Chapter 5: Cropland

CHAPTER 5

CROPLAND2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

5.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors 1
Stephen Michael Ogle (USA), Stephen John Wakelin (New Zealand), Leandro Buendia (Philippines), Brian
McConkey (Canada), Jeffrey Baldock (Australia), Hiroko Akiyama (Japan), Ayaka W. Mo Kishimoto (Japan),Ngonidzashe Chirinda (CGIAR, Zimbabwe), Martial Bernoux (FAO), Sumana Bhattacharya (India), Nares
Chuersuwan (Thailand), Muhammad Arif Rashid Goheer (Pakistan), Kristell Hergoualc’h (France), Shigehiro
Ishizuka (Japan), Rodel Diaz Lasco (Philippines), Xuebiao Pan (China), Himanshu Pathak (India), Kristiina
Regina (Finland), Atsushi Sato (Japan), Gabriel Vazquez-Amabile (Argentina), Changke Wang (China), Xunhua
Zheng (China).

Contributing Authors
Cody Alsaker (USA), Rémi Cardinael (France), Marife D. Corre (Germany), Ram Gurung (USA), Akinori Mori
(Japan), Johannes Lehmann (USA), Simone Rossi (Italy), Oliver van Straaten (Germany), Edzo Veldkamp(Netherlands), Dominic Woolf (USA), Kazuyuki Yagi (Japan), Xiaoyuan Yan (China)

1 Biomass Carbon Sections were prepared by lead authors, S.J. Wakelin, A. Sato, and R.D. Lasco; and contributing authors

R. Cardinael, S. Rossi.
Soil Carbon Sections were prepared by lead authors, S.M. Ogle, B. McConkey, J. Baldock, A. Mo Kishimoto, N. Chirinda,
M. Bernoux, K. Hergoualc’h, S. Ishizuka, X. Pan, K. Regina, G. Vazquez-Amabile, C. Wang; and contributing authors, C.
Alsaker, M.D. Corre, R. Gurung, J. Lehmann, A. Mori, O. van Straaten, E. Veldkamp, D. Woolf.
Rice Cultivation Section was prepared by lead authors, L. Buendia, H. Akiyama, S. Bhattacharya, N. Chuersuwan, M.A.R.
Goheer, H. Pathak, X. Zheng, S.M. Ogle; and contributing authors, K. Yagi,X. Yan.

5.2

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

Chapter 5: CroplandContents
Cropland6
5.1

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

5.2

Cropland Remaining Cropland............................................................................................................. 5.6
5.2.1 Biomass .......................................................................................................................................... 5.6
5.2.1.1

Choice of methods.............................................................................................................. 5.6

5.2.1.2

Choice of emission factors ................................................................................................. 5.7

5.2.1.3

Choice of activity data...................................................................................................... 5.13

5.2.1.4

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

5.2.1.5

Uncertainty assessment .................................................................................................... 5.14

5.2.2 Dead organic matter ..................................................................................................................... 5.14
5.2.3 Soil carbon .................................................................................................................................... 5.14
5.2.3.1

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

5.2.3.2

Choice of stock change and emission factors ................................................................... 5.26

5.2.3.3

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

5.2.3.4

Calculation steps for Tier 1 .............................................................................................. 5.37

5.2.3.5

Uncertainty assessment .................................................................................................... 5.38

5.2.4 Non-CO2 greenhouse gas emissions from biomass burning ......................................................... 5.38
5.3

Land Converted to Cropland .............................................................................................................. 5.39
5.3.1 Biomass ........................................................................................................................................ 5.39
5.3.1.1

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

5.3.1.2

Choice of emission factors ............................................................................................... 5.41

5.3.1.3

Choice of activity data...................................................................................................... 5.42

5.3.1.4

Calculation steps for Tier 1 and Tier 2 ............................................................................. 5.43

5.3.1.5

Uncertainty assessment .................................................................................................... 5.43

5.3.2 Dead organic matter ..................................................................................................................... 5.43
5.3.3 Soil carbon .................................................................................................................................... 5.43
5.3.3.1

Choice of method ............................................................................................................. 5.43

5.3.3.2

Choice of stock change and emission factors ................................................................... 5.44

5.3.3.3

Choice of activity data...................................................................................................... 5.46

5.3.3.4

Calculation steps for Tier 1 .............................................................................................. 5.47

5.3.3.5

Uncertainty assessment .................................................................................................... 5.48

5.3.4 Non-CO2 greenhouse gas emissions from biomass burning ......................................................... 5.48
5.4

Completeness, Time series, QA/QC, and Reporting .......................................................................... 5.48

5.5

Methane Emissions from Rice Cultivation......................................................................................... 5.49
5.5.1 Choice of method ......................................................................................................................... 5.49
5.5.2 Choice of emission and scaling factors ........................................................................................ 5.52
5.5.3 Choice of activity data .................................................................................................................. 5.57
5.5.4 Example Calculation for Tier 1 .................................................................................................... 5.58

Annex 5A.1 Estimation of default stock change factors for mineral soil C emissions/removals for cropland 5.61

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

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Volume 4: Agriculture, Forestry and Other Land Use

Annex 5A.2 Background for developing emission factors and scaling factors for methane emission from paddy
field, using scientific literature .................................................................................................... 5.63
Annex 5A.3 Parameterisation of the Tier 2 – Steady State Method for Mineral Soils .................................... 5.67
References..................................................................................................................................................... 5.70

Equations
Equation 5.0a (New)Annual Change in Soil C Stock for Mineral Soils using the Steady State Method..... 5.18

Equation 5.0b (New)Active Sub-Pool Soil C Stock For Mineral Soils using the Steady-State Method ...... 5.19

Equation 5.0c (New)

Slow Sub-Pool Soil C Stock for Mineral Soils using the Steady-State Method ......... 5.20

Equation 5.0d (New)

Passive Sub-Pool Soil C Stock for Mineral Soils using the Steady-State Method ..... 5.21

Equation 5.0e (New)Temperature Effect on Decomposition for Mineral Soils using the Steady-State Method.................................................................................................................................... 5.22

Equation 5.0f (New)

