Chapter 5: Cropland

CHAPTER 5

CROPLAND

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

Authors
Rodel D. Lasco (Philippines), Stephen Ogle (USA), John Raison (Australia), Louis Verchot (ICRAF/USA),
Reiner Wassmann (Germany), and Kazuyuki Yagi (Japan)
Sumana Bhattacharya (India), John S. Brenner (USA), Julius Partson Daka (Zambia), Sergio P. González (Chile),
Thelma Krug (Brazil), Yue Li (China), Daniel L. Martino (Uruguay), Brian G. McConkey (Canada), Pete Smith(UK), Stanley C. Tyler (USA), and Washington Zhakata (Zimbabwe)

Contributing Authors
Ronald L. Sass (USA) and Xiaoyuan Yan (China)

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: CroplandContents
5

Cropland5.1

Introduction ...........................................................................................................................................5.6

5.2

Cropland Remaining Cropland..............................................................................................................5.7

5.2.1
5.2.1.1

Choice of methods ...................................................................................................................5.7

5.2.1.2

Choice of emission factors.......................................................................................................5.8

5.2.1.3

Choice of activity data ...........................................................................................................5.10

5.2.1.4

Calculation steps for Tier 1 and Tier 2 ..................................................................................5.11

5.2.1.5

Uncertainty assessment..........................................................................................................5.12

5.2.2

Dead organic matter ....................................................................................................................5.12

5.2.2.1

Choice of method...................................................................................................................5.13

5.2.2.2

Choice of emission/removal factors.......................................................................................5.13

5.2.2.3

Choice of activity data ...........................................................................................................5.14

5.2.2.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.14

5.2.2.5

Uncertainty assessment..........................................................................................................5.15

5.2.3

Soil carbon...................................................................................................................................5.15

5.2.3.1

Choice of method...................................................................................................................5.15

5.2.3.2

Choice of stock change and emission factors ........................................................................5.16

5.2.3.3

Choice of activity data ...........................................................................................................5.19

5.2.3.4

Calculation steps for Tier 1....................................................................................................5.22

5.2.3.5

Uncertainty assessment..........................................................................................................5.23

5.2.4

5.3

Biomass .........................................................................................................................................5.7

Non-CO2 greenhouse gas emissions from biomass burning........................................................5.24

5.2.4.1

Choice of method...................................................................................................................5.24

5.2.4.2

Choice of emission factors.....................................................................................................5.24

5.2.4.3

Choice of activity data ...........................................................................................................5.25

5.2.4.4

Uncertainty assessment..........................................................................................................5.25

Land Converted to Cropland ...............................................................................................................5.25

5.3.1

Biomass .......................................................................................................................................5.26

5.3.1.1

Choice of methods .................................................................................................................5.26

5.3.1.2

Choice of emission/removal factors.......................................................................................5.28

5.3.1.3

Choice of activity data ...........................................................................................................5.29

5.3.1.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.30

5.3.1.5

Uncertainty assessment..........................................................................................................5.30

5.3.2

Dead organic matter ....................................................................................................................5.30

5.3.2.1

Choice of method...................................................................................................................5.31

5.3.2.2

Choice of emission/removal factors.......................................................................................5.32

5.3.2.3

Choice of activity data ...........................................................................................................5.33

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Volume 4: Agriculture, Forestry and Other Land Use

5.3.2.4

Calculation steps for Tiers 1 and 2 ........................................................................................5.33

5.3.2.5

Uncertainty assessment..........................................................................................................5.35

5.3.3
5.3.3.1

Choice of method...................................................................................................................5.35

5.3.3.2

Choice of stock change and emission factors ........................................................................5.36

5.3.3.3

Choice of activity data ...........................................................................................................5.37

5.3.3.4

Calculation steps for Tier 1....................................................................................................5.38

5.3.3.5

Uncertainty assessment..........................................................................................................5.39

5.3.4

5.4

Soil carbon...................................................................................................................................5.35

Non-CO2 greenhouse gas emissions from biomass burning........................................................5.39

5.3.4.1

Choice of method...................................................................................................................5.39

5.3.4.2

Choice of emission factors.....................................................................................................5.40

5.3.4.3

Choice of activity data ...........................................................................................................5.40

5.3.4.4

Uncertainty assessment..........................................................................................................5.41

Completeness, Time series, QA/QC, and Reporting ...........................................................................5.41

5.4.1

Completeness ..............................................................................................................................5.41

5.4.2

Developing a consistent time series.............................................................................................5.42

5.4.3

Quality Assurance and Quality Control.......................................................................................5.43

5.4.4

Reporting and Documentation.....................................................................................................5.43

5.5

Methane Emissions from Rice Cultivation..........................................................................................5.44

5.5.1

Choice of method ........................................................................................................................5.44

5.5.2

Choice of emission and scaling factors .......................................................................................5.48

5.5.3

Choice of activity data.................................................................................................................5.51

5.5.4

Uncertainty assessment ...............................................................................................................5.52

5.5.5

Completeness, Time series, QA/QC, and Reporting ...................................................................5.52

Annex 5A.1

Estimation of default stock change factors for mineral soil C emissions/removals
for cropland............................................................................................................................5.54

References.....................................................................................................................................................5.55

Equations

5.4

Equation 5.1

CH4 emissions from rice cultivation ....................................................................................5.45

Equation 5.2

Adjusted daily emission factor ............................................................................................5.48

Equation 5.3

Adjusted CH4 emission scaling factors for organic amendments ........................................5.50

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

FiguresFigure 5.1

Classification scheme for cropland systems .......................................................................5.21

Figure 5.2

Decision tree for CH4 emissions from rice production ........................................................5.47

TablesTable 5.1

Default coefficients for above-ground woody biomass and harvest cycles
in cropping systems containing perennial species .................................................................5.9

Table 5.2

Potential C storage for agroforestry systems in different ecoregionsof the world ...........................................................................................................................5.9

Table 5.3

Default above-ground biomass for various types of perennial croplands..............................5.9

Table 5.4

Examples of perennial cropland subcategories which a country may have.........................5.10

Table 5.5

Relative stock change factors (FLU, FMG, and FI) (over 20 years) for
different management activities on cropland.......................................................................5.17

Table 5.6

Annual emission factors (EF) for cultivated organic soils...................................................5.19

Table 5.7

Example of a simple disturbance matrix (Tier 2) for the impacts ofland conversion activities on carbon pools ..........................................................................5.27

Table 5.8

Default biomass carbon stocks removed due to land conversion to cropland .....................5.28

Table 5.9

Default biomass carbon stocks present on Land Converted to Croplandin the year following conversion .........................................................................................5.28

Table 5.10

Soil stock change factors (FLU, FMG, FI) for land-use conversions to Cropland...................5.37

Table 5.11

Default CH4 baseline emission factor assuming no flooding for less than 180 days
prior to rice cultivation, and continuously flooded during rice cultivation without
organic amendments ............................................................................................................5.49

Table 5.12

Default CH4 emission scaling factors for water regimes during the cultivation
period relative to continuously flooded fields .....................................................................5.49

Table 5.13

Default CH4 emission scaling factors for water regimes before the cultivation period .......5.50

Table 5.14

Default conversion factor for different types of organic amendment ..................................5.51

Boxes
Box 5.1

Relevant carbon pools for cropland.......................................................................................5.6

Box 5.2

Conditions influencing CH4 emissions from rice cultivation...............................................5.46

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

5 CROPLAND
5.1

INTRODUCTION

This section provides a tiered methodology for estimating and reporting greenhouse gas emissions fromcroplands. Cropland includes arable and tillable land, rice fields, and agroforestry systems where the vegetation
structure falls below the thresholds used for the Forest Land category, and is not expected to exceed thosethresholds at a later time. Cropland includes all annual 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 include cereals, oils seeds,
vegetables, root crops and forages. Perennial crops 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. 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 (mixed system) 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 (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 and cultivar, density, growth rates, and harvesting and pruning practices.
Carbon stocks in soils can be significant and changes in stocks can occur in conjunction with soil properties andmanagement practices, including crop type and rotation, tillage, drainage, residue management and organic
amendments. Burning of crop residue produces significant non-CO2 greenhouse gases and the calculation
methods are provided.
There is separate guidance for Cropland Remaining Cropland (CC) and Land Converted to Cropland (LC)because of the difference in carbon dynamics. Land-use conversions to Cropland from Forest Land, Grassland
and Wetlands usually result in a net loss of carbon from biomass and soils as well as N2O 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. Some changes, especially those dealing with soil carbon,
may take place in periods of time longer than one year. The guidance covers the carbon pools shown in Box 5.1.
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 5.2 below.
BOX 5.1
RELEVANT CARBON POOLS FOR CROPLAND

Biomass-

Above-ground biomass-

Below-ground biomass

Dead organic matter-

Dead wood-

LitterSoils (soil organic matter)
The new features of the 2006 IPCC Guidelines relative to 1996 IPCC Guidelines are the following:•

the whole Cropland section is new;•biomass carbon and soil carbon are in the same section;•

methane emissions from rice are included in the Cropland category;•

non-CO2 gas emissions from biomass burning (Cropland Remaining Cropland and Land Converted to
Cropland) are also included in the Cropland chapter; and•default values are provided for biomass on Cropland and agroforestry areas.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

5.2

CROPLAND REMAINING CROPLAND

This section provides guidelines on greenhouse gas inventory for croplands that have not undergone any landuse conversion for a period of at least 20 years as a default period 1. Section 5.3 provides guidelines on Land
Converted to Cropland more recently than this. The annual greenhouse gas emissions and removals from
Cropland Remaining Cropland include:•

Estimates of annual change in C stocks from all C pools and sources; and•Estimates of annual emission of non-CO2 gases from all pools and sources.

The changes in carbon stocks in Cropland Remaining Cropland are estimated using Equation 2.3.

5.2.1
5.2.1.1

BiomassC HOICE OF METHODS

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, rubber plantations, fruit and nut orchards, andpolycultures such as agroforestry systems. The default methodology for estimating carbon stock changes inwoody biomass is provided in Chapter 2, Section 2.2.1. This section elaborates this methodology with respect toestimating changes in carbon stocks in biomass in Cropland Remaining Cropland.
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.
Changes in carbon in cropland biomass (ΔCCCB) may be estimated from either: (a) annual rates of biomass gain
and loss (Chapter 2, Equation 2.7) or (b) carbon stocks at two points in time (Chapter 2, Equation 2.8). The first
approach (gain-loss method) provides the default Tier 1 method and can also be used at Tier 2 or 3 withrefinements described below. The second approach (the stock-difference method) applies either at Tier 2 or Tier3, but not Tier 1. It is good practice to improve inventories by using the highest feasible tier given national
circumstances. It is good practice for countries to use a Tier 2 or Tier 3 method if carbon emissions andremovals in Cropland Remaining Cropland is a key category and if the sub-category of biomass is consideredsignificant. It is good practice for countries to use the decision tree in Figure 2.2 in Chapter 2 to identify theappropriate tier to estimate changes in carbon stocks in biomass.
Tier 1The default 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 gathering or disturbance (according toEquation 2.7 in Chapter 2). Losses are estimated by multiplying a carbon stock value by the area of cropland on
which perennial woody crops are harvested.
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.
Under Tier 1, default factors shown in Table 5.1, are applied to nationally derived estimates of land areas.
Tier 2
Two methods can be used for Tier 2 estimation of changes in biomass. Method 1 (also called the Gain-Loss
Method) requires the biomass carbon loss to be subtracted from the biomass carbon increment for the reporting
year (Chapter 2, Equation 2.7). Method 2 (also called the Stock-Difference Method) requires biomass carbon
stock inventories for a given land-use area at two points in time (Chapter 2, Equation 2.8).
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 estimatesof carbon stocks at two points in time. Under Tier 2, carbon stock changes are estimated for above-ground and1

Countries using higher tier methods may use different time periods depending on the time taken for carbon stocks toequilibrate after change in land use.

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below-ground biomass in perennial woody vegetation. Tier 2 methods involve country-specific or regionspecific estimates of biomass stocks by major cropland types and management system, and estimates of stockchange as a function of major management system (e.g., dominant crop, productivity management). To theextent possible, it is good practice for countries to incorporate changes in perennial crop or tree biomass using
country-specific or region-specific data. Where data are missing, default data may be used.
Tier 3
A Tier 3 estimate will use a highly disaggregated Tier 2 approach or a country-specific method involving processmodelling and/or detailed measurement. Tier 3 involves inventory systems using statistically-based sampling ofcarbon stocks over time and/or process models, stratified by climate, cropland type and management regime. For
example, validated species-specific growth models that incorporate management effects such as harvesting andfertilization, with corresponding data on management activities, can be used to estimate net changes in croplandbiomass carbon stocks over time. Models, perhaps accompanied by measurements like those in forest
inventories, can be used to estimate stock changes and extrapolate to entire cropland areas, as in Tier 2.
Key criteria in selecting appropriate models are that 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-specific or region-specific field locations that are representative of climate, soil andcropland management systems in the country.

5.2.1.2

C HOICE OF EMISSION FACTORSEmission and removal factors required to estimate the changes in carbon stocks include (a) annual biomass
accumulation or growth rate, and (b) biomass loss factors which are influenced by such activities as removal(harvesting), fuelwood gathering and disturbance.

Above-ground woody biomass growth rate
Tier 1
Tables 5.1 to 5.3 provide estimates of biomass stocks and biomass growth rates and losses for major climatic
regions and agricultural systems. However, given the large variation in cropping systems, incorporating trees ortree crops, it is good practice to seek national data on above-ground woody biomass growth rate.
Tier 2
Annual woody biomass growth rate data can be, at a finer or disaggregated scale, based on national data sources
for different cropping and agroforestry systems. Rates of change in annual woody biomass growth rate should be
estimated in response to changes in specific management/land-use activities (e.g., fertilization, harvesting,thinning). Results from field research should be compared to estimates of biomass growth from other sources toverify that they are within documented ranges. It is important, in deriving estimates of biomass accumulation
rates, to recognize that biomass growth rates will occur primarily during the first 20 years following changes inmanagement, after which time the rates will tend towards a new steady-state level with little or no change
occurring unless further changes in management conditions occur.
Tier 3
For Tier 3, highly disaggregated factors for biomass accumulation are needed. These may include categorisationof species, specific for growth models that incorporate management effects such as harvesting and fertilization.
Measurement of above-ground biomass, similar to forest inventory with periodic measurement of above-groundbiomass accumulation, is necessary.

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Chapter 5: Cropland

TABLE 5.1
DEFAULT COEFFICIENTS FOR ABOVE-GROUND WOODY BIOMASS AND HARVEST CYCLES IN CROPPING SYSTEMSCONTAINING PERENNIAL SPECIESClimate region

Above-groundbiomass
carbon stockat harvest(tonnes C ha-1)

Harvest/Maturity
cycle
(yr)

Biomass
accumulation rate(G)
(tonnes C ha-1 yr-1)

Biomass carbon
loss (L)(tonnes C ha-1 yr-1)

Error
range1

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.

TABLE 5.2
POTENTIAL C STORAGE FOR AGROFORESTRY SYSTEMS IN DIFFERENT ECOREGIONS OF THE WORLD
Eco-region

SystemAbove-groundbiomass(tonnes ha-1)

Range(tonnes ha-1)

Humid tropical highHumid tropical low
Dry lowlands
Humid tropical
Dry lowlands
Humid topical
Humid tropical highHumid tropical low
Dry lowlands
Humid tropical low

Agrosilvicultural
Agrosilvicultural
Agrosilvicultural
Agrosilvicultural
Agrosilvicultural
Silvopastoral
Silvopastoral
Silvopastoral
Silvopastoral
Silvopastoral

41.0
70.5
117.0
120.0
75.0
39.5
143.5
151.0
132.5
16.5

29 - 53
39 - 102
39 - 195
12 - 228
68 - 8128 - 51
133 - 154
104 - 198
90 - 175
15 - 18

RegionAfricaS America
S America
SE Asia
SE Asia
Australia
N America
N America
N America
N AsiaSource: Albrecht and Kandji, 2003

TABLE 5.3
DEFAULT ABOVE-GROUND BIOMASS FOR VARIOUS TYPES OF PERENNIAL CROPLANDS (TONNES HA-1)
Cropland type

RegionAbove-groundbiomassOil Palm
Mature rubber
Young rubber
Young cinnamon (7 years)
Coconut
Improved fallow
2-year fallow
1-year fallow
6-year fallow (average)
Alley cropping
Multistorey system
Jungle rubber
Gmelina-cacao

SE Asia
SE Asia
SE Asia
SE Asia
SE Asia

Range

Error

136.0
178.0
48.0
68.0
196.0

62 - 202

78
90

E Africa
E Africa
SE Asia
SE Asia

35.0
12.0
16.0
2.9

27 - 44
7 - 214 - 64
1.5 - 4.5

SE Asia
SE Asia

304.0
116.0

16 - 8047

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40
89

References

Palm et al., 1999
Wasrin et al., 2000
Siregar & Gintings, 2000
Lasco et al., 2002

105

Albrecht and Kandji, 2003
Albrecht and Kandji, 2003
Lasco and Suson, 1999
Lasco et al., 2001

17
53

Tomich et al., 1998
Lasco et al., 2001

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Below-ground biomass accumulation
Tier 1The default assumption is that there is no change in below-ground biomass of perennial trees in agricultural
systems. Default values for below-ground biomass for agricultural systems are not available.
Tier 2This includes the use of actually measured below-ground biomass data from perennial woody vegetation.
Estimating below-ground biomass accumulation is recommended for Tier 2 calculation. 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). Limited data is available for below ground biomass thus, as far as
possible, empirically-derived root-to-shoot ratios specific to a region or vegetation type should be used.
Tier 3This includes the use of data from field studies identical to forest inventories and modelling studies, if stock
difference method is adopted.

Biomass losses from removal, fuelwood and disturbanceTier 1The default assumption is that all biomass lost is assumed to be emitted in the same year. Biomass removal,
fuelwood gathering and disturbance loss data from cropland source are not available. FAO provides totalroundwood and fuelwood consumption data, but not separated by source (e.g., Cropland, Forest Land, etc.). It is
recognized that statistics on fuelwood are extremely poor and uncertain worldwide. Default removal andfuelwood gathering statistics (discussed in Chapter 4, Section 4.2) may include biomass coming from croplandsuch as when firewood is harvested from home gardens. Thus, it is necessary to ensure no double counting oflosses occurs. If no data are available for roundwood or fuelwood sources from Cropland, the default approach
will include losses in Forest Land (Section 4.2) and will exclude losses from Cropland.
Tiers 2 and 3
National level data at a finer scale, based on inventory studies or production and consumption studies according
to different sources, including agricultural systems, can be used to estimate biomass loss. These can be obtained
through a variety of methods, including estimating density (crown coverage) of woody vegetation from air
photos (or high resolution satellite imagery) and ground-based measurement plots. Species composition, density
and above-ground vs. below-ground biomass can vary widely for different cropland types and conditions andthus it may be most efficient to stratify sampling and survey plots by cropland types. General guidance on survey
and sampling techniques for biomass inventories is given in Chapter 3, Annex 3A.3.

5.2.1.3

C HOICE OF ACTIVITY DATA

Activity data in this section refer to estimates of land areas of growing stock and harvested land with perennial
woody crops. The area data are estimated using the approaches described in Chapter 3. They should be regarded
as strata within the total cropland area (to keep land-use data consistent) and should be disaggregated dependingon the tier used and availability of growth and loss factors. Examples of Cropland subcategories are given in
Table 5.4.
TABLE 5.4
EXAMPLES OF PERENNIAL CROPLAND SUBCATEGORIES WHICH A COUNTRY MAY HAVE
Broad subcategories

Specific subcategories

Fruit orchards

Mango, Citrus, Apple

Plantation crops

Rubber, Coconut, Oil palm, Coffee, Cacao

Agroforestry systems

Hedgerow cropping (alley cropping), Improved fallow, Multi-storey
systems, Home gardens, Boundary planting, Windbreaks

Tier 1
Under Tier 1, annual or periodic surveys are used in conjunction with the approaches outlined in Chapter 3 toestimate the average annual area of established perennial woody crops and the average annual area of perennial
woody crops that are harvested or removed. The area estimates are further sub-divided into general climate
regions or soil types to match the default biomass gain and loss values. Under Tier 1 calculations, international
statistics such as FAO databases, and other sources can be used to estimate the area of land under perennial
woody crops.

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Chapter 5: Cropland

Tier 2
Under Tier 2, more detailed annual or periodic surveys are used to estimate the areas of land in different classesof perennial woody biomass crops. Areas are further classified into relevant sub categories such that all major
combinations of perennial woody crop types and climatic regions are represented with each area estimate. These
area estimates must match any country-specific biomass carbon increment and loss values developed for the Tier
2 method. If country-specific finer resolution data are only partially available, countries are encouraged to
extrapolate to the entire land base of perennial woody crops using sound assumptions from best available
knowledge.
Tier 3Tier 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 otherpotentially important regional variables (e.g., regional patterns of management practices). Furthermore, it is goodpractice to relate spatially explicit area estimates with local estimates of biomass increment, loss rates, andmanagement practices to improve the accuracy of estimates.

5.2.1.4

C ALCULATION STEPS FOR T IER 1 AND T IER 2

Summary of steps for estimating change in carbon stocks in biomass in Cropland
Remaining Cropland (∆C B ) using the Tier 1 and Tier 2 methods

Using the worksheets for Cropland (see Annex 1 –AFOLU Worksheets), calculate the change in biomass carbon
stocks of Cropland Remaining Cropland:
Step 1: Enter the subcategories of Cropland for the reporting year
Typically, there are various types of Cropland with woody perennial cover in a country with varying biomass
stocks and increments. Examples of these are: fruit orchards (e.g., mango, citrus), agricultural plantations (e.g.,coconut, rubber) and agroforestry farms.
Step 2: For each sub-category, enter the annual area of Cropland with perennial woody biomass
The area (A) in hectares of each sub-category of Cropland can usually be obtained from national land-use
agencies, Ministry of Agriculture, and Ministry of Natural Resources. Possible sources of data include: satellite
images, aerial photography and land-based surveys, and FAO database.
Step 3: For each sub-category, enter the mean annual carbon stocks in the biomass accumulation (intonnes C ha yr-1) of perennial woody biomass
The annual growth rates (ΔCG) for each sub-category of Cropland, from the biomass accumulation rates G in
Table 5.1, are entered in the appropriate column of worksheets.
Step 4: For each sub-category, enter the annual carbon stocks in biomass losses (in tonnes C ha yr-1)
If there is harvesting, the amount of carbon stocks from the biomass harvested (ΔCL) is entered in the appropriate
column. This can be estimated by multiplying the default above woody above ground biomass for various
croplands in Table 5.3 by the default carbon density of 0.5 tonne C/tonne biomass.
Step 5: Calculate the annual change of carbon stocks in biomass for each sub-category
The annual change of carbon stocks in biomass (ΔCB) is calculated using Equation 2.7 in Chapter 2.
Step 6: Calculate the total change in carbon stocks (ΔCB) by adding up all the values of the subcategory
estimates.

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 subjected to harvesting. The immature perennial
woody cropland area accumulates carbon at a rate of approximately 2.6 tonnes of above ground C
ha-1 yr-1. The area harvested looses all carbon in biomass stocks in the year of removal. Default
carbon stock losses for a tropical moist perennial woody cropland are 21 tonnes C ha-1 yr-1. Fromthese values, an estimated 234,000 tonnes C accumulates per year and 210,000 tonnes C are lost.
Using Equation 2.7 in Chapter 2, the net change in carbon stocks (above-ground) in the tropicalmoist environment are 24,000 tonnes C yr-1.

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5.2.1.5

U NCERTAINTY ASSESSMENT

The following discussion provides guidance on approaches for assessing uncertainty associated with estimates ofbiomass carbon for each tier method.
Tier 1The sources of uncertainty when using the Tier 1 method include the degree of accuracy in land area estimates(see Chapter 3) and in the default biomass carbon increment and loss rates. Uncertainty is likely to be low (<10%)
for estimates of area under different cropping systems since most countries annually estimate cropland area using
reliable methods. A published compilation of research on carbon stocks in agroforestry systems was used toderive the default data provided in Table 5.1 (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 parameter value has been assigned based on expert judgement. This information can beused, with a measure of uncertainty in area estimates from Chapter 3 of this Report, to assess the uncertainty inestimates of carbon emissions and removals in Cropland biomass using the Tier 1 methodology. Guidance on
uncertainty analysis is given in Volume 1, Chapter 3.
Tier 2The Tier 2 method will reduce overall uncertainty because country-specific emission and removal factors rates
should provide more accurate estimates of carbon increment and loss for crop systems and climatic regions
within national boundaries. It is good practice to calculate error estimates (i.e., standard deviations, standard
error, or ranges) for country-specific carbon increment rates and to use these variables in a basic uncertainty
assessment. It is good practice for countries to assess error ranges in country-specific coefficients and comparethem to those of default carbon accumulation coefficients. If country-specific 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 countryspecific rates with more 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 further reduce
uncertainty levels when associated with biomass carbon increment factors defined for those finer-scale landbases (e.g., when area of coffee plantations is multiplied by a coffee plantation coefficient, rather than by ageneric agroforestry default).
Tier 3Tier 3 approaches will provide the greatest level of certainty relative to Tiers 1 and 2 approaches. It is goodpractice to calculate standard deviations, standard errors, or ranges for all country-defined biomass increment
and loss rates. It is good practice for countries to develop probability density functions for model parameters to
use in Monte Carlo simulations. The uncertainty, particularly with respect to area estimates, is likely to be less orabsent for cropping systems.

