JANUARY 2007 OR ONE YEAR AFTER THE ENTRY
37
INTO FORCE OF THE KYOTO PROTOCOL FOR THE PARTY, WHICHEVER IS LATER
Information to bereportedDefinition of forest by
the Party

Detailed information

Reference inMarrakesh
38
Accords•

A single minimum land area value between 0.05 and 1 hectare;

8 (b)

•

The minimum width that defines the spatial configuration of that area (seeSection 4.2.2.5.1);

and•paragraph 16of the Annex
to draftA single minimum tree height value between 2 and 5 metres;
decision
Justification that such values are consistent with the information that has-/CMP.1
historically been reported to the Food and Agriculture Organization of the(LULUCF),
United Nations or other international bodies, and if they differ, explanation
FCCC/CP/why and how such values were chosen.
2001/13/Add.1
p.61•

A list of activities elected by the Party

8 (b)

•

Information on how the Party’s national system under Article 5, paragraph
1 will identify land areas associated with the elected activities

8 (c)

•

Information on how the Party interprets the definition of Art 3.4 activities(e.g., what activities are included under forest management)•
The Party’s own
precedence or hierarchy
among Article 3.4
activities

As outlined in Section 4.1.1 it is good practice to establish precedence
conditions and/or a hierarchy among 3.4 activities to facilitate theestimation and reporting procedures, and so that lands are allocated only to
one of the Article 3.4 activities.

••

Elected activities under
Article 3, paragraph 4

A single minimum tree crown cover value between 10 and 30%;

37

Paragraph 2 in draft decision -/CMP.1 (Modalities for accounting of assigned amounts), contained in document
FCCC/CP/2001/13/Add.2, p.56.

38

Entries in this column refer to relevant paragraphs in the Annex to draft decision -/CMP.1 (Modalities for the accounting of
assigned amounts), contained in document FCCC/CP/2001/13/Add.2, pp.57-72. In the table not necessarily all relevant
legal texts are referred to.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

TABLE 4.2.4b
SUPPLEMENTARY INFORMATION TO BE REPORTED FOR THE ANNUAL GREENHOUSE GAS INVENTORY DURING THE FIRSTCOMMITMENT PERIOD ACCORDING TO THE MARRAKESH ACCORDS. TEXT IN ITALICS INDICATES A DIRECT QUOTE FROM
THE RELEVANT PARAGRAPHS IN THE MARRAKESH ACCORDS
Information to bereported

Detailed information

Reference inMarakeshAccords39

Land related information
Approach for
geographical locationand identification ofunits of land

The geographical location of the boundaries of the areas that encompass:

6 (b)(i) Units of land subject to activities under Article 3, paragraph 3;(ii) Units of land subject to activities under Article 3, paragraph 3, which would
otherwise be included in land subject to elected activities under Article 3,
paragraph 4, […];
(iii) Land subject to elected activities under Article 3, paragraph 4.

Spatial assessment unit

The spatial assessment unit used for determining the area of accounting forafforestation, reforestation and deforestation

6 (c)Information on methods and approaches to estimate emissions and removals
Description of
methodologies used

The emissions and removals should be estimated using methodologies given in
the IPCC Guidelines as elaborated by this report, and using the principles as laidout in the draft decision -/CMP.1 (Land use, land-use change and forestry). The
methodologies used should be reported with information on the reporting
method for lands subject to Articles 3.3 and 3.4 (Reporting Method 1, 2 or a
combination thereof), the approach(es) used for land identification, and the tierlevel(s) for estimating the emissions and removals. National approaches,models, parameters and other related information should be describedtransparently indicating how they improve the accuracy of the reporting. The
assumptions and methodologies used for an inventory should be clearly
explained to facilitate replication and assessment of the inventory by users of
the report and taking into account the principles in paragraph 1, items (a), (b),(d), (g), (h) in the Marrakesh Accords, draft decision -/CMP.1 (Land use, landuse change and forestry), cf. document FCCC/CP/2001/13/Add.1, p.56.

see 6 (a)Justification whenomitting any carbon
pool

Information on which, if any, of the following pools: above-ground biomass,
below-ground biomass, litter, dead wood and/or soil organic carbon were notaccounted for, together with verifiable information that demonstrates that theseunaccounted pools were not a net source of anthropogenic greenhouse gas
emissions

6 (e)Information on indirect
factors on greenhousegas emissions andremovals

Information should also be provided which indicates whether or notanthropogenic greenhouse gas emissions by sources and removals by sinks fromland use, land-use change and forestry activities under Article 3 paragraph 3
and elected activities under Article 3 paragraph 4 factor out removals from:

7(a) Elevated carbon dioxide concentrations above pre-industrial levels;(b) Indirect nitrogen deposition; and(c) The dynamic effects of age structure resulting from activities prior to 1
January 1990
(See Section 4.2.3.5.)
Changes in data andmethods

39

Any changes in data or methodology since the report of the previous year, e.g.,in the choice of methods, activity data collection method, activity data,difficulties of detection (e.g., distinction between harvesting and deforestation
when estimating the D area), parameters used in the calculations should bereported in a transparent manner. The reporting should include information onwhether these changes have been applied also to reporting on previous
inventory years to ensure consistency of the time series.

10

Entries in this column refer to relevant paragraphs in the Annex to draft decision -/CMP.1 (Article 7), contained in
document FCCC/CP/2001/13/Add.3, pp.21-29. In the table not necessarily all relevant legal texts are referred to.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.4b (CONTINUED)
SUPPLEMENTARY INFORMATION TO BE REPORTED FOR THE ANNUAL GREENHOUSE GAS INVENTORY DURING THE FIRSTCOMMITMENT PERIOD ACCORDING TO THE MARRAKESH ACCORDS. TEXT IN ITALICS INDICATES A DIRECT QUOTE FROM
THE RELEVANT PARAGRAPHS IN THE MARRAKESH ACCORDS
Information to bereported

Detailed information

Other genericmethodological issues

Any additional relevant information on methodological issues, such asmeasurement intervals, disturbances, interannual variability (see Section 4.2.3)

Reference inMarakeshAccords40

Specific information for activities under Article 3, paragraphs 3 and 4
Article 3.3 specificinformation

Article 3.4 specificinformation•

Information that demonstrates that activities under Article 3, paragraph 3,began on or after 1 January 1990 and before 31 December of the last yearof the commitment period, and are directly human-induced;

8 (a)

•

Information on how harvesting or forest disturbance that is followed by there-establishment of a forest is distinguished from deforestation;

8 (b)

•

It is good practice to provide information on the size and geographicallocation of forest areas that have lost forest cover but which cannot beclassifed as deforested (and will therefore remain classified as forest with are-assessment in the next inventory).

A demonstration that activities under Article 3, paragraph 4, have occurredsince 1 January 1990 and are human induced

9 (a)Information related to the estimates of emissions by sources and removals by sinks(for reporting data, see Tables 4.2.5-4.2.6)
Estimates forgreenhouse gas
emissions by sources
and removals by sinks

Estimates of greenhouse gas emissions by sources and removals by sinks forhuman-induced activities under Article 3, paragraphs 3, and, if any, elected
activities under Article 3, paragraph 4, and for all geographical locations
reported in the current and previous years, since the beginning of thecommitment period or the onset of the activity, whichever comes later. In thelatter case the year of the onset of the activity must also be included.

see 6 (d)[…] Estimates for Article 3, paragraphs 3 and 4, shall be clearly distinguishedfrom anthropogenic emissions from the sources listed in Annex A to the Kyoto
Protocol.[…]5

Afforestation and
Reforestation

Information on emissions and removals of greenhouse gases from lands
harvested during the first commitment period following afforestation andreforestation on these units of land since 1990 consistent with the requirements
under paragraph 4 of the annex to draft decision –/CMP.1 (Land use, land-use
change and forestry).

8 (c)Cropland management,
grazing land
management andrevegetation

Anthropogenic greenhouse gas emissions by sources and removals by sinks foreach year of the commitment period and for the base year for each of theelected activities on the geographical locations identified, excluding emissions
reported under the Agriculture sector of the IPCC Guidelines.

9 (b), andparagraph 9 of
the annex todraft decision-/CMP.1(LULUCF),FCCC/CP/2001/13/Add.1, p.59

Absence of overlap
between 3.3 and 3.4
activities

Information that demonstrates that emissions by sources and removals by sinks
resulting from elected Article 3, paragraph 4, activities are not accounted forunder activities under Article 3, paragraph 3.

9 (c)Uncertainty of emission Estimates of emissions and removals shall be within levels of confidence as
6(d), footnote 5
and removal estimates elaborated by any IPCC good practice guidance adopted by the COP/MOP and
in accordance with relevant decisions of the COP/MOP on land use, land-use
change and forestry.

40

Entries in this column refer to relevant paragraphs in the Annex to draft decision -/CMP.1 (Article 7), contained in
document FCCC/CP/2001/13/Add.3, pp.21-29. In the table not necessarily all relevant legal texts are referred to.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

It is good practice to use coordinates as set out in Section 4.2.4.3.2 below for the reporting of the geographicallocation of the boundaries that encompass the units of land subject to activities under Article 3.3 and the lands
subject to elected activities under Article 3.4. This information can be summarised on a map for visualpresentation and data sharing. It is also good practice to report the land transition matrix below (Table 4.2.5) todemonstrate that the Party has accounted for all areas where afforestation, reforestation and deforestation and, if
elected, Article 3.4 activities have occurred. The diagonal cells of the table indicate the area of lands remaining
in the same category (e.g., FM land remaining FM land), while other cells indicate the areas of lands converted
to other categories (e.g., cropland converted to afforested land). It is good practice to explain any changes in the
total area over consecutive inventories.
It is good practice to use Tables 4.2.6a-c and Table 4.2.7 to submit annual estimates. For Article 3.3 and 3.4
activities (Tables 4.2.6a and 4.2.6b), data must be provided by geographical locations, whereas for projects(Table 4.2.6c) data must be filled in by project. The Marrakesh Accords also require that, in addition to the data
for the actual inventory year, a Party also reports this information for the base year for cropland management,
grazing land management, and revegetation. No reporting is necessary for those Article 3.4 activities that were
not elected by the Party.
When filling in these tables, care should be taken to insert carbon stock changes for each pool with proper signs.
Carbon stock changes are to be reported in units of carbon as positive when the carbon stock has increased, andas negative when the carbon stock has decreased. All changes are totalled for each geographic location, and thetotal values are then multiplied by 44/12 to convert carbon stock changes to CO2 emissions or removals. This
conversion also involves sign change from the equations used to make the estimates. Non-CO2 greenhouse gas
emissions are to be reported as positive, as these represent increases in abundances in the atmosphere.
Table 4.2.7 is a summary table of carbon stock changes resulting from activities under Articles 3.3 and 3.4 forthe inventory year. It is good practice to use the table also for the base year if cropland management, grazing
land management, and/or revegetation have been elected. This table summarises data of the compilation tables
by activity across all carbon pools and across all strata within a country.
In addition to the data in the Tables 4.2.6a-c and 4.2.7, respectively, it is good practice to report the underlying
assumptions and factors used for the calculation of the carbon stock changes and emissions of CH4 and N2O, aswell as for the calculation of the uncertainties. Such information can be obtained using the worksheets in Chapter
3 or from equivalent information supporting the estimates obtained using higher tiers or other methods.
The Marrakesh Accords contain a clause that carbon stock changes resulting from harvesting ofafforestation/reforestation land during the first commitment period will not result in a debit greater than thecredit previously accounted for that unit of land (see Table 4.2.4).41 If such units of land exist for the inventory
year, it is good practice to distinguish them from other afforestation/reforestation lands and to report them (andthe associated carbon stock changes and non-CO2 greenhouse gas emissions) separately in Tables 4.2.6 to 4.2.7.
Although this is an issue related to accounting, it is mentioned here because inventory data are likely to beneeded to implement the provision.
Finally, separate annual uncertainty estimates should be reported for each activity under Articles 3.3 and 3.4, foreach carbon pool, each greenhouse gas and geographical location. Estimates should be reported using tables
generated following the model of Tables 4.2.6a, b and c. Separate tables should be reported for the base year
when CM, GM and/or RV are elected. Uncertainty estimates are to be made at the 95% confidence limits
expressed as percent of the emissions by sources or removals by sinks (or changes in stocks) reported in Tables
4.2.6a, b and c.

41

Paragraph 4 in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.59.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.5
LAND TRANSITION MATRIX: LAND AREA (IN HA) SUBJECT TO THE VARIOUS ACTIVITIES IN THE INVENTORY YEAR AND THE PREVIOUS YEAR
Note that some of the transitions in the matrix may not be possible (e.g., once land has become subject to A, R, or D, it cannot become subject to FM, CM, GM, or RV in the next year)
INVENTORY YEAR:

Land in year prior to inventory
by activity

Land in inventory year by activity

4.44

A

R

D

FM if elected

CM if elected

GM if elected

RV if elected

Other

Total

A
R
D
FM if elected
CM if elected
GM if elected
RV if elected
Other
Total

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4
TABLE 4.2.6a
TABLE FOR REPORTING, FOR THE INVENTORY YEAR, CARBON STOCK CHANGES AND NON-CO2 EMISSIONS BY SOURCES AND REMOVALS BY SINKS FOR EACH OF THE FOLLOWING ACTIVITIES /1
1,2
1
LANDS: (I) A AND R NOT HARVESTED DURING THE FIRST COMMITMENT PERIOD; (II) A AND R HARVESTED DURING THE FIRST COMMITMENT PERIOD; (III) A AND R THAT ARE ALSO SUBJECT TO
3
3
ELECTED ARTICLE 3.4 ACTIVITIES ; (IV) D; (V) D THAT IS ALSO TO SUBJECT TO ELECTED ARTICLE 3.4 ACTIVITIES ; AND (VI) FM IF ELECTED. (I) PLUS (II) EQUALS ALL A AND R LANDS. (IV)EQUALS ALL D LANDS. (I) PLUS (II) PLUS (IV) EQUALS ALL A, R, AND D LANDS (ARTICLE 3.3). (VI) MUST NOT INCLUDE ANY A, R, OR D (ARTICLE 3.3) LANDS. (III) AND (V) ARE PROVIDED ONLY
4
FOR INFORMATION PURPOSES .
Activity:
Inventory year:
Increases (+) and Decreases (-) in Carbon Stock 6
5Geographical Location

Serial No.

9ID

Area of
ActivityAbovegroundbiomassBelowgroundbiomass

LitterDead wood(ha)(Gg C/yr)(Gg C/yr)(Gg C/yr)(Gg C/yr)

Soil

Total Carbon
Stock
Changes7

Emissions (+)
or Removals (-)
from Carbon
Stock Changes8

CH4
Emissions

N2O
Emissions(Gg C/yr)(Gg C/yr)(Gg CO2e/yr)(Gg/yr)(Gg/yr)1
2
3…
N
Total for the activity
Note that those countries that use Tier 1 or Tier 2 methods that allow separate reporting of increases (such as growth) and decreases (such as harvesting) of a pool should also do so by approppriately expanding the table.
In these cases, the net stock changes should also be reported, and these are subsequently used for the calculation of the total stock changes.
1

As afforestation (A) and reforestation (R) activities are treated in the same way, they can be reported together. The separation of afforestation and reforestation lands that are harvested from those that are not harvested
during the first commitment period is necessary because of the requirement set in paragraph 4 in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), cf. FCCC/CP/2001/13/Add.1, p.59.

2

If A and R lands have been harvested in the inventory year, then special carbon accounting rules apply that allow countries to limit debits from harvesting. This requires the tracking of “credits” earned on these lands inprevious inventory years or commitment periods.

3

Units of land subject to activities under Article 3.3 which would otherwise be included in land subject to elected activities under Article 3.4 must be reported (cf. paragraph 6 item (b) (ii) in the Annex to draft decision-/CMP.1 (Article 7), contained in FCCC/CP/2001/13/Add 3, p.22).
4
See paragraph 6, in particular 6 (b), of the Annex to draft decision -/CMP.1 (Article 7), contained in FCCC/CP/2001/13/Add 3, p.22.
5

Geographical location refers to the areas that encompass units of land subject to Article 3.3 and lands subject to Article 3.4 activities.

6

If a pool is not reported, the text “NR” (for “not reported”) must be entered, and it must be demonstrated that the pool is not a source.

7“Total carbon stock changes” is the sum of carbon stock changes of all five pools.

89

Emissions/Removals are calculated by multiplying total carbon stock changes by 44/12 to convert to CO2 followed by reversing the sign to follow conventions of emissions/removals reporting.
ID: unique identifier of the geographic location.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.6b
TABLE FOR REPORTING, FOR THE INVENTORY YEAR, CARBON STOCK CHANGES AND NON-CO2 EMISSIONS BY SOURCES AND REMOVALS BY SINKS FOR EACH OF THE FOLLOWING ARTICLE 3.4
ACTIVITIES/LANDS: (I) CM; (II) GM; (III) RV. SEPARATE TABLES (OR SEPARATE ROWS IN ONE TABLE) SHOULD BE USED TO REPORT THOSE ACTIVITIES THAT OCCUR ON MINERAL SOILS AND ON
ORGANIC SOILS. THE COLUMN “LIMING CO2 EMISSIONS” IS TO BE FILLED FOR GEOGRAPHICAL LOCATIONS WHERE THESE EMISSIONS APPLY. (SEE SECTIONS 4.2.8 AND 4.2.9 FOR DETAILS.)
THESE TABLES SHOULD ALSO BE PROVIDED FOR THE BASE YEAR
Activity:
Inventory year:
Increases (+) and Decreases (-) in Carbon Stock2
Geographical
Location1
Serial No.

6ID

Area of
ActivityAbovegroundbiomassBelowgroundbiomass

Litter

Dead
wood

Soil

(ha)

(Gg C/yr)(Gg C/yr)(Gg C/yr)(Gg C/yr)(Gg C/yr)

Total
CarbonStock
Changes3

Emissions (+) or
Removals (-)
from Carbon
Stock Changes4

Liming CO2
emissions

CH4
Emissions5

N2O
Emissions5(Gg C/yr)(Gg CO2e/yr)(Gg CO2e/yr)(Gg/yr)(Gg/yr)1
2
3…
N
Total for the activity
1

Geographical location refers to the areas that encompass lands subject to Article 3.4 activities.
If a pool is not reported, the text “NR” (for “not reported”) should be entered, and it must be demonstrated that the pool is not a source.
3“Total carbon stock changes” are the sum of carbon stock changes of all five pools.
4
Emissions/Removals are calculated by multiplying total carbon stock changes by 44/12 to convert to CO2 followed by reversing the sign to follow conventions of emissions/removals reporting.
5
For CM, GM and RV, if elected, methane and nitrous oxide emissions are reported here for transparency purposes only. They are reported and accounted along with the Kyoto Protocol Annex A
sources in the Agriculture sector.
6
ID: unique identifier of the geographic location.
2

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4
TABLE 4.2.6c
TABLE FOR REPORTING, FOR THE INVENTORY YEAR, CARBON STOCK CHANGES AND NON-CO2 EMISSIONS BY SOURCES AND REMOVALS BY SINKS FOR PROJECTS UNDER ARTICLE 6.
A COPY OF THIS TABLE MUST BE PROVIDED FOR EACH TYPE OF ACTIVITY.
Project activity:
Inventory year:
Increases (+) and Decreases (-) in Carbon Stock2
SerialnumberProject
ID1

Area of
Project

AbovegroundbiomassBelowgroundbiomass

LitterDead wood(ha)(Gg C/yr)(Gg C/yr)(Gg C/yr)(Gg C/yr)

Soil

Total Carbon
Stock Changes3

Emissions (+) or
Removals (-) from
Carbon Stock
Changes4

CH4
Emissions

N2O
Emissions(Gg C/yr)(Gg C/yr)(Gg CO2 e/yr)(Gg/yr)(Gg/yr)1
2
3…
N
Total for theactivity
1
Project ID is a unique identifier of the project.
2
If a pool is not reported, the text “NR” (for “not reported”) must be entered, and it must be demonstrated that the pool is not a source.
3“Total carbon stock changes” is the sum of carbon stock changes of all five pools if temporary plots are used, but if permanent plots are used, the change in stock in each component
should be summed by plot and the mean and confidence intervals be computed across all plots. See Section 4.3 for details.
4
Emissions/Removals are calculated by multiplying total carbon stock changes by 44/12 to convert to CO2 followed by reversing the sign to follow conventions of emissions/removals
reporting.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.7
SUMMARY TABLE OF GREENHOUSE GAS EMISSIONS BY SOURCES AND REMOVALS BY SINKS BY ARTICLES 3.3, 3.4 AND 6ACTIVITIES FOR THE INVENTORY YEAR. NOTE THAT EMISSIONS ARE TO BE REPORTED BY PROPERLY APPLYING ONE OF TWOREPORTING METHODS DETAILED IN SECTION 4.2.2.2.
Inventory year:

Activity

Areas

CO2
Emissions (+) or
Removals (-)

CH4 4

N2O 4

(ha)

(Gg CO2 e/yr)(Gg/yr)(Gg/yr)1

A and R not harvested during the first commitment period
A and R harvested during the first commitment period1
A and R that is also to subject to elected Article 3.4
activities1, 6D
D that is also to subject to elected Article 3.4 activities6
Article 3.4 FM if elected
Mineral Soils5
Article 3.4 CM if elected2

Organic Soils5
LimingMineral Soils5

2

Article 3.4 GM if elected

Organic Soils5
LimingMineral Soils5

Article 3.4 RV if elected2

Organic Soils5
LimingArticle 6 A and R activities3
Article 6 FM activities3
Article 6 CM activities3
Article 6 GM activities3
Article 6 RV activities3
1

As afforestation (A) and reforestation (R) activities are treated the same way, they can be reported together. The
separation of afforestation and reforestation lands that are harvested from those that are not harvested during the firstcommitment period is necessary because of the requirement set in paragraph 4 in the Annex to draft decision -/CMP.1(Land use, land-use change and forestry), cf. FCCC/CP/2001/13/Add.1, p.59.

2

If CM, GM and/or RV is elected, a copy of this table should be completed and reported for the base year.

3

Emissions and removals related to Article 6 projects hosted by the reporting Party, if any, should be reported in the final
five rows, recognizing that they are already implicitly included in the national estimates of activities under Articles 3.3
and 3.4 reported in this table. Double counting will be avoided at the accounting stage when converting Removal Units
into Emission Reduction Units.

4

For Article 3.4 CM, GM and RV, if elected, methane and nitrous oxide emissions are reported here for transparency
purposes only. They are reported and accounted along with the Kyoto Protocol Annex A sources in the Agriculture
sector.

5

The headings “Mineral soils” and “Organic Soils” follow the breakdown by sources and sinks in the CM, GM and RV
sections of Chapter 4. It should include all C pools, if applicable (i.e., shelterbelts…), occurring on croplands, grazing
lands or revegetation lands with mineral and organic soils, respectively and should be equal, for each activity, to the totalof the column “Total changes in carbon stocks” of Table 4.2.6b.

6

Afforestation (A), reforestation (R) and deforestation (D) lands, which are also subject to elected Article 3.4 activities, arealready included in the A/R and D totals.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.4.3.2

DOCUMENTATION

Documentation requirements under the Kyoto Protocol are outlined in the Marrakesh Accords as part of thedescription of the requirements for inventory management.42
It is good practice to document and archive all information, i.e., the underlying data and description of, orreference to, methods, assumptions and parameters used, which are used to produce estimates of emissions bysources and removals by sinks of greenhouse gases that would allow independent reviewers to follow the processof developing the reported estimates. Documented data and explanation of methods should be provided for bothsteps: the identification of land and the assessment of carbon stock changes and the emissions of non-CO2
greenhouse gases.
Documentation should also include information about uncertainty assessment (see also Section 4.2.4.2
Uncertainty Assessment), QA/QC procedures, external and internal reviews, verification activities and key
category identification (see Chapter 5, Cross-cutting Issues).

Activities def inition and identif ication
It is good practice to explain how the Marrakesh Accords definitions of the elected Article 3.4 activities havebeen interpreted according to national circumstances. For instance, if only a part of the managed forests reported
in the UNFCCC greenhouse gas inventory is included under forest management in the Kyoto reporting, thecriteria that are used to distinguish forests under “forest management” from “managed forests” should be
provided. Differences between croplands (or grasslands) in the UNFCCC greenhouse gas inventory and lands
undergoing cropland management (or grazing land management) under the Kyoto reporting should also be
documented.

Data documentation
In particular when using Reporting Method 1, the areas encompassed by the geographical boundaries resulting
from the stratification of a country, should be identified by unique serial numbers in the tables. These serial
numbers are to be cross-referenced to a database or other archive (the LULUCF Archive) specifying thelocations in terms of established legal or administrative boundaries, or by means of an existing coordinate
system, for example an established national grid system, the UTM (Universal Transverse Mercator) grid or
latitude and longitude.
The documentation of estimates of greenhouse gas emissions and removals must include:•

The sources of all data used in the calculations (i.e., complete citations for the statistical database(s) fromwhich data were collected);•

The information, rationale and assumptions that were used to develop reported data and results, in cases they
were not directly available from databases (for instance if interpolation or extrapolation methods have been
applied);•The frequency of data collection; and•Estimates of the associated uncertainties together with a description of the major sources of theuncertainties.

Description of the methods used in land identif ication and estimation ofemissions and removals
The methods should be documented with the following information:•

Choice of reporting methods for lands subject to Articles 3.3 and 3.4 (Reporting Method 1, 2) or adescription of the reporting method, if a combination of the two is used;•

Description of the approach used for geographical location and identification of the geographicalboundaries, lands, and units of land; references of maps used, if any;•

Choice of tier(s) used for estimating greenhouse gas emissions and removals;•

Methods used for estimating carbon stock changes, non-CO2 greenhouse gas emissions and magnitudes of
the corresponding uncertainties;

42

Paragraph 16 (a) in the Annex to the draft decision -/CMP.1 (Article 5.1), contained in FCCC/CP/2001/13/Add.3, p.9.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol•

Choice of activity data;•

If Tier 1 is used: all values of default parameters and emission/removal factors used;•

If Tier 2 is used: all values and references of default and national parameters and emission/removal factors
used;

•

If Tier 3 is used: description of, or references to, the scientific basis for the models used, description of theprocess by which carbon stock changes and emissions or removals are estimated;•

In case of Tier 2 or 3 the documentation should justify the use of specific parameters, factors or models;•Transparent and verifiable information that demonstrates that the pools not included in the reporting are notsources.

Analysis of f luctuations
It is good practice to explain significant fluctuations in reported emissions or removals between years. The
reasons for any changes in activity levels and in parameter values from year to year should be documented. If thereason for the changes is an improvement in methods, it is good practice to recalculate results for the preceding
years by using the new methods, new activity and/or new parameter values (see Chapter 5, Section 5.6 Time
series consistency and recalculations).

4.2.4.4

Q UALITY ASSURANCE AND QUALITY CONTROL

It is good practice to implement quality control checks as outlined in Chapter 5, Section 5.5 (Quality Assurance
and Quality Control) on category-specific QC Procedures, and expert review of the emission estimates.
Additional quality control checks as outlined in Tier 2 procedures in Section 5.5 and quality assurance
procedures may also be applicable, particularly if higher-tier methods are used to estimate carbon stock changes
and non-CO2 greenhouse gas emissions. A detailed treatment of inventory QA/QC for field measurement is
described in Appendix 4A.3 of the GPG2000.
Some important issues are highlighted and summarised below.
When compiling data, it is good practice to cross-check estimates of emissions and removals of greenhousegases against independent estimates. The inventory agency should ensure that estimates undergo quality control
by:•Cross-referencing aggregated production data (e.g., crop yield, tree growth) and reported area statistics withnational totals or other sources of national data (e.g., agriculture / forestry statistics);•

Back-calculating national emission/removal factors from aggregated emissions and other data;•

Comparing reported national totals with default values and data from other countries.

It is also good practice to verify that the sum of the disaggregated areas used to estimate the various
emissions/removals equals the total area under the activity, reported as per guidance in Chapters 2 and 3 (using
the LU/LUC matrix).

4.2.4.5

V ERIFICATIO N

Good practice guidance for verification is given in Chapter 5, Section 5.7 (Verification).

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.5

Afforestation and Reforestation

This section elaborates on the general discussion of methods applicable to all activities (Section 4.2 Methods for
estimation, measurement, monitoring and reporting of LULUCF activities under Article 3.3 and 3.4) and should
be read in conjunction with the general discussion presented earlier in this chapter.

4.2.5.1

D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS

Under the definitions of the Marrakesh Accords, both afforestation and reforestation refer to direct, humaninduced conversion of land to forest from another land use. The definitions do not include replanting or
regeneration following harvest or natural disturbance, because these temporary losses of forest cover are notconsidered deforestation. Harvest followed by regeneration is considered a forest management activity. The
distinction between the two activities is that afforestation occurs on land that has not been forest for at least 50
years, while reforestation occurs on land that has been forest more recently, though not since 31 December 1989.
For the identification of units of land, afforestation and reforestation will be discussed together because the two
definitions differ only by the time since the area was last forested, and because the same carbon reporting andaccounting rules apply to both activities. When calculating changes in carbon stocks following afforestation andreforestation, the assumptions about the initial size and composition of the litter, dead wood, and soil organiccarbon pools should reflect the preceding land-use type and history, rather than the distinction between
afforested and reforested sites.
The annual inventory should, at a minimum, identify (for Reporting Method 1 in Section 4.2.2.2):•

The geographical location of the boundaries of the areas that encompass units of land subject toafforestation/reforestation activities (including those units of land subject to activities under Article 3.3,
which would otherwise be included in land subject to elected activities under Article 3.4). The geographicalboundaries which are reported should correspond to strata in the estimation of land areas as described in
Section 5.3;•

For each of these areas, or strata, estimates of the area of the units of land affected byafforestation/reforestation activities in the two subcategories, namely those subject to Article 3.3, and thosesubject to Article 3.3 that would otherwise be subject to Article 3.4;•

The year of the start of afforestation/reforestation activities, which will be between 1 January 1990 and theend of the inventory year. Within the boundary of the areas afforestation/reforestation activities may havestarted in different years. It is good practice to group afforestation and reforestation units of land by age andto report the area in each age class separately; and•The area of units of land subject to afforestation/reforestation in each productivity class and species
combination to assign growth rate estimates and to support the calculation of carbon stock changes and nonCO2 greenhouse emissions.

A more comprehensive system (Reporting Method 2 in Section 4.2.2.2) identifies each unit of land subject toafforestation/reforestation activities since 1990 (again in the two subcategories – Article 3.3 and Article 3.3 thatwould otherwise be subject to Article 3.4), using the polygon boundaries, a coordinate system (e.g., theUniversal Transverse Mercator (UTM) Grid or Latitude/Longitude), or a legal description (e.g., those used byland-titles offices) of the location of the land subject to afforestation or reforestation activities. Chapter 2 (Basisfor consistent representation of land areas) discusses in detail the possible approaches for consistent
representation of land areas.

4.2.5.2

C HOICE OF METH ODS FOR IDENTIFYING UNI TS OF LANDSUBJECT TO DI RECT HUMAN - INDUCED AFFORESTATIO N /REFORESTATION

Parties need to report on the carbon stock changes and non-CO2 greenhouse gas emissions during thecommitment period on areas that have been subject to afforestation and reforestation (AR) activities since 1990.
The first step in this process is to make national parameter choices for the forest definition within the ranges
allowed by the Marrakesh Accords, namely 0.05 – 1 ha for minimum area, minimum tree crown cover of 1030% (or equivalent stocking level), minimum height at maturity of 2 to 5 meters and to report on theseparameters, in the annual greenhouse gas inventory as set out in Table 4.2.4a. As explained in Section 4.2.2.5.1 it

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

is also good practice to choose a parameter for the minimum width of forest areas. Once the parameters havebeen chosen, they will allow identification of units of land subject to afforestation and reforestation.
The identification of units of land subject to afforestation / reforestation activities requires the delineation ofareas that:• Meet or exceed the size of the country’s minimum area in the applied forest definition (i.e., 0.05 to 1 ha), and• Did not meet the definition of forest on 31 December 1989, and• Do meet the definition of forest at the time of the assessment and after 1 January 1990 as the result of direct
human-induced activities.
Note that the definition of forest can be met by young trees that do not yet meet the minimum height or crowncover criteria, provided that they are expected to reach these parameter thresholds at maturity.
It is good practice to distinguish those areas that did not meet the crown cover threshold in the definition offorest on 31 December 1989, for example because of recent harvest or natural disturbances, from those areas thatwere non-forest on that date, because only the latter areas are eligible for afforestation and reforestation activities
under the Marrakesh Accords. The Marrakesh Accords require that Parties provide information on the criteria
used to distinguish harvesting or forest disturbance that is followed by the re-establishment of a forest fromdeforestation.43 It is good practice to apply the same criteria when evaluating whether a unit of land meets thedefinition of forest. For example, if a country uses the criterion “time since harvest” to distinguish temporary
forest cover loss from deforestation, and specifies that a harvested area will regenerate within X years, then only
those areas that have been harvested more than X years prior to 31 December 1989 and that have not regenerated
would be eligible for reforestation, as only they would be considered non-forest on 31 December 1989. Similarly,
areas that have been disturbed by wildfire or other natural disturbances more than X years prior to 31 December
1989 and that have not regenerated to forest are classified as non-forest on 31 December 1989 and would
therefore be eligible for reforestation.
As discussed in Section 4.2.2.2 (Reporting methods for land subject to Article 3.3 and 3.4 activities), Parties
have the option to either report a complete inventory of all units of land subject to Article 3.3 activities, or to
stratify the land into areas, i.e., defining the boundaries of these areas, and to then develop for each area
estimates or inventories of the units of land subject to afforestation, reforestation and deforestation activities.
Combined approaches are also possible: complete spatial inventories of all units of land can be developed forsome strata, while estimates based on sampling approaches are developed for other strata in the country.
A Party’s choice of methods for the development of an inventory of afforestation and reforestation activities will
depend on the national circumstances. It is good practice to use Approach 3 in Chapter 2 (Basis for consistent
representation of land area, Section 2.3.2.3) for the identification of units of land subject to afforestation andreforestation since 1990. As discussed above, this requires that the spatial resolution of the systems in Approach
3 meets the requirements for the identification of the minimum forest area of 0.05 to 1 ha. The methods available
to identify lands subject to afforestation and reforestation activities are discussed in Section 4.2.8.2. It is goodpractice to provide information on uncertainties in the estimates of the total area of the units of land subject toafforestation and reforestation as discussed in Section 4.2.4.2 above.
It is good practice to provide documentation that all afforestation and reforestation activities included in theidentified units of land are direct human-induced. Relevant documentation includes forest management records
or other documentation that demonstrates that a decision had been taken to replant or to allow forest regeneration
by other means.
In some cases it may not be clear whether newly established trees will pass the forest threshold. The difference
between afforestation/reforestation activities and revegetation is that revegetation does not (and will not) meetthe Party’s definition of a forest (i.e., the height at maturity or the minimum crown closure). Where it isuncertain whether the trees on a unit of land will pass the thresholds of the definition of forest, it is good practice
not to report these areas as afforested or reforested land, and to await confirmation (at a later time) that theseparameter thresholds have been or will be passed. Prior to meeting the definition of afforestation or reforestation,
the carbon stock changes on these units of land could be reported in the land-use category in which the land wasreported prior to the land-use change, provided that this category is included in the national accounts, e.g., as
cropland or revegetation. (Note that this approach is consistent with the treatment of deforestation, i.e., units ofland that have not been confirmed as deforested remain in the forest category – see Section 4.2.6.2.1). A decision
tree for determining of whether an area will qualify for afforestation/reforestation or for revegetation is given in
Figure 4.2.5.

43

See paragraph 8(b) of the Annex to draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3,
p.23.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Figure 4.2.5

Decision tree for determining whether a unit of land qualifies for direct
human-induced (dhi) Afforestation/Reforestation (AR) or Revegetation(RV).
Did theunit of land meet thedefinition of a forest on
31 December
1989?

Yes

No
Are trees
growing on the unitof land after 31 December
1989?

No

Yes
Do the trees
exceed or have thepotential to exceed your selected
thresholds of crown coverand height atmaturity?

No

Yes
Are thetrees growing as
the result of direct
human-induced
activity?

No

Yes
Not eligible for dhi AR

Classify as dhi AR

No

Has yourcountry elected
Revegetation ?
Yes

Not eligible for RV

IsvegetationNo
Yes
growing as theresult of direct humaninduced activity
since 1990?

Is theunit of land
greater than
0.05 ha?#YesClassify as RV

No# See paragraph 1(e) in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.58.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Links with methodologies in this report and the IPCC Guidelines on reporting of land areas and carbon stockchanges and non-CO2 greenhouse gas emissions in inventories under the UNFCCC are given in the box below.

BOX 4.2.2
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 2.3 (Representing land areas): Cropland, grassland, wetland, settlements and other land
converted to forest land since 1990. Should include all transitions between 1990 and 2008, and inlater inventory years, transitions on an annual basis. Note that some areas that have turned into
forest since 1990 in the UNFCCC inventory may not have been converted through direct humaninduced activity.
LINKS WITH THE IPCC GUIDELINES

Not available in a format that meets requirements in the Marrakesh Accords for geographicallocation of the boundaries.

4.2.5.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCKCHANGES AND NO N -CO 2 EMISSIO NS

Estimation of carbon stock changes from afforestation and reforestation activities should be consistent with themethods set out in Chapter 3 and the equations it contains, and applied at the same or higher tier as used for
UNFCCC reporting. Growth characteristics of young trees differ from those of the managed forest as a whole,and special provisions may be needed where the UNFCCC inventory (prepared according to Section 3.2.2, Land
converted to forest land) is not sufficiently detailed to provide information that applies to young stands.
On areas subject to Article 3.3 activities, gross-net accounting rules are applied and information on carbon stockchanges in the base year (i.e., 1990) is therefore not required. Only the net changes in ecosystem carbon stocks
and the non-CO2 greenhouse gas emissions during each year of the commitment period are estimated andreported.
At Tier 1, biomass growth is determined using the data in Chapter 3, Section 3.2.2 (Land Converted to ForestLand).
At Tier 2, regional or national growth rates will be available as a function of stand age, species or site quality,
but data may be missing for stands between 0 and 23 years (the stand age reached in 2012 by trees planted in1990). Where biomass estimates exist for stands older than 23 years, biomass at younger ages can be estimatedby interpolating between the known value and biomass zero at age zero using a sigmoidal growth function fitted
to the data that are available for older stands.
At Tier 3, biomass growth rates should be established directly using measured data, validated growth models, or
empirical yield tables for the appropriate combinations of species and site conditions. It is good practice toinclude ground-based field measurements as part of any Tier 3 method, either as a component of a national (orproject) forest inventory or of a growth and yield forest monitoring system.
Determination of the size and dynamics of litter, dead wood and soil organic carbon pools prior to theafforestation activity may require the use of methods developed for cropland management or other land uses (seeChapter 3).
Links with methodologies in this report and the IPCC Guidelines on reporting of carbon stock changes and nonCO2 greenhouse gas emissions in inventories under the UNFCCC are given in the box below.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

BOX 4.2.3
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Chapter 3 Section 3.2.2 (Land converted to forest land)
LINKS WITH THE IPCC GUIDELINES

5A

Changes in forest and other woody biomass stocks (afforestation). To be determinedthrough separate monitoring for afforestation/reforestation activities

5C

Abandonment of managed lands (only portion that goes to forest)

5D

CO2 emissions and removals from soils (only afforestation/reforestation proportion)

5E

Other (CH4, N2O in managed forests) (only afforestation/reforestation proportion)

The default methods in the IPCC Guidelines do not cover belowground biomass, dead wood, litteror emissions of non-CO2 greenhouse gas.

4.2.5.3.1

POOLS AFFECTED BY AFFORESTATIO N/REFORESTATIONACTIVI TIES

Afforestation/reforestation activities often involve site preparation (slashing and possibly burning coarse biomass
residue, and tilling or ploughing on parts of or the whole area), followed by planting or seeding. These activities
may affect not only biomass pools, but also soil, as well as dead wood and litter, if (in the latter instances) landwith woody shrub or sparse tree cover was afforested.
The Marrakesh Accords require Parties to estimate carbon stock changes in all five pools (see Table 3.1.1)
during the commitment period unless the Party can demonstrate by transparent and verifiable information thatthe pool is not a source,44 for which good practice advice is set out in Section 4.2.3.1. It is good practice toinclude carbon stock changes and non-CO2 greenhouse gas emissions that result from pre-planting activities,
such as site preparation or shrub removals. Soil carbon may show some decline with afforestation of grasslands(e.g., Tate et al., 2003; Guo and Gifford. 2002). Net ecosystem losses of carbon after planting and seeding canpersist over many years. Therefore, estimates of pre-activity carbon stocks in the area may be required toinitialise the models used to estimate stock changes. Since there is no forest on the area prior to theafforestation/reforestation activity, the assessment should be done by methods described in the appropriate
sections of Chapter 3, e.g., Section 3.3 for cropland.
For afforestation or reforestation activities that begin during the commitment period, reporting for that unit ofland should begin at the beginning of the year in which the activity commences. 45 Site preparation andseeding/planting activities should be considered part of the activity, and associated emissions during thecommitment period should therefore be included.

4.2.5.3.2

HARVESTI NG OF AFFORESTATION/ REFORESTATION LANDDURING TH E COMMITMENT PERIOD

Some short rotation forests established through afforestation and reforestation activities may be affected byharvesting during the first commitment period. The Marrakesh Accords allow Parties to limit debits from such
harvests during the first commitment period.46
Although this is an accounting issue, it has implications for the design of carbon monitoring and reporting
systems for units of land subject to afforestation or reforestation since 1990. In particular, it is good practice toidentify the afforestation and reforestation lands on which harvesting occurs in the inventory year during thecommitment period, to track carbon stock changes and non-CO2 greenhouse gas emissions on these lands on a

44

Paragraph 21 in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.62.

45

Paragraph 6(d) in the Annex to draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3, p.23.

46“For the first commitment period, debits resulting from harvesting during the first commitment period following
afforestation and reforestation since 1990 shall not be greater than credits accounted for on that unit of land.” (cf.
paragraph 4 in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.59.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

year by year basis during the first commitment period, so that they can be compared with the amount of credits
received previously for these units of land.
The methods given in Chapter 3 for estimating non-CO2 greenhouse gas emissions on lands converted to forest
land are applicable for the afforestation and reforestation activities (see Section 3.2.2.4 Non-CO2 greenhousegases). If the units of land subject to afforestation and reforestation are subject to disturbances, then the Chapter
3 methods in other sections may also be applicable (see e.g., Section 3.2.1.4.3 Fires).

4.2.6

Deforestation

This section addresses specific methods applicable to deforestation activities and should be read in conjunction
with the general discussion in Sections 4.2.2 to 4.2.4.

4.2.6.1

D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS

Under the definitions of the Marrakesh Accords, deforestation refers to direct, human-induced conversion offorest to non-forest land. The definitions do not include harvest that is followed by regeneration since this isconsidered a forest management activity. Forest cover loss resulting from natural disturbances, such as wildfires,
insect epidemics or wind storms, are also not considered direct human-induced deforestation, since in most cases
these areas will regenerate naturally or with human assistance. Human activities (since 1990) such as cropland
management or the construction of roads or settlements, that prevent forest regeneration by changing land use on
areas where forest cover was removed by a natural disturbance, are also considered direct human-induced
deforestation.
The annual inventory should, at a minimum, identify: (for Reporting Method 1 in Section 4.2.2.2):•

The geographical location of the boundaries of the areas that encompass units of land subject to direct
human-induced deforestation activities. The geographical boundaries which are reported should correspondto strata in the estimation of land areas as described in Section 5.3;•

For each of these areas, or strata, an estimate of the area of the units of land affected by direct humaninduced deforestation activities, and the area of these units of land that are also subject to elected activities
under Article 3.4 (cropland management, grazing land management, revegetation);•

The year of the deforestation activities (1990 or later), which could be estimated through interpolation froma multi-year inventory; and•The area of units of land subject to direct human-induced deforestation in each of the new land-use
categories (cropland, grazing land, settlements) to support the calculation of carbon stock changes and nonCO2 greenhouse emissions.

A more comprehensive system (Reporting Method 2 in Section 4.2.2.2) identifies each unit of land subject todeforestation since 1990 using the polygon boundaries, a coordinate system (e.g., the Universal Transverse
Mercator (UTM) Grid or Latitude/Longitude), or a legal description (e.g., those used by land-titles offices) of thelocation of the land subject to deforestation activities. Chapter 2 (Basis for consistent representation of land areas)
discusses in detail the possible approaches for consistent representation of land areas.
Parties will need to use the methods outlined in Chapter 2 (Basis for consistent representation of land areas),taking into account Section 5.3 and the guidance in Section 4.2.2 to ensure that units of land subject todeforestation are adequately identified in land-use change and other inventory databases. The Marrakesh
Accords require that areas subject to direct human-induced deforestation since 1990 be reported separately fromareas subject to direct human-induced deforestation since 1990 that are also subject to elected activities under
Article 3.4. This will ensure that carbon stock changes in areas that have been deforested since 1990 (Article 3.3)
and that are subject to other elected land uses such as cropland management (Article 3.4) are not counted twice.
A Party’s choice of methods for the development of an inventory of units of land subject to deforestation
activities will depend on the national circumstances. For detecting deforestation areas it is good practice to use
Approach 3 in Section 2.3.2. Section 4.2.2.2 provides a general discussion of methods for the reporting on unitsof land subject to Article 3.3 activities.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.6.2

C HOICE OF METH ODS FOR IDENTIFYING UNI TS OF LANDSUBJECT TO DI RECT HUMAN - INDUCED DEFORESTATION

Annex B Parties to the Kyoto Protocol must report carbon stock changes and non-CO2 greenhouse gas emissions
during the commitment period on land areas that have been subject to direct human-induced deforestation
activities since 1990 (after 31 December 1989). The definition of deforestation is given by the Marrakesh
Accords.47 Deforestation for the purposes of the Kyoto Protocol involves the conversion of forest land to nonforest land. To quantify deforestation, forest must first be defined in terms of potential height, crown cover andminimum area as already described for afforestation and reforestation activities. The same parameter values forthe definition of forest must be used for determining the area of land subject to deforestation.
Once a Party has chosen its parameter values for the definition of forests, the boundaries of the forest area can be
identified for any point in time. Only areas within these boundaries are potentially subject to deforestation
activities. “Treed areas” that do not meet the minimum requirements of the country-specific forest definition cantherefore not be deforested.
The identification of units of land subject to deforestation activities requires the delineation of units of land that1.

Meet or exceed the size of the country’s minimum forest area (i.e., 0.05 to 1 ha), and2.

Have met the definition of forest on 31 December 1989, and3.

Have ceased to meet the definition of forest at some time after 1 January 1990 as the result of direct humaninduced deforestation.

Units of land can only be classified as deforested if they have been subject to direct human-induced conversion
from forest to non-forest land. Areas in which forest cover was lost as a result of natural disturbances are
therefore not considered deforested, even if changed physical conditions delay or prevent regeneration, provided
that these changes in physical conditions are not the result of direct human-induced actions. If, however, thenatural disturbance is followed by a non-forest land use, then this will prevent the regeneration of forest, and thedeforestation must be considered direct human induced. Forest areas that have been flooded as a result ofchanged drainage patterns (e.g., road construction or hydroelectric dams) and where the flooding has resulted ina loss of forest cover, are considered to be subject to direct human-induced deforestation.
Linkages with methodologies in this report and the IPCC Guidelines on reporting of land areas related todeforestation (conversion of forest to other land uses) in inventories under the UNFCCC are given in the box
below.

BOX 4.2.4
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Forest land converted to cropland, grassland, settlements, wetland, other land since 1990 asdetermined through Approach 3 in Chapter 2.
LINKS WITH THE IPCC GUIDELINES

Not available in a format that meets requirements in the Marrakesh Accords for geographicallocation of the boundaries.

47

Paragraphs 1(d), 3 and 5, respectively, in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry),
contained in document FCCC/CP/2001/13/Add.1, pp.58-59:“Deforestation” is the direct human-induced conversion of forested land to non-forested land.
For the purposes of determining the area of deforestation to come into the accounting system under Article 3, paragraph 3,
each Party shall determine the forest area using the same spatial assessment unit as is used for the determination ofafforestation and reforestation, but not larger than 1 hectare.
Each Party included in Annex I shall report, in accordance with Article 7, on how harvesting or forest disturbance that isfollowed by the re-establishment of a forest is distinguished from deforestation. This information will be subject to review
in accordance with Article 8.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.2.6.2.1

DISCRIMINATI NG BETWEEN DEFORESTATION AND
TEMPORARY LOSS OF FOREST COVER

Parties must report on how they distinguish between deforestation and areas that remain forests but where tree
cover has been removed temporarily48, notably areas that have been harvested or have been subject to otherhuman disturbance but for which it is expected that a forest will be replanted or regenerate naturally. It is goodpractice to develop and report criteria by which temporary removal or loss of tree cover can be distinguishedfrom deforestation. For example, a Party could define the expected time periods (years) between removal of tree
cover and successful natural regeneration or planting. The length of these time periods could vary by region,
biome, species and site conditions. In the absence of land-use change, such as conversion to cropland
management or construction of settlements, areas without tree cover are considered “forest” provided that thetime since forest cover loss is shorter than the number of years within which tree establishment is expected. Afterthat time period, lands that were forest on 31 December 1989, that since then have lost forest cover due to direct
human-induced actions and that failed to regenerate, are identified as deforested and the carbon stock changes
and non-CO2 greenhouse gas emissions for this land are to be recalculated and added to those of other deforested
areas.
Although the loss of forest cover is often readily identified, e.g., through change detection using remote sensing
images, the classification of this area as deforested is more challenging. It involves assessing the unit of land on
which the forest cover loss has occurred, as well as the surrounding area, and typically requires data frommultiple sources to supplement the information that can be obtained from remote sensing. In some cases a newland use can be determined from remote sensing images, for example where it is possible to identify agricultural
crops or infrastructure such as houses or industrial buildings. Information about actual or planned land-use
changes and actual or planned forest regeneration activities can be used to distinguish deforestation from
temporary loss in forest cover. Where such information is missing or unavailable, only the passage of time willtell whether or not the cover loss is temporary. In the absence of land-use change or infrastructure development,
and until the time for regeneration has elapsed, these units of land remain classified as forest. Note that this isconsistent with the approach suggested for afforestation and reforestation, i.e., units of land that have not been
confirmed as afforested/reforested remain classified as non-forest land. A Party may also choose a moreconservative approach. It could calculate, based on regional averages or other data, the proportion of the lands
without forest cover that is expected not to regenerate to forest and assign this proportion of the area to lands
subject to deforestation.
Regardless of the approach selected, it is good practice for Parties to identify and track the units of land with lossof forest cover that are not yet classified as deforested, and to report on their area and status in the annual
supplementary information (see Table 4.2.4b in Section 4.2.4.3) It is also good practice to confirm that, on theseunits of land, regeneration did occur within the expected time period. Units of land for which, at the end of acommitment period, no direct information was available to distinguish deforestation from other causes of coverloss, could be reassessed annually or at a minimum prior to the end of the next commitment period. If
regeneration did not occur or if other land-use activities are observed, then these units of land should bereclassified as deforested and the carbon stock changes recalculated accordingly (see also Chapter 5, Section 5.6
Recalculation and time series consistency).
The task of distinguishing temporary forest cover loss and deforestation can be supported by information on
harvested areas and areas subject to natural disturbances. In many countries, information on harvest cut blocks
and on natural disturbance events is more readily available than information on deforestation events. Suchinformation can be used to distinguish direct human-induced deforestation from temporary cover loss (e.g.,
harvest) or non-human induced disturbances (e.g., wildfire or insect outbreak). Attribution of the cause of forest
cover loss to the remaining areas would be made easier and would support the identification and verification ofunits of land subject to deforestation.
A decision tree for determining of whether a unit of land is subject to direct human-induced deforestation is
given in Figure 4.2.6.

48

Paragraph 8(b) in the Annex to draft decision -/CMP.1 (Article 7), contained in document FCCC/CP/2001/13/Add.3, p.23.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Figure 4.2.6

Decision tree for determining whether a unit of land is subject to direct
human-induced (dhi) Deforestation (D)No
Not eligible as dhi D

Was there
a forest cover loss below
the threshold after 31
December 1989?
Yes

No

Is therea reasonable
expectation that, within X
years1, the area will
regenerate toforest?

No

Was thecover loss followed by land-use
change to non-forest land
use?

Yes

Yes

Have X
years1 passed sincecover loss?

No

Classify as forest andre-evaluate next year

Yes
Does theland currently meet thedefinition of aforest?

YesClassify as forest

No
Was thecover loss due to
direct human-induced
activity?

Yes
Classify as dhi DNo

Not eligible as dhi D

1Refer to country-specific criteria for distinguishing harvesting from deforestation.

4.2.6.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCK CHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONSThe Marrakesh Accords specify that all carbon stock changes and non-CO2 greenhouse gas emissions during thecommitment period on units of land subject to direct human-induced deforestation since 1990 must be reported.
Where deforestation occurred between 1990 and the beginning of the commitment period, changes in the carbon
pools after the deforestation event need to be estimated for each inventory year of the commitment period. Post-

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

disturbance losses during the commitment period will result primarily from the continuing decay of dead wood,
litter and soil carbon remaining on the site after the deforestation event. These losses can be offset by increases
in biomass pools.
If the deforestation occurs during the commitment period, biomass carbon stocks will decrease but, depending on
deforestation practices, some of this biomass may be added to litter and dead wood pools. Their increase caninitially partly offset biomass carbon losses and delay emissions. In subsequent years, carbon is likely to bereleased from litter and dead wood pools through decay or burning.
On areas subject to Article 3.3 activities, gross-net accounting rules are applied49 and information on carbon
stock changes in the base year (i.e., 1990) is therefore not required. Only the net changes in ecosystem carbon
stocks and the non-CO2 greenhouse gas emissions during each year of the commitment period are estimated andreported.
For the estimation of carbon stock changes, it is good practice to use the same or a higher tier than is used for
estimating emissions from forest conversion in Sections 3.3.2/3.4.2/3.5.2/3.6/3.7.2 (Conversion from forest to
any other broad land-use category).
Carbon stock changes on lands subject to deforestation activities during the commitment period can be estimatedby determining the carbon stocks in all pools prior to and after the deforestation event. Alternatively, the stockchanges could be estimated from the carbon transfers out of the forest, e.g., the amount harvested or the fuelconsumed in the case of burning. For deforestation events that occur prior to the commitment period, knowledgeof pre-deforestation carbon stocks will also be useful for the estimation of post-disturbance carbon dynamics.
For example, estimates of emissions from decay of litter, dead wood, and soil organic carbon pools can bederived from data on pool sizes and decay rates. Information about pre-deforestation carbon stocks can beobtained from forest inventories, aerial photographs, satellite data, by comparison with adjacent remaining
forests, or can be reconstructed from stumps where these are remaining on the site. Information on the time since
deforestation, on the current vegetation and on management practices on that site is required for the estimation ofcarbon stock changes and non-CO2 greenhouse gas emissions.
Where units of land subject to deforestation become land under cropland management or grazing land
management, the established methodologies described in relevant sections of this report (Sections 3.3 Cropland,
3.4 Grassland, 4.2.8 Cropland management, 4.2.9 Grazing land management and 4.2.10 Revegetation) should be
used to estimate carbon stocks changes. The estimation of carbon stock changes on lands going to othercategories is covered in sections 3.5 to 3.7. Several of these categories may contain little or no carbon, or thechange in carbon may be very small. Box 4.2.5 summarises links with methodologies on estimation of carbon
stock changes and non-CO2 emissions in this report and with the IPCC Guidelines.

BOX 4.2.5
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Chapter 3 sections on “lands converted to …” (only portion that comes from forest). (Sections
3.3.2, 3.4.2, 3.5.2, 3.6, 3.7.2 and related Appendices).
LINKS WITH THE IPCC GUIDELINES

5B

CO2 emissions and non-CO2 emissions from burning and decay of biomass from Forestand grassland conversion (only portion that comes from forest)

5D

CO2 emissions and removals from soils (only D portion)

The default methodologies in IPCC Guidelines do not cover belowground biomass and deadorganic matter.

49

Except for Parties that fall under the provisions of the last sentence of Article 3.7.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.7

Forest Management

This section addresses specific methods for the identification of areas subject to forest management and thecalculation of carbon stock changes and non-CO2 greenhouse gas emissions for these areas. This section should
be read in conjunction with the general discussion in Sections 4.2.2 to 4.2.4.

4.2.7.1

D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS

Under the Marrakesh Accords, “Forest Management” is defined as “a system of practices for stewardship anduse of forest land aimed at fulfilling relevant ecological (including biological diversity), economic and social
functions of the forest in a sustainable manner”50. It includes both natural forests and plantations meeting theforest definition in the Marrakesh Accords with the parameter values for forests that have been selected andreported by the Party. Parties must decide by 31 December 2006 whether to include forest management in theirnational accounts and document their choices in the submission to the UNFCCC Secretariat.
There are two approaches conceivable that countries could choose to interpret the definition of forest
management. In the narrow approach, a country would define a system of specific practices that could include
stand-level forest management activities, such as site preparation, planting, thinning, fertilization, and harvesting,as well as landscape-level activities such as fire suppression and protection against insects, undertaken since1990. In this approach the area subject to forest management might increase over time as the specific practices
are implemented on new areas. In the broad approach, a country would define a system of forest management
practices (without the requirement that a specified forest management practice has occurred on each land), andidentify the area that is subject to this system of practices during the inventory year of the commitment period. 51
Section 4.2.2 (Generic methodologies for area identification, stratification and reporting) explains that thegeographical location of the boundaries of the areas containing land subject to forest management activities need
to be defined and reported. Two reporting methods are outlined in Section 4.2.2.2.
In Reporting Method 1 a boundary may encompass multiple forest management lands and other kinds of land
use such as agriculture or unmanaged forests. Any estimates of carbon stock changes resulting from forest
management are for the forest management areas only. In Reporting Method 2, a boundary defines 100% forest
management land without other kinds of land-use. In Reporting Method 2, a Party identifies the geographic
boundary of all lands subject to forest management throughout the country.
The Marrakesh Accords also specify that lands subject to forest management (Article 3.4) that are also subject to
Article 3.3 activities (in this case only afforestation and reforestation) be reported separately from those lands
that are subject to forest management only.

4.2.7.2

C HOICE OF METH ODS FOR IDENTIFYING LANDS SUBJECTTO FOREST MANAGEMENTLand subject to “Forest Management” as defined by the Marrakesh Accords is not necessarily the same area as“managed forests” in the context of the IPCC Guidelines used for UNFCCC reporting. The latter includes all
forests under direct human influence, including forests that may not meet the requirements of the Marrakesh
Accords. Most of the forest area that is subject to forest management under Article 3.4 of the Kyoto Protocol
would also be included in the area of “managed forests” of a Party. The relationships are summarized in Figure
4.2.7.

50

See paragraph 1 (f) in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in
document FCCC/CP/2001/13/Add.1, p.58.

51

In practice, the two approaches could lead to very similar results. For example, if the narrow approach includes landscapelevel activities such as fire suppression, then the area subject to these and other forest management activities could be thesame as the one resulting from the application of the broad approach.

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Figure 4.2.7

Relationship between different forest categories. Some of these lands may
also be subject to activities under Article 3.3 (afforestation or reforestation)as outlined in Figure 4.1.1. Thick arrows indicate where the majority of thearea included in a particular category for UNFCCC reporting is likely to be
included for Kyoto Protocol reporting. See Sections 4.2.7 and 4.2.7.1 forfurther explanations.

ALL FOREST LANDS

Unmanaged

ManagedUNFCCC reporting

KP Reporting
Does not meet Marrakesh
Accords’ eligibility criteria
for forest management

Meets Marrakesh Accords’
eligibility criteria for forest
management

Does not meet Marrakesh
Accords’ eligibility criteria
for forest management

Unbalanced accounting possible

It is good practice for each Party that elects forest management to provide documentation of how it applies the
Marrakesh Accords’ definition of forest management in a consistent way, and how it distinguishes areas subject
to forest management from areas that are not. Examples of country-specific decisions include the treatment oftree orchards or grazing lands with tree cover. It is good practice to base the assignment of land to activities
using criteria of predominant land use.
Figure 4.2.7 outlines the relationship between different forest categories. For UNFCCC reporting, countries havesubdivided their forest area into managed forests (those that are included in the reporting) and unmanaged forest(not included). The managed forests could further be subdivided into those areas that meet the Marrakesh
Accords’ eligibility criteria for forest management activities and those (if any) that do not.
Since most countries have in place policies to manage forests sustainably, and/or use practices for stewardshipand use of forest land aimed at fulfilling relevant ecological (including biological diversity), economic andsocial functions of the forest in a sustainable manner52, the total area of managed forest in a country will often be
the same as the area subject to forest management. It is good practice to define the national criteria for theidentification of land subject to forest management such that there is good agreement between the area ofmanaged forest (as reported under the UNFCCC) and the area of forest subject to forest management. Where
differences occur between the two, these should be explained and the extent of the differences should be
documented. In particular, where areas that are considered managed forest are excluded from the area subject toforest management, the reason for the exclusion should be provided, to avoid the perception of unbalanced
accounting (Figure 4.2.7). Unbalanced accounting can occur if areas that are considered a source are
preferentially excluded and areas considered a sink are included in the national reporting. The IPCC Report on
Definitions and Methodological Options to Inventory Emissions from Direct Human-Induced Degradation ofForests and Devegetation of Other Vegetation Types further addresses the issue of unbalanced accounting.
There may be national circumstances that justify the designation of areas that have been considered “unmanaged
forest” for UNFCCC reporting as land subject to forest management under the Kyoto Protocol. For example, aParty may have chosen to exclude forested national parks from the area of managed forest because they are notcontributing to the timber supply. But where these parks are managed to fulfil relevant ecological (including
biodiversity) and social functions, and are subject to forest management activities such as fire suppression, a52

See paragraph 1(f) in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in
document FCCC/CP/2001/13/Add.1, p.58.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

country may choose to include these forested national parks as lands subject to forest management (Figure 4.2.7).
In such cases, the country should consider including all areas subject to forest management activities in its
managed forest area for future UNFCCC reporting years.
Figure 4.2.8 gives the decision tree for determining whether land qualifies as subject to forest management. Land
that is classified as subject to forest management must meet the country’s criteria for forest. It is possible that
more than one direct human activity impacts the land. In such cases, national criteria need to be developed bywhich such lands are consistently assigned to the appropriate categories.
Figure 4.2.8

Decision tree for determining whether land qualifies as being subject toForest Management
Does
the land meet thedefinition of forest?

No

Yes

Yes

land to a
Is landIssubject
subject
to apractices
system ofsystem of…
practices…
asdefinedas defined in theMA?inthe MA?

Classify land as FMNo

Not eligible for FM

It is good practice to develop clear criteria for the distinction between land subject to forest management, andland subject to other Article 3.4 activities, and to apply these criteria consistently across space and time. For
example, forest areas that are predominantly managed for grazing could be included under forest management or
grazing land management, but not both. Similarly, fruit orchards can meet the definition of forest, but be undercropland management. It is good practice to consider the predominant human influence on land when deciding
its classification. Whether land is classified under forest management, or grazing land management/cropland
management has implications for the accounting rules that apply, as outlined in Table 4.1.1.
It is good practice for each Party to describe its application of the definition of forest management and todelineate boundaries of the areas that encompass land subject to forest management in the inventory year of thecommitment period. In most cases, this will be based on information contained in forest inventories including
criteria such as administrative, zoning (e.g., protected areas or parks) or ownership boundaries, since thedifference between managed and unmanaged forests or, possibly, between managed forest meeting the
Marrakesh Accords definition of forest management and managed forest not doing so, may be difficult or
impossible to detect by remote sensing or other forms of observation. Lands subject to afforestation andreforestation activities that also qualify as forest management lands must be identified separately from thoseareas meeting only the criteria of Article 3.3 or those only subject to forest management under Article 3.4.
Identification of these areas reduces the possibility of double counting.
The area of land subject to forest management can increase (or decrease) over time. For example, if a country
expands its road infrastructure into previously unmanaged forests and initiates harvesting activities, the area ofland subject to forest management is increasing and the associated carbon stock changes need to be estimatedaccordingly. Where changes in area occur over time, it is essential that the methods for carbon stock change
calculation are applied in the sequence outlined in Section 4.2.3.2. Failure to use the correct computational
methods may result in an apparent but incorrect increase in carbon stocks that is the result of the area change.
Once an area has been included in the carbon stock change reporting under the Kyoto Protocol it cannot be
removed, but it can change the reporting category (as outlined in Section 4.1.2). The area subject to forest
management can only decrease over time when area is lost through deforestation activities. Units of land that are
deforested are, however, subject to the rules of Article 3.3 and future carbon stock changes must be reported.
Thus, while the area reported under Article 3.4 would be decreasing, the area reported under Article 3.3 would
be increasing by the same amount.