Water Effect on Decomposition for Mineral Soils using the Steady-State Method.... 5.22

Equation 5.0g (New)C Input to the Active Soil C Sub-Pool for Mineral Soils using the Steady-State Method.................................................................................................................................... 5.23

Equation 5.0h (New)

Cropland C- input to soil for the Steady-State Method .............................................. 5.36

Equation 5.1

CH4 emissions from rice cultivation ........................................................................... 5.49

Equation 5.2 (Updated) Adjusted daily emission factor (Tier 1) ...................................................................... 5.51
Equation 5.2a (New)

Adjusted daily emission factor (Tier 2) ...................................................................... 5.51

Equation 5.3

Adjusted CH4 emission scaling factors for organic amendments ............................... 5.55

Equation 5A.2.1 (New) Effect of controlling variables on CH4 flux from rice fields ....................................... 5.64
Equation 5A.2.2 (New) Default emission factor for continuously flooded rice fields ...................................... 5.65

FiguresFigure 5.1

Classification scheme for cropping systems ............................................................... 5.34

Figure 5.2

Decision tree for CH4 emissions from rice production ............................................... 5.50

TablesTable 5.1 (Updated)

Default coefficients for above-ground biomass and harvest/maturity cycles inagroforestry systems containing perennial species ....................................................... 5.8

Table 5.2 (Updated)

Default coefficients for above- and below-ground biomass in agroforestry systems
containing perennial species ......................................................................................... 5.9

Table 5.3 (Updated)

Default maximum and time-averaged mean above-ground biomass and above groundbiomass accumulation rate for Perennial cropland monocultures ............................... 5.12

Table 5.4 (Updated)

Examples of classification of perennial crop systems ................................................. 5.14

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

Chapter 5: Cropland

Table 5.5 (Updated)

Relative carbon stock change factors (FLU, FMG, and FI) (over 20 years) for management
activities on cropland .................................................................................................. 5.27

Table 5.5a (New)Globally calibrated model parameters to be used to estimate SOC Changes for Mineral
Soils with the Tier 2 Steady-State Method ................................................................. 5.31

Table 5.5b (New)

Default values for nitrogen and lignin contents in crops for the Steady-State Method 5.32

Table 5.5c (New)

Default values for carbon to nitrogen ratios, nitrogen, and lignin contents in livestock
manure for the Steady-State Method........................................................................... 5.33

Table 5.7

Example of a simple disturbance matrix (Tier 2) for the impacts of land conversion
activities on carbon pools............................................................................................ 5.40

Table 5.8 (Updated)

Default biomass carbon stocks removed due to Land Conversion to Cropland .......... 5.41

Table 5.9 (Updated)

Default biomass carbon stocks present on Land Converted to Cropland in the year
following conversion .................................................................................................. 5.41

Table 5.10

Soil stock change factors (FLU, FMG, FI) for land-use conversions to cropland ........... 5.44

Table 5.11 (Updated)

Default CH4 baseline emission factor ......................................................................... 5.53

Table 5.11a (New)

Default Cultivation Period of Rice.............................................................................. 5.53

Table 5.12 (Updated)

Default CH4 emission scaling factors for water regimes during the cultivation period.................................................................................................................................... 5.54

Table 5.13 (Updated)

Default CH4 emission scaling factors for water regimes before the cultivation period5.55

Table 5.14 (Updated)

Default conversion factors for different types of organic amendments ...................... 5.56

Table 5.14a (New)Calculation for total harvested area............................................................................. 5.58

Table 5.14b (New)Calculation for Adjusted Daily Emission Factor ........................................................ 5.59

Table 5.14c (New)Calculation for Total Methane Emissions from Rice Cultivation ............................... 5.59

Table 5A.2.1 (New)

Description of the selected variables that control CH4 emissions from rice fields ..... 5.66

Table 5A.3.1 (New)Studies that were used to evaluate the model sensitivities and parameterise the Tier 2
steady state method for mineral soils .......................................................................... 5.67

Table 5A.3.2 (New)Sensitivity of Model Parameters, Parameter Values and minimum and maximum values
for the Tier 2 Steady-State Method for mineral soils .................................................. 5.68

Table 5A.3.3

Covariance Matrix for the three-pool Steady-State Method for mineral soils ............ 5.69

Boxes
Box 5.1a (New)

Understanding the basis for the Tier 2 Steady-State Method ...................................... 5.16

Box 5.1b (New)

Description of the Tier 2 Steady-State Method for estimating mineral SOC changes 5.17

Box 5.2 (Updated)

Conditions influencing CH4 emissions from rice cultivation

Box 5.2a (New)Good practice guidance for developing baseline emission factors (EF) for CH4
emissions from rice cultivation .................................................................................... 5.57

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5.52

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Volume 4: Agriculture, Forestry and Other Land Use

CROPLAND
5.1

INTRODUCTION

No refinement.

5.2

CROPLAND REMAINING CROPLAND

No refinement.

5.2.1

Biomass

5.2.1.1 C HOICE OF METHODS
Carbon can be stored in the biomass of croplands that contain perennial woody vegetation including, but notlimited to, monocultures such as tea, coffee, oil palm, coconut, rubber plantations, fruit and nut orchards, andpolycultures such as agroforestry systems. The default methodology for estimating carbon stock changes in woody
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 (CCCB) 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 with refinements
described below. The second approach (the stock-difference method) applies either at Tier 2 or Tier 3, but not Tier
1. It is good practice to improve inventories by using the highest feasible tier given national circumstances. It isgood practice for countries to use a Tier 2 or Tier 3 method if carbon emissions and removals in Cropland
Remaining Cropland is a key category and if the sub-category of biomass is considered significant. It is goodpractice for countries to use the decision tree in Figure 2.2 in Chapter 2 to identify the appropriate tier to estimatechanges in carbon stocks in biomass.
Tier 1The default method is to multiply the area of perennial woody cropland by a net estimate of biomass accumulationfrom growth and subtract losses associated with harvest or gathering or disturbance (according to Equation 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 steady state
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, updated default factors shown in updated Table 5.1, Table 5.2 and Table 5.3, are applied to nationallyderived estimates of land areas. For perennial cropland C uptake, multiply unharvested area that is still younger
than the age of maturity by the above-ground growth rate. If harvest and immature areas are unknown, it isassumed that in cropland remaining cropland, the annual harvest area is equal to total area divided by rotation
length in years. For perennial cropland C losses, the updated tables provide two types of carbon stocks of perennial
woody biomass per area. One is maximum carbon stock at harvest/maturity state (Lmax). This is appropriate for
estimating harvest loss due to crop renewal. The other is the mean carbon stock over the whole lifetime of the crop
(Lmean). This is used for loss due to conversion to another land use where the age of converted cropland is unknown.
These values should be used appropriately to calculate carbon losses following the guidance in 5.2.1.2.