5.2.2

Dead organic matter

Methods for estimating carbon stock changes associated with dead organic matter pools are presented in thissection for Cropland Remaining Cropland (CC). Methods are provided for two types of dead organic matter
pools: 1) dead wood and 2) litter. Chapter 1of this report provides detailed definitions of these pools.
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. Amounts of dead wood depend on the time of last disturbance, the amountof input (mortality) at the time of the disturbance, natural mortality rates, decay rates, and management.
Litter accumulation is a function of the annual amount of litterfall, which includes all leaves, twigs and small
branches, hay, fruits, flowers, and bark, minus the annual rate of decomposition. The litter mass is also
influenced by the time since the last disturbance, and the type of disturbance. Management such as wood andgrass harvesting, burning, and grazing dramatically alter litter properties, but there are few studies clearly
documenting the effects of management on litter carbon.
In general, croplands will have little or no dead wood, crop residues or litter, with the exception of agroforestry
systems which may be accounted under either Cropland or Forest Land, depending upon definitions adopted bycountries for reporting.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland5.2.2.1

C HOICE OF METHOD

The decision tree in Chapter 2, Figure 2.3 provides assistance in the selection of the appropriate tier level for the
implementation of estimation procedures. Estimation of changes in carbon stocks in DOM requires an estimateof changes in stocks of dead wood and changes in litter stocks (refer to Equation 2.17 in Chapter 2).
Each of the DOM pools (dead wood and litter) is to be treated separately, but the method for determiningchanges in each pool is the same.
Tier 1The Tier 1 method assumes that the dead wood and litter stocks are not present in Cropland or are at equilibrium
as in agroforestry systems and orchards. Thus, there is no need to estimate the carbon stock changes for thesepools.
Tiers 2 and 3
Tiers 2 and 3 allow for calculation of changes in dead wood and litter carbon due to management practices. Two
methods are suggested for estimating the carbon stock change in DOM.
Method 1 (Also called the Gain-Loss Method, Equation 2.18 in Chapter 2): Method 1 involves estimating thearea of cropland management categories and the average annual transfer into and out of dead wood and litter
stocks. This requires an estimate of area under Cropland Remaining Cropland according to: i) different climate
or cropland types; ii) management regime, or other factors significantly affecting dead wood and litter carbon
pools; and iii) the quantity of biomass transferred into dead wood and litter stocks as well as the quantity ofbiomass transferred out of the dead wood and litter stocks on per hectare basis according to different croplandtypes.
Method 2 (Also called the Stock-Difference Method, Equation 2.19 in Chapter 2): Method 2 involves
estimating the area of cropland and the dead wood and litter stocks at two periods of time, t1 and t2. The deadwood and litter stock changes for the inventory year are obtained by dividing the stock changes by the period(years) between two measurements. Method 2 is feasible for countries which have periodic inventories. Thismethod is more suitable for countries adopting Tier 3 methods. Tier 3 methods are used where countries havecountry-specific emission factors and national data. Country-defined methodology may be based on detailed
inventories of permanent sample plots for their croplands and/or models.

5.2.2.2

C HOICE OF EMISSION / REMOVAL FACTORS

Carbon fraction: The carbon fraction of dead wood and litter is variable and depends on the stage ofdecomposition. Wood is much less variable than litter and a value of 0.50 tonne C (tonne d.m.)-1 can be used forthe carbon fraction.
Tier 1
The assumption in Tier 1 is that the DOM carbon stocks in all Cropland Remaining Cropland are insignificant or
are not changing and therefore no emission/removal factors and activity data are needed. Countries experiencing
significant changes in cropland management or disturbances that are likely to affect DOM pools are encouraged
to develop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies.
Tier 2
It is good practice to use country-level data on DOM for different Cropland categories, in combination withdefault values, if country-specific or regional values are not available for some Cropland categories. Countryspecific values for the transfer of carbon from live trees that are harvested to harvest residues, and decomposition
rates (in the case of Method 1) or the net change in DOM pools (in the case of Method 2), can be derived fromcountry-specific data, taking into account the Cropland type, the rate of biomass utilization, harvesting practices,and the amount of damaged vegetation during harvesting operations.
Tier 3
For Tier 3, countries should develop their own methodologies and parameters for estimating changes in DOM.
These methodologies may be derived from Method 1 or 2 specified above, or may be based on other approaches.
The method used needs to be clearly documented.
National level disaggregated DOM carbon estimates should be determined as part of a national Croplandinventory, national level models, or from a dedicated greenhouse gas inventory programme, with periodic
sampling according to the principles set out in Chapter 3, Annex 3A.3. Inventory data can be coupled withmodelling studies to capture the dynamics of all Cropland carbon pools.

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Volume 4: Agriculture, Forestry and Other Land Use

5.2.2.3

C HOICE OF ACTIVITY DATA

Activity data consist of areas of Cropland Remaining Cropland summarised by major cropland types andmanagement practices. Total Cropland areas should be consistent with those reported under other sections of thischapter, notably under the biomass section of Cropland Remaining Cropland. Tying this information to nationalsoils, climate, vegetation, and other geophysical data makes it easier to assess changes in DOM.

5.2.2.4

C ALCULATION STEPS FOR T IERS 1 AND 2

The following summarizes steps for estimating change in DOM carbon stocks
Tier 1
Activity data are not needed as the DOM pool is assumed to be stable.
Tier 2 (Ga in-Lo ss Metho d) – Equat ion 2.18 in Chapter 2
Each of the DOM pools (dead wood and litter) is to be treated separately, but the method for each pool is thesame.
Step 1: Determine the categories or Cropland types and management systems to be used in this assessment andthe representative area. Area data should be obtained using the methods described in Chapter 3.
Step 2: Determine the net change in DOM stocks for each category. Identify values from inventories orscientific studies for the average inputs and outputs of dead wood or litter for each category. Countries should
use locally available data for inputs and outputs from these pools. Calculate the net change in the DOM pools by
subtracting the outputs from the inputs. Negative values indicate a net decrease in the stock.
Step 3: Determine the net change in DOM carbon stocks for each category based on Step 2. Multiply thechange in DOM stocks by the carbon fraction of the dead wood and litter to determine the net change in deadwood carbon stocks. The default value is 0.50 tonne C (tonne d.m.)-1 for dead wood and 0.40 tonne C (tonned.m.)-1 for litter.
Step 4: Determine the total change in the DOM carbon pools for each category by multiplying therepresentative area of each category by the net change in DOM carbon stocks for that category.
Step 5: Determine the total change in carbon stocks in DOM by taking the sum of the total changes in DOM
across all categories.
Tier 2 (Stock-D iff e rence Met hod) – Equat io n 2 .19 in Chapter 2
Each of the DOM pools is to be treated separately, but the method for each pool is the same.
Step 1: Determine the categories to be used in this assessment and the representative area as described for
Method 1.
Step 2: Determine the net change in DOM stocks for each category. From the inventory data, identify theinventory time interval, the average stock of DOM at the initial inventory (t1), and the average stock of DOM at
the final inventory (t2). Use these figures to calculate the net annual change in DOM stocks by subtracting the
DOM stock at t1 from the DOM stock at t2 and dividing this difference by the time interval. A negative value
indicates a decrease in the DOM stock.
Step 3: Determine the net change in DOM carbon stocks for each category. Determine the net change in DOM
carbon stocks by multiplying the net change in DOM stocks for each category by the carbon fraction of theDOM. The default value is 0.50 tonne C (tonne d.m.)-1 for dead wood and 0.40 tonne C (tonne d.m.)-1 for litter.
A Tier 3 approach requires country-specific or ecosystem-specific expansion factors. A Tier 2 approach can use
national level default expansion factors.
Step 4: Determine the total change in the DOM carbon pool for each activity category by multiplying therepresentative area of each activity category by the net change in DOM carbon stocks for that category.
Step 5: Determine the total change in carbon stocks in DOM by taking the sum of the total changes in DOM
across all activity categories.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland5.2.2.5

U NCERTAINTY ASSESSMENT

Uncertainty estimation is not required at Tier 1 since the DOM stocks are assumed to be stable. For Tiers 2 and3, area data and estimates of uncertainty should be obtained using the methods in Chapter 3. Carbon
accumulation and loss factors should be assessed locally.

5.2.3

Soil carbon

Cropland management modifies soil C stocks to varying degrees depending on how specific practices influence
C input and output from the soil system (Paustian et al., 1997; Bruce et al., 1999; Ogle et al., 2005). The main
management practices that affect soil C stocks in croplands are the type of residue management, tillage
management, fertilizer management (both mineral fertilizers and organic amendments), choice of crop andintensity of cropping management (e.g., continuous cropping versus cropping rotations with periods of barefallow), irrigation management, and mixed systems with cropping and pasture or hay in rotating sequences. In
addition, drainage and cultivation of organic soils reduces soil C stocks (Armentano and Menges, 1986).
General information and guidance for estimating changes in soil C stocks are found in Section 2.3.3 of Chapter
2 (including equations). That section should be read before proceeding with specific guidelines dealing with
Cropland soil C stocks. The total change in soil C stocks for Cropland is estimated using Equation 2.24 (Chapter2), which combines the change in soil organic C stocks for mineral soils and organic soils; and stock changes
associated with soil inorganic C pools (Tier 3 only). This section provides specific guidance for estimating soilorganic C stock changes. Soil inorganic C is fully covered by Section 2.3.3.1.
To account for changes in soil C stocks associated with Cropland Remaining Cropland, countries need at aminimum, estimates of the Cropland area at the beginning and end of the inventory time period. If land-use andmanagement data are limited, aggregate data, such as FAO statistics on Cropland, can be used as a starting point,along with expert knowledge about the approximate distribution of land management systems (e.g., medium, low
and high input cropping systems, etc.). Cropland management classes must be stratified according to climate
regions and major soil types, which can either be based on default or country-specific classifications. This canbe accomplished with overlays of land use on suitable climate and soil maps.

5.2.3.1

C HOICE OF METHOD

Inventories can be developed using a Tier 1, 2, or 3 approach, with each successive Tier requiring more detailand resources than the previous one. It is also possible that countries will use different tiers to prepare estimates
for the separate subcategories of soil C (i.e., soil organic C stocks changes in mineral soils and organic soils, andstock changes associated with soil inorganic C pools). Decision trees are provided for mineral soils (Figure 2.4)
and organic soils (Figure 2.5) in Section 2.3.3.1 (Chapter 2) to assist inventory compilers with selection of theappropriate tier for their soil C inventory.

Mineral soils
Tier 1
For mineral soils, the estimation method is based on changes in soil organic C stocks over a finite period
following changes in management that impact soil organic C. Equation 2.25 (Chapter 2) is used to estimatechange in soil organic C stocks in mineral soils by subtracting the C stock in the last year of an inventory time
period (SOC0) from the C stock at the beginning of the inventory time period (SOC(0 –T)) and dividing by thetime dependence of the stock change factors (D). In practice, country-specific data on land use and management
must be obtained and classified into appropriate land management systems (e.g., high, medium and low input
cropping), including tillage management, and then stratified by IPCC climate regions and soil types. Soilorganic C stocks (SOC) are estimated for the beginning and end of the inventory time period using default
reference carbon stocks (SOCref) and default stock change factors (FLU, FMG, FI ).
Tier 2
For Tier 2, the same basic equations are used as in Tier 1 (Equation 2.25), but country-specific information isincorporated to specify better the stock change factors, reference C stocks, climate regions, soil types, and/or theland management classification system.
Tier 3Tier 3 approaches may use dynamic models and/or detailed soil C inventory measurements as the basis for
estimating annual stock changes. Estimates from models are computed using coupled equations that estimate thenet change of soil C. A variety of models exist (e.g., see reviews by McGill et al., 1996; and Smith et al., 1997).
Key criteria in selecting an appropriate model include its capability of representing all of the relevant

2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

management practices/systems for croplands; model inputs (i.e., driving variables) are compatible with theavailability of country-wide input data; and verification against experimental data.
A Tier 3 approach may also be developed using a measurement-based approach in which a monitoring network
is sampled periodically to estimate soil organic C stock changes. A much higher density of benchmark sites will
likely be needed than with models to represent adequately the combination of land-use and management systems,
climate, and soil types. Additional guidance is provided in Section 2.3.3.1 of Chapter 2.

Organic soils
Tier 1Equation 2.26 (Chapter 2) is used to estimate C stock change in organic soils (e.g., peat-derived, Histosols).
The basic methodology is to stratify cultivated organic soils by climate region and assign a climate-specificannual C loss rate. Land areas are multiplied by the emission factor and then summed up to estimate annual Cemissions.
Tier 2
For Tier 2, the same basic equations are used as in Tier 1 (Equation 2.26), but country-specific information isincorporated to better specify emission factors, climate regions, and/or a land management classification system.
Tier 3Tier 3 approaches for organic soils use dynamic models and/or measurement networks, as described above formineral soils.

5.2.3.2

C HOICE OF STOCK CHANGE AND EMISSION FACTORS

Mineral soils
Tier 1
Table 5.5 provides Tier 1 approach default stock change factors for land use (FLU), input (FI) and management(FMG). The method and studies that were used to derive the default stock change factors are provided in Annex
5A.1 and References. The default time period for stock changes (D) is 20 years and management practice isassumed to influence stocks to a depth of 30 cm, which is also the depth for the reference soil C stocks in Table
2.3 (Chapter 2).
Tier 2
A Tier 2 approach entails the estimation of country-specific stock change factors. Derivation of input (FI) andmanagement factors (FMG) are based on comparisons to medium input and intensive tillage, respectively, because
they are considered the nominal practices in the IPCC default management classification (see Choice of Activity
Data). It is good practice to derive values for a higher resolution classification of management, climate and soil
types if there are significant differences in the stock change factors among more disaggregated categories basedon an empirical analysis. Reference C stocks can also be derived from country-specific data in a Tier 2 approach.
Additional guidance is provided in Chapter 2, Section 2.3.3.1.
Tier 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

Organic soils
Tier 1
Default emission factors are provided in Table 5.6 for cultivated organic soils. Assignment of emission factors
for perennial tree systems, such as fruit trees that are classified as Cropland, may be based on the factors forcultivated organic soils in Table 5.6 or forest management of organic soils (see Chapter 4). Shallower drainage
will lead to emissions more similar to forest management, while deeper drainage of perennial tree systems will
generate emissions more similar to annual cropping systems.
Tier 2Emission factors are derived from country-specific experimental data in a Tier 2 approach. It is good practicefor emission factors to be derived for specific land management categories of Cropland on organic soils and/or afiner classification of climate regions, assuming the new categories capture significant differences in C loss rates.
Additional guidance is given in Chapter 2, Section 2.3.3.1.
Tier 3
Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately
capture land-use and management effects.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

TABLE 5.5
RELATIVE STOCK CHANGE FACTORS (FLU, FMG, AND FI) (OVER 20 YEARS) FOR DIFFERENT MANAGEMENT ACTIVITIES ON
CROPLAND

Factorvalue
type

Land use(FLU)

Land use(FLU)

Land use(FLU)

Land use(FLU)

Tillage(FMG)

Temper-atureregimeMoisture
regime1

IPCC
defaults

Error2,3

Temperate/
Boreal

Dry

0.80

+ 9%

Moist

0.69

+ 12%

Dry

0.58

+ 61%

Moist/
Wet

0.48

+ 46%

Tropicalmontane4

n/a

0.64

+ 50%

Paddy
rice

All

Dry and
Moist/
Wet

1.10

+ 50%

Long-term (> 20 year) annual cropping of wetlands(paddy rice). Can include double-cropping with nonflooded crops. For paddy rice, tillage and input factors are
not used.

Perennial/
Tree
Crop

All

Dry and
Moist/
Wet

1.00

+ 50%

Long-term perennial tree crops such as fruit and nut trees,
coffee and cacao.

Dry

0.93

+ 11%

Moist/
Wet

0.82

+ 17%

Tropicalmontane4

n/a

0.88

+ 50%

All

Dry and
Moist/
Wet

1.00

NA

Dry

1.02

+ 6%

Moist

1.08

+ 5%

Dry

1.09

+ 9%

Moist/
Wet

1.15

+ 8%

n/a

1.09

+ 50%

Dry

1.10

+ 5%

Moist

1.15

+ 4%

Dry

1.17

+ 8%

Moist/
Wet

1.22

+ 7%

n/a

1.16

+ 50%

Level

Longterm
cultivated

Set
aside
(< 20
yrs)

Full

TropicalTemperate/
BorealandTropicalTemperate/
BorealTillage(FMG)

ReducedTropicalTropicalmontane4
Temperate/ Boreal

Tillage(FMG)

No-till

TropicalTropicalmontane4

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Description

Represents area that has been continuously managed for>20 yrs, to predominantly annual crops. Input and tillage
factors are also applied to estimate carbon stock changes.
Land-use factor was estimated relative to use of fulltillage and nominal (‘medium”) carbon input levels.

Represents temporary set aside of annually cropland (e.g.,conservation reserves) or other 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 by residues.

Primary and/or secondary tillage but with reduced soil
disturbance (usually shallow and without full soil
inversion). Normally leaves surface with >30% coverageby residues at planting.

Direct seeding without primary tillage, with only minimalsoil disturbance in the seeding zone. Herbicides are
typically used for weed control.

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TABLE 5.5 (CONTINUED)RELATIVE STOCK CHANGE FACTORS (FLU, FMG, AND FI) (OVER 20 YEARS) FOR DIFFERENT MANAGEMENT ACTIVITIES ON
CROPLAND

Factor
value
type

Input
(FI)

Input
(FI)

Input
(FI)

Input
(FI)

Temper-atureregimeMoisture
regime1

IPCC
defaults

Error2,3

Temperate/
Boreal

Dry

0.95

+ 13%

Moist

0.92

+ 14%

Dry

0.95

+ 13%

Moist/
Wet

0.92

+ 14%

Tropicalmontane4

n/a

0.94

+ 50%

MediumAll

Dry and
Moist/
Wet

1.00

NA

Dry

1.04

+ 13%

High

Temperate/
BorealandTropicalMoist/
Wet

1.11

+ 10%

n/a

1.08

+ 50%

Dry

1.37

+ 12%

Moist/
Wet

1.44

+ 13%

n/a

1.41

+ 50%

Level

Low

without
manureHigh –
withmanureTropicalTropicalmontane4
Temperate/
BorealandTropicalTropicalmontane4

Description

Low residue return occurs when there is due to removal ofresidues (via collection or burning), frequent barefallowing, production of crops yielding low residues (e.g.,vegetables, tobacco, cotton), no mineral fertilization or Nfixing crops.

Representative for annual cropping with cereals where allcrop residues are returned to the field. If residues are
removed then supplemental organic matter (e.g., manure)is added. Also requires mineral fertilization or N-fixing
crop in rotation.
Represents significantly greater crop residue inputs overmedium C input cropping systems due to additional
practices, such as production of high residue yielding
crops, use of green manures, cover crops, improved
vegetated fallows, irrigation, frequent use of perennial
grasses in annual crop rotations, but without manure
applied (see row below).

Represents significantly higher C input over medium C
input cropping systems due to an additional practice ofregular addition of animal manure.

1

Where data were sufficient, separate values were determined for temperate and tropical temperature regimes; and dry, moist, and wet
moisture regimes. Temperate and tropical zones correspond to those defined in Chapter 3; wet moisture regime corresponds to the
combined moist and wet zones in the tropics and moist zone in temperate regions.

2+ two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis toderive a default, uncertainty was assumed to be + 50% based on expert opinion. NA denotes ‘Not Applicable’, where factor values
constitute defined reference values, and the uncertainties are reflected in the reference C stocks and stock change factors for land use.

3

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.

4

There were not enough studies to estimate stock change factors for mineral soils in the tropical montane climate region. As an
approximation, the average stock change between the temperate and tropical regions was used to approximate the stock change for thetropical montane climate.

Note: See Annex 5A.1 for the estimation of default stock change factors for mineral soil C emissions/removals for Cropland.

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Chapter 5: Cropland

TABLE 5.6
ANNUAL EMISSION FACTORS (EF) FOR CULTIVATED ORGANIC SOILSIPCC default(tonnes C ha-1 yr-1)

Error 2

Boreal/Cool Temperate

5.0

+ 90%

Warm Temperate

10.0

+ 90%

Tropical/Sub-Tropical

20.0

+ 90%

Climatic temperature regime1

1

Climate classification is provided in Chapter 3.

2

Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean. Estimatesare based on Glenn et al., 1993; Kasimir-Klemedtsson et al., 1997; Freibauer and Kaltschmitt, 2001; Leifeld et al., 2005;
Augustin et al., 1996; Nykänen et al., 1995; Maljanen et al., 2001, 2004; Lohila et al., 2004; Ogle et al., 2003; Armentano
and Menges, 1986.

5.2.3.3

C HOICE OF ACTIVITY DATA

Mineral soils
Tier 1
Cropland systems are classified by practices that influence soil C storage. The default management classification
system is provided in Figure 5.1. Inventory compilers should use this classification to categorize management
systems in a manner consistent with the default Tier 1 stock change factors. This classification may be further
developed for Tiers 2 and 3 approaches. In general, practices that are known to increase C storage, such as
irrigation, mineral fertilization, organic amendments, cover crops and high residue yielding crops, have higher
inputs, while practices that decrease C storage, such as residue burning/removal, bare fallow, and low residue
crop varieties, have lower inputs. These practices are used to categorize management systems and then estimate
the change in soil organic C stocks. Practices should not be considered that are used in less than 1/3 of a given
cropping sequence (i.e., crop rotation), which is consistent with the classification of experimental data used toestimate the default stock change factors. Rice production, perennial croplands, and set-aside lands (i.e., lands
removed from production) are considered unique management systems (see below).
Each of the annual cropping systems (low input, medium input, high input, and high input w/organic amendment)
are further subdivided based on tillage management. Tillage practices are divided into no-till (direct seeding
without primary tillage and only minimal soil disturbance in the seeding zone; herbicides are typically used forweed control), reduced tillage (primary and/or secondary tillage but with reduced soil disturbance that is usually
shallow and without full soil inversion; normally leaves surface with >30% coverage by residues at planting) andfull tillage (substantial soil disturbance with full inversion and/or frequent, within year tillage operations, whileleaving <30% of the surface covered by residues at the time of planting). It is good practice only to consider
reduced and no-till if they are used continuously (every year) because even an occasional pass with a full tillage
implement will significantly reduce the soil organic C storage expected under the reduced or no-till regimes(Pierce et al., 1994; Smith et al., 1998). Assessing the impact of rotational tillage systems (i.e., mixing reduced,
no-till and/or full tillage practices) on soil C stocks will require a Tier 2 method.
The main types of land-use activity data are: i) aggregate statistics (Approach 1), ii) data with explicit
information on land-use conversions but without specific geo-referencing (Approach 2), or iii) data with explicit
information on land-use conversions and geo-referencing (Approach 3), such as land-use and management
inventories making up a statistically-based sample of a country’s land area (see Chapter 3 for discussion of
approaches). At a minimum, globally available land-use and crop production statistics, such as FAO databases(http://faostat.fao.org/), provide annual compilations of total land area by major land-uses, select management
data (e.g., irrigated vs. non-irrigated cropland), land area in ‘perennial’ crops (i.e., vineyards, perennial
herbaceous crops, and tree-based crops such as orchards) and annual crops (e.g., wheat, rice, maize, sorghum,etc.). FAO databases would be an example of aggregate data (Approach 1).
Management activity data supplement the land-use data, providing information to classify management systems,
such as crop types and rotations, tillage practices, irrigation, manure application, residue management, etc.
These data can also be aggregate statistics (Approach 1) or information on explicit management changes(Approach 2 or 3). Where possible, it is good practice to determine the specific management practices for land
areas associated with cropping systems (e.g., rotations and tillage practice), rather than only area by crop.
Remote sensing data are a valuable resource for land-use and management activity data, and potentially, expert

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Volume 4: Agriculture, Forestry and Other Land Use

knowledge is another source of information for cropping practices. It is good practice to elicit expert knowledge
using methods provided in Volume 1, Chapter 2 (eliciting expert knowledge).
National land-use and resource inventories, based on repeated surveys of the same locations, constitute activity
data gathered using Approach 2 or 3, and have some advantages over aggregated land-use and cropland
management data (Approach 1). Time series data can be more readily associated with a particular cropping
system (i.e., combination of crop type and management over a series of years), and the soil type can be
determined by sampling or by referencing the location to a suitable soil map. Inventory points that are selectedbased on an appropriate statistical design also enable estimates of the variability associated with activity data,
which can be used as part of a formal uncertainty analysis. An example of a survey using Approach 3 is the
National Resource Inventory in the U.S. (Nusser and Goebel, 1997).
Activity data 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 and climate maps of national
origin or global sources, such as the FAO Soils Map of the World and climate data from the United Nations
Environmental Program. A detailed description of the default climate and soil classification schemes is provided
in Chapter 3, Annex 3A.5. The soil classification is based on soil taxonomic description and textural data, while
climate regions are based on mean annual temperatures and precipitation, elevation, occurrence of frost, andpotential evapotranspiration.
Tier 2Tier 2 approaches are likely to involve a more detailed stratification of management systems than in Tier 1 (seeFigure 5.1) if sufficient data are available. This can include further subdivisions of annual cropping input
categories (i.e., low, medium, high, and high with amendment), rice cultivation, perennial cropping systems, andset-asides. It is good practice to further subdivide default classes based on empirical data that demonstrates
significant differences in soil organic C storage among the proposed categories. In addition, Tier 2 approaches
can involve a finer stratification of climate regions and soil types.
Tier 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or moredetailed data on the combinations of climate, soil, topographic and management data are needed, relative to theTiers 1 and 2 methods, but the exact requirements will depend on the model or measurement design.

Organic soils
Tier 1
In contrast to the mineral soil method, croplands on organic soils are not classified into management systems
under the assumption that drainage associated with all types of management for crops stimulates oxidation oforganic matter previously built up under a largely anoxic environment. However, in order to apply the method
described in Section 2.3.3.1 (Chapter 2), croplands do need to be stratified by climate region and soil type (seeChapter 3, Annex 3A.5 for guidance on soil and climate classifications).
Similar databases and approaches as those outlined for Mineral Soils in the Tier 1 discussion can be used forderiving area estimates. The land area with organic soils that are managed for Cropland can be determined using
an overlay of a land-use map on climate and soils maps. Country-specific data on drainage projects combinedwith land-use surveys can be used to obtain a more refined estimate of the relevant areas.
Tier 2Tier 2 approaches may involve a stratification of management systems if sufficient data are available. This can
include subdivisions of annual cropping systems by drainage class, crop type (Freibauer, 2003) or tillage
disturbance. In addition, Tier 2 approaches can involve a finer stratification of climate regions.
Tier 3Tier 3 approaches for organic soils are likely to include more detailed data on climate, soil, topographic andmanagement data, relative to the Tiers 1 and 2 methods, but the exact requirements will depend on the model ormeasurement design.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Figure 5.1

Classification scheme for cropland systems. In order to classify cropland
management systems, the inventory compiler should start at the top andproceed through the diagram answering questions (move across branches if
answer is yes) until reaching a terminal point on the diagram. The
classification diagram is consistent with default stock change factors in Table
5.5. C input classes (i.e., low, medium, high and high with organic
amendment) are further subdivided by tillage practice.