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Box 4.2.6 summarises links with methodologies in this report and with the IPCC Guidelines for theidentification of land areas.
BOX 4.2.6
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Forest land remaining forest land in Chapter 3.
LINKS WITH THE IPCC GUIDELINES

Not available in a format that meets requirements in the Marrakesh Accords for geographicallocation of the boundaries.

4.2.7.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCKCHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONS

The methods to estimate carbon stock changes in the various pools follow those in the IPCC Guidelines as
elaborated in Chapter 3 for above- and belowground biomass and soil organic carbon, with litter being the sameas the forest floor pool and dead wood the same as coarse woody debris, both definitions as described in Chapter
3 in Table 3.1.2.
On areas subject to forest management activities, gross-net accounting rules are applied and information on
carbon stock changes in the base year (i.e., 1990 in most cases) is therefore not required. Only the net changes inecosystem carbon stocks and the non-CO2 greenhouse gas emissions during each year of the commitment period
are estimated and reported.
In general, the LULUCF sector methods of the IPCC Guidelines as elaborated by Chapter 3 of this report are
applicable to forest management lands. They include “any forest which experiences periodic or on-going humaninterventions that affect carbon stocks” (p. 5.14, Reference Manual, IPCC, 1997). The tier structure should beapplied as follows:•

Tier 1 as elaborated in Chapter 3 assumes that the net change in the carbon stock for litter (forest floor),dead wood and soil organic carbon (SOC) pools is zero, but the Marrakesh Accords specify that above- andbelowground biomass, litter, dead wood and SOC should all be counted unless the country chooses not tocount a pool that can be shown not to be a source. Therefore Tier 1 can only be applied if the litter, deadwood and SOC pools can be shown not to be a source using the methods outlined in the Section 4.2.3.1. Tier
1 can also only be applied if forest management is not considered a key category, which can only be the caseif “forest land remaining forest land” in Chapter 3 are not a key category.

•

Tier 2 and 3 methods should be applied with all pools quantified unless the Party decides to exclude thosethat can be shown not to be a source, using the methods described in Section 4.3.2.1.

The information requirements for Kyoto Protocol reporting can only be satisfied with the information containedin the national UNFCCC inventory if:
1.

The areas subject to forest management are the same as the areas of the managed forest (Figure 4.2.8), (orwhere these are not the same the area and carbon stock changes of the areas subject to forest management
are known), and2.

The area and carbon stock changes of the managed forest within the geographic boundaries of each of thestrata used in a country are known, and3.

The area of the managed forest that was the result of direct human-induced afforestation or reforestation
since 1990 is known, along with the carbon stock changes on this area.

Where it is possible to extract this information from the UNFCCC inventory, the following steps will benecessary to prepare Kyoto Protocol reporting from the Party’s UNFCCC inventory:
1.

Calculate and then sum the carbon stock changes for remaining forests and transitions to forest including all
pools for each of the strata used in the country.

2.

Subtract carbon stock changes on areas (if any) that meet the criteria for managed forests but not for forest
management as defined by the Marrakesh Accords. If national circumstances lead to the situation that thearea subject to forest management under Article 3.4 contains areas that are not part of the managed forest,
then the carbon stock changes on this additional area have to be added.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

3.

Subtract the carbon stock changes on units of land subject to afforestation and reforestation from the totalremaining after Step 2, and report the results using reporting Table 4.2.5 and the means for displaying
mapped information.

A possibly more practicable alternative is to calculate and sum the carbon stock changes for each stratum (theareas defined by the location of the geographical boundaries) during each year of the commitment period on allland areas that are subject to forest management. To meet the Kyoto Protocol reporting requirements, national
forest carbon accounting systems should be able to track for all forest areas, whether these are classified as
managed forest (UNFCCC) or subject to Articles 3.3 and/or 3.4 of the Kyoto Protocol. Such systems can then be
used to calculate and report the net carbon stock changes in all relevant categories for both UNFCCC and Kyoto
Protocol reporting. Such a comprehensive approach would also ensure consistency among the methods used for
calculating and reporting carbon stock changes, because the same forest and land-use change inventories would
be the basis for the computations used in both UNFCCC and Kyoto Protocol reporting.
Box 4.2.7 summarises links with methodologies in this report and with the IPCC Guidelines to estimate carbon
stock changes and non-CO2 emissions.
BOX 4.2.7
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Chapter 3 Section 3.2.1 (Forest land remaining forest land)
The area subject to forest management may not be the same as the area of “forest land remaining
forest land” and estimates may have to be adjusted accordingly.
LINKS WITH THE IPCC GUIDELINES

5A

Changes in forest and other woody biomass stocks (subtract all afforestation andreforestation since 1990 - as determined above - from the 5A category estimate).

5D

CO2 emissions and removals from soils

5E

Other (CH4, N2O in managed forests)

The default methodologies in the IPCC Guidelines do neither cover belowground biomass, nor
dead organic matter.

Methods for estimating non-CO2 emissions from forests remaining forests are addressed in Chapter 3 (Section
3.2.1). The good practice guidance for choosing activity data and emission factors for the estimation of non-CO2
emissions as discussed in Chapter 3 also applies to forest management lands.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

4.2.8
4.2.8.1

Cropland Management
D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS“Cropland management” is the system of practices on land on which agricultural crops are grown and on landthat is set-aside or temporarily not being used for crop production.53 It is good practice to include, in land subject
to cropland management, all the lands in category (ii) of the land-use (LU) system of Chapter 2 (Section 2.2
Land-use categories), namely Cropland/arable/tillage.
To be included under cropland management are all lands under temporary (annuals) and permanent (perennials)
crops, and all fallow lands set at rest for one or several years before being cultivated again. Perennial crops
include trees and shrubs producing fruits, such as orchards (see exceptions below), vineyards and plantations
such as cocoa, coffee, tea and bananas. If these lands meet the threshold criteria for forests (see Footnote 6 inSection 4.1 for the definition of “forest” given in the Marrakesh Accords), it is good practice to include themunder cropland management or forest management, but not under both. Rice paddies are also included undercroplands, but associated methane emissions will be reported under the Agriculture Sector and not in the LUCF
sector in countries’ greenhouse gas inventories, as described in the IPCC Guidelines and GPG2000. Treed areas
such as orchards or shelterbelts that were established after 1990 and meet the definition of a forest can qualify asafforestation/reforestation, and if they do, can be included under those categories (see Section 4.1.2 General rules
for categorization of land areas under Articles 3.3. and 3.4). Arable land, which is normally used for cultivationof temporary crops but is temporarily used for grazing, can also be included under croplands.54
Given the potential diversity in national land use classification systems, it is good practice for countries tospecify what types of lands are included under cropland management in their national land use system and how
they are distinguished from grasslands/rangelands/pastures (as in land-use category (iii) described in Section 2.2)
and from the lands subject to afforestation/reforestation, forest management, grazing land management andrevegetation they are (or might be) reporting. For example, it is good practice to specify whether and to what
extent orchards or shelterbelts are included under cropland management. This will enhance the transparency of
the reporting and the comparability across Parties.
To use the proposed methodology for determining carbon stock changes on those lands, the total cropland area
needs to be subdivided into areas under various sets of management practices (which may overlap both in timeand space) for the base year and each of the years in the commitment period. The carbon emission and removal
factors depend on both the current and previous management on the land. Some areas may be emitting CO2,
some may be sequestering carbon, others may be in equilibrium, and this may change if management changes.
To obtain more disaggregated data on land uses and practices, a more comprehensive set of definitions of landuse and management systems within croplands for different climatic zones, such as those given in the IPCC
Guidelines, is needed. Broad families of practices under cropland management that affect carbon stocks include
tillage practices, rotations and cover crops, fertility management, plant residue management, erosion control andirrigation management (IPCC, 2000b, p.184). Further details can be found in Chapter 3 of this report.

4.2.8.1.1

1990 BASE YEAR

Cropland management, grazing land management and revegetation activities under Article 3.4 require net-net
accounting.55 For this purpose, greenhouse gas emissions and removals in the base year must be reported for anyof these Article 3.4 elected activities (cropland management, grazing land management and revegetation). Thisentails determining the total areas on which each of the activities occurred in the base year and calculating thecarbon stock changes for those areas. The non-CO2 greenhouse gas emissions are covered in the Agriculture
sector of the IPCC Guidelines in 1990 for those areas (see the text on non-CO2 gases in this section and Box
4.1.1, Examples 1 and 2 in Section 4.1.2).

53

Paragraph 1(g) in the Annex to draft decision -/CMP.1 (Land use, land-use change and forestry), contained in document
FCCC/CP/2001/13/Add.1, p.58.

54

http://www.unescap.org/stat/envstat/stwes-class-landuse.pdf

55

Net-net accounting refers to the provisions of paragraph 9 of the Annex to draft decision -/CMP.1 (Land use, land-use
change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.59-60.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

If the area under an Article 3.4 activity changes significantly between the base year and the commitment period,
this may lead to unbalanced estimates (that is, subtraction of stock changes on a land base that changes in size
over time (see Box 4.2.8)).
BOX 4.2.8
AN EXAMPLE OF CROPLAND MANAGEMENT AREAS IN 1990
AND THE COMMITMENT PERIOD (NET-NET ACCOUNTING)

In this example the area under cropland management in the base year expands to a larger area inthe reporting year during the commitment period. Some of the area was under cropland
management in both the base year and during the reporting period (a). Some of the area undercropland management in the base year is no longer under cropland management in the reporting
year (b). There are also areas under cropland management in the reporting year that were not undercropland management in the base year (c). Area (d) is under cropland management, but was
subject to deforestation which takes precedence. Under the Kyoto Protocol, the emissions andremovals in areas (a) + (b) in the base year are compared to emissions and removals in areas (a) +(c) – (d) in the reporting year.

b

a

c
d

Area under cropland
management only in base year

Area under cropland
management in base year
and in reporting period

EXCLUDED FROM c: Area
under forest in base year andcropland management inreporting period (not included incropland management reporting;reported under Article 3.3 asdeforestation land)

Area under cropland management
only in reporting year (could havebeen settlement, or grassland, or
wetland, in the base year)

This approach avoids having to track the carbon stock changes arising from activities not coveredby the Marrakesh Accords. Like other alternatives, it may have some policy implications. For
example, a simple change in area without a change in stock change per unit area could yield acredit or debit without there being an actual loss or gain by the atmosphere.

For most Parties with commitments under Annex B of the Kyoto Protocol, the base year is 1990. But under theprovisions of Article 4.6 of the UNFCCC, Parties with economies in transition (EITs) are granted someflexibility on the level of historical emissions chosen as a reference. As a consequence five EITs have a baseyear or period between 1985 and 1990 and hence they will need to assess the CO2 and other greenhouse gas
emissions and removals for those years. Historical data on land-use and management practices in 1990 (orappropriate year) and in years prior to 1990 are needed to establish the 1990 base year net emissions/removals ofsoil carbon from cropland management. Using the method described in Chapter 3 (Section 3.3.1.2.1.1. Change in
Carbon stocks in soils – Mineral soils), land-use/land management change is assumed to have an impact for 20
years; hence, in this approach, the net carbon stock change in 1990 is calculated from management during 1970
to 1990. If area and activity data are available for 1970 to 1990, the net carbon stock change during the 1990
base year can be established using the default carbon emission and removal factors as described above. The
duration of impact may be shorter or longer than 20 years. It is good practice to use a more appropriate time
period, based on country-specific data and measurements (see Tier 2 and Tier 3 approaches in Section 4.2.8.3.1).
If area and activity data are not available for 1970 to 1990 (or other appropriate time period) there is no historical
data upon which to establish carbon stock change during the base year (1990), which will therefore have to bereconstructed from other data if cropland management is selected for the first commitment period.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

The estimate of soil carbon stock change in the base year has a pronounced effect in net-net accounting. Where
reliable data are not available for 1970 to 1990 (or other applicable time period), countries can choose the mostappropriate of the following options:•

Choose not to elect cropland management as an activity under the Kyoto Protocol for the first commitment
period.

•Report an emission (loss of carbon) for 1990 (or appropriate base year) only if it can be verified that theland was, in the 20 years prior to the base year, subject to a management change (e.g., cultivation ofpreviously-forested lands) that leads to loss of soil carbon.

•

Use a default emission/removal factor of ‘0’ for 1990 if it can be shown that there have been few changes
in management practices on the applicable land in the 20 years prior to 1990.

•

Use data from another year shown to be a reliable proxy for the base year (e.g., 1989 in place of 1990).
The proxy year should be as close to 1990 as possible and, all else being equal, preference should be given
to a more recent year.

•

Use a country-specific methodology, shown to be reliable, to estimate base year soil carbon stock change in1990. It is good practice to verify that this methodology does not over- or underestimate
emissions/removals in the base year (see discussion of Tier 2 and 3 methods in Section 4.2.8.3). In mostcases, these methods also require historical data on management practices prior to 1990.

This approach may sometimes result in a conservative estimate of net soil carbon stock change but, in theabsence of reliable and verifiable data for calculating 1990 carbon stock change, will help avoid overestimating
the net removal of carbon from the atmosphere.

4.2.8.2

C HOICE OF METH ODS FOR IDENTIFYING LANDS

General guidance on identification of lands subject to cropland management is provided in Sections 4.1.1, 4.1.2,
4.2.1, and 4.2.2. Under the Marrakesh Accords, the geographical location of the boundaries of the area thatencompass land subject to cropland management needs to be reported annually, along with the total land areas
subject to this activity.
The geographical location of boundaries may include a spatially explicit specification of each land subject tocropland management, but does not have to. Instead, the boundaries of larger areas encompassing smaller lands
subject to cropland management may be provided, along with estimates of the area subject to cropland
management in each of the larger areas. In either case, the land subject to cropland management and the
management thereon need to be tracked through time because the continuity of management affects carbon
emissions and removals. For example, a Party wishing to claim carbon removals due to conversion to no-till of
10% of an area under cropland management must demonstrate that no-till has been practiced on the same land
for that period, since carbon accumulation in mineral soil depends on continuity of no-till (and the carbon
emission/removal factors have been derived for continuous no-till). The rate of carbon removal for the total area
therefore depends upon whether the same 10% of land has remained under no-till or if the 10% of no-till occurs
on a different portion of the area in different years; it is not sufficient merely to state that 10% of the cropland
management area has been under no-till for the whole period. It is good practice to follow continuously the
management of land subject to cropland management; this could be achieved either by continuously tracking
each land subject to cropland management from 1990 until the end of the commitment period (e.g. see Section
4.2.8.1 Definitional issues and reporting requirements), or by developing statistical sampling techniques,consistent with the advice in Section 5.3, that allow the management transitions on cropland management land to
be determined (see also Section 4.2.4.1 Developing a consistent time series).
At the national level criteria that could be relevant to subdivision for the purpose of stratification when setting up
a sampling strategy include:•

Climate•Soil type•

Degree of disturbance (e.g. tillage frequency and intensity)•

Level of organic input (e.g. plant litter, roots, manure, other amendments)•

Temporarily re-grassed lands (e.g. set-aside)•

Fallow lands•

Lands with woody biomass stocks (e.g. shelterbelts, orchards, other perennial plantations)

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4•

Lands converted to croplands since 1990 (land-use change) that are not in any other land-use category.

For all resulting subcategories under cropland management, the areas derived from the conversion of forests (i.e.,
deforestation) since 1990, need to be tracked separately as these will be reported as units of lands subject todeforestation.
At higher tiers further subdivision of the cropland management area may be necessary.
Methods to identify croplands with adequate disaggregation may include:•

National land-use and management statistics: in most countries, the agricultural land base including
croplands is usually surveyed regularly, providing data on distribution of different land uses, crops, tillage
practice and other aspects of management, often at sub-national regional level. These statistics may originate,in part, from remote sensing methods.
• Inventory data from a statistically based, plot-sampling system: land-use and management activities aremonitored at specific permanent sample plots that are revisited on a regular basis.
Further good practice guidance on identifying land areas is given in Chapter 2 (Basis for consistent
representation of land areas).
Links to related methods for cropland area identification in other chapters of this report and in the IPCC
Guidelines are given in Box 4.2.9 below:
BOX 4.2.9
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 2.3.2 (Three Approaches): Croplands that remain croplands or any conversion that leads tocroplands in Chapter 2 (except forests to croplands). Should include all transitions between 1990
(or 1970, where required for base year estimate) and 2008, and in later inventory years transitions
on an annual basis.56
LINKS WITH THE IPCC GUIDELINES

Not available in a format that meets requirements in the Marrakesh Accords for geographicallocation of the boundaries.

4.2.8.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCKCHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONSFor croplands, the IPCC Guidelines identify three potential sources or sinks of CO2 from agricultural soils:•

Net changes in organic carbon stocks of mineral soil associated with changes in land use and management•

Emissions of CO2 from cultivated organic soils•

Emissions of CO2 from liming of agricultural soils

Total annual emissions/removals of CO2 are calculated by summing emissions/removals from these sources (seeSection 3.3.1.2).
Carbon stock changes in other pools (aboveground, belowground biomass, litter and dead wood) should be
estimated if applicable (i.e., unless the Party to the Kyoto Protocol chooses not to report on a certain pool andprovides verifiable information that carbon stocks are not decreasing). For most crops, annual crop biomass canbe neglected, but trees, shelterbelts and woody crops on croplands need to be accounted for either under cropland
management, afforestation/reforestation or forest management. Relevant methods for estimating carbon stockchanges and non-CO2 greenhouse gas emissions from aboveground and belowground biomass, litter and deadwood can be found in the afforestation/reforestation or forest management sections (see Table 4.2.8) and Chapter
3 (see Box 4.2.10) of this report. The appropriate references are summarized in Table 4.2.8. The following
sections focus largely on the soil carbon pool. For generic decision trees, guiding the choice of methods also forother subcategories, see Figures 3.1.1 and 3.1.2 in Chapter 3.

56

If more than one land conversion occurs on the same land in the transition period of the matrix, then the transition period
may have to be shortened to reflect these transitions.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.2.8
SECTIONS WHERE METHODOLOGIES CAN BE FOUND FOR ESTIMATING DIFFERENT CARBON POOLS ON CROPLAND
Pools to be estimatedSection where methodologies can be found

Aboveground biomass

Section 4.2.5 (Afforestation and Reforestation) and Section 4.2.7 (Forest Management)

Belowground biomass

Section 4.2.5 (Afforestation and Reforestation) and Section 4.2.7 (Forest Management)

Litter and dead wood

Section 4.2.5 (Afforestation and Reforestation) and Section 4.2.7 (Forest Management)

Soil C

Section 4.2.8.3

Non-CO2

GPG2000 and Section 4.2.8.3.4 (only for emissions not covered by the IPCC
Guidelines and GPG2000 Agriculture chapters)

If the Party chooses not to account for a particular pool, then it needs to verifiably demonstrate that this pool is
not a source. Reporting requirements for such a choice can be found in Section 4.2.3.1.
For each of the carbon pools, different methodologies are used at different tiers to estimate net carbon emissions
and removals for the 1990 base year and the years during the commitment period. Since different methods may
yield different estimates (with different levels of uncertainty), it is good practice to use the same tier andmethodology to estimate carbon emissions/removals in 1990 and during the commitment period.
Methods used to estimate net soil carbon emissions and removals, both for the 1990 base year and thecommitment period, are described in detail in Chapter 3. Links to pertinent methods in Chapter 3 of this report
and the IPCC Guidelines are given in Box 4.2.10. The following sections provide a brief review of thesemethods already described earlier, identifying aspects specific to the Kyoto Protocol.
BOX 4.2.10
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 3.3.1.1

Change in biomass

Section 3.3.1.2

Change in carbon stocks in soils

LINKS WITH THE IPCC GUIDELINES

4

Non-CO2 greenhouse gases

5B

Forest and grassland conversion (conversion of grasslands to croplands)

5D

CO2 emissions and removals from soils

4.2.8.3.1

MINERAL SOILSFor carbon stock change from mineral soils, the decision tree in Figure 4.2.9 should be used to decide which tier
to use for reporting of cropland management under the Kyoto Protocol. For Article 3.4 activities it is goodpractice to use Tier 2 or Tier 3 for reporting carbon stock changes from mineral soils, if CO2 emissions fromcropland management is a key category.

Methods f or estimating carbon stock changes in mineral soils
Methods for estimating carbon stock changes fall into one of three tiers. These tiers are to be distinguished fromthe methods of estimating activity data (land areas). For estimating land areas, it is good practice to use themethods following Approach 2 or 3 (Chapter 2), taking into consideration the guidance in Section 4.2.2, for thehigher tiers in Chapter 3; for estimating carbon stock changes lower tiers may be used. The decision tree in
Figure 4.2.9 guides the choice of a good practice methodology.
Ti e r 1
The Tier 1 method for estimating carbon stock changes in mineral soils is described in Chapter 3 (Section 3.3.1.2:
Change in carbon stocks in soils) and is based on the method outlined in the IPCC Guidelines, pages 5.35–5.48
of the reference manual (IPCC, 1997). The default values given in the IPCC Guidelines, based on a 20-year
period, have been updated and used to derive annual carbon stock change factors. These are directly comparablewith the Tier 1 methods used for national greenhouse gas inventories given in Chapter 3 (LUCF sector goodpractice guidance).

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Figure 4.2.9

Decision tree for selecting the appropriate tier for estimating carbon stockchanges in mineral soils under cropland for Kyoto Protocol reporting (seealso Figure 3.1.1)

Are data
available tocalculate regional orcountry-specific carbon stockchanges associated withmanagement practices, andtheir duration of
impact?

No

Are
CO2 emissions
from croplands a key
category?

No

Yes
Yes
Obtain data from regionalor national measurement/
research programmes orother reliable sources

Is therea national carbon
inventory system
available that allows CM
activities to beaccounted forseparately?

No

Yes

Use Tier 3(Note 3)

Use Tier 2(Note 2)

Use Tier 1(Note 1)

Note 1: Use the matrix/database of default values.
Note 2: Use regionally specific parameters, soil data and duration of impact.
Note 3: Use more sophisticated modelling techniques, often linked to geographical databases.

It is good practice to follow continuously the management of land subject to cropland management. This could
be achieved either by continuously tracking each land subject to cropland management from 1990 until the endof the commitment period (e.g., see Section 4.2.7.1 Definitional issues and reporting requirements), or bydeveloping statistical sampling techniques, consistent with the advice in Section 5.3, that allow the managementtransitions on cropland management land to be determined (see Section 4.2.4.1 Developing a consistent timeseries).
Using the default values given in the IPCC Guidelines, average yearly rates of carbon stock change can becalculated for each soil type, climatic region and land-use or management change combination. These can beused as default annual “carbon stock change factors”57, and can be represented in a series of tables, a matrix or a
relational database. Such a system is shown schematically in Figure 4.2.10 where the numbers 1,2,3,… represent
different management practices.

57

See also footnote 32 above.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Figure 4.2.10

Conceptual illustration of the matrix of carbon stock change factors derived
for different land-use, land-management transitions for each set ofbiophysical combinations. These could be accessed via tables or a relational
database. For Tier 1, default values (see text above) are used for the carbon
stock change factor. Default values for management shifts in opposite
direction are the same, but of opposite sign. For example, if a shift frommanagement practice 1 to management practice 2 has a carbon stock change
factor of -0.5, then a shift from management practice 2 to management
practice 1 has a factor of + 0.5.

c = C stock change factor(Mg C/ha/yr)

1‘New’ Management‘New’ Management
12
4…
‘New’3Management
1
2
3
4
…
‘New’
Management=c=
1 c = – 0.52 c‘New’
3Management
4 c=
…
c=
c= -0.5c=c=Each ‘layer’represents a
different set ofbiophysicalconditions2
3
4…
c= -0.5 2 c =
c=
c=
1
3
4
…
c1= 0.5c=c =c =
c= -0.5c =c =
c=
2
1 0.5
c=
c=
c =c =
c= -0.5c =c =
c=
2
1 0.5
c=
c=
c=
c=
c2=
c=
c= 0.5c =c =
c =c =
c=
3
c2=
c=
c=
c=
c=
0.5
c
=
c
=
c=
3
2
c=
c=
c=
c=
c3=
c =c =
c =c =
c =c =
c=
4
c3=
c
=
c
=
c=
c
=
c
=
c
=
c=
4
3
c=
c=
c=
c=
..
c4=
c =c =
c =c =
c =c =
c=
. .
4=
c
c
=
c
=
c
=
c
=
c
=
c
=
c=
..
4
..
c=
c=
c=
c=
. .
c=
c=
c=
c=
..
..
c=
c=
c=
c=
.

‘Old’ Management‘Old’ Management‘Old’ Management‘Old’ Management‘Old’ Management

1

1

The yearly carbon stock change factor will often be more accurate than the default values for absolute carbon
stocks.58
These default carbon stock change factors have been compiled into a database so that default factors can beaccessed for each soil type, input level and land-use and land-management transition considered in the IPCC
Guidelines without referring to multiple tables. The database can be found in Annex 4A.1 (Tool for Estimationof Changes in Soil Carbon Stocks associated with Management Changes in Croplands and Grazing Lands basedon IPCC Default Data) on the attached CD-ROM (including instructions on how to use the database).
Calculating annual carbon stock change factors
The IPCC Guidelines assume a linear change in soil carbon stocks over a 20-year period after a change inmanagement, moving the soil carbon stock from an equilibrium position at t0 (year of management change) toanother equilibrium position at t20 (20 years after the change in management). The rate of carbon stock change
therefore is assumed to remain constant for the first 20 years after a management change and then becomes zeroas a new equilibrium has been reached.

58

The carbon stock change factor reflects a change in carbon stocks, which is much smaller than the absolute carbon stock;the change in carbon stocks can be reasonably correct even if the absolute values are not.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

The method for calculating annual carbon stock change factors is described in Chapter 3 (Section 3.3.1.2;
Equation 3.3.3). For a summary of the steps and a sample calculation, see Section 3.3.1.2.1.1: Choice of method(mineral soils).
Calculation of carbon stock change resulting from cropland management
Carbon stock change can be used to calculate a yearly emission/removal of carbon for up to 20 years after aland-use or land-management change by multiplying the carbon stock change factor by the area to which thechange has been applied as follows:
EQUATION 4.2.1
ANNUAL SOIL CARBON EMISSIONS/REMOVALS FROM CROPLAND MANAGEMENT∆CCM SOC = CSF ● A

Where:∆CCM SOC = annual change in carbon stock in soil organic carbon, Mg C yr-1
CSF

= carbon stock change factor, Mg C ha-1 yr-1

A
= area, ha(See also Equation 3.3.4 in Chapter 3)
For net-net accounting, the calculation shown in Equation 4.2.1 has to be performed for both the base year andreporting year. For discussion of the applicable area, see Section 4.1.2 (General rules for categorization of lands
areas under Articles 3.3 and 3.4).
Ti e r 2
The Tier 2 method also uses the methodology described in the IPCC Guidelines (Reference Manual andWorkbook), but now the default factors are replaced with country- or region-specific values shown to be morereliable (e.g., from literature values, long-term experiments or the local application of well-calibrated, welldocumented soil carbon models). Different regional data for soil carbon content (such as that available fromnational soil inventories) can also be used. Similarly, it is good practice to replace the default value for theduration of change (20 years) with a more appropriate value, if adequate information is available to justify it.
Regionally specific or local carbon stock change factors should be better than default factors at representing
actual carbon stock change in a given region. When replacing default carbon factors, rigorous criteria must be
applied to demonstrate that any change in factors does not lead to under- or overestimation of the soil carbonchange. Regional or country-specific factors should be based on measurements that are conducted frequently
enough and over a long enough time period and with sufficient spatial density to reflect variability of theunderlying biochemical processes, and documented in accessible publications.
The 20-year period over which soil carbon stock changes are assumed to change from one equilibrium position
to another is an approximation: in cooler climates, changes may take more than 20 years to reach a new
equilibrium (roughly 50 years); in tropical climates, a new equilibrium may be reached in shorter periods(roughly 10 years; Paustian et al., 1997). At Tier 2, different regional or country-specific values for the durationof impact of land-use or land-management change can be used where these exist or can be reliably estimated.
Alternatively an asymptotic model can also be fitted to data of soil carbon stock changes (see Figure 4.2.11;
compare to the “broken-stick” model used in the IPCC Guidelines in which a linear change occurs over 20 years
after which no further change occurs). Using this method, different carbon stock change factors could be appliedin different years after a land-use or management change so that stock changes are not underestimated soon after
a change (“a” on Figure 4.2.11), or overestimated as the soil approaches the new equilibrium (“b” on Figure
4.2.11).

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Schematic representation of a change in soil carbon stocks after a carbonsequestering management change is imposed represented by a broken-stickmodel of stock change (as used in the IPCC Guidelines where the time to a
new equilibrium is 20 Years) and by an asymptotic curve (for definitions of‘a’ and ‘b’ see text)

Soil C content

Figure 4.2.11b.
a.
Time to new equilibrium

Broken stick
Asymptotic

Management
ChangeTime
If for the duration of impact a value other than 20 years is used, this needs to be included in the matrix, as
represented schematically in Figure 4.2.12.
At Tier 2, default factors (e.g., input factors) associated with a different land-use or land-management change
can be replaced by more detailed relationships between the intensity of a practice (e.g., the amount of an organic
amendment applied to the soil) and a change in the yearly soil carbon emissions/removals. For example, in
Europe Smith et al. (2000) have developed such relationships (e.g., average yearly soil carbon stock change(tonnes C ha-1) = 0.0145 x amount of animal manure (tonnes dry matter ha-1 yr-1) added; recalculated from data
in Smith et al., 1997; R2 = 0.3658, n = 17, p < 0.01). Similar relationships could be derived from long-term datafor different soil types in different climatic regions. Alternatively, well-calibrated and well-evaluated models ofsoil carbon change (e.g., CENTURY (Parton et al., 1987), RothC (Coleman and Jenkinson, 1996)) could be used
to generate either stock change factors, or the intensity relationships described above, for different soils in
different climatic regions.
Rigorous criteria must be applied so that any carbon stock change is not under- or overestimated. It is goodpractice that stock change factors be based on experiments sampled according to the principles set out in Section
5.3, and to use the experimental values if they are more appropriate than the default values to the region andmanagement practice. Factors based on models should only be used after the model has been tested against
experiments such as those described above and any model should be widely evaluated, well-documented andarchived. It is good practice to provide confidence limits and/or uncertainty estimates associated with regional,
country-specific or local stock change factors.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Figure 4.2.12

Conceptual illustration of the matrix of carbon stock change factors derived
for different land-use, land-management transitions for each set ofbiophysical combinations. The Tier 2 method is extended by using regionallyspecific estimates of carbon factors or estimates of the duration of impact ofland-use/management change. Depending on how they are calculated, carbon
stock change factor (c) and duration (d) values for management shifts in
opposite direction will often be the same, but the ‘c’ value will have opposite
sign.

c = C stock change
factor (Mg C/ha/yr)
d = duration of C change(years)1‘New’ Management‘New’ Management
12
4…
‘New’3Management
1
2
3
4
…
‘New’
Management=c=
1 c = – 0.52 c‘New’
3Management
4 c=…
d =c =
d =c=
40-0.5yd =c =
d =c =

Each ‘layer’represents a
different set ofbiophysicalconditions2
3
4…
c= -0.5 2 c =
c=
c=
1
3
4
…
c1= 0.5c=c =c =
c= -0.5c =c =
c=
2
1
d =c=
400.5
y
d =-0.5c=
cd==c =c=
cd==c =
c=
2
1 0.5
c=
c=
c=
c=
c2=
c=
c= 0.5c =c =
c =c =c=3
d =c2=
d =0.5d==c =
d==c =
c=
c=
c
c
c=
3
2
c=
c=
c=
c=
c3=
c =c =
c =c =
c =c =c=4
d =c3=d=d=d==c =
c
=
c
=
c
=
c
=
c
c=
4
3
c=
c=
c=
c=
..
c4=
c =c =
c =c =
c =c =c=. . d =c4=
d
=
d
=
d
=
c
=
c
=
c
=
c
=
c
=
c
=
c=
..
4
..
c=
c=
c=
c=
. .
c=
c=
c=
c=
..
..
c=
c=
c=
c=
.

‘Old’ Management‘Old’ Management‘Old’ Management‘Old’ Management‘Old’ Management

11

Ti e r 3
Tier 3 methods that can be used for the national UNFCCC inventory (as described in Chapter 3, Section
3.3.1.2.1.1 Choice of method) are also likely to be used for reporting under the Kyoto Protocol. Compared withthe static matrix used at Tiers 1 and 2, Tier 3 can often better represent the management history of a land,
allowing better calculation of soil carbon changes resulting from multiple changes in management practices overtime. Furthermore soils can take much longer than 20 years to reach equilibrium, and Tier 3 (like Tier 2)
methods can take this into account. Large scale computing power makes possible a spatially disaggregatedsystem linked to management practice data which could keep track of carbon stock changes over time if linked
to rate equations with carbon contents, initialised at some point and cross-checked periodically. Tier 3 can also
be based on repeated statistical sampling consistent with the principles set out in Section 5.3 of sufficient density
to capture the soil types, climatic regions and management practices that occur. Tier 3 methods, therefore,
encompass a range of methodologies, more elaborate than Tier 2, usually based on sophisticated modelingtechniques, often linked to geographical databases.

Choice of carbon stock change f actors f or mineral soils
The carbon emission/removal factors used at each tier are described briefly in the following sections.
Tier 1: At Tier 1, average yearly carbon stock changes in mineral soils are calculated from default values bydividing the 20-year stock change by 20, as set out in Chapter 3, Equation 3.3.3. Full details of these factors andthe resulting stock change estimates can be found in the IPCC Guidelines, pages 5.35–5.48, and are provided in
the database described in Annex 4A.1. (Default values in Annex 4A.1 are slightly modified from those in the

IPCC Good Practice Guidance for LULUCF

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

IPCC Guidelines). For a summary of the steps and a sample calculation, see Section 3.3.1.2.1.1, Choice of
method (mineral soils).
Tier 2: At Tier 2, some or all of the default values for carbon stock change (Tier 1) are replaced by values
shown to be more reliable. These new values may be based on literature values, measured changes in carbon
stocks, on simple carbon models, or a combination of these. (See ‘Choice of management data for mineral soils’
below for some examples). It is good practice to show that the new values, compared to those they replace, aremore accurate for the conditions and practices to which they are applied.
Tier 3: For mineral soils, Tier 3 carbon stock change factors are country-derived, and may be calculated using
complex models. The carbon models used for Tier 3 are generally more complex than those in Tier 2, taking into
account soil (e.g., clay content, chemical composition, parent material), climate (e.g., precipitation, temperature,
evapotranspiration), and management factors (e.g., tillage, carbon inputs, fertility amendments, cropping system).
Good practice requires that the models be calibrated using measurements at benchmark sites, and that models
and assumptions used are described transparently.
In all cases, rigorous criteria must be applied so that any change in carbon stocks is neither under- or
overestimated; models used to estimate carbon stock changes should be well-documented and should be
evaluated using reliable experimental data for conditions and practices to which the models are applied. It isgood practice to provide estimates of confidence limits or uncertainty. Default carbon stock change factors may
also be replaced by values generated as part of national/regional carbon accounting systems (see Section 4.2.7.2
Choice of methods for identification of lands subject to Forest Management).

Choice of management data f or mineral soils
Area data on land uses and practices need to be available in accordance with Approach 2 or Approach 3 (Section
2.3.2), and guidance given in Section 4.2.2.3. The data on management required for each of three tiers are
outlined briefly here.
Tier 1: Using the IPCC Guidelines (see also Chapter 3, Section 3.3.1.2.1.1), impacts of land-use or land
management change are assumed, by default, to have an impact for 20 years. If area and activity data are
available for 20 years prior to the base year, a net carbon removal/emission for the base-year can be establishedusing the default carbon stock change factors described above. The land-use changes and management practices
at Tier 1 are the same as those given in the IPCC Guidelines: clearing of native vegetation with conversion tocultivated crops or pasture, land abandonment, shifting cultivation, differing residue addition levels, differingtillage systems and agricultural use of organic soils. Within these specific land-use or land-management changes,
activities are defined semi-quantitatively, e.g., “high input” vs. “low input” systems. Land-use or management
systems are not subdivided into finer levels of detail than this. Areas may be obtained from international data
sets (e.g., FAO), though some of these sources lack the spatial explicitness needed for reporting and may only be
helpful for cross-checking data. If area and activity data are available for 1970 through 1990, a 1990 baseline net
carbon stock change can be established using the default carbon stock change factors described above. If area
and activity data are not available for 1970 through 1990, see Section 4.2.7.2 for alternative options for
estimating the land areas.
Tier 2: The management practices at Tier 2 are the same as those given in the IPCC Guidelines and at Tier 1.
But for Tier 2, to make them country-specific, some management practices may be subdivided, or new ones may
be added. Within the agricultural management systems described in the IPCC Guidelines, management data
include descriptors such as “high input” and “low input”. These descriptors can be replaced at Tier 2 by more
explicit descriptors, for example, high organic amendment rates (e.g., >20 tonnes dry matter ha-1 yr-1), medium
organic amendment rates (e.g., 10-20 tonnes dry matter ha-1 yr-1), low organic amendment rates (e.g., <10 tonnes
dry matter ha-1 yr-1), and zero organic amendment. Further subdivisions could, for example, reflect different
forms of organic amendment, such as animal manure, cereal residues and sewage sludge, where corresponding
removal factors are available. An alternative to the use of more detailed descriptor categories is the use of
relationships similar to those derived for Europe by Smith et al. (1997, 1998, and 2000) and for the USA by Lal
et al. (1998). These could be based on a new, more comprehensive analysis of global data sets. Figures could
include the change in carbon stock associated with a given practice (e.g., zero till), or a relationship between
intensity of a practice and soil carbon change, e.g., average yearly soil carbon emission/removal (tonnes C ha-1)= 0.0145 x amount of animal manure (tonnes dry matter ha-1 yr-1) added; recalculated from data in Smith et al.,(1997; R2 = 0.3658, n = 17, p < 0.01). Alternatively, well-calibrated and well-evaluated models of soil carbonchange (e.g., CENTURY (Parton et al., 1986) RothC (Coleman and Jenkinson, 1996), or others) could be used togenerate either default carbon stock change factors, or to generate the intensity relationships described above foreach activity, for different soils in different climatic regions. These examples illustrate how practices can bemade more country-specific, but other refinements are also possible. Tier 2 methods may require area
descriptions of higher resolution than those in Tier 1. In any case, rigorous criteria must be applied so that any

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4change in emissions or removals is neither under- nor overestimated (see ‘Choice of carbon stock change factors
for mineral soils’ for discussion of criteria)
Tier 3: Management data used in the more complex Tier 3 methodologies need to be consistent with the levelof detail required by the model. It is good practice to use management data at a spatial resolution appropriate forthe model, and to have, or be able to estimate reliably, quantitative measures of the management factors requiredby the model.

4.2.8.3.2

CARBON STOCK CHANGES IN ORGANIC SOILSFor carbon stock changes in organic soils, the following decision tree (Figure 4.2.13) should be used to decide
which tier to use for reporting under the Kyoto Protocol.
Figure 4.2.13

Decision tree for selecting the tier at which to report carbon stock changes inorganic soils under the Kyoto Protocol (see also Figure 3.1.1)
Aredata available
to calculate regional orcountry-specific C stock change
factors, and are
regional soil data
available?

No

No

Yes
Obtain data from regional or
national measurement/research
programmes or other reliable
sourcesYes

Is anational carbon
inventory system
available that allows
cropland management
activities to beaccounted forseparately?

Are
CO2 emissions
from organic soils a
key category?

No

Yes

Use Tier 3(Note 3)

Use Tier 2(Note 2)

Use Tier 1(Note 1)

Note 1: Use the matrix/database of default values.
Note 2: Use region-specific parameters, soil data and duration of impact.
Note 3: Use more sophisticated modelling techniques, often linked to geographical databases.

Methods f or estimating CO 2 emissions/removals f rom organic soils
Tier 1: When organic soils are converted to agriculture, they are typically drained, cultivated, and limed,resulting in the oxidation of organic matter. The rate of carbon release will depend on climate, the composition(decomposability) of the organic matter, the degree of drainage and other practices such as fertilisation andliming. The Tier 1 method is set out in Section 3.3.1.2 which is based on the method given in the IPCC
Guidelines.

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

Tier 2: If more reliable country- or region-specific data is available on CO2 emissions from organic soils it isgood practice to use these values instead of Tier 1 defaults. Any data used should be shown to be more reliable
than defaults.
Tier 3: The complex systems described in Chapter 3 (LUCF sector good practice guidance) for national
greenhouse gas inventories may use methods or models for estimating CO2. These emissions may also be used
to estimate non-CO2 greenhouse gas emissions in an integrated way. However, the non-CO2 emissions should
be reported in the Agriculture sector, and double counting and omission should be avoided. It is good practice touse models which are calibrated using measurements at benchmark sites, and to describe models andassumptions used transparently.

Choice of carbon emission/removal f actors f or organic soils
Tier 1: The default carbon emission/removal factors for Tier 1 are provided in Chapter 3 (Table 3.3.5; Section
3.3.1.2.1.2).
Tier 2: For organic soils, it is good practice to replace the default values identified in Chapter 3 (Table 3.3.5;
Section 3.3.1.2.1.2) with country- or region-specific factors if these are shown to be more reliable than thedefaults. It is good practice to use replacement emission/removal factors based on experimental results derived
from experiments that are well-designed, with adequate sampling to give adequate statistical power. Anyemission or removal factors based on models should only be used after the model has been tested against
experiments, such as those described above, and any model should be widely evaluated, well-documented andarchived. It is good practice to provide confidence limits and/or uncertainty estimates associated with any
replacement emission/removal factors. Replacement emission/removal factors must be shown to better represent
local conditions or practice than default factors by comparing both default and replacement factors against
measurements or experiments within the region.
Tier 3: For organic soils, CO2 and non-CO2 greenhouse gas emissions or emissions/removals may be estimatedas part of processed-based modelling using national emission/removal factors. It is good practice to use suchmethods if they have been well-documented and evaluated. Before methods are applied they should bethoroughly tested and evaluated, as described for Tier 2.

Choice of management data f or organic soils
The same considerations apply as for management data for cropland management activities on mineral soils, as
described earlier in Section 4.2.8.3.1.

4.2.8.3.3

CO 2 EMI SSIONS FROM LIMING

Supplementary data provided for the Kyoto Protocol includes CO2 emissions from liming of croplands only if
cropland management is elected.

Methods f or estimating CO 2 emissions f rom liming
Liming is commonly used to ameliorate soil acidification. Carbonate minerals such as limestone CaCO3 anddolomite CaMg(CO3)2 are usually used. When added to acid soil these compounds release CO2 at a rate which
will vary according to soil conditions and the compound applied. Repeat applications are made every few years
but can be averaged out over time and the average annual rate is the basis for inventory calculations.
Tier 1: The Tier 1 method for estimating CO2 emissions from liming is identical to that described in Chapter 3(Section 3.3.1.2.1.1).
Tier 2: A Tier 2 method for liming uses national or regional figures in place of the default coefficients
described in Chapter 3 (Section 3.3.1.2.1.1) for soil CO2 emissions due to liming, where these are shown to be
more reliable.
Tier 3: The complex methods used at Tier 3 as described in Chapter 3 may account explicitly for liming. Thesemay integrate effects also on non-CO2 emissions. It is good practice to use such methods if they have been welldocumented and evaluated.

Choice of carbon emission f actors f or liming
It is good practice to use the default values given in the Chapter 3 (Section 3.3.1.2.1.1). If a Party chooses to use
alternative national emission factors (Tier 2), these should be justified by more detailed data on the compositionof the lime used. Tier 3 methods may in addition include the integrated effect of liming and management
practices on the non-CO2 emissions. It is good practice to use such factors if they have been well-documented
and evaluated.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.8.3.4

NON-CO 2 GREENHOUSE GASES

Methodologies for estimating N2O and CH4 emissions are given in the Agriculture Chapters of the IPCC
Guidelines and the GPG2000, which give methodologies for the following sources of agricultural emissions thatare related to cropland management (the list also applies to grazing land management and revegetation):
1) Direct N2O emissions from agricultural soils due to• Use of synthetic fertilisers,• Use of animal excreta as fertiliser,• Biological nitrogen fixation due to cultivation of legumes and other nitrogen fixing crops,• Crop residue and sewage sludge application,• Cultivation of soils with high organic content;
2) Indirect N2O emissions from nitrogen used in agriculture, including emissions from• Volatilisation and subsequent atmospheric deposition of NH3 and NOx (originating from the applicationof fertilisers and manures),• Nitrogen leaching and runoff;
3) CH4 emissions from rice cultivation;4) Non-CO2 emissions from burning of vegetation;5) CH4 from enteric fermentation;6) CH4 and N2O emissions from manure management.
These emissions should not be reported under cropland management but as agricultural emissions 59 and arecovered in Chapter 4 (Agriculture) of the GPG2000. Even for Parties that do not elect cropland management
under Article 3.4, these emissions should be reported as emissions from sources listed in the Annex A to the
Kyoto Protocol. Parties that elect cropland management should also report these emissions in the agriculture
sector and not include them under Article 3.4.
Non-CO2 emissions/removals on deforested lands converted to cropland (Article 3.3) need to be reported
separately from those under cropland management (Article 3.4). If non-CO2 emissions/removals on deforested
land cannot be determined directly, they may be estimated as a fraction of total non-CO2 emissions/removals
from cropland, corresponding to the area of total cropland on deforested land. For example, if 10% of thecropland area is on deforested land, then 10% of total cropland non-CO2 emissions/removals would be ascribed
to lands that have been subject to deforestation since 1990.
Some management practices adopted to increase soil carbon may also influence the emissions of non-CO2 gases.
Many of these effects are included in the Agriculture Chapters of the IPCC Guidelines and GPG2000, but there
may be other effects on non-CO2 gases not considered in the IPCC Guidelines and GPG2000 (see examples
presented in Box 4.2.11).

59

According to the Marrakesh Accords estimates of emissions from sources and removals by sinks from for Article 3.3 and3.4 activities are to be clearly distinguished from anthropogenic emissions from the sources listed in Annex A to the Kyoto
Protocol (cf. paragraph 5 in the Annex to draft decision -/CMP.1 (Article 7), contained in document
FCCC/CP/2001/13/Add.3, p.22).

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BOX 4.2.11
EXAMPLES OF POSSIBLE INFLUENCES OF CARBON STOCK CHANGES ON EMISSIONS OF NON-CO2 GASES

Example 1: Influence of reduced tillage on N2O emission.
Adoption of reduced or no-tillage often increases soil carbon in croplands. However, at the sametime it may also alter N2O emissions, through effects on porosity (and the fraction of the porosity
occupied by water), N cycling, temperature, and other factors (e.g., Weier et al., 1996; MacKenzie
et al., 1998; Robertson et al., 2000; Smith et al., 2001). The observations are inconclusive, withsome studies showing higher N2O emission under no-till than under tilled systems, and others
showing little effect or lower N2O emissions. The available data suggest that this variable response
depends on interactive effects of soil and climate, and that wetter environments with poorer
aeration, in which N2O emissions generally tend to be highest, are also associated with higher
emissions under no-till than under conventional tillage (e.g., Linn and Doran, 1984; Weier et al.,
1996; Vinten et al., 2002).
Example 2: Links between organic matter turnover and N2O emission.
Organic matter in soil is continually decomposing, resulting in the release of ammonia, and of
nitrate. A portion of this ‘available’ N may be converted to N2O. Consequently, practices that
increase the rate of organic matter decomposition (e.g., ploughing of grasslands, increased use of‘fallow’ periods) may stimulate N2O emissions. In contrast, re-planting grasslands and reducing‘fallow’ frequency may reduce N2O emissions. The significance and magnitude of these effects,
however, are not well-understood and it may not be possible to quantify them reliably at this stage.
Example 3: Effect of cropland management on CH4 oxidation.
Some practices that enhance soil carbon in croplands may also influence the rate of CH4 oxidation
in soils, negatively or positively (e.g., Smith et al., 2001). Often these effects are smaller thanthose on N2O, when expressed in units of CO2-equivalence.
Example 4: Effect of draining organic soils.
Emissions of CH4 may decrease as CO2 losses increase with soil drainage, and N2O emissions may
also be affected. (Note that the IPCC Guidelines assume that all carbon is lost as CO2; if this isdeparted from, it must be justified by scientifically sound and well-documented data. Methods for
estimating N2O emissions from cultivated organic soils are given in the Agriculture Chapters of
the IPCC Guidelines and GPG2000, and these emissions should be reported as described there to
avoid double-counting.).

The effects on non-CO2 emissions of these and other management practices may be included in higher tiermethods for agriculture, as noted in GPG2000 (Section 4.7, page 4.53 to 4.66). Where estimated, they shouldstill be reported with Agriculture, to avoid double counting. Examples of how these effects could be estimatedinclude:•Direct measurement of the non-CO2 greenhouse gases at representative sites;•

Estimation of emission rates based on literature values taking into account management, soil and climate.

4.2.9
4.2.9.1

Grazing land management
D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS

Grazing land management is the system of practices on land used for livestock production aimed at manipulating
the amount and type of vegetation and livestock produced. Grazing lands are, by definition, ‘managed’ to someextent, so the lands under grazing land management are in fact potentially all the lands within a country subject
to grazing; that is, all lands predominantly used for livestock production, based on criteria decided upon andexplicitly described by the country. Note that not all grasslands are necessarily grazing lands.
In order to ensure a comprehensive coverage, it is good practice to include all of the following lands in thegrazing lands category:•

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Improved pastures/grasslands/rangelands: These are lands subject to intensive, controlled grazing.
Management practices such as fertilizing/manuring, irrigation, reseeding, liming, or spraying are used tocontrol productivity. Lands used permanently for herbaceous forage crops are also included.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4•

Unimproved/natural pastures/grasslands/rangelands: These lands are usually composed of native vegetation
including hay and bushes, and grazing is mainly extensive. There is no or little grass management except
burning in some instances. However, the intensity, frequency, and seasonality of grazing and animal
distribution are managed (even by default) or can be specifically managed to prevent loss of stored carbon,
for example by avoiding overgrazing.

Pastures, rangelands or savannahs on which trees and shrubs are grown should be included under grazing land
management if the growing of forage crops or grazing is the most important activity on the area, based on
criteria established and explicitly stated by the country. Where treed lands meet the definition of a forest and thetrees have been established since 1990, the land should be included under the afforestation/reforestation category.
However, lands that meet the definition of ‘forest’ can be included in grazing land management, if grazing is thedominant activity, based on the criteria established by the country.
Set-aside lands, such as cultivated lands reverted to perennial grasslands, should be included under cropland
management if they are only temporarily set-aside (typically this is for 5 years or less, but any set-aside likely toreturn to cropland under the national conditions for set-aside should be counted as cropland). They should be
included under grazing land management if they are permanently set-aside. Protected lands, such as thosesubject to permanent cover programmes should be included under grazing land management if they are also used
for livestock production. Lands that are only temporarily used for grazing, as part of a cropping rotation, would
normally be included under cropland management. For consistency, the criteria used to distinguish between
cropland and grazing land and revegetation should be explicitly stated and applied consistently.
Given the potential overlap with other land-use categories, it is good practice for countries to specify what typesof lands are included under the category grazing land/rangeland/pastures in their national land-use system.
Moreover, countries should also specify how these lands differ from (a) lands in land-use category (ii) ofChapter 2 (cropland/arable/tillage), and (b) lands subject to other activities under Article 3.3 (AR) and Article
3.4 (FM, RV, CM – if elected). This will enhance the comparability of reporting across countries.
In addition, all lands that were forest on 31 December 1989 and that are subject to grazing land management in
the reporting year need to be identified, tracked and reported as a separate category (‘Deforestation’ lands thatwould otherwise be subject to grazing land management).
In order to allow the application of the proposed methodology for determining CO2 emissions/removals on thoselands, (i.e., area times a carbon stock change factor, the factor being positive, negative or null depending on
management and land use or land-use change), the total grazing land area needs to be subdivided into areas
under various sets of management practices (which may overlap both in time and space) for the base year and theyears in the commitment period. The carbon stock change factors depend on both the current and previous
management. Some areas may be emitting carbon, others may be sequestering CO2, others may be in equilibrium
and this may change if management changes.
To obtain more disaggregated data on land uses and practices, a more comprehensive definition of land use andmanagement systems within grazing lands/rangelands/pasture for different climatic zones can be developed.
Broad families of practices under grazing land management that affect carbon stocks include: herd management,
presence of woody plants, fertilization, irrigation, species composition, legume management, and fire
management (IPCC, 2000b, p.184 and p. 205). See also Chapter 3 (LUCF sector good practice guidance) andSection 4.2.9.2 below.

4.2.9.1.1

1990 BASE YEARSee Section 4.2.8.1 Definitional issues and reporting requirements.

4.2.9.2

C HOICE OF METH ODS FOR IDENTIFYING LANDS

General guidance on identification of lands relevant to grazing land management is provided in Sections 4.1.1,
4.1.2, 4.2.1, and 4.2.2. Under the Marrakesh Accords, the geographical location of the boundaries of the area thatencompass land subject to grazing land management need to be reported annually, along with the total land areas
subject to this activity. The geographical location of the boundaries may include a spatially explicit specificationof each land subject to grazing land management, but does not have to. This is analogous to the case for cropland
management as discussed in Section 4.2.8.1 (Definitional issues and reporting requirements). It is good practice
to follow continuously the management of land subject to grazing land management. This could be achieved
either by continuously tracking each land subject to grazing land management from 1990 until the end of thecommitment period (see Section 4.2.8.1), or by using statistical sampling techniques that allow the transitions ofmanagement on grazing land to be determined and that, at the same time, are consistent with the requirements of

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Section 5.3 (see also Section 4.2.4.1 Developing a consistent time series). At the national level, different layersof breakdown of the total grazing land area are needed, for instance using criteria that concern primary national
circumstances, management practices and other subdivisions. These could include:•

Climate•Soil type•

Degree of disturbance (e.g., compaction, disturbance by livestock foot action, frequency of burning, erosion)•Level of organic input (e.g., plant litter, roots, manure, other amendments)•

Lands that are intermittently grazed (e.g., set-aside, grass as part of a rotation)•

Grazing intensity (utilization percentage of the pasture)•

Treed lands (shelterbelts, orchards, other perennial plantations)•Lands converted to grazing-lands since 1990 (land-use change) that are not in any other land-use category.

For all of the resulting subcategories the areas under grazing land management that were derived fromconversion of forests (i.e., deforestation) since 1990 need to be tracked separately as these will be reported asunits of lands subject to deforestation.
At Tier 3 further subdivision of the area subject to grazing land management may be necessary.
Methods to identify lands subject to grazing land management with necessary disaggregation available in someAnnex I countries include the following:•

National land use and management statistics: the agricultural land base including land subject to grazing
land management is surveyed in most countries on a regular basis. These may be derived, in part, fromremote sensing of pasture and soil surface condition and changes in stocking rate.

•

Inventory data from a plot, statistically based, sampling system: land use and management activities aremonitored at specific permanent sample plots that are revisited on a regular basis.

Information on these areas would have to be compiled either for all lands affected by grazing land management
or summarised as estimates for all the strata (defined by the boundaries of the areas of land) that a Party chooses
to apply for the reporting of its land use statistics. Further good practice guidance on identifying land areas is
given in Chapter 2 (Basis for consistent representation of land areas).
Links to methods for area identification in other chapters of this report and IPCC Guidelines are given in Box
4.2.12.
BOX 4.2.12
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 2.3.2 (Three approaches): Grasslands (unmanaged or managed) that become managed
grasslands or any conversion that leads to managed grasslands in Chapter 2 (except forests tograsslands), provided that these managed grasslands are subject to grazing land management.
Should include all transitions between 1990 (or 1970, where required for base year estimate) and2008, and in later inventory years transitions on an annual basis.60
LINKS WITH THE IPCC GUIDELINES

Not available in a format that meets requirements in the Marrakesh Accords for geographicallocation of the boundaries.

60

If more than one land conversion happens on the same unit of land in the transition period of the matrix, then the transition
periods may have to be shortened to account for these transitions.

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

4.2.9.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCKCHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONS

Like for cropland management, methodologies at one of three tiers are used for estimating CO2
emissions/removals from mineral soils, organic soils and liming. The procedure is identical with different factors
being derived and different activity data being used (as described in more detail in the sections below).
Total annual soil emissions/removals of CO2 are calculated by summing:•

Net changes in organic carbon stocks of mineral soils•

Emissions of CO2 from organic soils•

Emissions of CO2 from liming

Carbon stock changes also need to be estimated for other carbon pools, as appropriate. For grazing lands with nowoody vegetation, annual crop biomass can be neglected where there is no long-term change in the cover.
However, carbon in biomass of trees, shelterbelts and woody crops on grazing lands need to be accounted forunder either (but not both) grazing-land management, afforestation/reforestation or forest management (unless an
Annex I Party to the Kyoto Protocol chooses not to and provides verifiable information that carbon stocks are
not decreasing). Methods for above- and belowground biomass, litter and dead wood can be found in theafforestation/reforestation or forest management sections and Chapter 3 (LUCF sector good practice guidance)of this report. For guidance in estimating carbon emissions/removals in pools other than in the soil, see Box
4.2.13 and Table 4.2.8. Figure 3.1.1 in Chapter 3 provides further guidance on selecting appropriate methods.

BOX 4.2.13
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 3.4.1.1 Change in biomass
Section 3.4.1.2 Change in carbon stocks in soils
LINKS WITH THE IPCC GUIDELINES

4

Non-CO2 greenhouse gases

5B

Forest and grassland conversion (conversion of grazing lands to croplands)

5D

CO2 emissions and removals from soils

4.2.9.3.1

MINERAL SOILS

The decision tree used for selecting the tier for estimating carbon stock changes in mineral soils under grazing
land management is analogous to the one used for croplands – see Figure 4.2.9 above.

Methods f or estimating carbon stock changes in mineral soils
The methods used for estimating carbon stock changes in mineral soils under grazing land management are
identical to those used for croplands. See the methods under Tiers 1, 2 and 3 described in Section 4.2.8.3.1(Mineral soils) and also in Chapter 3 (Sections 3.3.1.2, 3.4.1.2, 3.4.2.2). As for cropland management all
methods require that the lands subject to grazing land management be tracked continuously through time. At Tier
1, the database of default annual stock change factors in Annex 4A.1, is applicable also for grazing lands (seeSection 4.2.8.3.1). However, for Article 3.4 activities it is good practice to use Tier 2 or Tier 3 for estimating
carbon stock changes from mineral soils if CO2 emissions from grazing land management is a key category.

Choice of carbon emission/removal f actors f or mineral soils
The choice of carbon stock change factors at each tier follows the same lines as described under cropland
management. The carbon stock change factors are held within the same database. At higher tiers, as for cropland

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

management, carbon stock change factors can be calculated from literature values (e.g., Follett et al., 2000),
long-term experiments and model runs. It is good practice for replacement stock change factors, if based on
experimental results, to be derived from experiments that are well designed, with adequate sampling to giveadequate statistical power. Any factors based on models should only be used after the model has been tested
against experiments such as those described above, and any model should be widely evaluated, well-documented
and archived. It is good practice to provide confidence limits and/or uncertainty estimates associated with any
emission/removal factors. Emission/removal factors must be shown to represent local conditions or practice,based on measurements or experiments within the region.

Choice of land use and management data f or mineral soils
Like for cropland management, if area and management data are available for 1970 through 1990, a base year
(1990 or other) net carbon emission/removal can be established using the default carbon emission/removal
factors described above. If area and management data are not available for 1970 through 1990 the options
available are those already described for cropland (see Section 4.2.8.1.1: 1990 base year). Here only the activity
data required for each of three tiers are outlined briefly.
Tier 1: The management practices at Tier 1 are the same as those given in the IPCC Guidelines. The different
management impacts defined there are: clearing of native vegetation with conversion to cultivated crops or
pasture; land abandonment; shifting cultivation; differing residue addition levels; differing tillage systems;
agricultural use of organic soils for grazing. Within these specific land-use or land-management changes,
practices are defined semi-quantitatively, e.g., “high input” vs. “low input” systems. Land-use and management
systems are not subdivided to finer levels of detail than this. Areas may be obtained from international data sets
(e.g., FAO). If area and management data are available for 1970 through 1990, the 1990 base year net carbon
stock change can be established using the default carbon emission/removal factors described above. If area andmanagement data are not available for 1970 through 1990 the options available are those described above forcropland (see Section 4.2.8.1.1). If grazing land management is deemed a key category, then it is good practiceto use a Tier 2 or 3 method.
Tier 2: The management practices at Tier 2 are the same as those given in the IPCC Guidelines and at Tier 1.
To make them country-specific, however, some practices may be subdivided, or new ones may be added. For
example, within the agricultural management systems described in the IPCC Guidelines, management data
includes descriptors such as “high input” and “low input”; these descriptors could be replaced at Tier 2 by more
explicit descriptors; for example, high grazing level, medium grazing level, low grazing level, and zero grazing.
Further subdivision of activities may also be necessary; for example, different forms of grazing. An alternative tothe use of more detailed descriptor categories is the use of relationships relating the intensity of a practice (e.g.,
grazing level) with a change in the carbon emission/removal factor. Alternatively, well-calibrated and wellevaluated models of soil carbon change (e.g., CENTURY (Parton et al., 1986), RothC (Coleman and Jenkinson,
1996), or others) could be used to generate either default carbon emission/removal factors, or to generate theintensity relationships for each activity, for different soils in different climatic regions. These examples showhow, at Tier 2, activities can be made more country-specific, but other refinements are also possible. Rigorous
criteria must be applied so that any increase in the sink size is not under- or overestimated.
Tier 3: Management data used in the more complex Tier 3 approaches are likely to be subdivided as describedfor Tier 2 above.

4.2.9.3.2

CO 2 EMI SSIONS FROM ORGANIC SOILS

The decision tree for use with organic soils under grazing land management is identical to that from cropland
management, cf. Figure 4.2.13. The methods described under Tiers 1, 2 and 3 for cropland also apply to grazing
land, cf. Section 4.2.8.3.2 (Carbon stock changes in organic soils) and also Chapter 3 (Sections 3.3.1.2 and3.4.1.2). As for croplands, non-CO2 greenhouse gas emissions/removals from organic soils are also important,with some emissions (i.e., methane, CH4) decreasing as CO2 losses increase with soil drainage. It is importantwhen calculating changes in carbon emissions/removals from organic soils to also consider non-CO2 greenhousegas emissions, bearing in mind that, as a rule, these are covered in the Agriculture sector. However, note that the
IPCC Guidelines assume that all carbon is emitted as CO2; if this assumption is departed from, it must be
justified by scientifically sound and well-documented data.

Choice of carbon emission/removal f actors f or organic soils
Factors for organic soils are described in the equivalent subsection for cropland management (Section 4.2.8.3.2
Carbon stock changes in organic soils) and Chapter 3 (Sections 3.3.1.2 and 3.4.1.2).

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Methods for estimation, measurement, monitoring and reporting of LULUCF activities under Articles 3.3 & 3.4

Choice of management data f or organic soils
Management data for organic soils are as for IPCC Guidelines as described and amended above for mineral soils.

4.2.9.3.3

CO 2 EMI SSIONS FROM LIMINGFor carbon emissions from liming, the same methods can be used for land subject to grazing land management as
for those under cropland management (see Section 4.2.8.3.3 CO2 emissions from liming).