Tier 2

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

Chapter 5: CroplandTwo 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 estimates ofcarbon stocks at two points in time. Under Tier 2, carbon stock changes are estimated for above-ground and belowground biomass in perennial woody vegetation. Tier 2 methods involve country-specific or region-specificestimates of biomass stocks by major cropland types and management system and estimates of stock change as afunction of major management system (e.g., dominant crop, productivity management). To the extent possible, it
is good practice for countries to incorporate changes in perennial crop or tree biomass using country-specific orregion-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
Updated Tables 5.1 to 5.3 provide estimates of biomass stocks and/or biomass growth rates and losses for major
climatic regions and agricultural systems. Updated Table 5.1 provides default values of biomass growth and losses
applicable to agroforestry cropping systems in broad climate regions. Agroforestry systems are defined in Table
5.5. Updated Table 5.2 provides default sequestration rates in above- and below-ground biomass for agro-forestry
systems by region and climate zone. Updated Table 5.3 provides default values of biomass growth and losses forperennial cropping monoculture systems. Countries should use appropriate default values of above-groundbiomass growth rate relative to each climate region and cropping system from updated Table 5.1, Table 5.2 orTable 5.3. However, given the large variation in cropping systems, incorporating trees or tree crops, it is goodpractice 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.

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

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 5.1 (UPDATED1)DEFAULT COEFFICIENTS FOR ABOVE-GROUND BIOMASS AND HARVEST/MATURITY CYCLES IN AGROFORESTRY SYSTEMSCONTAINING PERENNIAL SPECIES2

ClimateRegionTropicalTemperate

Agroforestry
system3

Treedensity

Maximum aboveground biomass
carbon stock at
harvest ***Lmax

Harvest/Maturity
cycle**

Biomass
accumulati
on rate(G)*

Meanbiomass
carbon loss*** (Lmean)(Stemsha-1)(tonnes C ha-1)(yr)(tonnes C
ha-1 yr-1)(tonnes C ha-1
yr-1)

N

Fallow

69

6074

22.1 ± 52%

5 ± 50%4.42 ± 15%11.1 ± 26%

Hedgerow431481

9.4 ± 59%

20 ± 50%

0.47 ± 31%

4.7 ± 29%

Alley cropping

90

8568

47.4 ± 52%

20 ± 50%

2.37 ± 13%23.7 ± 26%

Multistrata

51

929

65.0 ± 54%

20 ± 50%

3.25 ± 21%32.5 ± 27%

Parkland7

152

11.8 ± 76%

20 ± 50%

0.59 ± 58%

5.9 ± 38%

ShadedPerennial

28

4236

48.0 ± 55%

20 ± 50%

2.4 ± 24%24.0 ± 28%

Silvoarable

22

880

72.2 ± 60%

20 ± 50%3.61± 33%

36.1 ± 30%

Silvopasture

18

1609

58.2 ± 80%

20 ± 50%

2.91 ± 63%

29.1 ± 40%

Hedgerow4

12816

26.1 ± 59%

30 ± 33%

0.87 ± 49%

13.1 ± 29%

Silvoarable

14

202

27.3 ± 62%

30 ± 33%

0.91 ± 52%

13.7 ± 31%

Silvopasture

10

854

69.9 ± 61%

30 ± 33%

2.33 ± 52%

35.0 ± 31%*Source: biomass carbon accumulation rate, G, from Cardinael et al. (2018). Uncertainty = 95% CI.
** Harvest/Maturity cycle and uncertainty are nominal estimates.
*** calculated (Lmax = G * Maturity cycle; Lmean = Lmax/2)
1

Replaces Table 5.1 from the 2006 IPCC Guidelines

2

See Table 5.3 for monocultures

3

See Table 5.4 for agroforestry system definitions

4

Biomass storage rates and tree density for hedgerows are presented per kilometer of hedgerows, not per hectare of agricultural field or per
hectare of hedgerow

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

Chapter 5: Cropland

TABLE 5.2 (UPDATED1)DEFAULT COEFFICIENTS FOR ABOVE- AND BELOW-GROUND BIOMASS IN AGROFORESTRY SYSTEMS CONTAINING PERENNIAL
SPECIES2ClimateRegionRegionTemperate(ALL)

Above-groundbiomass accumulation
rate (G)Below-groundbiomass
accumulation rate

(stems ha-1)(tonnes C ha-1 yr-1)(tonnes C ha-1 yr-1)