Continuous
perennial crops (e.g., fruits,coffee and nuts)?

Start

Yes

Perennial
Crop

Low C
input
No

No

Rice
Cultivation

Long term
paddy rice or irrigatedrice in rotation(>20 yrs)?

Yes

Low C
input

No

No
Practiceincreasing C
input3?

Medium C
input

Yes
No

Yes
Annualcrop with no N mineral
fertilization or N-fixing
crop?

High C
input
with organic
amendment

Yes

Annualcrop with residues
removed orburned1?

Yes

Organic
amendment?

No

Yes

Annualcrop with low residue2
or rotation with barefallow?

Medium C
input

Practiceincreasing C
input4?

No

Low C
input

Yes

Medium C
input

No
Annualcrop with organic
amendment?

Yes
Set-aside

No

Annualcrop with practiceincreasing C
input4?

Yes

High C
input

No
No

Non-croplandsystems (e.g.,
Forest land,
Grassland)

Yes

Converted
into another managed
land use?

Yes

Converted
into continuous perennial
cover5?
No

Medium C
input

Note:
1: Does not typically include grazing of residues in the field.
2: e.g. cotton, vegetables and tobacco.
3: Practices that increase C input above the amount typically generated by the low residues yielding varieties such as using organic
amendments, cover crops/green manures, and mixed crop/grass systems.
4: Practices that increase C input by enhancing residue production, such as using irrigation, cover crops/green manures, vegetated fallows,
high residue yielding crops, and mixed crop/grass systems.
5 Perennial cover without frequent harvest.
Note: Only consider practices, such as irrigation, residue burning/removal, mineral fertilizers, N-fixing crops, organic amendment, covercrops/green manures, low residue crop, or fallow, if used in at least 1/3 of cropping rotation sequence.

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5.2.3.4

C ALCULATION STEPS FOR T IER 1Mineral soils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha for Cropland Remaining
Cropland on mineral soils are as follows:
Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g.,
1990 to 1995, 1995 to 2000, etc.)
Step 2: Determine the amount Cropland Remaining Cropland by mineral soil types and climate regions in the
country at the beginning of the first inventory time period. The first year of the inventory time period will
depend on the time step of the activity data (0-T; e.g., 5, 10 or 20 years ago).
Step 3: Classify each Cropland into the appropriate management system using Figure 5.1.
Step 4: Assign a native reference C stock values (SOCREF) from Table 2.3 based on climate and soil type.
Step 5: Assign a land-use factor (FLU), management factor (FMG) and C input levels (FI) to each Cropland basedon the management classification (Step 2). Values for FLU, FMG and FI are given in Table 5.5.
Step 6: Multiply the factors (FLU, FMG, FI) by the reference soil C stock (SOCREF) to estimate an ‘initial’ soilorganic C stock (SOC(0-T)) for the inventory time period.
Step 7: Estimate the final soil organic C stock (SOC0) by repeating Steps 1 to 5 using the same native reference
C stock (SOCREF), but with land-use, management and input factors that represent conditions for each cropland in
the last (year 0) inventory year.
Step 8: Estimate the average annual change in soil organic C stocks for Cropland Remaining Cropland(∆CMineral) by subtracting the ‘initial’ soil organic C stock (SOC(0-T)) from the final soil organic C stock (SOC0),
and then dividing by the time dependence of the stock change factors (i.e., 20 years using the default factors). If
an inventory time period is greater than 20 years, then divide by the difference in the initial and final year of thetime period.
Step 9: Repeat steps 2 to 8 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.).
A numerical example is given below for Cropland Remaining Cropland on mineral soils, using Equation 2.25
and default reference C stocks (Table 2.3) and stock change factors (Table 5.5).

Example: The following example shows calculations for aggregate areas of cropland soil carbon
stock change. In a warm temperate wet climate on Mollisol soils, there are 1Mha of permanentannual cropland. The native reference carbon stock (SOCREF) for the region is 88 tonnes C ha-1. At
the beginning of the inventory calculation period (in this example, 10 yrs earlier in 1990), thedistribution 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 ● 0.69 ● 1 ● 0.92) +600,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1 ● 1) = 58.78 million tonnes C. In the last year of theinventory time period (in this example, the last year is 2000), there are: 200,000 ha of annual
cropping with full tillage and low C input, 700,000 ha of annual cropping with reduced tillage andmedium C input, and 100,000 ha of annual cropping with no-till and medium C input. Thus, totalsoil carbon stocks in the inventory year are: 200,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1 ● 0.92) +700,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1.08 ● 1) + 100,000 ha ● (88 tonnes C ha-1 ● 0.69 ● 1.15
● 1) = 64.06 million tonnes C. Thus, the average annual stock change over the period for the entire
area is: 64.06 – 58.78 = 5.28 million tonnes/20 yr = 264,000 tonnes C per year soil C stockincrease (Note: 20 years is the time dependence of the stock change factor, i.e., factor represents
annual rate of change over 20 years).

Organic soils
The steps for estimating the loss of soil C from drained organic soils are as follows:
Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g.,
1990 to 1995, 1995 to 2000, etc.)

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Chapter 5: Cropland

Step 2: Determine the amount of Cropland Remaining Cropland on organic soils for the last year of each
inventory time period.
Step 3: Assign the appropriate emission factor (EF) for annual losses of CO2 based on climate (from Table 5.6).
Step 4: Estimate total emissions by summing the product of area (A) multiplied by the emission factor (EF) for
all climate zones.
Step 5: Repeat for additional inventory time periods.
A numerical example is given below for Cropland Remaining Cropland on drained organic soils, using Equation
2.26 and default emission factors (Table 5.6).

Example: The following example shows calculations for aggregate areas of cropland soil carbon
stock change. In a warm temperate wet climate on Histosols, there are 0.4 Mha of permanentannual cropland on drained organic soils. The emission factor for this climate is 10.0 tonnes C ha-1
yr-1. Thus, annual soil carbon stock change for organic soils during the inventory time period is:
400,000 ha ● 10.0 tonnes C ha-1 = 4.0 million tonnes C yr-1.

5.2.3.5

U NCERTAINTY ASSESSMENT

Three broad sources of uncertainty exist in soil C inventories: 1) uncertainties in land-use and management
activity, and environmental data; 2) uncertainties in reference soil C stocks if using a Tier 1 or 2 approach(mineral soils only); and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model
structure/parameter error for Tier 3 model-based approaches, or measurement error/sampling variability
associated with Tier 3 measurement-based inventories. In general, precision of an inventory is increased andconfidence ranges are smaller with more sampling to estimate values for the three board categories, while
reducing bias (i.e., improve accuracy) is more likely to occur through the development of a higher Tier inventory
that incorporates country-specific information.
For Tier 1, uncertainties are provided with the reference C stocks in the first footnote in Table 2.3, stock change
factors in Table 5.5, and emission factor for organic soils in Table 5.6. Uncertainties in land-use andmanagement data will need to be addressed by the inventory compiler, and then combined with uncertainties forthe default factors and reference C stocks (mineral soils only) using an appropriate method, such as simple error
propagation equations. If using aggregate land-use area statistics for activity data (e.g., FAO data), the inventory
agency may have to apply a default level of uncertainty for the land area estimates (±50%). It is good practicefor the inventory compiler to derive uncertainties from country-specific activity data instead of using a default
level.
Default reference C stocks and stock change factors for mineral soils and emission factors for organic soils canhave inherently high uncertainties, particularly bias, when applied to specific countries. Defaults represent
globally averaged values of land-use and management impacts or reference C stocks that may vary from regionspecific values (Powers et al., 2004; Ogle et al., 2006). Bias can be reduced by deriving country-specific factors
using a Tier 2 method or by developing a Tier 3 country-specific estimation system. The underlying basis forhigher Tier approaches will be experiments in the country or neighbouring regions that address the effect of land
use and management on soil C. In addition, it is good practice to further minimize bias by accounting forsignificant within-country differences in land-use and management impacts, such as variation among climate
regions and/or soil types, even at the expense of reduced precision in the factor estimates (Ogle et al., 2006).
Bias is considered more problematic for reporting stock changes because it is not necessarily captured in theuncertainty range (i.e., the true stock change may be outside of the reported uncertainty range if there issignificant bias in the factors).
Uncertainties in land-use activity statistics may be reduced through a better national system, such as developing
or extending a ground-based survey with additional sample locations and/or incorporating remote sensing toprovide additional coverage. It is good practice to design a classification that captures the majority of land-use
and management activity with a sufficient sample size to minimize uncertainty at the national scale.
For Tier 2 methods, country-specific information is incorporated into the inventory analysis for purposes of
reducing bias. For example, Ogle et al. (2003) utilized country-specific data to construct probability distributionfunctions for US specific factors, activity data and reference C stocks for agricultural soils. It is good practice to2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

evaluate dependencies among the factors, reference C stocks or land-use and management activity data. In
particular, strong dependencies are common in land-use and management activity data because management
practices tend to be correlated in time and space. Combining uncertainties in stock change/emission factors,
reference C stocks and activity data can be done using methods such as simple error propagation equations orMonte-Carlo procedures to estimate means and standard deviations for the change in soil C stocks (Ogle et al.,
2003; Vanden Bygaart et al., 2004).
Tier 3 models are more complex and simple error propagation equations may not be effective at quantifying theassociated uncertainty in resulting estimates. Monte Carlo analyses are possible (Smith and Heath, 2001), but
can be difficult to implement if the model has many parameters (some models can have several hundredparameters) because joint probability distribution functions must be constructed quantifying the variance as wellas covariance among the parameters. Other methods are also available such as empirically-based approaches(Monte et al., 1996), which use measurements from a monitoring network to statistically evaluate therelationship between measured and modelled results (Falloon and Smith, 2003). In contrast to modelling,
uncertainties in measurement-based Tier 3 inventories can be estimated from the sample variance, measurement
error and other relevant sources of uncertainty.

5.2.4

Non-CO 2 greenhouse gas emissions from biomassburning

Non-CO2 emissions from Cropland Remaining Cropland (particularly CH4, CO, NOx and N2O) are usuallyassociated with burning of agriculture residues, which vary by country, crop, and management system. CO2
emissions from biomass burning do not have to be reported, since the carbon released during the combustion
process is assumed to be reabsorbed by the vegetation during the next growing season.
The percentage of the agricultural crop residues burnt on-site, which is the mass of fuel available for burning,should be estimated taking into account the fractions removed before burning due to animal consumption, decay
in the field, and use in other sectors (e.g., biofuel, domestic livestock feed, building materials, etc.). This isimportant to eliminate the possibility of double counting.
The methodology for estimating non-CO2 emissions from biomass burning in Cropland Remaining Croplandfollows the generic formulation in Equation 2.27 in Chapter 2. The estimates should be based on annual data.

5.2.4.1

C HOICE OF METHOD

The decision tree in Figure 2.6 in Chapter 2 provides general guidance on the choice of the appropriate Tier to be
used. The method of estimation of greenhouse gas emission from biomass burning involves the use of Equation
2.27 (Chapter 2). Under a Tier 1 approach, the activity data are normally highly aggregated, and combustion andemissions factors are the default values provided in Chapter 2. Under a Tier 2, estimates are generally developed
for the major crop types by climate zone, using country-specific residue accumulation rates and country-specificcombustion and emission estimates. Tier 3 is a very country-specific method involving process modelling and/or
detailed measurement.
All countries should strive for improving inventory and reporting approaches by applying the highest Tierpossible, given national circumstances. If burning in Cropland Remaining Cropland is a key category, countries
should use either Tier 2 or Tier 3 method.

5.2.4.2

C HOICE OF EMISSION FACTORS

Tier 1Countries applying a Tier 1 method should replace quantities MB and Cf in Equation 2.27 in Chapter 2 by theappropriate default fuel consumption value (MB x Cf) in Table 2.4. The default emission factors to be used areprovided in Table 2.5 for each greenhouse gas of interest.
Tier 2This method expands Tier 1 to include use of country-specific available fuel, combustion and emission factors.
Countries may estimate the amount of available fuel from crop production statistics and from the ratio of crop
yield and residue produced. Field studies are needed to estimate the fractions of crop residue removed from field(as fuel or fodder) and left as residue for burning for different crop systems. Countries should focus on the mostdominant crops being burnt or the systems with relatively high biomass per hectare and levels of emissions perunit of land (e.g., sugarcane, cotton).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Tier 3This tier makes use of models based on country-specific parameters, using national inventory data to ensure thatno burning of crop residues is omitted. Tier 3 depends on the field measurement of the amount of residues burnt
on-site for different cropping systems under different climate zones and management systems, based on
sampling methods described in Chapter 3 (Annex 3A.3). Countries should prioritize the development ofcountry-specific combustion and emission factors, by focusing on the most dominant crop residues being burnt.

5.2.4.3

C HOICE OF ACTIVITY DATA

Tier 1
Activity data includes estimates of land areas under the crop types for which agricultural residues are normallyburnt. This can be obtained in consultation with national agricultural governmental sectors, in the lack ofobjective data from satellite imagery, for example. Countries can also estimate the crop area planted from theannual crop production and an estimate of the average productivity per hectare. If no national estimates areavailable, FAO statistics can be used. It is good practice to cross check FAO data with national sources.
Tier 2
Under a Tier 2 method, countries should use more disaggregated area estimates (e.g., major crop types by
climate zone) with country-specific and crop management system-specific residue accumulation rates. This canbe accomplished through the use of more detailed annual or periodic surveys to estimate the areas of land under
different crop classes. Areas should be further classified into relevant categories such that all major combinationsof crop types and climatic regions are represented, with individual area estimates provided.
Tier 3Tier 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 crops by major climate and soil categories and other potentially
important regional variables (e.g., regional patterns of management practices) to be used in models. Countries
should strive to obtain spatially explicit area estimates to facilitate complete coverage of the cropland and ensurethat areas are not over- nor under-estimated. Additionally, spatially explicit area estimates can be related to
locally relevant emission rates and management impacts, improving the accuracy of the estimates. Area data fordifferent cropping systems used should be consistent with area used in earlier sections (Biomass, Dead organic
matter), though residues may be burnt on only a part of the total area.

5.2.4.4

U NCERTAINTY ASSESSMENT

Estimates of the area planted under each crop type for which residues are normally burnt may be highly
uncertain. Global statistics of crop production, which may be an indirect way to estimate area planted, if not
updated on a yearly basis, may be very uncertain. The fraction of the agricultural residue that is burnt in the field
is possibly the variable with most uncertainty. Tier 2 estimates are more precise, being based on country-specificparameters. It is good practice to provide error estimates (i.e., standard deviation, standard error, ranges) for
country-specific combustion and emission factors and areas burnt.

5.3

LAND CONVERTED TO CROPLAND

Globally, about 50% of the total land surface has been transformed by direct human action, 20% of land
ecosystems have been converted to permanent croplands, and 25% of the world’s forests have been cleared forvarious uses such as crop cultivation and pastures (Moore, 2002). Area under cropland has been increasing insome parts of the world to meet growing food and fibre demands. Most of the expansion of cropland in the lasttwo decades has occurred in Southeast Asia, parts of South Asia, the Great Lakes region of eastern Africa andthe Amazon Basin (Millennium Ecosystems Assessment, 2005). During the same period, forest destruction in thetropics averaged 12 million hectares per year according to Environmental Group Limited(http://www.environmental.com.au/). Deforestation rate during the 1990’s averaged 14.6 million ha per year.
Conversion to Cropland is a leading land-use change following tropical deforestation. Greenhouse gas emissions
and removals from Land Converted to Cropland can be a key source for many countries.
Estimation of annual greenhouse gas emissions and removals from Land Converted to Cropland includes thefollowing:•

Estimates of annual change in C stocks from all C pools and sources:oooBiomass (above-ground and below-ground biomass);
Dead organic matter (dead wood and litter);Soils (soil organic matter).

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Volume 4: Agriculture, Forestry and Other Land Use•

Estimates of non-CO2 gases (CH4, CO, N2O, NOx) from burning of above-ground biomass and DOM

5.3.1
5.3.1.1

BiomassC HOICE OF METHODSThis section provides guidance on methods for calculating carbon stock change in biomass due to the conversionof land from natural conditions and other uses to Cropland, including deforestation and conversion of pastureand grazing lands to Cropland. The methods require estimates of carbon in biomass stocks prior to and following
conversion, based on estimates of the areas of lands converted during the period between land-use surveys. As aresult of conversion to Cropland, it is assumed (in Tier 1) that the dominant vegetation is removed entirely
leading to emissions, resulting in near zero amounts of carbon remaining in biomass. Some type of cropping
system is planted soon thereafter increasing the amount of carbon stored in biomass. The difference between
initial and final 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 5.2.1 (Cropland Remaining Cropland).
It is good practice to consider all carbon pools (i.e., above ground and below ground biomass, dead organic
matter, and soils) in estimating changes in carbon stocks in Land Converted to Cropland. Currently, there isinsufficient information to provide a default approach with default parameters to estimate carbon stock change indead organic matter (DOM) pools2. DOM is unlikely to be important except in the year of conversion. It is
assumed that there will be no DOM in Cropland. In addition, the methodology below considers only carbon
stock change in above-ground biomass since limited data are available on below-ground carbon stocks inperennial Cropland.
The IPCC Guidelines describe increasingly sophisticated alternatives that incorporate greater detail on the areasof land converted, carbon stocks on lands, and loss of carbon resulting from land conversions. It is good practiceto adopt the appropriate tier depending on key source analysis, data availability and national circumstances. 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 sub-category ofbiomass is considered significant based on principles outlined in Volume 1, Chapter 4. Countries should use thedecision tree in Figure 1.3 to help with the choice of method. Land Converted to Cropland is likely to be a key
category for many countries and further biomass is likely to be a key source.
Tier 1The Tier 1 method follows the approach in Chapter 4 (Forest Land) where the amount of biomass that is clearedfor cropland is estimated by multiplying the area converted in one year by the average carbon stock in biomass in
the Forest Land or Grassland prior to conversion. It is good practice to account completely for all land
conversions to Cropland. Thus, this section elaborates on the method such that it includes different initial uses,
including but not limited to forests.
Equation 2.15 in Chapter 2 summarises the major elements of a first-order estimation of carbon stock change
from land-use conversion to Cropland. Average carbon stock change on a per hectare basis is estimated for eachtype 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 inbiomass prior to conversion), plus carbon stocks from one year of growth in Cropland following conversion. It isnecessary to account only for any woody vegetation that replaces the vegetation that was cleared during land-use
conversion. The GPG-LULUCF combines 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, BAFTER
and ΔCG to increase transparency.
At Tier 1, carbon stocks in biomass immediately after conversion (BAFTER) are assumed to be zero, since the land
is cleared of all vegetation before planting crops. Average carbon stock change per hectare 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, change in biomass of annual crops is considered zero because carbon gains in biomass fromannual growth are offset by losses from harvesting. Changes in biomass of perennial woody crops are counted
following the methodology in Section 2.3.1.1 (Change in carbon stocks in biomass in land remaining in a landuse category).
2

Any litter and dead wood pools (estimated using the methods described in Chapter 2, Section 2.3.2) should be assumed
oxidized following land conversion.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: CroplandThe default assumption for Tier 1 is that all carbon in biomass removed is lost to the atmosphere through burningor decay processes either on-site or off-site. Tier 1 calculations do not differentiate immediate emissions fromburning and other conversion related losses.
Tier 2The Tier 2 calculations are structurally similar to Tier 1, with the following distinctions. First, Tier 2 relies
largely on country-specific estimates of the carbon stocks in initial and final land uses rather than the default data.
Area estimates for Land Converted to Cropland are disaggregated according to original vegetation (e.g., from
Forest Land or Grassland) at finer spatial scales to capture regional and crop systems variations in countryspecific 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. Further, non-CO2
trace gas emissions occur as a result of burning. By partitioning losses to burning and decay, countries can also
calculate non-CO2 trace gas emissions from burning (Section 5.3.4).
The immediate impacts of land conversion activities on the five carbon stocks can be summarized in a
disturbance matrix, which describes the retention, transfers and releases of carbon in the pools in the original
ecosystem following conversion to Cropland. A disturbance matrix defines for each pool the proportion thatremains in that pool and the proportion that is transferred to other pools. A small number of transfers are
possible, and are outlined in a disturbance matrix in Table 5.7. The disturbance matrix ensures consistency of
the accounting of all carbon pools.
Biomass transfers to dead wood and litter can be estimated using Equation 2.20.
Tier 3
The Tier 3 method is similar to Tier 2, with the following distinctions: i) rather than relying on average annual
rates of conversion, countries can use direct estimates of spatially disaggregated areas converted annually foreach initial and final land use; ii) carbon densities and soil carbon stock change are based on locally specificinformation, which makes possible a dynamic link between biomass and soil; and iii) biomass volumes are basedon actual inventories. The transfer of biomass, to dead wood and litter following land-use conversion can be
estimated using Equation 2.20.

TABLE 5.7
EXAMPLE OF A SIMPLE DISTURBANCE MATRIX (TIER 2) FOR THE IMPACTS OF LAND CONVERSION ACTIVITIES ON CARBON
POOLS

To
From

Abovegroundbiomass

Belowgroundbiomass

Deadwood

LitterSoilorganic
matterHarvested wood
products

Atmosphere

Sum ofrow(must
equal 1)

Above-groundbiomass
Below-groundbiomass
Dead wood
LitterSoil organic
matterEnter the proportion of each pool on the left side of the matrix that is transferred to the pool at the top of each column. All of the poolson the left side of the matrix must be fully accounted, so the values in each row must sum to 1.
Impossible transitions are blacked out.

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5.3.1.2

C HOICE OF EMISSION / REMOVAL FACTORSThe emission/removal factors needed for the default method are: carbon stocks before conversion in the initialland use and after conversion to Cropland; and growth in biomass carbon stock from one year of croplandgrowth.
Tier 1
Default biomass carbon stock in initial land-use categories (BBEFORE) mainly Forest Land and Grassland areprovided in Table 5.8. Initial land-use based carbon stocks should be obtained for different Forest Land or
Grassland categories based on biome type, climate, soil management systems, etc. It is assumed that all biomass
is cleared when preparing a site for cropland use, thus, the default for BAFTER is 0 tonne C ha-1.
In addition, a value is needed for carbon stocks after one year of growth in crops planted after conversion (ΔCG).
Table 5.9 provides defaults for ΔCG. Separate defaults are provided for annual non-woody crops and perennial
woody crops. For lands planted in annual crops, the default value of ΔCG is 5 tonnes of C per hectare, based on
the original IPCC Guidelines recommendation of 10 tonnes of dry biomass per hectare (dry biomass has been
converted to tonnes carbon in Table 5.9). 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 provided in thissection are for one year of growth immediately following conversion, which usually give lower carbon stocks forperennial woody crops compared to annual crops.

TABLE 5.8
DEFAULT BIOMASS CARBON STOCKS REMOVED DUE TO LAND CONVERSION TO CROPLAND
Carbon stock in biomass before conversion (BBefore)(tonnes C ha-1)

Error range #Forest Land

See Chapter 4 Tables 4.7 to 4.12 for carbon stocks in a range of forest
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 4.3(Land Converted
to Forest Land)

Grassland

See Chapter 6 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.

TABLE 5.9
DEFAULT BIOMASS CARBON STOCKS PRESENT ON LAND CONVERTED TO CROPLAND
IN THE YEAR FOLLOWING CONVERSION

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

Error range#

5.0

+ 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 2Tier 2 methods should include some country-specific estimates for biomass stocks and removals due to land
conversion, and also include estimates of on-site 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.

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Chapter 5: Cropland

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 burnt on-site for both Forest Land and Grassland conversions.
Research studies suggest that the fraction is highly variable and could be as low as 0.2 (Fearnside, 2000; Barbosa
and Fearnside, 1996; and Fearnside, 1990). Updated default proportions of biomass burnt on-site are provided in
Chapter 4 (Forest Land) 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 andburnt is 0.35. This default takes into consideration research, which suggests the fraction should fall within therange 0.2 to 0.5 (e.g., Fearnside, 2000; Barbosa and Fearnside, 1996; and Fearnside, 1990). It is good practicefor countries to use 0.35 or another value within this range, provided that the rationale for the choice isdocumented. There is no default value for the amount of biomass taken off-site and burnt; countries will need todevelop a proportion based on national data sources. In Chapter 4 (Forest Land), the default proportion of
biomass oxidized as a result of burning is 0.9, as originally stated in the GPG-LULUCF.
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: 1) report all emissions from decay in one year, recognizing thatin reality they occur over a 10 year period, and 2) report all emission from decay on an annual basis, estimating
the rate as one tenth of the totals. If countries choose the latter option, they should add a multiplication factor of
0.10 to the equation.
Tier 3
Under Tier 3, all parameters should be country-defined using measurements and monitoring for more accurate
values rather than the defaults. Process based models and decay functions can also be used.