4.2.9.3.4

NON-CO 2 GREENHOUSE GASES

Methodologies for N2O and CH4 emissions from soils are given in the Agriculture chapter of GPG2000, which
gives methodologies for sources of agricultural soil emissions that are related to grazing land management (seealso Chapter 3, Section 3.4.1.3). Management practices adopted to increase soil carbon may also influence theemission of non-CO2 greenhouse gases. Often these effects will be covered by the methods described for
agriculture. For example, N2O emissions from adding more fertilizer to build soil organic matter will be directly
included. There may be other effects that are not covered by the default methods; for example, increasing thecarbon pools could also increase levels of organic nitrogen which, when mineralised, could become available asa substrate for denitrification and thus increase N2O production. Similarly, the cessation of tillage on conversionof croplands to grazing lands could, at some stage in the development of the grazing land make the soils moreanaerobic, thus potentially enhancing denitrification and N2O production (see Example 1 in Box 4.2.11). These
effects can be calculated in higher-tier methods, but still should be reported in the Agriculture sector, to avoid
double counting or omission.
Non-CO2 greenhouse gas emissions/removals on deforested lands converted to grazing land (Article 3.3) need tobe reported separately from those under grazing land management (Article 3.4). For further guidance, seecorresponding section on cropland management (Section 4.2.8.3.4).

4.2.104.2.10.1

Revegetation
D EFINITIO NAL ISSUES AND REPORTING REQ UIREMENTS“Revegetation” is a direct human-induced activity to increase carbon stocks on sites through the establishment ofvegetation that covers a minimum area of 0.05 hectares and does not meet the definitions of afforestation andreforestation. Land should be classified under revegetation if it meets the revegetation definition and takes place
after 1 January 1990 (see the decision tree in Figure 4.2.5 for further guidance). The methods for estimating carbon
stock changes from revegetation differ somewhat from those applied to cropland management or grazing land
management, and have similarities to those for afforestation and reforestation activities; even though revegetation is
distinct from afforestation/deforestation, it also typically affects the aboveground carbon pool significantly.
Revegetation implies that vegetation is established to replace the previous (sometimes minimal) ground coverthat had followed a land disturbance. For example, activities such as reclaiming/restoring herbaceous ecosystems
on carbon-depleted soils, environmental plantings, planting of trees, shrubs, grass or other non-woody vegetation
on various types of lands including urban areas, might all qualify as revegetation. Moreover, a tree planting may
not qualify for afforestation/reforestation because it does not meet (and is not expected to meet during thecommitment period) the minimum tree crown cover and/or minimum tree height chosen in the definition offorest, or because the consistent application of spatial configuration criteria (see Section 4.2.2.5) exclude it. In
such a case the planting may qualify as revegetation. Note that revegetation does not necessarily entail a change
in land use, in contrast to afforestation.
Set-aside lands such as cultivated lands subjected to revegetation should be included under cropland
management if they are only temporarily set-aside (typically this is for 5 years or less, but any set-aside likely toreturn to cropland under the national conditions for set-aside should be counted as cropland).
It is good practice for Parties electing revegetation to provide documentation describing how the included areas
meet the definition of revegetation and how they can be distinguished from other lands in land-use categories.

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4.2.10.2

C HOICE OF METH ODS FOR IDENTIFYING LANDS

General guidance on identification of lands subject to revegetation is provided in Sections 4.1.1, 4.1.2, 4.2.1, and4.2.2. Generally, all lands subject to revegetation since 1 January 1990 should be tracked consistent with thenational criteria that establish a hierarchy among Article 3.4 activities (if applicable) as explained in Section 4.1.
Under the Marrakesh Accords, the geographical locations of the boundaries of the areas that encompass lands
subject to revegetation need to be reported annually, along with the total land area subject to this activity.
The geographical location of the boundaries may include a spatially explicit specification of each land subject torevegetation, but does not have to. Instead, the larger area within which areas of land subject to revegetation areencompassed may be given. In either case, the lands subject to revegetation and the management thereon need to
be tracked continuously through time. Continuity in monitoring/reporting of management on land could beachieved either by continuously tracking each land subject to revegetation from 1990 until the end of thecommitment period (e.g., see Section 4.2.8.1 and 4.2.8.2), or by developing statistical sampling techniques,consistent with the requirements of Section 5.3, that allow the transition of different types of management on
revegetation land to be determined (see Section 4.2.4.1 Developing a consistent time series).
Links to pertinent methods in this report and in the IPCC Guidelines are provided in Box 4.2.14.

BOX 4.2.14
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 2.3.2 (Three Approaches): No information on revegetation area in Chapter 2 approaches.
Requires country-specific criteria on what constitutes revegetation. Should include all transitions
between 1990 (or 1970, where required for base year estimate) and 2008, and in later inventory
years transitions on an annual basis.61
LINKS WITH THE IPCC GUIDELINES

Revegetation is not addressed in the IPCC Guidelines.

Guidance on methods to identif y/monitor areas f or revegetation lands
Methods for monitoring revegetation lands are the same as those used for afforestation/reforestation anddeforestation lands (see Sections 4.2.5 and 4.2.6).

4.2.10.3

C HOICE OF METH ODS FOR ESTIMATING CARBON STOCKCHANGES AND NO N -CO 2 GREENHOUSE GAS EMISSIONSFor mineral soils, organic soils and for limed revegetation lands, the same methods and tier structures can be used as
described for cropland management and grazing land management. Methods for aboveground biomass, belowgroundbiomass, litter and dead wood on revegetation land, are described in Chapter 3, based on the IPCC Guidelines (see also
Box 4.2.15, Table 4.2.8, Figure 3.1.1). For urban soils, methods are described in Annex 3.B, Chapter 3.

61

If more than one land conversion happens on the same unit of land in the transition period of the matrix, then the transition
periods may have to be shortened to account for these transitions.

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BOX 4.2.15
LINKS WITH CHAPTER 2 OR 3 OF THIS REPORT

Section 3.4.2.1 Change in biomass
Section 3.4.2.2 Change in carbon stocks in soils
LINKS WITH THE IPCC GUIDELINES

4

Non-CO2 greenhouse gases

5A

Changes in forest and other woody biomass stocks (grasslands / tundra)

5C

Abandonment of managed lands (grasslands / tundra)

5D

CO2 emissions and removals from soils

5E
Other (e.g., dispersed trees that are managed but do not constitute a forest such asagroforestry, also referred to as “managed trees outside forests”)(not all five pools are included: belowground biomass and litter are missing)

4.2.10.3.1 CHOICE OF CARBON STOCK CHANGE FACTORS
There are no generic default values for revegetation activities in the IPCC Guidelines. A Party electing
revegetation may use Tier 1 methods to estimate changes in soil carbon since default values may exist (seeSection 4.2.8.3 (for cropland management), Section 4.2.9.3 (for grassland management) and also pertinent
sections in Chapter 3: Sections 3.3.1.2, 3.4.1.2, 3.4.2.2). However, for all other pools default values do not exist,
so it is good practice for a Party electing revegetation to provide country-specific values for stock change in eachcarbon pool and for pools not reported, to provide verifiable data that demonstrate that these are not declining in
carbon (see Section 4.2.3.1 Pools to be reported). If revegetation is deemed a key category, then it is goodpractice to use a Tier 2 or 3 method.
At Tier 2, it is good practice to provide verifiable methods and documentation to show how the carbon stockchange has been estimated for each pool elected under revegetation. For any carbon pool not elected, it is goodpractice to provide verifiable data that demonstrate that these are not declining (see Section 4.2.3.1 Pools to bereported).
At Tier 3 ecosystem carbon models, parameterised for the relevant plant functional types and soils included in
the selected revegetation area, could be used to estimate annual carbon emissions and removals. As with models
used for cropland management and grazing land management, they should be evaluated by testing against
experiments, well-documented and archived.

4.2.10.3.2 CHOICE OF MANAGEMENT DATA
It is good practice to provide detailed documentation specifying the practices included under revegetation andthe carbon emission/removal factors associated with each practice for each pool elected.

4.2.10.3.3 NON-CO 2 GREENHOUSE GASES
Methodologies for estimating N2O and CH4 emissions are given in the Agriculture chapters of the IPCC
Guidelines and the GPG2000, which give methodologies for sources of agricultural soil emissions on
revegetation land (the list of sources is similar to that described for cropland management – see Section 4.2.8.3).
These emissions should not be reported under revegetation but as emissions in the Agriculture sector fromsources listed in Annex A to the Kyoto Protocol, and they should clearly be distinguished from emissions fromrevegetation reported under Article 3.4 of the Protocol.
It is good practice to report the non-CO2 greenhouse gas emissions from sources on revegetation lands thatmight be affected by land-use practices under the Annex A sources inventory for the Kyoto Protocol. Thesesources belong to the inventory for the Agriculture sector (the list of sources is similar to that described forcropland management – see Section 4.2.8.3.4). Tier 3 methodologies may account for the detailed relationshipbetween carbon storage and non-CO2 greenhouse gas emissions if data are available to do so. Some examples of
relevant activities are given in Box 4.2.11. These emissions should still be reported in the Agriculture sector.

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Chapter 3 (Sections 3.3.2.2, 3.4.1.3, 3.4.2.3) provides further information on procedures for estimating non-CO2
greenhouse gas emissions.
Non-CO2 greenhouse gas emissions/removals on deforested lands subject to revegetation (Article 3.3) need to bereported separately from those under revegetation (Article 3.4). For further guidance, see corresponding section
under cropland management (Section 4.2.8.3.4).

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4.3

LULUCF PROJECTS

4.3.1

Introduction

This section provides good practice guidance for defining project boundaries, measuring, monitoring, andestimating changes in carbon stocks and non-CO2 greenhouse gases, implementing plans to measure and monitor,and developing quality assurance and quality control plans. The material is intended for use with projects under
Article 6 (Joint Implementation)62 and Article 12 (Clean Development Mechanism) of the Kyoto Protocol. It
does not address issues that are, at the time of writing, under the Subsidiary Body for Scientific andTechnological Advice (SBSTA) of the United Nations Framework Convention on Climate Change (UNFCCC)63,
in the context of Article 12 of the Kyoto Protocol.
Guidance is provided for those elements for which standard methods exist and are applicable for project
activities under Articles 6 and 12. In addition, guidance and/or recommendations are given on how to define
project boundaries and on aspects to be considered within a project’s baseline for activities under Article 6.
However, other elements of Article 12 project activities, such as definitions for “project boundary” and“baseline”, depend on decisions scheduled to be made at the ninth session of the Conference of the Parties (COP).
These are not included in this good practice guidance. In general the application of this good practice guidancein respect of Article 6 and Article 12 projects depends on the requirements of the relevant COP decisions,
including notably those relevant to Article 6 and the decisions which, at the time of writing, are undernegotiation in respect of LULUCF projects under Article 12.
Section 4.1.1 provides an overview of the steps required by Annex I Parties to meet the requirements forreporting changes in carbon stocks and emissions and removals of greenhouse gases associated with Article 6
projects under the Kyoto Protocol. Emissions and/or removals resulting from Article 6 projects are also part ofan Annex I host country’s annual inventory, and Section 4.1.3 elaborates the relationship between the estimation
and reporting of Article 3.3 and elected Article 3.4 activities on the one hand, and Article 6 project activities on
the other.
Reporting for project activities under Article 12 (comprising the validation, monitoring and verification reports)
involves the project participants, their contracted designated operational entity, the Parties involved and the
CDM Executive Board. The reports are also made publicly available upon transmission to the CDM Executive
Board. The modalities and procedures for reporting under Article 12 are also, at the time of writing, being
considered by the SBSTA. Hence, reporting requirements for Article 12 project activities are not included as partof this good practice guidance.
Estimating and monitoring anthropogenic changes in carbon stocks and non-CO2 greenhouse gas emissions andremovals at the project level involve several challenges and specific circumstances, which may not beappropriately captured within good practice guidance developed for national inventories. It is therefore
recommended to apply higher-tier methods, based on field measurements or field measurements in combination
with models (e.g., allometric equations, simulation models). The recommended multiple methods, presented as aseries of practical steps within a measuring, monitoring, and estimation plan, are detailed in Section 4.3.3 and its
subsections. Options for standard sampling and field measurement techniques are described, along with theadvantages and disadvantages of each. As clarified under Section 4.1.3, some areas with activities under Articles
3.3 and 3.4 can also be projects under Article 6. In such cases, it is good practice to use the same tier or a higher
tier for estimating carbon stock changes and greenhouse gas emissions as was used for the same land in the
UNFCCC inventory as specified in Chapter 3 of this report (refer to Section 4.2.3.4, Choice of method).

62

Guidelines for the implementation of Article 6 of the Kyoto Protocol are found in the Annex to the Draft decision –/CMP.1(Article 6), contained in document FCCC/CP/2001/13/Add.2, pp. 8-19.

63

In Decision 17/CP.7, the SBSTA was requested to develop definitions and modalities for including afforestation andreforestation project activities under the CDM in the first commitment period, taking into account the issues of nonpermanence, additionality, leakage, uncertainties, and socio-economic and environmental impacts, including impacts on
biodiversity and natural ecosystems. A decision on these definitions and modalities will be adopted at the ninth session of
the COP.

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4.3.1.1

D EFINITIO N OF P ROJECTS AND RELEVANCE TO A RTICLES
6 AND 12A LULUCF project can be defined as a planned set of eligible activities within a specific geographic locationthat have the purpose of resulting in net greenhouse gas removals that are additional to those that would occur inthe absence of the proposed project. A LULUCF project may be implemented by public or private entities, or a
combination of the two, including private investors, private enterprises, local and national governments, otherpublic institutions, and non-government organisations (NGOs).
For the first commitment period, eligible activities under Article 6 may include afforestation and reforestation,
forest management, grazing land management, cropland management, and revegetation. Under Article 12,
eligible activities for the first commitment period are limited to afforestation and reforestation. Under either
article, projects can comprise multiple activities. For example, under Article 6, a project could consist of a
combination of changes in both grazing and forest land management; under Article 12, a project could consist ofafforestation with timber species and multipurpose tree species.

4.3.2

Project Boundaries

The Marrakesh Accords specify that the project boundary for Article 6 shall “encompass all anthropogenic
emissions by sources and/or removals by sinks of greenhouse gases under the control of the project participantsthat are significant and reasonably attributable to the Article 6 project”.64 The definition for project boundary for
LULUCF activities under Article 12 remains, at time of writing, under consideration by SBSTA. Therefore, it isgood practice to identify all anthropogenic emissions by sources of greenhouse gases and removals by sinks
arising from activities and practices associated with LULUCF projects. In a general sense, project boundaries
can be thought of in terms of geographical area, temporal limits (project duration), and in terms of the project
activities and practices responsible for greenhouse gas emissions and removals that are significant andreasonably attributable to the project activities.

4.3.2.1

G EOGRAPHIC AREA

Projects may vary in size and may be confined to a single or several geographic areas. Depending on the rules
agreed for projects the area could be one contiguous block of land having a single owner or many small blocksof land spread more widely, perhaps having a large number of small land owners all being joined in some formof a cooperative or association. It is good practice to specify and clearly define spatial boundaries of the project
lands so as to facilitate accurate measuring, monitoring, accounting, and verifying the project. These boundaries
need to be identifiable by all stakeholders including project developers and Parties. It is good practice, whendescribing physical project boundaries, to include the following information:•

Name of the project area ( e.g., compartment number, allotment number, local name, etc.)•

Map(s) of the area (paper format and/or digital format, if available)•

Geographic coordinates•

Total land area•

Details of ownership•Land use and management history of the selected site(s).

The expectation is that boundaries remain unchanged during the duration of the project. In the event thatboundary changes are inevitable, subject to the rules agreed for projects, then these would need to be reported
and inclusions and/or exclusions of physical land area need to be surveyed using the above described methods(this would mean adjusting the net emissions or removals of greenhouse gases attributable to the project).
There are many different methods and tools that can be employed to identify and delineate physical projectboundaries. These include, amongst others, the following:•
64

Permanent boundary markers (e.g., fences, hedgerows, walls, etc.);

See Appendix B, paragraph 4(c) to draft decision -/CMP.1 (Article 6), contained in document FCCC/CP/2001/13/Add.2,
p.19.

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LULUCF Projects•

Remote sensing data e.g., satellite imagery from optical and/or radar sensor systems, aerial photographs,
airborne videos, etc.;•

Cadastral surveys (ground-based surveys to delineate property boundaries);•

Global Positioning Systems;•

Land records;•

National certified topographic maps with clearly defined topographic descriptions (e.g., rivers/creeks,
mountain ridges); and•Other nationally recognized systems.

Parties may opt to use any of these methods or tools, alone or in combination, provided accuracy is maintained.

4.3.2.2

T EMPORAL BOUNDARIES

Temporal boundaries (i.e., time boundaries), which are defined by the project starting and ending dates, should
be set so that the boundaries encompass all changes in carbon stocks and non-CO2 greenhouse gases emissions
and removals that are reasonably attributable to project practices. Different project types have different patterns
and rates of carbon accumulation as described in detail in the IPCC Special Report on LULUCF (Brown et al.,2000b). For afforestation and reforestation projects activities under Article 12, the issue of project duration andits relation to permanence is not discussed here because it is being addressed by SBSTA (see Section 4.3.1).

4.3.2.3

A CTIVI TIES AND P RACTICES

Different LULUCF projects have different direct human-induced changes in carbon stocks and non-CO2
greenhouse gases. Examples of different project types and the likely changes in carbon stocks and non-CO2
greenhouse gas emissions are provided in Box 4.3.1 (applicable to Articles 6 and 12, subject to the negotiations)and Boxes 4.3.2—4.3.4 (applicable to Article 6). Steps for identifying greenhouse gas emissions and removals
caused by the project include the following:•

List and describe the greenhouse gas emissions and removals resulting from the primary project practices—e. g. tree planting, crop tillage, changed forest harvesting, etc.

•

List and describe the greenhouse gas emissions and removals resulting from ancillary practices related toproject operation and management—e. g. land preparation, nursery management, planting, thinning,
logging—and describe these practices.

•

Evaluate and report the emissions and removals of project-related greenhouse gases (CO2, CH4, and N2O).
BOX 4.3.1
AFFORESTATION OR REFORESTATION PROJECTSTree planting on non-forested sites generally increases carbon stocks. These tree-planting projects
could include planting with commercial timber species, planting with non-commercial native
species, planting with multipurpose species (e.g., fruit trees, shade trees for coffee), or a
combination of these species groups. Tree planting may also change emissions of greenhousegases, in particular CO2, CH4 and N2O.
The list below contains factors that may be relevant for measuring and monitoring in addition tochanges in carbon stocks in pools defined by the Marrakesh Accords and decisions of the COP:

• Changes in emissions of greenhouse gases by burning of fossil fuels or biomass resulting fromsite preparation, monitoring activities, tree harvesting, and wood transportation.

• Changes in nitrous oxide emissions caused by nitrogen fertilization practices.
• Changes in nitrous oxide emissions from planting of leguminous trees.
• Changes in methane oxidation due to alteration of groundwater table level (particularly in highorganic soil types), tree planting and soil management.

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BOX 4.3.2
CROPLAND MANAGEMENT PROJECTS:CONVERSION FROM CONVENTIONAL TO ZERO TILLAGE IN AGRICULTURE

Switching from conventional to reduced or zero tillage may cause modifications in soil physical,
chemical and biological properties, as well as in water regimes, nutrient dynamics, fossil fuel use,
and other factors related to the greenhouse gas balance of the system. The list below contains
factors that may be taken into consideration for measuring and monitoring, in addition to changes
in the soil organic carbon pool:• Changes in nitrous oxide and methane emissions from soil.
• Changes in carbon dioxide emissions by transportation of agro-chemicals used in addition to
those in the baseline case.

• Changes in carbon dioxide emissions by burning of fossil fuels in farm equipment.

BOX 4.3.3
FOREST MANAGEMENT PROJECTS: REDUCED IMPACT LOGGING

Some logging practices in forests can cause damage to both vegetation and soils that seriously
impair regeneration. If adopted as part of sustainable forest management, reduced impact logging
is a technique that aims at minimizing these negative impacts, thus reducing carbon dioxide
emissions and improving the carbon removal capacity of regrowth. The list below contains factorsthat may be taken into consideration for measuring and monitoring in addition to changes incarbon stocks in relevant pools, particularly dead wood and soil organic carbon pools:• Changes in carbon dioxide emissions from burning of fossil fuels due to improved harvesting
and logging logistics.

• Changes in nitrous oxide and methane emissions from soil.

BOX 4.3.4
FOREST IMPROVEMENT PROJECTS:
ENRICHMENT PLANTING ON LOGGED-OVER FOREST OR SECONDARY GROWTH FOREST

Certain forest harvesting practices, such as selective logging, may cause poor residual tree growth.
Enrichment planting with high-growth, commercially-valuable, or multipurpose species usually
increases carbon stocks. The list below contains factors that may be taken into consideration formeasuring and monitoring in addition to changes in carbon stocks in relevant carbon pools:• Changes in nitrous oxide emissions from soils due to nitrogen inputs (fertilizers or use ofleguminous trees).

• Changes in carbon dioxide emissions by burning of fossil fuels for site preparation, logging andwood transportation, in addition to those in the baseline case.

• Changes in methane oxidation caused by changes in vegetation and soil management.

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4.3.3

Measuring, monitoring, and estimating changes incarbon stocks and non-CO2 greenhouse gas
emissions 65

A key aspect of implementing LULUCF projects for mitigating greenhouse gas emissions is the accurate andprecise estimation of greenhouse gas emissions and removals that are directly attributable to project activities.
Techniques and methods for measuring, monitoring, and estimating terrestrial carbon pools that are based oncommonly accepted principles of forest inventory, soil sampling, and ecological surveys are well established andapplicable to LULUCF projects (Paivinen et al., 1994; Pinard and Putz, 1997; MacDicken, 1997; Post et al.,
1999; Brown et al., 2000a, 2000b; Schlegel et al., 2001; Brown, 2002; Segura and Kanninen, 2002). These
techniques and methods will be elaborated further in this section.
Methods for measuring and estimating non-CO2 greenhouse gas emissions and removals are less well developed.
However, projects could include practices that affect non-CO2 greenhouse gases. Such practices include fertilizer
application to enhance tree growth (possible N2O emissions), wetland restoration (possible increase in CH4emissions), use of nitrogen-fixing plants (possible increase in N2O emissions) and biomass burning during site
preparation (possible change in N2O and CH4 emissions). Section 4.3.3.6 gives further advice on measuring,monitoring, and estimating emissions of non-CO2 greenhouse gases for LULUCF projects.
Although the methods described here are appropriate for most situations at present, scientists are constantly
developing new, and often more cost-effective, methods, and it is recommended to maintain awareness of the
progress in this area. For example, remote sensing technology is a fast developing field and new sensors arebeing tested and launched ( e.g., higher resolution sensors, radar systems) that could prove to be useful forplanning, stratifying, and measuring and monitoring projects more cost-effectively. Furthermore, costs could be
defrayed if measuring and monitoring carbon was combined with multipurpose resource inventories (Lund 1998).
Selective or partial accounting systems of the pools may be appropriate for projects as long as all pools for whichemissions are likely to increase as a result of the project (loss of carbon or emission of non-CO2 greenhousegases) are included (Brown et al., 2000b). However, for Article 12, the decision regarding the application of
selective accounting of the pools is still under discussion by SBSTA. Possible criteria affecting the selection ofcarbon-accumulating pools to measure and monitor include the following: magnitude of the pool and its rate ofchange; availability of appropriate methods; cost to measure; attainable accuracy and precision (cf. Section
4.3.3.3).
There is a trade-off between the desired precision level of carbon-stock estimates and cost that is related to thespatial variability of the carbon-stock changes within the project boundary. The more spatially variable thecarbon stocks in a project, the more sampling plots are needed to attain a given precision at the same confidence
level. This may result, in principle, in cost implications to implement the measuring and monitoring plan.
Stratification of the project lands into a reasonable number of relatively homogeneous units can reduce thenumber of plots needed for measuring, monitoring, and estimating. In general, the costs will increase with: thenumber of pools that need to be monitored; frequency of monitoring; precision level that is targeted; and thecomplexity of monitoring methods. The frequency of monitoring that is needed to detect change is related to therate and magnitude of change: the smaller the expected change, the greater the potential that frequent monitoring
will not detect a significant change. That is, frequency of monitoring should be determined by the magnitude of
expected change—more frequent monitoring is applicable if the expected magnitude of change is large.
It is also necessary to monitor the overall performance of the project site to demonstrate that the project has
accomplished what was originally proposed (e.g., that the project has achieved the targeted total planted area.)
Measuring carbon at sampling plots only will not accomplish this, and additional steps are needed to monitor theoverall performance of the project area.
Practical steps for designing and implementing a carbon measuring and monitoring plan are provided below,
with multiple methods for various carbon pools. All methods provided are a combination of default data, fieldmeasurements, and models. In other words, the methods described here are multi-tier approaches.

65

According to paragraph 53 in the Annex to the draft Decision -/CMP.1 (Article 12), project participants of Article 12
project activities are required to include the monitoring plan that provides for the collection and archiving of all relevant
data necessary for estimating or measuring anthropogenic emissions by sources or removals by sinks of greenhouse gases
occurring within the project boundary, cf. document FCCC/CP/2001/13/Add.2, p.38.

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The recommended practical steps for designing and implementing a plan to measure, monitor, and estimate
carbon-stock changes and non-CO2 greenhouse gas emissions are66:•

Develop the baseline.

•

Stratify the project area.

•

Identify the relevant carbon pools and non-CO2 greenhouse gases (this applies presently for Article 6 only;
pools to be included in Article 12 are presently being discussed by the SBSTA).

•

Design the sampling framework.

•

Identify the methods (field and models) for monitoring carbon pools and non-CO2 greenhouse gases.

•

Develop the monitoring plan, including the quality assurance/quality control plan.

The details on each one of these steps are described next.

4.3.3.1

B ASELINE

The baseline for an Article 6 project is the scenario that reasonably represents the anthropogenic emissions bysources and anthropogenic removals by sinks of greenhouse gases that would occur in the absence of the
proposed project. This implies the need to assess potential greenhouse gas emissions and removals in a mannerconsistent with those associated with the project. For Article 12, issues related to the definition, which pools,
gases, and activities the baseline shall include, how the baseline will be established, and choices of a baseline
methodology are presently under consideration by SBSTA.
Changes in the carbon stocks in the relevant carbon pools and the non-CO2 greenhouse gas emissions associatedwith the project need to be measured and monitored and then compared to those of the project’s baseline. There
are two aspects that have to be considered:•

The relevant carbon pools and non-CO2 greenhouse gas emissions prior to the start of project activity need
to be estimated. This estimation should preferably be based on measurements made on the same site where
the project will be established. It is possible to use alternative ways for estimating carbon stocks and nonCO2 greenhouse gas emissions, including for example, measurements on sites that are considered toreproduce, as far as possible, the initial condition of the project site (i.e., sites with similar soil type,vegetation cover and land-use history). Another possibility consists of using simulation models that havebeen calibrated for local conditions.
A projection67 of the carbon stocks in the relevant carbon pools and non-CO2 greenhouse gas emissions in
the project area has to be elaborated to estimate their trajectory without the project activity. The projectionof the carbon stocks and non-CO2 greenhouse gas emissions in the project area can be developed throughthe use of either, or both, of the following:•− Peer-reviewed simulation models (e.g., CO2fix —Masera et al., 2003; CENTURY—Parton et al., 1987;
or a locally developed model). Such models project the changes in carbon stocks of those components tobe measured in the project case in each land-use category over time, and in some cases, project non-CO2
greenhouse gas emissions too. It is recommended that these models be used to simulate changes in theselected carbon stocks and non-CO2 greenhouse gas emissions without the project activity at the start ofthe project.
− Control areas where the selected carbon pools and non-CO2 greenhouse gases are measured and monitored
over time. Data from the control areas can also be used in combination with the models in the previous
step to improve the simulation results.

66

For Article 12, it is recognized that leakage is an additional element in the monitoring plan; however, it has not beenaddressed here due to the ongoing work by SBSTA. For Article 6, leakage outside the project boundary is less of an issue
because it should be accounted for in national greenhouse gas inventories (Brown et al., 2000b).

67

The projection may require consideration of socio-economic and other factors that go well beyond the scope of inventory
guidance as set out in Appendix B to the draft decision -/CMP.1 (Article 6) (cf. document FCCC/CP/2001/13/Add.2, p.18),
and (for non-LULUCF projects) in section G of the draft decision -/CMP.1 (Article 12) dealing with the CDM (cf.
document FCCC/CP/2001/13/Add.2, pp.36-37). Provisions for LULUCF baseline projections are expected to be agreed
upon at COP10.

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4.3.3.2

S TRATIFICATION OF THE PROJECT AREA 68

At the start of a project, it is good practice to collect basic background information and data about the importantbio-physical, and socio-economic characteristics of the project area. The information and data include, e.g.,:land-use history; maps of soil, vegetation, and topography; and land ownership. It is good practice that the land
proposed for the project be geo-referenced. A geographic information system (GIS) would be useful for
integrating the data from different sources, which can then be used to identify and stratify the project area into
more or less homogeneous units.
It is good practice to stratify the project area (population of interest) into sub-populations or strata that form
relatively homogenous units, if the project is not homogenous. Stratification can be done prior to implementing
the measuring and monitoring plan (pre-stratification) or after (post-stratification) (see also Section 5.3.3). Poststratification defines the strata using auxiliary data after the field measurements have been made.
Stratification of the project area can increase the accuracy and precision of the measuring and monitoring in a
cost-effective manner. The size and spatial distribution of a project does not influence this step – one large
contiguous block of land or many small parcels are considered the population of interest and are stratified in thesame manner. In general, stratification decreases the costs of measuring and monitoring because it is expected todiminish the sampling effort necessary to achieve a given level of confidence caused by smaller variance in eachstratum than in the project area itself. The stratification should be carried out using criteria that are directly
related to the variables to be measured and monitored, e.g., the change in carbon stocks in trees for afforestation,or soil for cropland management.
For pre-stratification of an afforestation/reforestation project, the strata may be defined on the basis of one ormore variables such as the tree species to be planted (if several), age class (as generated by delay in practicalplanting schedules), initial vegetation (e.g., completely cleared versus cleared with patches or scattered trees),
and/or site factors (soil type, elevation, and slope etc.). For some afforestation/reforestation projects, the projectsite may appear to be homogeneous in all these and any other characteristics. However, it is possible that after
the first monitoring event, the change in carbon stocks is highly variable and that on further analysis it is foundthat the measurements can be grouped into similar classes—in other words can be post-stratified.
There is a trade-off between the number of strata and sampling intensity. The goal is to balance the number ofstrata identified against the total number of plots needed to adequately sample each stratum. There is no hard andfast rule, and project developers need to use their expert judgement in deciding on the number of strata to include.

4.3.3.3

S ELECTION OF CARBON POOLS AND NON -CO 2
GREENHOUSE GASES 69

The major carbon pools in LULUCF projects are: aboveground biomass, belowground biomass, litter, deadwood, and soil organic carbon, which in turn, can be further subdivided (Table 4.3.1; see also Chapter 3 andGlossary). The major non-CO2 greenhouse gases in LULUCF projects are N2O and CH4. For different types of
LULUCF projects, a decision matrix that illustrates the possible choices of carbon pools for measuring andmonitoring is shown in Table 4.3.1.
The selection of which pools to measure and monitor under agreed rules70 is likely to depend on several factors,
including expected rate of change, magnitude and direction of the change, availability and accuracy of methods
to quantify change, and cost to measure. Provisions could include that all pools that are expected to decrease as aresult of project activities must be measured and monitored, or that all pools that are expected to increase neednot be measured and monitored. In practical terms, the latter provision could be the case if monitoring costs arehigh relative to the expected increase in carbon stocks—which might be the case, for example, with understorey
herbaceous vegetation in an afforestation/reforestation project.

68

See Chapter 5, Section 5.3.3.1 for further discussion on stratification.

69

In paragraph 21 of the Annex to the draft decision -/CMP.1 (Land use, land-use change and forestry) it is stated: “A Party
may choose not to account for a given pool in a commitment period, if transparent and verifiable information is provided
that the pool is not a source.” (cf. document FCCC/CP/2001/13/Add.1, p. 62). The discussion in this section refers to
Article 6, and may also be applicable to Article 12, depending upon the decisions to be made by SBSTA.

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For Article 6 projects, see paragraph 21 of the Annex in the draft decision -/CMP.1 (Land use, land-use change andforestry), cf. document FCCC/CP/2001/13/Add.1, p. 62; rules for Article 12 projects are scheduled for adoption at COP9.

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TABLE 4.3.1
A DECISION MATRIX TO ILLUSTRATE POSSIBLE SELECTION CRITERIA OF POOLS TO MEASURE AND MONITOR IN LULUCF
PROJECTS (FOR EXPLANATION OF LETTERS AND NUMBERS IN THIS TABLE, SEE IMMEDIATELY BELOW THE TABLE)
Carbon pools
Dead Organic
MatterLiving biomass

Project type

Soil
Organic
CarbonAboveground:
trees

Aboveground:non-tree

Belowground

Litter

Dead
wood

Y1

M2

Y3

M4

M4

M5

Afforestation/reforestation
Forest management

Y1

M2

Y3

M4

Y4

M5

Cropland management

M1

M2

M3

M4

N

Y5

Grazing land management

M1

Y2

M3

M4

N

Y5

Revegetation

M1

Y2

M3

M4

M4

M5

Letters in the above table refer to the need for measuring and monitoring the carbon pools:Y=

Yes – the change in this pool is likely to be large and should be measured.

N=

No – the change is likely to be small to none and thus it is not necessary to measure this pool.

M=

Maybe – the change in this pool may need to be measured depending upon the forest type and/or management intensity of the
project.

Numbers in the above table refer to different methods for measuring and monitoring the carbon pools:
1=

Use the method for aboveground biomass of trees in Section 4.3.3.5.1.

2=

Use the method for aboveground biomass of non-trees vegetation in Section 4.3.3.5.1.

3=

Use the method for belowground biomass in Section 4.3.3.5.2.

4=

Use the method for litter and dead wood in Section 4.3.3.5.3.

5=

Use the method for soils in Section 4.3.3.5.4.

Source: modified from Brown et al., 2000b.

Changes in emissions of non-CO2 greenhouse gases may result from all project activities under Article 6; thesources of the non-CO2 greenhouse gases are biomass burning, fossil fuel combustion, and soil (see Boxes 4.3.1–
4.3.4). Furthermore, changes in grazing land management to enhance soil carbon, for example, can also change
emissions of non-CO2 greenhouse gases due to effects on livestock production (Sampson and Scholes, 2000).
Under Article 12, afforestation/reforestation activities may also change emissions of non-CO2 greenhouse gases
through practices such as those given in Box 4.3.1 (see also Section 4.3.3.6).

4.3.3.4

S AMPLING DESIGN

A discussion of general issues related to sampling design is given in detail in Section 5.3. For LULUCF projects,
permanent or temporary sampling plots could be used for sampling over time to estimate changes in the relevant
carbon pools and non-CO2 greenhouse gases. Both methods have advantages and disadvantages. Permanent
sample plots are generally regarded as statistically more efficient in estimating changes in forest carbon stocks
than temporary plots because typically there is high covariance between observations at successive sampling
events (Avery and Burkhart, 1983). Disadvantages of permanent plots are that their location could be known andthey could be treated differently (such as by fertilizer, irrigation, etc. to enhance the carbon stocks), or that they
could be destroyed or lost by disturbances over the project interval. The advantages of temporary plots is thatthey may be established more cost-effectively to estimate the carbon stocks of the relevant pools, their location
changes after each sampling interval, and they would not be lost by disturbances. The main disadvantage of
temporary plots is related to the precision in estimating the change in forest carbon stocks. Because individual
trees are not tracked (see Clark et al., 2001, for further discussion), the covariance term is non-existent and it will
be more difficult to attain the targeted precision level without measuring more plots. Thus any cost advantage
gained by using temporary over permanent forest plots may be lost by the need to install more temporary plots toachieve the targeted precision. For non-forestry based projects, where changes in carbon stocks of only soil or
herbaceous vegetation are measured and monitored, temporary plots could be used because the statisticaladvantage of permanent plots (high covariance) is lost (see next Section 4.3.3.4.1).

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4.3.3.4.1

THE NUMBER AND TYPE OF SAMPLE PLOTS

It is good practice to define the sample size for measuring and monitoring in each stratum on the basis of theestimated variance of the carbon stock in each stratum and the ratio of the area of the stratum to the total project
area. Typically, to estimate the number of plots needed for measuring and monitoring, at a given confidence
level, it is necessary to first obtain an estimate of the variance of the variable (for example, carbon stock of themain pools – trees in an afforestation/reforestation project or soil in a cropland management project) in eachstratum. This can be accomplished either from existing data of the type of project to be implemented (e.g., aforest or soil inventory in an area representative of the proposed project) or by making measurements on an
existing area representing the proposed project. For example, if the project is to afforest/reforest agriculturallands and the project will last for 20 years, then a measure of the carbon stocks in the trees of about 10-15 plots(for plot dimensions see Section 4.3.3.4.2) of an existing 20 year forest would possibly suffice. If the project area
comprises more than one stratum, then this procedure needs to be repeated for each of them. Such measurements
will provide estimates of the variance in each stratum.
The sample size (number of sample plots) needed can be calculated when the estimated variance in each stratum,area of each stratum, targeted precision level (based on sampling error only), and estimation error are known (seeSection 5.3.6.2; Freese, 1962; MacDicken, 1997; Schlegel et al., 2001; Segura and Kanninen, 2002). These sources
provide methods and equations to compute the number of sample plots within each stratum, taking into account thevariance and area of each stratum and the targeted precision at a given confidence level. Figure 4.3.1 illustrates therelationship between targeted precision level and number of sample plots (taking into consideration the varianceand area of each of the six strata present in this forest) and shows that to attain increasing levels of precision(expressed as plus/minus a given percentage of the mean with 95% confidence), an increasingly high number ofplots is needed. It is also recommended that an additional 10% of the calculated number of plots be installed toaccount for unexpected events that may make it impossible to re-locate all plots in the future.
Figure 4.3.1

An example of the relationship between the number of plots and theprecision level (+/- % of total carbon stock in living and dead biomass, with
95% confidence) for all strata combined, for a complex tropical forest in
Bolivia (the Noel Kempff Pilot Project); the project encompassed six strata
and 625 plots were actually installed (from data in Boscolo et al., 2000, andBrown et al., 2000a).

Number of plots

500
452

400
300
200

81

100

14

0
5

10

20

4
30

Precision Level +/- %Experience has shown that in the LULUCF sector, carbon stocks and the change in carbon stocks in complex
forests can be estimated to precision levels of within ±10% of the mean, with 95% confidence, at a modest cost
(Brown, 2002; http://www.winrock.org/REEP/NoelKmpff_rpt.html). National and regional forest inventories
that are used to assess growing stock of timber typically target precision levels of less than 10% of the mean (see
IPCC, 2000b).
The procedure described in the previous paragraph provides an estimate of the number of plots for various levelsof precision based only on sampling error. There are other sources of error when estimating carbon stocks, forexample, the errors from the use of allometric equations (model error) and from field and laboratory
measurements (measurement error). In general, the sampling error is the largest source of error and can account

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for up to 80% of the total error (Phillips et al., 2000). See Section 5.3.6.3 for more details on how to account forother sources of error.
When permanent sample plots are used to monitor changes in carbon stocks over time, it is good practice tolocate them systematically ( e.g., a uniform gid) with a random start, especially if stratified sampling is being
used. The goal is to avoid subjective choice of plot locations (plot centres, plot reference points, movement ofplot centres to more “convenient” positions). In the field, this is usually accomplished with the help of a GPS.
Permanent sample plots may also be located in control areas (i.e., in areas adjacent to the project area that arebiophysically similar to the project area) if it is expected that the reference case is likely to change over time
( e.g., abandoned agriculture land).
In the case of projects where planting of trees may occur over several years, it is good practice to measure andmonitor carbon stocks and non-CO2 greenhouse gases in age-class cohorts (a group of trees of similar age),treating each cohort class as a population. It is recommended to combine no more than two to three age classes
into a one-cohort class.
The carbon stocks and non-CO2 greenhouse gases can be measured in reference plots if needed. If this is done, anumber of plots similar to the number used in the project case will be required to maintain the targeted level ofprecision when comparing the with-project case to the baseline.

Estimating changes i n carbon stocks over time f rom plot data
A key component of a project is to measure, monitor, and estimate the quantity of carbon accruing on the project
area over the length of the project and over separate time periods. This is accomplished by estimating thechanges in carbon stocks over time. Projections of the amount of carbon accumulating can be made bycombining field measurements and models. However, if models are used, it is recommended to validate themwith field measurements and to recalibrate as necessary.
For monitoring forests using permanent plots, it is good practice to measure the growth of individual trees at
each time interval, keeping track of growth of survivors, mortality, and growth of new trees (ingrowth). Changes
in carbon stocks for each tree are then estimated and summed per plot. Changes in carbon stocks in dead organic
matter are also measured per plot and added to those for trees. Statistical analyses are then performed on net
carbon accumulation in biomass per plot. As discussed above, because these plots undergo repeatedmeasurements on basically the same components, there will be a high covariance term in the statistical analysis
and the uncertainty around the estimates of change should be within the level targeted by the sampling design.
For soil or non-forest vegetation (e.g., croplands or grazing lands), in contrast to the procedure indicated forforests, the same soil or plant sample cannot be monitored over time. Instead, on each sample collection, the unit
sampled (soil or plant sample) is destroyed for the analysis of its relevant components. Also, as variability
among samples can be high even at small spatial scales, the statistical concept of paired samples, even ifcollected only centimetres apart, cannot be reliably employed. Thus the changes in the mean carbon content
between two temporally-separated sample pools are best quantified by comparing means, via, for instance, theReliable Minimum Estimate (RME) approach (Dawkins, 1957), or by directly calculating the difference between
the means and associated confidence limits (Sokal and Rohlf, 1995). (The following discussion uses soil as an
example, but it could easily apply for vegetation on cropland and grazing land management projects).
The objective is to estimate the number of plots needed to establish the minimum change in the mean carbon stocks,
with 95% confidence, that has taken place from one monitoring event to the next, rather than to estimate thenumber of plots needed to establish that the two means are significantly different from each other. For the RME
approach (Figure 4.3.2), the monitoring results from plots are pooled to derive a mean for the sample population atTime 1 and Time 2. Change in soil carbon is estimated by subtracting the maximum estimate of the population mean
at Time 1 (mean at Time 1 plus half the 95% confidence interval at Time 1) from the minimum mean estimate at Time2 (mean at Time 2 minus half the 95% confidence interval at Time 2). The resulting difference represents, with 95%
confidence, the minimum reliable change in mean soil carbon from Time 1 to Time 2 (Figure 4.3.2).

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Figure 4.3.2

Illustration of the relationship between the magnitude of the Reliable
Minimum Estimate (RME) between Time 1 and Time 2 sampling periods andthe 95% confidence interval (the solid and dashed bars) around the mean soil
carbon content (shaded circle). The confidence interval is a function of thestandard error, defined as the ratio between the standard deviation and thesquare root of the sample size. The larger the sample size the smaller the
standard error and thus the smaller the 95% confidence interval. Hence,
RME1 is smaller than RME2 as a result of fewer samples.

Time 1RME1

RME2

Difference
between the twomeansTime 2

Both sampling intensity (i.e., number of soil samples) and frequency of sampling must be taken into
consideration when attempting to estimate changes in soil carbon over time. The minimum estimated change insoil carbon stocks between two means at a given level of confidence can be expressed as a percentage of theabsolute difference between the means. A targeted estimate ( e.g., 80% of the absolute difference between themeans), or alternatively, a targeted magnitude of change in soil carbon (not to exceed the absolute difference
between the means), can be achieved by adjusting sampling intensity, sampling frequency, or a combination ofboth (Figure 4.3.3).
In general, increasing the number of soil samples reduces the standard error around means separated in time, andbetter distinguishes the change that takes place (Figure 4.3.3). As high levels of variability in carbon among
sample units are typical of soils (coefficient of variation of ~ 30%), high sampling intensity is generally needed
to discern change. The resolution of change detection also depends on the magnitude of the change itself, and as
this is time-dependent, it is appropriate to consider frequency of sampling. Increasing the time interval between
sampling events is expected to increase the magnitude of the change that takes place, assuming the variancearound the means stays the same. Thus, the percentage and magnitude of the absolute change estimated also
increases (Figure 4.3.3). This is an important consideration, in that small changes expected with short samplingintervals may be undetectable, even with high sampling intensity. By assuming a rate of soil carbon
accumulation, sampling intervals can be designed to achieve a targeted estimate of the minimum change in soil
carbon. It is good practice to estimate the number of plots and sampling interval needed based on the variability
in carbon stocks and an assumed rate of carbon accumulation. For the details on how to estimate sample size forsoil sampling, refer to the RME method as described in MacDicken (1997), or by adapting the Minimum
Detectable Difference calculation (Zar, 1996) to solve for sample size for a targeted difference in means.

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Figure 4.3.3 An example of how the percent absolute change in mean soil carbon (with 95%
confidence) for an afforestation project varies in relation to the sampling intervaland sample size (n), assuming constant coefficient of variation (30%), constantannual rate of soil carbon accumulation of 0.5 tonnes C per hectare and year, andinitial soil carbon of 50 tonnes C per hectare (generated from unpublished data).

% absolute change in means

60%

n=100
n=90

40%

n=80
n=70
n=60
n=50

20%

0%
0

5

10

15

20

25

30

Sampling interval (years since forest initiation)

4.3.3.4.2 PLOT SHAPE AND SI ZE
The type of plots used in vegetation and forest inventories include: fixed area plots that can be nested orclustered, variable radius or point sampling plots (e.g., prism or relascope plots), or transects. It is recommended
to use permanent nested sample plots containing smaller sub-units of various shapes and sizes, depending on thevariables to be measured. For instance, in an afforestation/reforestation project, saplings could be measured in asmall circular plot; trees between 2.5 to 50 cm diameter at breast height (dbh) could be measured in a medium
circular plot; trees above 50 cm dbh could be measured in a larger circular plot; and understorey and fine litter
could be measured in four small square or circular plots located in each quadrant of the sample plot. The radiusand diameter limits for each circular plot would be a function of local conditions and expected size of the trees
through time.
The size of the sample plot is a trade-off between accuracy, precision, and time (cost) of measurement. The size ofthe plot is also related to the number of trees, their diameter, and variance of the carbon stock among plots. The plot
should be large enough to contain an adequate number of trees per plot to be measured. In general, it isrecommended to use a single plot varying between 100 m2 (for densely planted stand of 1,000 trees/ha or more) and
600 m2 (for sparsely planted stand of multi-purpose trees) in area for even-sized stands. For projects where it isexpected that the forest will be uneven-sized (e.g., through a combination of planting and natural regeneration), it isrecommended to use nested plots or even clusters of nested plots depending upon the forest characteristics. Whether
one uses circular or rectangular plots depends on local conditions. There are cases (e.g., rows of trees to serve as
windbreaks or sand dune stabilisation) where a number of transects may be the most appropriate sampling method
to use; and, the number of transects needed should be based on the variance, as described above.

4.3.3.5

F IELD MEASUREMENTS AND DATA ANALYSIS FOR
ESTIMATI NG CARB ON STOCKS

It is good practice to use standard techniques for field measurements of vegetation and soil. Details of suchtechniques are described in detail in MacDicken (1997) and Schlegel et al. (2001), among others. Any goodpractice method that requires ground-based field measurements should have a formal quality control plan (see

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Section 4.3.4). This section focuses on what constitutes good practices in conducting these measurements andanalysing them for carbon stock estimation.
For field measurements of carbon pools, the recommended sample unit is a permanent sample plot of nested
fixed radius subplots (see above). The project area should be stratified as described in Section 4.3.3.2, and thenumber of sample plots to be established for each stratum should be calculated.
All the biomass data obtained in field measurements must be expressed on an oven-dry basis, and converted tocarbon by multiplying the oven-dry matter values by the carbon fraction of dry biomass. This value varies
slightly depending on species and biomass component in question (trunk, branches, roots, understorey vegetationetc.) (see Chapter 3, Section 3.2). However, the value of 0.50 for the conversion is the approximation indicatedin the IPCC Guidelines, and should be applied if no local values are available.

4.3.3.5.1 ABOVEGROUND BIOMASS
Trees
There are two approaches for estimating aboveground biomass in trees: a direct approach using allometricequations, and an indirect approach using biomass expansion factors. For LULUCF projects, it is good practicewhen using permanent sample plots to estimate the carbon stock of trees through the direct approach. The
indirect approach is often used with temporary plots, a common practice in forest inventories. The details of both
approaches are presented next.

Direct approach
Step 1: The diameter at breast height (dbh; typically measured at 1.3 m above ground) of all the trees in thepermanent sample plots above a minimum diameter is measured. The minimum dbh is often 5 cm, but can varydepending on the expected size of trees —for arid environments where trees grow slowly, the minimum dbh may
be as small as 2.5 cm, whereas for humid environments where trees grow rapidly it could be up to 10 cm.
For afforestation/reforestation projects, small trees (e.g., saplings with dbh less than the minimum, but yet taller
than breast height) will likely dominate during the early stages of establishment. These can be readily included inthis approach by counting their number in a subplot.
Step 2: Biomass and carbon stock are estimated using appropriate allometric equations applied to the tree
measurements in Step 1. There are many multi-species allometric equations for native temperate and tropicalforest species (e.g., Araújo et al., 1999; Brown, 1997; Schroeder et al., 1997; Pérez and Kanninen, 2002 and2003; Tables 4.A.1 to 4.A.3 of Annex 4A.2). These equations are developed using variables, singly or in
combination, such as dbh, wood density, and total height as independent variables and aboveground biomass oftrees as the dependent variable. Further discussion regarding the development of these equations and their use
can be found in Brown (1997) and Parresol (1999).
The minimum diameter tree included in most of the allometric equations (Tables 4.A.1–4.A.3 in Annex 4A.2) issmaller than the recommended minimum dbh given in Step 1 above, thus the biomass of these small trees can be
estimated from the same allometric regressions. A typical approach is to estimate the common dbh of thesaplings, usually the mid-point between the smallest size observed and the minimum diameter, estimate thebiomass for this diameter sapling, and multiply this estimated biomass by the number of saplings counted. If theallometric equation does not include trees of the small size classes, an alternative approach to estimating theaboveground biomass is to grow and harvest about 10-15 such saplings planted in a site nearby the project area.
Step 3: When allometric equations developed from a biome-wide database, such as those in Annex 4A.2, Tables
4.A.1 and 4.A.2, are used, it is good practice to verify the equation by destructively harvesting, within the
project area but outside the sample plots, a few trees of different sizes and estimate their biomass and thencompare against a selected equation. If the biomass estimated from the harvested trees is within about +/- 10% ofthat predicted by the equation, then it can be assumed that the selected equation is suitable for the project. If thisis not the case, it is recommended to develop local allometric equations for the project use. For this, a sample oftrees, representing different size classes, is destructively harvested, and its total aboveground biomass isdetermined. The number of trees to be destructively harvested and measured depends on the range of size classes
and number of species—the greater the heterogeneity the more trees are required. If resources permit, the wooddensity (specific gravity) and the carbon content can be determined in the laboratory. Finally, allometricequations are constructed relating the biomass with values from easily measured variables, such as the dbh andtotal height. Further discussion of the development of local allometric equations is presented in Brown (1997),
MacDicken (1997), Schlegel et al. (2001) and Segura and Kanninen (2002).
Table 4.A.1 of Annex 4A.2 presents general allometric equations for estimating the aboveground biomass (kgdm/tree) for different forest types using the diameter at breast height as the independent variable. Theseequations are based on a multi-species database that contains biomass data for more than 450 individuals.

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In many tropical regions, palm trees of various species are common, both in restored forests and in abandoned
pastures. Table 4.A.2 (Annex 4A.2) presents some allometric equations for estimating the aboveground biomassof several common palm species in tropical America. Biomass of palms does not relate well to their dbh; insteadheight is used alone as the independent variable.
Table 4.A.3 (Annex 4A.2) presents examples of allometric equations for individual species commonly used in
the tropics. However, as discussed above, any project would need to assess the applicability of particularallometric equations for local conditions. This will be particularly important if species are grown in mixtures. If
not, it is good practice either to validate existing equations with data collected at the project site or to develop
local allometric equations based on field measurements.

Indirect approach
An alternative approach for estimating aboveground biomass of forests, particularly commercial plantations, is tobase it on the volume of the commercial component71 of the tree for which there are often many equations or
methods available for estimating this component. The indirect method is based on factors developed at the standlevel, for closed canopy forests, and cannot be used for estimating biomass of individual trees. There are two
ways of obtaining estimates of the commercial volume in this approach:
Method 1:
Step 1 : As with the direct approach, the diameter of all trees above some minimum diameter is measured.
Step 2 : The volume of the commercial component of each tree is then estimated based on locally derived
methods or equations. The volume is then summed for all trees and expressed as volume per unit area (e.g.,
m3/ha).
Method 2:
Steps 1 and 2 combined: There are field instruments ( e.g., relascope) that measure volume directly. Using this
instrument or other appropriate means, the volume of each tree in the plots is measured. The sum for all trees isthen expressed as volume per unit area.
Once the volume of the commercial component is estimated, it then needs to be converted to biomass and thenestimates of the other tree components, such as branches, twigs, and leaves need to be added. This method isexpressed in Equation 4.3.1 (Brown, 1997) (see also Section 3.2.1.1 on use of BEF and Annex 3A.1, Table
3A.1.10):EQUATION 4.3.1
ESTIMATION OF ABOVEGROUND BIOMASS OF FORESTS
Aboveground biomass = Commercial tree volume ● D ● BEF
Where:
Aboveground biomass, tonnes of dry matter ha-1
Commercial tree volume, m3 ha-1
D = volume-weighted average wood density, tonnes of oven-dry matter per m3 of green volume
BEF = biomass expansion factor (ratio of aboveground oven-dry biomass of trees to oven-dry biomass ofcommercial volume), dimensionless.
Wood density values of most commercially important species are generally available (see, for example, Brown,
1997; Fearnside, 1997; and Annex 3A.1 Table 3A.1.9) or relatively straightforward to measure. Most publisheddensity values are for mature individuals; if wood densities are not available for young individuals, it isrecommended that measurements be made. The BEF is significantly related to the commercial biomass for mostforest types (in these examples, volume is over-bark for all trees with a dbh of 10 cm and above), generally starting
high (>4.0) at low volumes, then declining at an exponential rate to a constant low value (about 1.3-1.8) at highvolumes. Thus, using one value for the BEF for all values of standing volume is incorrect. It is recommended toeither develop a local regression equation for this relationship or use those in Annex 3A.1 Table 3A.1.10 or frompublished sources ( e.g., Brown, 1997; Brown and Schroeder, 1999; Fang et al., 2001). Additional discussion on the
topic of converting commercial volume to biomass is provided in Section 3.2.1.1 of this report.
71

It is important to state whether the volume is estimated as over or under bark; in case of under-bark volume, the expansion
factor needs to take bark into account.

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If a significant amount of effort is required to develop local BEFs, involving, for instance, harvest of trees, thenit is recommended not to use this approach but rather to use the resources to develop local allometric equations
as described under the direct approach above. The direct approach generally results in more precise biomass
estimates than the indirect approach because the calculations of the former involve only one step (e.g., dbh tobiomass), whereas the indirect approach involves several steps (diameter and height to volume, volume tovolume-based biomass, estimation of BEF based on volume, product of three variables to biomass).

Non-tree veget ation
Non-tree vegetation such as herbaceous plants, grasses, and shrubs can occur as components of a forestry project orof cropland and grazing land management projects. Herbaceous plants in forest understorey can be measured bysimple harvesting techniques of up to four small subplots per permanent or temporary plot. A small frame (either
circular or square), usually encompassing about 0.5 m2 or less, is used to aid this task. The material inside the frame
is cut to ground level, pooled by plot, and weighed. Well-mixed sub-samples from each plot are then oven dried todetermine dry-to-wet matter ratios. These ratios are then used to convert the entire sample to oven-dry matter. For
cropland and grazing land management projects, the same approach can be used in temporary plots because, as
mentioned above, there is no statistical advantage over using permanent plots (Section 4.3.3.4.1).
For shrubs and other large non-tree vegetation it is good practice to measure the biomass by destructive
harvesting techniques. A small sub-plot depending on the size of the vegetation is established and all the shrub
vegetation is harvested and weighed. An alternative approach, if the shrubs are large, is to develop local shrub
allometric equations based on variables such as crown area and height or diameter at base of plant or some otherrelevant variable (e.g., number of stems in multi-stemmed shrubs). The equations would then be based on
regressions of biomass of the shrub versus some logical combination of the independent variables. The
independent variable or variables would then be measured in the sampling plots.

4.3.3.5.2 BELOWGROUND BIOMASS
Trees
Methods for measuring and estimating aboveground biomass are relatively well established. However, thebelowground biomass (roots) is difficult and time-consuming to measure and estimate in most ecosystems, andmethods are generally not standardized (Körner, 1994; Kurz et al., 1996; Cairns et al., 1997; Li et al., 2003). A
review of the literature shows that typical methods include spatially distributed soil cores or pits for fine andmedium roots, and partial ones to complete excavation and/or allometry for coarse roots. Live and dead roots are
generally not distinguished and hence root biomass is generally reported as the total of live and dead.
A comprehensive literature review by Cairns et al. (1997) included more than 160 studies covering native
tropical, temperate, and boreal forests that reported both belowground biomass and aboveground biomass. The
average belowground to aboveground dry biomass ratios based on these studies was 0.26, with a range of 0.18
(lower 25% quartile) to 0.30 (upper 75% quartile). The belowground to aboveground dry biomass ratios did notvary significantly with latitudinal zone (tropical, temperate, boreal), soil texture (fine, medium, coarse), or tree
type (angiosperm, gymnosperm). Further analyses of the data produced a significant regression equation of
belowground biomass density versus aboveground biomass density when all data were pooled. Inclusion of age
or latitudinal belt significantly improved the model (Cairns et al., 1997). Given the lack of standard methods andthe time-consuming nature of monitoring belowground biomass in forests, it is good practice to estimate
belowground biomass from either estimated aboveground biomass based on the equations in Table 4.A.4, Annex
4A.2, or from locally derived data or models.
The data used to develop the belowground biomass equations in Table 4.A.4 were based on native forests, andmay not apply to plantations. Ritson and Sochacki (2003) reported that belowground to aboveground biomass
ratios of plantations of Pinus pinaster varied between 1.5 and 0.25, decreasing with increasing tree size and/or
age. For commercial plantation species, it is likely that research on belowground biomass exists that could be
used. Failing that, it is good practice to use an estimate for belowground biomass by using the average
belowground to aboveground biomass ratios, such as those in Annex 3A.1, Table 3A.1.8.

Non-tree veget ation
In non-forest project types (e.g., cropland and grazing land management), where large changes in the
belowground biomass from non-tree vegetation are expected to occur, the carbon stock in the belowgroundbiomass pool needs to be estimated (Table 4.3.1). For non-tree vegetation, it is not possible to estimate
belowground biomass from aboveground biomass data and therefore, on-site measurements may be required.

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Direct measurement of belowground biomass requires collecting soil samples, usually in the form of cores ofknown diameter and depth, separating the roots from soil, and oven-drying and weighing the roots. It is
recommended to perform the following steps for direct measurement of belowground biomass in the field:•

The sampling design should follow the procedures detailed earlier in Section 4.3.3.4.

•

Because a large proportion of non-tree root biomass is usually present in the upper soil layers, in mostsituations sampling to a depth of 0.3-0.4 m should suffice. In cases where samples are collected at deeper
depths, it is recommended to split the sample into two or more layers, clearly recording the depth of each layer.

•

Separation of roots from soil can be performed by using root washing devices (Cahoon and Morton, 1961;
Smucker et al., 1982) for maximum recovery. If these devices are not available, simpler procedures ( e.g.,
placing soil samples on a sieve and washing roots with high pressure water) may yield recovery of a
relatively large proportion of root biomass.

•

Non-root belowground biomass (e.g., stolons, rhizomes and tubers) should be considered as part of the
belowground biomass pool.

•

Roots should be oven-dried at 70 oC until dry and then weighed. The resulting weight should be divided by
the cross sectional area of the sample core to determine belowground biomass on a per-area basis.

The core-break method has been found to be a rapid method for evaluating root distributions in the field (Böhm,
1979; Bennie et al., 1987). With this technique, cores are removed from different soil depths, broken in half, andthe visible root axes on each cross-sectional surface area are counted and averaged. To convert root counts toestimates of root length density or biomass requires calibration equations for each crop species, soil type, andmanagement practice. Calibration equations should be developed locally and may change with crop development
or soil depth (Drew and Saker, 1980; Bennie et al., 1987; Bland, 1989).

4.3.3.5.3 DEAD O RGANI C MATTER
LitterLitter can be directly sampled using a small frame (either circular or square), usually encompassing an area of
about 0.5 m2, as described above for herbaceous vegetation (four subplots within the sample plot). The frame isplaced in the sample plot and all litter within the frame is collected and weighed. A well-mixed sub-sample iscollected to determine oven dry-to-wet weight ratios to convert the total wet mass to oven-dry mass.
An alternative approach for systems where the litter layer is well-defined and deep (more than 5 cm), is todevelop a local regression equation that relates depth of the litter to the mass per unit area. This can be done bysampling the litter in the frames as mentioned above and at the same time measuring the depth of the litter. At least
10-15 such data points should be collected, ensuring that the full range of the expected litter depth is sampled.

Dead wood
Dead wood, both standing and lying, does not generally correlate well with any index of stand structure (Harmon
et al., 1993). Methods have been developed for measuring biomass of dead wood and have been tested in many
forest types and generally require no more effort than measuring live trees (Brown, 1974; Harmon and Sexton,
1996; Delaney et al., 1998). For dead wood lying on the ground, the general approach is to estimate the volume
of logs by density class (often related to its decomposition state, but not always) and then convert to mass as a
product of volume and density, for each density class. There are two approaches that can be used to estimate thevolume of dead wood present, depending upon the expected quantity present.
Method 1 – when the quantity is expected to be a relatively small proportion of the aboveground biomass (i.e.,
about 10-15%, based on expert judgement): A time-efficient method is the line-intersect method, and it is goodpractice to use at least 100 m length of line, generally divided into two 50 m sections placed at right angles
across the plot centre. The diameters of all pieces of wood that intersect the line are measured and each piece ofdead wood is also classified into one of several density classes. If the intersected log is elliptical in shape theminimum and maximum diameters need to be measured. The volume per hectare is estimated for each density
class as follows (for more details on the derivation of this equation see Brown (1974)):
EQUATION 4.3.2
VOLUME OF LYING DEAD WOOD
Volume (m3/ha) = π2 ● (D12 + D22 +….+ Dn2) / (8 ● L)

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Where:
D1, D2,…, Dn =diameter of each of n pieces intersecting the line, in centimetres (cm). The roundequivalent of an elliptically shaped log is computed as the square root of(Dminimum • Dmaximum) for that log.