N

Silvoarable

2833

2.97 ± 75%

0.77

EuropeSilvopasture

4225

2.17 ± 47%

0.56

Hedgerow3

128160.87 ± 49%

0.23

Silvoarable

7111

0.59 ± 29%

0.14

Silvopasture1571

0.97 ± 75%

0.11

Silvopasture

1400

1.18 ± 75%

0.52

Hedgerow3

128160.87 ± 49%

0.23

Silvoarable

9271

1.12 ± 62%

0.28

Silvopasture

6

312

1.81 ± 44%

0.48

Silvoarable

576

0.52 ± 102%

0.14

Silvopasture

4

1667

3.11 ± 91%

1.03

Hedgerow3

128160.87 ± 49%

0.23

Silvoarable

14

202

0.91 ± 54%

0.23

Silvopasture

10

854

2.33 ± 52%

0.70

Fallow

22-

5.61 ± 21%

2.54

Hedgerow32

1667

0.48 ± 75%

0.12

Alley cropping

20

1000

1.88 ± 28%

0.45

Multistrata

3

2771

1.63 ± 26%

0.46

Parkland7

152

0.59 ± 58%

0.21

Fallow

91250

5.61 ± 59%

0.53

Alley cropping

15

10430

2.79 ± 24%

0.67

Silvoarable

6540

6.24 ± 36%

1.62

Silvopasture

17

1609

3.07 ± 62%%

0.84

Fallow

31

1250

5.61 ± 22%

1.95

Hedgerow32

1667

0.48 ± 75%

0.12

Alley cropping

35

5041

2.27 ± 19%

0.54

Multistrata

3

2771

1.63 ± 26%

0.46

Parkland7

152

0.59 ± 58%

0.21

Silvoarable

6540

6.24 ± 36%

1.62

Silvopasture

17

1609

3.07 ± 62%

0.84

Alley cropping

28

7233

2.75 ± 22%

0.59

Multistrata

3

1902

2.98 ± 28%

0.72

Shaded Perennial

5-

1.82 ± 34%

0.44

Silvoarable

5-5.09 ± 39%

1.22

South
AmericaAllregions

Warm
Temperate

Tree density

Asia

North
America
Cool
Temperate

Agroforestry
systemEuropeAll
Regions

AfricaAsia
TropicalDry

All
Regions

Tropical
Moist

Africa2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

TABLE 5.2 (UPDATED) (CONTINUED)DEFAULT COEFFICIENTS FOR ABOVE- AND BELOW-GROUND BIOMASS IN AGROFORESTRY SYSTEMS CONTAINING PERENNIAL
SPECIES2ClimateRegionRegionAgroforestry
systemTree density

Above-groundbiomass accumulation
rate (G)Below-groundbiomass
accumulation rate

(stems ha-1)(tonnes C ha-1 yr-1)(tonnes C ha-1 yr-1)

N

Fallow1-

5.30 ± 75%

1.27

Multistrata

21

628

3.03 ± 30%

0.73

Shaded Perennial

2

1481

2.07 ± 36%

0.50

Silvoarable

11

1065

1.5 ± 44%

0.35

CentralAmerica

Alley cropping

15

25000

2.28 ± 23%

0.55

South
America

Shaded Perennial

6

4131

3.06 ± 66%

0.71

Fallow1-

5.30 ± 75%

1.27

Alley cropping

43

13733

2.59 ± 17%

0.58

Multistrata

24

802

3.02 ± 26%

0.73

Shaded Perennial

13

3071

2.43 ± 40%

0.57

Silvoarable

16

1065

2.63 ± 42%

0.62

Fallow

30

7521

3.12 ± 15%

1.12

Fallow3-

6.21 ± 53%

1.49

Multistrata

2-

2.89 ± 75%

0.69

Shaded Perennial

1

1477

3.16 ± 75%

0.71

Fallow2-2.00 ± 75%

0.48

Multistrata

11-

4.83 ± 50%%

1.16

Shaded Perennial

2

1608

1.79 ± 75%

0.42

Silvopasture1-

0.06 ± 75%

0.01

Hedgerow3

11110

0.43 ± 75%

0.10

Alley cropping

12

1203

1.88 ± 51%

0.45

Multistrata

1-

3.25 ± 75%

0.78

Shaded Perennial

10

5967

2.28 ± 42%

0.51

Fallow

2-

4.76 ± 75%

1.14Multistrata

10

475

2.6 ± 42%

0.70

Shaded Perennial

2-

2.96 ± 75%

0.71

Fallow

7-

4.59 ± 45%

1.10

Hedgerow3

11110

0.43 ± 75%

0.10

Alley cropping

12

1203

1.88 ± 51%

0.45

Multistrata

24

475

3.25 ± 31%

0.91

Shaded Perennial

15

4766

2.36 ± 29%

0.54

Silvopasture1-

0.06 ± 75%

0.01

Asia

Tropical
Moist

AllRegions

TropicalmontaneAfricaAfricaAsia

Tropical
Wet

CentralAmericaSouth
AmericaAll
Regions

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

Chapter 5: Cropland

TABLE 5.2 (UPDATED) (CONTINUED)DEFAULT COEFFICIENTS FOR ABOVE- AND BELOW-GROUND BIOMASS IN AGROFORESTRY SYSTEMS CONTAINING PERENNIAL
SPECIES2ClimateRegionTropicalAll

Agroforestry
systemRegionAll
Regions

Tree density

Above-groundbiomass accumulation
rate (G)Below-groundbiomass accumulation
rate(stems ha-1)(tonnes C ha-1 yr-1)(tonnes C ha-1 yr-1)

N

Fallow

69

6074

4.42 ± 15%

1.49

Hedgerow3

31481

0.47 ± 31%

0.11

Alley cropping

90

8568

2.37 ± 13%

0.55

Multistrata

51

929

3.25 ± 21%

0.80

Parkland7

152

0.59 ± 58%

0.21

Shaded Perennial

28

4236

2.40 ± 24%

0.55

Silvoarable

22

880

3.61 ± 33%

0.89

Silvopasture

18

1609

2.91 ± 63%

0.79

Source: Cardinael et al. (2018).
1

Replaces Tables 5.2 and 5.3 from the 2006 IPCC Guidelines

2

See Table 5.3 for monocultures.

4

Biomass storage rates and tree density for hedgerows are presented per kilometer of hedgerows, not per hectare of agricultural field or per
hectare of hedgerow* Where N < 3 a nominal uncertainty estimate of ± 75% is given.