5.3.1.3

C HOICE OF ACTIVITY DATA

All tiers require estimates of land areas converted to Cropland. The same area estimates should be used for bothbiomass and soil C calculations on Land Converted to Cropland. Higher tiers require greater specificity of areas.
At a minimum, the area of Forest Land and natural Grassland converted to Cropland should be identified
separately for all tiers. This implies at least some knowledge of the land uses prior to conversion. This may also
require expert judgment if Approach 1 in Chapter 3 of these guidelines is used for land area identification.
Tier 1
Separate estimates are required of areas converted to Cropland from initial land uses (i.e., Forest Land,
Grassland, Settlements, etc.) to final crop land type (i.e., annual or perennial) (ATO_OTHERS). 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. Although,to allow other pools to equilibrate and for consistency with land area estimation overall, land areas should
remain in the conversion category for 20 years (or other period reflecting national circumstances) following
conversion. The methodology assumes that area estimates are based on a one-year time frame, which is likely torequire estimation on the basis of average rates on land-use conversion, determined by measurements estimates
made at longer intervals. 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 the judgement of country
experts. Under Tier 1 calculations, international statistics such as FAO databases, IPCC GPG Reports and othersources, supplemented with sound assumptions, can be used to estimate the area of Land Converted to Croplandfrom 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
It is good practice for countries 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 through 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 to Cropland 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 such 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). Periodic land-use change matrix need to be developed giving the initialand final land-use areas at disaggregated level based on remote sensing and field surveys.

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Volume 4: Agriculture, Forestry and Other Land Use

5.3.1.4

C ALCULATION STEPS FOR T IERS 1 AND 2

The following summarizes steps for estimating change in carbon stocks inbiomass (∆C B ) using the default methods

Using the worksheet provided for Land Converted to Cropland (see Annex 1, AFOLU Worksheets), calculate
the change in biomass carbon stocks in Land Converted to Cropland as follows:
Step 1: Enter the subcategories of croplands for the reporting year. The subcategories of croplands used in
Section 5.2 may also be used to fill out the appropriate column in the worksheet.
Step 2: For each sub-category, enter the annual area of land converted to Cropland (ATO_OTHERS). Data forannual area may be obtained from various sources such as the Ministry of Forestry, Ministry of Agriculture,
Ministry of Planning, or Mapping Office within a country.
Step 3: For each sub-category, enter the carbon stocks in biomass immediately after conversion to Cropland(BAFTER), in tonnes C ha-1. Biomass and carbon data may be default values or country-specific values.
Step 4: For each sub-category, enter the carbon stocks in biomass immediately before conversion to Cropland(BBEFORE), in tonnes C ha-1. Biomass and carbon data may be default values or country-specific values.
Step 5: Calculate the carbon stocks change per area (CCONVERSION) for the type of conversion when land isconverted to Cropland (Equation 2.16).
Step 6: Obtain the values for change in carbon stocks from one year of cropland growth (ΔCG) and the decreasein biomass carbon due to losses ((ΔCL) using Table 5.1. Enter the values in the appropriate column.
Step 7: Calculate the annual change in carbon stocks in biomass in Land Converted to Cropland (ΔCB) using
Equation 2.15.
Step 8: Sum up all the annual changes in carbon stocks in biomass.

5.3.1.5

U NCERTAINTY ASSESSMENT

Tier 1The sources of uncertainty in this method are from the use of global or national average rates of conversion andfrom 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 thismethod have error ranges associated with them. A published compilation of research on carbon stocks inagroforestry systems was used to derive the default data provided in Section 5.2 (Schroeder, 1994). Whiledefaults 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. Land Converted to Cropland is likely to be a key source category for many countries and allefforts should be made to reduce uncertainty.
Tier 2The Tier 2 method uses at least some country-defined defaults, which will improve the accuracy of estimates,
because they better represent conditions relevant to the country. Use of country-specific values should entail
sufficient sample sizes and or use of expert judgment to estimate uncertainties. This, together with uncertainty
estimates on activity data derived using the advice in Chapter 3, should be used in the approaches to uncertainty
analysis as described in Volume 1, Chapter 3 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 and their associateduncertainties can be done using Monte-Carlo procedures to estimate means and confidence intervals for theoverall inventory. The uncertainty is likely to be less than for other tiers since estimates of carbon stock changes
are based on more measurements and more refined models.

5.3.2

Dead organic matter

Forest Land, Grassland, Settlements, and other land-use categories could be potentially converted to Cropland
which, in general will have little or no dead wood or litter, with the exception of agroforestry systems. Methods
are provided for two types of dead organic matter pools: 1) dead wood, and 2) litter. Chapter 1of this reportprovides detailed definitions of these pools.

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Chapter 5: Cropland

Dead wood is a diverse pool which is difficult to measure, with associated uncertainties about rates of transfer tolitter, soil, or emissions to the atmosphere.
Litter accumulation depends on litterfall, which includes all leaves, twigs and small branches, fruits, flowers, andbark, minus the rate of decomposition. The litter mass is also influenced by the time since the last disturbance,
and the type of disturbance. During the early stages of cropland development, litter increases rapidly.
Management such as vegetation harvesting and burning dramatically alter litter stocks, but there are very few
studies clearly documenting the effects of management on litter carbon.
In general, croplands will have little or no dead wood or litter, and therefore these pools can often be assumed toapproach zero after conversion, the exception being agroforestry systems which may be accounted either under
Cropland or Forest Land, depending upon definitions adopted by countries for reporting. It is likely that the same
will be true of many land uses prior to conversion, so that corresponding carbon pools prior to conversion canalso be assumed to be zero. The exceptions are forest, agro-forests, and wetlands converted to Cropland, which
could have significant carbon in DOM pools, as well as forest areas around settlements that may have been
defined as Settlements based on nearby use rather than land cover.
Estimating change in carbon stocks in DOM for lands converted to Cropland under higher tiers requires a twophase approach. During the first phase, there is often an abrupt change in DOM associated with the land-use
change, particularly then the change is deliberate and associated with land preparation operations (e.g., clearing
and burning). The second phase accounts for decay and accumulation processes during a transition period to a
new steady-state system. At some point in time, the cropland ecosystem should reach an equilibrium at whichtime it can be considered Cropland Remaining Cropland and accounted for under that category. The transition
period should be 20 years, but some countries can determine the appropriate transition period more accurately athigher tiers.
To account for the transition period, lands converted to Cropland should be treated as annual cohorts. That is,land converted in a given year should be accounted for with Phase 1 methods in the year of conversion, and with
Phase 2 methods for the subsequent 19 years. At the end of the 20 year period, the land area for that given year
is added to the land area being accounted under the Cropland Remaining Cropland category.

5.3.2.1

C HOICE OF METHOD

The decision tree in Figure 2.3 in Chapter 2 provides assistance in the selection of the appropriate tier level forthe implementation of estimation procedures. Estimation of changes in carbon stocks in DOM requires anestimate of changes in stocks of dead wood and changes in litter stocks (refer to Equation 2.17 in Chapter 2).
Each of the DOM pools (dead wood and litter) is to be treated separately, but the method for each pool is thesame.
Tier 1
A Tier 1 approach involves estimating the area of each type of land conversion using only the major conversion
categories (e.g., Forest Land to Cropland). The immediate and abrupt carbon stock change (Phase 1) in deadwood and litter due to conversion of other lands to Cropland under Tier 1 will be estimated using Equation 2.23
in Chapter 2. C0 in Equation 2.23 is likely to be zero and there is no need to divide Ton. The Tier 1 default
assumes removal of all dead wood and litter during conversion and that there is no dead wood or litter thatremains or accumulates in Land Converted to Cropland. Countries where this assumption is known to be false(e.g., where slash and burn agriculture is widely practiced) are encouraged to use a higher tier when accounting
for lands converted to Cropland. Additionally, it is assumed that croplands achieve their steady-state biomass
during the first year following conversion. Thus, for Tier 1, Phase 2 has no transition period and lands converted
to Cropland are transferred to Cropland Remaining Cropland in the second year following conversion.
There are no default values available for dead wood or litter in most systems. For forests, there are no global
default values for dead wood, but there are values for litter (Table 2.2 in Chapter 2). These values are in terms of
tonnes C ha-1, not in terms of litter stocks. Countries should make best estimates and use local data from forestry
and agricultural research institutes to provide best estimates of the dead wood and litter in the initial system prior
to conversion.
Tier 2Tier 2 approaches require greater disaggregation than that used in Tier 1. Activity data should be reported bymanagement regimes. Tier 2 also employs the two-phase approach described above.
As recommended above in the biomass section, the immediate impacts of land conversion activities on the fivecarbon stocks can be summarized in a disturbance matrix. The disturbance matrix describes the retention,transfers and releases of carbon in the pools in the original ecosystem following conversion to Cropland. A
disturbance matrix defines the proportion of the carbon stock that remains in that pool and the proportion that is2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Usetransferred to other pools. A small number of transfers are possible, and are outlined in the disturbance matrix inTable 5.7. Use of a disturbance matrix ensures consistency of the accounting of all carbon pools.
The immediate and abrupt carbon stock change in dead wood due to conversion of other lands to Cropland under
Tiers 2 and 3 will be estimated using Equation 2.23 in Chapter 2 as suggested in Tier 1. During the transition
period, pools that gain or lose carbon often have a non-linear loss or accumulation curve that can be represented
through successive transition matrices. For Tier 2, a linear change function can be assumed; a Tier 3 approachbased upon this method should use the true shapes of the curves. These curves should be applied to each cohortthat is under transition during the reporting year to estimate the annual change in the dead wood and litter carbon
pools.
For the calculation of changes in dead wood and litter carbon during the transition phase, two methods aresuggested:
Method 1 (Also called the Gain-Loss Method, Equation 2.18 in Chapter 2): Method 1 involves estimating thearea of each type of land conversion and the average annual transfer into and out of dead wood and litter stocks.
This requires an estimate of area under Land Converted to Cropland according to different climate or croplandtypes, management regime, or other factors significantly affecting dead wood and litter carbon pools and thequantity of biomass transferred into dead wood and litter stocks as well as the quantity of biomass transferred outof the dead wood and litter stocks on per hectare basis according to different cropland types.
Method 2 (Also called the Stock-Difference Method, Equation 2.19 in Chapter 2): Method 2 involves
estimating the area of Land Converted to Cropland and then estimating dead wood and litter stocks at two
periods of time, t1 and t2. The dead wood and litter stock changes for the inventory year are obtained by dividing
the stock changes by the period (years) between two measurements. The stock difference method is feasible forcountries, which have periodic inventories. This method is more suitable for countries adopting Tier 3 methods.
Tier 3 methods are used where countries have country-specific emission factors, and substantial national data.
Country-defined methodology may be based on detailed inventories of permanent sample plots for theircroplands and/or models.
Tier 3
For Tier 3, countries should develop their own methodologies and parameters for estimating changes in DOM.
These methodologies may be derived from both methods specified above, or may be based on other approaches.
The method used needs to be clearly documented.
Method 2 may be suitable for countries adopting Tier 3 methods. Tier 3 methods are used where countries havecountry-specific emission factors, and substantial national data. Country-defined methodology may be based on
detailed inventories of permanent sample plots for their grasslands and/or models.

5.3.2.2

C HOICE OF EMISSION / REMOVAL FACTORS

Carbon Fraction: The carbon fraction of dead wood and litter is variable and depends on the stage ofdecomposition. Wood is much less variable than litter and a value of 0.50 tonne C (tonne d.m.)-1 can be used forthe carbon fraction.
Tier 1
For Tier 1, it is assumed that the dead wood and litter carbon stocks in lands converted to Cropland are all lost
during the conversion and that there is no accumulation of new DOM in the Cropland after conversion.
Countries experiencing significant conversions of other ecosystems to Cropland that have a significant
component of dead wood or litter (e.g., slash and burn systems for clearing land, agroforestry, etc.) are
encouraged to develop domestic data to quantify this impact and report it under Tier 2 or 3 methodologies.
Tier 2
It is good practice to use country-level data on dead wood and litter for different Cropland categories, in
combination with default values, if country or regional values are not available for some conversion categories.
Country-specific values for transfer of carbon from live trees and other crops that are harvested to harvest
residues and decomposition rates, in the case of Method 1 (Gain-Loss Method), or the net change in DOM pools,in the case of Method 2 (Stock-Difference Method), can be derived from domestic expansion factors, taking into
account the Cropland type, the rate of biomass utilization, harvesting practices and the amount of damaged
vegetation during harvesting operations. Country-specific values for disturbance regimes should be derived fromscientific studies.
Tier 3National level disaggregated DOM carbon estimates should be determined as part of a national land-use
inventory, national level models, or from a dedicated greenhouse gas inventory programme, with periodic

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Croplandsampling according to the principles set our in Chapter 3 Annex 3A.3. Inventory data can be coupled withmodelling studies to capture the dynamics of all Cropland carbon pools.
Tier 3 methods provide estimates of greater certainty than lower tiers and feature a greater link between
individual carbon pools. Some countries have developed disturbance matrices that provide a carbon reallocation
pattern among different pools for each type of disturbance. Other important parameters in a modelled DOM
carbon budget are decay rates, which may vary with the type of wood and microclimatic conditions, and site
preparation procedures (e.g., controlled broadcast burning, or burning of piles).

5.3.2.3

C HOICE OF ACTIVITY DATA

Activity data should be consistent with the activity data used for estimating changes in biomass on land areas
converted to Cropland. This can be obtained, consistent with the general principles set out in Chapter 3 and as
described earlier through national statistics, from forest services, conservation agencies, municipalities, survey
and mapping agencies. Cross-checks should be made to ensure complete and consistent representation ofannually converted lands in order to avoid possible omissions or double counting. Data should be disaggregatedaccording to the general climatic categories and Cropland types. Tier 3 inventories will require morecomprehensive information on the establishment of new croplands, with refined soil classes, climates, and spatial
and temporal resolution. All changes having occurred over the number of years selected as the transition period
should be included with transitions older than the transition period (default 20 years) reported as a subdivision of
Cropland Remaining Cropland.
All tiers require estimates of land areas converted to Cropland. The same area data should be used for biomass
calculations, dead organic matter and the soil carbon estimates. 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 3. 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 Approaches 1 and 2 from Chapter 3 are used toestimate land area are being used, supplementary surveys may be needed to identify the area of land being
converted from Forest Land to Cropland. As pointed out in Chapter 3, where surveys are being set up, it willoften be more accurate to determine directly areas undergoing conversion, than to estimate these from thedifferences in total land areas under particular uses at different times.

5.3.2.4

C ALCULATION STEPS FOR T IERS 1 AND 2

Tier 1
Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of
conversion by year (Aon). Area data should be obtained using the methods described in Chapter 3. Higher tiersrequire greater detail but the minimum requirement for inventories to be consistent with the IPCC Guidelines
when using Tier 1 is that the areas of Forest Land conversion to Cropland can be identified separately.
Step 2: For each activity category, determine the dead wood and litter stocks (separately) per hectare prior toconversion (ΔCo).
Step 3: For each activity category, determine the stocks in the dead wood and litter (separately) per hectare forthe particular type of cropland after conversion (ΔCn). For Tier 1, dead wood and litter stocks following
conversion are assumed to be equal to zero.
Step 4: Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by
subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock.
Step 5: Convert the net change in the individual stock to units of tonnes C ha-1 by multiplying the net stockchange by the carbon fraction of that stock (0.40 tonne C (tonne d.m.)-1 for litter, and 0.50 tonne C (tonne d.m.)-1
for dead wood).
Step 6: Multiply the net change in each C stock by the area converted during the reporting year, to get theannual change in carbon stocks in dead wood and litter (ΔCDOM).

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Tier 2
Step 1: Determine the categories of land conversion to be used in this assessment and the representative area of
conversion by year. When calculating for lands in the transition phase, representative areas for each category at
different stages of conversion are required.
Step 2: Abrupt changes•

Determine the activity categories to be used in this assessment and the representative areas. The category
consists of definitions of the type of conversion and, if applicable, the nature of management of the previous
land cover and cropland management, for example: ‘conversion of logged tropical seasonal forest to cereal
crops’.

•For each activity category, determine the dead wood and litter stocks (separately) per hectare prior toconversion.

•

For each activity category, determine the stocks in the dead wood and litter (separately) per hectare
following one year of conversion to Cropland.

•

Calculate the net change of dead wood and litter stocks per hectare for each type of conversion by
subtracting the initial stocks from the final stocks. A negative value indicates a loss in the stock.

•

Convert the net change in the individual stock to units of tonnes C ha-1 as mentioned in Tier 1.

•

Multiply the net change in each C stock by the area converted during the reporting year.

Step 3: Transitional changes•

Determine the activity categories and cohorts to be used in this assessment and the representative areas. The
category consists of definitions of the type of conversion and, if applicable, the nature of management of theprevious land cover and cropland management, for example: ‘conversion of logged tropical seasonal forest
to cattle pasture using exotic grasses’.

•

Determine the annual change rate for dead wood and litter stocks (separately) by activity type using either
Method 1 (Gain-Loss Method) or Method 2 (Stock-Difference Method) (see below) for each cohort of lands
that are currently in the transition phase between conversion and a new steady-state cropland system.

•

Determine the dead wood and litter stocks in the cohort during the previous year (usually taken from theprevious inventory).

•

Calculate the change in dead wood and litter stocks for each cohort by adding the net change rate to theprevious year’s stocks.

•

Convert the net change in the individual stock to units of tonnes C ha-1 as described in Tier 1.

•

Multiply the net change in each C stock by the area in each cohort for the reporting year.

Method 1 (Gain-Loss Method; see Equation 2.18 in Chapter 2)•

Determine the average annual inputs of dead wood and litter (separately).

•

Determine the average annual losses of dead wood and litter (separately).

•

Determine the net change rate in dead wood and litter by subtracting the outputs from the inputs.

•

A Tier 2 approach requires country-specific and cropping system-specific stock change factors and the best
available local data should be used (and documented).

Method 2 (Stock-Difference Method; see Equation 2. 19 in Chapter 2)•

Determine the inventory time interval, the average stocks of dead wood and litter at the initial inventory, andthe average stocks of dead wood and litter at the final inventory.

•

Use these figures to calculate the net change in dead wood and litter stocks by subtracting the initial stockfrom the final stock and dividing this difference by the number of years between inventories. A negativevalue indicates a loss in the stock.

•

A Tier 2 approach requires country-specific and cropping system-specific stock change factors and the best
available local data should be used (and documented).

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2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland5.3.2.5

U NCERTAINTY ASSESSMENT

This section considers source-specific uncertainties relevant to estimates made for lands converted to Cropland.
Sources of uncertainty include the degree of accuracy in land area estimates, carbon increase and loss, carbon
stock, fraction of land area burnt, and expansion factor terms. Error estimates (i.e., standard deviations, standard
error, or ranges) must be calculated for each of the country-defined terms used in a basic uncertainty assessment.

Emission factor uncertainties
These will be the same as the uncertainties associated with estimation of the litter and dead organic matter stocks
per unit area on the previous land use. Uncertainties need not be estimated where zero carbon density in litterand dead organic matter pools is assumed for Cropland. Where this is not the case, uncertainties should beassessed by analysis of local data and should both exceed a factor of about 2.

Activity data uncertainties
Area data and estimates of uncertainty should be obtained using the methods in Chapter 3. Tiers 2 and 3
approaches may also use higher resolution activity data, such as area estimates for different climatic regions orfor cropland management systems within national boundaries. This will reduce uncertainty levels whenassociated with carbon accumulation factors defined at the same resolution.

5.3.3

Soil carbon

Land is typically converted to Cropland from native lands, managed Forest Land and Grassland, but occasionallyconversions can occur from Wetlands and seldom Settlements. Regardless of soil type (i.e., mineral or organic),
the conversion of land to Cropland will, in most cases, result in a loss of soil C for some years following
conversion (Mann, 1986; Armentano and Menges, 1986; Davidson and Ackerman, 1993). Possible exceptions
are irrigation of formerly arid lands and conversion of degraded lands to Cropland.
General information and guidance for estimating changes in soil C stocks are provided in Section 2.3.3 ofChapter 2 (including equations), and that section needs to be read before proceeding with a consideration of
specific guidelines dealing with cropland soil C stocks. The total change in soil C stocks for Land Converted to
Cropland is estimated using Equation 2.24 (Chapter 2), which combines the change in soil organic C stocks(SOC stocks) for mineral soils and organic soils; and stock changes associated with soil inorganic C pools (Tier
3 only). This section provides specific guidance for estimating soil organic C stock changes; see Section 2.3.3.1
for discussion on soil inorganic C (no additional guidance is provided in the Cropland section below).
To account for changes in soil C stocks associated with Land Converted to Cropland, countries need to have, at aminimum, estimates of the areas of Land Converted to Cropland during the inventory time period. If land-use
and management data are limited, aggregate data, such as FAO statistics, can be used as a starting point, alongwith knowledge of country experts of the approximate distribution of land-use types being converted and theirassociated management. If the previous land uses and conversions are not unknown, SOC stocks changes canstill be computed using the methods provided in Cropland Remaining Cropland, but the land base area will
likely be different for croplands in the current year relative to the initial year in the inventory. It is critical,
however, that the total land area across all land-use sectors be equal over the inventory time period (e.g., 7
million ha may be converted from Forest Land and Grassland to Cropland during the inventory time period,
meaning that croplands will have an additional 7 Million ha in the last year of the inventory, while grasslands
and forests will have a corresponding loss of 7 Million ha in the last year). Land Converted to Cropland is
stratified according to climate regions and major soil types, which could either be based on default or countryspecific classifications. This can be accomplished with overlays of climate and soil maps, coupled with spatiallyexplicit data on the location of land conversions.

5.3.3.1

C HOICE OF METHOD

Inventories can be developed using a Tier 1, 2 or 3 approach with each successive tier requiring more detail andresources than the previous one. It is also possible that countries will use different tiers to prepare estimates forthe separate subcategories of soil C (i.e., soil organic C stocks changes in mineral soils and organic soils; andstock changes associated with soil inorganic C pools). Decision trees are provided for mineral soils (Figure 2.4)
and organic soils (Figure 2.5) in Section 2.3.3.1 (Chapter 2) to assist inventory compilers with selection of theappropriate tier for their soil C inventory.

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Volume 4: Agriculture, Forestry and Other Land Use

Mineral soils
Tier 1
Soil organic C stock changes for mineral soils can be estimated for land-use conversion to Cropland using
Equation 2.25 in Chapter 2. For Tier 1, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and C stock in
the last year of the inventory time period (SOC0) are computed from the default reference soil organic C stocks(SOCREF) and default stock change factors (FLU, FMG, FI). Annual rates of stock changes are estimated as thedifference in stocks (over time) divided by the time dependence (D) of the Cropland stock change factors(default is 20 years).
Tier 2The Tier 2 method for mineral soils also uses Equation 2.25 in Chapter 2, but incorporates country-specificreference C stocks and/or stock change factors, and possibly more disaggregated land-use activity andenvironmental data.
Tier 3Tier 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 estimate soil C change fromland-use conversions to Cropland, and may employ models, data sets and/or monitoring networks. If possible, it
is recommended that Tier 3 methods be integrated with estimates of biomass removal and the post-clearance
treatment of plant residues (including woody debris and litter), as variation in the removal and treatment ofresidues (e.g., burning, site preparation) will affect C inputs to soil organic matter formation and C losses
through decomposition and combustion. It is important that models be evaluated with independent observations
from country-specific or region-specific field locations that are representative of the interactions of climate, soil
and cropland management on post-conversion change in soil C stocks.

Organic soils
Tier 1 and Tier 2
Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term
cropped organic soils. Carbon losses are computed using Equation 2.26 (Chapter 2). Additional guidance on theTiers 1 and 2 approaches are given in the Cropland Remaining Cropland section (Section 5.2.3).
Tier 3
A Tier 3 approach will involve more detailed and country-specific models and/or measurement-based
approaches along with highly disaggregated land-use and management data (see mineral soils above for further
discussion).

5.3.3.2

C HOICE OF STOCK CHANGE AND EMISSION FACTORS

Mineral soils
Tier 1For native unmanaged land, as well as for managed forest lands, settlements and nominally managed grasslands
with low disturbance regimes, soil C stocks are assumed equal to the reference values (i.e., land-use, disturbance(forests only), management and input factors equal 1), while it will be necessary to apply the appropriate stockchange factors to represent previous land-use systems that are not the reference condition, such as improved anddegraded grasslands. It will also be necessary to apply the appropriate stock change factor to represent input andmanagement effects on soil C storage in the new cropland system. Default reference C stocks are found in Table
2.3 (Chapter 2). See the appropriate land-use chapter for default stock change factors.
In the case of transient land-use conversions to Cropland, the stock change factors are given in Table 5.10, anddepend on the length of the fallow (vegetation recovery) cycle in a shifting cultivation system, representing an
average soil C stock over the crop-fallow cycle. Mature fallow denotes situations where the non-croplandvegetation (e.g., forests) recovers to a mature or near mature state prior to being cleared again for cropland use,
whereas in shortened fallow, vegetation recovery is not attained prior to re-clearing. If land already in shiftingcultivation is converted to permanent Cropland (or other land uses), the stock change factors representing
shifting cultivation would provide the ‘initial’ C stocks (SOC(0-T)) in the calculations using Equation 2.25
(Chapter 2).

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Chapter 5: Cropland

TABLE 5.10
SOIL STOCK CHANGE FACTORS (FLU, FMG, FI) FOR LAND-USE CONVERSIONS TO CROPLAND
Factor value
type

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

Land use

Land use

Shifting cultivation– Mature fallow

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

Climateregime

IPCC
default

Error

All

1

NA

Tropical1NA

Tropical

0.64

+ 50%

Tropical

0.8

+ 50%

Definition#

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
to regrowth.

Managed forest(default value is 1)

Managed grassland(See default values in Table 6.2)

Cropland(See default values in Table 5.5)

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.

Tier 2Estimation of country-specific stock change factors is probably the most important development associated with
the Tier 2 approach. Differences in soil organic C stocks among land uses are computed relative to a reference
condition, using land-use factors (FLU). Input factors (FI) and management factors (FMG) are then used to further
refine the C stocks of the new cropland system. Additional guidance on how to derive these stock change factors
is given in Croplands Remaining Croplands, Section 5.2.3.2. See the appropriate chapter for specific information
regarding the derivation of stock change factors for other land-use categories (Forest Land in Section 4.2.3.2,
Grassland in 6.2.3.2, Settlements in 8.2.3.2, and Other Land in 9.3.3.2).
Reference C stocks can also be derived from country-specific data in a Tier 2 approach. However, reference
values should be consistent across the land uses (i.e., Forest Land, Cropland, Grassland, Settlements, Other
Land), and thus must be coordinated among the various teams conducting soil C inventories for AFOLU.
Tier 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

Organic soils
Tier 1 and Tier 2
Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term
cropped organic soils. Tier 1 emission factors are given in Table 5.6, while Tier 2 emission factors are derived
from country-specific or region-specific data.
Tier 3
Constant emission rate factors per se are less likely to be estimated in favor of variable rates that more accurately
capture land-use and management effects. See Chapter 2, Section 2.3.3.1 for further discussion.