L = the length of the line, in metres (m).
An additional multiplier is often introduced to Equation 4.3.2 to correct the bias introduced by the non-horizontal
orientation of the pieces (Brown and Roussopolos, 1974). However, this correction is not required for coarse
dead wood, as this bias decreases with piece diameter. For more details see Harmon and Sexton (1996).
Method 2 – when the quantity is expected to be a relatively large proportion of the aboveground biomass (i.e.,
more than about 15%, based on expert judgement): When the quantity of dead wood lying on the forest floor is
expected to be high and variably distributed, as in slash left behind after logging, it is good practice to do acomplete inventory of the wood in the sampling plots. It is recommended to measure all the dead wood in a
subplot of the sampling plots (see also Harmon and Sexton, 1996, for details on the methods). For a complete
census, the volume of each piece of dead wood lying within the circle is calculated based on the diametermeasurements taken at 1 m intervals along each piece of dead wood in the plot. The volume of each piece is thenestimated as the volume of a truncated cylinder based on the average of the two diameter measurements and the
distance between them (usually 1 m). As with Method 1, each piece of dead wood is also classified into a density
class. The volume is summed for each density class and, using the appropriate factor (based on the area of theplot), expressed on a m3/ha basis for each density class.
Density measurements: Experience shows that three density classes are sufficient—sound, intermediate androtten. An objective and consistent way to distinguish between them is needed. A common practice in the field is
to strike the wood with a “machete”—if the blade bounces off it is sound, if it enters slightly is it intermediate,
and if it causes the wood to fall apart it is rotten (“machete test”). Samples of dead wood in each density class arethen collected to determine their wood density. Mass of dead wood is then the product of volume per density
class (from above equation) and the wood density for that class. Thus a key step in this method is to classify thedead wood into its correct density class and then to adequately sample a sufficient number of logs in each class
to represent the wood densities present. It is good practice to sample at least 10 logs of each different density
class. In forests with palms or early colonizers or hollow logs, it is also good practice to treat these as separate
groups and sample them the same way.
For projects based on few species and where the rate of decomposition of wood is well known for given species
or forest types, models could be locally developed for estimating the density of the dead wood at different stagesof decomposition (Beets et al., 1999). Volume of wood would still need to be estimated based on either Method
1 or 2 above, but the density could be estimated based on the model of decomposition.
Standing dead wood is measured as part of the tree inventory. Standing dead trees should be measured according
to the same criteria as live trees. However, the measurements that are taken and the data that are recorded varyslightly from live trees. For example, if the standing dead tree contains branches and twigs and resembles a live
tree (except for leaves) this would be noted in the field data. From the measurement of its dbh, its biomass can be
estimated using the appropriate allometric equation as for live trees, subtracting out the biomass of leaves (about
2-3% of aboveground biomass). However, a dead tree can contain only small and large branches, or only large
branches, or no branches – these conditions need to be recorded in the field measurements and the total biomass
can be reduced accordingly; in particular if only large branches remain, the biomass estimated from theappropriate allometric equation is reduced by about 20% to account for the absence of smaller branches and twigs.
When a tree has no branches and is just the bole, then its volume can be estimated from measurements of its basal
diameter, height, and an estimate of its top diameter; and its biomass can be calculated with its density class.

4.3.3.5.4 SOI L ORGANIC CARBON
The soil organic carbon pool is estimated from soil samples taken in the sample plots. Soil samples are usuallytaken with a metallic cylinder at different depths or by the excavation method. It is good practice to collect acomposite sample (recommended to collect about two to four such samples per composite) in each plot anddepth. These are then mixed and homogenized to make one composite sample for each depth and plot. To
estimate the soil carbon stock, an additional composite sample needs to be collected for bulk density
measurements at each depth and plot (see also Section 3.2.1.3.1.1 and Section 3.2.1.3.1.2 for further discussion
on soil organic carbon).
In coarse textured, stony soils, sampling bulk density by soil cores is inadequate and will probably overestimate
the bulk density of the fine soil in the horizon (Blake and Hartage, 1986; Page-Dumroese et al., 1999). Instead,the excavation method is recommended, supplemented with an estimate of the percent volume occupied bystones. If significant non-soil areas (e.g., large rocky outcrops) exist in the project site, these should be

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eliminated at the start of the project during stratification; estimates of soil carbon should only be scaled to thearea where soil exists.
The depth to which the soil carbon pool should be measured and monitored may vary according to project type,site conditions, species, and expected depth at which change will take place (see Chapter 3 and other sections in
Chapter 4 for additional details). In most cases, soil organic carbon concentrations are highest in the uppermost
layer of soil and decrease exponentially with depth. However, the relationship of soil organic carbon
concentrations with soil depth can vary as a result of such factors as the depth distribution of roots, transport ofsoil organic carbon within the soil profile, and erosion/deposition. It is good practice to measure the soil carbon
pool to a depth of at least 30 cm. This is the depth where the changes in the soil carbon pool are likely to be fastenough to be detected during the project period. In cases where a project is using deep-rooted plants, it may be
useful to measure and monitor the soil carbon pool to depths greater than 40 cm. However, this increases thecosts of measuring and monitoring.
If soils are shallower than 30 cm then it is important that the depth of each soil sample collected be measured andrecorded. Calculations to estimate the soil carbon stocks need to account for varying soil depth over the project
area and soil depth should therefore be taken into account in the stratification.
The two most commonly used methods for soil carbon analysis are: the dry combustion method and the WalkleyBlack method (wet oxidation method). MacDicken (1997) discusses advantages and disadvantages of thesemethods for soil analysis. The Walkley Black method is commonly used in laboratories that have few resources,
as it does not require sophisticated equipment. However, in many countries, professional labs exist that use thedry combustion method, and the cost can often be modest. It is good practice, especially where soil carbon is asignificant aspect of the project, to use the dry combustion method. Because the dry combustion method includes
carbonates, it is important that the soils that could contain carbonates be pre-tested and the inorganic carbon beremoved by acidification.
There are two ways to express soil carbon – on an equal mass or equal volume basis. There are advantages anddisadvantages to both methods. To express changes in soil carbon on an equal mass basis requires that thechange in the soil bulk density be known ahead of the sampling so that adjustments can be made to collect anequal mass of soil. Alternatively, the adjustments can be made as part of the calculations. It is likely that projects
designed to enhance soil organic carbon will also cause the soil bulk density to decrease. If it is expected that thesoil bulk density will change significantly during the course of the project, it is recommended to assess theimpact of expressing the changes in soil carbon on an equal mass or equal volume basis on the total projectedchange in soil carbon stocks. Otherwise, it is recommended that the changes in soil carbon stocks be reported onan equal volume basis, as it is commonly done.
The soil carbon stock per unit area on an equal volume basis is then calculated as:
EQUATION 4.3.3
SOIL ORGANIC CARBON CONTENT
SOC = [SOC] ● Bulk Density ● Depth ● CoarseFragments ● 10
Where:
SOC= the soil organic carbon stock for soil of interest, Mg C ha-1

[SOC]

= the concentration of soil organic carbon in a given soil mass, g C (kg soil)-1(from lab analyses)

Bulk Density= the soil mass per sample volume, Mg m-3

Depth= sampling depth or thickness or soil layer, m

CoarseFragments= 1 – (% volume of coarse fragments / 100) 72

The final multiplier of 10 is introduced to convert units to Mg C ha-1.

4.3.3.6

E STIMATING CHANGES I N NON -CO 2 GREENHOUSE GASEMISSIONS AND REMOVALS

Although the primary purpose of LULUCF projects is to increase carbon stocks relative to a baseline, practices
included as part of LULUCF projects may also result in changes in non-CO2 greenhouse gas emissions and72

In soils with coarse fragments ( e.g., soils developed on till or coarse alluvium, or with high concentration of roots), SOC isadjusted for the proportion of the volumetric sample occupied by the coarse fraction (>2 mm fraction).

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removals. Such practices, associated with the LULUCF sector, include, for instance, biomass burning ( e.g.,during site preparation); change in livestock production (caused, for example, by changes in forage species ingrazing land management); application of synthetic and organic fertilizers to soils; cultivation of nitrogen fixing
trees, crops, and forages; flooding and drainage of soils. In addition, land-use practices that disturb soils, e.g.,tillage for crop cultivation or for afforestation/reforestation site preparation, may affect non-CO2 emissions andremovals from soils. Table 4.3.2 lists possible LULUCF project practices that can affect non-CO2 emissions andremovals. However, the definitions and modalities for Article 12, which are under negotiation at the time of thiswriting, may determine which of these practices are to be included in measurement, monitoring, and reporting of
Article 12 project activities.
TABLE 4.3.2
POSSIBLE LULUCF PROJECT PRACTICES THAT MAY RESULT IN EMISSIONS OR REMOVALS OFNON-CO2 GREENHOUSE GASES

Practice

Effect on non-CO2 gases

Emission or removal process

Biomass Burning

Source of CH4 and N2O

Combustionb

Synthetic and Organic
Fertilizer Application

Source of N2O

Nitrification/denitrification of fertilizers and organic
amendments applied to soils

Reduced CH4 removal

Suppression of soil microbial oxidation of CH4

Source of N2O

Nitrification/denitrification of soil N from enhanced
biological N fixation

Source of CH4

Anaerobic decomposition of organic material in soils

Cultivation of N-Fixing
Trees, Crops, and Forages
Soil Re-FloodingaReduced/Eliminated source of N2O Reduces mineralization of soil organic matter
Reduced/Eliminated source of CH4

Reduction of anaerobic decomposition of organic
material

Source of N2O

Mineralization of soil organic matter and subsequent
nitrification/denitrification of mineralised nitrogen

Source of N2O

Mineralization of soil organic matter and subsequent
nitrification/denitrification of mineralised nitrogen

Reduced CH4 removal

Suppression of soil microbial oxidation of CH4

Soil Drainage

Soil DisturbanceAnimal digestion (CH4)

Changes in Grazing Land
Managementc

Increased or decreased source of
CH4 and N2O from effects onlivestock

Anaerobic decomposition of manure stored in manure
management systems and applied/deposited on soils(CH4)
Nitrification/denitrification of N in manure stored inmanure management systems and applied/deposited on
soils (N2O)aBiomass burning is also a source of carbon monoxide, oxides of nitrogen, and non-methane volatile organic compounds.
These emissions are not addressed here because these gases are not considered under the Kyoto Protocol.

bSome experiments have indicated that open biomass burning (i.e., field burning of vegetation) results in elevatedemissions of N2O from soils for up to six months after burning (cf. Chapter 5 of Volume 3 of the IPCC Guidelines).
However, other experiments have found no long-term effect on soil N2O emissions, so this process is not addressed
further here.

cChanges in the species mix of grazing land plants for enhancing soil carbon, for example, could affect livestock
production and thus the non-CO2 greenhouse gases they produce.

In general, it is recommended to estimate the net greenhouse gas emissions and removals from these practices
with project-specific activity data and site-specific emission factors. It is also recommended to derive theemission factors from either well-designed and well-implemented field measurements at either the project site(s)or at sites that are considered to reproduce the conditions of the project site(s); or from validated, calibrated, andwell-documented simulation models implemented with project site-specific input data. The IPCC Guidelines, as
amended by GPG2000, and Chapter 3 of this report provide default Tier 1 methods and emission factors for
estimating emissions from many of these practices at the national level (see Table 4.3.3). However, these
documents provide limited good practice guidance for either measurement of, or simulation modelling of,emissions and removals from many of these practices. Because these practices fall within IPCC national
inventory sectors other than Land-Use Change and Forestry (e.g., the Energy or Agriculture sectors), it is beyond

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the scope of this report to provide detailed good practice guidance for measuring, monitoring, and estimating
emissions and removals from these practices.
Changes in non-CO2 greenhouse gas emissions or removals caused by these practices may be small relative to
net changes in carbon stocks over the lifetime of the LULUCF project. Therefore, when any of these practices
are part of a LULUCF project, it is recommended first to estimate the likely annual net changes in non-CO2
emissions or removals over the lifetime of the project based upon project activity data and the default IPCC
methods and emission factors provided in the IPCC Guidelines, as amended by GPG2000 and Chapter 3 of thisreport. If the expected average annual net change in non-CO2 emissions or removals is relatively small, e.g., lessthan about 10% of expected average total annual net carbon stock changes on a CO2-equivalent basis, use of thedefault IPCC emission factors may be adequate. However, if the expected average annual net change in non-CO2
emissions or removals from an activity is relatively large, e.g., greater than about 10% of expected averageannual net carbon stock changes on a CO2-equivalent basis, it is recommended to develop project-specificemission factors, either through measurement or simulation models.
TABLE 4.3.3
LOCATION OF IPCC DEFAULT METHODS AND DATA FOR ESTIMATION OF
NON-CO2 GREENHOUSE GAS EMISSIONS AND REMOVALS
Practice

Biomass Burning

Synthetic andOrganic Fertilizer a
Application

Location of IPCC Default Methods and Data•

Emission ratio methodologies and emission ratios for confined burning for energy
production in the Energy chapter of the IPCC Guidelines and the GPG2000.

•

Emission ratio methodologies and emission ratios for open field burning in the Agriculture
chapter of the IPCC Guidelines and the GPG2000.

•

Emission ratio and emission factor methodology, and combustion efficiencies, emission
ratios, and emission factors for open field burning in forest, grassland, and savanna
ecosystem types in Chapter 3 of this Report (see Section 3.2.1.4, Section 3.4.1.3, and Annex
3A.1).

•

Emission factor method, fertilizer nitrogen contents, volatilisation and leaching/runoff rates,and default emission factors for N2O emissions in the Agriculture chapter of the IPCC
Guidelines and the GPG2000. Note: Both direct and indirect N2O emissions should be
estimated, even though some of the indirect emissions may occur outside of a project’s
geographic boundaries.

•

N2O emissions from fertilized soils may be affected by liming (see Section 3.2.1.4 of thisReport). However, because liming has been found to both enhance and reduce N2O
emissions from fertilization, default emission factors for fertilizer application to limed soils
are not provided•

Emission factor method, biomass nitrogen content, and emission factor for crops and forages
in the Agriculture chapter of the IPCC Guidelines and the GPG2000. The method is basedon the amount of nitrogen in the aboveground biomass produced annually, which is used as aproxy for the additional amount of nitrogen available for nitrification and denitrification.
Default methods have not been developed for leguminous trees (see Section 3.2.1.4 ofChapter 3 of this Report).

•

Methods and area-based N2O emission factors for drainage of forest soils and drainage of
wetlands in Appendix 3a.2 and Appenddix 3a.3, respectively, of this Report.

•

Methods and emission factors for CH4 are not provided.

•

Method and N2O emission factors for cultivation of organic soils (i.e., histosols) in the
Agriculture chapter of the IPCC Guidelines and the GPG2000.

•

For disturbance of mineral soils, methods and emission factors for estimating increases in
N2O emissions in lands converted to croplands in Section 3.3.2.3 of this Report.

•

Methods and emission factors for CH4 are not provided.

•

Emission factor methodologies for animal digestion and manure application/deposition in
the Agriculture chapter of the IPCC Guidelines and the GPG2000. Emission factors anddata for deriving emission factors, as well as emission estimation models for some animal
types are also provided. Project-specific emission factors for some animal types can be
developed by applying project-specific data (e.g., animal weight and feed digestibility) to the
IPCC emission estimation models.

Cultivation of NFixing Trees, Crops,
and Forages

Soil Re-Flooding
and Drainage

Soil Disturbance

Changes in Grazing
Land Managementa The term fertilizer is used here to encompass both synthetic and organic fertilizers, e.g., urea and compost, as well as organic soil
amendments such as uncomposted crop residues.

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4.3.3.7

M ONITORI NG CHANGES IN GREENHOUSE GAS EMISSIONSAND REMOVALS FROM PROJECT OPERATION PRACTICESGreenhouse gas emissions from the direct use of energy in project operations can be significant. Such directenergy use includes both fuels and electricity consumed in both mobile and stationary equipment. Examples ofmobile sources include tractors used for site preparation, fertilizer application, tillage, or planting; road transport
to and from sites for monitoring; light-rail transport such as for the transport of logs out of the forest; airtransport such as in helicopter logging; and water transport of logs from the forest. Stationary equipment, which,for most LULUCF projects, will typically constitute a less significant source of greenhouse gas emissions thanmobile sources, could include machinery such as soil mixers and potting equipment in nurseries, irrigation
pumps, and lighting. Project operators need to determine and report the greenhouse gas emissions from direct
fossil fuel and electricity use in mobile and stationary equipment.
Carbon dioxide is the primary greenhouse gas emitted from fossil fuel consumption in stationary and mobile
equipment. Because N2O and CH4 emissions are likely to make up a relatively small proportion of overall energy
use emissions from projects, estimation of these emissions is at the discretion of the user.
Greenhouse gas emissions from stationary sources can be estimated by applying appropriate emission factors to
the fuel quantity or electricity consumed (see the Energy chapters of the IPCC Guidelines and the GPG2000).
Emissions from mobile sources can be estimated with either a fuel-based approach, or a distance-based approach(see Box 4.3.5 and the Energy chapters of the IPCC Guidelines and the GPG2000).
BOX 4.3.5
GUIDANCE ON ESTIMATING GREENHOUSE GAS EMISSIONS FROM MOBILE SOURCES
Direct greenhouse gas emissions from the use of vehicles can be estimated through either of two
methodologies:
Fuel-based approach
Distance-based approach
The choice of methodology is dependent on data availability. However, the fuel-based method is
the preferred method for all modes of transport as the method is associated with lower uncertainty.
In this case, the quantity of fossil fuel, usually gasoline and/or diesel fuel that is combusted during
project practices needs to be monitored and recorded. For a detailed description of themethodologies, see the IPCC Guidelines and the GPG2000.

4.3.3.8

C ONSI DERATIONS FOR THE MONITORING PLAN

The monitoring plan has specific meaning in the context of Articles 6 and 12 of the Kyoto Protocol. The plan
includes, but is not limited to, planning of the measurement that will show how the project affects carbon stocks
and emissions of non-CO2 greenhouse gases over time. This subsection provides general advice relevant tomeasurement aspects of the plan only.

4.3.3.8.1 MONI TORI NG PROJECTS WITH SMALL-SCALE LANDOWNERSMonitoring projects that could involve multiple small-scale landholders, working on small but discrete parcels ofland spread over a region requires attention. As described above (Section 4.3.3.2), whether the project is one
contiguous parcel made up of one or two large land owners or many small parcels spread over a large area withmany small land owners, the project land can be delineated and stratified using standard techniques. It is notexpected that each parcel would be monitored as if constituting a separate project, but instead can be treated as
one project and monitored for carbon at the project level as described above. However, because the project isspread out over many land owners, it is good practice to develop monitoring protocols for the project level, andthen to develop indicators that can be monitored at the parcel level to ensure project-level performance (see Box
4.3.6).

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BOX 4.3.6
MONITORING PROJECTS INVOLVING MULTIPLE SMALL-SCALE LANDHOLDERS
Monitoring the changes in carbon stocks and non-CO2 greenhouse gas emissions and removals
when projects are constituted by multiple small-scale landholders will require the monitoring
system to be split between two levels: (1) the project level and (2) the parcel level, as follows:
Level 1: project level
For each activity to be implemented within the project area, it is good practice to develop atechnical description, setting out the management objectives, the species, the soil, climatic andvegetation conditions suitable for the activity, the expected inputs in terms of materials and labour
and the expected outputs in terms of growth and yield of products. The technical descriptions
should also include tables relating readily measured indicators at the parcel level (for example
diameter at breast height or top height) to estimates of carbon stocks. These tables may be
produced with reference to Section 4.3.3.5, using either direct or indirect methods. Good practicealso entails establishment of a number of sample plots within the project area to maintain andimprove the calibration of these tables (according to Section 4.3.3.4). Each technical description
should also include a set of parameters used to determine the baseline carbon stocks, against which
the carbon uptake is to be measured. A similar set of indicators that are readily measured at theplot level should be tabulated against baseline carbon stocks.
Level 2: parcel levelWithin each parcel the following measurements can then be taken: 1) cross-check to determine
whether the activity implemented in the parcel falls within the parameters set out in the technicaldescription (e.g., correct species, planting density, climate, etc); 2) measurement of baseline
indicators; and 3) measurement of activity indicators.
The changes in carbon stocks are then estimated with reference to the tables in the relevant
technical descriptions. Quality assurance procedures should examine the data collection
procedures at both levels within such projects.

4.3.3.8.2 FREQ UENCY OF CARBON MONITORING
The frequency of monitoring should take into consideration the carbon dynamics of the project and costs
involved. In the tropics, changes in the carbon stock in trees and soils in an afforestation/reforestation project canbe detected with measurements at intervals of about 3 years or less (Shepherd and Montagnini, 2001). In thetemperate zone, given the dynamics of forest processes, they are generally measured at 5-year intervals (e.g.,
many national forest inventories). For carbon pools that respond more slowly, such as soil, even longer periods
could be used. Thus it is recommended that for carbon accumulating in the trees, the frequency of monitoringshould be defined in accordance with the rate of change of the carbon stock, and be in accordance with therotation length (for plantations) and cultivation cycle (for croplands and grazing lands).

4.3.3.8.3 OVERALL PROJECT SITE PERFORMANCE
Monitoring only the changes in carbon stocks and non-CO2 greenhouse gases in the permanent monitoring plots
does not necessarily provide the information for assessing whether the project is accomplishing the samechanges in carbon stocks across the entire project and whether the project is accomplishing what it set out todo— e.g., plant several thousand hectares of trees. Periodic visits to the carbon monitoring plots will only showthat the carbon in those plots (which were randomly located and should be representative of the population) is
accumulating with known accuracy and precision at a given confidence level. As the project developers willknow the location of the plots, it is also important that through time comprehensive checks are made to ensurethat the overall project is performing the same way as the plots. This can be accomplished through third-party
field verification using indicators of carbon stock changes, such as tree height for afforestation/reforestation
projects and crop productivity for cropland management projects. It is good practice for project developers to
produce such indicators that can readily be field-verified across the project area. To monitor overall project site
performance (i.e., project activities are being performed over the entire project area), one of several methods canbe used, depending upon the level of technology and resources available, such as:•

Visual site visits with photographic documentation. It is recommended to thoroughly inspect the total area
planted in each region and that a selection of photographs be taken and dated. The field reports and photos
should be part of the permanent record.

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Digital aerial imagery, using multi-spectral sensors (particularly infra-red), of GPS located transects acrosseach planted area. As above, full documentation and digital photographs, dated, should be part of the project’s
records.

•

Remote sensing with use of very high-resolution satellite data ( e.g., Ikonos, QuickBird) or high resolution
satellite data ( e.g., Spot, Landsat, RadarSat, Envisat ASAR). The decision on which satellite imagery to use
will depend on size of project (100s to 1,000s of ha), location (mostly under high cloud cover or often freeof clouds), and project resources.

4.3.4

Quality Assurance and Quality Control Plan

Monitoring requires provisions for quality assurance (QA) and quality control (QC) to be implemented via a
QA/QC plan. The plan should become part of project documentation and cover procedures as described below
for: (1) collecting reliable field measurements; (2) verifying methods used to collect field data; (3) verifying data
entry and analysis techniques; and (4) data maintenance and archiving. If after implementing the QA/QC plan it
is found that the targeted precision level is not met, then additional field measurements need to be conducteduntil the targeted precision level is achieved.

4.3.4.1

P ROCEDURES TO ENSURE RELI ABLE F IELDMEASUREMENTS

Collecting reliable field measurement data is an important step in the quality assurance plan. Those responsible
for the measurement work should be fully trained in all aspects of the field data collection and data analyses. It isgood practice to develop Standard Operating Procedures (SOPs) for each step of the field measurements, whichshould be adhered to at all times. These SOPs should detail all phases of the field measurements and containprovisions for documentation for verification purposes and so that future field personnel can check past results
and repeat the measurements in a consistent fashion.
To ensure the collection of reliable field data, it is good practice to ensure that:•Field-team members are fully cognisant of all procedures and the importance of collecting data as accurately
as possible;•

Field teams install test plots if needed in the field and measure all pertinent components using the SOPs;•

All field measurements are checked by a qualified person in cooperation with the field team and correct any
errors in techniques;•

A document is filed with the project documents that show that these steps have been followed. The
document will list all names of the field team and the project leader will certify that the team is trained;•

New staff are adequately trained.

4.3.4.2

P ROCEDURES TO VERIFY FIELD DATA COLLECTION

To verify that plots have been installed and the measurements taken correctly, it is good practice:•

To re-measure independently every 8-10 plots, and to compare the measurements to check for errors; anyerrors found should be resolved, corrected and recorded. The re-measurement of permanent plots is to verify
that measurement procedures were conducted properly.

•

At the end of the field work, to check independently 10-20% of the plots. Field data collected at this stage
will be compared with the original data. Any errors found should be corrected and recorded. Any errors
discovered should be expressed as a percentage of all plots that have been rechecked to provide an estimateof the measurement error.

4.3.4.3

P ROCEDURES TO VERIFY DATA ENTRY AND ANALYSIS

Reliable carbon estimates require proper entry of data into the data analyses spreadsheets. Possible errors in thisprocess can be minimised if the entry of both field data and laboratory data are reviewed using expert judgement
and, where necessary, comparison with independent data to ensure that the data are realistic. Communication

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between all personnel involved in measuring and analysing data should be used to resolve any apparent
anomalies before the final analysis of the monitoring data is completed. If there are any problems with themonitoring plot data that cannot be resolved, the plot should not be used in the analysis.

4.3.4.4

D ATA MAINTENANCE AND STORAGE

Because of the relatively long-term nature of these projects, data archiving (maintenance and storage) will be an
important component of the work (see also Section 5.5.6). Data archiving should take several forms and copiesof all data should be provided to each project participant.
Copies (electronic and/or paper) of all field data, data analyses, and models; estimates of the changes in carbon
stocks and non-CO2 greenhouse gases and corresponding calculations and models used; any GIS products; andcopies of the measuring and monitoring reports should all be stored in a dedicated and safe place, preferably offsite.
Given the time frame over which the project will take place and the pace of production of updated versions ofsoftware and new hardware for storing data, it is recommended that the electronic copies of the data and report
be updated periodically or converted to a format that could be accessed by any future software application.

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Annex 4A.1

Annex 4A.1 Tool for Estimation of Changes in Soil Carbon
Stocks associated with Management Changes in
Croplands and Grazing Lands based on IPCC
Default Data(see the attached CD-ROM)

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Annex 4A.2

Examples of allometric equations for estimating
aboveground biomass and belowground biomassof trees

TABLE 4.A.1
ALLOMETRIC EQUATIONS FOR ESTIMATING ABOVEGROUND BIOMASS (KG DRY MATTER PER TREE)OF TROPICAL AND TEMPERATE HARDWOOD AND PINE SPECIESForest typea

R2/sample
size

DBH range(cm)

Tropical moist hardwoods

0.98/226

5 - 148

Tropical wet hardwoods

0.92/176

4 - 112

Y = 0.887 + [(10486 • (DBH)2.84) / ((DBH2.84) + 376907)]

Temperate/tropical pines

0.98/137

0.6 - 56

Y = 0.5 + [(25000 • (DBH)2.5) / ((DBH2.5) + 246872)]

Temperate US eastern
hardwoods

0.99/454

1.3 - 83.2

EquationY = exp[–2.289 + 2.649 • ln (DBH) – 0.021 • (ln(DBH))2]
Y = 21.297 – 6.953 • (DBH) + 0.740 • (DBH)

2

Where
Y= aboveground dry matter, kg (tree)-1
DBH =diameter at breast height, cm
ln = natural logarithm
exp = “e raised to the power of”
aTropical moist generally represent areas with rainfall of between 2000 to 4000 mm/year in the lowlands;
tropical wet is suited for areas with rainfall greater than 4000 mm/year in the lowlands (see Brown, 1997 forfurther discussion).

Sources: Updated from Brown, 1997; Brown and Schroeder, 1999; Schroeder et al., 1997

TABLE 4.A.2
ALLOMETRIC EQUATIONS FOR ESTIMATING ABOVEGROUND BIOMASS OF PALM TREES (KG DRY MATTER PER TREE)COMMON IN TROPICAL HUMID FORESTS OF LATIN AMERICA. THE NUMBER OF HARVESTED TREES WAS 15 FOR EACH
SPECIESR2

Height range(HT in m)

Chrysophylla sp

0.94

0.5-10.0

Attalea cohune

0.94

0.5-15.7

Sabal sp0.82

0.2-14.5

Attalea phalerata

0.62

1-11

Euterpe precatoria &Phenakospermun guianensis

0.75

1-33

Equation

Palm species

Y = 0.182 + 0.498 • HT + 0.049 • (HT)2
Y = 10.856 + 176.76 • (HT) – 6.898 • (HT)2
Y = 24.559 + 4.921 • HT + 1.017 • (HT)Y = 23.487 + 41.851 • (ln(HT))

2

2

Y= 6.666 + 12.826 • (HT0.5) • ln(HT)Where
Y = aboveground dry matter, kg (tree)-1
HT = height of the trunk, meters (for palms this is the main stem, excluding the fronds)
ln = natural logarithm
Source: Delaney et al., 1999; Brown et al., 2001

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Annex 4A.2

TABLE 4.A.3
EXAMPLES OF ALLOMETRIC EQUATIONS FOR ESTIMATING ABOVEGROUND BIOMASS (KG OF DRY MATTER PER TREE) OF
SOME INDIVIDUAL SPECIES COMMONLY USED IN THE TROPICS

Equation

Species

Y = 0.153 • DBH2.382

Tectona grandis b

Y = 0.0908 • DBH

2.575

c

Y = 0.0103 • DBH

2.993

Tectona grandis

Bombacopsis quinatumd

R2

Height for
DBH/BA(cm)a

Diameter
range
(cm)

Source

0.98

130

10-59

1

0.98

130

17-45

2

0.97

130

14-46

3

Y = 1.22 • DBH • HT • 0.01

Eucalyptus sp.

e

0.97

130

1-31

4

Y = 0.08859 • DBH2.235

Pinus pinaster f

0.98

10

0-47

5

Y = 0.97 + 0.078 • BA – 0.00094 • BA +0.0000064 • BA3

Bactris gasipaes g

0.98

100

2-12

6

Y = –3.9 + 0.23 • BA + 0.0015 • BA2

Theobroma grandiflora g

0.93

30

6-18

6

0.99

150

6-20

6

0.94

30

8-17

6

0.99

130

8-26

6

2

2

2

Y = –3.84 + 0.528 • BA + 0.001 • BA

Hevea brasiliensis

Y = –6.64 + 0.279 • BA + 0.000514 • BA2

Citrus sinensis g2

Y = –18.1 + 0.663 • BA + 0.000384 • BA

g

Bertholletia excelsa

g

Where
Y = aboveground dry matter, kg (tree)-1
DBH =diameter, cm
HT = total height of the tree, meters
BA = basal area, cm2aHeight for DBH/BA is height above ground where diameter or basal area was measured, cm
b

87 individuals at ages of 5-47 years.

c

9 individuals at age of 20 years.

d

17 individuals at ages of 10-26 years.

ePooled values for 458 individuals of Eucalyptus ovata, E. saligna, E. globulus and E. nites at ages of 2-5 years.

f

148 individuals at ages of 1-47 years.

g

7-10 individuals at age of 7 years.

Sources: (1) Pérez and Kanninen, 2003; (2) Kraenzel et al., 2003; (3) Pérez and Kanninen, 2002; (4) Senelwa and Sims, 1998;(5) Ritson and Sochacki, 2003; (6) Schroth et al., 2002.

IPCC Good Practice Guidance for LULUCF

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Chapter 4: Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol

TABLE 4.A.4
ALLOMETRIC EQUATIONS FOR ESTIMATING BELOWGROUND OR ROOT BIOMASS OF FORESTSALTHOUGH ADDITION OF AGE AND LATITUDE DID NOT INCREASE THE R2 BY VERY MUCH, THE COEFFICIENTS WEREHIGHLY SIGNIFICANT

Conditions and independent variables

Equation

Sample size

R2

All forests, ABD

Y=exp[–1.085 + 0.9256 • ln(ABD)]

151

0.83

All forests, ABD and AGE

Y=exp[–1.3267+0.8877•ln(ABD)+0.1045•ln(AGE)

109

0.84

Tropical forests, ABD

Y=exp[–1.0587 + 0.8836 • ln(ABD)]

151

0.84

Temperate forests, ABD

Y=exp[–1.0587 + 0.8836 • ln(ABD) + 0.2840]

151

0.84

Boreal forests, ABD

Y=exp[–1.0587 + 0.8836 • ln(ABD) + 0.1874]

151

0.84

Where
Y= root biomass in Mg ha-1 of dry matter
ln = natural logarithm
exp = “e to the power of”
ABD = aboveground biomass in Mg ha-1 of dry matter
AGE = age of the forest, years
Source: Cairns et al., 1997

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IPCC Good Practice Guidance for LULUCF

References

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5CROSS-CUTTING ISSUESIPCC Good Practice Guidance for LULUCF

Chapter 5: Cross-Cutting Issues

AUTHORS AND REVIEW EDITORS
Coordinating Lead Authors
Newton Paciornik (Brazil) and Kristin Rypdal (Norway)

Lead Authors
Rainer Baritz (Germany), Simon Barry (Australia), Albertus Johannes Dolman (Netherlands),
Marlen Eve (USA), Michael Gillenwater (USA), Michael Kohl (Germany), Dina Kruger (USA),
Bo Lim (UK/UNDP), Raisa Makipaa (Finland), Giorgio Matteucci (European Commission),
Toshinori Okuda (Japan), Keith Porter (Jamaica), María José Sanz-Sanchez ( Spain), T.P. Singh (India),Göran Ståhl (Sweden), Riccardo Valentini (Italy), and Martina Van Der Merwe (South Africa)

Contributing Authors
Sandra Brown (USA), Ketil Flugsrud (Norway), Gen Inoue (Japan), Gerald Kaendler (Germany),
Anders Lindroth (Sweden), Kenlo Nishida (Japan), Steve Ogle (USA), Mats Olsson (Sweden),
Gareth Philips (USA), Fran Sussman (USA), Yoshiki Yamagata (Japan), Ed Vine (USA),
and Christian Wirth (Germany)

Review Editors
Jamidu Katima (Tanzania) and Tom Wirth (USA)

5.2

IPCC Good Practice Guidance for LULUCF

Chapter 5: Cross-Cutting Issues

Contents
5.1

INTRODUCTION

5.7

5.2

IDENTIFYING AND QUANTIFYING UNCERTAINTIES

5.8

5.2.1

Introduction .................................................................................................................................... 5.8

5.2.2

Methods to Combine Uncertainties .............................................................................................. 5.10

5.2.2.1 Tier 1 - Simple propagation of errors ..................................................................................... 5.10
5.2.2.2 Estimating uncertainties by category using Monte Carlo analysis (Tier 2) ............................. 5.11
5.2.3

Practical Considerations for Quantifying Uncertainties of Input Data.......................................... 5.14

5.2.4

Example of Uncertainty Analysis ................................................................................................ 5.16

5.2.5

Reporting and Documentation ...................................................................................................... 5.20

5.3

SAMPLING

5.21

5.3.1

Introduction .................................................................................................................................. 5.21

5.3.2

Overview on Sampling Principles ................................................................................................ 5.21

5.3.3

Sampling Design .......................................................................................................................... 5.22

5.3.3.1 Use of auxiliary data and stratification ................................................................................... 5.22
5.3.3.2 Systematic sampling ............................................................................................................... 5.23
5.3.3.3 Permanent sample plots and time series data .......................................................................... 5.23
5.3.4

Sampling Methods for Area Estimation ....................................................................................... 5.24

5.3.4.1 Estimation of areas via proportions ........................................................................................ 5.25
5.3.4.2 Direct estimation of area ......................................................................................................... 5.25
5.3.5

Sampling Methods for Estimating Greenhouse Gas Emissions and Removals ........................... 5.25

5.3.6

Uncertainties in Sample Based Surveys ....................................................................................... 5.26

5.3.6.1 Types of errors ........................................................................................................................ 5.26
5.3.6.2 Sample size and sampling error .............................................................................................. 5.27
5.3.6.3 Quantifying errors in sample based surveys ........................................................................... 5.28
METHODOLOGICAL CHOICE − IDENTIFICATION OF KEY CATEGORIES

5.4

5.29

5.4.1

Introduction .................................................................................................................................. 5.29

5.4.2

Quantitative Approaches to Determining Key Categories ........................................................... 5.29

5.4.2.1 Tier 1 method to identify key categories of sources and sinks ............................................... 5.33
5.4.2.2 Tier 2 method to identify key categories of sources and sinks ............................................... 5.36
5.4.3

Qualitative Considerations ........................................................................................................... 5.38

5.4.4

Identifying Key Categories under Kyoto Protocol Articles 3.3 and 3.4 ....................................... 5.38

5.4.5

Application of the Results ............................................................................................................ 5.40

5.4.6

Reporting and Documentation ..................................................................................................... 5.41

5.4.7

Derivation of Threshold for the Tier 1 Key Category Analysis ................................................... 5.42

5.4.7.1 Assumptions about uncertainties .................................................................................................. 5.42

IPCC Good Practice Guidance for LULUCF

5.3

Chapter 5: Cross-Cutting Issues

5.4.7.2 Emission level ........................................................................................................................... 5.43
5.4.7.3 Trend ......................................................................................................................................... 5.44
5.4.8 Example of Tier 1 Key Category Analysis ...................................................................................... 5.45
5.5

QUALITY ASSURANCE AND QUALITY CONTROL
5.5.1

Introduction .................................................................................................................................. 5.49

5.5.2

QA/QC Plan ................................................................................................................................. 5.50

5.5.3

General QC Procedures (Tier 1).................................................................................................... 5.51

5.5.4

Source or Sink Category-Specific QC Procedures (Tier 2)............................................................ 552

5.5.5

QA Review Procedures ................................................................................................................ 5.53

5.5.6

Documentation, Archiving and Reporting ................................................................................... 5.54

5.5.7

Issues under Kyoto Protocol Articles 3.3 and 3.4 ........................................................................ 5.55

5.6

TIME SERIES CONSISTENCY AND RECALCULATIONS

5.56

5.6.1

Introduction .................................................................................................................................. 5.56

5.6.2

Time Series Consistency and Methodological Change ................................................................ 5.56

5.6.3

Recalculation and Periodic Data .................................................................................................. 5.58

5.6.4

Issues under Kyoto Protocol Articles 3.3 and 3.4 ........................................................................ 5.60

5.6.5

Reporting and Documentation ..................................................................................................... 5.60

5.7

VERIFICATION

5.61

5.7.1

Introduction .................................................................................................................................. 5.61

5.7.2

Verification Approaches .............................................................................................................. 5.62

5.7.3

Guidance for Verification of LULUCF Inventories ..................................................................... 5.69

5.7.4

Specific Issues Linked to the Kyoto Protocol .............................................................................. 5.71

5.7.5

Reporting and Documentation ..................................................................................................... 5.72

5.7.6

Some Details for Verification Approaches ................................................................................... 5.73

References

5.4

5.49

5.77

IPCC Good Practice Guidance for LULUCF

Chapter 5: Cross-Cutting Issues

Equations
Equation 5.2.1 Estimating category uncertainties (Tier 1) ..................................................................... 5.10
Equation 5.2.2 Overall uncertainty in national emissions (Tier 1)......................................................... 5.11
Equation 5.4.1 Level assessment (Tier 1).............................................................................................. 5.33
Equation 5.4.2 Trend assessment (Tier 1) ............................................................................................. 5.34
Equation 5.4.3 Trend assessment with zero current year emissions ..................................................... 5.35
Equation 5.4.4 Level assessment (Tier 2).............................................................................................. 5.37
Equation 5.4.5 Trend assessment (Tier 2) ............................................................................................. 5.37

FiguresFigure 5.3.1

Principle of sampling .................................................................................................... 5.21

Figure 5.3.2

Simple random layout of plots (left) and systematic layout (right) .............................. 5.23

Figure 5.3.3

Use of different configurations of permanent and temporary sampling units
for estimating changes .................................................................................................. 5.24

Figure 5.3.4

Relationship between the standard error of the area estimate, s(A), the proportion of
the land-use class, p, and the sample size, n ................................................................. 5.27

Figure 5.4.1

Decision tree to identify key categories of sources and sinks ...................................... 5.30

Figure 5.4.2

Decision tree to choose a good practice method .......................................................... 5.41

Figure 5.4.3

Cumulative uncertainty plotted against cumulative emissions ..................................... 5.43

Figure 5.4.4

Fraction of emissions required to reach 90% of sum of contribution fromuncertainties in different inventories. With and without LULUCF .............................. 5.43

Figure 5.4.5

Fraction of emissions required to reach 90% of sum of contribution fromtrend uncertainty in different inventories. With and without LULUCF ....................... 5.44

Figure 5.6.1

Recalculated estimate for 2003 based on linear extrapolation ...................................... 5.59

IPCC Good Practice Guidance for LULUCF

5.5

Chapter 5: Cross-Cutting Issues

TablesTable 5.3.1

Example of area estimation via proportions ................................................................. 5.25

Table 5.4.1

Suggested IPCC source/sink categories for LUCF and non-LULUCF ........................ 5.31

Table 5.4.2

Spreadsheet for the Tier 1 analysis – Level assessment including LULUCF
categories ...................................................................................................................... 5.34

Table 5.4.3

Spreadsheet for the Tier 1 analysis – Trend assessment including LULUCF
categories ...................................................................................................................... 5.35

Table 5.4.4

Relationship between activities identified in Chapter 3 and Chapter 4 and IPCC
source/sink categories for LULUCF ............................................................................. 5.39

Table 5.4.5

Key category analysis summary ................................................................................... 5.42

Table 5.4.6

Assumed uncertainties to determine a key category threshold including LULUCF .... 5.42

Table 5.4.7

Example of a level assessment for an Annex I country ................................................ 5.45

Table 5.4.8

Trend analysis with LULUCFs ..................................................................................... 5.47

Table 5.5.1

Tier 1 general inventory level QC procedures .............................................................. 5.51

Table 5.6.1

Summary of approaches to obtain consistency in time series ....................................... 5.57

Table 5.7.1

Applicability of verification approaches for land area identification and for carbon
pools and non-CO2 greenhouse gases ........................................................................... 5.63

Table 5.7.2

Features of some of the main remote sensing platforms .............................................. 5.76

Boxes

5.6

Box 5.2.1

Example of uncertainty expression ................................................................................. 5.9

Box 5.2.2

Level of aggregation of the Tier 1 analysis .................................................................. 5.11

Box 5.2.3

Tier 2 uncertainty assessment for changes in agricultural soil C in the U.S.A ............. 5.14

Box 5.2.4

Uncertainties of estimates based on models ................................................................. 5.16

Box 5.5.1

Definitions of Quality Assurance and Quality Control ................................................ 5.49

Box 5.5.2

Expert peer review ........................................................................................................ 5.54

Box 5.6.1

Example case where a national forest inventory is conducted very 5 years ................. 5.58

Box 5.6.2

Example of modelling the emissions of a site over time .............................................. 5.59

Box 5.7.1

Definition of verification for the inventory .................................................................. 5.61

Box 5.7.2

Guidance for selecting inventory components for verification andverification approaches ................................................................................................. 5.69

Box 5.7.3

Verification of LULUCF Sector in a National Inventory ............................................. 5.70

Box 5.7.4

Guidance for verifying carbon pools and activities ...................................................... 5.71

Box 5.7.5

Verification of LULUCF under the Kyoto Protocol ..................................................... 5.72

Box 5.7.6

Programs/networks relevant to LULUCF ..................................................................... 5.73

IPCC Good Practice Guidance for LULUCF

Introduction

5.1

INTRODUCTION

Several general or cross-cutting issues need to be considered when preparing national greenhouse gas inventories
of emissions and removals. This chapter provides good practice guidance on six such issues identified in theGood Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000,
IPCC, 2000), building on the previous discussion to take into account the specific characteristics of the land use,land-use change and forestry (LULUCF) sector. The six issues are:•Uncertainty Assessment: Estimates of uncertainty need to be developed for all categories in an inventory
and for the inventory as a whole. GPG2000 provides practical guidance for estimating and combining
uncertainties, along with a discussion of the conceptual underpinnings of inventory uncertainty. Section 5.2,
Identifying and Quantifying Uncertainties, of this chapter, discusses the key types of uncertainty in the
LULUCF sector and provides specific information on how to apply the good practice guidance of GPG2000
to this sector.

•

Sampling: Data for the LULUCF sector often are obtained from sample surveys; for example land areas,
biomass stock and soil carbon, and such data typically are used for estimating changes in land use or carbon
stocks. Section 5.3, Sampling, gives good practice guidance for the planning and use of sample surveys forthe reporting of emissions and removals of greenhouse gases at the national level. This section also gives an
overview of the relationship between sampling design and uncertainty estimates.

•

Key Category Analysis: Chapter 7 of GPG2000, Methodological Choice and Recalculation, presents theconcept of key source analysis. As originally designed it applied only to source categories. Section 5.4,
Methodological Choice − Identification of Key Categories, of this chapter, expands the original approach toenable the identification of key categories that are sources or sinks. Good practice guidance is provided on
how to identify key categories for the LULUCF sector for the inventory under the UNFCCC, and additional
guidance is provided for identifying key categories associated with the supplementary information provided
under Articles 3.3 and 3.4 of the Kyoto Protocol.

•

Quality Assurance (QA) and Quality Control (QC): A QA/QC system is an important part of inventory
development, as described in Chapter 8 of GPG2000. Section 5.5 of this chapter describes those aspects of
the QA/QC system that are needed for the LULUCF sector and provides specific good practice guidance on
conducting Tier 2 quality control checks for this sector, building on information provided in Chapter 2,
Basis for Consistent Representation of Land Areas, and Chapter 3, LUCF Sector Good Practice Guidance,of this report. QA/QC issues specific to the Kyoto Protocol are also presented.

•

Time Series Consistency: Ensuring the time series consistency of inventory estimates is essential if one is tohave confidence in reported inventory trends. In Chapter 7 of GPG2000, several methods are provided for
ensuring time series consistency in cases where it is not possible to use the same methods and/or data overthe entire period. In Section 5.6, Time Series Consistency and Recalculations, of this chapter, these methods
are discussed with respect to specific situations that can arise in the development of emission and removal
estimates for the LULUCF sector.

•

Verification: Conducting verification activities can improve inventory quality as well as lead to betterscientific understanding. Verification approaches and practical guidance for verifying estimates in the
LULUCF sector are described in Section 5.7 of this chapter.

This chapter provides the information needed to apply good practice guidance in the LULUCF sector. It does
not repeat all information from GPG2000, however. Thus, readers may wish to refer to GPG2000 for additional
background information. Specific situations in which reference to GPG2000 may be useful are mentioned in thesubsections that follow.

IPCC Good Practice Guidance for LULUCF

5.7

Chapter 5: Cross-Cutting Issues

5.2

IDENTIFYING AND QUANTIFYINGUNCERTAINTIES

5.2.1

Introduction

This section describes good practice in estimating and reporting uncertainties associated with estimates ofemissions and removals in the LULUCF sector and shows how to incorporate the LULUCF sector into theprocedure introduced in Chapter 6, Quantifying Uncertainties in Practice, of GPG2000 for the assessment of
combined uncertainties across the inventory.
The definition of good practice requires that inventories should be accurate in the sense that they are neither
over- nor underestimated as far as can be judged, and that uncertainties are reduced as far as practicable. There is
no predetermined level of precision; uncertainty is assessed to help prioritise efforts to improve the accuracy of
inventories in the future and guide decisions on methodological choice. Uncertainties are also of interest when
judging the level of agreement between national inventories and emission or removal estimates made bydifferent institutions or approaches.
Inventory estimates can be used for a range of purposes. For some purposes, only the national total matters,
while for others, the detail by greenhouse gas and source or sink category is important. In order to compile thedata to the intended purpose, users need to understand the actual reliability of both the total estimate and its
component parts. For this reason, the methods used to communicate uncertainty must be practical, scientificallydefensible, robust enough to be applicable to a range of source and sink categories, methods and national
circumstances, and presented in ways comprehensible to all inventory users.
There are many reasons for actual emissions and removals to differ from the number calculated in a national
inventory. Some sources of uncertainty (e.g., sampling error or limitations on instrument accuracy) may generate
well-defined, easily characterised estimates of the range of potential error. Other sources of uncertainty, forexample systematic errors, are more difficult to identify and quantify (Rypdal and Winiwarter, 2001). Thissection describes how to account for both well-defined statistical uncertainties and less specific information
characterising other forms of uncertainty in the LULUCF sector, and discusses the implications for theuncertainty of both the total inventory and its components.
Ideally, emission and removal estimates and uncertainty ranges would be derived from source-specific measured
data. Since it is not practical to measure every emission source or sink category in this way, some of theestimates are based on the known characteristics of typical sites taken to be representative of the population of
all sites. This approach introduces additional uncertainties, because it must be assumed that the entire population
behaves, on average, like the sites that have been measured. Random sampling of a target population allows
quantitative estimation of uncertainties. Large systematic errors (implying biased estimates) can occur in cases
where an estimate with known precision is based on a population which is different from the population where theestimate is to be applied. In practice, expert judgement will often be necessary to define the uncertainty ranges.
The pragmatic approach for producing quantitative uncertainty estimates in this situation is to use the best
available estimates − a combination of the available measured data, model outputs, and expert judgement. The
methods proposed in this section can therefore be used with the category-specific default uncertainty ranges
discussed in Chapters 2 to 4 in this report, and also allow for new empirical data to be incorporated as they
become available.
Consistent with Chapter 6 of GPG2000 (Quantifying Uncertainties in Practice), uncertainties should be reported
as a confidence interval giving the range within which the underlying value of an uncertain quantity is thought tolie for a specified probability. The IPCC Guidelines suggest the use of a 95% confidence interval, which is theinterval that has a 95% probability of containing the unknown true value. This may also be expressed as apercentage uncertainty, defined as half the confidence interval width divided by the estimated value of thequantity (see Box 5.2.1). The percentage uncertainty is applicable when either the underlying probability density
function is known or when a sampling scheme or expert judgement is used. Furthermore, this notion can bereadily used to identify the categories for which efforts to reduce uncertainty should be prioritised.
This section is consistent with Chapter 6 and Annex 1 (Conceptual Basis for Uncertainty Analysis) of GPG2000,
while providing additional information on how to assess uncertainties in the LULUCF sector. Much of the
discussion focuses on issues related to CO2 emissions and removals, which were not addressed in the previous
report. Uncertainty estimates for emissions of non-CO2 gases can also be prepared, following the guidance from
Chapter 6 of GPG2000. Methods to combine uncertainties are described in Section 5.2.2, practicalconsiderations for quantifying uncertainties in input data in Section 5.2.3, an example of an uncertainty analysis

5.8

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

for the LULUCF sector is presented in Section 5.2.4, and Section 5.2.5 addresses reporting and documentation
issues. Because of the importance of well-designed sampling programmes to reduce uncertainties when
preparing LULUCF inventories for many countries, specific guidance on the design of sampling programmes forland areas and biomass stock, as well as guidance on assessment of associated uncertainties is provided
separately in Section 5.3.

BOX 5.2.1
EXAMPLE OF UNCERTAINTY EXPRESSIONµ -2σµ µ-2-2σσµµ --σ
σ

µ

µ +σ

µ +2σ

In the GPG2000, the percentage uncertainty is defined as:% uncertainty =1 (95% Confidence Interval width)
2
×100
µ

% uncertainty =1 (4σ)
2σ20
2×100 =
×100 =
×100 = 20%µµ100

For this example:

Where:
σ = standard deviation
σ = var iance =10µ = the mean of the distribution
Note that this uncertainty is twice the relative standard error (in %), a commonly used statisticalestimate of relative uncertainty.

IPCC Good Practice Guidance for LULUCF

5.9

Chapter 5: Cross-Cutting Issues

5.2.2

Methods to Combine Uncertainties

Estimated carbon stock changes, emissions and removals arising from LULUCF activities have uncertainties
associated with area or other activity data, biomass growth rates, expansion factors and other coefficients. Thissection describes how to combine these uncertainties at the category level and how to estimate the uncertainty inlevel and trend in the inventory as a whole. It assumes that the uncertainties of the various input data estimates
are available, either as default values given in Chapters 2, 3 and 4 of this report, expert judgement, or estimates
based of sound statistical sampling (Section 5.3).
In GPG2000, two methods for the estimation of combined uncertainties are presented: a Tier 1 method using
simple error propagation equations, and a Tier 2 method using Monte Carlo or similar techniques. Both methods
are applicable when dealing with the LULUCF sector. However, some specific considerations have to behighlighted, because net emissions can be negative if both emissions and removals are taken into account.
Inventory agencies may also apply national methods for estimating the overall uncertainty, e.g., error
propagation methods that avoid the simplifying approximations associated with the Tier 1 method. In this case, it
is good practice to clearly document such methods.
Use of either Tier 1 or Tier 2 will provide insight into how individual categories and greenhouse gases contributeto the uncertainty in total emissions in any given year, and to the trend in total emissions between years. Being
spreadsheet based, the Tier 1 method is easy to apply, and it is good practice for all countries to undertake anuncertainty analysis according to Tier 1. Inventory agencies may also undertake uncertainty analysis according
to Tier 2 or national methods. The uncertainty estimates of the LULUCF sector can be combined with theuncertainty estimates of the non- LULUCF sector (derived using the good practice methods outlined in
GPG2000) to obtain the total inventory uncertainty.

5.2.2.1

T IER 1 – S IMPLE P ROPAGATION OF E RRORSThe Tier 1 method for combining uncertainties is based on the error propagation equation introduced in Section
A1.4.3.1 (Error Propagation Equation) in the Annex 1 (Conceptual Basis for Uncertainty Analysis) of GPG2000.
Practical guidance on how to apply the Tier 1 method for uncertainty analysis of emission estimates is provided in
Section 6.3.2 (Tier 1 – Estimating Uncertainties by Source Category with Simplifying Assumptions) of GPG2000.
For the estimation of trend uncertainties, the method described in Section 6.3.2 of GPG2000 can be used whenemissions and removals are summed. Table 6.1, Tier 1 Uncertainty Calculation and Reporting, of GPG2000 can
also be applied with the implementation of a Tier 1 uncertainty calculation including the LULUCF sector.
Equation 5.2.1 can be used to estimate the uncertainty of a product of several quantities, e.g., when an emission
estimate is expressed as the product of an emission factor and activity data. It applies where there is no
significant correlation among data and where uncertainties are relatively small (standard deviation less thanabout 30% of the mean). The equation can also be used to give approximate results where uncertainties are larger
than this. Where significant correlation exists, Equation 5.2.1 can be modified based on the equation provided in
Section A1.4.3.1 of GPG2000, or the data can be aggregated following the guidance in Box 5.2.2 in this section
and the paragraphs on dependence and correlation in Section 5.2.2.2.
EQUATION 5.2.1
ESTIMATING CATEGORY UNCERTAINTIES (TIER 1)

U total = U12 + U 22 + ... + U 2n
Where:
UtotalUi

5.10=

percentage uncertainty in the product of the quantities (half the 95% confidence
interval divided by the total and expressed as a percentage);=percentage uncertainties associated with each of the quantities, i = 1, …, n

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

BOX 5.2.2
LEVEL OF AGGREGATION OF THE TIER 1 ANALYSIS

Correlation among input data to the uncertainty analysis often exists. Examples are cases where
the same activity data or emission factors are used in several estimates that are to be added in alater step. Often, these correlations cannot be detected statistically, especially if default values orcoarse area statistics are used. However, a qualitative assessment of the likely correlation can still
be made by evaluating, e.g., whether or not estimates are derived from the same source or if there
are other logical dependencies that would cause the errors of different estimates to deviate in thesame direction (if the correlation is positive). One possibility to avoid the correlation due to suchdependencies is to aggregate the source/sink categories to a level where they are eliminated. For
example, the emission factors for all carbon pools on a certain land-use class can be added before
they are multiplied with activity data. This aggregation gives more reliable results overall,although it results in some loss of detail in reporting on uncertainties. Table 5.4.2 of Section 5.4
gives guidance on aggregation level for key category analysis that also may be applied for the Tier
1 uncertainty analysis.

Where uncertain quantities are to be combined by addition or subtraction, as when deriving the overall
uncertainty in national estimates, Equation 5.2.2 can be used. Equation 5.2.2 is adapted from Equation 6.3 in
GPG2000. However, the inclusion of LULUCF sector in the analysis can result in the summing of emissions andremovals, the latter considered with a negative sign; therefore, the absolute value of the sum of all category
estimates should be used in the denominator.
EQUATION 5.2.2
OVERALL UNCERTAINTY IN NATIONAL EMISSIONS (TIER 1)
UE =(U1 • E1)2 + (U2 • E2 )2 + m + (Un • En )2
E1 + E2 + m + En

where:
UE=

percentage uncertainty of the sum

Ui=percentage uncertainty associated with source/sink iEi=emission/removal estimate for source/sink i

As with Equation 5.2.1, Equation 5.2.2 assumes that there is no significant correlation among emission andremoval estimates and that uncertainties are relatively small. However, it still can be used to give approximate
results where uncertainties are relatively large. Where significant correlation exists and the level of correlation is
known, Equation 5.2.1 can be modified based on the equation provided in Section A1.4.3.1 in Annex 1 of
GPG2000. Otherwise, categories should be aggregated, if possible (see Box 5.2.2), or Monte Carlo analysis (Tier2) may be used.

5.2.2.2

E STIMATING U NCERTAI NTIES BY C ATEGORY U SI NG
M ONTE C ARLO A NALYSIS (T IER 2)

Monte Carlo analysis is suitable for detailed category-by-category Tier 2 assessment of uncertainty. This section
expands guidance on Monte Carlo analysis given in Chapter 6 of GPG2000 by providing guidance specific to the
LULUCF sector. GPG2000 should be consulted as background, although some of the material from Chapter 6 isreproduced here.
Monte Carlo analysis is especially useful where extensive country-specific land use data exist. It can handle
varying degrees of correlation (both in time and between categories) and can be used to assess uncertainty incomplex models as well as simple ‘management factor (or emissions factor) times activity data’ calculations. A
general description of the Monte Carlo method can be found in Fishman (1996), and statistical software
packages are readily available – some of which include Monte Carlo algorithms that are very user-friendly.
Winiwarter and Rypdal (2000) and Eggleston et al. (1998) provide examples of Monte Carlo analysis applied tonational greenhouse gas inventories to estimate uncertainties both in overall emissions and emissions trends.
Ogle et al. (2003) document a Monte Carlo analysis of uncertainty for the agricultural soil portion of the

IPCC Good Practice Guidance for LULUCF

5.11

Chapter 5: Cross-Cutting Issues

LULUCF carbon inventory in the United States. A brief example of the application of Monte Carlo analysis isprovided in Box 5.2.3 based on Ogle et al. (2003).

BACKGRO UND ON MONTE CARLO ANALYSIS
Monte Carlo analyses are designed to select random values for estimation parameters and activity data fromprobability distribution functions (PDF), and then calculate the corresponding change in carbon (or carbon
equivalent) stocks. This procedure is repeated many times to provide a mean value and a range of uncertainty(i.e., a PDF for the emissions and removals) resulting from variability in model input variables as represented by
PDFs. Monte Carlo analyses can be performed at the category level, for aggregations of categories or for theinventory as a whole.
Variability in the input variables is quantified in probability distribution functions, describing the pattern ofpossible values for the variable. PDFs may need truncation if certain thresholds are known to occur in the input
variables. For example, estimates of base soil carbon could be small but would never be negative (soils can nothave less than 0 percent carbon), therefore a distribution that would otherwise take negative values would need
to be truncated at 0, although both negative and positive numbers are meaningful in cases such as where a
process can lead to either a sink or a source term.
PDFs can be based on field data, expert judgement, or a combination of the two and can be linked to account forinterdependencies, notably correlations across time or between gases for activity data and correlations among
management factors. If these interdependencies are not taken into account, the estimated uncertainty may be toolarge or too small depending on the correlations, and the results are less meaningful.
After constructing PDFs, a Monte Carlo analysis is conducted as an iterative process. A set of input values are
selected at random within each PDF, after that the model is run with those values producing an estimate for the
output of interest, and then the process is repeated many times over and over, providing a PDF for the inventory
estimate as a whole.

ESTIMATI NG UNCERTAINTIES IN LEVELS AND TRENDS
As with all methods, Monte Carlo analysis only provides satisfactory results if it is properly implemented, andthe results will only be valid to the extent that the input data, including PDFs, correlations, and any expert
judgements, are sound. The Monte Carlo approach consists of five clearly defined steps. Only the first two steps
require effort from the user, the remainder being handled by the software package.
Step 1:

Specify uncertainties in the input variables. This includes estimation parameters and LULUCF activity
data, their associated means and probability distribution functions (PDFs), and any correlations. The
uncertainties can be assessed following the guidance in Section 5.2.3 (Practical Consideration for
Quantifying Uncertainties of Input Data) and Section 5.2.4 (Example of Uncertainty Analysis) of thischapter. For guidance on assessment of correlations, see below.

Step 2:

Set up software package. The emission inventory calculation, the PDFs, and the correlation values
should be set up in the Monte Carlo package. The software performs the subsequent steps. In somecases, the inventory agency may decide to set up its own programme to run a Monte Carlo simulation;
this can be done using statistical software.

Step 3:

Select input values. Input values will normally be the good practice estimates applied in thecalculation. This is the start of the iterations. For each input data item, a number is randomly selectedfrom the PDF of that variable.

Step 4:

Estimate carbon stocks. The variables selected in Step 3 are used to estimate carbon stocks for the baseyear and the current year (i.e., beginning and end of the inventory period; year t−20 and year t) basedon input values.

Step 5:Iterate and monitor results. The calculated total from Step 4 is stored, and the process then repeatsfrom step 3. The mean of the totals stored gives an estimate of the carbon stock, and the variability
represents uncertainty. Many repetitions are needed for this type of analysis. The number of iterations
can be determined in two ways: by setting the number of model runs, a priori, such as 10,000 andallowing the simulation to continue until reaching the set number, or by allowing the mean to reach a
relatively stable point before terminating the simulation.

5.12

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

The Monte Carlo method can also be used to estimate uncertainties in the trend (changes between two years)
resulting from LULUCF activities. The procedure is a simple extension of that described previously. The Monte
Carlo analysis needs to be set up to estimate stocks for both years simultaneously. The procedural steps are thesame as described above, except for variations in Step 1 and 2:
Step 1:The same procedure as described above, except that it needs to be done for both the base year and thecurrent year, and consequently additional interdependencies must be considered. For many LULUCF
categories, the same emission factor will be used for each year (i.e., the emission factors for both years
are 100% correlated). Activity data for land use and emissions are often correlated across time, andthis will need to be represented in the model as well.

Step 2:The software package should be set up as previously described, except that the PDFs will need tocapture the relationship between carbon stocks in the base year and current year. Where the input data
are assumed to be 100% correlated between years (as will be the case for many LULUCF estimation
parameters), the same random number is used to generate the emission factor values from the PDF in
both years.

SPECIFYING PROBABILITY DI STRIBUTIONS FOR INVENTORY INPUTS
Data used in an uncertainty analysis can be derived from field trials or from expert judgement. These data need
to be synthesized in such a way as to produce the probability distribution functions. Some key questions to ask
regarding the data include:•

Are the data representative of management practices and other national circumstances?

•

What is the averaging time associated with the data set, and is it the same as for the assessment?

Usually, available data will represent an annual average for an estimation parameter or an annual total for
activity data.
Monte Carlo simulation requires that the analyst specifies probability distributions (see Fishman 1996) thatreasonably represent each model input for which the uncertainty is to be quantified. The probability distributions
may be based on advice in Chapter 3 of this report, or be obtained by a variety of methods, including statisticalanalysis of data, or the elicitation of expert judgement as described in Chapter 6 of GPG2000. A key
consideration is to develop the distributions for the input variables to the emission/removal calculation model sothat they are based upon consistent underlying assumptions regarding averaging time, location, and otherconditioning factors relevant to the particular assessment (e.g., climatic conditions influencing agricultural
greenhouse gas emissions). See also Section 5.2.3 (Practical Considerations for Quantifying Uncertainties of
Input Data) for further guidance.

ASSESSI NG THE CO NTRIBUTION OF EACH INVENTO RY INPUT TOOVERALL UNCERTAINTY
Ideally, the amount of effort devoted to characterizing uncertainty in an inventory input should be proportional toits importance to the overall uncertainty assessment. It would not be a good use of limited resources to spend
large amounts of time exhaustively collecting data and expert judgements for a source/sink category that has
little effect on overall uncertainty. Thus, countries are encouraged to identify which inputs to particular
categories are particularly significant with respect to the overall uncertainty of the inventory as a mean toprioritise improvements. Similarly, it would be a shortcoming of an assessment not to devote reasonable
resources to quantifying the uncertainties associated with the inputs to which the overall uncertainty in theinventory is highly sensitive. Thus, many analysts suggest an approach in which the first iteration of uncertainty
analysis is an assessment of the main sources of uncertainty. This information will enhance the assessment ofoverall uncertainty and can be very useful in documentation. Methods for assessing the importance of each input
are described in references such as Morgan and Henrion (1990), Cullen and Frey (1999), and others. See also
Section 5.4 (Methodological Choice − Identification of Key Categories).