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TABLE 5.3 (UPDATED1)DEFAULT MAXIMUM AND TIME-AVERAGED MEAN ABOVE-GROUND BIOMASS AND ABOVE GROUND BIOMASSACCUMULATION RATE FOR PERENNIAL CROPLAND MONOCULTURES (TONNES HA-1)

DomainMaximum
above-groundbiomass
carbon stockat harvest(Lmax)

Cropping system

Harvest/Maturity
cycle
(yr)

(tonnes C ha-1)

Temperate

Abovegroundbiomass
accumulation rate (G)(tonnes C ha1 yr-1)

Meanbiomass
carbon stock(Lmean)References(tonnes C
ha-1)

Olive

9.1 ± 15%20 ± 23%

0.46 ± 27%

6.9 ± 25%

[1]

Orchard e.g. apple

8.5 ± 19%

20 ± 42%

0.43 ± 46%

6.4 ± 25%

[1]Vine e.g. grape

5.5 ± 18%

20 ± 18%

0.28 ± 26%

2.8 ± 25%

[1]

Short Rotation
Coppice12.69 ± 40%

43.2 ± 40%

6.35 ± 40%[2] + adjustment from[3]

Oil palm Elaeis
guineensis

60.0 ± 41%25

2.4 ± 41%30.0 ± 41%[4]

Rubber Hevea
brasiliensis

80.2 ± 15%27

3.0 ± 13%40.1 ± 15%[5]

Tea Camelia
sinensis20.7 ± 50%

300.7 ± 25%

18.3 ± 25%[6]TropicalAll[1] Canaveira, P. et al. 2018.
[2] Hauk S, Knoke T, Wittkopf S 2013[3] Krasuska E, Rosenqvist H. 2012
[4] Chave, J. 2015[5] Blagodatsky, S., Xu, J., Cadisch, G. 2016[6] Zhang M, et al. 2017
1

Updated Table 5.3 from 2006 IPCC Guidelines

Below-ground biomass accumulation
Tier 1The default assumption is that there is no change in below-ground biomass of perennial trees in agricultural
systems. There are limited below-ground biomass data for agricultural systems.
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. Estimates are provided inTable 5.2. 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 groundbiomass thus, as far as possible, empirically-derived root-to-shoot ratios specific to a region or vegetation typeshould 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 remova l, fuelwood and disturbanceTier 1The default assumption is that all biomass lost is assumed to be emitted in the same year. Limited biomass removal,
fuelwood gathering and disturbance loss data from cropland source are available. Food and Agriculture
Organization of the United Nations (FAO) provides total roundwood 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 and fuelwood gathering statistics (discussed in Chapter 4, Section
4.2) may include biomass coming from cropland such as when firewood is harvested from home gardens. Thus, itis necessary to ensure no double counting of losses 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

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Chapter 5: Croplandlosses from Cropland. Updated Tables 5.1 and 5.3 provide default values of maximum carbon stock per area (Lmax)
and mean carbon stock per area (Lmean). Countries should use Lmax in updated Table 5.1 and 5.3 in the case thatperennial woody biomass is replaced at or over the year of harvest/maturity under a nominal harvest/maturity cycle
assuming that perennial cropland is harvested and regenerated back into perennial cropland. Carbon losses are
estimated by multiplying annual area of harvested/replaced cropland by Lmax. Countries should use Lmean in
updated Table 5.1 and 5.3 in the case that carbon removal has occurred by land use change where the age of theperennial crop removed is unknown. Carbon losses are estimated by multiplying the annual area of land conversion
by Lmean. When perennial cropland is converted to another type of cropland, losses are reported in croplandremaining cropland. When perennial cropland is converted to non-cropland land uses, losses are reported in
relevant land converted categories
Tiers 2 a nd 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 andabove-ground vs. below-ground biomass can vary widely for different cropland types and conditions and thus it
may be most efficient to stratify sampling and survey plots by cropland types. General guidance on survey andsampling 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
updated Table 5.4.
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.
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.

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TABLE 5.4 (UPDATED1)
EXAMPLES OF CLASSIFICATION OF PERENNIAL CROP SYSTEMS
Crop system

Agroforestry

Monoculture

Description

Fallows

Land rested from cultivation, but comprises planted and managed trees, often
leguminous, shrubs and herbaceous cover crops before it is cultivated again.
Includes improved and natural fallows and can be implemented before any of thefollowing systems.

Hedgerows

Linear plantation around fields, including shelterbelts, windbreaks, boundary
plantings and live fences.

Alley cropping

Fast-growing, usually leguminous, woody species (mainly shrubs) grown in crop
fields, usually at high densities. The woody species are regularly pruned and theprunings are applied as mulch into the alleys as a source of organic matter andnutrients. Also known as intercropping.

Multistratasystems

Multistorey combinations of a large number of various trees and perennial andannual crops. They include home gardens and agroforests.

Parklands

Intercropping of agricultural crops or grazing land under low density maturescattered trees. Typical of dry areas like Sahel (e.g. Faidherbia albida).

Shadedperennial-crop
systems

Growing shade-tolerant species such as cacao and coffee under, or in between,
overstorey shade trees that can be used for timber or other commercial tree products

Silvoarable
systems

Woody species planted in parallel tree rows to allow mechanization andintercropped with an annual crop; usually used for timber (e.p. Juglans spp), butalso for fuel (e.p. Populus spp). Usually low tree density per hectare.

Silvopastoral
systems

Woody species planted on permanent grasslands, often grazed.

Plantations

Monoculture plantation crops such as tea, coffee and cacao grown without shadetrees, as well as oil palms, rubber and coconuts.

Vine systems

A plantation of vines, typically producing grapes used for winemaking, but also
kiwifruit or passionfruit.

Orchards
systems

Land planted with woody vegetation, often fruit trees (eg. apple, pear, plum, nut
trees). Understory vegetation is usually mowed or grazed.

Source: Cardinael et al. (2018), adapted from Nair et al. (2009)
Within the FAOSTAT land use classification system most perennial crop systems will be classified under 6650 (Land under permanent
crops). Fallows may be reported under 6655 (Land with temporary fallow), and parklands and silvopastoral systems under 6655 (Landunder permanent meadows and pastures), Land that meets the forest definition will be reported as Forest land.
1

Updated Table 5.4 in the 2006 IPCC Guidelines

5.2.1.4 C ALCULATION STEPS FOR T IER 1 AND T IER 2
No refinement.

5.2.1.5 U NCERTAINTY ASSESSMENT
No refinement.

5.2.2

Dead organic matterNo refinement.