5.3.3.3

C HOICE OF ACTIVITY DATA

Mineral soils
Tier 1 and Tier 2
For purposes of estimating soil carbon stock change, area estimates of Land Converted to Cropland should be
stratified according to major climate regions and soil types. This can be based on overlays with suitable climate
and soil maps and spatially-explicit data of the location of land conversions. Detailed descriptions of the default
climate and soil classification schemes are provided in Chapter 3, Annex 3A.5. Specific information is provided

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in the each of the land-use chapters regarding treatment of land-use/management activity data (Forest Land in
Section 4.2.3.3, Cropland in 5.2.3.3, Grassland in 6.2.3.3, Settlements in 8.2.3.3, and Other Land in 9.3.3.3).
One critical issue in evaluating the impact of Land Converted to Cropland on soil organic C stocks is the type ofland-use and management activity data. Activity data gathered using Approach 2 or 3 (see Chapter 3 fordiscussion about approaches) provide the underlying basis for determining the previous land use for Land
Converted to Cropland. In contrast, aggregate data (Approach 1, Chapter 3) only provide the total amount ofarea in each land at the beginning and end of the inventory period (e.g., 1985 and 2005). Approach 1 data are
not sufficient to determine specific transitions. In this case all Cropland will be reported in the Cropland
Remaining Cropland category and in effect transitions become step changes across the landscape. This makes it
particularly important to achieve coordination among all land sectors to ensure that the total land base isremaining constant over time, given that some land area will be lost and gained within individual sectors during
each inventory year due to land-use change.
Tier 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or moredetailed data on the combinations of climate, soil, topographic and management data are needed, relative to Tier
1 or 2 methods, but the exact requirements will be dependent on the model or measurement design.

Organic soils
Tiers 1 and 2
Land Converted to Cropland on organic soils within the inventory time period is treated the same as long-term
cropped organic soils, and guidance on activity data is discussed in Section 5.2.3.3.
Tier 3
Similar to mineral soils, Tier 3 approaches will likely require more detailed data on the combinations of climate,soil, topographic and management data. Relative to Tier 1 or 2 methods, the exact requirements will bedependent on the model or measurement design.

5.3.3.4

C ALCULATION STEPS FOR T IER 1Mineral soils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change per ha of Land Converted to Croplandon mineral soils are as follows:
Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g.,
1990 to 1995, 1995 to 2000, etc.)
Step 2: Determine the amount of Land Converted to Cropland by mineral soil types and climate regions in the
country at the beginning of the first inventory time period. The first year of the inventory time period will
depend on the time step of the activity data (0-T; e.g., 5, 10 or 20 years ago).
Step 3: For Grassland converted to Cropland, classify previous grasslands into the appropriate management
system using Figure 6.1. No classification is needed for other land uses at the Tier 1 level.
Step 4: Assign native reference C stock values (SOCREF) from Table 2.3 based on climate and soil type.
Step 5: Assign a land-use factor (FLU), management factor (FMG) and C input levels (FI) to each grassland basedon the management classification (Step 2). Values for FLU, FMG and FI are given in Table 6.2 for grasslands.
Values are assumed to be 1 for all other land uses.
Step 6: Multiply the factors (FLU, FMG, FI) by the reference soil C stock to estimate an ‘initial’ soil organic C
stock (SOC(0-T)) for the inventory time period.
Step 7: Estimate the final soil organic C stock (SOC0) by repeating Steps 1 to 5 using the same native reference
C stock (SOCREF), but with land-use, management and input factors that represent conditions for the cropland in
the last (year 0) inventory year.
Step 8: Estimate the average annual change in soil organic C stocks for land converted to Cropland (∆CMineral)by subtracting the ‘initial’ soil organic C stock (SOC(0-T)) from the final soil organic C stock (SOC0), and thendividing by the time dependence of the stock change factors (i.e., 20 years using the default factors). Note: if aninventory time period is greater than 20 years, then divide by the difference in the initial and final year of thetime period.
Step 9: Repeat Steps 2 to 8 if there are additional inventory time periods (e.g., 1990 to 2000, 2001 to 2010, etc.).
Note that Land Converted to Cropland will retain that designation for 20 years. Therefore, inventory time

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Chapter 5: Croplandperiods that are less than 20 years may need to refer to the previous inventory time period to evaluate if a parcelof land is considered Land Converted to Cropland or Cropland Remaining Cropland.
A numerical example is given below for Forest Land converted to Cropland on mineral soils, using Equation
2.25 and default reference C stocks (Table 2.3) and stock change factors (Table 5.5).
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,with intensive tillage and low residue C inputs then SOC0 = 70 tonnes C ha-1 ● 0.48 ● 1 ● 0.92 =30.9 tonnes C ha-1. Thus the average annual change in soil C stock for the area over the inventory
time period is calculated as (30.9 tonnes C ha-1 – 70 tonnes C ha-1) / 20 yrs = -2.0 tonnes C ha-1
yr-1.

Organic soils
Calculation steps and example are the same as described in Section 5.2.3.4 above.

5.3.3.5

U NCERTAINTY ASSESSMENT

Uncertainty analyses for Land Converted to Cropland are fundamentally the same as Cropland Remaining
Cropland. Three broad sources of uncertainty exists: 1) uncertainties in land-use and management activity andenvironmental data; 2) uncertainties in reference soil C stocks if using a Tier 1 or 2 approach (mineral soils only);and 3) uncertainties in the stock change/emission factors for Tier 1 or 2 approaches, model structure/parameter
error for Tier 3 model-based approaches, or measurement error/sampling variability associated with a Tier 3
measurement-based inventories. See the uncertainty section in Cropland Remaining Cropland for additional
discussion (Section 5.2.3.5).

5.3.4

Non-CO 2 greenhouse gas emissions from biomassburning

Greenhouse gas emissions from conversion of non-cropland, particularly Forest Land and Grassland to Cropland,
are likely to be key source category for many countries. Greenhouse gas emissions from Land Converted to
Cropland occur from incomplete combustion of biomass and dead organic matter (DOM) in the initial land-use
category before conversion. CO2 emissions are accounted for in the new land-use category (Land Converted to
Cropland). The most significant non-CO2 emissions in this section arise from conversion of Forest Land to
Cropland, but it may also occur as a result of the conversion from Grassland to Cropland. It is very unlikely that
Cropland originates from conversion of the other land-use categories (Settlements, Wetlands, or Other Land).
In the tropics, it is common practice to burn the forest residues successively, until most (or all) of the forest
residues and DOM is cleared, and agriculture can be established. In some places, up to three or four burnings arenecessary. Part of the above-ground forest biomass removed during the process of conversion of Forest Land to
Cropland may be transferred to harvested wood products, and an amount may be removed from the site to be
used as fuel wood (hence, burnt off-site). Whatever remains is normally burnt on-site.
Methods for estimating CO2 emissions from fire for Land Converted to Cropland are described in Section 2.4 in
Chapter 2.
Non-CO2 emissions from biomass burning in unmanaged Forest Land, if followed by a land-use conversion,
shall be reported, since the converted land is considered to be managed land.
The approach to be used to estimate non-CO2 emissions from biomass burning in Land Converted to Cropland isessentially the same as for Cropland Remaining Cropland.

5.3.4.1

C HOICE OF METHOD

The decision tree in Figure 2.6 in Chapter 2 provides guidance on the choice of the Tier level to be applied bycountries when reporting non-CO2 emissions from Land Converted to Cropland. Countries experiencing
significant scale conversion of non-cropland, particularly from Forest Land, to cropland should strive to adopt
Tier 2 or 3 methods.

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The choice of method is directly related to the availability of national data on the area of converted land burnt,
the mass of fuel available, and combustion and emission factors. When using higher tiers, country-specific dataon the mass of available fuel is used to represent the amount of biomass removed for conversion, and transferredto harvested wood product (if applicable), removed for fuel use and burnt off-site.
Countries should strive to report using a Tier 2 or Tier 3 method whenever greenhouse gas emissions frombiomass burning in Land Converted to Cropland is a key category. If models have been developed and validated,countries should apply a Tier 3 method even in those cases where Land Converted to Cropland is not a key
category.

5.3.4.2

C HOICE OF EMISSION FACTORS

Tier 1
The mass of fuel combusted is critical for estimating greenhouse gas emissions. Default data to support
estimation of emissions under a Tier 1 approach are given in Tables 2.4 – 2.6 in Chapter 2. Countries need tojudge how their vegetation types relate to the broad vegetation categories described in the default tables. For Tier
1, it should be assumed that all of the carbon in above-ground biomass and DOM in the previous land category is
lost immediately after conversion. Default values for biomass prior to conversion can be found in the chapters
relating to the respective land uses (e.g., default factors for Forest Land are to be found in the chapter dealing
with biomass in Forest Land). For calculation of non-CO2 emissions, estimates of the amount of fuel actually
burnt (Table 2.4) should be used.
Tier 2
In a Tier 2 method, country-specific estimates of mass of fuel available should be used. Data should bedisaggregated according to forest types, in the case of Forest Land converted to Cropland. Combustion andemission factors that reflect better the national conditions (climate zone, biome, burning conditions) should be
developed and uncertainty ranges provided. In addition, unlike Tier 1, where it is assumed that all of the carbon
in above-ground and DOM is lost immediately after conversion, in a Tier 2 method the transfers of biomass toharvested wood products and fuelwood (burnt off-site) should be estimated to provide a more reliable estimate of
the mass of fuel available for combustion.
Tier 3
Under a Tier 3, all the parameters required for estimating CO2 and non-CO2 emissions should be developed
nationally for different land types subjected to conversion to Cropland.

5.3.4.3

C HOICE OF ACTIVITY DATA

The activity data needed to estimate non-CO2 emissions from biomass burning refers to the area affected by thisactivity. Countries shall stratify the area converted to Cropland by Forest Land and Grassland converted, since
the amount of fuel available for burning may present large variations from one category of land use to another.
The most critical conversion is from Forest Land to Cropland, due to large biomass involved per hectare. It isgood practice to ensure the area data used for non-CO2 estimation is consistent with that used for biomass andDOM sections.
Tier 1Countries applying a Tier 1 method should estimate the areas converted to Cropland from initial land uses
(Forest Land, Grassland, etc.). Countries using Approach 1 of Chapter 3 should strive to further stratify Land
Converted to Cropland from different land-use categories. The conversion should be estimated on a yearly basis.
Estimates can be derived by applying a rate of conversion to Cropland to the total area cropped annually. The
rate can be estimated on the basis of historical knowledge, judgement of country experts, and/or from samples of
converted areas and assessment of the final land use. Alternatively, estimates can be derived using data frominternational sources, such as FAO, to estimate the area of Forest Land and Grassland area annually converted,and using expert judgement to estimate the portion of this area converted to Cropland.
Tier 2
Countries should, where possible, use actual area estimates for all possible conversions to Cropland. Multitemporal remotely sensed data of adequate resolution should provide better estimates of land-use conversion
than the approaches used in Tier 1. The analysis may be based on full coverage of the territory or on
representative sample areas selected, from which estimates of the area converted to Cropland in the entire
territory can be derived.
Tier 3
The activity data in Tier 3 should be based on the Approach 3 method presented in Chapter 3, where the total
annual area converted to Cropland (from Forest Land, Grassland, or other land-use category) is estimated. It is

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Chapter 5: Croplandgood practice to develop a land-use change matrix as suggested in Chapter 3, in a spatially explicit manner. The
data should be disaggregated according to type of biome, climate, soils, political boundaries, or a combination ofthese parameters.

5.3.4.4

U NCERTAINTY ASSESSMENT

Tier 1The sources of uncertainty arise from: (i) use of global or national average rates of conversion or coarse
estimates of land areas converted to Cropland; (ii) estimate of the area converted that is burnt; (iii) mass ofavailable fuel; and (iv) combustion and emission factors. Uncertainties associated with emission and combustion
factors are provided, and those related to items (i) and (ii) can vary significantly depending on the method usedin their estimation.
As a result of these uncertainties it is unlikely that the estimate of area burnt will be known to better than 20%
and the emissions per unit area to within a factor of 2 using Tier 1 methods.
Tier 2The use of area estimates produced from more reliable sources (remotely sensed data, sample approach) will
improve the accuracy relative to Tier 1 and Approach 1 of Chapter 3. These sources will also provide better
estimates of the areas that are converted and burnt. Taking into account the biomass transferred to harvested
wood products or removed from the site as fuelwood, and the biomass left on-site to decay, will eliminate a bias
(overestimation) in the estimates. Estimates of emission or combustion factors, if accompanied by error ranges(in the form of standard deviation), will allow uncertainty associated with Land Converted to Cropland to beassessed.
Tier 3
Uncertainty is less and is dependent on the accuracy of remote sensing and field surveys, and of the modelling
approach used and associated data inputs.

5.4

COMPLETENESS, TIME SERIES, QA/QC, AND
REPORTING

Material presented here supplements the general guidance on these issues that is provided in Volume 1.

5.4.1

Completeness

Tier 1
A complete Cropland inventory for Tier 1 has three elements: 1) carbon stock changes and non-CO2 (CH4, CO,
N2O, NOx) emissions from biomass burning have been estimated for all Land Converted to Cropland andCropland Remaining Cropland during the inventory time period, 2) inventory analysis addressed the impact of
all management practices described in the Tier 1 methods, and 3) the analysis accounted for climatic and soil
variation that impacts emissions and removals (as described for Tier 1).
The latter two elements require assignment of management systems to cropland areas and stratification by
climate regions and soil types. It is good practice for countries to use the same area classifications for biomass
and soil pools in addition to biomass burning (to the extent that classifications are needed for these source
categories). This will ensure consistency and transparency, allow for efficient use of land surveys and other datacollection tools, and enable the explicit linking between carbon dioxide emissions and removals in biomass andsoil pools, as well as non-CO2 emissions from biomass burning.
For biomass and soil C stock estimations, a cropland inventory should address the impact of land-use change(Land Converted to Cropland) and management. However, in some cases, activity data or expert knowledge
may not be sufficient to estimate the effects of agroforestry, crop rotation practices, tillage practices, irrigation,
manure application, residue management, etc. In those cases, countries may proceed with an inventory
addressing land use alone, but the results will be incomplete and omission of management practices must be
clearly identified in the reporting documentation for purposes of transparency. If there are omissions, it is goodpractice to collect additional activity data for future inventories, particularly if biomass or soil C is a key source
category.
C stock changes may not be computed for some cropland areas if greenhouse gas emissions and removals are
believed to be insignificant or constant through time, such as non-woody cropland where there are no
management or land-use changes. In this case, it is good practice for countries to document and explain thereason for omissions.

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For biomass burning, non-CO2 greenhouse gases should be estimated for all major categories of crop residues,
taking care to account for removal of residues from the field for other purposes such as energy production, andfor losses of residues resulting from grazing and decomposition during the period between harvests and burning
operations. Where there is conversion of Forest Land to Cropland, the emissions from the burning of DOM andcleared tree biomass should be included.
Tier 2
A complete Tier 2 inventory has similar elements as Tier 1 but incorporates country-specific data: to estimate C
stock change factors, reference soil C stocks, residue estimates (fuel load), combustion and emission factors forbiomass burning; and to develop climate descriptions and soil categories in addition to improve management
system classifications. Moreover, it is good practice for a Tier 2 inventory to incorporate country-specific data
for each component. Inventories are still considered complete, however, if they combine country- specific datawith Tier 1 defaults.
Tier 3
In addition to the Tiers 1 and 2 considerations, completeness of Tier 3 inventories will depend on thecomponents of the country-specific evaluation system. In practice, Tier 3 inventories are likely to fully accountfor emissions and removals from croplands using more, finely resolved data on climate, soils, biomass burningand management systems. It is good practice for inventory compilers to describe and document the elements ofthe country-specific system, demonstrating the completeness of the approach and data sources. If gaps are
identified, it is good practice to gather additional data and further develop the country-specific system.

5.4.2

Developing a consistent time series

Tier 1
Consistent time series are essential for evaluating trends in emissions or removals. In order to maintainconsistency, compilers should apply the same classifications and factors over the entire inventory time period,
including climate, soil types, management system classifications, C stock change factors, reference soil C stocks,
residue estimates (fuel load), combustion factors, and non-CO2 emission factors. Defaults are provided for all ofthese characteristics and so consistency should not be an issue. In addition, the land base should also remain
consistent through time, with the exception of Land Converted to Cropland or Cropland converted to other land
uses.
Countries should use consistent sources of activity data on land use, management and biomass burning,
throughout the inventory. Sampling approaches, if used, should be maintained for the duration of the inventory
time period to ensure a consistent approach. If subcategories are created, countries should keep transparent
records of how they are defined and apply them consistently throughout the inventory.
In some cases, sources of activity data, definitions or methods may change over time with availability of new
information. Inventory compilers should determine the influence of changing data or methods on trends, and if
deemed significant, emissions and removals should be re-calculated for the time series using methods provided
in Chapter 5 of Volume 1.
For C stock changes, one key element in producing a consistent time series is to ensure consistency between C
stocks for lands converted to Cropland that were reported in previous reporting periods and the state of thosestocks reported for those lands that are remaining Cropland in the current reporting period. For example, if 50
tonnes of the above-ground live biomass was transferred to the dead organic matter pool for land converted from
Forest Land to Cropland in the previous reporting period, reporting in this period must assume that the starting
carbon stocks in the dead organic matter pool was 50 tonnes for those lands.
Tier 2
In addition to the issues discussed under Tier 1, there are additional considerations associated with introductionof country-specific information. Specifically, it is good practice to apply new values or classifications derived
from country-specific information across the entire inventory and re-calculate the time series. Otherwise,
positive or negative trends in C stocks or biomass burning may be partly due to changes associated with theinventory methods at some point in the time series, and not representative of actual trends.
It is possible that new country-specific information may not be available for the entire time series. In those cases,
it is good practice to demonstrate the effect of changes in activity levels versus updated country-specific data or
methods. Guidance on recalculation for these circumstances is presented in Chapter 5 of Volume 1.
Tier 3
Similar to Tiers 1 and 2, it is good practice to apply the country-specific estimation system throughout the entire
time series. Inventory agencies should use the same measurement protocols (sampling strategy, method, etc.)
and/or model-based system throughout the inventory time period.

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Chapter 5: Cropland

5.4.3

Quality Assurance and Quality Control

Tier 1
It is good practice to implement Quality Assurance/Quality Controls with internal and external review of
Cropland inventory data. Internal reviews should be conducted by the agency in charge of the inventory, while
external review is conducted by other agencies, experts or groups who are not directly involved with thecompilation.
Internal review should focus on the inventory implementation process to ensure that: 1) activity data have been
stratified appropriately by climate regions and soil types; 2) management classifications/descriptions have been
applied appropriately; 3) activity data have been properly transcribed into the worksheets or inventorycomputation software; and 4) C stock change factors, reference soil C stocks, residue estimates (fuel load), andbiomass burning combustion and emission factors have been assigned appropriately. Quality Assurance/Quality
Control measures may involve visual inspection as well as built-in program functions to check data entry andresults. Summary statistics can also be helpful, such as summing areas by strata within worksheets to determine
if they are consistent with land-use statistics. Total areas should remain constant over the inventory period, andareas by strata should only vary by land-use or management classification (climate and soil areas should remain
constant).
External reviews need to consider the validity of the inventory approach, thoroughness of inventory
documentation, methods explanation and overall transparency. It is important to evaluate if the total area ofcropland is realistic, and reviewers should cross-check area estimates across land-use categories (i.e., Forest
Land, Cropland, Grassland, etc.) to ensure that the sum of the entire land base for a country is equal across every
year in the inventory time period.
Tier 2
In addition to the Quality Assurance/Quality Controls measures under Tier 1, the inventory agency should
review the country-specific climate regions, soil types, management system classifications, C stock change
factors, reference C stocks, residue estimates (fuel load), combustion factors and/or non-CO2 emission factors forbiomass burning. If using factors based on direct measurements, the inventory agency should review themeasurements to ensure that they are representative of the actual range of environmental and management
conditions, and were developed according to recognized standards (IAEA, 1992). If accessible, it is goodpractice to compare country-specific factors with Tier 2 stock change and emission factors used by othercountries with comparable circumstances, in addition to the IPCC defaults.
Given the complexity of emission and removal trends, specialist in the field should be involved in the external
review to critique the residue fuel load estimates, stock change factors, combustion and emission factors, as wellas country-specific climate regions, soil types, and/or management system descriptions.
Tier 3
Country-specific inventory systems will likely need additional Quality Assurance/Quality Control measures, but
this will be dependent on the systems that are developed. It is good practice to develop a Quality
Assurance/Quality Control protocol that is specific to the country’s advanced inventory system, archive thereports, and include summary results in reporting documentation.

5.4.4

Reporting and Documentation

Tier 1
In general, it is good practice to document and archive all information required to produce the national inventory
estimates. For Tier 1, inventory compilers should document activity data trends and uncertainties for croplands.
Key activities include land-use change, use of mineral fertilizers, agroforestry practices, organic amendments,
tillage management, cropping rotations, residue management (including burning), irrigation practices, extent of
mixed cropping systems, water management in rice systems, and land-use change.
It is good practice to archive actual databases, such as agricultural census data, and procedures used to process
the data (e.g., statistical programs); definitions used to categorize or aggregate activity data; and procedures used
to stratify activity data by climate regions and soil types (for Tier 1 and Tier 2). The worksheets or inventory
software should be archived with input/output files that were generated to produce the results.
In cases where activity data are not available directly from databases or multiple data sets were combined, theinformation, 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 uncertainty. Use of expert
knowledge should be documented and correspondences archived.

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It is good practice to document and explain trends in biomass and soil C stocks, as well as biomass burning interms of the land-use and management activity. Changes in biomass stocks should be linked directly to land use
or changes in agroforestry practices, while trends in soil C stocks may be due to land use or shifts in key
management activities, as described above. Biomass burning emissions from residues will depend on the extent
to which burning is used to prepare fields for planting. Significant fluctuations in emissions between years
should be explained.
Countries need to include documentation on completeness of their inventory, issues related to time seriesconsistency or lack thereof, and a summary of Quality Assurance/Quality Control measures and results.
Tier 2
In addition to the Tier 1 considerations, inventory compilers should document the underlying basis for countryspecific C stock change factors, reference soil C stocks, residue estimated (fuel loads), combustion and emission
factors for biomass burning, management system classifications, climate regions and/or soil types. Furthermore,
it is good practice to archive metadata and data sources for information used to estimate country-specific values.
Reporting documentation should include the country-specific factors (i.e., means and uncertainties). It is goodpractice to include a discussion in the inventory report about differences between country-specific factors andTier 1 defaults as well as Tier 2 factors from regions with similar circumstances as the reporting country. If
different emission factors, parameters and methods are used for different years, the reasons for these changes
should be explained and documented. In addition, inventory agencies should describe country-specificclassifications of management, climate and/or soil types, and it is recommended that improvements in theinventory methods based on the new classifications be documented. For example, tillage management practices
may be subdivided into additional categories beyond the Tier 1 classes (i.e., reduced, no-till and full tillage), but
further subdivisions will only improve inventory estimates if the stock change or emission factors differ
significantly among the new categories.
When discussing trends in emissions and removals, a distinction should be made between changes in activity
levels and changes in methods from year to year, and the reasons for these changes need to be documented.
Tier 3Tier 3 inventory will need similar documentation about activity data and emission/removal trends as lower tierapproaches, but additional documentation should be included to explain the underlying basis and framework of
the country-specific estimation system. With measurement-based inventories, it is good practice to document the
sampling design, laboratory procedures and data analysis techniques. Measurement data should be archived,along with results from data analyses. For Tier 3 approaches that use models, it is good practice to document themodel version and provide a model description, as well as permanently archive copies of all model input files,
source code and executable programs.

5.5

METHANE EMISSIONS FROM RICECULTIVATION

Anaerobic decomposition of organic material in flooded rice fields produces methane (CH4), which escapes tothe atmosphere primarily by transport through the rice plants (Takai, 1970; Cicerone and Shetter, 1981; Conrad,
1989; Nouchi et al., 1990). The annual amount of CH4 emitted from a given area of rice is a function of thenumber and duration of crops grown, water regimes before and during cultivation period, and organic andinorganic soil amendments (Neue and Sass, 1994; Minami, 1995). Soil type, temperature, and rice cultivar also
affect CH4 emissions.
These new guidelines for computing CH4 emissions incorporate various changes as compared to the 1996
Guidelines and the GPG2000, namely (i) revision of emission and scaling factors derived from updated analysis
of available data, (ii) use of daily – instead of seasonal – emission factors to allow more flexibility in separating
cropping seasons and fallow periods, (iii) new scaling factors for water regime before the cultivation period andtiming of straw incorporation, and (iv) inclusion of Tier 3 approach in line with the general principles of the2006 revision of guidelines. The revised guidelines also maintain the separate calculation of N2O emission fromrice cultivation (as one form of managed soil) which is dealt with in Chapter 11.

5.5.1

Choice of method

The basic equation to estimate CH4 emissions from rice cultivation is shown in Equation 5.1. CH4 emissions are
estimated by multiplying daily emission factors by cultivation period3 of rice and annual harvested areas4. In its
3

In the case of a ratoon crop, ‘cultivation period’ should be extended by the respective number of days.

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Chapter 5: Cropland

most simple form, this equation is implemented using national activity data (i.e., national average cultivation
period of rice and area harvested) and a single emission factor. However, the natural conditions and agricultural
management of rice production may be highly variable within a country. It is good practice to account for thisvariability by disaggregating national total harvested area into sub-units (e.g., harvested areas under different
water regimes). Harvested area for each sub-unit is multiplied by the respective cultivation period and emission
factor that is representative of the conditions that define the sub-unit (Sass, 2002). With this disaggregatedapproach, total annual emissions are equal to the sum of emissions from each sub-unit of harvested area.