SPECIFYING DEPENDENCE AND CORRELATIO N AMONG INVENTORYINPUTS
A key issue that should be considered by analysts when setting up a probabilistic analysis is whether there are
dependencies or correlations among model inputs. Ideally, it is preferable to define the model so that the inputs
are as statistically independent as possible. Therefore, rather than trying to estimate uncertainties separately foreach LULUCF subcategory, it may be more practical to estimate uncertainty for aggregated categories, for whichgood estimates and cross-checks may be available. Dependencies, if they exist, may not always be important tothe assessment of uncertainties. Dependencies among inputs will matter only when they exist between two inputs
to which the uncertainty is particularly sensitive and when the dependencies are sufficiently strong. In contrast,

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Chapter 5: Cross-Cutting Issues

weak dependencies among inputs, or strong dependencies among inputs to which the uncertainty in the inventory
is insensitive, will be of relatively little importance to the analysis. Of course, some interdependencies areimportant and failure to account for those relationships can result in misleading results.
Dependencies can be assessed by evaluating the correlation among the input variables through statisticalanalyses. For example, Ogle et al. (2003) accounted for dependencies in tillage management factors, which were
estimated from a common set of data in a single regression-type model, by determining the covariance between
factors for reduced tillage and no-till management, and then using that information to generate tillage factor
values with appropriate correlation during a Monte Carlo simulation. Box 5.2.3 discusses this study in moredetail. One should consider the potential for correlations among input variables and focus on those that would
likely have the largest dependencies (e.g., applying management factors for the same practice in different yearsof an inventory, or correlations among management activities from one year to the next). Additional discussions
and examples are given in Cullen and Frey (1999) and Morgan and Henrion (1990). These documents also
contain reference lists with citations to relevant literature.

BOX 5.2.3
TIER 2 UNCERTAINTY ASSESSMENT FOR CHANGES IN AGRICULTURAL SOIL C IN THE U.S.A

Ogle et al. (2003) have performed a Monte Carlo analysis for assessing the changes in carbon inagricultural soil in the United States. The method in the IPCC Guidelines requires inputs for
management factors (i.e., the quantitative coefficients representing the change in soil organiccarbon following a land use or management change), reference carbon stocks (i.e., the amount ofsoil organic carbon in the soils under the baseline condition), and the land use and management
activity data. The management factors were estimated from about 75 published studies using linear
mixed effect models. PDFs were derived for the management effect at a depth of 30 cm following
20 years since its implementation. Reference stocks were estimated using the National Soil Survey
Characterization Database of the United States department of agriculture – national resource
conservation service (USDA-NRCS) with carbon stock estimates from about 3700 soil samples
across the United States. PDFs were based on the mean and variance from the samples, taking into
account the spatial autocorrelation due to clumped distribution patterns. The land use andmanagement activity data were recorded in the National Resources Inventory (NRI; USDANRCS), which tracks agricultural land management at more than 400,000 points in the UnitedStates and supplemented with data on tillage practices from the Conservation Technology
Information Center (CTIC). The Monte Carlo analysis was implemented using a commerciallyavailable statistical software package and code developed by U.S. analysts. Their analysis
accounted for interdependencies between estimation parameters that were derived from common
datasets. For example, factors for set-aside lands and land-use change between cultivated anduncultivated conditions were derived from a single regression analysis using an indicator variable
for set-asides, and hence were interdependent. Their analysis also accounted for interdependencies
in land use and management activity data. When simulating input values, factors were considered
completely interdependent from the base year and current year in the inventory because the effectof management was assumed not to change during the inventory period. As such, factors were
simulated with identical random seed values. In contrast, reference carbon stocks for the various
climates by soil zones used in the IPCC analysis were simulated independently, with different
random seeds, because stocks for each zone were constructed from separate sets of data. U.S.
analysts chose to use 50,000 iterations for the Monte Carlo analysis. Ogle et al. (2003) estimatedthat mineral soils gained an average of 10.7 Tg C yr -1 between 1982 and 1997, with a 95%
confidence interval ranging from 6.5 to 15.2 Tg C yr -1. In contrast, organic soils lost an average of
9.4 Tg C yr -1, ranging from 6.4 to 13.3 Tg C yr -1. Further, Ogle et al. (2003) found that thevariability in management factors contributed 90% of the overall uncertainty for final inventory
estimates of soil carbon change.

5.2.3

Practical Considerations for Quantifying
Uncertainties of Input Data

Before uncertainties in an inventory category can be assessed, information on the uncertainties of the input datais needed. Chapter 3 of this report provides guidance on the uncertainties related to the choice of methods (tiers)
and uncertainties in default parameters. For key categories, it is good practice to make an independent
assessment of the uncertainty associated with the data used in order to prepare the national estimates. The

5.14

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

following sections provide general guidance on some of the issues that should be considered for the threemethodological tiers described in Chapter 3, and issues associated with the Kyoto Protocol described in Chapter 4.
Chapter 2 describes the sources of uncertainty likely to be encountered in determining land areas associated withland use and land-use change activities. These depend on national circumstances, and on how countries
specifically apply the three approaches, or the mix of approaches, used to categorise land area. Given the
differences in national approaches, it is difficult to give general quantitative advice, although Table 2.3.6 in
Chapter 2 provides illustrative ranges and advice on how to reduce uncertainties associated with the land
classification. The advice in Chapter 2 is relevant to all tiers addressed in the following three subsections.

QUANTIFYING UNCERTAINTIES WHEN ESTIMATES OF EMISSIONS ANDREMOVALS ARE BASED ON TI ER 1 METHODS
Tier 1 methods to estimate emissions and removals from the LULUCF sector use country-specific area estimates(land area and changes in land area by categories) and default values of estimation parameters needed to
calculate the source/sink strength of a specific category. The uncertainty associated with Tier 1 methods will
likely be high because the suitability of the available default parameters to a country’s circumstances is notknown. The application of default data in a country or region that has very different characteristics from those of
the source data can lead to large systematic errors (i.e., highly biased estimates of emissions or removals). A
qualitative uncertainty estimation of the default values used in Tier 1 or the verification approaches described in
Section 5.7 can help to identify potential bias of the estimates.
Ranges of uncertainty estimates for default estimation parameters are given in Chapter 3. Estimates of
uncertainties in other estimation parameters (e.g., harvest data) have to be based on national sources or expert
judgment reflecting national circumstances. Uncertainties in estimating the areas associated with land use andland-use change activities are obtained as described above. Overall uncertainty estimates for the LULUCF sector
are obtained by combining uncertainties as described in Section 5.2.2 (Methods to Combine Uncertainties).

QUANTIFYING UNCERTAINTIES WHEN ESTIMATES OF EMISSIONS ANDREMOVALS ARE BASED ON TI ER 2 METHODS
Tier 2 methods described in Chapter 3 make use of country specific data within the framework established atTier 1. In this case it is good practice to assess the uncertainty of these data given national circumstances. These
data are often only broadly defined, presumably with very little stratification according to climate/management/
disturbance categories. Mostly, they will be assessed in top-down approaches on the basis of cross-referenced
background values, or combined estimates from non-representative data sources including expert judgement. It isgood practice to assess uncertainty estimates for such default values using literature evaluation, expert
judgement or comparisons with countries with similar conditions. By tracing the original data, it might bepossible to improve the uncertainty assessment. Uncertainties in estimating the areas associated with land use
and land-use change activities are obtained as described in the opening to Section 5.2.3. For emission factors (forexample of wetlands or non-CO2 trace gases from biomass burning), countries may have direct measurementsfrom a few samples for certain reporting categories. Overall uncertainty estimates are then obtained by
combining uncertainties as described in Section 5.2.2.

QUANTIFYING UNCERTAINTIES WHEN ESTIMATES OF EMISSIONS ANDREMOVALS ARE BASED ON TI ER 3 METHODSIn Tier 3, extensive and representative country-specific information on carbon stock changes (in forestry, forexample, gains by growth, and losses by harvest, as well as losses due to natural mortality or disturbance) is usedin estimates of emissions and removals. In this case, the uncertainty of all estimation parameters entering the
calculation, including possible systematic errors, should be assessed. Uncertainties in estimating the areas
associated with land use and land-use change activities are obtained as already described. While the random error
component can be quantified in bottom-up approaches using in-situ inventory information (see Section 5.3 onsampling), the systematic error requires additional focus. The specific errors introduced by e.g., sampling andmodel conversions have to be considered (Lehtonen et al., 2004). It is good practice to combine all error
components (random and systematic) for each parameter (including expansion and conversion factors), and tocombine the corresponding uncertainty estimates for the emission and removal estimates for each category (see also
specific recommendations on assessing the uncertainty of estimates from sample based surveys in Section 5.3).
Depending on the national Tier 3 approach, the important driving factors for the carbon cycle might be identified
and parameterised in the subsections of Section 3.2.1. This allows for the application of dynamic models for
extrapolation and verification purposes (see Section 5.7 on verification). Therefore, special attention should be
paid to uncertainties of estimates based on models (Box 5.2.4).

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Chapter 5: Cross-Cutting Issues

BOX 5.2.4
UNCERTAINTIES OF ESTIMATES BASED ON MODELS

Models used in inventory construction can range from purely empirical/statistical relationships todetailed process based models. In practice, most models are constructed with elements of both.
There are many issues to consider in quantifying the uncertainties in the estimates produced bythese models. A few general comments can be made although it is beyond the scope of thisdocument to review all relevant models. Overall uncertainty in models can be derived from two
main components: uncertainty in the structure of the model and uncertainty in the parametervalues. The first source of uncertainty is difficult to quantify. Comparison with observational fielddata can indicate that either the structure of the model or the parameter values or both are incorrect(Oreskes et al., 1984). It is therefore important to test the validity of the models, and to use only
models that are validated for the intended purpose. If a model is not well validated, a validation
programme should complement its use. The uncertainty associated with parameter values can be
more easily quantified by combining statistical estimates or expert judgments of parameter
uncertainty with sensitivity, or Monte Carlo analysis. A sensitivity analysis should be performed
before a model is used so as to determine its usefulness for prediction. A model that is highly
sensitive to a parameter with high uncertainty may not be the best choice for inventory purposes.
Given that the model structure is adequate, the final point to consider is the uncertainty of
estimates produced by models. In this case, there are typically two error components to consider:
uncertainty due to parameter uncertainty and uncertainty due to inherent variation in the
population that cannot be captured by the model. When making these estimates, both sources of
uncertainty should be considered in any calculation.

QUANTIFYING UNCERTAINTIES WHEN ESTIMATES OF EMISSIONS ANDREMOVALS ARE BASED ON SUPPLEMENTARY REQUIREMENTS OF THE
KYOTO PROTOCOL
The general methods to combine uncertainties as described in Section 5.2.2 (Methods to Combine Uncertainties)
can also be applied in the reporting of estimates under the Kyoto Protocol. However, some of the major factors
influencing the uncertainties might be different. For example, the overall uncertainty of the inventory of the
LULUCF sector might be more sensitive to uncertainties in detection of land-use categories and changes withinthem for categories under Articles 3.3 and 3.4 of the Kyoto Protocol. In addition, the net-net accounting which is
required for the reporting for agriculture-related activities, introduces some specific issues, which are addressedin more detail in Sections 4.2.4.2 and 4.2.8.1. For example, the uncertainty in the base year estimate may bedifferent from that of the commitment period. On the other hand, there are special requirements for methodologicalchoice for the reporting according under the Kyoto Protocol (as described in Chapter 4). It is necessary for reporting
purposes to conduct separate uncertainty assessments for activities under Articles 3.3 and 3.4 of the Kyoto Protocol.
The requirements and level of detail of the analysis is described in Section 4.2.4.3 of Chapter 4.

5.2.4

Example of uncertainty analysis

Chapter 6 , Quantifying Uncertainties in Practice, gives a general example on how uncertainties can be combinedin its Appendix 6A.2. This approach can also be used for LULUCF sector provided all LULUCF calculations are
expressed as products of area (or other activity data) and emission or removal factors. Since LULUCF estimates
are in general approximately proportional to area more complex estimation procedures than multiplying activity
data with a single emission factor can all be expressed in this form, with uncertainties associated with theequivalent emission or uptake factor estimated by expert judgement or by using the standard relationships forerror propagation.
In this section an example is given that illustrates the steps for the Tier 1 uncertainty assessment, applied for the
LULUCF Tier 1 approach using two typical activities. It considers a simple case where carbon stock changes,
emissions and removals are estimated for two sub-categories within the forest land category: i) forest land thatremains as forest land, and ii) the conversion of forest land to grassland. Non-CO2 gases and emissions fromsoils are not considered here. The example concentrates on simple numerical estimates of uncertainty, not taking
into account correlations between input parameters.
The estimation involves four steps.
Step 1:

5.16

Estimate emissions or removals related to each activity; forest land remaining as forest, and conversion
from forest to grassland.

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

Step 2:

Assessment of uncertainties related to both activities.

Step 3:

Assessment of the total uncertainties from the LULUCF sector.

Step 4:

Combination of LULCF uncertainties with other source categories.

Step 1: Estimate emissions or removals f or each activity
Before conducting an uncertainty assessment, estimates of the carbon stock change are prepared for bothsubcategories: forest land remaining forest land and forest land converted to grassland. These estimates should
be prepared following the detailed guidance in Chapters 3 of this report.

Forest Land Rem aining Forest Land
Section 3.2.1.1.1.1 in Chapter 3 gives two methods for estimating carbon stock changes in biomass; in thisexample we only apply Method 1 which requires the biomass carbon loss to be subtracted from the biomass
increment (Equation 3.2.2):∆CFF LB = (∆CFFG − ∆CFF L )where:∆CFF LB = annual change in carbon stocks in living biomass (includes above- and belowground biomass)
on forest land remaining forest land, tonnes C yr-1
∆CFFG = average annual increase in carbon due to biomass growth (also called biomass increment),
tonnes C yr-1
∆CFF L = annual average decrease in carbon due to biomass loss, tonnes C yr-1.
To simplify the example we assume that there is no biomass loss, so that ∆CFF L= 0. Hence in this example,∆CFF LB = ∆CFFG . The biomass increment ∆CFFG is calculated according to Equation 3.2.4 as:∆CFFG = ∑ij (Aij ● GTOTALij ) ● CF
where:∆CFFG = average annual increase in carbon due to biomass increment in forest land remaining forest
land by forest type and climatic zone, tonnes C yr -1
Aij= area of forest land remaining forest land, by forest type (i= 1 to n) andclimatic zone ( j=1 to m), ha

GTOTALij = annual average increment rate in total biomass in units of dry matter by
forest type (i= 1 to n) and climatic zone ( j=1 to m), , tonnes d.m. ha-1 yr -1
CF= carbon fraction, tonnes C (tonnes d.m.)-1 (default value 0.5, with 2% uncertainty)

In this example, the area of forest land remaining as forest is assumed to be 10 million hectares. Assume further
that there is only one forest type and one climatic zone, so that n = m = 1, which simplifies the expression of∆CFFG above to be:∆CFFG = A ● GTOTAL ● CF
where GTOTAL is now the annual average increment rate in total biomass, averaged over the whole land area. In
general, the value for GTOTAL can be calculated from Equation 3.2.5 in Section 3.2.1.1.1.1 for each forest typeand climatic zone, taking into account the parameter values in Annex 3A.1.1 In the present example, a default
value of 3.1 tonnes d.m. ha-1 yr-1, with a default percent uncertainty of 50%, is given for GTOTAL, so for the
average annual increase in carbon stock due to biomass increment on forest land remaining forest land is:∆CFF LB = ∆CFFG = 10,000,000 ● 3.1 ● 0.5 tonnes C yr-1 = 15,500,000 tonnes C yr-1

Forest Land Converted to Grassland
The basic method for Tier 1 to estimate carbon stock changes in biomass due to conversion of forest land to
grassland is given in Section 3.4.2.1.

1

Default values for the average annual aboveground biomass GW and the root-to-shoot ratio R entering Equation 3.2.5 canbe found in Annex 3A.1, in Tables 3A.1.5, 3A.1.6 and 3A.1.8 (for R).

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Chapter 5: Cross-Cutting Issues

Equation 3.4.13 gives the annual carbon stock change from the conversion of forest land into grassland,assuming the year of conversion, as:∆CLGLB = AConversion ● (CConversion + CGrowth)
CConversion = CAfter – CBefore
where:∆CLGLB=

Annual change in carbon stocks in living biomass as a result of land use conversion to
grassland from some initial land use, tonnes C yr-1

AConversion =Annual area of land converted to grasslands from some initial use, ha yr-1

CConversion =Carbon stocks removed when lands are converted from some initial use to grassland,
tonnes C ha-1

CGrowth=Carbon stocks from one year of growth of grassland vegetation after conversion,
tonnes C ha-1

CAfter=Carbon stocks in biomass immediately after conversion to grassland, tonnes C ha-1

CBefore=Carbon stocks in biomass immediately before conversion to grassland, tonnes C ha-1

If the default values are expressed as biomass per hectare, it will be necessary to convert to carbon using CF of
0.5 as a default, with an uncertainty for CF of 2%.
In this example, the area of forest converted to grassland is 500 hectares. The emission factors and the associateduncertainties are provided in Chapter 3.2.1.1.2 and Table 3.4.9 in Section 3.4.2.1 of Chapter 3. For this example
we assume that:= 80 tonnes C ha−1, with percent uncertainty of 24%

CFLB= CBefore

CAfter= 0 tonne C ha-1, with percent uncertainty of 0%

CGLB= CGrowth = 3 tonnes C ha−1, with percent uncertainty of 60%

Replacing the above values in the equation gives:∆CLGLB = AFG ● ( – CFLB + CGLB )= 500 ha ● ( −80 + 3 ) tonnes C ha−1

= −38,500 tonnes C

Step 2: Assessment of uncertainties f or each activity
Forest Land Rem aining Forest Land
The uncertainty associated with estimated forest land area must be determined based on expert judgement. If theestimate is based on national surveys with designed statistical sampling (see Section 5.3, Sampling and Table
2.3.6 in Chapter 2) then statistical methods can be used to calculate the uncertainty.
In this example, it is assumed that the area of managed forest comes from administrative records. The agency
that compiles them used a good practice method and an uncertainty in the area estimates of 20%, based on
expert judgement.
The uncertainty of the annual biomass growth depends on the uncertainty of input parameters. If the country isusing default parameters, uncertainty will be high and can be only roughly estimated with expert judgment (seeChapter 3). If the annual growth in biomass is calculated according to Equation 3.2.4 and converted to carbon
with CF, then the uncertainty estimate of the growth in biomass carbon ( U ∆C FF ) is obtained as:G

U ∆CFF = U AFF + U GTOTAL + U CF
2

2

2

G

If we define U GC TOTAL as the percentage uncertainty of the annual biomass growth in terms of carbon per unit
area (i.e., the combined uncertainty of GTOTAL ● CF), then:U GCTOTAL = U G TOTAL 2 + U CF 2

U GCTOTAL = 50% 2 + 2% 2 = 50.04%

5.18

IPCC Good Practice Guidance for LULUCF

Identifying and Quantifying Uncertainties

Before the combined uncertainties of the activity information AFF (area of forest land remaining forest land) andthe emission factor (annual biomass growth in terms of carbon, GCTOTAL) can be calculated, it must be
determined whether they are correlated. In this example, the inputs are derived from independent sources, and it
is reasonable to assume that they are not correlated. Consequently, Equation 5.2.1 can be used to give theU ∆CFF as:GU ∆ C FF=GU A FF + U GC TOTAL
22= 20% 2 + 50.04%2 = 53.8%
where:U ∆C FF
U AFF=

percent uncertainty of the change in carbon stock=

percent uncertainty of the forest land area estimates

G

Forest land converted to Grassland
It is also necessary to estimate the uncertainty associated with the carbon stock change resulting from land-use
change. Depending on the source, type and density of the data, statistical error estimates might not be possible,
and expert judgement will be used. In this example, since the carbon stock immediately after the conversion
CAfter is assumed to be zero with certainty, the uncertainty of the carbon stock change, as calculated with
Equation 3.4.13, has three components: the uncertainty in carbon stock immediately before the conversion UCF,
(F = Forest), the uncertainty in carbon stock of grassland vegetation after the conversions UCG, (G = Grassland)
and the uncertainty associated with the estimate of the area that has been converted UAFG. Using Equation 5.2.2
and the example values for the carbon stocks and uncertainties as given in Step 1 above, the percent uncertaintyof the carbon stock change per hectare UФ is estimated as:UΦ ==( U C F • C F ) 2 + ( U CG • C G ) 2
CF + CG(24% • ( −80)) 2 + (60% • 3) 2
− 80 + 3= 25%

The total uncertainty for biomass carbon stock change for this simplified example of land-use change is thencalculated using Equation 5.2.1, combining the uncertainty in carbon stock change per hectare with theuncertainty in the estimate of the converted area, which – in our example – is assumed to be 30%. Hence:
U ∆CFG = U A FG 2 + U Φ 2= 30%2 + 25%2 = 39%

Step 3: Assessment of the total uncertainties f rom the LULUCF sector
In this simple example, the uncertainty of the LULUCF Sector is estimated by combining the uncertainty of theestimates of the two activities. Uncertainties for a real world case with more category estimates can be combinedin the same way.

Total uncertainty for this example
Estimateof the associatedcarbon stock change(tonne C yr -1)

U∆C

Forest Land Remaining as Forest

15 500 000

53.8%

Forest Land Converted to Grassland-38 50039%

15 461 500

54%

Land-Use Category

Total

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Chapter 5: Cross-Cutting Issues

The overall uncertainty is then estimated from Equation 5.2.2 to be:

U TOTAL =(53.8% • 15500000) 2 + (39% • (−38500)) 2
15500000 + (−38500)= 54%

The overall uncertainty from these two LULUCF activities, when expressed as percent uncertainty is 54%. The
uncertainty expressed as the relative standard error of the estimate is obtained by dividing the percent uncertainty
by 2. It should be noted that the formula implies correlations among the estimates due to the reliance on identicalconversion and expansion factors for both activities. In practice, however, this correlation may be small. If not,the calculations should be done for independent samples, e.g., during Tier 2 uncertainty analysis (such as Monte
Carlo).

Step 4: Combination of LULUCF uncertainties with other source categories
Finally, the uncertainty estimate for the LULUCF sector can be combined by uncertainty estimates for othersource categories using either a Tier 1 or Tier 2 method.

5.2.5

Reporting and documentation

The general advice on reporting given in GPG2000 is also applicable for the LULUCF sector. The result of a
Tier 1 uncertainty analysis for the LULUCF sector can be reported adding the lines for the relevant LULUCF
categories to Table 6.1 in Section 6.3 in Chapter 6 of GPG2000, with taking the guidance given in Section 6.3.2
of GPG2000 into account.
According to GPG2000, the analysis can be performed using CO2 equivalent emissions calculated using global
warming potentials (GWP) specified by COP3, Decision 2/CP.3.2

2

The methodology is also generally applicable using other weighting schemes.

5.20

IPCC Good Practice Guidance for LULUCF

Sampling

5.3

SAMPLING

5.3.1

Introduction

Data for the LULUCF sector are often obtained from sample surveys and typically are used for estimatingchanges in land use or in carbon stocks. National forest inventories are important examples of the type of surveys
used. This section provides good practice guidance for the use of data from sample surveys for the reporting ofemissions and removals of greenhouse gases, and for the planning of sample surveys in order to acquire data forthis purpose. Sampling also is important for monitoring Kyoto Protocol projects, and Chapter 4 provides specificrecommendations consistent with this section. This section provides good practice guidance concerning:•

Overview on sampling principles (Section 5.3.2);•

Sampling design (Section 5.3.3);•

Sampling methods for area estimation (Section 5.3.4);•

Sampling methods for estimating emissions and removals of greenhouse gases (Section 5.3.5);•

Uncertainties in sample based surveys (Section 5.3.6).

Useful general references on sampling include: Raj (1968), Cochran (1977), De Vries (1986), Thompson (1992),
Särndal et al. (1992), Schreuder et al. (1993), Reed and Mroz (1997), and Lund (1998).

5.3.2

Overview on Sampling Principles

Sampling infers information about an entire population by observing a fraction of it: the sample (see Figure
5.3.1). For example, changes of carbon in tree biomass at regional or national levels can be estimated from thegrowth, mortality and cuttings of trees on a limited number of sample plots. Sampling theory then provides themeans for scaling up the information from the sample plots to the selected geographical level. Properly designed
sampling can greatly increase efficiency in the use of inventory resources. Furthermore, field sampling is generally
needed in developing LULUCF inventories because, even if remote sensing data provide complete territorial
coverage, there will be a need for ground-based data from sample sites for interpretation and verification.
Figure 5.3.1

Principle of sampling

Selection

Population

SampleInference
Standard sampling theory relies on random selection of a sample from the population; each unit in the population
has a specific probability of being included in the sample. This is the case when sample plots have been
distributed entirely at random within an area, or when plots have been distributed in a systematic grid system aslong as the positioning of the grid is random. Random sampling reduces the risk of bias and allows for an
objective assessment of the uncertainty of the estimates. Therefore, randomly sampled data generally should be
used where available, or when setting up new surveys.
Samples may also be taken at subjectively chosen locations, which are assumed to be representative for the
population. This is called subjective (or purposive) sampling and data from such surveys are often used in

IPCC Good Practice Guidance for LULUCF

5.21

Chapter 5: Cross- Cutting Issues

greenhouse gas inventories (i.e., when observations from survey sites that were not selected randomly are used torepresent an entire land category or subdivision). Under these conditions, observations about, for example, forest
type might be extrapolated to areas for which they are not representative. However, due to limited resources
greenhouse gas inventories may need to make use of data also from subjectively selected sites or research plots.
In this case, it is good practice to identify, in consultation with the agencies responsible for the sites or plots, theland areas for which the subjective samples can be regarded as representative.

5.3.3

Sampling Design

Sampling design determines how the sampling units (the sites or plots) are selected from the population and thus
what statistical estimation procedures should be applied to make inferences from the sample. Random sampling
designs can be divided into two main groups, depending on whether or not the population is stratified (i.e.,
subdivided before sampling) using auxiliary information. Stratified surveys will generally be more efficient interms of what accuracy can be achieved at a certain cost. On the other hand, they tend to be slightly more
complex, which increases the risk of non- sampling errors due to incorrect use of the collected data. Sampling
designs should aim for a good compromise between simplicity and efficiency, and this can be promoted byfollowing three aspects of good practice as set out below:•

Use of auxiliary data and stratification;•

Systematic sampling;•

Permanent sample plots and time series data.

5.3.3.1

U SE OF AUXI LIARY DATA AND STRATIFICATIO N

One of the most important sampling designs which incorporate auxiliary information is stratification, whereby
the population is divided into subpopulations on the basis of auxiliary data. These data may consist ofknowledge of legal, administrative boundaries or boundaries of forest administrations which will be efficient tosample separately, or maps or remote sensing data distinguishing between upland and lowland areas or between
different ecosystem types. Since stratification is intended to increase efficiency, it is good practice to use
auxiliary data when such data are available or can be made available at low additional cost.
Stratification increases efficiency in two main ways: (i) by improving the accuracy of the estimate for the entire
population; and (ii) by ensuring that adequate results are obtained for certain subpopulations, e.g., for certain
administrative regions.
On the first issue, stratification increases sampling efficiency if a subdivision of the population is made so thatthe variability between units within a stratum is reduced as compared to the variability within the entire
population. For example, a country may be divided into a lowland region (with certain features of the land- use
categories of interest) and an upland region (with different features of the corresponding categories). If eachstratum is homogeneous a precise overall estimate can be obtained using only a limited sample from eachstratum. The second issue is important for purposes of providing results at a specific degree of accuracy for all
administrative regions of interest, but also in case sampled data are to be used together with other existingdatasets, which have been collected using different protocols with the same administrative or legal boundaries.
Use of remote sensing or map data for identifying the boundaries of the strata (the land- use class subdivisions to
be included in a sample survey) can introduce errors where some areas may be incorrectly classified as
belonging to the stratum whilst other areas that do belong to the specific class are missed. Errors of this kind canlead to substantial bias in the final estimates, since the area identified for sampling will then not correspond to
the target population. Whenever there is an obvious risk that errors of this kind may occur, it is good practice tomake an assessment of the potential impact of such errors using ground truth data.
When data for the reporting of greenhouse gas emissions or removals are taken from existing large-scaleinventories, such as national forest inventories, it is convenient to apply the standard estimation procedures ofthat inventory, as long as they are based on sound statistical principles. In addition, post- stratification (i.e.,
defining strata based on remote sensing or map auxiliary data after the field survey has been conducted) meansthat it may be possible to use new auxiliary data to increase efficiency without changing the basic field design(Dees et al. 1998). Using this estimation principle, the risk for bias pointed out in the previous paragraph also
can be avoided.

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IPCC Good Practice Guidance for LULUCF

Sampling

5.3.3.2

S YSTEMATIC SAMPLING

Sample based forest or land-use surveys generally make use of sample points or plots on which thecharacteristics of interest are recorded. One important issue here regards the layout of these points or plots. It is
often appropriate to allocate the plots in small clusters in order to minimise travel costs when covering large
areas with a sample based survey. With cluster sampling, the distance between plots should be large enough to
avoid major between-plot correlation, taking (for forest sampling) stand size into account. An important issue iswhether plots (or clusters of plots) should be laid out entirely at random or systematically using a regular grid,
which is randomly located over the area of interest (see Figure 5.3.2). In general, it is efficient to use systematic
sampling, since in most cases this will increase the precision of the estimates. Systematic sampling also
simplifies the fieldwork.
Figure 5.3.2

Simple random layout of plots (left) and systematic layout (right)

Somewhat simplified, the reason why systematic random sampling generally is superior to simple random
sampling is that sample plots will be distributed evenly to all parts of the target area.3 With simple random
sampling, some parts of an area may have many plots while other parts will not have any plots at all.

5.3.3.3

P ERMANENT SAMPLE PLOTS AND TIME SERI ES DATAGreenhouse gas inventories must assess both current state and changes over time (e.g., in areas of land- use types
and carbon stocks). Assessment of changes is most important and it involves repeated sampling over time. The
time interval between measurements should be determined based on the frequency of the events that causechanges, and also on the reporting requirements. Generally, sampling intervals of 5-10 years are adequate in the
LULUCF sector, and in many countries data from well designed surveys are already available for many decades,
especially in the forest sector. Nevertheless, since estimates for the reporting are required on an annual basis,interpolation and extrapolation methods of the kind described in Section 5.6 will need to be applied. Wheresufficiently long time series are not available, it may be necessary to extrapolate backwards in time to capture thedynamics of carbon stock changes, using the good practice guidance in Section 5.6 in conjunction with goodpractice guidance in Chapters 3 and 4 about the periods required and assumptions to be made.
When undertaking repeated sampling, the required data regarding the current state of areas or carbon stocks areassessed on each occasion. Changes are then estimated by calculating the difference between the state at time t + 1
from the state at time t. Three common sampling designs can be used for change estimation:•

The same sampling units are used on both occasions (permanent sampling units);•

Different, independent sets of sampling units are used on both occasions (temporary sampling units);•

Some sampling units can be replaced between occasions while others remain the same (sampling withpartial replacement).

Figure 5.3.3 shows these three approaches.

3

In unusual cases when there is a regular pattern in the terrain that may coincide with the systematic grid system, systematic
sampling may lead to less precise estimates than simple random sampling. However, such potential problems generally canbe handled by orienting the grid system in another direction.

IPCC Good Practice Guidance for LULUCF

5.23

Chapter 5: Cross- Cutting Issues

Figure 5.3.3

Use of different configurations of permanent and temporary sampling units
for estimating changes

Identical set

Independent sets

Sampling with partial replacement(permanent plots)(temporary plots)(permanent and temporary plots)

Sampling unit measured at occasion 1
Sampling unit measured at occasion 2
Permanent sample plots generally are more efficient in estimating changes than temporary plots because it iseasier to distinguish actual trends from differences that are only due to changed plot selection. However, there
are also some risks in the use of permanent sample plots. If the locations of permanent sample plots become
known to land managers (e.g., by visibly marking the plots), there is a risk that management of the permanentplots will differ from the management of other areas. If this occurs, the plots will no longer be representative andthere is an obvious risk that the results will be biased. If it is perceived that there might be a risk of the above
kind, it is good practice to assess some temporary plots as a control sample in order to determine if theconditions on these plots deviate from the conditions on the permanent plots.
The use of sampling with partial replacement can address some of the potential problems with relying on
permanent plots, because it is possible to replace sites that are believed to have been treated differently.
Sampling with partial replacement may be used, although the estimation procedures are complicated (Scott and
Köhl 1994; Köhl et al. 1995).
When only temporary plots are used, overall changes still can be estimated but it will no longer be possible tostudy land- use transfers between different classes unless a time dimension can be introduced into the sample.
This can be done by drawing on auxiliary data, for example maps, remote sensing or administrative records
about the state of land in the past. This will introduce additional uncertainty into the assessment which it may bedifficult to quantify other than by expert judgement.

5.3.4

Sampling Methods for Area Estimation

Chapter 2 presents different approaches for assessing areas or changes in areas of land- use classes. Many ofthese approaches rely on sampling. Areas and changes in areas can be estimated in two different ways using
sampling:•

Estimation via proportions;•

Direct estimation of area.

The first approach requires that the total area of the survey region is known, and that the sample survey provides
only the proportions of different land-use classes. The second approach does not require the total area to be known.
Both approaches require assessment of a given number of sampling units located in the inventory area. Selectionof sampling units may be performed using simple random sampling or systematic sampling (see Figure 5.3.2).
Systematic sampling generally improves the precision of the area estimates, especially when the different landuse classes occur in large patches. Stratification, which is discussed in Section 5.3.3.1, also may be applied to
improve the efficiency of the area estimates; in this case it is good practice to perform the procedures described
below independently in each stratum.
In estimating proportions it is assumed that the sampling units are dimensionless points, although a small areaaround each point must be considered when the land- use class is determined. Sample plots may also be used forarea estimation, although this principle is not further elaborated here.

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IPCC Good Practice Guidance for LULUCF

Sampling

5.3.4.1

E STIMATION OF AREAS VIA PROPORTIONS

The total area of an inventory region is generally known. In this case the estimation of the areas of different
land-use classes can be based on assessments of area proportions. When applying this approach, the inventory
area is covered by a certain number of sample points, and land- use is determined for each point. The proportionof each land- use class then is calculated by dividing the number of points located in the specific class by the
total number of points. Area estimates for each land use class are obtained by multiplying the proportion of eachclass by the total area.
Table 5.3.1 provides an example of this procedure. The standard error of an area estimate is obtained as
A (p i • (1 − p i )) (n − 1) , where pi is the proportion of points in the particular land- use class, A the known total

area, and n the total number of sample points.4 The 95% confidence interval for Ai, the estimated area of land use
class i, will be given approximately by ±2 times the standard error.
TABLE 5.3.1
EXAMPLE OF AREA ESTIMATION VIA PROPORTIONS
Sampling procedure

Estimation of proportions

Estimated areas of land use classes

Standard error

pi = ni / n

Ai = pi · As(Ai)p1 = 3/ 9 ≅ 0.333

A1= 300 has(A1)= 150.0 ha

p2 = 2/ 9 ≅ 0.222

A2= 200 has(A2)= 132.2 ha

p3 = 4/ 9 ≅ 0.444

A3= 400 has(A3)= 158.1 ha

Sum = 1.0

Total = 900 ha

Where:
A=

total area (= 900 ha in the example)

Ai =estimated area of land use class ini=number of points located in land- use class in=total number of points

Estimates of areas involved in land- use change can be made by introducing classes of the type Aij where land
use changes from class i to class j between successive surveys.

5.3.4.2

D IRECT ESTIMATI ON OF AREA

Whenever the total inventory area is known, it is efficient to estimate areas, and area changes, via assessment ofproportions, since that procedure will result in the highest accuracy. In cases where the total inventory area is notknown or is subject to unacceptable uncertainty, an alternative procedure that involves a direct assessment ofareas of different land-use classes can be applied. This approach can only be used when systematic sampling isapplied; each sample point will represent an area corresponding to the size of the grid cell of the sample layout.
For example, when sample points are selected from a square systematic grid with 1000 metres distance between
the points, each sample point will represent an area of 1km ● 1km = 100 ha. Thus, if 15 plots fall within a
specific land-use class of interest the area estimate will be 15 ● 100 ha = 1500 ha.

5.3.5

Sampling Methods for Estimating Greenhouse Gases
Emissions and Removals

Sampling is needed not only for area estimation, but also for estimating the state of carbon stocks and emissions
and removals of greenhouse gases. As a basis for this, assessment of variables such as tree biomass and soil

4

Note that this formula is only approximate when systematic sampling is applied.

IPCC Good Practice Guidance for LULUCF

5.25

Chapter 5: Cross- Cutting Issues

carbon content is made on the plots. Measurements of these quantities can be made directly on site, or bylaboratory analysis of samples, or deduced using models based on correlated variables (such as standard
measurements of tree height and diameter) to obtain actual stock, or emissions and removals, of greenhousegases at the plot level.
Only general guidelines can be given regarding the use of sampling for direct estimation of greenhouse gas
emissions or removals. Compared to traditional forest or land- use inventories, the assessments on the plots tend
to be slightly more complicated, particularly for the soil carbon pool. An important issue in random sampling
surveys is the layout of plots e.g., tree measurements or soil sampling. It is important that this layout isconducted according to strict procedures rather than leaving it to the surveyors to choose appropriate spots formeasurements or selecting samples.
Often, inventories of greenhouse gases will be incorporated into on-going national forest or land- use monitoring
programmes. In this case it is generally good practice to use the established procedures of those inventories, bothfor purposes of estimating the quantities of interest and the corresponding uncertainties. However, the effects ofmodel conversion errors in final conversion steps (e.g., when applying biomass expansion factors) in this caseneed to be taken into account. This is further discussed in the next section.

5.3.6

Uncertainties in Sample Based Surveys

The methods described in Chapters 3 and 4 are linked with default uncertainty ranges for the default values
presented, and Section 5.2 of this chapter describes how to combine uncertainties in order to estimate the overall
uncertainty of an inventory. If an inventory agency uses default values, they can refer to the uncertainty ranges
provided in Chapters 3 and 4. When implementing higher tier methods, however, the inventory agency often willuse country- specific values and data obtained through research, literature review, field sampling, or remotesensing. Where country- specific data are used, inventory agencies need to develop their own uncertainty
estimates, based on expert judgment or – if sampling has been used – based on direct assessment of the precisionof the derived data or estimates.
The possibility to derive uncertainty estimates based on formal statistical procedures is a very important
advantage of applying sampling procedures in comparison to other methods; the reliability of the information
can be assessed based on the data acquired.
Thus, when data from random sampling are used for purposes of greenhouse gas inventory reporting, it is goodpractice to base the assessment of uncertainties on sampling principles, rather than using default values or expert
judgement. These uncertainties can then be combined with the uncertainties of other data or models usedaccording to the guidance in Section 5.2 of this chapter.
This section describes the different sources of errors in sample surveys and their effects on overall uncertainty inestimates. Good practice guidance is given on how to assess uncertainties in sample based surveys. The
discussion on causes of errors is general, and is valid also when data are derived using non-random sampling
schemes (e.g., data from research plots) and then scaled up on the basis of area estimates to obtain results onnational level. The discussion of the sources of errors first describes errors in assessments at the sample unit
level, and then discusses issues in scaling up to estimates for some larger area.

5.3.6.1

T YPES OF ERRORS

Typically for LULUCF inventories, sampling data are acquired from sample plots in the field. To obtain
estimates for some larger area (e.g., a country), measurements made at the plot level need to be scaled up.
Several kinds of errors may occur in these steps:•

First, whenever measurements are carried out measurement errors due to various imperfections in technique
or instrumentation often occur. Measurement errors often are systematic, always deviating in a certain
direction from the true value. Such errors then will be propagated during the process of scaling up.
Measurement errors also may be random. In this case the average error is zero and the deviations are just as
likely to be positive as negative. The latter kinds of errors are less harmful than the systematic ones,
although they may lead to systematic errors when basic measurements are applied in models for deriving thequantity of interest (e.g., the volume of a tree).

•Second, the quantities of interest are not always measured directly, but models are applied to derive them.
For example, the amount of carbon in a tree usually is calculated by first deriving the tree volume based onmodels that use parameters such as tree species, diameter, and height as input variables, and then using othermodels or static expansion factors to convert volume to biomass and biomass to carbon. When applying

5.26

IPCC Good Practice Guidance for LULUCF

Samplingmodels, model errors will occur since models seldom are able to predict target quantities exactly. Modelerrors may be both random and systematic. The sizes are likely to vary depending on the values of the input
variables. As shown by Gertner and Köhl (1992), systematic model errors sometimes contributesignificantly to the overall uncertainty.
•

When plot level measurements are scaled up to some larger area, sampling errors occur due to the fact thatconditions across the larger area vary and measurements have only been made at the sample locations. The
average conditions within the selected sample plots seldom coincide exactly with the average conditions
within the entire area of interest. Sampling errors (using random sampling designs and unbiased estimators)
are only random, and these effects can be reduced by increasing the sample size, as discussed below andshown in Figure 5.3.4.

•

If upscaling is based on complete cover information (e.g., from remote sensing) rather than a sample based
survey, uncertainty will be introduced due to land areas being incorrectly classified. Classification errors canbe identified and corrected if a sample survey is conducted for studying the extent of such errors. In this case,surveys should be based on random sampling in order to avoid the likely systematic errors of a subjectively
selected sample.

•

Data registration and calculation errors are the final types of error that may occur. These errors are lesstechnical yet potentially important sources of uncertainty in connection with sample-based surveys. Dataregistry should be made directly to field computers or different people should independently register data fromfield forms to computer media in order to avoid registration errors. Calculations need to be checked according
to the basic principles of Quality Assurance in Section 5.5. The effects of registration and calculation errors are
difficult to assess. Often they are detected and can be corrected for when they cause major deviations fromplausible values. When they only cause minor deviations, they are likely to remain undetected.

5.3.6.2

S AMPLE SI ZE AND SAMPLING ERRO R

The relation between sampling errors, population variance, and sample size is commonly understood; increasing
sample size results in higher precision and heterogeneous populations (i.e., those with large within population
variation) require larger sample sizes to reach a certain precision. Where area proportions are to be estimated,sampling errors do not only depend on sample size but on the proportion itself. For a given sample size, thesampling error is largest for land- use class proportions p = 0.5; it decreases for p approaching 0 or 1.
The effect of different land-use class proportions (from p = 0.1 to p = 0.9) and sample sizes (from n = 100 to n =1,000) on the sampling error of the area estimate is shown in Figure 5.3.4 for two different area sizes (1,000 haand 100,000 ha).
Figure 5.3.4

Relationship between the standard error of the area estimate s(A), theproportion of the land- use class p, and the sample size nArea = 1000ha

Area = 100000ha
s(A)

s(A)

6000
4000
2000
0
0.2

1000
750
500
0.4

p

n

100
80
60
40
20
0
0.2

1000
750
500

250
0.6
0.8

0

IPCC Good Practice Guidance for LULUCF

0.4
p

250

0.6
0.8

n

0

5.27

Chapter 5: Cross- Cutting Issues

5.3.6.3

Q UANTIFYING ERRORS IN SAMPLE BASED SURVEYS

In basic sampling theory, the quantities connected to the population units are assumed to be observed without
errors. Moreover, the variables of interest (e.g., removals of greenhouse gases) are assumed to be directly
recorded at the sampling units; thus no errors due to model conversions need to be considered. In this case,provided adequate statistical estimators have been used, the sample-based estimates of totals (e.g., removals ofgreenhouse gases at the national level) are unbiased and the corresponding precision can be assessed based on
the data acquired.
In many cases (e.g., sampling for area estimation) the above assumptions can be considered valid, and then it isgood practice to assess the uncertainty of the estimates strictly according to the principles of sampling theory,
taking into account what sampling design and estimator were used. The details of such calculations are provided
in sampling textbooks such as the references that are introduced in Section 5.3.1. Model errors may enter into theoverall uncertainty estimates in different ways. One important case is when the models only give rise to random
errors at the level of individual sampling units (e.g., if biomass models have been applied to plot-level tree data).
In such cases, the random model errors will inflate the between-plot variability, which will lead to an increased
uncertainty of the overall estimates. In this case the standard methods of estimating uncertainties according tosampling theory still can be used, with good approximation, without modifications. Thus, under these conditions
it is good practice to apply standard sampling theory for deriving the uncertainty estimates, rather than theapproaches of Section 5.2.
When models are likely to give rise to (unknown) systematic errors or when they have been used only at some
final conversion step (like biomass expansion factors applied to estimates of total volume) the uncertainties
introduced should be accounted for. In this case it is good practice to use the Tier 1 – or Tier 2 – approach of
Section 5.2 for deriving overall uncertainty.
In general, it is good practice to assess the applicability of core models for the target population through pilot
studies. When models are applied on datasets representing conditions and measurement procedures far different
from the ones they were derived upon, there is an obvious risk that the models will incur systematic errors.
Measurement errors can lead to substantial systematic errors, especially in case changes are estimated based on
repeated measurements and the systematic error levels vary over time. The size of measurement errors can only
be estimated by careful control measurements – on a subsample of the plots – although such check assessments
are in some cases difficult to implement (e.g., in soil surveys). In case greenhouse gas inventory reporting isbased on sampling, it is good practice to conduct careful check assessments on a (small) fraction of the plots, inorder to assess the size of the measurement errors. This fraction may be in the order of 1% to 10% depending on
the actual sample size and the cost of the control survey, as well as the level of training and experience of thesurveyors.
For some variables it is possible to obtain true measurement values through very accurate control procedures,and in such cases the goal should be to estimate the size of the systematic measurement errors. In other cases it
may be impossible to measure/assess a true value, and in such cases only the variability between surveyors
should be reported.
If major measurement errors are found in a carefully conducted control survey, it is good practice to correct forthese errors before the final estimates of greenhouse gas emissions/removals are calculated.

5.28

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

5.4

METHODOLOGICAL CHOICE IDENTIFICATION OF KEY CATEGORIES

5.4.1

Introduction

This chapter addresses how to identify key categories 5 in a national inventory including LULUCF.
Methodological choice for individual source and sink categories is important in managing overall inventory
uncertainty. In the decision trees in Chapters 3 and 4 of this report, specific guidance is given for each category
and each activity under Articles 3.3 and 3.4 of the Kyoto Protocol using the concept of key categories. Generally,
inventory uncertainty is lower when emissions and removals are estimated using a higher tier. However, thesegenerally require extensive resources for data collection, so it may not be feasible to use higher tier methods forevery category of emissions and removals. It is therefore good practice to make the most efficient use ofavailable resources by identifying those categories that have the greatest contribution to overall inventory
uncertainty. By identifying these key categories in the national inventory, inventory agencies can prioritise theirefforts and improve their overall estimates. It is good practice for each inventory agency to identify its national
key categories in a systematic and objective manner. Such a process will lead to improved inventory quality, as
well as greater confidence in the emission estimates that are developed.
Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (GPG2000,
IPCC, 2000) identifies a key source category as “one that is prioritised within the national inventory systembecause its estimate has a significant influence on a country’s total inventory of direct greenhouse gases in termsof the absolute level of emissions, the trend in emissions, or both”. The concept of key sources was originallyderived for emissions excluding the LULUCF sector and as implemented in GPG2000 has enabled countries to
identify those source categories that should be estimated using higher tiers if sufficient resources are available.
In this report, the definition is expanded to also cover LULUCF emissions by sources and removals by sinks. In
this document whenever the term key category is used, it includes both sources and sinks. The inclusion of the
LULUCF categories in the key category analysis facilitates the determination of priorities across all sectors of
the national inventory and, where relevant, for Kyoto Protocol supplementary information as well.
Any inventory agency that has prepared a national greenhouse gas inventory will be able to identify key
categories in terms of their contribution to the absolute level of national emissions. For those inventory agencies
that have prepared a time series, the quantitative determination of key categories should include evaluation ofboth the absolute level and the trend of emissions and removals. Some key categories may only be identified
when their influence on the trend of the national inventory is taken into account.
The quantitative approaches to determine key categories are described in Section 5.4.2 (Quantitative Approaches
to Determining Key Categories). Both a basic Tier 1 approach and a Tier 2 approach, which takes uncertainties
into account, are described. In addition to making a quantitative determination of key categories, it is goodpractice to consider qualitative criteria, particularly when a Tier 1 assessment is performed or lower tier estimation
methods are used. These qualitative criteria are described in Section 5.4.3 (Qualitative Considerations). The goodpractice guidance provided in Sections 5.4.2 and 5.4.3 is applicable to the full inventory of emissions and removals.
For estimates prepared under Articles 3.3 and 3.4 of the Kyoto Protocol, there are additional considerations as
described in Section 5.4.4. The guidance on the application of results is given in 5.4.5. The derivation of thresholds
for the Tier 1 level and trend assessments taking the LULUCF sector into account is described in Section 5.4.7.
Finally, Section 5.4.8 gives an example of the application of the Tier 1 key category analysis.

5.4.2

5

Quantitative Approaches to Determining Key
Categories

In GPG2000 the concept was named “key source categories” and dealt with the inventory excluding the LULUCF sector.
However, because an inventory including the LULUCF sector can consist of both emissions and removals, the term "keycategory" is used here to better reflect that both sources and sinks are included. In the context of the UNFCCC inventory,
categories are land-use categories as described Table 3.1.1 in Chapter 3. In the context of the Kyoto Protocol, each activity
under Articles 3.3 and 3.4 (if elected) is a category.

IPCC Good Practice Guidance for LULUCF

5.29

Chapter 5: Cross- Cutting Issues

In each country’s national inventory, certain categories are particularly significant in terms of their contribution
to the overall uncertainty in the inventory. It is important to identify these key categories so that resources
available for inventory preparation may be prioritised and the best possible estimates prepared.
Two tiers for performing the key category analysis are described, consistent with the two-tiered quantitative
approach to identify key source categories described in Chapter 7 (Methodological Choice and Recalculations)of GPG2000. In the sections below, this approach is adapted to allow the incorporation of LULUCF categories.
The approach adapted for integrating the LULUCF categories is designed to address three objectives: (i) toenable continued assessment of key source categories without LULUCF (as is described in GPG2000); (ii) toassess the relative importance of LULUCF categories by integrating them into the overall key category analysis;and (iii) to be consistent with guidance and decisions of the Conference of the Parties to the UNFCCC and the
Kyoto Protocol regarding the identification of key categories.
With these objectives in mind, the key quantitative category analysis should be performed as follows:(i)

The key (source) categories should first be identified for the inventory excluding LULUCF (i.e., keycategories should be identified for the energy, industrial processes, solvent and other product use,
agriculture, and waste sectors) following the guidance in GPG2000, Chapter 7 (Methodological Choice
and Recalculation).

(ii)

The key category analysis then should be repeated for the full inventory including the LULUCF categories.
It is possible that some non-LULUCF categories identified as key in the first analysis will not appear as key
when the LULUCF categories are included. In this case, these categories should still be considered as key. Ina few cases, in countries with small net LULUCF emissions or removals, the integrated analysis may
identify additional non-LULUCF categories as key. In this situation, the analysis performed for the nonLULUCF sectors should be used to identify the key categories in those sectors, and the additional nonLULUCF categories identified in the combined analysis should not be considered as key.

Any agency that has developed an essentially complete greenhouse gas inventory can perform a Tier 1 Level
Assessment to identify key source or sink categories for the overall emission level. Those inventory agencies thathave developed emission inventories for more than one year will also be able to perform a Tier 1 Trend
Assessment to identify key categories that influence the trend in emissions. If national category uncertainties orparameter uncertainties are available, inventory agencies can use Tier 2 to identify key categories. The Tier 2
approach is more detailed than the Tier 1 and is likely to reduce the number of key categories identified. The
Tier 2 approach may also take into account a higher complexity, for example assessing key activity data andestimation parameters separately. If both Tier 1 and Tier 2 analysis have been performed it is good practice to
use the results of the Tier 2 analysis.
Figure 5.4.1

Decision tree to identify key categories of sources and sinks
Areinventory
data available formore than
one year?

Determine key categories
using the Tier 1 Level
Assessment and qualitative
considerations

No

Yes
Are
country- specificuncertainty estimatesavailable for eachcategory
estimate?

No

Determine key categories
using the Tier 1 Level andTrend Assessment andqualitative considerations

Yes
Determine key categories using theTier 2 Level and Trend
Assessments (incorporating national
uncertainty estimates) andqualitative considerations

5.30

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

The decision tree in Figure 5.4.1 shown above illustrates how inventory agencies can determine which approach
to use for the identification of key categories. This figure was modified from the Figure 7.1 in Chapter 7 of
GPG2000 to make it applicable to the LULUCF sector.

AGGREGATIO N LEVEL
The results of the analysis of key categories will be most useful if the analysis is done at the appropriate level ofdetail. For the LULUCF sector, the recommended level of analysis is the level of category nomenclature used in
Chapter 3, which is listed in Table 5.4.1 along with “special considerations” which provide additional
information on the key category analysis for various categories. Table 5.4.1 is adapted from Table 7.1 from
Chapter 7 of GPG2000 to include the categories of the LULUCF sector. It is reprinted with all source categories
and sectors included so as to facilitate the development of an integrated key category analysis. Each suggested
category for LULUCF activities in Table 5.4.1 comprises several subcategories and it is good practice to further
evaluate the significance of these subcategories for purposes of choosing appropriate methods and prioritisingresources. Following guidance provided in GPG2000, it is good practice to identify subcategories as key if theyaccount for 25-30 percent of the overall emissions or removals of the category. Table 3.1.3 in Chapter 3 lists thesubcategories associated with each category given in Table 3.1.1 in Chapter 3 for purposes of this analysis. For
example, carbon stock changes in soil and biomass can be distinguished within the “forest land remaining forestland” category. If a country prepares its estimates following the LUCF categories from the IPCC Guidelines,
they can map their estimates onto the categories listed in Table 5.4.1 by following the guidance given by Table
3.1.1 in Section 3.1.2 and details in the respective sections of Chapter 3.
Countries may choose to perform the quantitative analysis at a more detailed level. In this case possible
correlations should be taken into account (see the Tier 2 approach for uncertainty assessments described in
Section 5.2, Identifying and Quantifying Uncertainties). The assumptions about such correlations should be thesame when assessing uncertainties and identifying key categories. Table 5.4.1 indicates subcategories that can be
distinguished without the need to take correlations into account.
If data are available, the analysis can be performed for emissions and removals separately within a given
category. If this is not feasible it is important to apply the qualitative criteria to identify key categories insituations where emissions and removals cancel or almost cancel. See Section 5.4.3 for qualitative considerations.
TABLE 5.4.1
SUGGESTED IPCC SOURCE/SINK CATEGORIES FOR LULUCF AND NON-LULUCF a
Source/Sink Categories to be Assessed in Key Category
Analysis

Special Considerations

LULUCF
Forest land remaining forest land
Croplands remaining croplands
Grassland remaining grassland
Wetland remaining wetland
Settlements remaining settlements

Assess key categories separately for CO2, CH4 and N2O. If
the category is key, assess the significance of subcategories
by identifying those that contribute 25-30% to the total
level of emissions or removals from the category. For
information on the subcategories associated with eachcategory, see Table 3.1.1 and 3.1.3 in Chapter 3.

Conversion to forest land
Conversion to cropland
Conversion to grassland
Conversion to wetlandb

Conversion to settlements

In addition to the guidance above, assess the impact of all
deforestation occurring within the country according to thequalitative guidance provided in the sixth bullet Section
5.4.3.

Conversion to other land
ENERGYCO2 Emissions from Stationary Combustion

Disaggregate to the level where emission factors are
distinguished. In most inventories, this will be the main
fuel types. If emission factors are determined
independently for some subsource categories, these should
be distinguished in the analysis.

Non- CO2 Emissions from Stationary Combustion

Assess CH4 and N2O separately.

Mobile Combustion: Road Vehicles

Assess CO2, CH4 and N2O separately.

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Chapter 5: Cross- Cutting Issues

TABLE 5.4.1 (Continued)
SUGGESTED IPCC SOURCE/SINK CATEGORIES FOR LULUCF AND NON- LULUCF a
Mobile Combustion: Water- borne Navigation

Assess CO2, CH4 and N2O separately.

Mobile Combustion: Aircraft

Assess CO2, CH4 and N2O separately.

Fugitive Emissions from Coal Mining and HandlingIf this source is key, it is likely that underground mining willbe the most significant subsource category.

Fugitive Emissions from Oil and Gas Operations

This source category comprises several subsource categories
which may be significant. Inventory agencies should assess thissource category, if it is key, to determine which subsource
categories are most important.

INDUSTRIAL PROCESSESCO2 Emissions from Cement Production
CO2 Emissions from Lime Production
CO2 Emissions from the Iron and Steel Industry
N2O Emissions from Adipic Acid and Nitric Acid
Production

Assess adipic acid and nitric acid separately.

PFC Emissions from Aluminium Production
Sulfur hexafluoride (SF6) from Magnesium Production
SF6 Emissions from Electrical Equipment
SF6 Emissions from Other Sources of SF6SF6 Emissions from Production of SF6
PFC, HFC, SF6 Emissions from Semiconductor
Manufacturing

Assess emissions from all compounds jointly on a GWPweighted basis, since they are all used in similar fashions
in the process.

Emissions from Substitutes for Ozone Depleting
Substances (ODS Substitutes)

Assess emissions from all HFCs and PFCs used as substitutes
for ODS jointly on a GWP- weighted basis, given theimportance of having a consistent method for all ODS sources.

HFC- 23 Emissions from HCFC- 22 Manufacture
AGRICULTURE
CH4 Emissions from Enteric Fermentation in Domestic
Livestock

If this source category is key, it is likely that cattle, buffalo andsheep will be the most significant subsource categories.

CH4 Emissions from Manure Management

If this source category is key, it is likely that cattle andswine will be the most significant subsource categories.

N2O Emissions from Manure Management
CH4 and N2O Emissions from Savanna Burning

Assess CH4 and N2O separately.

CH4 and N2O Emissions from Agricultural Residue
Burning

Assess CH4 and N2O separately.

Direct N2O Emissions from Agricultural Soils
Indirect N2O Emissions from Nitrogen Used inAgriculture
CH4 Emissions from Rice Production
WASTE
CH4 Emissions from Solid Waste Disposal Sites
Emissions from Wastewater Handling

Assess CH4 and N2O separately.

Emissions from Waste Incineration

Assess CO2 and N2O separately.

OTHER

Other sources of direct greenhouse gas emissions not listed
above should also be included, if possible.

ab

In some cases, inventory agencies modify this list of IPCC source categories to reflect particular national circumstances.
Reservoirs can be distinguished in the analysis.

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IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

The analysis can be performed using CO2-equivalent emissions calculated using global warming potentials(GWP) specified in the Guidelines for the preparation of national communications by Parties included in Annex
I to the Convention, Part I: UNFCCC reporting guidelines on annual inventories (UNFCCC Guidelines) and theAnnex to the Kyoto Protocol6. Each greenhouse gas from each source and sink category should be consideredseparately, unless there are specific methodological reasons for treating gases collectively. In the LULUCF
sector, for example, estimates will be prepared for emissions or removals of CO2, N2O and CH4. The key
category evaluation should be performed for each of these gases separately because the methods, emission
factors and related parameters differ for each gas.

5.4.2.1

T IER 1 METHOD TO IDENTIFY KEY CATEGORIES OF
SOURCES AND SINKSThe Tier 1 method to identify key categories assesses the influence of various categories of sources and sinks on
the level, and possibly the trend, of the national greenhouse gas inventory. When the national inventory estimates
are available for several years, it is good practice to assess the contribution of each category to both the level andtrend of the national inventory. If only a single year’s inventory is available, a Level Assessment should be
performed.
The Tier 1 method can be readily completed using a spreadsheet analysis. Tables 5.4.2 and 5.4.3 illustrate theformat of the analysis. Separate spreadsheets are suggested for the Level and Trend Assessments because it isnecessary to sort the results of the analysis according to two different columns, and the output of the sorting
process is more difficult to track if the analyses are combined in the same table. Both tables use a format similar
to that described in Chapter 6 of GPG2000 (IPCC, 2000), Quantifying Uncertainties in Practice. Section 5.4.8
illustrates the application of the Tier 1 approach.

LEVEL ASSESSMENT
The contribution of each source or sink category to the total national inventory level is calculated according toEquation 5.4.1:
EQUATION 5.4.1
LEVEL ASSESSMENT (TIER 1)
Key Category Level Assessment = Source or Sink Category Estimate/ Total Contribution
Lx,t *= Ex,t* / Et*
Where:
Lx,t* = level assessment for source or sink x in year t. The asterisk (*) indicates that contributions
from all categories (including LULUCF categories) are entered as absolute values.
Ex,t* = Ex,t : absolute value of emission or removal estimate of source or sink category x in year t
Et*= ∑ E x ,t : total contribution, which is the sum of the absolute values of emissions and removals
xin year t. The asterisk (*) indicates that contributions from all categories (including LULUCF
categories) enter as absolute values.
Because both emissions and removals are entered with positive sign7, the Total Contribution
may be larger than a country’s total emissions less removals.8
Table 5.4.2 outlines a spreadsheet that can be used for the Level Assessment. This spreadsheet is to be applied inaddition to the assessment for non- LULUCF sources, as described in GPG2000, Table 7.2 in Chapter 7

6

The methodology is also generally applicable using other weighting schemes, but the threshold for the Tier 1 analysis wasderived based on the GWP concept and may be different under other weighting schemes.

7

Removals are entered with absolute values to avoid an oscillating cumulative value Lx,t as could be the case if removals
were entered with negative signs, and thus to facilitate straightforward interpretation of the quantitative analysis.

8This equation can be used in any situation, regardless of whether the national greenhouse gas inventory is a net source (asis most common) or a net sink.

IPCC Good Practice Guidance for LULUCF

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Chapter 5: Cross- Cutting Issues(Methodological Choice and Recalculation). Section 5.4.8 provides an example of the application of the Tier 1
method.
TABLE 5.4.2
SPREADSHEET FOR THE TIER 1 ANALYSIS – LEVEL ASSESSMENT INCLUDING LULUCF CATEGORIESA
IPCC Source/Sink
Categories

B
Direct Greenhouse
Gas

C
Base orCurrent Year Estimateof Emissions orRemovals(absolute value)D
Level Assessmentwith LULUCF,
from column C

E
Cumulative Total of
Column D

Total

Where:
Column A : list of IPCC categories of sources and sinks (see Table 5.4.1)
Column B : direct greenhouse gas
Column C : base year or current year emissions or removals of each greenhouse gas, in CO2- equivalent
units. Removal estimates entered with absolute values (positive signs)
Column D : level assessment with LULUCF from column C, following Equation 5.4.1
Column E : cumulative total of Column D
In the table, the calculations necessary for the Level Assessment are computed in Column D, following Equation
5.4.1. Thus, the value of the Level Assessment including LULUCF should be entered in column D for eachcategory. All entries in Column D should be positive because absolute values of sinks are entered for removal
estimates in Column C. The sum of all entries in Column D is entered in the total line of this table (note that thistotal will not be the total net emission (or net removal)). Once the entries in Column D are computed, thecategories should be sorted in descending order of magnitude and the cumulative total summed in Column E.
Key categories including LULUCF are those that, when summed together in descending order of magnitude, add
up to 95 % of the total in Column D. The rationale for the choice of threshold for the Tier 1 method is explainedin the Section 5.4.7. The method builds on GPG2000 and Rypdal and Flugsrud (2001). It is also good practice toexamine categories identified between the 95 and 97 % threshold carefully with respect to the qualitative criteria(see Section 5.4.3).
The Level Assessment should be performed for all years for which inventory estimates are available. If previous
inventory estimates have not changed, there is no need to recalculate the previous years’ analysis. If any
estimates have changed or been recalculated, however, the analysis for that year should be updated. Any
category that meets the threshold in any year should be identified as a key category.