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. 1997a; 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 and

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Chapter 5: Croplandintensity of cropping management (e.g., continuous cropping versus cropping rotations with periods of bare fallow),
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 can beaccomplished 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 method, 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, and stockchanges associated with soil inorganic C pools). Decision trees are provided for mineral soils (Figure 2.5) andorganic soils (Figure 2.6) 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 followingchanges in management that impact soil organic C. Equation 2.25 (Chapter 2) is used to estimate change in soilorganic C stocks in mineral soils by subtracting the C stock in the last year of an inventory time period (SOC 0)
from the C stock at the beginning of the inventory time period (SOC(0 –T)) and dividing by the time dependence ofthe stock change factors (D). In practice, country-specific data on land use and management must be obtained andclassified 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. Soil organic C stocks (SOC) are estimatedfor the beginning and end of the inventory time period using default reference carbon stocks (SOCref) and default
stock change factors (FLU, FMG, FI ).
Tier 2Developing Country-Specific Factors for the Default Equations
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 and reference C stocks with more disaggregation of climate
regions, soil types, and/or the land management classification. See Section 2.3.3.1, Chapter 2, Volume IV for
more information.
Biochar C Amendments
Tier 2 methods for biochar C amendments utilize a top-down approach in which the total amount of biochar
generated and added to mineral soil is used to estimate the change in soil organic C stocks with country-specificfactors. See Section 2.3.3.1, Chapter 2, Volume IV for more information.
Steady-State Method
The Tier 2 steady-state method is a three sub-pool steady-state C model that provides an optional alternative
method for estimating soil C stock changes in the 0-30 cm layer of mineral soils in Cropland Remaining Cropland.2
This Tier 2 steady-state method estimates C stock changes from combinations of tillage and C-input management
activities under conditions defined by the soil texture and the weather. The method is not appropriate for ricecultivation and is not parameterised to estimate the change in soil organic C stocks due to biochar C amendments.
2 The

Tier2 Steady state method may be applicable to other land uses, but this will require further development andparameterisation than provided in this section.

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This is an approach with intermediate complexity between Tier 1 and Tier 3 methods, and is based on a steadystate solution to the three soil organic C sub-pools in the Century ecosystem model (Ogle et al. 2012; Parton et al.
1987; Paustian et al. 1997b).
The Tier 2 steady-state method addresses more complexity in soil C dynamics than Tier 1 or Tier 2 using default
equations, by subdividing soil organic C into three separate sub-pools with fast (Active sub-pool), intermediate(Slow sub-pool), and long turnover times (Passive sub-pool). The turnover time of C within each sub-pool
determines the length of time that C remains in the soil. The Tier 2 steady-state method incorporates spatial andtemporal variation in climate, organic carbon inputs to soils, soil properties and management practices. However,compilers can further develop and/or parameterise this model given appropriate datasets, which would be a Tier 3
method (See Section 2.5.2 for more information about developing a Tier 3 model-based approach). See Boxes
5.1A and 5.1B for more information about the method.
BOX 5.1A (NEW)UNDERSTANDING THE BASIS FOR THE TIER 2 STEADY STATE METHODThe Tier 2 steady-state method, based on a soil C model, features intermediate complexity between
Tier 1 and Tier 3 methods. It allows a compiler to estimate C stock changes in a more disaggregatedway compared to Tier 1, but lacks the full complexity of Tier 3 methods. The model parameters
were determined using a Bayesian Calibration method (See Annex 5A.3), and application of thismethod will generate SOC stock change factor that are specific to climate, soil and management
conditions in a country. Consequently, the resulting stock change factors are more disaggregatedthan the default Tier 1 methods that are derived at a global scale with limited disaggregation tobroadly-defined climate regions.
It is noteworthy that Tier 2 methods are often based directly on the Tier 1 equations with countryspecific factors, but this is not a requirement for a Tier 2 method (See Volume 4, Chapter 1, Box1.1). This method is analogous to the Tier 2 methods for estimating CH4 emissions from entericfermentation (Volume 4, Chapter 10), with a set of equations for calculating gross energy intake inorder to derive a country-specific emission factor. The Tier 2 equations are used to derive stockchange factors from country-specific data on crop type, yields, tillage, organic amendments, soil
texture, and weather. The Tier 2 steady-state method uses management activity data that are
typically more available in a country than that required to apply the methods for the default
equations. The method gives the countries with these data an option to develop C stock change thatare more responsive to their particular conditions than the Tier 1 approach. The Tier 1 equations
require detailed information on the combination of crops types, tillage practices, manure
amendments, mineral fertilization, irrigation management, grazing management, green manures, andfallows for individual parcels of land in the inventory. Although several of these activity data areneeded for the Tier 2 steady-state method, much of the data requirements with the default equations
are represented by the C inputs to the soil that are derived from crop yields, thereby eliminating
several data requirements.
This method differs from Tier 3 methods that utilize process-based models that yield a fully dynamic
time series by simulating changes in management and environmental conditions through time. This
Tier 2 method does not simulate C change but simply calculates an annual C stock change from thecurrent C stock to the future steady-state soil C stock calculated based on current conditions. In
addition, the steady-state method is much less complex with about 20 parameters compared to the
100s to 1000s parameters that are often found in Tier 3 process-based models. Consequently, thedata and resource requirements are considerably less intensive than typical process-based model
applications (See examples in Box 2.2d, Chapter 2, Volume IV).
The Tier 2 steady state method introduces additional interannual variation into the final resultscompared to Tier 1, by representing the impact of drivers such as weather on C inputs to soils andlosses associated with decomposition of soil organic matter. Using this method may require
additional quality assurance, quality control and verification (see Volume 1, Chapter 6, Section
6.11).

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Chapter 5: Cropland

BOX 5.1B (NEW)DESCRIPTION OF THE TIER 2 STEADY STATE METHOD FOR ESTIMATING MINERAL SOIL
ORGANIC CARBON STOCK CHANGESThe Tier 2 steady-state method is adapted from the Century Ecosystem Model (Parton et al. 1987)
and estimates changes in soil organic C for the top 30cm of the soil profile. In this model, the stockof the soil carbon sub-pools is initialised by running the model with climate and carbon input dataassociated for a period of 5-20 years prior to the start of the inventory (or longer if data are available).
A proportion of biomass C (C input to the soil) is transferred to soil litter, and then divided into
fraction, β, that goes to metabolic components with the remaining fraction (Cinput- β) going tostructural components 1. The structural component is composed of more recalcitrant, ligno-cellulose
plant materials. The metabolic component is composed of more readily decomposed organic matter.
Decomposition products are transferred according to calculated fractional transfer coefficients (f1 tof8) to and between three soil organic matter sub-pools, active, slow and passive. The active sub-poolis microbial (bacteria and fungi) biomass and associated metabolites with a rapid turnover (months
to years), the slow sub-pool has intermediate stability and turnover (decades), and the passive subpool is mineral-protected C and microbial decomposition products with long turnover times(centuries). Irrespective of the turnover time the approach is used to estimate the stock of each subpool and how they change over time. The total soil organic carbon stock and stock change iscalculated as the sum of the values derived for each sub-pool.