EQUATION 5.1
CH4 EMISSIONS FROM RICE CULTIVATION
CH 4 Rice = ∑ ( EFi , j , k • ti , j , k • Ai , j , k • 10 −6 )i, j, kWhere:
CH4 Rice= annual methane emissions from rice cultivation, Gg CH4 yr-1

EFijk

= a daily emission factor for i, j, and k conditions, kg CH4 ha-1 day-1

tijk = cultivation period of rice for i, j, and k conditions, day
Aijk = annual harvested area of rice for i, j, and k conditions, ha yr-1
i, j, and k = represent different ecosystems, water regimes, type and amount of organic amendments, andother conditions under which CH4 emissions from rice may vary
The different conditions that should be considered include rice ecosystem type, flooding pattern before andduring cultivation period, and type and amount of organic amendments. Other conditions such as soil type, andrice cultivar can be considered for the disaggregation if country-specific information about the relationshipbetween these conditions and CH4 emissions are available. The rice ecosystem types and water regimes during
cultivation period are listed in Table 5.12. If the national rice production can be subdivided into climatic zones
with different production systems (e.g., flooding patterns), Equation 5.1 should be applied to each region
separately. The same applies if rice statistics or expert judgments are available to distinguish management
practices or other factors along administrative units (district or province). In addition, if more than one crop is
harvested during a given year, emissions should be estimated for each cropping season taking into accountpossible differences in cultivation practice (e.g., use of organic amendments, flooding pattern before and during
the cultivation period).
The decision tree in Figure 5.2 guides inventory agencies through the process of applying the good practice
IPCC approach. Implicit in this decision tree is a hierarchy of disaggregation in implementing the IPCC method.
Within this hierarchy, the level of disaggregation utilised by an inventory agency will depend upon theavailability of activity and emission factor data, as well as the importance of rice as a contributor to its national
greenhouse gas emissions. The specific steps and variables in this decision tree, and the logic behind it, arediscussed in the text that follows the decision tree.
Tier 1
Tier 1 applies to countries in which either CH4 emissions from rice cultivation are not a key category or countryspecific emission factors do not exist. The disaggregation of the annual harvest area of rice needs to be done forat least three baseline water regimes including irrigated, rainfed, and upland. It is encouraged to incorporate as
many of the conditions (i, j, k, etc.) that influence CH4 emissions (summarized in Box 5.2) as possible.
Emissions for each sub-unit are adjusted by multiplying a baseline default emission factor (for field with no preseason flooding for less than 180 days prior to rice cultivation and continuously flooded fields without organic
amendments, EFc) by various scaling factors as shown in Equation 5.2. The calculations are carried out for eachwater regime and organic amendment separately as shown in Equation 5.1.

4

In case of multiple cropping during the same year, ‘harvested area’ is equal to the sum of the area cultivated for each
cropping.

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BOX 5.2
CONDITIONS INFLUENCING CH4 EMISSIONS FROM RICE CULTIVATION

The following rice cultivation characteristics should be considered in calculating CH4 emissions aswell as in developing emission factors:
Regional differences in rice cropping practices: If the country is large and has distinct
agricultural regions with different climate and/or production systems (e.g., flooding patterns), a
separate set of calculations should be performed for each region.
Multiple crops: If more than one crop is harvested on a given area of land during the year, and thegrowing conditions vary among cropping seasons, calculations should be performed for eachseason.
Water regime: In the context of this chapter, water regime is defined as a combination of (i)ecosystem type and (ii) flooding pattern.
Ecosystem type: At a minimum, separate calculations should be undertaken for each rice
ecosystem (i.e., irrigated, rainfed, and deep water rice production).
Flooding pattern: Flooding pattern of rice fields has a significant effect on CH4 emissions (Sass etal., 1992; Yagi et al., 1996; Wassmann et al., 2000). Rice ecosystems can further be distinguishedinto continuously and intermittently flooded (irrigated rice), and regular rainfed, drought prone,and deep water (rainfed), according to the flooding patterns during the cultivation period. Also,
flooding pattern before cultivation period should be considered (Yagi et al., 1998; Cai et al., 2000;
2003a; Fitzgerald et al., 2000).
Organic amendments to soils: Organic material incorporated into rice soils increases CH4emissions (Schütz et al., 1989; Yagi and Minami, 1990; Sass et al., 1991). The impact of organic
amendments on CH4 emissions depends on type and amount of the applied material which can be
described by a dose response curve (Denier van der Gon and Neue, 1995; Yan et al., 2005).
Organic material incorporated into the soil can either be of endogenous (straw, green manure, etc.)
or exogenous origin (compost, farmyard manure, etc.). Calculations of emissions should consider
the effect of organic amendments.
Other conditions: It is known that other factors, such as soil type (Sass et al., 1994; Wassmann etal., 1998; Huang et al., 2002), rice cultivar (Watanabe and Kimura, 1998; Wassmann and Aulakh,2000), sulphate containing amendments (Lindau et al., 1993; Denier van der Gon and Neue, 2002),
etc., can significantly influence CH4 emissions. Inventory agencies are encouraged to make every
effort to consider these conditions if country-specific information about the relationship between
these conditions and CH4 emissions is available.

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Chapter 5: Cropland

Figure 5.2

Decision tree for CH 4 emissions from rice production

Start

Are
there different
agroecological zones in
the country?

Yes

Calculate emissions for eachagro-ecological zone.

Yes

Calculate emissions for each
cropping (i.e., dry season-,wet season-, early-, single-,
late-cropping).

Yes

Calculate emissions using
country-specific methods forhigher level ofdisaggregation as basis for
the Tier 3 method.

No
Are there
multiple rice cropping
during the sameyear?
No
Are countryspecific methods,
including modelling or direct
measurement approach,available?

Box 3: Tier 3NoAre
country-specificemission factors available fordifferent water
regime?

Yes

Calculate emissions using
the Tier 2 method.
Box 2: Tier 2NoIs riceproduction a key source
category1?

Yes

Collect data forTier 2 or Tier 3
method.

No

Calculate emissions using theTier 1 default emission factor andscaling factors together withactivity data for harvested area
and cultivation period.
Box 1: Tier 1

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

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EQUATION 5.2
ADJUSTED DAILY EMISSION FACTOR
EFi = EFc • SFw • SF p • SFo • SFs ,rWhere:
EFi = adjusted daily emission factor for a particular harvested area
EFc = baseline emission factor for continuously flooded fields without organic amendments
SFw = scaling factor to account for the differences in water regime during the cultivation period (fromTable 5.12)
SFp = scaling factor to account for the differences in water regime in the pre-season before the cultivation
period (from Table 5.13)
SFo = scaling factor should vary for both type and amount of organic amendment applied (from Equation
5.3 and Table 5.14)
SFs,r = scaling factor for soil type, rice cultivar, etc., if available

Tier 2Tier 2 applies the same methodological approach as Tier 1, but country-specific emission factors and/or scaling
factors should be used. These country-specific factors are needed to reflect the local impact of the conditions (i, j,
k, etc.) that influence CH4 emissions, preferably being developed through collection of field data. As for Tier 1
approach, it is encouraged to implement the method at the most disaggregated level and to incorporate themultitude of conditions (i, j, k, etc.) that influence CH4 emissions.
Tier 3Tier 3 includes models and monitoring networks tailored to address national circumstances of rice cultivation,
repeated over time, driven by high-resolution activity data and disaggregated at sub-national level. Models canbe empirical or mechanistic, but must in either case be validated with independent observations from country or
region-specific studies that cover the range of rice cultivation characteristics (Cai et al., 2003b; Li et al., 2004;
Huang et al., 2004). Proper documentation of the validity and completeness of the data, assumptions, equations
and models used is therefore critical. Tier 3 methodologies may also take into account inter-annual variability
triggered by typhoon damage, drought stress, etc. Ideally, the assessment should be based on recent satellite data.

5.5.2

Choice of emission and scaling factors

Tier 1
A baseline emission factor for no flooded fields for less than 180 days prior to rice cultivation and continuously
flooded during the rice cultivation period without organic amendments (EFc) is used as a starting point. The
IPCC default for EFc is 1.30 kg CH4 ha-1 day-1 (with error range of 0.80 - 2.20, Table 5.11), estimated by astatistical analysis of available field measurement data (Yan et al., 2005, the data set used in the analysis isavailable at a web site5).
Scaling factors are used to adjust the EFc to account for the various conditions discussed in Box 5.2, which resultin adjusted daily emission factors (EFi) for a particular sub-unit of disaggregated harvested area according toEquation 5.2. The most important scaling factors, namely water regime during and before cultivation period andorganic amendments, are represented in Tables 5.12, 5.13, and 5.14, respectively, through default values.
Country-specific scaling factors should only be used if they are based on well-researched and documented
measurement data. It is encouraged to consider soil type, rice cultivar, and other factors if available.

5

http://www.jamstec.go.jp/frcgc/

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Chapter 5: Cropland

TABLE 5.11
DEFAULT CH4 BASELINE EMISSION FACTOR ASSUMING NO FLOODING FOR LESS THAN 180 DAYS PRIOR TO RICECULTIVATION, AND CONTINUOUSLY FLOODED DURING RICE CULTIVATION WITHOUT ORGANIC AMENDMENTS-1

Emission factor

Error range

1.30

0.80 - 2.20

-1

CH4 emission (kg CH4 ha d )Source: Yan et al., 2005

Water regime during the cultivation period (SFw): Table 5.12 provides default scaling factors and error
ranges reflecting different water regimes. The aggregated case refers to a situation when activity data are only
available for rice ecosystem types, but not for flooding patterns (see Box 5.2). In the disaggregated case,flooding patterns can be distinguished in the form of three subcategories as shown in Table 5.12. It is goodpractice to collect more disaggregated activity data and apply disaggregated case SFw whenever possible.

TABLE 5.12
DEFAULT CH4 EMISSION SCALING FACTORS FOR WATER REGIMES DURING THE CULTIVATION PERIOD RELATIVE TO
CONTINUOUSLY FLOODED FIELDS

Aggregated case
Water regime

Scalingfactor
(SFw)

Error
range

Scalingfactor
(SFw)

Error
range

Upland a

0

-

0

-

1

0.79 - 1.26

0.60

0.46 - 0.80

Intermittently flooded – multiple aeration

0.52

0.41 - 0.66

Regular rainfed

0.28

0.21 - 0.37

0.25

0.18 - 0.36

0.31

ND

Continuously flooded
Irrigated b

Disaggregated caseIntermittently flooded – single aeration

Rainfed anddeep water cDrought prone

0.78

0.27

0.62 - 0.98

0.21 - 0.34

Deep water
ND: not determinedaFields are never flooded for a significant period of time.

b

Fields are flooded for a significant period of time and water regime is fully controlled.
• Continuously flooded: Fields have standing water throughout the rice growing season and may only dry out for harvest (end-season
drainage).
• Intermittently flooded : Fields have at least one aeration period of more than 3 days during the cropping season.
- Single aeration: Fields have a single aeration during the cropping season at any growth stage (except for end-season drainage).
- Multiple aeration: Fields have more than one aeration period during the cropping season (except for end-season drainage).

cFields are flooded for a significant period of time and water regime depends solely on precipitation.
• Regular rainfed: The water level may rise up to 50 cm during the cropping season.
• Drought prone: Drought periods occur during every cropping season.
• Deep water rice: Floodwater rises to more than 50 cm for a significant period of time during the cropping season.

Note: Other rice ecosystem categories, like swamps and inland, saline or tidal wetlands may be discriminated within each sub-category.
Source: Yan et al., 2005

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Water regime before the cultivation period (SFp): Table 5.13 provides default scaling factors for water regime
before the cultivation period which can be used when country-specific data are unavailable. This table
distinguishes three different water regimes prior to rice cultivation, namely:1.

Non-flooded pre-season < 180 days, which often occurs under double cropping of rice;2.

Non-flooded pre-season > 180 days, e.g., single rice crop following a dry fallow period; and3.

Flooded pre-season in which the minimum flooding interval is set to 30 days; i.e., shorter flooding periods(usually done to prepare the soil for ploughing) will not be included in this category.

When activity data for the pre-season water status are not available, aggregated case factors can be used. It isgood practice to collect more disaggregated activity data and apply disaggregated case of SFp. Scaling factors for
additional water regimes can be applied if country-specific data are available.

TABLE 5.13
DEFAULT CH4 EMISSION SCALING FACTORS FOR WATER REGIMES BEFORE THE CULTIVATION PERIOD
Aggregated case

Water regime prior to rice cultivation (schematicpresentation showing flooded periods as shaded)

Scalingfactor (SFp)

Error
range

< 180 dNon flooded preseason <180 d

CROP> 180 dNon flooded preseason >180 d
Flooded preseason (>30 d)a,b

CROP

1.22

1.07 - 1.40

> 30 dCROPa

Short pre-season flooding periods of less than 30 d are not considered in selection of SFpb

For calculation of pre-season emission see below (section on completeness)

Disaggregated case
Scalingfactor (SFp)

Error
range

1

0.88 - 1.14

0.68

0.58 - 0.80

1.90

1.65 - 2.18

Source: Yan et al., 2005

Organic amendments (SFo): It is good practice to develop scaling factors that incorporate information on thetype and amount of organic amendment applied (compost, farmyard manure, green manure, and rice straw). On
an equal mass basis, more CH4 is emitted from amendments containing higher amounts of easily decomposablecarbon and emissions also increase as more of each organic amendment is applied. Equation 5.3 and Table 5.14
present an approach to vary the scaling factor according to the amount of different types of amendment applied.
Rice straw is often incorporated into the soil after harvest. In the case of a long fallow after rice straw
incorporation, CH4 emissions in the ensuing rice-growing season will be less than the case that rice straw isincorporated just before rice transplanting (Fitzgerald et al., 2000). Therefore, the timing of rice straw
application was distinguished. An uncertainty range of 0.54-0.64 can be adopted for the exponent 0.59 in
Equation 5.3.

EQUATION 5.3
ADJUSTED CH4 EMISSION SCALING FACTORS FOR ORGANIC AMENDMENTS⎛⎞
SFo = ⎜⎜1 + ∑ ROAi • CFOAi ⎟⎟
i
⎝
⎠

0.59

Where:
SFo = scaling factor for both type and amount of organic amendment applied
ROAi = application rate of organic amendment i, in dry weight for straw and fresh weight for others,
tonne ha-1
CFOAi = conversion factor for organic amendment i (in terms of its relative effect with respect to straw
applied shortly before cultivation) as shown in Table 5.14.

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TABLE 5.14
DEFAULT CONVERSION FACTOR FOR DIFFERENT TYPES OF ORGANIC AMENDMENT
Conversion factor(CFOA)

Error range

Straw incorporated shortly (<30 days) before cultivationa

1

0.97 - 1.04

Straw incorporated long (>30 days) before cultivationa

0.29

0.20 - 0.40

Compost0.05

0.01 - 0.08

Farm yard manure

0.14

0.07 - 0.20

Green manure

0.50

0.30 - 0.60

Organic amendmentaStraw application means that straw is incorporated into the soil, it does not include case that straw just placed on the soil surface, northat straw was burnt on the field.

Source: Yan et al., 2005

Soil type (SFs) and rice cultivar (SFr): In some countries emission data for different soil types and rice cultivar
are available and can be used to derive SFs and SFr, respectively. Both experiments and mechanistic knowledge
confirm the importance of these factors, but large variations within the available data do not allow one to define
reasonably accurate default values. It is anticipated that in the near future simulation models will be capable ofproducing specific scaling factors for SFs and SFr.
Tier 2Inventory agencies can use country-specific emission factors from field measurements that cover the conditionsof rice cultivation in their respective country. It is good practice to compile country-specific data bases onavailable field measurements which supplement the Emission Factor database6 by other measurement programs(e.g., national) not yet included in this data base. However, certain standard QA/QC requirements apply to thesefield measurements (see Section 5.5.5).
In Tier 2, inventory agencies can define the baseline management according to the prevailing conditions found intheir respective country and determine country-specific emission factors for such a baseline. Then, inventory
agencies can also determine country-specific scaling factors for management practices other than the baseline. Incase where country-specific scaling factors are not available, default scaling factors can be used. However, thismay require some recalculation of the scaling factors given in Tables 5.12 to 5.14 if the condition is different
from the baseline.
Tier 3Tier 3 approaches do not require choice of emission factors, but are instead based on a thorough understandingof drivers and parameters (see above).

5.5.3

Choice of activity data

In addition to the essential activity data requested above, it is good practice to match data on organic
amendments and soil types to the same level of disaggregation as the activity data. It may be necessary tocomplete a survey of cropping practices to obtain data on the type and amount of organic amendments applied.
Activity data are primarily based on harvested area statistics, which should be available from a national statistics
agency as well as complementary information on cultivation period and agronomic practices. The activity data
should be broken down by regional differences in rice cropping practices or water regime (see Box 5.2).
Harvested area estimates corresponding to different conditions may be obtained on a countrywide basis throughaccepted methods of reporting. The use of locally verified areas would be most valuable when they arecorrelated with available data for emission factors under differing conditions such as climate, agronomic
practices, and soil properties. If these data are not available in-country, they can be obtained from international
data sources: e.g., IRRI (1995) and the World Rice Statistics on the website of IRRI 7 (International Rice
Research Institute), which include harvest area of rice by ecosystem type for major rice producing counties, arice crop calendar for each country, and other useful information, and the FAOSTAT on the website of FAO8.
6

http://www.jamstec.go.jp/frcgc/

7

http://www.irri.org/science/ricestat/8

http://faostat.fao.org/

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The use of locally verified areas would be most valuable when they are correlated with available data foremission factors under differing conditions such as climate, agronomic practices, and soil properties. It may benecessary to consult local experts for a survey of agronomic practices relevant to methane emissions (organic
amendments, water management, etc.).
Most likely, activity data will be more reliable as compared to the accuracy of the emission factors. However, forvarious reasons the area statistics may be biased and a check of the harvested area statistics for (parts of) the
country with remotely sensed data is encouraged.
In addition to the essential activity data requested above, it is good practice, particularly in Tiers 2 and 3
approaches, to match data on organic amendments and other conditions, e.g., soil types, to the same level ofdisaggregation as the activity data.

5.5.4

Uncertainty assessment

The general principles of uncertainty assessment relevant for national emission inventories are elucidated inVolume 1, Chapter 3. The uncertainty of emission and scaling factors may be influenced by natural variability,
such as annual climate variability, and variability within units that are assumed to be homogenous, such as
spatial variability in a field or soil unit. For this source category, good practice should permit determination of
uncertainties using standard statistical methods when enough experimental data are available. Studies to quantify
some of this uncertainty are rare but available (e.g., for soil type induced variability). The variability found insuch studies is assumed to be generally valid. For more detail, see Sass (2002).
Important activity data necessary to assign scaling factors (i.e., data on cultural practices and organic
amendments) may not be available in current databases/statistics. Estimates of the fraction of rice farmers using
a particular practice or amendment must then be based on expert judgement, and the uncertainty range in theestimated fraction should also be based on expert judgement. As a default value for the uncertainty in thefraction estimate as ± 0.2 (e.g., the fraction of farmers using organic amendment estimated at 0.4, the uncertainty
range being 0.2 - 0.6). Volume 1, Chapter 3 provides advice on quantifying uncertainties in practice including
combining expert judgements and empirical data into overall uncertainty estimates.
In the case of CH4 emissions from rice cultivation, the uncertainty ranges of Tier 1 values (emission and scaling
factors) can be adopted directly from Tables 5.11-5.14. Ranges are defined as the standard deviation about themean, indicating the uncertainty associated with a given default value for this source category. The exponent inEquation 5.3 is provided with an uncertainty range of 0.54 - 0.64. Uncertainty assessment of Tier 2 and Tier 3
approaches will depend on the respective data base and model used. Therefore, it is good practice to apply
general principles of statistical analysis as outlined in Volume 1, Chapter 3 as well as model approaches as
outlined in Volume 4, Chapter 3, Section 3.5.

5.5.5

Completeness, Time series, QA/QC, and Reporting

COMPLETENESS
Complete coverage for this source category requires estimation of emissions from the following activities, where
present:•

If soil submergence is not limited to the actual rice growing season, emissions outside of the rice growing
season should be included (e.g., from a flooded fallow period). For further information, see Yagi et al., 1998;
Cai et al., 2000; and Cai et al. 2003a;•

Other rice ecosystem categories, like swamp, inland-saline or tidal rice fields may be discriminated withineach sub-category according to local emission measurements;•

If more than one rice crop is grown annually, these rice crops should be reported independently according to
the local definition (e.g., early rice, late rice, wet season rice, dry season rice). The rice crops may fall into
different categories with a different seasonally integrated emission factor and different correction factors forother modifiers like organic amendments.

DEVELOPING A CONSISTENT TIME SERIESAs for other sources and categories, the methods for estimating CH4 emissions from rice fields should be appliedconsistently to every year in the time series and at the same level of disaggregation. If detailed activity level data
are unavailable for earlier years, emissions for these years should be recalculated according to the guidance
provided in Volume 1, Chapter 5. If there have been significant changes in agricultural practices affecting CH4emissions over the time series, the estimation method should be implemented at a level of disaggregation which

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Chapter 5: Croplandis sufficient to discern the effects of these changes. For example, various trends in (Asian) rice agriculture suchas the adoption of new rice varieties, increasing use of inorganic fertiliser, improved water management,changing use of organic amendments, and direct seeding may lead to increases or decreases in overall emissions.
To weigh the impact of these changes, it may be necessary to use model studies.

REPORTING AND DOCUMENTATION
It is good practice to document and archive all information required to produce the national emissions inventory
estimates as outlined in Volume 1, Chapter 8. It is good practice to document the emission estimate by reporting
the information required to fill out the rice worksheet in the Guidelines. Inventory agencies that do not use theworksheets should provide comparable information. If the emission estimate is disaggregated by region,
information on each region should be reported.
The following additional information should be reported, if available, to ensure transparency:•

Water management practices;•

The types and amounts of organic amendments used. (Incorporation of rice straw or residues of the previous(non-rice) crop should be considered an organic amendment, although it may be a normal production
practice and not aimed at increasing nutrient levels as is the case with manure additions);•

Soil types used for rice agriculture;•

Number of rice crops grown annually; and•Most important rice cultivars grown.

Inventory agencies using country-specific emission factors should provide information on the origin and basis ofeach factor, compare them to other published emission factors, explain any significant differences, and attempt
to place bounds on the uncertainties.

INVENTORY QUALITY ASSESSMENT/QUALITY CONTROL (QA/QC)
It is good practice to implement quality control checks as outlined in Volume 1, Chapter 6, and expert review ofthe emission estimates. Additional quality control checks as outlined in Tier 2 procedures in Volume 1, Chapter
6, and quality assurance procedures may also be applicable, particularly if higher tier methods are used todetermine emissions from this source.
A detailed treatment of inventory QA/QC for field measurement is given by Sass (2002). Some important issues
are highlighted and summarised below.
Measurements of standard methane emissions: The inventory QC procedures used at the rice field level will
be determined largely by local scientists. There are, however, certain internationally determined procedures toobtain ‘standard emission factors’ that should be common to all monitoring programmes. Instructions for
obtaining standard emission factors are contained in IAEA (1992) and IGAC (1994). It is desirable for eachlaboratory in every reporting country to obtain this standard emission factor to ensure the intercomparibility andintercalibration of extended data sets used to establish country-specific emission factors.
Compiling national emissions: Before accepting emissions data, the inventory agency should carry out anassessment of data quality and sampling procedures. This type of review requires close cooperation with national
laboratories to obtain enough information to verify the reported emissions. The assessment should include
sample recalculations, an assessment on reliability of agronomic and climate data, an identification of potential
bias in the methodology, and recommendations for improvement.
It is, at present, not possible to cross-check emissions estimates from this source category through externalmeasurements. However, the inventory agency should ensure that emissions estimates undergo quality control by:•

Cross-referencing aggregated crop yield and reported field area statistics with national totals or othersources of crop yield/area data;•

Back-calculating national emission factors from aggregated emissions and other data; and•Cross-referencing reported national totals with default values and data from other countries.

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Annex 5A.1

Estimation of default stock change factors formineral soil C emissions/removals for cropland

Default stock change factors are provided in Table 5.5 that were computed using a global dataset of experimental
results for tillage, input, set-aside, and land use. The land-use factor represents the loss of carbon that occurs
after 20 years of continuous cultivation. Tillage and input factors represent the effect on C stocks after 20 years
following the management change. Set-aside factors represent the effect of temporary removal of cultivatedcropland from production and placing it into perennial cover for a period of time that may extend to 20 years.
Experimental data (citations provided in reference list) were analyzed 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 pointin the dataset. Similarly, time series data were not aggregated, even though those measurements were conductedon the same plots. Consequently, random effects were used to account for the dependencies in times series data
and among data points representing different depths from the same study. If significant, a country level random
effect was used to assess an additional uncertainty associated with applying a global default value to a specificcountry (included in the default uncertainties). 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 thetables). 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 factor, which represents the average loss of carbon at 20 years or longer time period
following cultivation. Users of the Tier 1 method can approximate the annual change in carbon storage by
dividing the inventory estimate by 20. Variance was calculated for each of the factor values, and can be usedwith simple error propagation methods or to construct probability distribution functions with a normal density.

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Chapter 5: Cropland

References
IPCC (1997). Revised 1996 IPCC Guidelines for National Greenhouse Inventories. Houghton J.T., Meira Filho
L.G., Lim B., Tréanton K., Mamaty I., Bonduki Y., Griggs D.J. Callander B.A. (Eds). Intergovernmental
Panel on Climate Change (IPCC), IPCC/OECD/IEA, Paris, France.
IPCC (2000). Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R.,
Martinsen T., Meijer J., Miwa K., Tanabe K. (Eds). Intergovernmental Panel on Climate Change (IPCC),
IPCC/OECD/IEA/IGES, Hayama, Japan.
IPCC (2003). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Penman J., Gytarsky M.,
Hiraishi T., Krug, T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K., Wagner F.
(Eds).Intergovernmental Panel on Climate Change (IPCC), IPCC/IGES, Hayama, Japan.