TREND ASSESSMENT
The contribution of each source or sink category to the trend in the total inventory can be assessed if more than
one year of inventory data are available, according to Equation 5.4.2.
EQUATION 5.4.29
TREND ASSESSMENT (TIER 1)
Source or Sink Category Trend Assessment =(Source or Sink Category Level Assessment) ● (Source or Sink Category Trend – Total Trend) Tx,t* = Ex,t* / Et ●  [( Ex,t – EX,0 ) / Ex,t ] – [ ( Et – E0 ) / Et] 
Where:
Tx,t*9= trend assessment, which is the contribution of the source or sink category trend to the overall
inventory trend. The Trend Assessment is always recorded as an absolute value, i.e., a negative

Norwegian Pollution Control Authority with Rypdal and Flugsrud (2001).

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IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

value is always recorded as the equivalent positive value. The asterisk (*) indicates that, incontrast to Equation 7.2, in Chapter 7 of the GPG2000, LULUCF sources and sinks can beevaluated using this equation.
= Ex,t absolute value of emission or removal estimate of source or sink category x in year t

Ex,t*Ex,t and Ex,0 = real values of estimates of source or sink category x in years t and 0, respectively
Et and E0= ∑ E x , t and ∑ E x ,0 total inventory estimates in years t and 0, respectively. Et and E0 differxxfrom Et* and E0* in Equation 5.4.1 in that removals are not entered as absolute values.
The Source or Sink Category Trend is the change in the source or sink category emissions or removals over time,computed by subtracting the base year (year 0) estimate for source or sink category x from the current year (yeart) estimate and dividing by the current year estimate.10
The Total Trend is the change in the total inventory emissions (or removals) over time, computed by subtracting
the base year (year 0) estimate for the total inventory from the current year (year t) estimate and dividing by thecurrent year estimate.
In circumstances where the current year emissions for a given category are zero, the expression may bereformulated to avoid zero in the denominator (Equation 5.4.3).11
EQUATION 5.4.3
TREND ASSESSMENT WITH ZERO CURRENT YEAR EMISSIONS 12

Tx,t* =  EX,0 / Et 
The Trend Assessment will identify categories that have a different trend as compared to the trend of the overall
inventory. As differences in trend are more significant for the overall inventory level for larger categories ofemissions and removals (in absolute terms), the results of the trend difference (i.e., the category trend minus total
trend) is multiplied by  Ex,t* / Et to provide appropriate weighting. Thus, key categories will be those where thecategory trend diverges from the total trend, weighted by the level of emissions or removals of the category.
Table 5.4.3 outlines a spreadsheet that can be used for the Trend Assessment. This spreadsheet is to be applied inaddition to the assessment for non-LULUCF sources, as described in GPG2000, Chapter 7, Methodological
Choice and Recalculation, Table 7.3. Section 5.4.8 provides and example of the application of the Tier 1 method.

TABLE 5.4.3
SPREADSHEET FOR THE TIER 1 ANALYSIS – TREND ASSESSMENT INCLUDING LULUCF CATEGORIES
A

B

IPCC
Source/Sink
Categories

DirectGreenhouse
Gas

C
Base Year
Estimate

D
Current Year
Estimate

E
Trend
Assessment

F%
Contribution
to Trend

G
Cumulative
Total of
Column F

Total

10

Although it is common to look at growth rates in the form of (Et – E0) / E0, where the growth rate is measured from an
initial value in year 0, the functional form of Equation 7.2 in Chapter 7 of GPG2000 has been designed to minimise
occurrences of division by zero and to enable analysis of the importance of source categories with very low emissions in
the base year (e.g., substitutes for ozone depleting substances).

11

Although this equation was not shown in GPG2000, it is also generally applicable to non- LULUCF categories as it has
been derived from Equation 5.4.2.

12This results applies when Ex,t=0 is inserted into Equation 5.4.2.

IPCC Good Practice Guidance for LULUCF

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Chapter 5: Cross-Cutting Issues

Where:
Column A : list of IPCC categories (see Table 5.4.1)
Column B : direct greenhouse gas
Column C : base year estimate of emissions or removals from the national inventory data,
in CO2-equivalent units. Sinks are entered with signed values (positive or negative values).
Column D : current year emissions estimate from the most recent national inventory data,
in CO2-equivalent units. Sinks are entered with signed values
Column E : trend assessment from Equation 5.4.2, recorded as an absolute value
Column F : percentage contribution to the total of assessments in column E
Column G : cumulative total of Column F, calculated after sorting the entries in Column F in descendingorder of magnitude
The LULUCF categories identified in this analysis should be considered key in addition to those identified in theanalysis that does not include LULUCF emissions and removals. If additional non-LULUCF categories are
identified as key when LULUCF is included in the analysis, these should not be initially considered key, butshould be carefully examined using the qualitative considerations.
The entries in Columns A, B and either C or D should be identical to those used in the Table 5.4.2, Spreadsheet
for the Tier 1 Analysis - Level Assessment. The base year estimate in Column C is always entered in the
spreadsheet, while the current year estimate in Column D will depend on the year of analysis. The absolute valueof Tx,t should be entered in Column E for each category of sources and sinks, following Equation 5.4.2, and thesum of all the entries entered in the total line of the table.13 The percentage contribution of each category to thetotal of Column E should be computed and entered in Column F. The categories (i.e., the rows of the table)
should be sorted in descending order of magnitude, based on Column F. The cumulative total of Column Fshould then be computed in Column G. Key categories are those that, when summed together in descendingorder of magnitude, add up to more than 95 % of the total of Column E. An example of a Tier 1 analysis for thelevel and trend is given in Section 5.4.8.

5.4.2.2

T IER 2 METHOD TO IDENTIFY KEY CATEGORIES OF
SOURCES AND SINKS

The more sophisticated Tier 2 approach to identify key categories of sources and sinks is based on the results of
the uncertainty analysis described in Section 5.2 (Identifying and Quantifying Uncertainties) in this report, and in
GPG2000, Chapter 6 (Quantifying Uncertainties in Practice). The Tier 2 approach is consistent with, but notrequired for, good practice. Inventory agencies are encouraged to use Tier 2 if possible, because it will provide
additional insight into the reasons that particular categories are key and can assist in prioritising activities toimprove inventory quality and reduce overall uncertainty. It should be recognized that because the Tier 1 is a
simplified approach, the Tier 1 and Tier 2 approaches could result in a few differences in key categories. In suchsituations, the results of the Tier 2 approach should be utilized.
In particular, it is important to bear in mind that a LULUCF category can comprise large fluxes, and emissions
and removals may cancel out. In a Tier 2 analysis it may be possible to make the assessment at the level of evenmore detailed sub-estimates. In this case correlations need to be evaluated and modeled when appropriate. Whenthe analysis is based on Tier 1, these cases should be evaluated using the qualitative criteria as described in
Section 5.4.3.

APPLICATION OF UNCERTAINTY ESTIMATES TO IDENTIF Y KEY
SOURCES AND SINKS CATEGORI ES
The key category analysis may be enhanced by incorporating the national category uncertainty estimatesdeveloped in Section 5.2. Uncertainty estimates based on the Tier 1 approach described in Section 5.2 are
sufficient for the purpose, but estimates based on the Tier 2 uncertainty assessment approach should be used if

13

Unlike the Level Assessment, where all entries will be positive, in the Trend Assessment negative values will occur if
emissions of the source category decline by more in percentage terms than emissions in the overall inventory, or grow by asmaller amount. In this analysis the negative and positive values are considered equivalent, and the absolute values of these
are recorded in the table.

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IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

available. The category uncertainties are incorporated by weighting the Tier 1 Level and Trend Assessment
results by the category’s relative uncertainty. The key category equations are shown below.

LEVEL ASSESSMENT
Equation 5.4.4 describes the Tier 2 Level Assessment including uncertainty. The results of this assessment (LUx,t)
is identical to the results of quantifying uncertainties in practice, as shown in Column H of Table 6.1 of Chapter
6 of GPG2000. So if that table has been completed, it is not necessary to recalculate Equation 5.4.4.
EQUATION 5.4.4
LEVEL ASSESSMENT (TIER 2)

Level Assessment with Uncertainty = Tier 1 Level Assessment ● Relative Category Uncertainty
LUx,t = Lx,t ● Ux,tWhere:
LUx,t =

Level assessment with uncertainty

Lx,t

=

computed as in Equation 5.4.1

Ux,t

=

relative category uncertainty in year t calculated as described in Section 5.2. The relative
uncertainty will always have a positive sign.

The key categories are identified by accounting for those that add up to 90 % of the total value of the total LUx,t.
This 90 % was the bases for the derivation of the threshold used in the Tier 1 analysis (see Section 5.4.7 andRypdal and Flugsrud (2001)).

TREND ASSESSMENT
Equation 5.4.5 shows how the Tier 2 Trend Assessment can be expanded to include uncertainty.
EQUATION 5.4.5
TREND ASSESSMENT (TIER 2)

Trend Assessment with Uncertainty = Tier 1 Trend Assessment ● Relative Category Uncertainty
TUx,t = Tx,t ● Ux,tWhere:
TUx,t =

trend assessment with uncertainty

Tx,t

=

trend assessment computed in Equation 5.4.2

Ux,t

=

relative category uncertainty in year t calculated as described in Section 5.2. The relative
uncertainty will always have a positive sign.

The key categories are identified by accounting for those that add up to 90 % of the total value of the total TUx,t.
This 90 % was the basis for the derivation of the threshold used in the Tier 1 analysis (see Section 5.4.7 andRypdal and Flugsrud (2001)).

INCORPORATING MONTE CARLO ANALYSISIn Section 5.2 (Identifying and Quantifying Uncertainties), Monte Carlo analysis is presented as the Tier 2
approach for quantitative uncertainty assessment. Whereas the Tier 1 uncertainty analysis is based on simplified
assumptions to develop uncertainties for each category, Monte Carlo types of analyses can handle large
uncertainties, complex probability density functions, correlations and complex emission estimation equations
among other things. The output of the Tier 2 uncertainty analysis can be used directly in Equations 5.4.4 and 5.4.5.
If uncertainties are asymmetrical, the larger difference between the mean and the confidence limit should be used.
Monte Carlo analysis or other statistical tools can also be used to perform a sensitivity analysis to directly
identify the principal factors contributing to the overall uncertainty. Thus, a Monte Carlo or similar analysis canbe a valuable tool for a key category analysis. The method can, for example, be used to analyze moredisaggregated sources categories (by modelling correlations) and emission factors and activity data separately (toidentify key parameters rather than key categories). The analysis of key parameters can be based on Equations

IPCC Good Practice Guidance for LULUCF

5.37

Chapter 5: Cross-Cutting Issues

5.4.4 and 5.4.5 above, by compiling correlations coefficients between input and output (Morgan and Henrion,1990) or on other appropriate techniques.

5.4.3

Qualitative Considerations

In some cases, the results of the Tier 1 or Tier 2 analysis of key categories may not identify all categories thatshould be prioritised in the inventory system. In GPG2000, a list of qualitative criteria was provided to address
specific circumstances that could not be readily reflected in the quantitative assessment. These criteria should beapplied to categories not identified in the quantitative analysis, and if additional categories are identified they canbe added to the list of key categories.
The qualitative considerations identified in Chapter 7 of GPG2000 have been refined slightly to reflect the
LULUCF sector:•

Mitigation techniques and technologies: If emissions from a category are being reduced or removals increased
through the use of climate change mitigation techniques, it is good practice to identify these categories as key.

•

High expected growth of emissions or removals: If the inventory agency expects emissions or removals
from a category to grow significantly in the future, they are encouraged to identify that category as key.
Some of these categories will be identified by the Trend Assessment or will be identified in the future.
Because it is important to implement a higher tier good practice method as soon as possible, however, early
identification using qualitative criteria is important.

•High uncertainty: If the inventory agency is not taking uncertainty explicitly into account by using the Tier 2
method to identify key categories, they may want to identify the most uncertain categories as key. This isbecause the largest reductions in overall inventory uncertainty can be achieved by improving estimates ofhighly uncertain categories.

•

Unexpectedly high or low emissions or removals: When emissions or removals are far higher or lower thanwould be expected using the methods in the IPCC Guidelines or those described in Chapters 3 and 4 of thisreport (for example, due to the use of a national emission factor), these categories should be designated as
key. Particular attention should also be paid to QA/QC (Section 5.5) and documentation for these categories.

•

Large stocks: When a small net flux results from the subtraction of large emissions and removals, theuncertainty can be very high. Thus, when moving from the Tier 1 to higher tier estimation methods the orderof IPCC Source Categories may change and previously insignificant categories may become significant.

•Deforestation: In the quantitative key category analysis, deforestation is spread out under the different landuse change categories (e.g., Lands converted to grassland are considered separately from Lands converted tocropland). To ensure consistency with the IPCC Guidelines, countries should identify and sum up theemission estimates associated with forest conversion to any other land category. “Deforestation” should beconsidered key if the sum is larger than the smallest category considered key in the quantitative analysis. In
this case, countries can further examine which land conversions are significant (i.e., account for more than
30 percent) of the estimate and classify them as key.

•Completeness: Neither the Tier 1 nor the Tier 2 approach gives correct results if the inventory is notcomplete. The analysis can still be performed, but there may be key categories among those not estimated.
In these cases it is good practice to qualitatively examine potential key categories applying the qualitative
considerations above. IPCC Guidelines (IPCC, 1997), GPG2000 (IPCC, 2000) and this report list potential
categories of sources and sinks. The inventory of a country with similar national circumstances can also
often give good indications on potential key categories.

For each key category identified, the inventory agency should determine if certain subcategories are particularly
significant (i.e., represents a significant share of the emissions or removals). It is good practice to identify what
subcategories are particularly important and focus efforts towards methodological improvements on thesesubcategories.

5.4.4

Identifying Key Categories under Kyoto Protocol
Articles 3.3 and 3.4

The concept of key categories can also be used for choosing the good practice estimation methods for emissions
and removals due to activities under Articles 3.3 and 3.4 of the Kyoto Protocol to the UNFCCC. The key

5.38

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

categories for Kyoto Protocol reporting can be identified following the guidance in this section. Detailed
guidance is provided in Chapter 4 on how to take the key category determination into account in methodologicalchoice for estimates prepared under the Kyoto Protocol.
Taking into consideration that there is not any experience with the preparation of these estimates under the
Kyoto Protocol, it is suggested that the basis for assessment of key categories under Articles 3.3 and 3.4 of the
Kyoto Protocol is the same as the assessment made for the UNFCCC inventory. Whenever a category is
identified as key in the UNFCCC inventory, the associated activity under the Kyoto Protocol should beconsidered as key in reporting under the Kyoto Protocol.14 The identification of key categories under the Kyoto
Protocol will also have to include some qualitative assessments as there is not always an unambiguous
correspondence between the UNFCCC categories and Kyoto Protocol activities. A country may also undertake aquantitative Tier 2 approach to identify the key categories of their inventory including the Kyoto Protocol
activities. The results of this assessment will in most circumstances result in fewer LULUCF key categories.
Table 5.4.4 can be used to establish the relationship between categories in Chapter 3 and Chapter 4 for purposesof identifying key categories under Articles 3.3 and 3.4 of the Kyoto Protocol.

TABLE 5.4.4
RELATIONSHIP BETWEEN ACTIVITIES IDENTIFIED IN CHAPTER 3 AND CHAPTER 4
AND IPCC SOURCE/SINK CATEGORIES FOR LULUCF
1Chapter 3 Categories

2
Chapter 4 Categories

3
Key category if item in Column 1
was identified as key in theanalysis of the UNFCCC
inventorya

FOREST LAND
Forest land remaining forest land (managed)

FM, GM, CM

Land converted to forest land (managed)

AR

CROPLAND
Cropland remaining cropland

CM, RVLand converted into cropland

D, RV, CM

GRASSLAND
Rangeland and grassland remaining rangelandand grassland (managed)

GM, RVLand converted to rangeland and grassland(managed)

D, RV , GM

WETLANDS
Wetlands remaining wetlands (managed)

RVLand converted to wetlands

D, RV

SETTLEMENTS
Settlements remaining settlements

RVLand converted to settlements

D, RV

ab

OTHER LAND

Other land remaining other land
Land converted to other landDa

Article 3.4 activities only when electedb
Theoretically revegetation can occur in both subcategories.
FM: forest management, AR: afforestation and reforestation, CM: cropland management, D: deforestation, RV: revegetation,
GM: grazing land management.

14This applies also when there only are partial overlaps with the UNFCCC inventory.

IPCC Good Practice Guidance for LULUCF

5.39

Chapter 5: Cross-Cutting Issues

The left column lists the categories of Chapter 3 that may have been used in the key category analysis of the
UNFCCC inventory15. If any of these are identified as key, the Kyoto Protocol activities in the corresponding
right column should initially be considered key. However, as in some cases several Kyoto Protocol activities
potentially can be key, it is good practice to qualitatively examine which of the possible activities actually are
key. For example, if land converted to rangeland and grassland was identified as key, this can involve
deforestation, revegetation, grassland management or land-use changes not covered by the Kyoto Protocol. The
land area affected by revegetation may be much smaller than the land area of the Chapter 3 category in which it
occurs. If this is the case, and if revegetation is identified as potentially key according to Table 5.4.4, thencountries may separately assess the importance of greenhouse gas emissions and removals in revegetationcompared to the other category (or categories). It is good practice to explain and document which of thepotential key categories are finally identified as key for Kyoto Protocol reporting.
In addition, it is good practice to take into account the following considerations in the key category
determination for estimates prepared under Articles 3.3 and 3.4 of the Kyoto Protocol:•

As shown in Table 5.4.4., several activities under the Kyoto Protocol can occur in more than one category of
the UNFCCC inventory. In such cases, it is good practice to consider the total emissions and removals fromthe activity for purposes of the key category analysis. When this approach is needed, an activity should beconsidered key if the emissions or removals from the sum are greater than the emissions from the smallest
category that is identified as key in the UNFCCC inventory (including LULUCF).

•

If, when using the quantitative methods, a category is not identified as key for the present year but it isanticipated to strongly increase in the future, it should be designated as key. This could, for example, occur
with a large-scale afforestation programme producing only small sinks in initial years, but with the
expectation of larger yields later.

•

In some cases, it is possible that the emissions or removals from an activity under the Kyoto Protocol could
exceed the emissions or removals of the associated category in the UNFCCC inventory. In such a case the
Kyoto Protocol activity should be identified as key if its emissions/removals exceed the emissions of thesmallest category that is identified as key in the UNFCCC inventory (including LULUCF).

For each key category, the inventory agency should determine if certain subcategories are particularly significant(i.e., represent a significant share of the emissions or removals). For example, if cropland management has been
elected and is identified as key, it is good practice to identify what subcategories are particularly important andfocus efforts towards methodological improvements on these subcategories. As described in Section 5.4.2.2, thequantitative key category assessment can only be made at a more disaggregated level if correlations between
input data can be taken into account.
Because there will be special requirements related to methodologies and verification for estimates for LULUCF
projects under Articles 6 and 12 of the Kyoto Protocol, projects have not been integrated into the key category
concept. Section 4.3 in Chapter 4 gives good practice guidance on how these estimates should be prepared for
the LULUCF inventories for reporting under the Kyoto Protocol.

5.4.5

Application of the Results

Identification of key categories in national inventories is important because the resources available for preparing
inventories are finite and their use should be prioritised. It is essential that estimates be prepared for all
categories, in order to ensure completeness. As far as possible, key categories should receive special
consideration in terms of two important inventory aspects. Figure 5.4.2 illustrates a decision tree to choose agood practice method, which is modified from Figure 7.4 of Chapter 7 of GPG2000 to make it applicable to the
LULUCF sector.

15

If the analysis was based on the IPCC source/sink categories (1996) the transformation will be less precise. The mapping isshown in Chapter 3, Section 3.1.

5.40

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

Figure 5.4.2

Decision tree to choose a good practice methodIsthe category
considered a key
category?

No

Choose agood practicemethod appropriate
to available data

Yes

Are thedata available tofollow category-specific goodpractice guidance related to
the key
categories?

Yes

Estimate emissions andremovals following
category-specific goodpractice guidance forkey categories

NoCan
data be collectedwithout significantly
jeopardising the resources
for other key
categories?

No

Yes

Make arrangements
to collect data

Choose a good practicemethod appropriate toavailable data anddocument why categoryspecific guidance cannot
be followed

First, additional attention ought to be focused on key categories with respect to methodological choice. As
shown in the decision tree in Figure 5.4.2, inventory agencies are encouraged to use category-specific goodpractice methods for key categories, unless resources are unavailable. For most categories, higher tier (i.e., Tiers
2 and 3) methods are suggested for key categories, although this is not always the case. For guidance on thespecific application of this principle to key categories, inventory agencies should refer to the decision trees in
Chapter 3. There may be special requirements for methodological choice when reporting under Articles 3.3 and3.4 of the Kyoto Protocol. These requirements are explained in Chapter 4 of this report.
Second, it is good practice for key categories to receive additional attention with respect to quality assurance andquality control (QA/QC). In Section 5.5, detailed guidance is provided on QA/QC for the LULUCF categories in
the inventory.

5.4.6

Reporting and Documentation

It is good practice to clearly document the key categories in the inventory. This information is essential for
explaining the choice of method for each category. In addition, inventory agencies should list the criteria bywhich each category was identified as key (e.g., level, trend, or qualitative), and the method used to conduct thequantitative key category analysis (e.g., Tier 1 or Tier 2). Table 5.4.5 can be used to document the results of the
key category analysis.

IPCC Good Practice Guidance for LULUCF

5.41

Chapter 5: Cross-Cutting Issues

TABLE 5.4.5
KEY CATEGORY ANALYSIS SUMMARY
Quantitative Method Used for Key Category Analysis:

Tier 1 ❐Tier 2 ❐

A

B

C

D

E

IPCC Source/Sink
Category

Direct Greenhouse
Gas

Key Category Flag
(Yes or No)

If C is Yes, Criteria
for Identification

Comments

Where:
Column A : list of IPCC categories – entry should be the same as column A in Tables 5.4.2 and 5.4.3
Column B : direct greenhouse gas – entry should be the same as column B in Tables 5.4.2 and 5.4.3
Column C : key category flag – enter ‘Yes’ if the category is key
Column D : criteria by which key category was identified – for each key category identified in Column
C, enter one or more of the following: ‘Level’ for Level Assessment, ‘Trend’ for Trend
Assessment, or ‘Qualitative’ for qualitative criteria
Column E : comments - enter any explanatory material

5.4.7

Derivation of Threshold for the Tier 1 Key Category
Analysis

The thresholds for the level and trend were derived using the same methodology as used in GPG2000, but with amore complete data set, longer time series and with LULUCF included. The GPG2000 method of determining
the threshold was documented in more detail in Flugsrud et al. (1999). For the level threshold, the relationshipbetween the percentage of the emissions and the sum of uncertainties of each source or sink category wascompiled for the reported greenhouse gas inventories of 30 Parties included in Annex I to the United Nations
Framework Convention on Climate Change (UNFCCC). As in GPG2000 the threshold was determined to cover
90 % of the sum of uncertainties of each category as this typically gives 10 to 15 key source categories (Rypdal
and Flugsrud 2001). The analysis is based on data received from the UNFCCC Secretariat for 1990 and 1999 (byMay 2002). The dataset used to determine the trend threshold is more limited, including only 16 countries, as
fewer countries have reported sufficiently detailed data for both years.

5.4.7.1

A SSUMPTIONS ABOUT UNCERTAINTIESThe analysis is based on the assessment of uncertainties in Table 5.4.6 . Sensitivity analysis shows the results to
be rather robust with respect to the assumptions made about uncertainties. For the sources under non-LULUCF
sectors the assumed uncertainties are: CO2 5%, CH4 25 %, N2O 100%. Non-CO2 greenhouse gases (N2O andCH4) were included for the LULUCF sector to the extent that they have been reported, assuming uncertainties as
for the non-LULUCF sector.
TABLE 5.4.6
ASSUMED UNCERTAINTIES TO DETERMINE A KEY CATEGORY THRESHOLD INCLUDING LULUCFNet CO2 emissions or removal uncertainties
Changes in forest and woody biomass

5.42± 50 %

Forest and grassland conversion- 50 to + 100 %Abandonment of managed land

- 50 to + 100 %Emissions and removals from soil

- 50 to + 100 %Other LULUCF- 50 to + 100 %IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

5.4.7.2

E MISSION LEVELIn GPG2000 the threshold value was determined to be 95% of total emissions. The pattern of emission estimatesneeded to account for 90% of the sum of category uncertainties in the dataset including LULUCF is similar tothe one seen previously (as shown in Figure 5.4.3 below).
Figure 5.4.3

Cumulative uncertainty plotted against cumulative emissions

100 %Cumulative uncertainty

90 %
80 %
70 %
60 %
50 %
40 %
30 %
20 %
10 %
0%

0%

90 %

99 %
99.9 %
Cumulative emission

99.99 %Note:

The dotted lines show the division of the 95% threshold at 90% of sum of contribution from uncertainties.

Source:Data reported by Parties to the UNFCCC and assumed uncertainties.

Fraction of emissions required to reach 90% of sum of contribution fromuncertainties in different inventories. With and without LULUCF (with
LULUCF using absolute values of emissions).

Fraction of emissions required toreach 90% of uncertainty

Figure 5.4.4

99% - < 100%98% - < 99%97% - < 98%
96% - < 97%
95% - < 96%94% - < 95%
93% - < 94%
92% - < 93%
91% - < 92%
90% - < 91%
85% - < 90%
70% - < 85%

0

2

4

6

8

10

Number of inventories
Without LUCF
Source:With LUCF

Data reported by Parties to the UNFCCC and assumed uncertainties

IPCC Good Practice Guidance for LULUCF

5.43

Chapter 5: Cross-Cutting Issues

Figure 5.4.4 shows that when emissions and removals from LULUCF are included, a slightly smaller fraction of
total emissions (by absolute value) is required to account for 90% of sum of source and sink category
uncertainties. For the 30 inventories analysed, the median fraction was 97.1% without LULUCF and 96.8% with
LULUCFs. The reason is that some of emissions or removals from LULUCF are large and with high uncertainty.
The threshold would need to be very high to be able to identify all Tier 2 key categories in all inventories. It is
important to bear in mind that the Tier 2 approach is the most rigorous approach to determine key categories as
the uncertainty is taken into account. A high threshold would mean that many non-key categories according toTier 2 are defined in the Tier 1 approach. For this reason, it was determined to be most effective to set thethreshold to 95% and to advise countries to apply qualitative criteria to the categories between 95 and 97%.
The conclusion is that the previously determined threshold of 95% is also recommended for the integrated
analysis including LULUCF categories.

5.4.7.3

T REND

The threshold was set to identify 90% of the sum of Tx,t* (Equation 5.4.2) in the inventories. Figure 5.4.5 shows
the same pattern for trend as Figure 5.4.4 for the level. When emissions and removals from LULUCF are
included, a smaller fraction of total assessment (by absolute value) is required to account for 90% of the sum of
Tx,t*16. The reason is again that some of the emissions and removals from LULUCF have large contribution totrend and high uncertainty.
Fraction of emissions required to reach 90% of sum of contribution fromtrend uncertainty in different inventories. With and without LULUCF (with
LULUCF using absolute values of emissions).

Fraction of emissions required toreach 90% of trend uncertainty

Figure 5.4.5

98% - < 100%96% - < 98%
94% - < 96%92% - < 94%
90% - < 92%
88% - < 90%
86% - < 88%
84% - < 86%
82% - < 84%
80% - < 82%

0

1

2

3

4

5

6

Number of inventories
Without LUCF
Source:16

With LUCF

Data reported by Parties to the UNFCCC and assumed uncertainties

The available data did not make it feasible to include HFCs, PFCs and SF6 in the analysis. However, these gases should be
included, if possible, when the method is applied.

5.44

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

5.4.8

Example of Tier 1 Key Category Analysis

The example illustrates the application of the Tier 1 approach based on the submitted inventory for an Annex I
country. Both the level and trend assessment is shown.
TABLE 5.4.7
EXAMPLE OF A LEVEL ASSESSMENTa
A

B

Direct
IPCC Source
Greencategories
house(IPCC 1996)
Gases
Sum

Base orBase orCurrent
Current
Year
Year
Estimate
Estimate
nonLULUCF
LULUCFC
D’
E’D
E
Base or
Level
Level
Cumulative
Current Assessment
Assessment
Total of
CumulativeYear
without
with
Column D
Total of
Estimate LULUCF,
LULUCF, (additional
Column D’
Absolute from column
from column LULUCF
Value
C
C
sources)

535375- 61309643884b1

1

1.AA.3CO2138822..

138822

0.259

0.259

0.216

0.216

1.AA.4CO2102167..

102167

0.191

0.450

0.159

0.374

5.A

CO2

..

- 84861

84861

..

0.450

0.132

0.506

1.AA.2

CO2

77213

..

77213

0.144

0.594

0.120

0.626

1.AA.1

CO2

61389

..

61389

0.115

0.709

0.095

0.721

4.D

N2O

51152

..

51152

0.096

0.805

0.079

0.801

4.A

CH4

27942

..

27942

0.052

0.857

0.043

0.844

6.A

CH4

16440

..

16440

0.031

0.887

0.026

0.870

5.B

CO2

..

12540

12540

..

0.887

0.019

0.889

2.B

N2O

11093

..

11093

0.021

0.908

0.017

0.906

2.A

CO2

10371

..

10371

0.019

0.928

0.016

0.923

5.E

N2O

..

5550

5550

..

0.928

0.009

0.931

1.B.2

CO2

4006

..

4006

0.007

0.935

0.006

0.937

4.B

CH4

3644

..

3644

0.007

0.942

0.006

0.943

2.C

CO2

3443

..

3443

0.006

0.948

0.005

0.948

5.D

CO2

..

3370

3370

..

0.948

0.005

0.954

1.AA.3

N2O

3174

..

3174

0.006

0.954

0.005

0.959

4.B

N2O

3109

..

3109

0.006

0.960

0.005

0.963

1.AA.4

CH4

2817

..

2817

0.005

0.965

0.004

0.968

2.B

CO2

2723

..

2723

0.005

0.970

0.004

0.972

1.B.1

CH4

2658

..

2658

0.005

0.975

0.004

0.976

6.C

CO2

2287

..

2287

0.004

0.980

0.004

0.980

1.B.2

CH4

1906

..

1906

0.004

0.983

0.003

0.983

5.E

CH4

..

1880

1880

..

0.983

0.003

0.986

1.AA.4

N2O

1456

..

1456

0.003

0.986

0.002

0.988

3.A

CO2

823

..

823

0.002

0.987

0.001

0.989

1.AA.2

N2O

796

..

796

0.001

0.989

0.001

0.990

1.AA.1

N2O

683

..

683

0.001

0.990

0.001

0.991

6.B

N2O

665

..

665

0.001

0.991

0.001

0.992

3.D

CO2

658

..

658

0.001

0.993

0.001

0.993

IPCC Good Practice Guidance for LULUCF

5.45

Chapter 5: Cross-Cutting Issues

TABLE 5.4.7 (CONTINUED)
EXAMPLE OF A LEVEL ASSESSMENT FOR AN ANNEX I COUNTRYa
A

B

Direct
IPCC Source
Greencategories
house(IPCC 1996)
Gases

Base orBase orCurrent
Current
Year
Year
Estimate
Estimate
nonLULUCF
LULUCFC
D’
E’D
E
Base or
Level
Level
Cumulative
Current Assessment
Assessment
Total of
CumulativeYear
without
with
Column F
Total of
Estimate LULUCF,
LULUCF, (additional
Column D
Absolute from column
from column LULUCF
Value
C
C
sources)

2.D

CO2

656

..

656

0.001

0.994

0.001

0.994

3.D

N2O

613

..

613

0.001

0.995

0.001

0.995

4.D

CH4

482

..

482

0.001

0.996

0.001

0.996

6.C

N2O

402

..

402

0.001

0.997

0.001

0.997

6.C

CH4

368

..

368

0.001

0.997

0.001

0.997

6.D

CH4

359

..

359

0.001

0.998

0.001

0.998

1.AA.3

CH4

312

..

312

0.001

0.999

0.000

0.998

6.B

CH4

282

..

282

0.001

0.999

0.000

0.999

5.B

CH4

..

236

236

..

0.999

0.000

0.999

4.C

CH4

163

..

163

0.000

0.999

0.000

0.999

3.B

CO2

136

..

136

0.000

1.000

0.000

1.000

1.AA.2

CH4

81

..

81

0.000

1.000

0.000

1.000

2.B

CH4

55

..

55

0.000

1.000

0.000

1.000

5.C

CO2

..

- 48

48

..

1.000

0.000

1.000

1.AA.1

CH4

28

..

28

0.000

1.000

0.000

1.000

5.B

N2O

..

24

24

..

1.000

0.000

1.000

1.B.2

N2O

0

..

0

0.000

1.000

0.000

1.000

a

Shaded cells of the table show values for cumulative assessment that identifies key categories for the level.

bThis sum differs from the sum of the two columns to the left because removals are summed up as absolute values.

5.46

IPCC Good Practice Guidance for LULUCF

Methodological Choice – Identification of Key Categories

TABLE 5.4.8
TREND ANALYSIS WITH LULUCFSa
A

B

C

D

E

F

G

IPCC Source
Categories(IPCC 1996)

DirectGreenhouse
Gas

Base Year
Estimate

Current Year
Estimate

Trend
Assessment% Contribution
to Assessment

Cumulative
Total of
Column F

486002474066

0.162226

1Sum
1.AA.3CO21191561388220.046486

0.28655

0.28655

2.B

N2O

27775

11093

0.03292

0.202928

0.489477

5.A

CO2- 75330- 84861

0.023418

0.144352

0.63383

1.AA.4CO294375

1021670.020804

0.128239

0.762069

1.AA.1

CO2

65495

61389

0.005139

0.031676

0.793745

2.A

CO2

13016

10371

0.004784

0.029492

0.823237

1.AA.2

CO2

76919

77213

0.004491

0.027681

0.850918

1.AA.3

N2O

1208

3174

0.004106

0.025310.876228

1.B.1

CH4

4331

2658

0.003225

0.019882

0.896109

4.A

CH4

30058

27942

0.002834

0.017467

0.913576

5.B

CO2

11710

12540

0.00230.014175

0.927751

6.A

CH4

17917

16440

0.002134

0.0131520.940903

2.C

CO2

4550

3443

0.002046

0.012613

0.953516

5.D

CO2

4051

3370

0.001197

0.007376

0.960892

4.D

N2O

52898

51152

0.000918

0.005659

0.966551

1.B.2

CH4

2199

1906

0.000493

0.003041

0.969592

2.B

CO2

3007

2723

0.000433

0.002667

0.972259

6.C

CO2

2133

2287

0.000425

0.002620.974879

1.B.2

CO2

4306

4006

0.000398

0.002456

0.977336

4.B

CH4

3537

3644

0.000398

0.002453

0.979789

5.E

N2O

5494

5550

0.000394

0.002428

0.982217

1.AA.4

CH4

3043

2817

0.000313

0.001927

0.984143

1.AA.4

N2O

1338

1456

0.000310.001913

0.986056

1.AA.1

N2O

561

683

0.000278

0.001714

0.98777

1.AA.3

CH4

453

312

0.000267

0.001648

0.989418

6.D

CH4

246

359

0.000245

0.001513

0.990931

3.B

CO2

252

136

0.000226

0.001394

0.992325

1.AA.2

N2O

731

796

0.000170.001049

0.993374

3.A

CO2

920

823

0.000153

0.000943

0.994317

6.B

N2O

612

665

0.000140.000861

0.995178

5.E

CH4

1861

1880

0.000134

0.000824

0.996002

4.B

N2O

3249

3109

0.000124

0.000766

0.996768

6.C

CH4

320

368

0.000115

0.000708

0.997477

6.C

N2O

357

402

0.000112

0.000689

0.998166

3.D

N2O

596

613

6.56E- 05

0.000404

0.99857

IPCC Good Practice Guidance for LULUCF

5.47

Chapter 5: Cross-Cutting Issues

TABLE 5.4.8 (CONTINUED)TREND ANALYSIS WITH LULUCFSa
A

B

C

D

E

F

G

IPCC Source
Categories(IPCC 1996)

DirectGreenhouse
Gas

Base Year
Estimate

Current Year
Estimate

Trend
Assessment% Contribution
to Assessment

Cumulative
Total of
Column F

6.B

CH4

259

282

5.91E- 05

0.000365

0.998935

5.B

CH4

221

236

4.27E- 05

0.000263

0.999198

1.AA.1

CH4

46

28

3.52E- 05

0.000217

0.999415

4.D

CH4

482

482

2.6E-05

0.000160.999575

4.C

CH4

180

163

2.57E- 05

0.000159

0.999733

2.D

CO2

681

656

1.65E- 05

0.0001010.999835

3.D

CO2

681

658

1.12E- 05

6.92E- 05

0.999904

2.B

CH4

53

55

6.85E- 06

4.22E- 05

0.999946

5.B

N2O

22

24

4.42E- 06

2.72E- 05

0.999974

5.C

CO2

- 48

- 48

2.43E- 06

1.5E-05

0.999989

1.AA.2

CH4

82

81

7.13E- 07

4.39E- 06

0.999993

1.B.2

N2O

..

0

5.74E- 07

3.54E- 06

0.999996

1.B.2

N2O

..

0

5.74E- 07

3.54E- 06

1aAdditional LULUCFs identified are shaded.

5.48

IPCC Good Practice Guidance for LULUCF

Quality Assurance and Quality Control

5.5

QUALITY ASSURANCE AND QUALITYCONTROL

5.5.1

Introduction

The IPCC Good Practice Guidance and Uncertainty Management (GPG2000, IPCC, 2000), Chapter 8, Quality
Assurance and Quality Control, defines quality assurance (QA) and quality control (QC), and provides guidanceon the elements of a QA/QC system, taking into account the need for transparency and review. It also discusses
the practical issues that inventory agencies must consider when allocating resources to QA/QC across the entire
inventory and how to rationalise the prioritisation resources for the LULUCF sector. This section enumerates thetypes of procedures that an inventory agency should undertake in order to ensure that the inventory estimates andtheir contributing data are of high quality, with particular emphasis on issues in the LULUCF sector. The
procedures also contribute to developing an inventory that can be readily assessed in terms of quality andcompleteness.
BOX 5.5.1
DEFINITIONS OF QUALITY ASSURANCE AND QUALITY CONTROL

Quality Control (QC) is a system of routine technical activities, to measure and control the qualityof the inventory as it is being developed. The QC system is designed to:(i) provide routine and consistent checks to ensure data integrity, correctness, and completeness;(ii) identify and address errors and omissions;(iii) document and archive inventory material and record all QC activities.
QC activities include general methods such as accuracy checks on data acquisition andcalculations and the use of approved standardised procedures for emission calculations,
measurements, estimating uncertainties, archiving information and reporting. Higher tier QC
activities include technical reviews of source or sink categories, activity and emission factor data,
and methods.
Quality Assurance (QA) activities include a planned system of review procedures conducted bypersonnel not directly involved in the inventory compilation/development process. Reviews,
preferably by independent third parties, should be performed upon a finalised inventory following
the implementation of QC procedures. Reviews verify that data quality objectives were met,
ensure that the inventory represents the best possible estimates of emissions and sinks given thecurrent state of scientific knowledge and data available, and support the effectiveness of the QC
programme.
Source: IPCC (2000).
Box 5.5.1 presents the definitions of quality control and quality assurance used in GPG2000. GPG2000 also
identified the following elements of a complete QA/QC system:•

An inventory agency responsible for coordinating QA/QC activities;•

A QA/QC plan;•

General QC procedures (Tier 1) that cross-cut all inventory categories;•

Source or sink category-specific QC procedures (Tier 2) that require knowledge of data and methods;•

QA review procedures;•

Reporting, documentation, and archiving procedures.

The inventory methods for the LULUCF sector require specific good practice guidance for QA/QC in all but thefirst of these elements. In addition, verification issues and issues related to the Kyoto Protocol can affect QA/QC
good practice. These two issues are addressed in Sections 5.7 and 5.5.7, respectively.
Estimating emissions and removals from LULUCF activities involves several important – although notnecessarily unique – issues. The primary difference between the LUCF sector and other sectors in the IPCC
Guidelines (IPCC, 1997) (i.e., energy, agriculture) is that the LUCF sector focuses on calculating the net

IPCC Good Practice Guidance for LULUCF

5.49

Chapter 5: Cross- Cutting Issues

emissions or removals.17 In particular, the QA/QC system must recognise that the LULUCF sector is unique
because CO2 can be both removed from and emitted to the atmosphere. From the perspective of inventory
QA/QC, however, more important considerations in the LULUCF sector focus on the complexity of the data thatare needed for preparation of accurate estimates of emissions and removals from LULUCF. Four importantfeatures of LULUCF inventory methods that generally affect QA/QC are highlighted below.
•

Representativeness of input data: LULUCF activities affect large geographical areas. Because of the sizeof these areas – coupled with the complex nature of the biological processes taking place – it is impracticalto rely entirely on direct measurements of greenhouse gas emissions and removals in producing national
inventories. Consequently, inventories rely on data produced using sampling through field measurements
and land surveys. Further, a complete set of samples is not likely to be taken on an annual basis, but instead
will be taken periodically (e.g., every four years). Samples may also be augmented with remote sensing datathat allow more complete coverage.

•

Need for historical data: Greenhouse gas emissions and removals related to LULUCF is a function of pastland-use activities, which continue to affect current (i.e., inventory year) CO2 emissions or removals. Thus,
both past and current land use and forestry activities influence current emissions and removals. For thisreason, sufficient historical data are needed to assess present day emissions, and so the datasets used in the
LULUCF sector may cover a longer historical period than other source categories (e.g., 20 to 100 years).
However, many countries benefit from the fact that forestry and some other land-use data have beencollected for a long time, so detailed and comprehensive – although not necessarily accurate – data sources
may be available.18 Time series consistency is an important QA/QC issue and is discussed in more detail in
Section 5.6.

•

Complex interactions and variability of the biological processes: The complex interactions and inherent
variability of the biological processes associated with forests, soils, and other LULUCF components canlead to the need for use of more sophisticated models19 than those employed for estimating emissions frommost other source categories. The data, assumptions, and other characteristics of the model may not always
be transparent. QA/QC needs to focus on documenting model characteristics and assumptions, checking
model outputs, identifying areas for improvement, checking the model algorithms, and documenting theresults of those checks.

•

Variability in the magnitude and nature of the data: Greenhouse gas emissions or removals can be small
net fluxes resulting from large gross fluxes or differences between large stocks, for example slow changes inlarge soil organic carbon stocks in soils. In addition, different types of activities lead to different types ofchanges. For example, forest management is likely to result in small and dispersed changes per unit area
over large areas, whereas large scale deforestation results in relatively large and immediate net emissions.
For these reasons, QA/QC procedures should involve the assessment of the suitability of the selected
methods for estimation of the greenhouse gas in each case, from direct measurements to sophisticatedmodels.20

5.5.2

QA/QC Plan

As discussed in GPG2000, a QA/QC plan is a fundamental element of a QA/QC system, and it is good practice
to develop one. The plan should, in general, outline the QA/QC activities that will be implemented, and include ascheduled time frame that follows inventory preparation from its initial development through to final reporting in
any year. It should also contain an outline of the processes and schedule to review all source and sink categories.
For LULUCF source and sink categories, the plan should describe the specific QC procedures that have been or
will be implemented in addition to special QA review procedures employed. These procedures should be

17

It should be noted, however, that subtracting major components during an emission source category calculation, is notunique to LULUCF sector. For example, thoroughly estimating carbon storage in non-energy fossil fuel feedstocks
involves a complicated analysis of fossil fuel processing and fates in order to subtract the amount of carbon in those fuelsthat is not combusted or oxidized. These adjustments to fossil fuel combustion calculations can be quite significant relative
to a country’s overall emissions inventory.

18

Of course these data will have been collected for reasons other than estimating greenhouse gas emissions and removals.

19

Numerical or process models interpolate activity data for intermediate years between samples, extrapolate sample datafrom measures of timber volume or other metrics to total biomass carbon, and attempt to capture other complexities andsubtleties of the relationship of forestry and land- use change to emissions and removals of CO2 and other gases.

20

The issue of methodological choice is discussed in detail at the subcategory level in Chapter 3 of this report.

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IPCC Good Practice Guidance for LULUCF

Quality Assurance and Quality Control

formulated in such a way that they address the four features described in Section 5.5.1, the representation of land
areas in Chapter 2 (Basis for Consistent Representation of Land Areas), LULUCF sector methodologies in
Chapter 3 (LUCF Sector Good Practice Guidance), and, if relevant, the methods used for accounting emissions
and removals under Article 3.3 and 3.4 of Kyoto Protocol in Chapter 4 (Supplementary Methods and Good
Practice Guidance Arising form the Kyoto Protocol).

5.5.3

General QC Procedures (Tier 1)

It is good practice to implement the generic QC checks as outlined in GPG2000, Chapter 8 (Quality Assurance
and Quality Control) Tier 1 General Inventory Level QC Procedures. These general techniques focus on theprocessing, handling, documenting, archiving, and reporting procedures that should be used for all inventory
source and sink categories. Table 5.5.1 lists the generic Tier 1 QC checks from Table 8.1 in GPG2000. Thesechecks have been revised to make them applicable to sinks as well as sources. In cases where estimates for the
LULUCF sector are prepared by institutions other than the inventory agency, the inventory agency is still
responsible for ensuring that Tier 1 QC procedures are performed and that both findings and procedures are
documented.
TABLE 5.5.1
TIER 1 GENERAL INVENTORY LEVEL QC PROCEDURES
QC Activity
Check that assumptions andcriteria for the selection ofactivity data, emission
factors and other estimation
parameters are documented.
Check for transcription
errors in data input andreference.
Check that emissions andremovals are calculated
correctly.
Check that parameter andunits are correctly recorded
and that appropriate
conversion factors are used.

Check the integrity of
database files.

Check for consistency indata between categories.
Check that the movement of
inventory data amongprocessing steps is correct.

Check that uncertainties inemissions and removals are
estimated or calculated
correctly.

Procedures•

Cross- check descriptions of activity data, emission factors and other estimation
parameters with information on source and sink categories and ensure that these areproperly recorded and archived.

•

Confirm that bibliographical data references are properly cited in the internal
documentation.

•

Cross- check a sample of input data from each source category (either measurementsor parameters used in calculations) for transcription errors.

•

Reproduce a representative sample of emission or removal calculations.

•

Selectively mimic complex model calculations with abbreviated calculations to judge
relative accuracy.

•

Check that units are properly labelled in calculation sheets.

•

Check that units are correctly carried through from beginning to end of calculations.

•

Check that conversion factors are correct.

•

Check that temporal and spatial adjustment factors are used correctly.

•

Confirm that the appropriate data processing steps are correctly represented in the
database.

•

Confirm that data relationships are correctly represented in the database.

•

Ensure that data fields are properly labelled and have the correct design specifications.

•

Ensure that adequate documentation of database and model structure and operation arearchived.

•

Identify parameters (e.g., activity data, and constants) that are common to multiple
categories of sources and sinks, and confirm that there is consistency in the values
used for these parameters in the emissions calculations.

•

Check that emission and removal data are correctly aggregated from lower reportinglevels to higher reporting levels when preparing summaries.

•

Check that emission and removal data are correctly transcribed between different
intermediate products.

•

Check that qualifications of individuals providing expert judgement for uncertainty
estimates are appropriate.

•

Check that qualifications, assumptions and expert judgements are recorded. Check that
calculated uncertainties are complete and calculated correctly.

•

If necessary, duplicate error calculations on a small sample of the probability
distributions used by Monte Carlo analyses.

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Chapter 5: Cross- Cutting Issues

TABLE 5.5.1 (CONTINUED)TIER 1 GENERAL INVENTORY LEVEL QC PROCEDURES

Undertake review ofinternal documentation.

Check time seriesconsistency.

Undertake completenesschecks.

Compare estimates toprevious estimates.

5.5.4

•

Check that there is detailed internal documentation to support the estimates and enablereproduction of the emission and removal and uncertainty estimates.

•

Check that inventory data, supporting data, and inventory records are archived andstored to facilitate detailed review.

•

Check integrity of any data archiving arrangements of outside organisations involved
in inventory preparation.

•

Check for temporal consistency in time series input data for each category of sources
and sinks.

•

Check for consistency in the algorithm/method used for calculations throughout thetime series.

•

Check recalculation method.

•

Confirm that estimates are reported for all categories of sources and sinks and for all
years from the appropriate base year to the period of the current inventory.

•

Check that known data gaps that result in incomplete emissions estimates are
documented.

•

For each category, current inventory estimates should be compared to previous
estimates, if available. If there are significant changes or departures from expected
trends, re- check estimates and explain any difference.

Source or Sink Category-Specific QC Procedures(Tier 2)

It is good practice to supplement the Tier 1 QC checks related to data processing, handling and reporting with
Tier 2 source or sink category-specific procedures for key categories (i.e., with the additional quality control
checks outlined in GPG2000, Section 8.7, Source Category-Specific QC Procedures (Tier 2)). Tier 2 proceduresshould be implemented on a case-by-case basis. These checks may be applicable, particularly if higher tierinventory methods are used to prepare emission and removal estimates. The Tier 2 QC procedures are directed at
specific types of data used in the methods and require knowledge of the source or sink category, the types of data
available, and the parameters associated with emissions or removals.
In some cases, the quantity and complexity of data that will be used to develop estimates of emissions andremovals from LULUCF may lead to some difficulties for implementing Tier 2 QC checks and investigations. At
the same time, this complexity makes it all the more important that rigorous Tier 2 data quality investigations be
performed and that they be done in cooperation with the institutions that are primarily responsible for collecting
and analyzing LULUCF data. These institutions may be numerous and somewhat diverse because of theallocation of land management responsibilities within each country. Investigating the quality of the input data
used in LULUCF models and other calculations will require extensive cooperation and communication withthese institutions to better understand their existing QA/QC procedures.
While source and sink category-specific checks are described in Chapter 3 of this report, Tier 2 QC for the
LULUCF sector should focus on the following types of checks:•

The inventory agency should check that land areas are properly classified and that no double counting oromissions of land area have occurred (see Section 2.3.2 of Chapter 2 and Table 2.3.1) This land areaclassification should be consistent with Chapter 2 (Basis for Consistent Representation of Land Areas). In
particular, it is important to check consistency and possible double-counting between the agriculture sector
and the LULUCF sector.

•

The inventory agency should investigate the completeness of source and sink categories in the LULUCF
sector, by examining the land-use categories and the subcategories to the extent appropriate, as described in
Chapter 3 (see Table 3.1.1 and Table 3.1.2 in Section 3.1.1). This is particularly important because of thecomplicated relationships among several of the LULUCF categories (e.g., abandoned lands regrowing andchanges in woody biomass stocks) and between LULUCF categories and other source categories (e.g.,biomass cleared and biomass fuel combustion). This classification should be consistent with Chapter 3,(LUCF Sector Good Practice Guidance). The inventory agency should also assess whether estimates ofparticular categories cover all relevant geographical areas (e.g., territories), sub-source or sink categories,
pools, or activities.

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IPCC Good Practice Guidance for LULUCF

Quality Assurance and Quality Control•

The inventory agency should periodically check the consistency of the time-series activity data, because of
the long history of data needed to estimate emissions for a single year. The activity and other data usedshould represent a consistent land area for the country, and have been collected using methods which do not
introduce temporal biases. Discontinuities in the time series of emissions or other data used in thecalculation of emissions or removals should be explained. The direction and magnitude of theemissions/removals estimates for individual LULUCF source or sink categories and their subcategories
should be compared and assessed as to the reasonableness and causes of these changes, considering thepossible impact of climate variability on time scales (for example at the scales of decades).

•Because of the relative importance of sampling data for preparing estimates, the inventory agency should
examine the sampling and extrapolation protocols that have been used, determine what review the protocols
have undergone, identify any internal QA/QC procedures that were in place, and consider other relevant
factors. See also Section 5.3, Sampling of this report. Additional information on secondary data
investigations can be found in Section 8.7.2.1, National Level Activity Data, of Chapter 8 of GPG2000.

•

Because the multiple uses of remote sensing techniques and data for preparing the LULUCF inventory, theinventory agency should provide documentation about the data and tools being used (i.e., type of imagery
and processing) at the level of detail needed for each case.

•

Models can be a necessary part of the national inventory process. They provide the opportunity to create
regional or national estimations when scientific knowledge or available information is limited to specificlocations or conditions. Because models are a means of extrapolating and/or interpolating what one knowsin order to estimate what one is less sure of, simply assuming that the model chosen is providing accurate
output for the inventory needs to be carefully avoided. If QA/QC associated with models is inadequate or
not transparent, the inventory agency should attempt to establish checks on the models and data. In
particular, the inventory agency should check the following:(i)Appropriateness of model assumptions, extrapolations, interpolations, calibration-based
modifications, data characteristics, and their applicability to the greenhouse gas inventory method
and national circumstances;(ii)

Availability of model documentation, including descriptions, assumptions, rationale, and scientificevidence and references supporting the approach and parameters used to model land-use processes;(iii)

Types of QA/QC procedures performed by model developers and data suppliers and whether or nottheir quality control procedures are adequate;(iv)

Existence of plans to periodically evaluate and update or replace assumptions with appropriate newmeasurements. Key assumptions may be identified by performing sensitivity analyses.

5.5.5

QA Review ProceduresGood practice for QA procedures requires an expert review to assess the quality of the inventory, and also to
identify areas where improvements could be made. The inventory may be reviewed as a whole or in parts. QA
procedures are used in addition to Tier 1 and Tier 2 QC. The objective in QA implementation is to involve
reviewers that can conduct an unbiased review of the inventory. It is good practice to use QA reviewers thathave not been involved in preparing the inventory. Preferably, these reviewers would be independent experts
from other agencies or a national or international expert or group not closely connected with national inventory
compilation. Where third party reviewers outside the inventory agency are not available, staff from another partof the inventory agency not involved in the portion being reviewed can also fulfil QA roles.
It is good practice for inventory agencies to conduct a basic expert peer review (Tier 1 QA) prior to inventory
submission, in order to identify potential problems and make corrections where possible. It is also good practice
to apply this review to all source and sink categories and sectors in the inventory. However, this will not always
be practical due to timing and resource constraints. Key categories should be given priority, as well as categories
where significant changes in methods or data have been made. Inventory agencies may also choose to perform
more extensive peer reviews or audits or both as additional QA procedures within the available resources.
Inventory agencies should also consider applying the techniques and procedures for the LULUCF sector
described in Section 5.7, Verification, of this report, subject to the availability of data for these techniques andresource constraints. Priority should be given to key source and sink categories in the application of these more
rigorous verification techniques. The comparison of emission or removal estimates or other relevant data for the
LULUCF sector with data external to the inventory process can help to establish the reliability of individualcomponents. Verification of the inventory may be especially useful for the LULUCF sector, because of thepotentially large uncertainties surrounding the inventory estimates. Expert reviews and Tier 2 QC investigations

IPCC Good Practice Guidance for LULUCF

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Chapter 5: Cross- Cutting Issues

are critical first steps in verification. Box 5.5.2 provides further discussion on conducting an expert peer review
for the LULUCF sector.
BOX 5.5.2
EXPERT PEER REVIEW

Expert peer review consists of a review of calculations or assumptions by experts in relevant
technical fields. This procedure is generally accomplished by reviewing documentation associatedwith the methods and results, but usually does not include rigorous certification of data or
references such as might be undertaken in an audit. The objective of the expert peer review is toensure that the inventory’s results, assumptions, and methods are reasonably judged by thoseknowledgeable in the specific field. Expert review processes in the LULUCF sector may involve
technical experts as well as researchers. Where a country has formal stakeholder and public review
mechanisms in place, these reviews can supplement but not replace expert peer review.
In the LULUCF sector, the complexity of models may make peer review more difficult, as well as
more important. Consequently, good practice should include:●

Identifying whether the major models used for the analysis have undergone peer review; if
not, the inventory agency should initiate a peer review process for the models separately, oras part of, the inventory peer review process.

●

Determining whether the documentation of the models, input data, and other assumptions,etc., is sufficiently thorough and sufficient to support the peer review.

There are no standard tools or mechanisms for expert peer review, and its use should be consideredon a case-by-case basis. If there is a high level of uncertainty associated with an emission or
removal estimate for a category, expert peer review may provide information to improve theestimate, or at least to better quantify the uncertainty. Effective peer reviews often involve
identifying and contacting key independent organizations or institutions, including researchorganizations. In the LULUCF sector, for example, the participation of researchers and researchorganizations is often needed when applying verification techniques and procedures (see Section
5.7), especially with regards to more complicated models. It is good practice to obtain the relevant
expertise in development and review of methods, data acquisition, and models.

5.5.6

Documentation, Archiving and Reporting

It is good practice to document and archive all information required to produce the national inventory estimatesas outlined in GPG2000 (Chapter 8, Quality Assurance and Quality Control, Section 8.10.1, Internal
Documentation and Archiving) including the results of the verification activities and changes in data inputs andmethods from previous years. To ensure transparency, documentation should be sufficient to enable theassessment of the estimates of emissions for key categories. Documentation and archiving procedures in the
LULUCF sector should be focus on the following issues:•

Because of the likely use of sample data and because annual data are unlikely to be available for areas,
stocks and estimation parameters, documentation of the consistency of time series data and methods forinterpolating between samples and years is particularly important.

•

Because of the importance of clear land-use classification in each year and accurate verifiable tracking of
categories over time, documentation should be provided on land-use categories.

•

Because of the complexity of LULUCF data and models, providing thorough documentation allows internal
QC checks and investigations and external QA reviews to operate effectively:

5.54

(i)

The rationale for the choice of models and their consistency with the good practice guidance
provided in Chapter 3 should be discussed, documented, and archived;(ii)

Archives should contain documentation provided by the model developers on the assumptions andworkings of the model, including data sources, source code (if available) and other information(such as sensitivity analyses);(iii)

Documentation should include data on QA/QC procedures governing models, both existingprocedures or documentation available from model developers, and efforts to institute additional or
expanded procedures.

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Quality Assurance and Quality Control

5.5.7

Issues under Kyoto Protocol Articles 3.3 and 3.4

It is good practice to follow the Tier 1 and Tier 2 QC procedures described in Section 5.5.3 and 5.5.4 forestimates reported under Articles 3.3 and 3.4 of the Kyoto Protocol 21 . For the most part, the QA/QC
requirements for estimates of LULUCF prepared under the Kyoto Protocol will be similar to those for any otherinventory estimates, but there is a need to undertake additional checks according to Chapter 4. A summary ofthese Tier 2 QC checks is given below:•

Identify the geographical location of the boundaries of the area that encompasses land subjected to theactivities under Articles 3.3 and 3.4 (if elected). Special care is needed for Kyoto Protocol reporting on theattribution of specific activities to relevant land categories in tracking the shifts of an area of land from one
category to another, when different activities are taking place, one after the other, within or between
commitment periods under the Kyoto Protocol. It is also important to take into account the special
requirements for methodological choice as explained in Chapter 4.

•

Check availability of data for estimation of net-net accounting for some activities under Article 3.4 of the
Kyoto Protocol. It is important to document estimates both for the base year and commitment period. It isparticularly important to document any approximations required to estimate data for the base year.

•

Ensure that the historical data undergo QC checks that are as rigorous as the current year data.

•

Check the analysis conducted to determine that a pool which is not being reported is not a source.

21

The present section only deals with activities specified in Article 3.3 and 3.4 under Kyoto Protocol of the United Nations
Framework Convention on Climate Change (UNFCCC), it does not address projects (under Article 6 or 12 of Kyoto
Protocol).

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Chapter 5: Cross- Cutting Issues

5.6

TIME SERIES CONSISTENCY AND
RECALCULATIONS

5.6.1

Introduction

Greenhouse gas inventories for LULUCF categories typically rely upon numerous data inputs, assumptions, andmodels brought together in a consistent and transparent way. Because a major interest in an inventory is its trend,
it is critical to ensure that inventory totals estimated for different years are as comparable as practically possible.
According to the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories (GPG2000, IPCC, 2000), it is most appropriate to use the same methodology and consistent sourcesof data for all inventory years. If this is not possible, time-series consistency can be approximated using
techniques described in this section. Recalculations imply changes in earlier estimates due to changes inmethodology or methodological refinements.
It is anticipated that the use of recalculation methodologies in LULUCF category inventories will be particularly
important for two reasons. First, the development of inventory methods and interpolation/extrapolation tools(models) for this sector is ongoing and it is anticipated that changes to the methods of many countries will occur
over time due to the complexity of processes involved. This will be the result of either changes in tiers or
modification of national methods. The second reason that recalculation issues are important is that certain dataneeded to calculate an inventory for the LULUCF categories may not be collected annually. For example, forest
inventory data may be compiled only once in a five or ten year period. In these cases, methods are needed to
extrapolate and interpolate from infrequent data to develop an annual time series.
This section discusses general issues of time series consistency and the use of recalculation in the LULUCF
sector. Section 5.6.2 considers the impact of methodological change and methodological refinements (either dataor models) and the associated recalculation techniques that can be used to ensure the consistency of the inventory
over time. The issue of developing annual inventories when data are only available at a lower frequency (e.g.,
every 5 years) is covered in Section 5.6.3. Issues particular to the Kyoto Protocol are addressed in Section 5.6.4.

5.6.2

Time Series Consistency and Methodological Change

As inventory methods improve and more relevant data become available, it is good practice to apply this new
information if it improves the reliability and accuracy of the inventory.22 When modifying methods or input data,care must be taken to ensure that changes in the inventory through time reflect real changes in emissions or
removals and not simply the pattern of methodological refinements. For example, if a country moves from a Tier
1 method in one year to a higher tier in the next, any change in emissions and/or removals between the two years
will reflect both the different methods as well as real changes. When different methods are used in two different
periods there is potential for the time series to be inconsistent for the two periods. The standard method for
ensuring consistency is to recalculate the estimates using the same method for all inventory years, if possible.
The purpose of this recalculation is to ensure that the entire time series reflects the new data and/or method. If it
is not possible to use the new data or methods throughout the time series, alternatives must be considered.
The GPG2000, Section 7.3, Recalculations, describes methods for recalculation and time series consistency andit should be referred to for a general description of good practice guidance in this area. The discussion in the
GPG2000 is not sector-specific and can be applied directly to the LULUCF sector. However, given the ongoingrefinement of data and methods in this sector, it is anticipated that the use of recalculation techniques will be
particularly important. Following the GPG2000, it is good practice to recalculate previously reported inventory
estimates when:•

Errors have been identified in the previous inventory data, models, or methods that affect the inventory levelor trend. If errors are corrected in follow-up inventories, but recalculation is not conducted to correct priorinventories, erroneous reporting of the inventory would result;•

Available data have changed. The availability of data is a critical determinant of the appropriate method, andthus changes in available data may lead to changes or refinements in methods. As inventory agencies gain
experience and devote additional resources to preparing greenhouse gas emissions inventories, it is expected

22

New methods or data that are not judged to improve the ultimate inventory estimate and therefore are not used, may
provide useful information for analyzing uncertainty, QA/QC, and verification.

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IPCC Good Practice Guidance for LULUCF

Time Series Consistency and Recalculations

that data availability will improve.23 Overall, though, inventory agencies should choose methods and collectdata consistent with the identification of key source and sink categories, as discussed in Section 5.4.5.
•

The previously used method is not consistent with good practice guidance for that source/sink category as
described in Chapters 2, 3 or 4.

•

A source/sink category has become key. A source or sink category might not be considered key in the baseyear, depending on the criteria used, but could become key in a future year. For example, a country mightlaunch afforestation programs that could result in a considerable increase of afforested lands, or experiencelarge conversions of forested areas in urban developments which could result in sizable increase of
deforestation. Inventory agencies anticipating these types of significant changes and resulting changes tohigher tier methods in a category may want to consider this possibility before it becomes key.

•

The previously used method is insufficient to reflect mitigation activities in a transparent manner. As
techniques and technologies for reducing emissions or enhancing removals are introduced, inventory
agencies should use methods that can account for the resulting decrease changes in emissions or removals ina transparent manner. Where the previously used methods are insufficiently transparent, it is good practice
to change or refine them.

•

The capacity for inventory preparation has increased. Over time, the human and/or financial capacity to
prepare inventories may increase. If inventory agencies increase inventory capacity, it is good practice tochange or refine methods so as to produce more accurate, complete or transparent estimates, particularly for
key categories.