Decomposition rates for sub-pools depend on the decay rate constants, temperature effects, andmoisture effects. Decomposition of the active and slow sub-pools is also influenced by the soil
texture (sand content) and tillage practice. Sub-pools with longer turnover times imply that the C
remains in the soil for more years before the organic matter is decomposed and carbon is respired as
CO2 by the soil decomposer community. As decomposition occurs in each sub-pool, some of thedecomposing C is transferred to other sub-pools and components (arrows in the diagram) and someof the C is converted into CO2 and lost from the soil (not identified with arrows). The transfer of C
to the next sub-pool or component at steady state is determined by the transfer coefficients (f).
Higher transfer coefficients imply that more of the C is transferred to the next sub-pool or component
rather than converted into CO2. The steady-state solution for this model is discussed further inPaustian et al. (1997) and Ogle et al. (2012).
1

This approach is not intended to be used for estimation of dead organic matter. Compilers should apply the dead organic
matter methods in section 5.2.2.

The land base is stratified as fine as possible to include the spatial variation in climate, soil properties, irrigation,and tillage practices. However, there will be practical limits to the level of stratification given the resolution ofdata and national circumstances for inventory compilation. The method can be applied by subdividing the country
into grid cells or regions, such as counties, districts or municipalities. Each grid cell or region would contain a2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

single combination of climate, soil properties and tillage practices and have an area of land assigned to the unit.
Within each grid cell or region, the compiler will determine the C input using country-specific equations, oralternatively a generic equation can be used (Equation 5.0h). Compilers will also need values for the parameters
defining the quality of the C input (lignin and nitrogen content) or use generic values available in Tables 5.5b and5.5c. The type of tillage applied within each grid cell or region will need to be compiled to determine the correct
value for tillage parameter. Monthly average temperature, precipitation and potential evapotranspiration is neededfor each grid cell or region. This information is available from global datasets, such as the Climatic Research Unit
(CRU) climate dataset3, if country-specific data are not available. The average sand content is needed for each gridcell or region, which is available from Harmonized World Soil Database4 or from Soil Grids5, if country-specificdata are not available. If global data sources are used, it is important to understand and acknowledge the uncertainty
associated with these data products to estimate confidence intervals for the resulting changes in soil C stocks.
The following sections provide the equations and steps involved with application of the method within a grid cellor region (e.g., counties, districts or municipalities). The equations estimate water and temperature effects on
decomposition; the size of the active, slow and passive soil carbon sub-pools; and the change in total SOC. The
values of default parameters are given in Table 5.5a. All constants in the equations are considered globally
applicable and should not be altered when applying this Tier 2 steady-state method. The change in soil C stock is
calculated annually, multiplied by the area of the grid cell or region and the product summed across all grid cells
or regions to determine the annual inventory soil C stock change.

Equations for the Ti er 2- Steady State Method for Mineral Soils
Calculate SOC Stock Changes
The change in SOC stock is calculated using Equation 5.0a.
EQUATION 5.0A (NEW)
ANNUAL CHANGE IN SOIL C STOCK FOR MINERAL SOILS USING THE STEADY STATE METHOD

CMineral = FSOCi • Ai
i

FSOC = SOC yi − SOC( y −1)ii

SOC y i = ACTIVE y i + SLOWy i + PASSIVE y i
Where:

CMineral= annual SOC stock change factor for mineral soil, summed across all i grid cells or regions,
tonnes C

FSOC= annual stock change factor for mineral soils in grid cell or region i, tonnes C ha-1

Ai

= Area of grid cell or region i , ha

SOC y i

= SOC stock at the end of the current year y for grid cell or region i , tonnes C ha-1

SOC( y −1)i

= SOC stock at the end of the previous year for grid cell or region i , tonnes C ha-1

ACTIVEyi= active sub-pool SOC stock in year y for grid cell or region i , tonnes C ha-1 (see Equation

i

5.0b)

SLOWyi= slow sub-pool SOC stock in year y for grid cell or region i , tonnes C ha-1 (see Equation
5.0c)

PASSIVEyi= passive sub-pool SOC stock in year y for grid cell or region i , tonnes C ha-1 (see Equation
5.0d)

3 https://crudata.uea.ac.uk/cru/data/hrg/(23/10/2018)

4 http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/5 https://soilgrids.org/#!/?layer=TAXNWRB_250m&vector=1

5.18(23/10/2018)(23/10/2018)2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 5: CroplandAll subsequent equations associated with the steady state method (Equations 5.0b – 5.0g) are to be completed
separately using data derived for each grid cell or region to yield values specific to the grid cell or region. The
subscripts i have been left off the equations to simplify the presentation of the equations. All calculations denotedin Equations 5.0b – 5.0g will need to be completed for each individual grid cell or region included in the inventory
process.