BIOMASS
Albrecht, A. and Kandji, S.T. (2003). Carbon sequestration in tropical agroforestry systems. Agriculture,
Ecosytems and Environment 99: 15-27.
Hairiah, K. and Sitompul, S.M. (2000). Assessment and simulation of above-ground and below-ground carbon
dynamics. Report to Asia Pacific Network (APN). Brawijaya University, Faculty of Agriculture, Malang,Indonesia..
Lasco, R.D. and Suson, P.D. (1999). A Leucaena Leucocephala -based indigenous fallow system in central
Philippines: the Naalad system. Intl Tree Crops Journal 10: 161-174.
Lasco, R.D., Lales, J.S., Arnuevo, M.T., Guillermo, I.Q., de Jesus, A.C., Medrano, R., Bajar, O.F. and Mendoza,
C.V. (2002). Carbon dioxide (CO2) storage and sequstration of land cover in the Leyte Geothermal
Reservation. Renewable Energy 25: 307-315.
Lasco, R.D., Sales, R.F., Estrella, R., Saplaco, S.R., Castillo, A.S.A., Cruz, R.V.O. and Pulhin, F.B. (2001).
Carbon stocks assessment of two agroforestry systems in the Makiling Forest Reserve, Philippines.
Philippine Agricultural Scientist 84: 401-407.
Millennium Ecosystems Assessment (2005). Ecosystems and Human Well-being: A Synthesis. Island Press,
Washington DC. 137pp.
Moore III, B. (2002). Chapter 2 Challenges of a changing earth. In, Challenges of a Changing Earth (W. Steffen,
J. Jaeger, D.J. Carson, and C. Bradshaw, eds). Berlin: Springer-Verlag. Pp. 7-17.
Palm, C.A., Woomer, P.L., Alegre, J., Arevalo, L., Castilla, C., Cordeiro, D.G., Feigl, B., Hairiah, K., KottoSame, J., Mendes, A., Maukam, A., Murdiyarso, D., Njomgang, R., Parton, W.J., Ricse, A., Rodrigues,
V., Sitompus, S.M. and van Noordwijk, M. (1999). Carbon sequestration and trace gas emissions inslash-and-burn and alternative land-uses in the Humid Tropics. ACB Climate Change Working Group.
Final Report Phase II, Nairobi, Kenya.
Siregar, C.A. and Gintings, Ng. (2000). Research activities related to ground biomass measurement at Forestry
Research Development Agency. Paper presented at the Workshop on LUCC and Greenhouse Gas
Emissions Biophysical Data. Institut Pertanian Bogor. Indonesia, 16 December 2000.
Tjitrosemito, S. and Mawardi, I. (2000). 'Terrestrial carbon stock in oil palm plantation', Paper presented at the
Science Policy Workshop on Terrestrial Carbon Assessment for Possible Trading under CDM Projects,
Bogor, Indonesia 28-29 February 2000.
Tomich, T.P., van Noordwijk, M., Budidarsono, S., Gillison, A., Kusumanto, T., Murdiyarso, D., Stolle, T. andFagi, A.M. (1998). Alternative to slash and burn in Indonesia. Summary Report and Synthesis of Phase
II. ASB-Indonesia, Report No. 8, ICRAF, Bogor, Indonesia.
Wasrin, U.R., Rohiani, A, Putera, A.E. and Hidayat, A. (2000). Assessment of above-ground C-stock using
remote sensing and GIS technique. Final Report, Seameo Biotrop, Bogor, 28p.

SOILS
Armentano, T.V. and Menges, E.S. (1986). Patterns of change in the carbon balance of organic soil-wetlands ofthe temperate zone. Journal of Ecology 74: 755-774.
Augustin, J., Merbach, W., Schmidt, W. and Reining, E. (1996). Effect of changing temperature and water table
on trace gas emission from minerotrophic mires. Journal of Applied Botany-Angewandte Botanik 70, 4551.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.55

Volume 4: Agriculture, Forestry and Other Land Use

Bruce, J.P., Frome, M., Haites, E., Janzen, H., Lal, R. and Paustian, K. (1999). Carbon sequestration in soils.
Journal of Soil and Water Conservation 54:382-389.
Davidson, E.A. and Ackerman, I.L. (1993). Changes in soil carbon inventories following cultivation ofpreviously untilled soils. Biogeochemistry, 20:161–164.
Falloon, P. and Smith, P. (2003). Accounting for changes in soil carbon under the Kyoto Protocol: need for
improved long-term data sets to reduce uncertainty in model projections. Soil Use and Management, 19,265-269.
Freibauer, A. (2003). Biogenic Emissions of Greenhouse Gases from European Agriculture. European Journalof Agronomy 19(2): 135-160.
Freibauer, A. and Kaltschmitt, M. (eds). (2001). Biogenic greenhouse gas emissions from agriculture in Europe.
European Summary Report of the EU concerted action FAIR3-CT96-1877, Biogenic emissions ofgreenhouse gases caused by arable and animal agriculture, 220 p.
Glenn, S.M., Hayes, A. and Moore, T.R. (1993). Methane and carbon dioxide fluxes from drained peatland soils,
southern Quebec. Global Biogeochemical Cycles 7:247-257
Kasimir-Klemedtsson, A., Klemedtsson, L., Berglund, K., Martikainen, P., Silvola, J. and Oenema, O. (1997).
Greenhouse gas emissions from farmed organic soils: a review. Soil Use and Management 13:245-250.
Leifeld, J., Bassin, S. and Fuhrer, J. (2005). Carbon stocks in Swiss agricultural soils predicted by land-use, soil
characteristics, and altitude. Agriculture Ecosystems & Environment 105, 255-266.
Lohila, A., Aurela, M., Tuovinen, J.P. and Laurila, T. (2004). Annual CO2 exchange of a peat field growing
spring barley or perennial forage grass. Journal of Geophysical Research 109, D18116
Maljanen, M., Martikainen, P.J., Walden, J. and Silvola, J. (2001). CO2 exchange in an organic field growing
barley or grass in eastern Finland. Global Change Biology 7, 679-692.
Maljanen, M., Komulainen, V.M., Hytonen, J., Martikainen, P. and Laine, J. (2004). Carbon dioxide, nitrous
oxide and methane dynamics in boreal organic agricultural soils with different soil characteristics. Soil
Biology and Biochemistry 36, 1801-1808.
Mann, L.K. (1986). Changes in soil carbon storage after cultivation. Soil Science 142:279-288.
McGill, W. B. (1996). Review and classification of ten soil organic matter models. In: Powlson D.S., Smith P.,
and Smith J.U. (eds.). Evaluation of Soil Organic Matter Models Using Existing Long-Term Datasets.
Springer-Verlag, Heidelberg: pp. 111-132.
Monte, L, Hakanson, L., Bergstrom, U., Brittain, J. and Heling, R. (1996). Uncertainty analysis and validation ofenvironmental models: the empirically based uncertainty analysis. Ecological Modelling 91:139-152.
Nusser, S.M. and Goebel, J.J. (1997). The National Resources Inventory: a long-term multi-resource monitoring
programme. Environmental and Ecological Statistics 4:181-204.
Nykänen, H., Alm, J., Lang, K., Silvola, J. and Martikainen, P.J. (1995). Emissions of CH4, N2O and CO2 from avirgin fen and a fen drained for grassland in Finland. Journal of Biogeography 22, 351-357.
Ogle, S.M., Breidt, F.J., Eve, M.D. and Paustian, K. (2003). Uncertainty in estimating land-use and management
impacts on soil organic carbon storage for U.S. agricultural lands between 1982 and 1997. Global Change
Biology 9:1521-1542.
Ogle, S.M., Breidt, F.J. and Paustian, K. (2005). Agricultural management impacts on soil organic carbon
storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry72:87-121.
Ogle, S.M., Breidt, F.J. and Paustian, K. (2006). Bias and variance in model results associated with spatial
scaling of measurements for parameterization in regional assessments. Global Change Biology 12:516523.
Paustian, K, Andren, O., Janzen, H.H., Lal, R., Smith, P., Tian, G., Tiessen, H., van, Noordwijk, M. and
Woomer, P.L. (1997). Agricultural soils as a sink to mitigate CO2 emissions. Soil Use and Management
13:230-244.
Pierce, F. J., Fortin, M.-C. and Staton, M.J. (1994). Periodic plowing effects on soil properties in a no-till
farming system. Soil Science Society of America Journal 58:1782-1787.

5.56

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Chapter 5: CroplandPowers, J. S., Read, J. M., Denslow, J. S. and Guzman, S. M. (2004). Estimating soil carbon fluxes following
land-cover change: a test of some critical assumptions for a region in Costa Rica. Global Change Biology
10:170-181.
Smith, J.E. and Heath, L.S. (2001). Identifying influences on model uncertainty: an application using a forest
carbon budget model. Environmental Management 27:253-267.
Smith, P., Powlson, D.S., Smith, J.U. and Elliott, E.T. (eds) (1997). Evaluation and comparison of soil organic
matter models. Special Issue, Geoderma 81:1-225.
Smith, P., Powlson, D.S., Glendining, M.J. and Smith, J.U. (1998) Preliminary estimates of the potential for
carbon mitigation in European soils through no-till farming. Global Change Biology 4: 679-685.
VandenBygaart, A.J., Gregorich, E.G., Angers, D.A., et al. (2004). Uncertainty analysis of soil organic carbon
stock change in Canadian cropland from 1991 to 2001. Global Change Biology 10:983-994.

ESTIMATION OF DEFAULT STOCK CHANGE FACTORS FOR MINERAL SOIL CEMISSIONS/REMOVALS FOR CROPLAND: ANNEX 5A.1
Agbenin, J.O. and Goladi, J.T. (1997). Carbon, nitrogen and phosphorus dynamics under continuous cultivation
as influenced by farmyard manure and inorganic fertilizers in the savanna of northern Nigeria.
Agriculture, Ecosystems and Environment 63:17-24.
Ahl, C., Joergensen, R.G., Kandeler, E., Meyer, B. and Woehler, V. (1998). Microbial biomass and activity insilt and sand loams after long-term shallow tillage in central Germany. Soil and Tillage Research 49:93104.
Alvarez, R., Russo, M.E., Prystupa, P., Scheiner, J.D. and Blotta, L. (1998). Soil carbon pools underconventional and no-tillage systems in the Argentine Rolling Pampa. Agronomy Journal 90:138-143.
Angers, D.A., Bolinder, M.A., Carter, M.R., Gregorich, E.G., Drury, C.F., Liang, B.C., Voroney, R.P., Simard,
R.R., Donald, R.G., Beyaert, R.P. and Martel, J. (1997). Impact of tillage practices on organic carbon andnitrogen storage in cool, humid soils of eastern Canada. Soil and Tillage Research 41:191-201.
Angers, D.A., Voroney, R.P. and Cote, D. (1995). Dynamics of soil organic matter and corn residues affected bytillage practices. Soil Science Society of America Journal 59:1311-1315.
Anken, T., Weisskopf, P., Zihlmann, U., Forrer, H., Jansa, J. and Perhacova, K. (2004). Long-term tillage systemeffects under moist cool conditions in Switzerland. Soil and Tillage Research 78:171-183.
Baer, S.G., Rice, C.W. and Blair, J.M. (2000). Assessment of soil quality in fields with short and long term
enrollment in the CRP. Journal of Soil and Water Convservation 55:142-146.
Balesdent, J., Mariotti, A. and Boisgontier, D. (1990). Effect of tillage on soil organic carbon mineralization
estimated from 13C abundance in maize fields. Journal of Soil Science 41:587-596.
Barber, R.G., Orellana, M., Navarro, F., Diaz, O. and Soruco, M.A. (1996). Effects of conservation andconventional tillage systems after land clearing on soil properties and crop yield in Santa Cruz, Bolivia.
Soil and Tillage Research 38:133-152.
Bauer, A. and Black, A.L. (1981). Soil carbon, nitrogen, and bulk density comparisons in two cropland tillage
systems after 25 years and in virgin grassland. Soil Science Society of America Journal 45:166-1170.
Bayer, C., Mielniczuk, J., Martin-Neto, L. and Ernani, P.R. (2002). Stocks and humification degree of organic
matter fractions as affected by no-tillage on a subtropical soil. Plant and Soil 238:133-140.
Bayer, C., Mielniczuk, J., Amado, T.J.C., Martin-Neto, L. and Fernández, S.V. (2000). Organic matter storage ina sandy clay loam Acrisol affected by tillage and cropping systems in southern Brazil. Soil and Tillage
Research 54:101-109.
Beare, M.H., Hendrix, P.F. and Coleman, D.C. (1994). Water-stable aggregates and organic matter fractions inconventional- and no-tillage soils. Soil Science Society of America Journal 58: 777-786.
Beyer, L. (1994). Effect of cultivation on physico-chemical, humus-chemical and biotic properties and fertilityof two forest soils. Agriculture, Ecosystems and Environment 48:179-188.
Black, A.L. and Tanaka, D.L. (1997). A conservation tillage-cropping systems study in the Northern Great
Plains of the United States. Pages 335-342 in Paul, E.A., K. Paustian, E.T. Elliott, and C.V. Cole, editors.
Soil Organic Matter in Temperate Agroecosystems: Long-term Experiments in North America. CRC
Press. Boca Raton, FL.

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Blanco-Canqui, H., Gantzer, C.J., Anderson, S.H. and Alberts, E.E. (2004). Tillage and crop influences on
physical properties for an Epiaqualf. Soil Science Society of America Journal 68:567-576.
Bordovsky, D.G., Choudhary, M. and Gerard, C.J. (1999). Effect of tillage, cropping, and residue management
on soil properties in the Texas rolling plains. Soil Science 164:331-340.
Borin, M., Menini, C. and Sartori, L. (1997). Effects of tillage systems on energy and carbon balance in northeastern Italy. Soil and Tillage Research 40:209-226.
Borresen, T. and Njos, A. (1993). Ploughing and rotary cultivation for cereal production in a long-term
experiment on a clay soil in southeastern Norway. 1. Soil properties. Soil and Tillage Research 28:97-108.
Bowman, R.A. and Anderson, R.L. (2002). Conservation Reserve Program: Effects on soil organic carbon andpreservation when converting back to cropland in northeastern Colorado. Journal of Soil and Water
Conservation 57:121-126.
Bremer, E., Janzen, H.H. and Johnston, A.M. (1994). Sensitivity of total, light fraction and mineralizable organic
matter to management practices in a Lethbridge soil. Canadian Journal of Soil Science 74:131-138.
Burke, I.C., Lauenroth, W.K. and Coffin, D.P. (1995). Soil organic matter recovery in semiarid grasslands:
implications for the Conservation Reserve Program. Ecological Applications 5:793-801.
Buschiazzo, D.E., Panigatti, J.L. and Unger, P.W. (1998). Tillage effects on soil properties and crop productionin the subhumid and semiarid Argentinean Pampas. Soil and Tillage Research 49:105-116.
Buyanovsky, G.A. and Wagner, G.H. (1998). Carbon cycling in cultivated land and its global significance.
Global Change Biology 4:131-141
Buyanovsky, G.A., Kucera, C.L. and Wagner, G.H. (1987). Comparative analysis of carbon dynamics in nativeand cultivated ecosystems. Ecology 68:2023-2031.
Cambardella, C.A. and Elliott, E.T. (1992). Particulate soil organic-matter changes across a grassland cultivation
sequence. Soil Science Society of America Journal 56:777-783.
Campbell, C.A. and Zentner, R.P. (1997). Crop production and soil organic matter in long-term crop rotations inthe semi-arid northern Great Plains of Canada. Pages 317-334 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.
Campbell, C.A., McConkey, B.G., Zentner, R.P., Selles, F. and Curtin, D. (1996). Long-term effects of tillage
and crop rotations on soil organic C and total N in a clay soil in southwestern Saskatchewan. Canadian
Journal of Soil Science 76:395-401.
Campbell, C.A., Lafond, G.P., Moulin, A.P., Townley-Smith, L. and Zentner, R.P. (1997). Crop production andsoil organic matter in long-term crop rotations in the sub-humid northern Great Plains of Canada. Pages
297-315 in: E.A. Paul, E.T. Elliott, K. Paustian, and C.V. Cole. Soil organic matter in temperate
agroecosystems: Long-term experiments in North America. CRC Press, Boca Raton, FL.
Campbell, C.A., Bowren, K.E., Schnitzer, M., Zentner, R.P. and Townley-Smith, L. (1991). Effect of crop
rotations and fertilization on soil organic matter and some biochemical properties of a thick black
Chernozem. Canadian Journal of Soil Science 71: 377-387.
Campbell, C.A., Zentner, R.P., Selles, F., Biederbeck, V.O., McConkey, B.G., Blomert, B. and Jefferson, P.G.
(2000). Quantifying short-term effects of crop rotations on soil organic carbon in southwestern
Saskatchewan. Canadian Journal of Soil Science 80:193-202.
Campbell, C.A., Biederbeck, V.O., McConkey, B.G., Curtin, D. and Zentner, R.P. (1999). Soil quality - effect oftillage and fallow frequency. Soil organic matter quality as influenced by tillage and fallow frequency in asilt loam in southwestern Saskatchewan. Soil Biology and Biochemistry 31:1-7.
Campbell, C.A., Biederbeck, V.O., Wen, G., Zentner, R.P., Schoenau, J. and Hahn, D. (1999). Seasonal trends inselected soil biochemical attributes: Effects of crop rotation in the semiarid prairie. Canadian Journal of
Soil Science 79:73-84.
Campbell, C.A., Biederbeck, V.O., Zentner, R.P. and Lafond, G.P. (1991). Effect of crop rotations and cultural
practices on soil organic matter microbial biomass and respiration in a thin Black Chernozem. Canadian
Journal of Soil Science 71: 363-376.
Carter, M.R., Johnston, H.W. and Kimpinski, J. (1988). Direct drilling and soil loosening for spring cereals on afine sandy loam in Atlantic Canada. Soil and Tillage Research 12:365-384.

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Carter, M.R., Sanderson, J.B., Ivany, J.A. and White, R.P. (2002). Influence of rotation and tillage on forage
maize productivity, weed species, and soil quality of a fine sandy loam in the cool-humid climate ofAtlantic Canada, Soil & Tillage Research, 67:85-98.
Carter, M.R. (1991). Evaluation of shallow tillage for spring cereals on a fine sandy loam. 2. Soil physical,
chemical and biological properties. Soil and Tillage Research 21:37-52.
Chan, K.Y., Roberts, W.P. and Heenan, D.P. (1992). Organic carbon and associated soil properties of a red Earthafter 10 years of rotation under different stubble and tillage practices. Australian Journal of Soil Research
30: 71-83.
Chan, K.Y. and Mead, J.A. (1988). Surface physical properties of a sandy loam soil under different tillage
practices. Australian Journal of Soil Research 26:549-559.
Chaney, B.K., Hodson, D.R. and Braim, M.A. (1985). The effects of direct drilling, shallow cultivation andploughing on some soil physical properties in a long-term experiment on spring barley. J. Agric. Sci.,
Camb. 104:125-133.
Clapp, C.E., Allmaras, R.R., Layese, M.F., Linden, D.R. and Dowdy, R.H. (2000). Soil organic carbon and 13C
abundance as related to tillage, crop residue, and nitrogen fertilization under continuous corn managementin Minnesota. Soil and Tillage Research 55:127-142.
Collins, H.P., Blevins, R.L., Bundy, L.G., Christenson, D.R., Dick, W.A., Huggins, D.R. and Paul, E.A. (1999).
Soil carbon dynamics in corn-based agroecosystems: results from carbon-13 natural abundance. Soil
Science Society of America Journal 63:584-591.
Corazza, E.J. et al. (1999). Behavior of different management systems as a source or sink of C-CO2 in relation tocerrado type vegetation. R.Bras Ci.Solo 23:425-432.
Costantini, A., Cosentino, D. and Segat, A. (1996). Influence of tillage systems on biological properties of a
Typic Argiudoll soil under continuous maize in central Argentina. Soil and Tillage Research 38:265-271.
Dalal, R.C. (1989). Long-term effects of no-tillage, crop residue, and nitrogen application on properties of a
Vertisol. Soil Science Society of America Journal 53:1511-1515.
Dalal, R.C. and Mayer, R.J. (1986). Long-term trends in fertility of soils under continuous cultivation and cereal
cropping in Southern Queensland. I. Overall changes in soil properties and trends in winter cereal yields.
Australian Journal of Soil Research 24:265-279.
Dalal R.C., Henderson P.A. and Glasby J.M. (1991). Organic matter and microbial biomass in a vertisol after 20
yr of zero tillage. Soil Biology and Biochemistry 23:435-441.
Dick, W.A. and Durkalski, J.T. (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,
editors. Advances in Soil Science: Management of Carbon Sequestration in Soil. CRC Press Inc. Boca
Raton, FL.
Dick, W.A., Edwards, W.M. and McCoy, E.L. (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, and C.V. Cole. Soil organic matter in temperate agroecosystems: Long-term
experiments in North America. CRC Press, Boca Raton, FL.
Doran, J.W., Elliott, E.T. and Paustian, K. (1998). Soil microbial activity, nitrogen cycling, and long-termchanges in organic carbon pools as related to fallow tillage management. Soil and Tillage Research 49:318.
Duiker, S.W. and Lal, R. (1999). Crop residue and tillage effects on carbon sequestration in a luvisol in central
Ohio. Soil and Tillage Research 52:73-81.
Edwards, J.H., Wood, C.W., Thurlow, D.L. and Ruf, M.E. (1992). Tillage and crop rotation effects on fertility
status of a Hapludult soil. Soil Science Society of America Journal 56:1577-1582.
Eghball, B., Mielke, L.N., McCallister, D.L. and Doran, J.W. (1994). Distribution of organic carbon andinorganic nitrogen in a soil under various tillage and crop sequences. Journal of Soil and Water
Conservation 49: 201-205.
Fabrizzi, K.P., Moron, A. and Garcia, F.O. (2003). Soil carbon and nitrogen organic fractions in degraded vs.
non-degraded Mollisols in Argentina. Soil Science Society of America Journal 67:1831-1841.
Fitzsimmons, M.J., Pennock, D.J. and Thorpe, J. (2004). Effects of deforestation on ecosystem carbon densities
in central Saskatchewan, Canada. Forest Ecology and Management 188: 349-361.

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Fleige, H. and Baeumer, K. (1974). Effect of zero-tillage on organic carbon and total nitrogen content, and theirdistribution in different N-fractions in loessial soils. Agro-Ecosystems 1:19-29.
Follett, R.F. and Peterson, G.A. (1988). Surface soil nutrient distribution as affected by wheat-fallow tillage
systems. Soil Science Society of America Journal 52:141-147.
Follett, R.F., Paul, E.A., Leavitt, S.W., Halvorson, A.D., Lyon, D. and Peterson, G.A. (1997). Carbon isotope
ratios of Great Plains soils and in wheat-fallow systems. Soil Science Society of America Journal
61:1068-1077.
Follett, R.F., Pruessner, E.G., Samson-Liebig, S.E., Kimble, J.M. and Waltman, S.W. (2001). Carbon
sequestration under the Conservation Reserve Program in the historic grassland soils of the United Statesof America. Pages 1-14 in Lal, R., and K. McSweeney, editors. Soil Management for Enhancing Carbon
Sequestration. SSSA Special Publication. Madison, WI.
Franzluebbers, A.J. and Arshad, M.A. (1996). Water-stable aggregation and organic matter in four soils underconventional and zero tillage. Canadian Journal of Soil Science 76:387-393.
Franzluebbers, A.J., Hons, F.M. and Zuberer, D.A. (1995). Soil organic carbon, microbial biomass, andmineralizable carbon and nitrogen in sorghum. Soil Science Society of America 59:460-466.
Franzluebbers, A.J., Langdale, G.W. and Schomberg, H.H. (1999). Soil carbon, nitrogen, and aggregation in
response to type and frequency of tillage. Soil Science Society of America Journal 63:349-355.
Freitas, P.L., Blancaneaux, P., Gavinelly, E., Larre-Larrouy, M.-C. and Feller, C. (2000). Nivel e natureza do
estoque organico de latossols sob diferentes sistemas de uso e manejo, Pesq.agropec.bras. Brasilia 35:
157-170.
Freixo, A.A., Machado, P., dos Santos, H.P., Silva, C.A. and Fadigas, F. (2002). Soil organic carbon andfractions of a Rhodic Ferralsol under the influence of tillage and crop rotation systems in southern Brazil.
Soil and Tillage Research 64:221-230.
Frye, W.W. and Blevins, R.L. (1997). Soil organic matter under long-term no-tillage and conventional tillage
corn production in Kentucky. Pages 227-234 in: E.A. Paul, E.T. Elliott, K. Paustian, and C.V. Cole. Soilorganic matter in temperate agroecosystems: Long-term experiments in North America. CRC Press, Boca
Raton, FL.
Garcia-Prechac, F., Ernst, O., Siri-Prieto, G. and Terra, J.A. (2004). Intergrating no-till into crop-pasturerotations in Uruguay. Soil and Tillage Research 77:1-13.
Gebhart, D.L., Johnson, H.B., Mayeux, H.S. and Polley, H.W. (1994). The CRP increases soil organic carbon.
Journal of Soil and Water Conservation 49:488-492.
Ghuman, B.S. and Sur, H.S. (2001). Tillage and residue management effects on soil properties and yields ofrainfed maize and wheat in a subhumid subtropical climate. Soil and Tillage Research 58:1-10.
Girma, T. (1998). Effect of cultivation on physical and chemical properties of a Vertisol in Middle Awash
Valley, Ethiopia. Communications in Soil Science and Plant Analysis 29:587-598.
Graham, M.H., Haynes, R.J. and Meyer, J.H. (2002). Soil organic matter content and quality: effects of fertilizer
applications, burning and trash retention on a long-term sugarcane experiment in South Africa. Soil
Biology and Biochemistry 34:93-102.
Grandy, A.S., Porter, G.A. and Erich, M.S. (2002). Organic amendment and rotation crop effects on the recovery
of soil organic matter and aggregation in potato cropping systems. Soil Science Society of America
Journal 66:1311-1319.
Gregorich, E.G., Ellert, B.H., Drury, C.F. and Liang, B.C. (1996). Fertilization effects on soil organic matterturnover and corn residue C storage. Soil Science Society of America Journal 60:472-476.
Grunzweig, J.M., Sparrow, S.D., Yakir, D. and Chapin III, F.S. (2004). Impact of agricultural land-use change
on carbon storage in boreal Alaska. Global Change Biology 10:452-472.
Hadas, A., Agassi, M., Zhevelev, H., Kautsky, L., Levy, G.J., Fizik, E. and Gotessman, M. (2004). Mulchingwith composted municipal solid wastes in the Central Negev, Israel II. Effect on available nitrogen andphosphorus and on organic matter in soil. Soil and Tillage Research 78:115-128.
Halvorson, A.D., Wienhold, B.J. and Black, A.L. (2002). Tillage, nitrogen, and cropping system effects on soil
carbon sequestration. Soil Science Society of America Journal 66:906-912.
Halvorson, A.D., Reule, C.A. and Follett, R.F. (1999). Nitrogen fertilization effects on soil carbon and nitrogenin a dryland cropping system. Soil Science Society of America Journal 63:912-917.