•

new methods become available. In the future, new methods may be developed that take advantage of newtechnologies or improved scientific understanding. For example, remote-sensing technology and site
specific modelling is making it feasible to estimate emissions from land clearing activities more accurately
than by using simple aggregate emission factor/activity data. Inventory agencies should ensure that their
methods are consistent with the IPCC Guidelines and with this report.

Once the need for recalculation is determined, there are a variety of approaches that may be considered toaddress potential inconsistencies in the time series. The choice of recalculation method typically depends on thedata that are available to perform the recalculations. GPG2000 discusses several methods, and these aresummarised in Table 5.6.1. The approaches described in GPG2000 are conceptually fully applicable to the
LULUCF sector.
TABLE 5.6.1
SUMMARY OF APPROACHES TO OBTAIN CONSISTENCY IN TIME SERIES

23

ApproachApplicability

Comments

Total Recalculation

Required data are available for alltime periods.

•

Good practice, if possible.

Interpolation

Data needed for recalculation using
the new method are available forintermittent years during the timeseries.

•Emissions estimates can be linearly interpolated forthe periods when the new method cannot be applied.

•

Most reliable if the trend over time is constant.

Trend Extrapolation

Data for the new method are notcollected annually and are notavailable at the beginning or the endof the time series.

•

Should not be used if the trend is changing (in thiscase, the surrogate method may be more appropriate).

•

Should not be done for long periods.

•

Most reliable when the overlap between two or more
sets of annual emissions estimates can be assessed.

•

If the relationship between the two methods during
the period of overlap is irregular, this approachshould not be used for recalculation.

•

Multiple indicative data sets (singly or in
combination) should be tested in order to determine
the most strongly correlated.

•

Should not be done for long periods.

Overlap

Data necessary to apply both thepreviously used and the new
method must be available for atleast one year.

Surrogate

Emission factors or activity data
used in the new method are strongly
correlated with other well- known
and more readily available
indicative data.

In some circumstances data collections may be reduced which can also lead to a change or refinement in method.

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Chapter 5: Cross- Cutting Issues

It is not feasible to list all possible issues that may arise when recalculating or to provide detailed
recommendations about the appropriate recalculation technique in all instances. Each case should be treated on
its merits and the recalculation methodology chosen should be based on a trade off between the cost to
implement it and the overall impact on the time series consistency.
Over several years of inventory preparation, a variety of methodological changes may occur. In simple cases(e.g., when moving between tiers), sampling or experimentation may provide country-specific emission factors.
In this case, it is good practice to recalculate the time series incorporating these new emission factors, with theavailable activity data. More complicated situations can also arise. For example:•

The instruments used to collect activity data may change through time, and it is impossible to go back intime to apply the new instrument. For example, clearing events can be estimated by the use of satellite
imagery, but the satellites available for this work change or degrade through time. In this case, the overlap
method is most applicable.

•

Some data sources may not be available annually because of resource constraints. In this case, interpolation
between years or extrapolation for years after the last year with measured data available may be most appropriate.

•

Emissions and removals from LULUCF typically depend on past land use activity. Thus, data must cover alarge historical period (20-100 years), and the quality of such data will often vary through time. Overlap,
interpolation or extrapolation techniques may be necessary in this case.

•

The calculation of emission factors will typically require a combination of sampling and modelling work.
Time series consistency must apply to the modelling work as well. Models can be viewed as a way oftransforming input data to produce output results. In most cases where changes are made to the data inputs
or mathematical relationships in a model, the entire time series of estimates should be recalculated (seeTable 5.6.1). In circumstances where this is not feasible due to available data, variations of the overlap
method could be applied.

5.6.3

Recalculation and Periodic Data

National resource or environmental inventories, such as national forest inventories, only in rare cases cover theentire country on an annual basis. Instead, they are generally carried out every fifth or tenth year, or region-byregion, implying that national level estimates can only be directly obtained once the inventory in every region
has been completed.
When data are available at a frequency that is less than annual, several issues arise. First, the estimates need to be
updated each time new data become available, and the years between the available data need to be recalculated
in some way. The second issue is producing inventories for years after the last available data point and before
new data are available. In this case, new estimates should be extrapolated based on available data, and thenrecalculated when new data become available.
The choice of method to achieve time series consistency will depend on the particular data available. If surrogate
data (i.e., alternative datasets that can be used as a proxy for missing data) are available, they can be a useful
guide for extrapolating the trend in periodic data and subsequently interpolating the same data following the next
data collection cycle. If there are no available surrogates or other information, then the only technique available
is to extrapolate, with a recalculated interpolation of the estimates when the new observations are available. Thus,
it is good practice to attempt to find reliable surrogate data to guide extrapolation and interpolation when thefundamental data used for the inventory estimates are not available annually. Two examples of practical
approaches are given in Box 5.6.1 and Box 5.6.2.
BOX 5.6.1
EXAMPLE CASE WHERE A NATIONAL FOREST INVENTORY IS CONDUCTED EVERY 5 YEARSConsider a case where a national forest inventory is conducted every 5 years. Estimates of several
types of required data (e.g., tree growth) will therefore only be obtained at certain intervals. On theassumption that growth is on average reasonably stable between years, inventory estimates for theyears after the last available data should be made using extrapolations of past estimates (i.e., tree
growth trends). In Figure 5.6.1, a biomass estimate for 2003 for a plot is obtained in this way,although the latest measurement was made in 2000. The trend between 1995 and 2000 is simply
extrapolated linearly. In practice, a log scale might be used to accommodate exponential behaviour
but this is not considered for this simple example. Also, extrapolation can be improved using
surrogate data or more sophisticated modelling taking into account parameters influencing theparameter we want to extrapolate.

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Time Series Consistency and Recalculations

BOX 5.6.1 (CONTINUED)
EXAMPLE CASE WHERE A NATIONAL FOREST INVENTORY IS CONDUCTED EVERY 5 YEARS

Next, once the new data for 2005 are collected (Figure 5.6.1), the estimates for the intermediate
years (2001-2004) need to be recalculated using an appropriate approach (e.g., a combination ofinterpolation and surrogate approaches). In this example, the estimates for all of these intermediate
years (2001-2004) would be recalculated, since the estimate for 2005 turned out to be lower thanthe extrapolated trend.
F IGURE 5.6.1
R ECALCULATED ESTIMATE FOR 2003 BASED ON LINEAR EXTRAPOLATION

65
60

Original Extrapolation
Actual (Periodic) Data

55
50
45
40
1985

1990

1995

2000

2005

BOX 5.6.2
EXAMPLE OF MODELLING THE EMISSIONS OF A SITE OVER TIME

Consider modelling the emissions of a site over time. This might be useful in a country-specificapproach if the inventory was based on tracking either a sample or complete population of sites.
Typically, it would not be cost-effective to physically visit all sites annually to assess land-use
change. Instead remote sensing technologies could be employed to measure changes such as clearing,with the much greater coverage of the technique offsetting the lower precision of the data comparedto ground visits. Because of the costs of acquiring and processing the remote sensing data, it might
not be either feasible or cost effective to generate the remotely sensed data on an annual basis.
Instead, it might be generated every several years and the intervening periods interpolated.
When a clearing event is identified through periodic surveys or remote sensing, it is necessary toallocate emissions to one or more of the years preceding the event. In the absence of any surrogate
or additional information indicating which year or years the event took place, it is good practice toallocate emissions from the clearing event in equal increments to each year. For example, if remotesensing shows that a particular site was forested during 1997, but was cleared by 2000, then theclearing may have occurred in 1998, 1999 or 2000.
The presence of surrogate information may change the approach to the analysis. In making
estimates in the period before new satellite data become available, (i.e., for the original 1999 and2000 inventories) extrapolation from previous years is necessary, perhaps with the use ofadministrative records. It is good practice to make the most reliable extrapolation possible, subject
to the best available data and resource constraints, recognising that estimates will be revised in the
future when more detailed information is available.
As an extension for the uncertainty analysis for this category, the clearing event could be
randomised to one of the three years (i.e., assigned to each year with probability 1/3).
Analogously, a Monte Carlo approach could repeatedly assign the clearing event to a random year
and then calculate the uncertainty in the emissions or removals for the sector. This would
incorporate the additional uncertainty in the exact time of clearing into the estimate. If approximate
clearing rates are known from administrative records, it may be used to adjust the interpolation
probabilities. For example, if the clearing rate in 1998 is estimated to be twice that of 1999 and2000 then we could estimate the probability for the above example to be 1/2 that it was cleared in1998 and 1/4 that its was cleared in 1999 or 2000.

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Chapter 5: Cross- Cutting Issues

5.6.4

Issues under Kyoto Protocol Articles 3.3 and 3.4

In general, good practice for ensuring time series consistency and performing recalculations for estimates of
LULUCF prepared under the Kyoto Protocol reporting of supplementary information will be similar to those forany other inventory estimates. However, there are some special issues that are specific to Articles 3.3 and 3.4that it is good practice to take into account:•

The need to report on an annual basis the geographical location of the boundaries of the area that encompass
land subject to the activity. During the commitment period under the Kyoto Protocol, it will be necessary to
update the identification of such areas if new lands are brought under Articles 3.3 and 3.4. Thus, it will benecessary to ensure consistent representation of these areas over the period back to 1990 or the onset of any
activities under Articles 3.3 and 3.4, as well as to adequately track shifts among categories in those lands. It
is good practice to use the methods described in Section 5.6.

•

The need to make recalculations due to updated information on non-annual data (see Chapter 4 for a moredetailed description on how to deal with non-annual data).

5.6.5

Reporting and Documentation

In all cases, the calculations performed to ensure time series consistency should be carefully documented
because of the complicated processes and large temporal and geographical scales typically involved in the
LULUCF sector. The good practice guidance provided in GPG2000 on documentation of time seriesconsistency applies fully to this sector. The GPG2000 states that clear documentation of recalculations isessential for transparent emissions estimates, and to demonstrate that the recalculation is an improvement inaccuracy and completeness. In general, the following information should be provided whenever recalculations
are undertaken:•

The effect of the recalculations on the level and trend of the estimate (by providing the estimates preparedusing both the previously used and new methods).

•

The reason for the recalculation (see Section 7.2.1, Quantitative approaches to identify key source categories,
of GPG2000, for further discussion of this issue).

•A description of the changed or refined data, models, assumptions, factor values, and/or method.

•

Justification for the methodological change or refinement in terms of an improvement in accuracy,transparency, or completeness.

•

The approach used to recalculate previously submitted estimates.

•

The rationale for selecting the approach, which should include a comparison of the results obtained using
the selected approach and other possible alternatives, ideally including a simple graphical plot of emissions
or removals versus time or relevant activity data, or both.

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Verification

5.7

VERIFICATION

5.7.1

Introduction

The purpose of verifying national greenhouse gas inventories is to establish their reliability and to check theaccuracy of the reported numbers by independent means. Verification can be performed at several levels: project,national and international.
The overall goals of verification are to:•Provide inputs to improve inventories;•

Build confidence on estimates and trends;•

Help to improve scientific understanding.

These goals can be achieved through internal or external inventory checks. Internal verification is generally
performed by inventory agencies, while other bodies (e.g., other government agencies, private companies,
research consortiums, independent scientists, non- governmental organisations) will carry out externalverification.
The Glossary of IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas
Inventories (GPG2000, IPCC 2000) defines verification as shown in Box 5.7.1 (see also Glossary):
BOX 5.7.1
DEFINITION OF VERIFICATION FOR THE INVENTORY

Verification refers to the collection of activities and procedures that can be followed during theplanning and development, or after the completion of an inventory, that can help to establish its
reliability for the intended application of the inventory.
In general, verification as discussed in Annex 2, Verification, of GPG2000 is also relevant to the LULUCF
sector. There are many approaches to verification, including: comparison of the inventory estimates withindependent assessments, procedures and datasets; peer and public review; and direct measurement of emissions
and removals of greenhouse gases. Verification approaches can also include examination of specific aspects of
the inventory, such as underlying data (collection, transcription, and analysis), emission factors, activity data,assumptions and rules used for the calculations (suitability and application of methods, including models), andupscaling procedures. No matter which verification approaches are used or what aspects of the inventory areverified, it is good practice to conduct verification using data and methods that are independent from those used
to prepare the inventory.
To some extent specific approaches for verification are needed for LULUCF sector because of the uniqueness of
estimation methods. Ideally, verification of LULUCF activities would be based on complete accounting ofemissions and removals at the national scale, measured by independent methods at different levels and, possibly,complemented by top-down approaches based on atmospheric measurements. Such verification would becomplex and resource intensive, and will be possibly performed by research consortiums and/or programmes. It
is more likely that inventory agencies would apply some more limited verification approaches or seek to address
their verification needs through already ongoing research activities. The external verification approaches
described in this section may help inventory agencies to evaluate their results.
This section presents a range of verification approaches and provides practical guidance on how to apply them tothe entire national inventory, or parts of it. Section 5.7.2 describes some of the approaches available for verifying
inventory estimates and/or the data on which they are based. Section 5.7.3 provides practical recommendations
for verifying LULUCF Inventories. Section 5.7.4 considers some of the verification issues that are specific to the
Kyoto Protocol 24 . Section 5.7.5 addresses reporting and documentation issues. QA/QC is closely related toverification, and it is covered in Section 5.5 of this chapter. Finally, some details for verification approaches are
given in Section 5.7.6.

24

Verifiability is a requirement under Article 3.3 of the Kyoto Protocol and for Articles 3.3 and 3.4 under paragraph 17 of theAnnex to the draft LULUCF decision agreed in Marrakesh (see FCCC/CP/2001/13/Add.1, page 61).

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Chapter 5: Cross-Cutting Issues

5.7.2

Verification Approaches

An inventory agency (or an external group) may decide to verify the entire inventory, a part of it or theunderlying data and models from which the inventory estimates have been calculated. This section describes
approaches that can be used to verify inventory estimates, including some techniques that allow the verificationof the overall inventory, and many that can be used to verify selected elements of an inventory. The criteria for
selecting verification approaches include: scale of interest, costs, desired level of accuracy and precision,
complexity of design and implementation of the verification approaches, and the required level of expertiseneeded to verify. For each approach, a technical description is given with reference to its applicability (e.g., for aparticular category, types of data). Guidance for the application of the approach is also provided, and Table 5.7.1
contains information to assist in identifying the most suitable approaches for particular categories or inputs.
Table 5.7.1 addresses verification approaches for land area classification, major carbon pools and non- CO2 gases,
although it is not exhaustive. The general applicability of the verification approaches for the estimations ofemissions and removals from LULUCF sector for the reporting under the Kyoto Protocol is described in Section
5.7.4.
Generally, the most significant emissions and removals related to LULUCF are of carbon dioxide (CO2).
However, the LULUCF sector also includes non-CO2 greenhouse gases (mainly emissions) from the fertilisationof forests, land clearing, soil preparation for afforestation/reforestation, grasslands and croplands management
and other practices. These non- CO2 greenhouse gases include methane (CH4), nitrous oxide (N2O), carbon
monoxide (CO), oxides of nitrogen (NOx), and non- methane volatile organic compounds (NMVOCs). Emissions
and removals of CO2 can be determined and verified directly as changes in carbon stocks in biomass or soils. For
non- CO2 gases, fluxes can be measured to verify annual emission estimates.
There are many approaches that can be used to verify emission and removal estimates for the LULUCF sector.
An overall verification exercise may include cross-checking of the results at different geographical scales, fromregional to global. Such cross-checking, however, requires considerable time and it is likely to be implemented
over multiple years rather than on single year basis. Compared to fossil fuel emissions, LULUCF activities are
more difficult to assess over short time periods, because biospheric carbon is often difficult to monitor and slow
to equilibrate. The assessment of the net anthropogenic impacts on biospheric carbon would consequently
require a long-term perspective (Nilsson et al. 2001).
Table 5.7.1 summarises the applicability of a range of verification approaches to different aspects of LULUCF
inventory estimation. More detailed descriptions of the approaches are given in the following part in this section.

APPROACH 1: COMPARISON TO OTHER I NFORMATI ON
Comparison of the LULUCF inventory to other independently compiled inventories or data sets can be a usefuland efficient means of verification. Two broad types of verification are possible under this approach:comparison with independent inventories (Approach 1a) or comparison with international programmes anddatasets (Approach 1b).

Approach 1a: Comparison w ith independent i nventories
In some countries, it may be possible to verify the national LULUCF estimates prepared by the inventory agency
with inventories put together by other organisations (i.e., other national, regional/provincial agencies, researchorganisations, etc.). Such external inventories can be used for verification if the same underlying data have notbeen used to produce the reported estimates and if the relationships between sectors and categories in the
different inventories can be assessed. In this respect, it is good practice to ensure that the same dataset has notbeen already used to calculate/estimate some of the reported LULUCF category. When comparing independent
inventories it is also important to take into account the uncertainties in the estimates.
Another effective verification approach is to compare inventory information between countries or groups ofcountries. Such comparison could be made for overall estimates of particular source/sink categories, default
assumptions and/or data used to compile the national inventory. This approach can be quite inexpensive toperform, but care must be taken to ensure that the characteristics of the selected countries are, in fact,comparable (i.e., they should have similar climatic or biome characteristics). Sometimes data based oninventories from other countries can be better related to national circumstances than those calculated withgeneral default emission factors or activity data, and can in turn be used to improve the inventory.
The comparison of inventory data or estimates with other inventories can be an inexpensive and fairly simple
verification approach. In general, it does not require skilled technicians or highly trained personnel, particularly
when compared to the requirements of approaches like remote sensing or modelling. It can be applied to allelements of an estimate, including land area classification, inventories of various carbon pools, estimates of nonCO2 gases, and activities like afforestation, reforestation and deforestation. The key determinant in its

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applicability is the availability of alternative inventories against which to compare. It is good practice to use thisapproach if such inventories are available. If such comparisons identify significant differences, the causes should
be investigated, in order to correctly interpret the results and flag possible areas for further inventory checks.

TABLE 5.7.1
APPLICABILITY OF VERIFICATION APPROACHES FOR LAND AREA IDENTIFICATION
AND FOR CARBON POOLS AND NON- CO2 GREENHOUSE GASES
Approach 1

Approach 2

Approach 3

Approach 4

Approach 5

Comparison
with otherinventories andotherindependent
datasets

Applying
higher tiermethods

Directmeasurement

Remotesensing

Modelling

Suitable, if data
are available

Suitable, if data
are available

Not applicable

Suitable

Not applicable

Abovegroundbiomass

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable(ground dataneeded)Suitable (regression,
ecosystem andgrowth models)

Belowgroundbiomass

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable, (regression,
ecosystem andgrowth models)Dead wood

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventory- basedmodels)LitterSuitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Applicable(ecosystem andinventory- basedmodels)Soil organic
matterSuitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystem
and inventory- basedmodels)Non- CO2
greenhousegases

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Emissionfactors

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Not applicable

Suitable (ecosystemmodels)Land area
Carbon pools

Activity/land-based report
Forest,grassland,
cropland, otherland uses

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable,particularly toidentify land
cover/land use
and theirchanges

Suitable,
Data-intensive,
Can be an alternative
approach whenestimates from direct
measurements andremote sensing are
not available

Afforestation,
Reforestation,
Deforestation,
projects

Suitable, if data
are available

Suitable, if data
are available

Suitable(resource-intensive)

Suitable,particularly toidentify land
cover/land use
and theirchanges

Not practical

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Chapter 5: Cross-Cutting Issues

Approach 1b: Comparisons w ith International Programmes and Datasets
A number of research and monitoring initiatives are currently under way at the international level, both atregional/continental scale (research projects, monitoring networks, etc.) and at global scale (remote sensing of
the biosphere, global data archiving centre, networks of similar research initiatives between regions, etc).
For the LULUCF sector, most of this research is linked to the quantification of the role of terrestrial ecosystems,
particularly forests, in the carbon cycle, from ecosystem to global scale. In this respect, many of the results
gathered by research and monitoring networks could be relevant for verification of LULUCF reporting, as well
as for other cross-cutting issues such as those linked to QA/QC and uncertainties.
The scale and aggregation level (national, regional, etc.) of the data and information that can be gathered fromsuch programmes and datasets may be useful in different phases and levels of the verification process (internaland external auditing, comparison with data collected by other agencies, etc.).
As with Approach 1a, the comparison of inventory data or estimates with independent datasets can be an
inexpensive and straightforward verification approach. It can be applied to any element of an inventory forwhich there is an alternative source of data. Generally, it is most applicable for land area classification, although
it can also be used to verify selected elements of estimates of carbon pools, non- CO2 greenhouse gases, andactivities, while data coming from research networks can be used to verify country-specific data (emission
factors). As mentioned for the previous approach, when using an international dataset for verification purposes,
it is good practice to ensure that the same dataset has not been already used to calculate or estimate someelements of the reported LULUCF category. This situation can occur particularly when the internationallyavailable programmes and datasets are compiled from national statistics or include the results of specific studies
performed in the territory of the country that is planning to use the data for verification. The analysis of theeventual differences emerging from the comparison with internationally available data- sets and inventories
should be devoted particularly to the identification of the possible reasons for such differences, with the final
objective of overall inventory improvement. Links to some international programmes and datasets that could be
useful for verification purposes can be found in Box 5.7.6, Links and Networks Relevant to LULUCF, in Section
5.7.6. Other useful links to open sources for land-use/land- cover data may be found in Chapter 2, Annex 2.A.2,
Examples of International Land Cover Datasets.

APPROACH 2: APPLYING HIGHER TIER METHODS
A country may not have sufficient data or resources to use higher tier methods for its total inventory of emissions
and removals from all of the various categories of the LULUCF sector. In some cases, however, the country may
have access to more comprehensive datasets for specific areas (e.g., a region or subcategory). In this case, thecountry could conduct verification of part of its estimate using a higher tier method. As an example, if
greenhouse gas emissions and removals in managed forests have been estimated using the Tier 1 methods, aninventory agency may consider performing verification by applying, over a portion of the forested area, countryspecific data (Tier 2 or Tier 3). In this case, biomass and growth equations would have to be available ordeveloped in selected areas at least for homogeneous growth conditions (biome, climatic regions), forest age
classes and management regimes.
The application of higher tier methods for parts of an inventory can be an effective verification technique if thenecessary data, derived from the more detailed method are available. This approach can be applied at a variety of
scales, from plot to national level. Costs will vary depending upon the scope of the verification. In general,development of higher tier estimates for verification can be fairly simple and may use the already available
inventory expertise. A key issue with this approach is whether to use the partial higher tier estimates as a part ofthe inventory itself or as a verification approach.

APPROACH 3: DIRECT MEASUREMENTS OF EMISSI ONS AND REMOVALS
OF GREENHOUSE GASES
Direct measurements are a verification approach for various carbon pools, as well as non-CO2 greenhouse gas
emissions and LULUCF activities. However, this approach is not generally applicable for verifying land areaclassification. The scale of the approach can vary from plot to national level. At limited scale, direct
measurements can provide country-specific default factors and activity data, while larger scale approaches canbe used for verification of sectoral estimates and specific activities. Costs can vary substantially, depending uponsample size and the desired accuracy. With a large sample size, accuracy can be quite high. When applying thisapproach, the most significant challenges are generally designing the sampling strategy and measurement
protocols. Once the infrastructure is in place, measurements collections are generally not technically difficult,although they can be labour intensive.
When performing direct measurements of emissions and removals of greenhouse gases in the LULUCF sector,
temporal and spatial variability needs to be properly considered, because emissions/removals in a given year are

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Verification

not necessarily indicative of long-term trends. This is due to the fact that most of the emissions and removals inthe sector are linked to biological processes and subject to climate variability. The problem can be partly
addressed by using average, cumulative measurements or smoothing over several years to get representative
results. Furthermore, the effect of inter-annual variability of data tends to decrease as larger areas are considered.
Thus, direct measurements over larger areas or with longer measurement intervals are more likely to reflect theeffect of management practices (see Chapter 4, Section 4.2.3.7, Interannual Variability). While recognising theseissues in using direct measurements as a verification tool, they can still be useful in several ways to verify
LULUCF sector estimates and background data, as described below.

Living Biom ass (aboveground and belowground biom ass)
Reported carbon stock changes in biomass can be verified by direct measurements of stock changes. Currently
available techniques allow reasonably accurate measurement of changes in aboveground biomass at periodic
intervals, although, in mature forests, the annual changes in stocks can be small for the size of the pool. Methods
for estimating belowground biomass are also available, although the sampling is more difficult than foraboveground biomass. This approach can be used particularly in forests, but it is also suitable for the changes inliving biomass in other land-uses which contain woody biomass while not matching the definition for forest land(e.g., agro- forestry systems, revegetated grasslands, etc.).
There is a variety of ways in which direct measurements can be used to verify biomass estimates. For example, acountry may decide to collect forest inventory data by direct measurements more frequently than they typicallydo, e.g., on a 5-10 year interval, for a selected subsample of plots or for a region. An inventory agency may alsouse direct measurements to derive local allometric relationships including belowground biomass that could be
used to verify stock changes for the entire living biomass component. Direct measurements could also be used asa verification tool for young forest stands or lands which are undergoing biomass regrowth, as the available
allometric equations and biomass expansion factors are normally not applicable for these pools. Available
ecosystem studies could be used to derive species-specific biomass expansion factors, which could be comparedagainst the default factors used for reporting and also to check growth rate of specific forest types.

Dead Organic Matter (dead wood and litter)
As for aboveground and belowground biomass, stocks of dead organic matter (litter and dead wood) can also be
estimated from direct measurements. However, in forests, litter and dead wood pools are highly variable both inspace and in time (e.g., seasonal changes in litter, sudden changes due to natural or human disturbances) and aproper sampling scheme would be needed to accurately assess stocks of dead organic matter. It is expected thatlitter pools are not changing significantly in mature forests and verification should be preferably directed toafforestation/reforestation areas and to forest stands that are undergoing major management operations such as
harvesting, site preparation, thinning, etc.
Generally, ecosystem studies are measuring aboveground litter input using netted traps (foliage and twigs) andlitter stocks through collection of litter in several plots (also for coarse dead wood). Such studies, if available,
could serve to check Tier 1 default factors eventually used for reporting.

Soils (soil organic m atter)
Verification of emissions and removals from soils could also be undertaken. As for aboveground biomass,
sensitive methods for estimating soil carbon stocks are available. Repeated soil sampling over a certain area,
region or at national scale can be a relevant approach for detecting changes in soil carbon in different land uses
(forests, grasslands, croplands). However, for ecosystems that are not undergoing land- use changes or are notsubject to significant management operation (e.g., harvesting of a mature forest, improvement of a grassland,ploughing of croplands, etc), changes in soil carbon stocks could be small and difficult to assess accurately overshort periods.
Greenhouse gas emissions and removals from soils can be measured at several sampling points in a plot using
portable or transportable gas- sampling systems (cuvettes and gas analyser). Measurements at the sampling
points would then need to be upscaled to plot/ecosystem levels, taking into account the significant spatial
variability typical of soil-related gas emissions and removals. Both CO2 and other greenhouse gases (N2O, CH4)
have been measured with this approach (Butterbach-Bahl et al., 2002; Janssens et al, 2001). Direct
measurements of greenhouse gas fluxes obtained in this way can be useful also in comparing emissions before
and after the application of a specific management practice (Steinkamp et al., 2001; Butterbach-Bahl and Papen,
2002). Directly measured values can be used to verify the default emission factors eventually used at lower tiers.
Verification of changes in soil carbon in land that is undergoing transition in use can be performed by comparing
measured carbon stocks in the land that has undergone the transition against carbon stocks of lands where theformer land use is still present. In such case, care should be taken to ensure paired sites are well matched interms of factors that may influence soil carbon turnover rates (e.g., soil type, native vegetation, drainage,
topography, etc.).

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Chapter 5: Cross-Cutting Issues

Measurem ents of Greenhouse Gas Fluxes at Ecosystem Scale
Direct measurements of ecosystem fluxes of greenhouse gases can be used to verify, at a local scale, reportedchanges in carbon stocks. These flux observations are usually conducted by micrometeorological techniques,
such as eddy covariance, using canopy towers placed inside forests or other ecosystems, mainly for CO2
exchanges measurements (Aubinet et al., 2000). Generally, they provide data on the Net Ecosystem Exchange(NEE, see footnote 26). This approach is suitable for the comprehensive estimation of carbon emissions andremovals at plot/ecosystem scale, providing data that can be compared with activity data/emission factors anddefault values used in deriving the emissions/removals for a particular LULUCF category. However, there arelimitations in the upscaling of these results to regional and national level, as temporal and spatial variability,
long-term trends and disturbances needs to be properly considered (Körner, 2003). Direct measurements ofecosystem net fluxes require significant investments in equipment and have limitation for possible locations(depending on topography, vegetation and canopy structure). Once implemented, such measurements can be
performed on a continuous basis, providing an estimation of the interannual variability of the balance of CO2
emissions and removals of a certain ecosystem. Due to its complexity, it is likely that ecosystem fluxes will bemeasured by research institutes/networks. If such experiments are available within a country, the inventory
agency may consider using these results for verification.

APPROACH 4: REMOTE SENSING
Remote sensing is an effective approach for verifying land-cover/land- use attribution, detection of land-coverchange and estimations of land areas under conversion and abandonment. In addition, remote sensing can beused to estimate changes in aboveground biomass. Both of these uses of remote sensing for verification are
described below. Remote sensing is not applicable to the verification of belowground biomass, litter, dead woodor soil organic matter.
Remote sensing can be employed at scales ranging from plot to continental level. However, extracting accurate
and repeatable information from remotely sensed imagery can be a demanding task, and is likely to requireconsiderable technical expertise. The cost will depend upon the scope and scale of the programme. Costs can be
relatively low if archived data are available. If frequent measurement and extensive data interpretation are
required, however, both costs and the need for skilled expertise can increase substantially. Among other factors,
the accuracy of remote sensing will depend upon the scale at which it is used and the source of the images.
Generally, it can be quite accurate, but ground truthing is needed to improve result accuracy.

Approach 4a: Remote Sensing to Verif y Land Use and Land-use Changes
Remote sensing is the most direct tool that can be used for verification of the area involved in conversion offorests and grassland to other land-use types (cropland, settlements, etc), the abandonment of managed land, andfor fire detection (which is one of the main factors causing conversions in the tropics). However, if a country has
used remote sensing techniques for the consistent representation of land areas (see Chapter 2, Section 2.4.4.1), orfor the attribution of land- use and activities related to specific aspects of the Kyoto Protocol (see Chapter 4,
Section 4.2.2), care must be taken to ensure that the remote sensing data used for verification are independent of
those used for inventory development. From a technical point of view, remote sensing can be considered averification ex-post, comparing consecutive surveys taken in different years.
It is also important to bear in mind that although remote sensing will in many cases readily detect changes inland cover (e.g., from a vegetation cover to bare ground), it may not always provide adequate and accurate
information on changes in land use or vegetation types (e.g., from Crop A to Crop B)25. For example, detecting
clear-cuts in forests based on remotely- sensed data alone is relatively easy, but it is more difficult to distinguishwhether these are part of on- going forest management or represent deforestation (see also Chapter 4, Section
4.2.6.2.1). Similarly, separation of unmanaged pine forest from managed coniferous plantation forest has beenreported to be difficult, with accuracies of only about 50% (Okuda and Nakane, 1988). Distinguishing between
different crop types is a further area where remote sensing can have difficulty. The combination of frequent
observation by moderate spatial resolution sensors and detailed observation by high-resolution sensors cansometimes solve this problem.
Due to interactions with the atmosphere, clouds in particular, the use of optical remote sensing data may have
limits in certain regions of the globe (e.g., boreal and tropical zones) or periods of the year. In this respect,
Synthetic Aperture Radar (SAR) sensors are better suited to this purpose, as data acquisition can be performed
regardless of sunlight and cloud cover. Even using new sensors such as SAR, it would be challenging to estimate
or verify land-use and land-cover changes on a yearly basis. In part, the challenges result from the resources(personnel and funding) that are needed for such efforts. Nonetheless, as the temporal and spatial resolution of25

In some cases land cover might change, but not the land use, and vice versa.

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satellite sensors improve, detection of sudden and/or recent changes in land use or cover may become possible
on an annual or even more frequent basis.

Approach 4b: Remote sensing to verif y changes in living biomass
Satellite remote sensing and its image products may also be appropriate for assessing biomass and biomasschanges at the major ecosystem level (e.g., grassland vs. forest). Carbon stocks in forests can be estimated using
correlations between spectral image data and biomass, provided that adequate data (not used for inventory
estimates) are available to represent the range in forest biomes and management regimes for which estimates are
required (Trotter et al. 1997). Correlation equations, may be affected by several parameters (canopy andunderstorey type, season, illumination, satellite- viewing geometry) (Okuda et al., 2003), and must in general be
developed for each forest type. In addition, vegetation indices (e.g., the Normalised Difference Vegetation Index,
NDVI) have also been used for the estimation of above ground biomass (see Section 5.7.6 for an overview onsuch indices).
Another approach is to employ Synthetic Aperture Radar (SAR) data that provide structural, rather than spectral,
information about the monitored land cover. For some forest types, wood biomass can be estimated with acertain level of accuracy, using the relationships between biomass and radar intensity (amplitude, backscattering)(Rauste et al., 1994; Foody et al., 1997; Luckman et al., 1998; Saatchi et al., 2000; Terhikki Manninen andUlander, 2001) or indirectly, for instance by linking SAR derived tree heights with in situ derived allometric
relationships. SAR data are suitable for assessing relative incremental changes in aboveground biomass stocks
between two or more points in time, particularly when changes are relevant. Time sequences rather than singledate imagery allows characterisation of change trends and minimisation of errors in the estimations.
Both optical and SAR sensors have limitations in rough topographic terrain and in areas with heterogeneous
canopy cover. The accuracy level of remotely sensed data varies with the geometric and radiometriccharacteristics of the sensors, including change in sensor calibration over time. The imaging data used should bechosen according to the geographical scale of the target area and the desired degree of resolution. Specifications(sensor type, spatial resolution, availability, etc.) of various satellite sensors are listed in Table 5.7.2, in Section
5.7.6.
Other approaches for area and biomass verification using imagery data may include:•

Airborne photography (for the vertical canopy structure of forest, labour-intensive);•

Laser profiler (LIDAR canopy height and structure, accuracy still to be examined, experimental, expensive);•

Comparison with maps/data produced by independent agencies using remote sensing.

APPROACH 5: VERIFICATION USING MODELS
Models can be used to verify estimates of carbon pools, activity data and also the overall inventory. Generally,
they are not used in verification of land area classification. For specific land-use categories under the UNFCCC
and activities selected under the Kyoto Protocol, models can be an attractive option when direct measurements
combined with remote sensing are not feasible. Modelling costs can vary significantly, depending on the specificapplications, availability of appropriate tools, and the degree of resolution desired. Starting costs associated withmodel design and calibration are generally much higher than ongoing running costs. Verification using models isquite complex and requires a high level of technical expertise.
There are two very different types of modelling approaches for verification purposes: bottom-up models andtop-down models. Bottom- up models scale up from lower scale processes to higher aggregation levels, whereas
top-down models follow the other direction and try to infer smaller scale processes from larger scalemeasurements. Although in principle both approaches may be used for verification purposes at the national level,
the top-down models are more suited for continental scale verification. Bottom- up models can be used fromsite/plot level scale to regional and national and even continental level, provided the input data are available.
Models that are used for verification purposes, as models used in inventory preparation, need to be well
documented and should have undergone peer-review. Input parameters, data, functions and assumptions shouldhave been subjected to scrutiny, which is typically referred to as validation. The term validation is used in thegenerally accepted meaning of testing adequately the performance of a model, which is not equal to say that themodel is the only true representation of reality (Oreskes et al, 1994).
As with other approaches, it should be noted that models have their advantages and drawbacks, and so far there
is no such thing as a “best model”. For avoiding some of the possible biases associated with model choice, anensemble of identically calibrated models could be used (Alexandrov et al., 2002). Expert advice is often
required to use models as verification tools.

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Approach 5a: Bottom-up Modelling
There are several types of bottom-up models that can be used for verification:
Ecosystem and growth models can simulate growth of vegetation and the fate of carbon at sufficiently longtime scales, which can be used for verification. They compute biomass growth and fluxes of carbon, water, andnitrogen, and are able to provide estimates of gross primary production (GPP)26 and net primary production(NPP)26 of carbon per unit area in forests (Kramer et al., 2002) and other vegetation types. They can be used toverify Tier 1 and Tier 2 component estimates of biomass and fluxes, and also to derive “emission factors” and/or
country- specific parameters relevant to Tier 2 calculations (see Table 5.7.1). In the case of forests, there arebasically two classes of ecosystem models that can be applied: those that focus on physiology andbiogeochemistry of the ecosystem, and those that are based on forest inventories. Well- known examples of thesetwo classes are FOREST-BGC (Waring and Running 1998), Biome-BGC (Running and Coughlan, 1988;
Running and Hunt, 1993; Running, 1994) and inventory based models (Kauppi et al., 1992; Nabuurs et al., 1997;
Birdsey, 1996; Kurz and Apps, 1999)
Recently, a new generation of terrestrial carbon cycle models have been developed to integrate the effects ofchanges in climate, atmospheric chemistry, disturbance rates on NPP, NEP26 and NBP26 (e.g., Landsberg andWaring, 1997; Chen et al., 2000a; Chen et al., 2000b; McGuire et al., 2001). Using spatial data from remotesensing (e.g., land cover, burnt area, and leaf area index) and georeferenced datasets of climate, atmosphericchemistry and soil inventory, these process-based models can scale up site-level data (e.g., ecosystem flux
measurements) to regional and national scales. Without direct dependence on a forest inventory, the data
estimated using these models could be used to compare to forest inventory-based carbon accounting. However,
the ability of models in which land representation is based on remote sensing to quantify the carbon stockchanges resulting from land use changes at small scale (e.g., afforestation, reforestation and deforestation) is
limited by the spatial resolution of the remote sensing information.
If models are used to aggregate results and to provide data on biomass changes at national scale, model
parameterisation needs to be adequately performed, taking into account the different land use and land coverexisting in a country. As an example, to use model results as verification for forest inventory data, theparameterisation should be performed at least for the main tree species.
Regression models have been used to calculate NPP from basic meteorological data (e.g., Chikugo models,
Uchijima and Seino, 1985). NPP values derived from regression and process-based models can be used forcross-checking of Tier 1 and Tier 2 data at large scale (see Table 5.7.1).
Modelling approaches using Geographical Information Systems (GIS) that incorporate ground truth data
provide more accurate values than remote sensing approaches. GIS based data, such as topography and canopy
cover and structural features such as climate can also be used to drive ecosystem and growth models in order toretrieve spatially explicit results. Accordingly, at continental and global scales, GIS modelling can be used toverify national land survey methodologies (Mollicone et al., 2003).

Approach 5b: Top-dow n Modelling and Large-Scale Approaches
Top-down models could be used for the verification of carbon stocks and stock changes from regional to global
scales. These approaches are not easily applicable to country level estimates, but can be used for aggregatedcountries, large regions or continents. For countries with very large land area or with features that allow toseparation of in-country from external air- mass movements (e.g., North America, Boreal Zone- Siberia,
Australia, United Kingdom, etc.), regional/continental scale approaches can be useful also at national scale.
While top-down modelling can provide overall constraints on regional carbon budgets, they are not suitable forverification of sectoral carbon budgets, because they cannot separate the contribution of emissions and removals
from different land-use categories or management activities -as required for the reporting under the UNFCCC
and the Kyoto Protocol. Moreover, top-down modelling approaches include emissions and removals from landuse categories that are not subject to reporting under either the UNFCCC or the Kyoto Protocol (e.g., nonmanaged lands). Nevertheless, at larger scales, atmospheric measurements of greenhouse gas concentrations andisotopic composition should in principle be able to prove if the aggregate actions taken under UNFCCC and the
Kyoto Protocol will be effective with respect to the trend in atmospheric greenhouse gas concentrations (Schulze
et al., 2002).
26

GPP: Gross Primary Production, given by the gross photosynthesis; NPP: Net Primary Production, net photosynthesis or
GPP minus autotrophic respiration (from above- and belowground living plant biomass); NEP: Net Ecosystem Production,
the net emissions or removals of carbon (CO2), or NPP minus heterotrophic respiration (soil organic matter and soilorganic carbon decomposition, animals), when NEP is measured using flux techniques in correctly defined as NEE, Net
Ecosystem Exchange; NBP: Net Biome Production, the net emissions or removals of carbon at large scale (biome), whichtakes into account also natural and human-induced disturbances (fire, windtrows, harvest, NBP=NEP-disturbances). NBP
is the term that finally is reflected in the global carbon budget (i.e., the atmosphere).

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Inverse models calculate fluxes from concentration measurements and atmospheric transport models. They canbe used to determine overall carbon dynamics at continental to global scales, but have limited ability to separate
the contribution of different land-use categories or management activities to the total budget. By measuring the
spatial and temporal distribution of CO2 concentrations, it is possible to detect terrestrial and oceanic carbon
fluxes. Inverse models are also used to calculate fluxes of methane and other greenhouse gases.
Incorporating airborne observations and using regional- level transport models in the inverse analysis canimprove the estimates, as can the consideration of spatially distributed emissions/removals data. The
implementation of inverse modelling approaches is under continuous development, requiring scientificcollaboration and a networked system among nations. It is probable that such estimations will be independent
from country data and will be valuable for overall verification at regional to continental level (see Gurney et al.,2002 for a comparison of several inverse modelling results at continental scale).
At national level, another large scale approach that can be used for overall verification is the use of tall towers,
which are generally available within a country (e.g., TV towers, transmission towers), to measure the CO2
gradients (Bakwin et al., 1995). This approach can be combined with the use of inverse modelling to derive
regional/national balances of emissions and removals. Once in place, the system can be automated and is notvery expensive.

5.7.3

Guidance for Verification of LULUCF Inventories

Several components of an inventory can be identified by inventory agencies (or external groups) for verification
including emissions/removals estimates, input data, and assumptions. The questions in Box 5.7.2 can be used byan inventory agency as guidance for the development of a verification plan.

BOX 5.7.2
GUIDANCE FOR SELECTING INVENTORY COMPONENTS FOR VERIFICATION AND VERIFICATION APPROACHES

Which criteria can be used to choose the inventory elements for verification?
If any source/sink category is “key”, it should be given priority for verification. However,
emissions and removals that are not “key” can also be selected for verification, especially if theseare of relevance to mitigation policies or their uncertainty is high. If a pool is expected to change
significantly over the inventory reporting period, particular attention should also be devoted to it.
How will the inventory elements be verified?
Selection of the verification approach will depend largely on the suitability/availability of theapproach for the inventory agency or the country-specific conditions. Additional criteria are: thetype of data to be verified, the spatial scale of the inventory coverage, the quantity and quality ofthe data to be verified, and the accuracy, precision and cost of the approach itself. The approaches
and criteria for choosing them are elaborated in Table 5.7.1 and described in detail in Section
5.7.2.

If a country undertakes internal verification of its inventory, it is good practice to ensure that:•

Sufficient independent expertise is available;•

Documentation of the verification is included in the national inventory report;•

Uncertainty estimates and QA/QC documentation is included in the report;•

Other available national verification activities are described;•

Applied verification methods are transparent, rigorous and scientifically sound;•

Verification results are reasonable and well-explained;•

Final calculations can be reasonably linked to underlying data and assumption.

The checklist in the Box 5.7.3 summarises some of the tools that can be used for internal verification of an
inventory, with particular emphasis on the LULUCF sector. A specific box is provided also for Kyoto Protocol
aspects (see Section 5.7.4, Box 5.7.5).

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BOX 5.7.3
VERIFICATION OF INVENTORY OF LULUCF SECTOR IN A NATIONAL INVENTORY

A. Checks:
Does the inventory of the LULUCF sector document the data and assumptions used for estimating
emissions and removals for all IPCC source/sink categories?
Have all important carbon pools been included in the inventory?
If some LULUCF emission/removal categories have been omitted, does the report explain why?
Are emissions and removals reported as positive and negative terms, respectively?
For the total area of the inventory of the LULUCF sector, are the overall changes in land- use forthe inventory year equal to zero within the confidence limit?
Are any discontinuities in trends from base year to end year evaluated and explained?
B. Comparisons of emissions and removals from LULUCF:
Compare the inventory of the LULUCF sector with independently prepared national inventories
for the same country or compare regional sub- sets of the national inventory with independently
prepared inventories for those regions. (Table 5.7.1, Approach 1).
Compare the inventory of the LULUCF sector with national inventories for a different, but similarcountry (Table 5.7.1, Approach 1).
Compare activity data and/or emission factors of the inventory of the LULUCF sector withindependent international databases and/or other countries. For example, compare Biomass
Expansion Factors of similar species with data from countries with similar forest conditions(Table 5.7.1, Approach 1).
Compare the inventory of the LULUCF sector with results calculated using another tiermethodology, including defaults (Table 5.7.1, Approach 2).
Compare the inventory of the LULUCF sector with available high- intensity studies andexperiments (Table 5.7.1, Approach 1-3).
Compare land areas and biomass stocks used in the inventory with remote sensing(Table 5.7.1, Approach 4).
Compare the inventory of the LULUCF sector with models (Table 5.7.1, Approach 5).
C. Comparisons of uncertainties:
Compare uncertainty estimates with uncertainty reported in the literature.
Compare uncertainty estimates with those from other countries and the IPCC default values.
D. Direct measurements:
Carry out direct measurements (such as local forest inventory, detailed growth measurements
and/or ecosystem fluxes of greenhouse gases, Table 5.7.1, Approach 3).

Taking into account resource limitation, the information provided in the national inventory report should beverified as far as possible, particularly for key categories. The verification approaches in Box 5.7.3 can beapplied as follows:•

The checks listed under A are essential and, ideally, these should have been conducted as part of QA/QC.

•

It is good practice to perform verification with at least one of the approaches listed in Box 5.7.3 under B(see Table 5.7.1 and Section 5.7.2 for more information on the applicable approaches).

•

If independent estimates on emissions and removals of greenhouse gases by LULUCF are not available,
then internal or external verification will most probably be limited to scrutiny of the data and methods(Smith, 2001). Under these circumstances, it is good practice for the inventory agency to carry out thesechecks and to provide sufficient documentation in its national inventory report and other supporting material
to facilitate external verification.

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Inventory agencies, taking into account country-specific circumstances and the availability of resources, canassess the proper combination of approaches for verifying their LULUCF inventories. Approaches 1, 2 and3 are feasible for verifying several components of the inventory. Among those listed, Approaches 1 and 2
can be easily implemented by an inventory agency with low to moderate resources. Remote sensing is themost suitable method for the verification of land areas. Direct measurements (under D in Box 5.7.3) arerelevant, although this approach can be resource-intensive and, on a large scale, costs may be a constraint.
Models can be used as an alternative when direct measurements combined with remote sensing is not
feasible.

5.7.4

Specific Issues Linked to the Kyoto Protocol

In general, the same approaches discussed in Section 5.7.2 can be used for verifying both an inventory submitted
under the UNFCCC and reporting under the Kyoto Protocol. Although, the cost of measuring changes in carbon
stocks for a given area increases as both desired precision and landscape heterogeneity increase, the same
principles of good practice apply to projects and national inventories.
An inventory agency can use the questions in Box 5.7.4 to help guide the development of a verification plan for
supplementary information reported under Articles 3.3 and 3.4 of the Kyoto Protocol.
BOX 5.7.4
GUIDANCE FOR VERIFYING CARBON POOLS AND ACTIVITIESWhich carbon pools to verify?
It is good practice to focus verification on those carbon pools that are expected to be most relevantto the Kyoto Protocol but also on non-CO2 greenhouse gas emissions. The Marrakesh Accords list
the following pools: aboveground and belowground biomass, litter, dead wood and soil organiccarbon. As stated in the Marrakesh Accords, a Party may exclude particular pools from reporting,if verifiable information is provided showing that the pool has not been a source of greenhousegases for activities under Article 3.3 and elected activities under Article 3.4, or for projects.
Therefore the information required is different for selected (changes of the pools following advice
provided on Chapter 3 and 4) and non- selected pools (additional information demonstrating thatthey are not a source). As for LULUCF inventories, if a pool is expected to change significantly
over the inventory reporting period, particular attention should also be devoted to it.
Which activities to verify?
According to the Marrakesh Accords, a Party has to report activities under Article 3.3 and may
choose only certain activities under Article 3.4 of the Kyoto Protocol. For all obligatory or elected
activities, elements which are specific to the reporting under Kyoto Protocol inventories include:
the identification of the areas in which such activities have taken place, the demonstration that theactivities have occurred since 1st January 1990 and are human induced, and the establishment of
the “1990” base year (reference year for reforestation activities and base year for net- net
accounting).

Specific verification related to estimates developed under Articles 3.3 and 3.4 of the Kyoto Protocol may include:•

For lands introduced into a reporting under Kyoto Protocol, it is good practice to verify such lands using
geographical and statistical information, such as remote sensing data. Even if georeferencing was not
required, this would facilitate verification (Smith, 2001).

•

The reporting of greenhouse gas emissions and removals of most Article 3.3 and 3.4 activities requirereference to 1990 or pre-1990 data (classification of forest/non forest lands for 1990, net- net accounting forcropland management, grazing land management, revegetation, etc.). In some cases, these data may not be
available or their reliability may be limited and estimates may be used, subject to advice in Chapter 4
Section 4.2.8.1. In such cases, it is good practice to verify the estimation approach and values, as much as
possible.

Emissions and removals from project activities can be reported under Articles 6 and 12 of the Kyoto Protocol,
and Chapter 4 of this report lists different types of projects and suggests the type of information that may need tobe verified for each. While many of the approaches presented in Section 5.7.2 are useful for project verification,

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additional rules are being developed under the Kyoto Protocol and the Marrakesh Accords27. This factor notwithstanding, verification of projects is generally easier than national level verification. For projects, boundaries,
carbon pools and lifetimes are all factors that can be well established, and hence verified. Generally, projects
with good monitoring and reporting plans are likely to be easier to verify.
As with inventories of LULUCF sector, inventory agencies, taking specific circumstances and the resource
availability into account, may choose the proper combination of approaches for verifying supplementary
information reported under the Kyoto Protocol. Among these approaches, remote sensing is the most suitable forthe verification of land areas. Direct measurements are relevant, although this approach can be resource intensive.
Models can be used as an alternative when direct measurements combined with remote sensing is not feasible.
Some verification steps, which are unique to the Kyoto Protocol, are presented in Box 5.7.5.
BOX 5.7.5
VERIFICATION OF LULUCF UNDER THE KYOTO PROTOCOL

Checks:If a Party reports that an activity has occurred on forest land, is the definition of ‘forest’ provided
and consistent with activities and land units reported? Is information on selected crown cover andtree height provided?
Are changes in all carbon pools reported (aboveground and belowground biomass, dead wood,
litter, soil organic carbon)? If not, is the reason and documentation for omitting a pool given?
Are geographical boundaries of land areas specified for the activities eligible under Articles 3.3
and 3.4?
Is the total land area reported under Article 3.3 and 3.4 constant or increasing throughout
subsequent or contiguous commitment periods?
Is information provided that demonstrates that the elected activities under Article 3.4 occurredsince 1990 and are human induced?
For Article 3.3, is information provided to distinguish deforestation from harvesting (clear-cut) orforest disturbance followed by re-establishment of a forest?

The checks listed in Box 5.7.5 are essential and, ideally, should have been conducted as part of QA/QC. In
addition to these specific checks, the comprehensive list presented in Box 5.7.3 under items B to D can be used
to identify additional useful verification activities.

5.7.5

Reporting and Documentation

When an inventory agency has undertaken verification activities, it is good practice to report and document thefollowing items:•

Information that has been verified;•

Criteria that were used for the selection of verification priorities;•

Verification approaches, along with relevant data that were collected;•

Any limitations in the approaches that have been identified;•

Eventual comparisons that have been performed with independent inventories, datasets, scientific literature,etc;•

Any feedback received from external reviewers, with a summary of key comments;•

Main conclusions of the verification;•

Actions taken as a result of the verification process;27

The verification to which the paragraph refers is to be considered in the context of the present chapter (as defined in
Section 5.7.1). According to the Marrakesh Accords, projects have to be subjected to specific “verification”, as defined indraft decision -/CMP.1 (Article 6), -/CMP.1 (Article 12) and their annexes (FCCC/CP/2001/13/Add.2).

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Any recommendations for inventory improvements or research at national/international level arising fromthe findings.

Inventory agencies are also encouraged to provide information on the external verification activities by otherbodies, to the extent that they are relevant to the inventory and that any such information can be readily collected
and summarised.
If modelling has been used for verification, it is good practice to fully document the modelling process. Other
information to be reported includes: sources of input data, a discussion of model and data assumptions, anddescription of procedures and analysis. Because of the volume of input data, and the number of variables that areneeded for a typical large model, documentation may be dense, technical and lengthy. It is good practice toreport the above information comprehensively and transparently. The information to be included should allow athird party to fully understand the verification process, and to corroborate the results if needed.

5.7.6

Some Details for Verification Approaches

COMPARISONS WITH INTERNATIONAL PROGRAMS AND DATASETS
For an inventory agency that is willing to compare an inventory or part of it against datasets coming frominternational monitoring and research programmes, it can be useful to follow the links provided in Box 5.7.6.
Obviously, the Box is not exhaustive of all programmes available, but it provides information for some of thosethat are more relevant to LULUCF.
BOX 5.7.6
PROGRAMMES AND NETWORKS RELEVANT TO LULUCFFLUXNET (Ameriflux, CarboEuroflux)
Network of ecosystem flux measurements, mostly on forest stands, but also other land use type
Common database, links to ecosystem studies
http://www- eosdis.ornl.gov/FLUXNET/index.html
CarboEurope (funded by the European Commission)
Cluster of projects aimed at understanding the carbon balance of Europe with different approaches(flux measurements, ecosystem studies, regional and continental budgeting, inverse modelling,
ecosystem modelling)
http://www.bgc-jena.mpg.de/public/carboeur/International Geosphere-Biosphere Programme (IGBP)Net Primary Production data sets, coordination of international research efforts, global change andterrestrial ecosystem, etc
http://www.igbp.kva.se/cgi-bin/php/frameset.php
http://www.gcte.org/
Long Term Ecological Research (forests, grasslands)
Network of ecosystem ecological studies present in different countries
http://www.lternet.edu/
FAO
Database of terrestrial ecosystem research sites (TEM), Global Terrestrial Observing System(GTOS ), Global Climate Observing System (GCOS), Forest Resource Assessments (FRA)
http://www.fao.org/
Monitoring networks:
ICP Forests
The common European Union International Cooperative Programme on Forest (EU/ICP Forests)
works on two levels with standardised protocols and methods in 35 countries. The systematic gridnet has approximately 6000 Level I points where limited number of surveys are carried out,
whereas the intensive monitoring grid net has 860 Level II plots in the mayor forest types of theEuropean continent where large number of surveys are carried out.
http://www.icp- forests.org/
ICP/IM and EMEP
The multi-disciplinary ICP Integrated Monitoring programme (ICP/IM) and the Co-operative
Programme for Monitoring and Evaluation of the Long- Range Transmissions of Air Pollutants in
Europe (EMEP)

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BOX 5.7.6 (CONTINUED)
PROGRAMMES AND NETWORKS RELEVANT TO LULUCF

A part of the monitoring strategy and evaluation of the effects under the Long-range
Transboundary Air Pollution Convention of the United Nation's Economic Commission for Europe(UNECE). The EMEP programme relies on three main elements: (1) collection of emissions data,(2) measurements of air and precipitation quality and (3) modelling of atmospheric transport anddeposition of air pollution.
http://www.vyh.fi/eng/intcoop/projects/icp_im/im.htm
http://www.emep.int/
Global Carbon Project
The Global Carbon Project is a project of the Earth System Science Partnership of the International
Geosphere Biosphere Programme (IGBP) World Climate Research Programme (WCRP) and theInternational Human Dimensions Programme (IHDP). The scientific goal of the Global Carbon
Project is to develop a complete picture of the global carbon cycle, including both its biophysicaland human dimensions together with the interactions and feedbacks between them.
http://www.globalcarbonproject.org/
The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)
A source for biogeochemical and ecological data collected on the ground, from aircraft, by satellite
or generated by computer models. The scale of data ranges from site- specific to global, andduration range from days to years. ORNL Environmental Sciences Division (ESD) operates the
ORNL-DAAC for Biogeochemical Dynamics as part of the National Aeronautics and Space
Administration's (NASA) Earth Science Enterprise (ESE) programme.
http://www- eosdis.ornl.gov/

REMOTE SENSI NG
Overview of Available Remote Sensing Sensors
Optical satellite data going from coarse to high resolution, are available worldwide from NOAA AVHRR, SPOT
Vegetation, ERS/ATSR, MODIS, Envisat MERIS, Landsat TM/ETM and several other sensors. Multifrequency/polarisation radar, that was only recently made available from NASA AIRSAR missions, is also very
useful for vegetation classification. Those sensors, being sensitive to vegetation structural characteristics,
provide an excellent complementary data source to optical remote sensing. Such radar data will begin to become
more available with Envisat ASAR and the launch of RadarSat 2. The accuracy of remotely sensed data varies
with the geometric and radiometric characteristics of the sensors. Specifications (sensor type, spatial resolution,
availability, etc.) of various satellite sensors are listed in Table 5.7.2, further information can be found at
http://idisk.mac.com/alexandreleroux/Public/agisrs/arsist.html. The imaging data used should be chosen
according to the geographical scale of the target area and the desired degree of resolution. The use of different
sensors can be a solution to circumvent the limitation of remote sensing in areas of persistent cloud cover (e.g.,
optical and radar data).

Use of Remote Sensing to Derive Vegetation Parameters
Net Primary Production (NPP) is known to be positively correlated with photosynthetically active radiation(PAR), which also can be estimated from NDVI (Normalised Difference Vegetation Index) and solar radiation.
The functional relationship between optical remote sensing data (including indices like the NDVI) and carbon
stocks is that canopy reflectance is related to leaf area index (LAI), and LAI in turn has a strong functional
relationship to woody biomass and NPP (Gholz, 1982; Waring, 1983). An alternative interpretation of therelationship is that reflectance is related to the fraction of absorbed photosynthetically active radiation (fAPAR),
which over longer time periods is linearly correlated to NPP (e.g., Monteith, 1977; Landsberg and Waring, 1997).
The NDVI has been widely used to estimate both LAI and fAPAR from remotely sensed data.
NDVI and solar radiation determined by remote sensing, coupled with meteorological measurements andgeographical information system (GIS) data, can be used to make estimates also at larger scales (regional to
global). NDVI has also been used to derive growing season duration, a parameter that has been shown to beclosely linked to the Net Ecosystem Exchange (NEE, the net carbon sink) measured by ecosystem fluxes,
particularly in deciduous forests (Baldocchi et al., 2001). However, when this approach is used, care must be
taken in considering that fine- scale differences are difficult to handle and that not all vegetation successional
phases are properly covered by NDVI (recovery processes, etc.). Furthermore, most of the ecosystem parameters

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derived from correlations with NDVI are likely to be species and/or biome specific. Moreover, the NDVI is
influenced by factors other than canopy LAI or fAPAR, and the relationships have a tendency to saturate at LAI
values above about 3 m2 m-2 (Moreau and Li, 1996; Carlson and Ripley, 1997; Gemmell and McDonald, 2000),
although, for conifer canopies, the saturation did not occur for LAI up to 10 m2 m-2 (Chen et al., 2002). Becauseof the saturation, NDVI derived from LANDSAT images was found to be poorly correlated with stand structure
variables or total aboveground biomass within forest stands in the tropics. In general, NDVI-based approaches toestimating LAI or fAPAR will be a function of soil reflectance, fractional cover, biome type, andillumination/viewing conditions. These factors result in a wide variation in the equations used for estimating LAI(or fAPAR) from NDVI (Moreau and Li 1996), and users should consider this if selecting or deriving equations.
If spectral indices are to be used as the basis for constructing a relationship with LAI or fAPAR, consideration
should be given to using an index that is less affected by variations in parameters such as soil reflectance(Kaufman and Tanré, 1992; Huete et al., 1997). The Enhanced Vegetation Index (EVI) is perhaps the mostpromising of these, and is both simple to implement for most sensors and linearly related to fAPAR (Huete et al.,
1997; Gobron et al., 2000). For datasets for which 1-km pixels are sufficient, users may be able to use the
MODIS or MERIS fAPAR data and MODIS LAI data. In addition, software is freely available to generate highquality fAPAR values (Gobron et al., 2000) from data acquired by the SeaWIFS, MERIS, VEGETATION, or
GLI sensors.
Above ground biomass can be estimated efficiently also by LIDAR airborne sensing that measure the canopy
surface and ground elevation height at the same time, by emitting laser pulses with wavelengths that reflect overthe canopy surface but pass through trees and reflect off the ground as well. However, because of the small
diameter beams of laser, mapping large areas requires extensive flying missions (Dubayah and Drake, 2000).
The Laser Vegetation Imaging Sensor (LVIS) by airborne or satellite instruments such as Vegetation Canopy
LIDAR with large footprints will possibly solve such problems (Blair et al., 1999; Means et al, 1999; Dubayah
and Drake, 2000). One can also estimate vegetation structure from optical satellite data using the Bi-Directional
Reflectance property based on the Sun-Target-Sensor Geometry.

Use of Remote Sensing f or Fire Detection and Burnt area
Remote sensing is also frequently applied for forest fire detection.. Examples of forest fire or fire scars detection
at different scales range from detection of 1 ha burn scars on a national basis using Landsat TM (e.g.,
ITALSCAR, 2003: Regional Burned Forest Mapping in Italy, http://www.esa.int/dup) or for European Union’s
Member States (http://natural- hazards.jrc.it/fires/) to the use of ERS SAR in Indonesia (Page et al., 2002), to
global detection of active fires (ATSR World Fire Atlas, 2003: http://earth.esa.int/ionia/FIRE/), burn scars(GLOBSCAR, 2003 Global Burned Forest Mapping, http://earth.esa.int/ionia/FIRE/; GLOBCARBON, 2003:
Global Land Products for Carbon Model Assimilation, http://www.esa.int/dup) and burnt areas (Global BruntArea 2000: http://www.gvm.sai.jrc.it/fire/gba2000_website/index.htm). As an example, a recent study using
remote sensing techniques has estimated the total area deforested due to fires in the humid tropics between 1990
and 1997, arriving at a different number to that reported by FAO statistics, that are using deforestation datareported by countries and experts (Achard et al., 2002).

IPCC Good Practice Guidance for LULUCF

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Chapter 5: Cross-Cutting Issues

TABLE 5.7.2 FEATURES OF SOME OF THE MAIN REMOTE SENSING PLATFORMS
Satellite

Sensor nameCountry(Operation)
USA
EU
Japan
USA
USA
EuropeEuropeUSA
EuropeUSA
USA
USA
FrenchJapan/USA
India
Japan
Japan
Japan
USA
USA
USA
USA
USA
Japan
Japan
EuropeEuropeCanadaGermany

SpatialResolution(m) at nadir
1100
1150
250, 1000
250, 500, 1000
275, 550, 1000
1000
1000
400
300 (Land)80
30, 120
15, 30, 60(2.5), 10, 20
15, 30, 90
6 / 2318*24
10
2.5
0.82 / 3.3
0.82/ 3.3
0.61 / 2.5
10, 3030
18
10, 100
30
30, 100, 150(3, 8), 10, 30
1-3, 3-15

NOAA (POES)
SPOT
ADEOS-II
Terra/Aqua
Terra
ERS-1/2
Envisat
NPOESS
Envisat
Landsat
Landsat
LandsatSPOT
Terra
IRS-1C/D
JERS-1ALOS
ALOS
IKONOS
Orbview-3
QuickBird
EO-1
EO-1
JERS-1
ALOS
ERS-1/2
EnvisatRadarsat-1/2
TerraSAR
LIDAR
VCL

AVHRR
Vegetation
GLI
MODIS
MISR
ATSR-1/2
AATSR
VIRS
MERIS
MSS
TM
ETM+
HRV/HRVIR/HRG
ASTER
PAN/LISS-3
OPS (VNIR)AVNIR-2
PRISM
Pan/Multi
Pan/Multi
Pan/Multi
ALI
Hyperion
SAR
PALSAR
AMI
ASAR
SAR
SAR

(km)
2700
2250
1600
2330
360
500
500
3000
1150
185
185
185
60
60
70 / 141
75
70
35/70
11
8
17
185
7.5
75
70, 250-350
100
100, 400(20), 50, 100
10, 40-60

VCL

USA

25

8

Swath

Sensor type
Spectral informationand scaleType Scale VNIR SWIR TIR SAR
O
Co-G
M
S
M
O
Co-G
M
S
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
M
M
O
Co-G
M
M
O
R
M
O
R
M
M
S
O
R
M
M
S
O
R
M
(S)
O
R
M
M
M
O
R
M
S
O
R
M
O
R
M
O
R
S
O
R
M
O
R
M
O
R
M
O
R
M
M
O
R
H
H
S
R
L
S
R
L
S
R
C
S
R
C
S
R
C
S
R
X/L
L

R

S

Data availability (acquisition period)
1980 - 1990 1990 - 1999 2000 - 2007 2008 - 2012
A
A
A
A
PA
PA
MA
PA
MA
A
PA
PA
PA
A
MA
PA
MA
A
PA
MA
A
A
PA
A
PA
A
A
PA
A
A
A
PA
PA
PA
PA
A
PA
MA
A
MA
PA
MA
PA
MA
PA
PA
PA
PA
MA
PA
PA
MA
PA
MA
PA
A
MA
PA
MA

-

PA

MA

O: optical; S: synthetic aperture radar; L: LIDAR; Co: continental; G: global; R: regional; S: single band; M: multiple band; H: hyper band. A: available for the entire period;
PA: available for a portion of the period; MA: may be available during the period

5.76

IPCC Good Practice Guidance for LULUCF

ANNEX A

GLOSSARY

IPCC Good Practice Guidance for LULUCF

Annex A: Glossary

ABOVEGROUND BI OMASSAll living biomass above the soil including stem, stump, branches, bark, seeds, and foliage.
Note: In cases where forest understorey is a relatively small component of the aboveground biomass carbon
pool, it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers
are used in a consistent manner throughout the inventory time series as specified in Chapter 5.

ABSOLUTE ERROR
Maximum tolerable error which is defined as an actual range independent of the value of the variable being estimated.

ACTIVITYA practice or ensemble of practices that take place on a delineated area over a given period of time.

ACCOUNTING
The rules for comparing emissions and removals as reported with commitments.

ACCURACY
Inventory definition: Accuracy is a relative measure of the exactness of an emission or removal estimate.
Estimates should be accurate in the sense that they are systematically neither over nor under true emissions or
removals, so far as can be judged, and that uncertainties are reduced so far as is practicable. Appropriate
methodologies conforming to guidance on good practices should be used to promote accuracy in inventories.
(FCCC/SBSTA/1999/6/Add. 1)
Statistical definition: Accuracy is a general term which describes the degree to which an estimate of a quantity
is unaffected by bias due to systematic error. It should be distinguished from precision as illustrated below.

ACTIVITY DATA
Inventory definition: Data on the magnitude of human activity resulting in emissions or removals taking place
during a given period of time.
In the LULUCF sector, data on land areas, management systems, lime and fertilizer use are examples of activity data.

AFFORESTATION 1
The direct human-induced conversion of land that has not been forested for a period of at least 50 years toforested land through planting, seeding and/or the human-induced promotion of natural seed sources.
1

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

G.2

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Annex A: Glossary

ANTHROPOGENI C
Man-made, resulting from human activities. In the IPCC Guidelines, anthropogenic emissions are distinguishedfrom natural emissions. Many of the greenhouse gases are also emitted naturally. It is only the man-madeincrements over natural emissions which may be perturbing natural balances.
In this LULUCF-GPG, all emissions and removals of managed lands are seen as anthropogenic.

ARITHMETIC MEAN
Statistical definition: The sum of the values divided by the number of values.

BASIC WOOD DENSITY
Ratio between oven dry mass and fresh stem-wood volume without bark. It allows the calculation of woody
biomass in dry matter mass.

BELOWGROUND BIOMASSAll living biomass of live roots. Fine roots of less than (suggested) 2mm diameter are sometimes excluded
because these often cannot be distinguished empirically from soil organic matter or litter.

BIAS
Inventory definition: A systematic error of the observation method, whose value in most cases is unknown. It
can be introduced by using measuring equipment that is improperly calibrated, by selecting items from a wrong
population or by favouring certain elements of a population, etc.

BIOMASS
Organic material both aboveground and belowground, and both living and dead, e.g., trees, crops, grasses, tree
litter, roots etc. Biomass includes the pool definition for above - and below - ground biomass.

BIOMASS ACCUMULATIO N RATESNet build up of biomass, i.e., all increments minus all losses. When carbon accumulation rate is used, only one
further conversion step is applied: i.e., the use of 50% carbon content in dry matter (default value).
The biomass accumulation rates can be calculated using Equation 3.2.4 in Chapter 3 of this report.

BIOMASS EXPANSION FACTOR (BEF)
A multiplication factor that expands growing stock, or commercial round-wood harvest volume, or growing
stock volume increment data, to account for non-merchantable biomass components such as branches, foliage,
and non-commercial trees.

BACK-CASTING
The opposite of forecasting. Predicting conditions in the past from current conditions.

BOREALSee polar/boreal.

IPCC Good Practice Guidance for LULUCF

G.3

Annex A: Glossary

BOTTOM-UP MODELLING
A modelling approach which starts from processes at a detailed scale (i.e., plot/stand/ecosystems scale) andprovides results at a larger, aggregated scale (regional/national/continental/global).

BURNI NG/FIRE CO MPLETENESS
The share of the total amount of biomass in a given unit or area which burns in a fire. Often used in combination
with combustion efficiency.

CANOPY COVER
The percentage of the ground covered by a vertical projection of the outermost perimeter of the natural spread of
the foliage of plants. Cannot exceed 100%. (Also called crown closure)Same as crown cover.

CARBON ACCUMULATION RATES
See biomass accumulation rates.

CARBON BUDGETThe balance of the exchanges of carbon between carbon pools or between one specific loops (e.g., atmosphere –biosphere) of the carbon cycle. The examination of the budget of a pool or reservoir will provide information
whether it is acting as a source or a sink.

CARBON CYCLE
All parts (pools) and fluxes of carbon; usually thought of as a series of the four main pools of carbon
interconnected by pathways of exchange. The four pools are atmosphere, biosphere, oceans and sediments.
Carbon exchanges from pool to pool by chemical, physical and biological processes.

CARBON FLUX
Transfer of carbon from one pool to another in units of measurement of mass per unit of area and time (e.g.,tonnes C ha-1 yr-1).

CARBON POOL
The reservoir containing carbon.

CARBON RESERVE
Prefer to use carbon stock. See carbon stock.

CARBON STOCK
The quantity of carbon in a pool.

G.4

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Annex A: Glossary

CARBON STOCK CHANGE
The carbon stock in a pool can change due to the difference between additions of carbon and losses of carbon.
When the losses are larger than the additions, the carbon stock becomes smaller, and thus the pool acts as a source
to the atmosphere; when the losses are smaller than the additions, the pools acts as a sink to the atmosphere.

CLOSED FORESTS
Forests characterised by canopy cover higher than 40%.

CARBON DIOXI DE EQUIVALENT
A measure used to compare different greenhouse gases based on their global warming potentials (GWPs). The
GWPs are calculated as the ratio of the radiative forcing of one kilogramme greenhouse gas emitted to theatmosphere to that from one kilogramme CO2 over a period of time (usually 100 years).

CENSUS
Data collected by interrogation of (human) population. Usually the total population of interest is interviewed (butsometimes sampled).

CHRONOSEQUENCE
Chronosequences consist of measurements taken from similar but separate locations that represent a temporalsequence in land use or management, for example, years since deforestation. Efforts are made to control all otherbetween-site differences (e.g., by selecting areas with similar soil type, topography, previous vegetation).
Chronosequences are often used as a surrogate for experimental studies or measurements repeated over time at
the same location.

COEFFICIENT OF VARIATION
Statistical definition: The coefficient of variation, vx is the ratio of the population standard deviation, σx, andmean, µx, where vx = σx /µx. It also frequently refers to the sample coefficient of variation, which is the ratio of
the sample standard deviation and sample mean.2

COMBUSTION EFFICIENCY
The fraction of the combusted carbon that is released in the form of CO2.

COMMERCIAL HARVEST
See fellings.

COMPARABILITY
Inventory definition: Comparability means that estimates of emissions and removals reported by Parties ininventories should be comparable among Parties. For this purpose, Parties should use the methodologies andformats agreed by the Conference of the Parties (COP) for estimating and reporting inventories.

2‘Coefficient of variation’ is the term, which is frequently replaced by ‘error’ in a statement like ‘the error is 5%’.

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G.5

Annex A: Glossary

COMPLETENESS
Inventory definition: Completeness means that an inventory covers all sources and sinks for the full geographic
coverage, as well as all gases included in the IPCC Guidelines in addition to other existing relevant source/sink
categories which are specific to individual Parties (and therefore may not be included in the IPCC Guidelines).

CONFIDENCE
Inventory definition: The term ‘confidence’ is used to represent trust in a measurement or estimate. Having
confidence in inventory estimates does not make those estimates more accurate or precise; however, it will
eventually help to establish a consensus regarding whether the data can be applied to solve a problem. This usageof confidence differs substantially from the statistical usage in the term confidence interval.

CONFIDENCE INTERVAL
Statistical definition: A confidence interval is the range in which it is believed that the true value of a quantity
lies. The level of belief is expressed by the probability, whose value is related to the size of the interval. It is oneof the ways in which uncertainty can be expressed (see estimation, statistical definition).
In practice a confidence interval is defined by a probability value, say 95%, and confidence limits on either sideof the mean value x. In this case the confidence limits L1 and L2 would be calculated from the probability density
function such that there was a 95% chance of the true value of the quantity being estimated by x lying between
L1 and L2. Commonly L1 and L2 are the 2.5 percentile and 97.5 percentile respectively.
Example: ‘An emission is between 90 and 100 kt with a probability of 95%.’ Such a statement can be provided
when the confidence interval is calculated (the numerical values in this example are arbitrarily chosen).

CONFUSION MATRIXThe conventional technique that establishes a matrix showing, for any given classification of land, theprobability of misclassification by one of the other candidate classifications.

CONSISTENCY
Inventory definition: Consistency means that an inventory should be internally consistent in all its elements
over a period of years. An inventory is consistent if the same methodologies are used for the base year and all
subsequent years and if consistent data sets are used to estimate emissions or removals from sources or sinks.
Under certain circumstances referred to in paragraphs 10 and 11 of FCCC/SBSTA/1999/6/Add.1, an inventory
using different methodologies for different years can be considered to be consistent if it has been recalculated ina transparent manner taking into account any good practices.
Statistical definition: A statistical estimator for a parameter is said to be consistent, if the estimator tends
towards the parameter as the size of the sample used for the estimator increases – i.e., precision is improved byan increasing number of observations.

CONVERSION
Change of one land use to another.

CORRELATION
Statistical definition: Mutual dependence between two quantities. See correlation coefficient.

G.6

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Annex A: Glossary

CORRELATION CO EFFICIENT
Statistical definition: A number lying between –1 and +1, which measures the mutual dependence between two
variables which are observed together. A value of +1 means that the variables have a perfect direct straight line
relation; a value of –1 means that there is a perfect inverse straight line relation; and a value of 0 means that
there is no straight line relation. It is defined as the covariance of the two variables divided by the product of
their standard deviations.

COUNTRY-SPECIFI C DATA
Data for either activities or emissions that are based on research carried out on domestic sites.

COVARIANCE
Statistical definition: The covariance between two variables is a measure of the mutual dependence between two
variables.
The sample covariance of paired samples of random variables X and Y is calculated using the following formula:
1 n
2
s xy
= ∑ ( x i − x )( y i − y ) where xi, yi, i = 1,…,n are items in the sample and x and y are sample means.
n i

CROPLANDThis category includes arable and tillage land, and agro-forestry systems where vegetation falls below thethreshold used for the forest land category, consistent with the selection of national definitions.

CROPLAND MANAGEMENT 3
The system of practices on land on which agricultural crops are grown and on land that is set aside or
temporarily not being used for crop production.

CROSS- CUTTI NG ISSUES
Matters that arise in more than one part of the good practice guidance. In this report identifying and quantifying
uncertainties, sampling, methodological choice – identification of key categories, quality assurance and quality control,
time series consistency and recalculation, and verification are addressed in a separate chapter called “Cross-cutting
Issues”.

CROWN COVER
See canopy cover.

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

3

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

IPCC Good Practice Guidance for LULUCF

G.7

Annex A: Glossary

DECISION TREEInventory definition: A decision tree is a flow chart describing the specific ordered steps which need to befollowed to develop an inventory or an inventory component in accordance with the principles of good practice.

DEFORESTATION 4
The direct human-induced conversion of forested land to non-forested land.

DISTURBANCES
Processes that reduce or redistribute carbon pools in terrestrial ecosystems.

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

DRY BIOMASS
See dry matter.

DRY MATTER ( d.m. )Dry matter refers to biomass that has been dried to an oven-dry state, often at 70ºC.

EMISSIONS
The release of greenhouse gases and/or their precursors into the atmosphere over a specified area and period of time.

EMISSION FACTOR
Inventory definition: A coefficient that relates the activity data to the amount of chemical compound which isthe source of later emissions. Emission factors are often based on a sample of measurement data, averaged todevelop a representative rate of emission for a given activity level under a given set of operating conditions.

ERROR
Statistical definition: In statistical usage, the term ‘error’ is a general term referring to the difference between
an observed (measured) value of a quantity and its ‘true’ (but usually unknown) value and does not carry the(pejorative) sense of a mistake or blunder.

ERROR MATRIXSee confusion matrix.

4

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

G.8

IPCC Good Practice Guidance for LULUCF

Annex A: Glossary

ESTIMATION
Inventory definitions: The process of calculating emissions.
Statistical definition: Estimation is the assessment of the value of a quantity or its uncertainty through theassignment of numerical observation values in an estimation formula, or estimator. The results of an estimation
can be expressed as follows:••a point estimation which provide a number which can be used as an approximation to a parameter (such as
the sample standard deviation which estimates the population standard deviation), or
an interval estimate specifying a confidence level.

Example: A statement like ‘The total emission is estimated to be 100 kt and its coefficient of variation is 5%’ is based
upon point estimates of the sample mean and standard deviation, whereas a statement such as ‘The total emission lies
between 90 and 110 kt with probability 95%’ expresses the results of estimation as a confidence interval.

EXPERT JUDGEMENT
Inventory definition: A carefully considered, well-documented qualitative or quantitative judgement made in
the absence of unequivocal observational evidence by a person or persons who have a demonstrable expertise in
the given field.

EXTREME VALUE
Statistical definition: The extreme values of a sample are the maximum and minimum values of the sample.
The statistical theory of extreme values is concerned with estimating the distributions of these extreme values forlarge numbers of sampled values.

FELLINGSStanding volume of all trees live or dead, measured overbark to a specified minimum diameter at breast heightthat are felled during the reference period, including those parts of trees that are not removed from the forest.
Removals are a subset of fellings (the commercial part destined for processing).

FERMENTED HORI ZO N (F)A horizon consisting of partly decomposed litter in which macroscopically recognisable parts of plants remain.
Fine organic matter, consisting of macro-fauna excrements is almost always existing, but is less in substancethan the recognisable plant material.

FOREST 5
Forest is a minimum area of land of 0.05 – 1.0 hectares with tree crown cover (or equivalent stocking level) of
more than 10 – 30 per cent with trees with the potential to reach a minimum height of 2 – 5 metres at maturity insitu. A forest may consist either of closed forest formations where trees of various storeys and undergrowthcover a high portion of the ground or open forest. Young natural stands and all plantations which have yet toreach a crown density of 10 – 30 per cent or tree height of 2 – 5 metres are included under forest, as are areas
normally forming part of the forest area which are temporarily unstocked as a result of human intervention suchas harvesting or natural causes but which are expected to revert to forest.
Remark: Forests are not defined for reporting under the Convention. The IPCC Guidelines encourage countries
to use detailed ecosystem classifications in the calculations and in reporting broad specified categories to ensure
consistency and comparability of national data across countries.

5

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

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G.9

Annex A: Glossary

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

FOREST LANDThis category includes all land with woody vegetation consistent with thresholds used to define forest land in thenational GHG inventory, sub-divided at the national level into managed and unmanaged and also by ecosystem
type as specified in the IPCC Guidelines.6 It also includes systems with vegetation that currently falls below, but
is expected to exceed, the threshold of the forest land category.

FOREST MANAGEMENT 7
A system of practices for stewardship and use of forest land aimed at fulfilling relevant ecological (including
biological diversity), economic and social functions of the forest in a sustainable manner.

GOOD PRACTICE
Inventory definition: Good Practice is a set of procedures intended to ensure that greenhouse gas inventories
are accurate in the sense that they are systematically neither over nor underestimates so far as can be judged, andthat uncertainties are reduced so far as possible.
Good Practice covers choice of estimation methods appropriate to national circumstances, quality assurance andquality control at the national level, quantification of uncertainties and data archiving and reporting to promotetransparency.

GRASSLANDThis category includes rangelands and pasture land that is not considered as cropland. It also includes systems
with vegetation that fall below the threshold used in the forest land category and is not expected to exceed,
without human intervention, the thresholds used in the forest land category. This category also includes all
grassland from wild lands to recreational areas as well as agricultural and silvo-pastural systems, subdivided into
managed and unmanaged, consistent with national definitions.

GRAZING LAND MANAGEMENT 8
The system of practices on land used for livestock production aimed at manipulating the amount and type ofvegetation and livestock produced.

GRID CELL
The unit of land defined by the boundaries of an imaginary grid imposed on a map. May also be called a raster
cell or a pixel.

GROSS ANNUAL I NCREMENT
The average annual increment of volume over the reference period of all trees measured to a specified minimum
diameter at breast height (varies by country). Includes increment of trees which have been felled or die.
6

Forest management has particular meaning under the Marrakesh Accords, which may require subdivision of the managed
forest as described in Chapter 4.
7
In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land use, land-use change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.
8
In the context of the Kyoto Protocol, as stipulated by the Marrakesh Accords, cf. paragraph 1 of the Annex to draft decision-/CMP.1 (Land use, land-use change and forestry) contained in document FCCC/CP/2001/13/Add.1, p.58.

G.10

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Annex A: Glossary

GROUND TRUTH
A term used for data obtained by measurements on the ground, usually as validation for, e.g., satellite data.

GROWING STOCK
The living tree component of the standing volume (measured in m3 overbark).

HIGH ACTIVITY CLAY (HAC) SOILS
Soils with high activity clay (HAC) minerals are lightly to moderately weathered soils which are dominated by
2:1 silicated clay minerals (in FAO classification included: Vertisols, Chernozems, Phaezems, Luvisols).

HARMONISATION OF DEFINITIONSIn this context it is meant to standardize or to increase comparability and/or convergence between definitions.

HUMUS HORIZO N ( H)
Horizon consisting by far of finely distributed organic matter (but still on top of the mineral soil horizons).
Macroscopically recognisable parts of plants remain, but occur to much lesser extent than the finely distributedorganic matter. The horizon can contain mineral soil particles.

INCREMENT
See gross and net annual increment.

INVENTORIES CONSISTENT WITH GO OD PRACTICE
Those inventories which contain neither over- nor underestimates so far as can be judged, and in which
uncertainties are reduced as far as is practicable.

IMPROVED PASTURES/ GRASSLAND/ RANGELAND
Land subject to intensive, controlled grazing often subject to fertilisation and/or regular re-establishment of thegrass cover.

KEY CATEGORY
A category that is prioritised within the national inventory system because its estimate has a significant influence
on a country’s total inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend inemissions, or both.

KEY SOURCE
See key category.

LAND COVER
The type of vegetation covering the earth’s surface.

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G.11

Annex A: Glossary

LAND USE
The type of activity being carried out on a unit of land.
In GPG-LULUCF this term is used for the broad land-use categories defined in Chapter 2. It is recognized thatthese land categories are a mixture of land cover (e.g,, Forest, Grassland, Wetlands) and land use (e.g., CroplandSettlements) classes.

LFH LAYERS
Soil horizons. For details see individual definitions under litter horizon, fermented horizon and humus horizon.

LITTER
Includes all non-living biomass with a diameter less than a minimum diameter chosen by the country (forexample 10 cm), lying dead, in various states of decomposition above the mineral or organic soil. This includes
litter, fumic, and humic layers. Live fine roots (of less than the suggested diameter limit for belowgroundbiomass) are included in litter where they cannot be distinguished from it empirically.

LITTER HORI ZON ( L)A horizon consisting of relatively fresh dead plant material, it may be colourised, but does not containexcrements from soil fauna. It is not or only partly fragmented.

LOGNORMAL DISTRIBUTION
Statistical definition: The lognormal distribution is an asymmetric distribution, which starts from zero, rises toa maximum and then tails off more slowly to infinity. It is related to the normal distribution: X has a lognormal
distribution if ln (X) has a normal distribution.

The PDF of the lognormal distribution is given by:f ( x) =−1

σ l x 2π(ln x − µ l ) 2

e

2σ l2

, for 0 ≤ x ≤ ∞ .

The parameters required to specify the function are: µl the mean of the natural log transform of the data; and σl 2
the variance of the natural log transform of the data. The data and information that the inventory compiler canuse to determine the input parameters are: mean = µ; variance = σ 2; and the relationships:µ l = lnµ2
(σ 2 + µ 2 )

and

σl =

σ 2

ln  2 + 1 .
µ




LOW ACTIVI TY CLAY (LAC) SOILS
Soils with low activity clay (LAC) minerals are highly weathered soils dominated by 1:1 clay mineral andamorphous iron and aluminium oxides (in FAO classification included: Acrisols, Nitosols, Ferrasols).

G.12

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Annex A: Glossary

MANAGED FOREST
All forests subject to some kind of human interactions (notably commercial management, harvest of industrial
round-wood (logs) and fuelwood, production and use of wood commodities, and forest managed for amenity
value or environmental protection if specified by the country), with defined geographical boundaries.

MANAGED GRASSLAND
Grasslands on which human-induced activities are carried out, such as grazing or hay removal.

MEAN
Statistical definition: The mean, population mean, expectation or expected value is, broadly speaking, ameasure of a central value around which values sampled from a probability distribution tend to lie. The sample
mean or arithmetic average is an estimator for the mean. It is an unbiased and consistent estimator of the
population mean (expected value) and is itself a random variable with its own variance value. The sample meanis the sum of values divided by the number of values:

x =

1 n
∑ x i (xi, where i = 1,…., n are items of a sample).
n i

MEDIAN
Statistical definition: The median or population median is a value which divides the integral of a probability
density function (PDF) into two halves. For symmetric PDFs, it equals the mean. The median is the 50th
population percentile.

The sample median is an estimator of the population median. It is the value that divides an ordered sample into
two equal halves. If there are 2n + 1 observations, the median is taken as the (n + 1)th member of the orderedsample. If there are 2n, it is taken as being halfway between the nth and (n + 1)th .

METADATA
Information about data; i.e., the description of which parameters and variables are stored in a database: theirlocation, time of recording, accessibility, representativeness, owner, etc.

MODEL
Statistical definition: A model is a quantitatively-based abstraction of a real-world situation which may
simplify or neglect certain features to better focus on its more important elements.

Example: the relationship that emissions equal an emission factor times an activity level is a simple model. The
term ‘model’ is also often used in the sense of a computer software realisation of a model abstraction.

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

MONTE CARLO METHOD
Inventory definition: The principle of Monte Carlo analysis is to perform the inventory calculation many times
by electronic computer, each time with the uncertain emission factors or model parameters and activity datachosen randomly (by the computer) within the distribution on uncertainties specified initially by the user.
Uncertainties in emission factors and/or activity data are often large and may not have normal distributions. In

IPCC Good Practice Guidance for LULUCF

G.13

Annex A: Glossary

this case the conventional statistical rules for combining uncertainties become very approximate. Monte Carloanalysis can deal with this situation by generating an uncertainty distribution for the inventory estimate that is
consistent with the input uncertainty distributions on the emission factors, model parameters and activity data.

NET ANNUAL I NCREMENT
Average annual volume over the given reference period of gross increment minus natural mortality, of all trees
to a specified minimum diameter at breast height.

NET- NET ACCOUNTING
The carbon sink or source in the reporting year minus the carbon sink or source in the base year. This is theaccounting method for grazing land management, cropland management and revegetation under Article 3.4.

NORMAL DI STRIBUTION
Statistical definition: The normal (or Gaussian) distribution has the PDF given in the following equation and isdefined by two parameters (the mean µ and the standard σ deviation) .
f (x) =

1

σ 2π

−

e

( x − µ)2
2σ 2

, for - ∞ ≤ x ≤ ∞ .

OPEN FORESTS
Forests characterised by crown cover between 10 and 40% (FAO), or below the canopy cover threshold asadopted by the Party.

ORGANIC SOILS
Soils are organic if they satisfy the requirements 1 and 2, or 1 and 3 below (FAO, 1998):
1.
2.
3.

Thickness of 10 cm or more. A horizon less than 20 cm thick must have 12 percent or more organic carbon
when mixed to a depth of 20 cm;
If the soil is never saturated with water for more than a few days, and contains more than 20 percent (byweight) organic carbon (about 35 percent organic matter);If the soil is subject to water saturation episodes and has either:
At least 12 percent (by weight) organic carbon (about 20 percent organic matter) if it has no clay;(i)or
At least 18 percent (by weight) organic carbon (about 30 percent organic matter) if it has 60 percent(ii)
or more clay; orAn intermediate, proportional amount of organic carbon for intermediate amounts of clay.
(iii)

OTHER LAND ( AS A LAND- USE CATEGORY)This category includes bare soil, rock, ice, and all unmanaged land areas that do not fall into any of the other fivecategories. It allows the total of identified land areas to match the national area, where data are available.

PASTURE
Grassland managed for grazing.

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Annex A: Glossary

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

PERCENTILE
Statistical definition: The kth percentile or population percentile is a value which separates the lowest kth part ofthe integral of the probability density function (PDF) – i.e., an integral of a PDF tail from the kth percentile
towards lower probability densities.

PERENNIAL CROPS
Multiple year crops, includes trees and shrubs, in combination with herbaceous crops e.g., agroforestry, ororchards, vineyards and plantations such as cocoa, coffee, tea, oil palm, coconut, rubber trees, and bananas,
except where these lands meet the canopy cover threshold criteria for forest land.

POLAR/BOREAL
Mean annual temperature (MAT) is less than 0 oC.

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

POPULATIO N
Statistical definition: The population is the totality of items under consideration. In the case of a randomvariable, the probability distribution is considered to define the population of that variable.

PRACTICE
An action or set of actions that affect the land, the stocks of pools associated with it or otherwise affect theexchange of greenhouse gases with the atmosphere.

PRECISION
Inventory definition: Precision is the inverse of uncertainty in the sense that the more precise something is, theless uncertain it is.
Statistical definition: Closeness of agreement between independent results of measurements obtained understipulated conditions (see also accuracy).

PROBABILITY
Statistical definition: A probability is a real number in the scale 0 to 1 attached to a random event. There aredifferent ways in which probability can be interpreted. One interpretation considers a probability as having thenature of a relative frequency (i.e., the proportion of all outcomes corresponding to an event), whilst anotherinterpretation regards a probability as being a measure of degree of belief.

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Annex A: Glossary

PROBABILITY DENSITY FUNCTION
Statistical definition: A probability density function (PDF) is a mathematical function which characterizes theprobability behaviour of population. It is a function f(x) which specifies the relative likelihood of a continuous
random variable X taking a value near x, and is defined as the probability that X takes a value between x and
x+dx, divided by dx where dx is an infinitesimally small number.

PROBABILITY DISTRIBUTION
Statistical definition: A function giving the probability that a random variable takes any given value or belongs
to a given set of values. The probability on the whole set of values of the random variable equals 1.

PROPAGATION OF UNCERTAINTIESStatistical definition: The rules for propagation of uncertainties specify how to algebraically combine thequantitative measures of uncertainty associated with the input values to the mathematical formulae used ininventory compilation, so as to obtain corresponding measures of uncertainty for the output values. See Chapter
6, Quantifying Uncertainties in Practice, and Annex 1, Conceptual Basis for Uncertainty Analysis of GPG2000.

QUALITY ASSURANCE
Inventory definition: Quality Assurance (QA) activities include a planned system of review procedures
conducted by personnel not directly involved in the inventory compilation/development process to verify thatdata quality objectives were met, ensure that the inventory represents the best possible estimate of emissions andsinks given the current state of scientific knowledge and data available, and support the effectiveness of thequality control (QC) programme.

QUALITY CONTRO L
Inventory definition: Quality Control (QC) is a system of routine technical activities, to measure and control
the quality of the inventory as it is being developed. The QC system is designed to:(i)Provide routine and consistent checks to ensure data integrity, correctness, and completeness;(ii)

Identify and address errors and omissions;(iii) Document and archive inventory material and record all QC activities.
QC activities include general methods such as accuracy checks on data acquisition and calculations and the useof approved standardised procedures for emission calculations, measurements, estimating uncertainties,
archiving information and reporting. Higher tier QC activities include technical reviews of source categories,
activity and emission factor data, and methods.

RADAR DATA
Remotely-sensed data from the microwave portion of the electromagnetic spectrum, sent from and collected byaircraft or satellite after reflection from the target.

RANDOM ERRO R
See systematic and random errors.

G.16

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Annex A: Glossary

RANDOM VARI ABLE
Statistical definition: A variable that may take any of the values of a specified set of values and with which is
associated a probability distribution. A random variable which may take only isolated values is said to be‘discrete.’ A random variable that may take any value within a finite or infinite interval is said to be ‘continuous’.

RASTER DATA
Information stored on regular grid of points.

RASTER IMAGES
Raster data means information stored on a regular grid of points, as opposed to polygon data, which isinformation stored as the coordinates of an outline area sharing a common attribute.

REFORESTATION 9
Direct human-induced conversion of non-forested land to forested land through planting, seeding and/or thehuman-induced promotion of natural seed sources, on land that was forested but that has been converted to nonforested land. For the first commitment period, reforestation activities will be limited to reforestation occurringon those lands that did not contain forest on 31 December 1989.

RELATIVE ERROR
Maximum tolerable error which is a fraction of the value of the variable being estimated.

REMOTELY SENSED DATA
Data generally acquired by means of scanners or cameras onboard aircraft or satellites.

REMOTE SENSI NG
Practice of acquiring and using data from satellites and aerial photography to infer or measure land cover/use.
May be used in combination with ground surveys to check the accuracy of interpretation.

REMOVALS
Removals are a subset of fellings (the commercial part destined for processing). The ‘Removals’ term should
only be used in this forestry context, not as synonym for carbon sink.

REPORTING
The process of providing estimates to the UNFCCC.

RESOLUTIO N
Smallest unit of land about which land cover or use can be determined. High resolution means the resolvableland units are small.

9

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

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Annex A: Glossary

RESERVOIRS
Water bodies regulated for human activities (energy production, irrigation, navigation, recreation etc.) where
substantial changes in water area due to water level regulation occur. The term should not be used in the context
of a carbon reservoir.

REVEGETATION 10
A direct human-induced activity to increase carbon stocks on sites throught the establishment of vegetation thatcovers a minimum area of 0.05 hectares and does not meet the definitions of afforestation and reforestation
contained here.

SAMPLE
Statistical meaning: A sample is a finite set of observations drawn from a population.

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

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

SENSITI VITY
Statistical definition: A sensitivity is a measurement of how responsive one quantity is to a change in anotherrelated quantity. The sensitivity of a quantity Y that is affected by changes in another quantity X, is defined as
the change in Y divided by the change in X that caused the changes in Y.

SENSITIVITY ANALYSIS
Statistical definition: Sensitivity analysis is a study of a model algorithm to determine how sensitive (or stable) it is
to variations of its input data or underlying assumptions. It is performed by varying input values or model equations
and observing how the model output varies correspondingly. The aim of such a sensitivity analysis can include:•

Observing the range of output values corresponding to input variables lying within ‘reasonable’ ranges; and•Calculating finite difference approximations for elasticities and sensitivities as required by somemethodologies for studying error propagation within a system.

SEQUESTRATION
The process of increasing the carbon content of a carbon pool other than the atmosphere. It is preferred to use theterm “sink”.

10

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

G.18

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Annex A: Glossary

SETTLEMENTS
This category includes all developed land, including transportation infrastructure and human settlements of any
size, unless they are already included under other categories. This should be consistent with the selection ofnational definitions.

SIMPLE RANDOM SAMPLE
Statistical definition: A sample of n items chosen from a population such that every possible sample has thesame probability of being chosen.

SINK
Any process, activity or mechanism which removes a greenhouse gas, an aerosol, or a precursor of a greenhousegas from the atmosphere. Notation in the final stages of reporting is the negative (-) sign.

SKEWNESS
Statistical definition: Skewness is a measure of asymmetry of a PDF. It is a simple function of two moments of
the PDF, given by:µ3µ3

where µ2, µ3, and σ are central moments. Symmetric distributions have γ = 0. The same nameσ3
is frequently used for sample skewness, in which case both population moments are replaced by sample
moments.

γ =µ 23 2=

SOIL ORGANIC MATTER
Includes organic carbon in mineral and organic soils (including peat) to a specified depth chosen by the country andapplied consistently through the time series. Live fine roots (of less than the suggested diameter limit for belowground biomass) are included with soil organic matter where they cannot be distinguished from it empirically.

SOURCEAny process or activity which releases a greenhouse gas, an aerosol or a precursor of a greenhouse gas into theatmosphere. Notation in the final stages of reporting is the positive (+) sign.

SPATIAL INTERPOLATIO N
Inference about the characteristics of land from known information on surrounding land locations.

SPATIALLY EXPLICIT
Mapped or otherwise geographically referenced.

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

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Annex A: Glossary

STANDARD DEVIATION
Statistical definition: The population standard deviation is the positive square root of the variance. It isestimated by the sample standard deviation that is the positive square root of the sample variance.

STANDI NG VOLUME
Volume of standing trees, living or dead, above stump measured overbark to a predefined top diameter. Includes
all trees with diameter above a given diameter at breast height (dbh). The minimum dbh and the top diametervary by country and are usually country defined.

STATISTIC
Statistical definition: A statistic is a function of the sample random variables.

STATISTICS
Statistical definition: Statistics can refer either in a general sense to the compilation of data, frequently abouthuman activities, or in a more specific sense to the branch of science concerned with the systematic numericaltreatment of data derived from aggregates of items.

SYSTEMATIC AND RANDOM ERRO RS
Statistical definition: Systematic error is the difference between the true, but usually unknown, value of aquantity being measured, and the mean observed value as would be estimated by the sample mean of an infinite
set of observations. The random error of an individual measurement is the difference between an individual
measurement and the above limiting value of the sample mean.

SYSTEMATIC ERRO R
Statistical definition: See systematic and random errors.

TEMPERATE, COLD
Mean annual temperature (MAT) is between 0 – 10 oC.

TEMPERATE, WARM
Mean annual temperature (MAT) is between 10 – 20 oC.

TIME SERIES
Statistical definition: A time series is series of values which are affected by random processes and which areobserved at successive (usually equidistant) points in time.

TOP-DOWN MODELLING
A modelling approach which aims to infer processes and parameters at a smaller scale from measurements taken
at an aggregated scale (regional/national/continental/global).

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Annex A: Glossary

TRANSPARENCY
Inventory definition: Transparency means that the assumptions and methodologies used for an inventory
should be clearly explained to facilitate replication and assessment of the inventory by users of the reported
information. The transparency of inventories is fundamental to the success of the process for the communicationand consideration of information.

TREND
Inventory definition: The trend of a quantity measures its change over a time period, with a positive trend value
indicating growth in the quantity, and a negative value indicating a decrease. It is defined as the ratio of thechange in the quantity over the time period, divided by the initial value of the quantity, and is usually expressed
either as a percentage or a fraction.

TROPICAL
Mean annual temperature (MAT) is more than 20 oC.

UNCERTAINTY
Statistical definition: An uncertainty is a parameter, associated with the result of measurement thatcharacterises the dispersion of the values that could be reasonably attributed to the measured quantity (e.g., thesample variance or coefficient of variation).
Inventory definition: A general and imprecise term which refers to the lack of certainty (in inventory
components) resulting from any causal factor such as unidentified sources and sinks, lack of transparency, etc.

UNCERTAINTY ANALYSIS
Statistical definition: An uncertainty analysis of a model aims to provide quantitative measures of theuncertainty of output values caused by uncertainties in the model itself and in its input values, and to examine
the relative importance of these factors.

UNIFORM DI STRIBUTION
Statistical definition: A random variable with a uniform or rectangular distribution is confined to lie within arange over which all values are equally probable. If the upper and lower limits of the range are a and b
respectively, the PDF is a flat function from a to b (the two parameters defining the PDF).

The PDF of a uniform distribution is given by: 1

for a ≤ x ≤ bf ( x) =  b − a 0elsewhere
where
a+b
µ =
2
is the mean and(b − a) 2
12
is the variance.

σ2 =IPCC Good Practice Guidance for LULUCF

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Annex A: Glossary

VALIDATION
Inventory definition: Validation is the establishment of sound approach and foundation. In the context ofemission inventories, validation involves checking to ensure that the inventory has been compiled correctly inline with reporting instructions and guidelines. It checks the internal consistency of the inventory. The legal use
of validation is to give an official confirmation or approval of an act or product.

VARIABILI TY
Statistical definition: This refers to observed differences attributable to true heterogeneity or diversity in a
population. Variability derives from processes which are either inherently random or whose nature and effects
are influential but unknown. Variability is not usually reducible by further measurement or study, but can becharacterised by quantities such as the sample variance.

VARIANCE
Statistical definition: The variance or population variance is a parameter of a PDF, which expresses thevariability of the population. It is the second central moment of a random variable. The sample variance isdefined as a measure of dispersion, which is the sum of the squared deviations of observations from their average,
divided by one less than the number of observations.

s2 =

1 n
2
∑ ( x i − x)n −1 i

VARIANCE OF SAMPLE MEAN
Statistical definition: The mean of a sample taken from a population is itself a random variable with its own
characteristic behaviour and its own variance. For such sample means, the appropriate estimate of the variance is
not the sample variance, which estimates the variability associated with a single simple value, but a lower value,equal to the sample variance divided by the sample size.

VERIFICATIO N
Inventory definition: Verification refers to the collection of activities and procedures that can be followed
during the planning and development, or after completion of an inventory that can help to establish its reliability
for the intended applications of that inventory.

Typically, methods external to the inventory are used to check the truth of the inventory, including comparisonswith estimates made by other bodies or with emission and uptake measurements determined from atmosphericconcentrations or concentration gradients of these gases.

WALL- TO-WALL MAPPING
Complete spatial coverage of a land area, e.g., by satellite data.

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

WETLANDS
This category includes land that is covered or saturated by water for all or part of the year (e.g., peatland) andthat does not fall into the forest land, cropland, grassland or settlements categories. This category can be

G.22

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Annex A: Glossary

subdivided into managed and unmanaged according to national definitions. It includes reservoirs as a managed
sub-division and natural rivers and lakes as unmanaged sub-divisions.

References
Intergovernmental Panel on Climate Change (IPCC). (2000). Penman J., Kruger D., Galbally I., Hiraishi T.,
Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen T., Meijer J., Miwa K., and Tanabe K. (Eds).
Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
IPCC/OECD/IEA/IGES, Hayama, Japan.
Intergovernmental Panel on Climate Change (IPCC). (2000). Watson R., Noble I.R., Bolin B., Ravindranath,
N.H., Verardo D.J. and Dokken D.J. (Eds) Land use, Land-use Change, and Forestry: A Special Report.
Cambridge University Press. Cambridge, UK.
Intergovernmental Panel on Climate Change (IPCC). (2001). Houghton J. T. et al. (Eds.). Climate Change 2001:
The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.
FAO (1998). World Reference Base for Soil Resources. World Soil Resources Reports 84. FAO, Rome. 88 pp.
ISBN 92-5-104141-5
FAO. (2002). Proceedings of the Expert Meeting on Harmonizing Forest-related Definitions for use by various
stakeholders. FAO, Rome, Italy.
Mekkink P. (1999). Soils of Forest Reserves in the Netherlands. Report 98/35, Staring Centre, Wageningen, The
Netherlands.
UNFCCC. (2001). Paragraphs 1(a) - (e) in the Annex to draft decision -/CMP.1 (Land use land-use change andforestry), contained in document FCCC/CP/2001/13/Add.1, p.58.

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G.23

ANNEX B

BASIC INFORMATIONIPCC Good Practice Guidance for LULUCF

Annex B: Basic Information

BASIC INFORMATION

Prefixes and multiplication factors
Multiplication Factor

Abbreviation

PrefixSymbol1 000 000 000 000 000

1015

peta

P

1 000 000 000 000

10

12

tera

T

1 000 000 000

109

giga

G

1 000 000

10

6

mega

M

1 000

103

kilo

k

100

10

2

hecto

h

10

101

deca

da

0.1

10

-1

deci

d

0.01

10-2

centi

c

0.001

10

-3

milli

m

0.000 001

10-6

micro

µ

Abbreviations for chemical compounds
CH4

MethaneN2O

Nitrous oxide

CO2

Carbon dioxide

CO

Carbon monoxide

NOX

Nitrogen oxides

NMVOC

B.2

Non-methane volatile organic compound

NH3

Ammonia

CFCs

Chlorofluorocarbons

HFCs

Hydrofluorocarbons

PFCs

Perfluorocarbons

SF6

Sulphur hexafluoride

CCl4

Carbon tetrachloride

C2F6

Hexafluoroethane

CF4

Tetrafluoromethane

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Annex B: Basic Information

Standard equivalents
1 tonne of oil equivalent (toe)1 x 1010 calories

103 toe41.868 TJ

1 short ton

0.9072 tonne

1 tonne1.1023 short tons

1 tonne1 megagram

1 kilotonne

1 gigagram

1 megatonne

1 teragram

1 gigatonne

1 petagram

1 kilogram

2.2046 lbs

1 hectare

104 m2

1 calorieIT

4.1868 Joules

1 atmosphere101.325 kPa

Units and abbreviations
cubic metrem3

hectare

ha

gram

g

tonne

t

joule

J

degree Celsius℃

calorie

cal

year

yr

capitacap

gallongal

dry matter

d.m.

IPCC Good Practice Guidance for LULUCF

B.3

ANNEX CABBREVIATIONS AND ACRONYMS

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Annex C: Abbreviations and Acronyms

AARS

Asian Association on Remote Sensing

ESE

Earth Science Enterprise – (NASA)

ABD

Aboveground Biomass Density

ESRI

AGO

Australian Greenhouse Gas OfficeEnvironmental Systems ResearchInstitute

ANPP

Aboveground net primary
productivity (g/m2/year)

ETM+

Enhanced Thematic Mapper PlusEU

European Union

ASAR

Advanced Synthetic Aperture RadarFAO

FAO Statistical Database

ASB

Alternatives to slash and burn

FAOSTAT

Ave.

Average

Food and Agriculture Statistical
Database

AVHRR

Advanced Very High Resolution
Radiometer

FF

Forest land remaining forest land

FIA

Forest Inventory and Analysis

BEF

Biomass Expansion Factor

FRA

Forest Resource Assessment

BNPP

Belowground net primary
productivity (g/m2/year)

GBC

Green building challenge

GCOS

Global Climate Observing System

C stock

Carbon stock

GG

Grassland remaining grassland

CC

Crop land remaining cropland

GHG

Greenhouse Gas

C&I

Criteria and Indicators

GIS

Geographic Information System

CBD

Convention on Biological Diversity

GLCF

Global Land Cover Facility

CDM

Clean Development Mechanism

GPG

Good Practice GuidanceCI

Confidence Interval

GPG2000

CIFOR

Centre for International Forestry
Research

IPCC Good Practice Guidance andUncertainty Management in
National Greenhouse Gas Inventories

CLC

CORINE Land Cover

GPP

Gross Primary Production

COP

Conference of the Parties

GPS

Global Positioning System

CORINE

Cordination de l’Information surl’Environnement

GTOS

Global Terrestrial Observing System

GWP

Global Warming Potential

CRP

Conservation Reserve Programme

HACHigh activity clay

CSIRO

Commonwealth Scientific andIndustrial Research Organisation

HT

Total heightCT

Conventional till

ICOD

International Commission on Large
Dams

CTIC

Conservation Technology
Information Center

ICP

International Cooperative
Programme

D

Wood density

ID

Identificationd.m.

Dry matter

IGBP

DAAC

Distributed Active Archive Centre

International Geosphere-Biosphere
Programme

dbh

Diameter at Breast Height

IHDP

DOC

Dead Organic Carbon

International Human Dimensions
Programme

DOM

Dead Organic Matter

IM

Integrated Monitoring

EF

Emission factor

IPCC

Intergovernmental Panel on Climate
ChangeEIT

Economies in Transition

IRW

Industrial Roundwood

EMEP

Co-operative Programme for
Monitoring and Evaluation of theLong-Range Transmissions of Air
Pollutants in Europe

ISCGM

International Steering Committee for
Global Mapping

JI

Joint Implementation

Estimate

LAC

Low activity clay

ES

AA.2

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Annex C: Abbreviations and Acronyms

LAI

Leaf Area Index

LC

Lands converted to cropland

LCDB

Land-use /Cover Database

LF

Lands converted to forest land

LG

Lands converted to grassland

LHF

Litter-Humus-Fermented (Soil Layers)

LIDAR

Light Detection and Ranging

LO

Lands converted to other land

LS

Lands converted to settlements

LVIS

Laser Vegetation Imaging Sensor

LW

Lands converted to wetlands

M&M

Measuring and Monitoring

MA

Marrakesh Accords

MAT

Mean annual temperature

Max.

Maximum

MDD

Minimum Detectable Difference

Min.

Minimummin.

minuteMOP

Meeting of the Parties

MSS

Multispectral Scannern.s.

Not significant

NASA

National Aeronautics and Space
Administration

NZLCDBNew Zealand Land-use /Cover
Database

OFP

Other Fibre Pulp

ONC

Operational Navigational Chart

OO

Other land remaining other land

ORNL

Oak Ridge National Laboratory

PDF

Probability Density Function

PEFC

Pan European Forest Certification

PET

Potential evapotranspiration

PI

Partial Inventory

PWC

Perennial woody crops

QA

Quality Assurance

QC

Quality Control

R/S

Root-to-shoot ratio

RBD

Root biomass density

RFP

Recovered Fibre Pulp

RGP

Rangelands, Grasslands, Pastures

RP

Recovered Paper

RS

Remote Sensing

S

Sulphur

SAR

Synthetic Aperture Radar

SD

Standard deviation

SFM

Sustainable Forest Management

NBP

Net Biome Production

SOM

Soil Organic Matter

NC

Non-carbon

SOC

Soil Organic Carbon

NCAS

National Carbon Accounting System

SPOT

n.d.

Not determined

Système Probatoire d’Observation dela Terre

NDVI

Normalised Difference Vegetation
Index

SS

Settlements remaining settlements

TNPP

Total net primary productivity(g/m2/year)

TOR

Terms of Reference

UNECE

United Nations Economic
Commission for Europe

UNCED

United Nations Conference on
Environment and Development

NEE

Net Ecosystem Exchange

NEP

Net Ecosystem Production

NF

Non-Federal

NGOs

Non-government Organizations

NI

National Inventory

NMHC

Non-methane hydrocarbons

NOAA

National Oceanic and Atmospheric
Administration

NPI

National Peatland Inventory

NPP

UNEP-GRID United Nations Environment
Programme – Global Resource
Information Database
UNFCCC

United Nations Framework
Convention on Climate Change

Net Primary Production

URL

Uniform Resource Locator

NRCS

Natural Resources Conservation
Service

USDA

United States Department ofAgriculture

NRI

National Resources Inventory

USGS

United States Geologic Survey

IPCC Good Practice Guidance for LULUCF

AA.3

Annex C: Abbreviations and Acronyms

UTM

Universal Transverse Mercator

VOCs

Volatile organic compounds

WCD

World Commission on Dams

WCRP

World Climate Research Programme

WRBWorld Reference Base

WP

Wood pulp

WSO

Wetlands, Settlements, and Other land

WW

Wetlands remaining wetlands

yr

Year

AA.4

IPCC Good Practice Guidance for LULUC

ANNEX D

LIST OF REVIEWERS

IPCC Good Practice Guidance for LULUCF

Annex D: List of Reviewers

List of Reviewers
Argentina
Ginzo, H.
Glaz, D.
Marin, N.
Nine, M.
Norverto, C.

Ministry of Foreign Affairs
Secretary of Environment andSustainable Development
Secretary of Environment andSustainable Development
Ministerio de Relaciones
Exteriores,Comercio Internacional
Secretaría de Agricultura, Ganadería,
Pesca y Alimentos

Australia
Barrett, D.

Cooperative Research Centre forGreenhouse Accounting (CRC GA)
Barry, S.
CRC GA
Brack, C.
CRC GA
Carter, J.
CRC GA Queensland Natural
Resources and Mines (Qld NR&M)
Cowie, A.
CRC GA State Forest of New South
Wales (SF NSW)
Dalal, R.
CRC GA Qld NR&M
Dean, C.
CRC GA
Farquar, G.
CRC GA Australian National
Univeristy
Gardner, D.
CRC GA SF NSW
Gifford, R.
CRC GA Commonwealth Scientific &Industrial Research Organisation(CSIRO)Henry, B.
CRC GA Qld NR&M
Kirschbaum, M. CRC GA
Mitchell, C.
CRC GA
Mokany, K.
CRC GA
Montagu, K. CRC GA NSW SF
Raison, J.
CRC GA CSIRO
Ritson, P.
CRC GA CALM, WA
Turner, B.
CRC GA
Ximenes, F.
CRC GA SF NSWAustria
Radunsky, K.

Federal Environment Agency

Azerbaijan
Government of Azerbaijan
Belgium
Vanderstraeten, M.
Walle, I.
Ghent University
Benin
Guendehou, S. Benin Centre of Scientific andTechnical Research (CBRST)
Brazilde Oliveira Santos, M.
dos Santos, M.
Fearnside, P. National Amazon Research Institute(INPA)
Fujihara, M.
Price Waterhouse Coopers
Gualda, R.

LR.2

Brazil (Continued)
Krug, T.
Inter-American Institute for Global
Change Research (IAI)
Miguez, J.D. Ministry of Science and Technology
Monteiro Lourenço, R.
Paciornik, N. Ministry of Science and Technology
Tavares de Lima, I.
Vianna Rodrigues, R.
CanadaBernier, P.
Blain, D.
Boehm, M.
Boysen, E.
Brierley, T.

Canadian Forest Service
Environment Canada
Agriculture and Agri-Food Canada
Ontario Ministry of Natural Resources
Department of Agriculture and AgriFood
Chang, L.
Environment Canada
Chen, J.
University of Toronto
Chen, W.
Narural Resources Canada CCRS
Fernandes, R. Natural Resources Canada CCRS
Graham, P.
Canadian Forest Service
Gray, P.
Ontario Ministry of Natural Resources
Henderson, L Environment Canada
Jaques, A.
Government of Canada / Environment
CanadaLeckie, D.
Canadian Forest Service
Lempriere, T. Canadian Forest Service
Magnussen, S. Canadian Forest Service
McConkey, B Agricultue and Agri-Food Canada
McGovern, M. Environment Canada
Trofymow, J. Canadian Forest Service
Varfalvy, L.
Hydro-Québec
Wellisch, M. Environment Canada
Chile
Bahamondez,C. Centro de Información Forestal(CINFOR)
China, Peoples Republic ofChen, Z.
China Meteorological Administration
Gao, Y.
China Meteorological Administration
Kong, X.
Ministry of Foreign Affairs
Li, L.
State Development Planning
Commission
Li, Y.
Chinese Academy of Agriculture
Liu, H.
National Meteorological Center
Liu, S.
Chinese Academy of Forestry
Lv, X.
Ministry of Science and Technology
Ma, A.
State Development Planning
Commission
Qin, D.
China Meteorological Administration
Wang, B.
China Meteorological Administration
Wang, X.
State Forestry Administration
Xu, D.
Chinese Academy of Forestry
Yan, C.
Ministry of Agriculture
Yang, Z.
National Satellite MeteorologicalCenterYi, X.
Ministry of Foreign Affairs
Ying, N.
China Meteorological Administration

IPCC Good Practice Guidance for LULUCF

Annex D: List of Reviewers

China, Peoples Republic of (Continued)
Zhang, L.
National Satellite MeteorologicalCenterZhang, X.
Chinese Academy of Forestry
Zheng, G.
China Meteorological Administration
Colombia
Government of Colombia

Germany (Continued)
Glatzel, S.
University of Göttingen
Heinemeyer, O. BMVEL- Bundesforschungsanstalt
für Landwirtschaft (FAL)
Rogasik, J.
FAL/PB
Schmitz
BMVEL
Strich, S.
BMVEL
Strogies, M.
Federal Environment Agency

Congo, Democratic Republic of
Ntombi
IPCC/Point Focal

Iceland
The Ministry for the Environment

FinlandAlm, J.
Finnish Forest Research InstituteEsala, M.
MTT Agrifood Research Finland
Heikinheimo, P. Ministry of Environment
Heino, P.
Finnish Forest Industries Federation
Laine, J.
Helsinki University
Lapveteläinen, T. Minisrty of Agriculture and Forestry
Lehtonen, A. Finnish Forest Research InstituteMonni, S.
VTT Technical Research Centre of
Finland
Muukkonen, J. STAT, Statistics Finland
Myllys, M.
MTT Agrifood Research Finland
Pajukallio, A. Ministy of Environment
Perälä, P.
MTT Agrifood Research Finland
Pingoud, K.
VTT Technical Research Centre of
FinlandRegina, K.
MTT Agrifood Research Finland
Savolainen, I. VTT Technical Research Centre of
Finland
Seppänen, A. Ministy of Environment
Smolander, A. Finnish Forest Research InstituteStarr, M.
Finnish Forest Reseach InstituteTomppo, E.
Finnish Forest Research Institute
Vainio-Mattila,M.Ministry of Agriculture and Forestry
Yli-Halla, M. MTT Agrifood Research Finland

IrelandFay, D.

FranceLoisel, C.
Pignard, G.

Riedacker, A.
Seguin, B.

Office National des Forêts (ONF) Inventaire Forestier National (INF)
ONF - INF, Institut National de laRecherche Agronomique (INRA) Mission Interministérielle de l'Effet
de Serre (MIES)ONF - IFN, INRA -MIES
IFN

Germany
Appel, V.

Bundesministerium fürVerbraucherschutz, Ernährung und
Landwirtschaft (BMVEL)
Augustin, S.
Bundesforschungsanstalt für Forstund Holzwirtschaft (BFH)
Beerbaum
BMVEL
Benndorf, R. Federal Environment Agency (UBA)
Butterbach-Bahl, K.Institut für Meteorologie und
Klimaforschung Atmosphärische
Umweltforschung GarmischPartenkirchen (IMK-IFU)
Dieter, M.
BFH

IPCC Good Practice Guidance for LULUCF

Gallagher, G.
Hendrick, E.
Michael, Y.
Italy
Lumicisi, A.
Tubiello, F.

The Irish Agriculture and Food
Development Authority (TEAGASC)National Council for Forest Researchand Development (COFORD)
COFORDAT ENVIRON

Italian Ministry of Environment andTerritory
Columbia University

Japan
Fujimori, T.

Japan Forest Technological
Association
Handa, M.
Organization for Landscape and
Urban Greenery Technology
Development
Harada, T.
Forestry Agency, Ministry ofAgriculture, Forestry and Fisheries
Hayashi, Y.
National Institute for AgroEnvironmental Sciences
Higashi, M.
Ministry of Land, Intrastructure andTransport
Hiranuma, K. Ministry of Agriculture, Forestry and
Fisheries
Honda, Y.
Chiba University
Inoue, G.
National Institute for Environmental
Studies
Ishizuka, M.
Forestry and Forest Products
Research Institute
Itakura, T.
Ministry of Eduation, Culture,
Sports, Science and Technology
Itakura, K.
Ministry of Land, Intrastructure andTransport
Kato, J.
Ministry of Land, Infrastructure andTransport
Kobayashi, S. Forestry and Forest Products
Research InstituteKohyama, T. Hokkaido University
Koike, T.
Hokkaido University Forests, FSC
Matsumoto, M. Forestry and Forest Products
Research Institute (FFPRI)
Minami, K.
National Institute for AgroEnvironmental Sciences
Morikawa, Y. Waseda University
Muto, N.
Forestry Agency, Ministry ofAgriculture, Forestry and FisheriesNara, C.
Ministry of the Environment

LR.3

Annex D: List of Reviewers

Japan (Continued)
Nouchi, I.
National Institute for AgroEnvironmental Sciences
Ogiwara, H.
Forestry Agency, Ministry ofAgriculture, Forestry and Fisheries
Ohta, S.
Forestry and Forest Products
Research Institute (FFPRI)
Oikawa, K.
University of Tsukuba
Okuda, T.
National Institute for Environmental
Studies
Shibasaki, R. University of Tokyo
Shimizu, K.
Forestry and Forest Products
Research InstituteShirato, Y.
National Institute for AgroEnvironmental Sciences
Sweda, T.
Ehime University
Takahashi, M. Forestry and Forest Products
Research InstituteTaniyama, I. National Institute for AgroEnvironmental Sciences
Tanouchi, H. Forestry and Forest Products
Research InstituteTonosaki, M. Forestry and Forest Products
Research Institute
Tsuruta, H.
National Institute of AgroEnvironmental Sciences
Watanabe, T. Forestry Agency, Ministry ofAgriculture, Forestry and Fisheries
Yagi, K.
National Institute for AgroEnvironmental Sciences
Yamagata, Y. National Institute for Environmental
StudiesYasuoka, Y.
University of Tokyo
Kenya
Ambenje, P.
Marigi, S.
Munah, P.
Njihia, J.
Wairoto, J.

Kenya Meteorological Department

Malaysia
Ministry of Science, Technology and theEnvironment (MOSTE)
Chan, K.
Malaysian Palm Oil Board (MPOB)
Netherlands
Abeelen, C.
Batjes, N.

Clabbers, B.
Dirkse, G.
Groesz, F.
Moors, E.
Nabuurs, G.
Paasman, J.
Trines, E.
van Tol, G.

LR.4

Netherlands Agency for Energy andthe Environment (Novem)
International Soil Reference andInformation Centre (ISRIC)/WDC
for soils
Ministerie Landbouw, Natuur en
Visserij (LNV)
ALTERRANEO
ALTERRAALTERRABosdata
LNV
EC-LNVNew ZealandBaisden, T.
Barton, J.
Beets, P.
Edwards, S.

Landcare Research
New Zealand Climate Change Office
Forest Research
Ministry of Agriculture and Forestry(MAF)
Ford-Robertson, J. Ford-Robertson Initiatives Ltd
Goulding, C. Forest ResearchJebson, M.
MAF
Lane, P.
MAF
Maclaren, P. Piers Maclaren and Associates
Manley, B.
School of Forestry, Canterbury
University
Novis, J.
MAF
Plume, H.
New Zealand Climate Change Ofice
Reisinger, A. New Zealand Climate Change Office
Robertson, K. Force Consulting Ltd
Rys, G.
MAF
Smith, B.
MAF
Stephens, P.
Landcare ResearchTate, K.
Landcare Research
Trotter, C.
Landcare ResearchWard, M.
New Zealand Climate Change Office
Wratt, D.
National Institute of Water andAtmospheric Research (NIWA)
Nigeria
Obioh, I.

DERD, OAU

NorwayAalde, H.

Norwegian Institute of Land
Inventory (NIJOS)
de Wit, H.
Skogforsk / NIJOS
Eldhuset, T.
Skogforsk
Flugsrud, K. Statistic Norway
Grønlund, A. Norwegian Centre for Soil andEnvironmental Research
Klokk, T.
Directorate for Nature Mangement
Lindstad, B.
Agricultural University of Norway
Nilsen, P.
Skogforsk
Njøs, A.
Jordforsk
Pettersen, M. Norwegian Pollution Control Authority
Rosland, A.
Norwegian Pollution Control Authority
Rypdal, K.
Center for International Climate andEnvironmental Research Oslo
(CICERO)
Sletnes, A.
Ministry of Agriculture
Solberg, B.
Directorate for Nature Mangement
Tomter, S.
NIJOSTorvanger, A. CICERO
Utseth, A.
Directorate for Nature Mangement
Vestgarden, L. Jordforsk
Oman
bin Ali Al-Hakmani, M.
Ministry of Regional Municipalities,
Environment and Water Resources
PolandOlecka, A.

Institute of Meteorology and Water
Management, Meteorology Centre

IPCC Good Practice Guidance for LULUCF

Annex D: List of Reviewers

PortugalFerreira, C.
Seixas, J.

DG Forests
New University Lisbon

Russian Federation
Bedritsky, A. Russian Federal Service for
Hydrometeorology andEnvironmental Monitoring
Filipchuk, A. All-Russian Scientific and ResearchInstitute for Forestry andMechanization / International Centeron Forests / Institute for Silviculture
and Forest Mechanization(VNIILM)
Gytarsky, M. Institute of Global Climate andEcology
Karaban, R.
Institute of Global Climate andEcology
Moiseev, B.
International Center on Forests
Nazarov, I.
Institute of Global Climate andEcology
Romanovskaya, A. Institute of Global Climate andEcology
Spain
Díaz, C.
OECC-MMASanchez-Pena, G. Ministry for Environment
Sanz, M.
Centro de Estudios Ambientales delMediterraneo (CEAM)
Vallejo, R.
University of Barcelona
Sri Lanka
Punyawardena, B. Department of Agriculture
Sudan
Elgizouli, I.
Elhassan, N.

Higher Council for Environment andNatural Resources (HCENR)
HCENR

SwedenBoström, B.

Environmental Protection Agency /Swedish Energy Agency
Eriksson, H.
National Board of Forestry /Environmental Protection Agency
Jacobsson, J. Naturvårds-verket
Lilliesköld, M. Swedish Environmental
Protection AgencyÖsterberg, K. Swedish University of Agricultural
Sciences / Swedish Environmental
Protection AgencyStåhl, G.
Swedish University of Agricultural
Sciences
Switzerland
Filliger, P.
Robledo, C.

TurkeySensoy, S.

TuvaluFry, I.

Turkish State Meteorological Service(TSMS)

Departmentt of Environment

United Kingdom
Broadmeadow, M. Forest ResearchGrace, J.
Edinburgh University
Gregory, S.
FC
Penman, J.
Department for Environment, Food
& Rural Affairs (DEFRA)
Smith, K.
University of Edinburgh
United States
Andrasko, K. USEPA
Birdsey, R.
USDA Forest Service
Buford, M.
USDA Forest Service R&D
DeAngelo, B. USEPA
Franzluebbers, A. USDA ARS
Goklany, I.
USDA Forest Service
Higgs, M.
USDA Forest Service R&D
Hohenstein, W USDA
Hrubovcak, J. USDA
Irving, W.
USEPA
Kruger, D.
USEPA
Lund, H.
Forest Information Services
Norfleet, L.
USDA NRCS
Sampson, R. The Sampson Group, Inc.
Sperow, M.
West Virginia University
Stokes, B.
USDA Forest Service R&D
Wirth, T.
USEPA
European Comission (EC)
Herold, A.
Öko-Institut
Matteucci, G. Joint Research Centre, Institute forEnvironment and Sustainability(JRC IES)
Seufert, G.
JRC IES
Wenning, M.
United Nations Food and Agriculture Organisation
Friedrich, T. FAO
Holmgren, P. FAO
Killmann, W. FAO
Schoene, D.
FAO
United Nations Framework Convention on
Climate Change
Forner, C.
UNFCCC
Granholm, H. UNFCCC

Romero, J.

Government of Switzerland
Swiss Federal Laboratories For
Material Testing and Research (EMPA)
Government of Switzerland

European Space Agency
Arino, O.
ESA
Dees, M.
GAF, Munich
Fernández-Prieto, D. ESA
Volden, E.
ESA

Tajikistan
Novikov, V.

Tajik Hydrometeo-rological Service

World Wildlife Fund
Rakonczay, Z. WWF

IPCC Good Practice Guidance for LULUCF

LR.5