5.60

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

Halvorson, A.D., Vigil, M.F., Peterson, G.A. and Elliott, E.T. (1997). Long-term tillage and crop residue
management study at Akron, Colorado. Pages 361-370 in: E.A. Paul, E.T. Elliott, K. Paustian, and C.V.
Cole. Soil organic matter in temperate agroecosystems: Long-term experiments in North America. CRC
Press, Boca Raton, FL.
Hansmeyer, T.L., Linden, D.R., Allan, D.L. and Huggins, D.R. (1998). Determining carbon dynamics under notill, ridge-till, chisel, and moldboard tillage systems within a corn and soybean cropping sequence. Pages
93-97 in Lal R., J.M. Kimble, R.F. Follett, and B.A. Stewart, editors. Advances in Soil Science:Management of Carbon Sequestration in Soil. CRC Press. Boca Raton, FL.
Hao, X., Chang, C. and Lindwall, C.W. (2001). Tillage and crop sequence effects on organic carbon and total
nitrogen content in an irrigated Alberta soil. Soil and Tillage Research 62:167-169.
Harden, J.W., Sharpe, J.M., Parton, W.J., Ojima, D.S., Fries, T.L., Huntington, T.G. and Dabney, S.M. (1999).
Dynamic replacement and loss of soil carbon on eroding cropland. Global Biogeochemical Cycles
14:885-901.
Havlin, J.L. and Kissel, D.E. (1997). Management effects on soil organic carbon and nitrogen in the East-Central
Great Plains of Kansas. Pages 381-386 in Paul, E.A., K. Paustian, E.T. Elliott, and C.V. Cole, editors.
Soil Organic Matter in Temperate Agroecosystems: Long-term Experiments in North America. CRC
Press. Boca Raton, FL.
Hendrix, P.F. (1997). Long-term patterns of plant production and soil carbon dynamics in a Georgia piedmont
agroecosystem. Pages 235-245 in: E.A. Paul, E.T. Elliott, K. Paustian, and C.V. Cole. Soil organic matterin temperate agroecosystems: Long-term experiments in North America. CRC Press, Boca Raton, FL.
Hernanz, J.L., Lopez, R., Navarrete, L. and Sanchez-Giron, V. (2002). Long-term effects of tillage systems androtations on soil structural stability and organic carbon stratification in semiarid central Spain. Soil and
Tillage Research 66:129-141.
Hulugalle, N.R. (2000). Carbon sequestration in irrigated vertisols under cotton-based farming systems.
Communications in Soil Science and Plant Analysis 31:645-654.
Hussain, I., Olson, K.R., Wander, M.M. and Karlen, D.L. (1999). Adaption of soil quality indices andapplication to three tillage systems in southern Illinois. Soil and Tillage Research 50:237-249.
Ihori, T., Burke, I.C., Lauenroth, W.K. and Coffin, D.P. (1995). Effects of cultivation and abandonment on soilorganic matter in Northeastern Colorado. Soil Science Society of America Journal 59:1112-1119.
Jackson, L.E., Ramirez, I., Yokota, R., Fennimore, S.A., Koike, S.T., Henderson, D.M., Chaney, W.E., Calderon,
F.J. and Klonsky, K. (2004). Agriculture, Ecosystems and Environment 103:443-463.
Janzen, H.H. (1987). Soil organic matter characteristics after long-term cropping to various spring wheat
rotations. Canadian Journal of Soil Science 67:845-856.
Jastrow, J.D., Miller, R.M. and Lussenhop, J. (1998). Contributions of interacting biological mechanisms to soil
aggregate stabilization in restored prairie. Soil Biology and Biochemistry 30:905-916.
Karlen, D.L., Kumar, A., Kanwar, R.S., Cambardella, C.A. and Colvin, T.S. (1998). Tillage system effects on
15-year carbon-based and simulated N budgets in a tile-drained Iowa field. Soil and Tillage Research
48:155-165.
Karlen, D.L., Rosek, M.J., Gardner, J.C., Allan, D.L., Alms, M.J., Bezdicek, D.F., Flock, M., Huggins, D.R.,
Miller, B.S. and Staben, M.L. (1999). Conservation Reserve Program effects on soil quality indicators.
Journal of Soil and Water Conservation 54:439-444.
Karlen, D.L., Wollenhaupt, N.C., Erbach, D.C., Berry, E.C., Swan, J.B., Eash, N.S. and Jordahl, J.L. (1994).
Long-term tillage effects on soil quality. Soil and Tillage Research 32:313-327.
Knowles, T.A. and Singh, B. (2003). Carbon storage in cotton soils of northern New South Wales. Australian
Journal of Soil Research 41:889-903.
Kushwaha, C.P., Tripathi, S.K. and Singh, K.P. (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. (1998). Soil quality changes under continuous cropping for seventeen seasons of an alfisol in western
Nigeria. Land Degradation and Development 9:259-274.
Lal, R., Mahboubi, A.A. and Fausey, N.R. (1994). Long-term tillage and rotation effects on properties of acentral Ohio soil. Soil Science Society of America Journal 58:517-522.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.61

Volume 4: Agriculture, Forestry and Other Land Use

Larney, F.J., Bremer, E., Janzen, H.H., Johnston, A.M. and Lindwall, C.W. (1997). Changes in total,mineralizable and light fraction soil organic matter with cropping and tillage intensities in semiarid
southern Alberta, Canada. Soil and Tillage Research 42:229-240.
Lilienfein, J., Wilcke, W., Vilela, L., do Carmo Lima, S., Thomas, R. and Zech, W. (2000). Effect of no-tillage
and conventional tillage systems on the chemical composition of soil solid phase and soil solution ofBrazilian savanna. J. Plant Nutr. Soil Sci. 163: 411-419.
Ludwig, B., John, B., Ellerbrock, R., Kaiser, M. and Flessa, H. (2003). Stabilization of carbon from maize in asandy soil in a long-term experiment. European Journal of Soil Science 54:117-126.
McCarty, G.W., Lyssenko, N.N. and Starr, J.L. (1998). Short-term changes in soil carbon and nitrogen pools
during tillage management transition. Soil Science Society of America Journal 62:1564-1571.
Mielke, L.N., Doran, J.W. and Richards, K.A. (1986). Physical environment near the surface of plowed and notilled soils. Soil and Tillage Research 7:355-366.
Mikhailova, E.A., Bryant, R.B., Vassenev, I.I., Schwager, S.J. and Post, C.J. (2000). Cultivation effects on soil
carbon and nitrogen contents at depth in the Russian Chernozem. Soil Science Society of America Journal
64:738-745.
Mrabet, R., Saber, N., El-brahli, A., Lahlou, S. and Bessam, F. (2001). Total, particulate organic matter andstructural stability of a Calcixeroll soil under different wheat rotations and tillage systems in a semiarid
area of Morocco. Soil & Tillage Research 57: 225-235.
Nyborg, M., Solberg, E.D., Malhi, S.S. and Izaurralde, R.C. (1995). Fertilizer N, crop residue, and tillage altersoil C and N content in a decade. Pages 93-99 in Lal, R., J. Kimble, E. Levine, and B.A. Stewart, editors.
Advances in Soil Science: Soil Management and Greenhouse effect. CRC Press. Boca Raton, FL.
Parfitt, R.L., Theng, B.K.G., Whitton, J.S. and Shepherd, T.G. (1997). Effects of clay minerals and land use onorganic matter pools. Geoderma 75:1-12.
Patwardhan, A.S., Chinnaswamy, R.V., Donigian Jr., A.S., Metherell, A.K., Blevins, R.L., Frye, W.W., andPaustian, K. (1995). Application of the Century soil organic matter model to a field site in Lexington, KY.
Pages 385-394 in Lal, R., J. Kimble, E. Levine, and B.A. Stewart, editors. Advances in Soil Science:
Soils and Global Change. CRC Press, Boca Raton, FL.
Paustian, K. and Elliott, E.T. Unpublished data. Field sampling of long-term experiments in U.S. and Canada for
EPA carbon sequestration project.
Pennock, D.J. and van Kessel, C. (1997). Effect of agriculture and of clear-cut forest harvest on landscape-scalesoil organic carbon storage in Saskatchewan. Canadian Journal of Soil Science 77:211-218.
Pierce, F.J. and Fortin, M.-C. (1997). Long-term tillage and periodic plowing of a no-tilled soil in Michigan:
Impacts, yield, and soil organic matter. Pages 141-149 in: E.A. Paul, E.T. Elliott, K. Paustian, and C.V.
Cole. Soil organic matter in temperate agroecosystems: Long-term experiments in North America. CRC
Press, Boca Raton, FL.
Potter, K.N., Torbert, H.A., Johnson, H.B. and Tischler, C.R. (1999). Carbon storage after long-term grassestablishment on degraded soils. Soil Science 164:718-723.
Potter, K.N., Torbert, H.A., Jones, O.R., Matocha, J.E., Morrison Jr., J.E., and Unger, P.W. (1998). Distribution
and amount of soil organic C in long-term management systems in Texas. Soil and Tillage Research
47:309-321.
Potter, K.N., Jones, O.R., Torbert, H.A. and Unger, P.W. (1997). Crop rotation and tillage effects on organiccarbon sequestration in the semiarid southern Great Plains. Soil Science 162:140-147.
Powlson, D.S. and Jenkinson, D.S. (1982). A comparison of the organic matter, biomass, adenosine triphosphateand mineralizable nitrogen contents of ploughed and direct-drilled soils, J. Agric. Sci. Camb. 97:713-721.
Rasmussen, P.E and Albrecht, S.L. (1998). Crop management effects on organic carbon in semi-arid PacificNorthwest soils. Pages 209-219 in Lal R., J.M. Kimble, R.F. Follett, and B.A. Stewart, editors. Advancesin Soil Science: Management of Carbon Sequestration in Soil. CRC Press. Boca Raton, FL.
Reeder, J.D., Schuman, G.E. and Bowman, R.A. (1998). Soil C and N changes on Conservation Reserve
Program lands in the Central Great Plains. Soil and Tillage Research 47:339-349.
Rhoton, F.E., Bruce, R.R., Buehring, N.W., Elkins, G.B., Langdale, C.W. and Tyler, D.D. (1993). Chemical andphysical characteristics of four soil types under conventional and no-tillage systems. Soil and Tillage
Research 28: 51-61.

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Chapter 5: Cropland

Robles, M.D. and Burke, I.C. (1997). Legume, grass, and conservation reserve program effects on soil organic
matter recovery. Ecological Applications 7:345-357.
Ross, C.W. and Hughes, K.A. (1985). Maize/oats forage rotation under 3 cultivation systems, 1978-83 2. Soilproperties. New Zealand Journal of Agricultural Research 28:209-219.
Sa, J.C.M., Cerri, C.C., Dick, W.A., Lal, R., Filho, S.P.V., Piccolo, M.C. and Feigl, B.E. (2001). Organic matter
dynamics and carbon sequestration rates for a tillage chronosequence in a Brazilian Oxisol. Soil Science
Society of America Journal 65:1486-1499.
Saffigna, P.G., Powlson, D.S., Brookes, P.C. and Thomas, G.A. (1989). Influence of sorghum residues andtillage on soil organic matter and soil microbial biomass in an Australian vertisol. Soil Biology andBiochemistry 21: 759-765.
Saggar, S., Yeates, G.W. and Shepherd, T.G. (2001). Cultivation effects on soil biological properties, microfauna
and organic matter dynamics in Eutric Gleysol and Gleyic Luvisol soils in New Zealand. Soil and Tillage
Research 58:55-68.
Sainju, U.M., Singh, B.P. and Whitehead, W.F. (2002). Long-term effects of tillage, cover crops, and nitrogen
fertilization on organic carbon and nitrogen concentrations in sandy loam soils in Georgia, USA. Soil and
Tillage Research 63:167-179.
Salinas-Garcia, J.R., Hons, F.M. and Matocha, J.E. (1997). Long-term effects of tillage and fertilization on soilorganic matter dynamics. Soil Science Society of America Journal 61:152-159.
Schiffman, P.M., and Johnson, W.C. (1989). Phytomass and detrital carbon storage during forest regrowth in thesoutheastern United States Piedmont. Canadian Journal of Forest Research 19:69-78.
Sherrod, L.A., Peterson, G.A., Westfall, D.G. and Ahuja, L.R. (2006). Cropping intensification enhances soilorganic carbon and nitrogen in a no-till agroecosystem. Soil Science Society of America Journal. (inpress).
Sidhu, A.S. and Sur, H.S. (1993). Effect of incorporation of legume straw on soil properties and crop yield in amaize-wheat sequence. Tropical Agriculture (Trinidad) 70:226-229.
Six, J., Elliot, E.T., Paustian, K. and Doran, J.W. (1998). Aggregation and soil organic matter accumulation incultivated and native grassland soils. Soil Science Society of America Journal 62:1367-1377.
Six, J., Paustian, K., Elliott, E.T. and Combrink, C. (2000). Soil structure and organic matter: I. Distribution of
aggregate-size classes and aggregate-associated carbon. Soil Science Society of America Journal 64:681689
Slobodian, N., van Rees, K., and Pennock, D. (2002). Cultivation-induced effects on below-ground biomass andorganic carbon. Soil Science Society of America Journal 66:924-930.
Solomon, D., Fritzsche, F., Lehmann, J., Tekalign, M. and Zech, W. (2002). Soil organic matter dynamics in thesubhumid agroecosystems of the Ethiopian Highlands: evidence from natural 13C abundance andparticle-size fractionation. Soil Science Society of America Journal 66: 969-978.
Sparling, G.P., Schipper, L.A., Hewitt, A.E. and Degens, B.P. (2000). Resistance to cropping pressure of two
New Zealand soils with contrasting mineralogy. Australian Journal of Soil Research 38:85-100.
Stenberg, M., Stenberg, B. and Rydberg, T. (2000). Effects of reduced tillage and liming on microbial activity
and soil properties in a weakly-structured soil. Applied Soil Ecology 14:135-145.
Tabeada, M.A., Micucci, F.G., Cosentino, D.J. and Lavado, R.S. (1998). Comparison of compaction induced byconventional and zero tillage in two soils of the Rolling Pampa of Argentina. Soil and Tillage Research
49:57-63.
Tiessen, H., Stewart, J.W.B. and Bettany, J.R. (1982). Cultivation effects on the amounts and concentration ofcarbon, nitrogen, and phosphorus in grassland soils. Agronomy Journal 74:831-835.
Unger, P.W. (2001). Total carbon, aggregation, bulk density, and penetration resistance of cropland and nearby
grassland soils. Pages 77-92 in: R. Lal (ed.). Soil carbon sequestration and the greenhouse effect. SSSA
Special Publication No. 57, Madison, WI.
Vanotti, M.B., Bundy, L.G. and Peterson, A.E. (1997). Nitrogen fertilizer and legume-cereal rotation effects on
soil productivity and organic matter dynamics in Wisconsin. Pages 105-119 in: E.A. Paul, E.T. Elliott, K.
Paustian, and C.V. Cole. Soil organic matter in temperate agroecosystems: Long-term experiments inNorth America. CRC Press, Boca Raton, FL.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.63

Volume 4: Agriculture, Forestry and Other Land Use

Varvel, G.E. (1994). Rotation and nitrogen fertilization effects on changes in soil carbon and nitrogen.
Agronomy Journal 86:319-325.
Voroney, R.P., van Veen, J.A. and Paul, E.A. (1981). Organic C dynamics in grassland soils. 2. Model validationand simulation of the long-term effects of cultivation and rainfall erosion. Canadian Journal of Soil
Science 61:211-224.
Wander, M.M., Bidart, M.G. and Aref, S. (1998). Tillage impacts on depth distribution of total and particulateorganic matter in three Illinois soils. Soil Science Society of America Journal 62:1704-1711.
Wanniarachchi, SD, Voroney, R.P., Vyn, T.J., Beyaert, R.P. and MacKenzie, A.F. (1999). Tillage effects on the
dynamics of total and corn-residue-derived soil organic matter in two southern Ontario soils. Canadian
Journal of Soil Science 79: 473-480.
Westerhof, R., Vilela, L., Azarza, M. and Zech, W. (1998). Land-use effects on labile N extracted withpermanganate and the nitrogen management index in the Cerrado region of Brazil. Biology and Fertilityof Soils 27:353-357.
Wu, T., Schoenau, J.J., Li, F., Qian, P., Malhi, S.S., Shi, Y. and Xu, F. (2004). Influence of cultivation andfertilization on total organic carbon and carbon fractions in soils from the Loess Plateau of China. Soiland Tillage Research 77:59-68.
Yang, X.M. and Kay, B.D. (2001). Impacts of tillage practices on total, loose- and occluded-particulate, andhumified organic carbon fractions in soils within a field in southern Ontario. Canadian Journal of Soil
Science 81: 149-156.
Yang, X.M. and Wander, M.M. (1999). Tillage effects on soil organic carbon distribution and storage in a silt
loam soil in Illinois. Soil and Tillage Research 52:1-9.
Zeleke, T.B., Grevers, M.C.J., Si, B.C., Mermut, A.R. and Beyene, S. (2004). Effect of residue incorporation on
physical properties of the surface soil in the South Central Rift Valley of Ethiopia. Soil and Tillage
Research 77:35-46.
Zhang, H., Thompson, M.L. and Sandor, J.A. (1988). Compositional differences in organic matter amongcultivated and uncultivated Argiudolls and Hapludalfs derived from loess. Soil Science Society ofAmerica Journal 52:216-222.

RICE CULTIVATION
Cai, Z.C., Tsuruta, H. and Minami, K. (2000). Methane emission from rice fields in China: measurements andinfluencing factors. Journal of Geophysical Research 105(D13): 17231–17242.
Cai, Z.C., Tsuruta, H., Gao, M., Xu, H. and Wei, C.F. (2003a). Options for mitigating methane emission from apermanently flooded rice field. Global Change Biology 9: 37-45.
Cai, Z.C., Sawamoto, T., Li, C.S., Kang, G.D., Boonjawat, J., Mosier, A. and Wassmann, R. (2003b). Field
validation of the DNDC model for greenhouse gas emissions in East Asian cropping systems, Global
Biogeochemical Cycles 17(4): 1107 doi:10.1029/2003GB002046,2003.
Cicerone, R.J. and Shetter, J.D. (1981). Sources of atmospheric methane: Measurements in rice paddies and a
discussion. Journal of Geophysical Research 86: 7203-7209.
Conrad, R. (1989). “Control of methane production in terrestrial ecosystems”. In: Exchange of Trace Gases
between Terrestrial Ecosystems and the Atmosphere, M.O. Andreae and D.S. Schimel(eds.), 39-58.
Denier van der Gon, H.A.C. and Neue, H.U. (1995). Influence of organic matter incorporation on the methane
emission from a wetland rice field. Global Biogeochemical Cycles 9: 11-22.
Denier van der Gon, H.A.C. and Neue, H.U. (2002). Impact of gypsum application on the methane emission
from a wetland rice field. Global Biogeochemical Cycles 8: 127-134.
Fitzgerald, G.J., Scow, K.M. and Hill, J.E. (2000). Fallow season straw and water management effects on
methane emissions in California rice. Global Biogeochem. Cycles, 14: 767-775.
Huang, Y., Jiao, Y., Zong, L.G., Zheng, X.H., Sass, R.L. and Fisher, F.M. (2002). Quantitative dependence of
methane emission on soil properties, Nutrient Cycling in Agroecosystems 64(1-2): 157-167.
Huang, Y, Zhang, W., Zheng, X.H., Li, J. and Yu, Y.Q. (2004). Modeling methane emission from rice paddies
with various agricultural practices. Journal of Geophysical Research-Atmospheres 109 (D8): Art. No.
D08113 APR 29 2004.

5.64

2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: Cropland

IAEA (1992). Manual on measurement of methane and nitrous oxide emissions from agriculture. IAEATECDOC-674, pp. 91.
IGAC (1994). Global measurements standards of methane emissions for irrigated rice cultivation. Sass, R.L. and
H.-U. Neue (eds.) IGAC Core Project Office, Cambridge, Mass., USA, 10 pp.
IPCC (International Panel on Climate Change) (1997). Revised 1996 IPCC Guidelines for National Greenhouse
Gas Inventories Workbook (Volume 2). Cambridge University Press, Cambridge.
IPCC (International Panel on Climate Change) (2000). Good Practice Guidance and Uncertainty Management in
National Greenhouse Gas Inventories. Cambridge University Press, Cambridge.
IRRI (1995). World rice statistics 1993-94, International Rice Research Institute, Los Banõs, pp. 260.
Li, C.S., Mosier, A., Wassmann, R., Cai, Z.C., Zheng, X.H., Huang, Y., Tsuruta, H., Boonjawat, J. and Lantin, R.
(2004). Modeling greenhouse gas emissions from rice-based production systems: Sensitivity analysis andupscaling, Global Biogeochemical Cycles 18, doi: 10.1029/2003GB00204, 2004.
Lindau, C.W., Bollich, P.K., de Laune, R.D., Mosier, A.R. and Bronson, K.F. (1993). Methane mitigation inflooded Louisiana rice fields. Biology and Fertility of Soils 15: 174-178.
Minami, K. (1995). The effect of nitrogen fertilizer use and other practices on methane emission from flooded
rice. Fertilizer Research 40: 71-84.
Neue, H.U. and Sass, R. (1994). Trace gas emissions from rice fields. In: Prinn R.G. (ed.) Global AtmosphericBiospheric Chemistry. Environmental Science Res. 48. Plenum Press, New York, pp. 119-148.
Nouchi, I., Mariko, S. and Aoki, K. (1990). Mechanism of methane transport from the rhizosphere to theatmosphere through rice plants. Plant Physiology 94: 59-66.
Sass, R. (2002). CH4 emissions from rice agriculture. In ’Background Papers, IPCC Expert Meetings on Good
Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. IPCC-NGGIP,
p. 399-417, available at http://www.ipcc-nggip.iges.or.jp/.
Sass, R.L., Fisher, F.M., Harcombe, P.A. and Turner, F.T. (1991). Mitigation of methane emission from rice
fields: Possible adverse effects of incorporated rice straw. Global Biogeochemical Cycles, 5: 275-287.
Sass, R.L., Fisher, F.M., Wang, Y.B., Turner, F.T. and Jund, M.F. (1992). Methane emission from rice fields:
The effect of floodwater management. Global Biogeochemical Cycles 6: 249-262
Sass, R. I., Fisher, F. M., Lewis, S. T., Jund, M. F. and Turner, F. T. (1994). Methane emissions from rice fields:
Effect of soil properties. Global Biogeochemical Cycles 2, 135-140, 1994.
Schütz, H., Holzapfel-Pschorn, A., Conrad, R., Rennenberg, H. and Seiler, W. (1989). A 3-year continuousrecord on the influence of daytime, season, and fertilizer treatment on methane emission rates from an
Italian rice paddy. Journal of Geophysical Research 94: 16405-16416.
Takai, Y. (1970). The mechanism of methane fermentation in flooded paddy soil. Soil Science and Plant
Nutrition 16: 238-244.
Wassmann, R., and Aulakh, M.S. (2000). The role of rice plants in regulating mechanisms of methane emissions.
Biology and Fertility of Soils 31: 20-29.
Wassmann, R., Neue, H.U., Bueno, C., Lantin, R.S., Alberto, M.C.R., Buendia, L.V., Bronson, K., Papen, H.
and Rennenberg, H. (1998). Methane production capacities of different rice soils derived from inherent
and exogenous substracts. Plant and Soil 203: 227-237.
Wassmann, R, Buendia, L.V., Lantin, R.S., Makarim, K., Chareonsilp, N. and Rennenberg, H. (2000).
Characterization of methane emissions from rice fields in Asia. II. Differences among irrigated, rainfed,
and deepwater rice. Nutrient Cycling in Agroecosystems 58: 107–119.
Watanabe, A. and Kimura, M. (1998). Factors affecting variation in CH4 emission from paddy soils grown withdifferent rice cultivars: A pot experiment. Journal of Geophysical Research 103: 18947-18952.
Yagi, K. and Minami, K. (1990). Effect of organic matter application on methane emission from some Japanese
paddy fields. Soil Science and Plant Nutrition 36: 599-610.
Yagi, K, Tsuruta, H., Kanda, K. and Minami, K. (1996). Effect of water management on methane emission form
a Japanese rice paddy field: Automated methane monitoring. Global Biogeochemical Cycles 10: 255-267.
Yagi, K., Minami, K. and Ogawa, Y. (1998). Effect of water percolation on methane emission from rice paddies:a lysimeter experiment. Plant and Soil 198: 193-200.

2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.65

Volume 4: Agriculture, Forestry and Other Land Use

Yan, X., Yagi, K., Akiyama, H. and Akimoto, H. (2005). Statistical analysis of the major variables controlling
methane emission from rice fields. Global Change Biology 11, 1131-1141, doi: 10/1111/j.13652486.2005.00976.x.

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2006 IPCC Guidelines for National Greenhouse Gas Inventories