Calculate the size of the Active SOC Sub -pool
The size of the active SOC sub-pool is calculated using Equation 5.0b. The calculations for each sub-pool
EQUATION 5.0B (NEW)ACTIVE SUB-POOL SOIL C STOCK FOR MINERAL SOILS USING THE STEADY-STATE METHOD()

ACTIVE y = ACTIVE y −1 + ACTIVE y* − ACTIVE y −1 • 1 yr • ka

ACTIVE y* =kaka = k faca •t fac • w fac • ( 0.25 + ( 0.75 •sand ) ) • till fac
Where:

ACTIVEy= active sub-pool SOC stock in year y, tonnes C ha-1

ACTIVEy −1 = active sub-pool SOC stock in previous year, tonnes C ha-1
ACTIVE y*= steady state active sub-pool SOC stock given conditions in year y, tonnes C ha-1ka

= decay rate for active SOC sub-pool, year-1

= C input to the active SOC sub-pool, tonnes C ha-1 year-1 (see Equation 5.0g)

k faca= decay rate constant under optimal conditions for decomposition of the active SOC subpool, year-1 (see Table 5.5a)t fac= temperature effect on decomposition, dimensionless (see Equation 5.0e)

wfac= water effect on decomposition, dimensionless (see Equation 5.0f)

till fac= tillage disturbance modifier on decay rate for active and slow sub-pools, dimensionless (seeTable 5.5a)sand= fraction of 0-30 cm soil mass that is sand (0.050 – 2mm particles), dimensionless

NOTE: If the estimated ka value is above 1, then set the value of ka to 1 in the equation for calculating ACTIVEy
in the first equation. The ‘1 year’ designation in the equation is because the model is applied to estimate changes
over a single year, which is needed so that units cancel appropriately in the calculation.

Calculate the size of the Slow SOC Sub -pool
The size of the slow SOC sub-pool is calculated using Equation 5.0c.

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EQUATION 5.0C (NEW)SLOW SUB-POOL SOIL C STOCK FOR MINERAL SOILS USING THE STEADY-STATE METHOD()

SLOWy = SLOWy −1 + SLOWy* − SLOWy −1 • 1yr • k s()[( Cinput •LC ) • f3 ] +  ACTIVE y* • ka • f 4 SLOWy* =ks
k s = k facs • t fac • w fac • till fac
f 4 = 1 − f 5 − ( 0.17 + 0.68 • sand )Where:

SLOWy= slow sub-pool SOC stock in y, tonnes C ha-1

SLOWy −1

= slow sub-pool SOC stock in previous year, tonnes C ha-1

SLOWy*= steady state slow sub-pool SOC stock given conditions in year y, tonnes C ha-1

ks

= decay rate for slow SOC sub-pool, year-1

Cinput= total carbon input, tonnes C ha-1 year-1

LC= lignin content of carbon input, proportion (see Table 5.5b and 5.5c) for default values,
otherwise compile country-specific values)

ACTIVE y* = steady state active sub-pool SOC stock given conditions in year y, tonnes C ha-1ka

= decay rate for active carbon sub-pool in the soil, year-1

k facs= decay rate constant under optimal condition for decomposition of the slow carbon sub-pool,year-1 (see Table 5.5a)t fac= temperature effect on decomposition, dimensionless (see Equation 5.0e)

wfac= water effect on decomposition, dimensionless (see Equation 5.0f)

till fac= tillage disturbance modifier on decay rate for active and slow sub-pools, dimentionless (seeTable 5.5a)

f3= fraction of structural component decay products transferred to the slow sub-pool,
proportion (see Table 5.5a)

f4= fraction of active sub-pool decay products transferred to the slow sub-pool, proportion (seeEquation 5.0c)

f5= fraction of active sub-pool decay products transferred to the passive sub-pool, proportion(see Table 5.5a)sand= fraction of 0-30 cm soil mass that is sand (0.050 – 2mm particles), proportion

NOTE: If the estimated k s value is above 1, then set the value of k s to 1 in the equation for calculating SLOWyin the first equation. The ‘1 year’ designation in the equation is because the model is applied to estimate changes
over a single year, which is needed so that units cancel appropriately in the calculation.

Calculate the size of the Passive C Sub -pool
The size of the slow SOC sub-pool is calculated using Equation 5.0d.

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Chapter 5: Cropland

EQUATION 5.0D (NEW)PASSIVE SUB-POOL SOIL C STOCK FOR MINERAL SOILS USING THE STEADY-STATE METHOD()

PASSIVE y = PASSIVE y −1 + PASSIVE y* − PASSIVE y −1 • 1yr • k p

(

)

)

 ACTIVE * • k • f  + ( SLOW * • k • f ]
a
5
s
6
y
y
 
PASSIVE y* = kpk P = k fac p • t fac • w fac
Where:PASSIVEy= passive sub-pool SOC stock in year y, tonnes C ha-1

PASSIVEy −1 = passive sub-pool SOC stock in previous year, tonnes C ha-1
PASSIVEy*= steady state passive sub-pool SOC given conditions in year y, tonnes C ha-1

kP

= decay rate for passive SOC sub-pool, year-1

ACTIVE y*= steady state active sub-pool SOC stock given conditions in year y, tonnes C ha-1ka

= decay rate for active carbon sub-pool, year-1

SLOWy *= steady state slow sub-pool SOC stock given conditions in year y, tonnes C ha-1

ks

= decay rate for slow carbon sub-pool, year-1

k fac p= decay rate constant under optimal conditions for decomposition of the slow carbon subpool, year-1 (see Table 5.5a)t fac= temperature effect on decomposition, dimensionless (see Equation 5.0e)

wfac= water effect on decomposition, dimensionless (see Equation 5.0f)

f5= fraction of active sub-pool decay products transferred to the slow sub-pool, proportion(seeTable 5.5a)

f6= fraction of slow sub-pool decay products transferred to the passive sub-pool, proportion(seeTable 5.5a)

NOTE: If the estimated k p value is above 1, then set the value of k p to 1 in the equation for calculating

PASSIVEy in the first equation. The ‘1 year’ designation in the equation is because the model is applied toestimate changes over a single year, which is needed so that units cancel appropriately in the calculation.

Calculate Temperature Effect on Decomposition
Calculate the temperature effect on soil organic matter decomposition using Equation 5.0e.

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Volume 4: Agriculture, Forestry and Other Land Use

EQUATION 5.0E (NEW)TEMPERATURE EFFECT ON DECOMPOSITION FOR MINERAL SOILS USING THE STEADY-STATEMETHODt fac =
 t −tempi 
Ti =  max t −t max opt 

0.2

1 12
Ti
12 i =1

  t −temp 2.63  i• exp  0.076 • 1−  max  
 t −t  max opt   Where:t fac= annual average air temperature effect on decomposition, dimensionlessTi= monthly average air temperature effect on decomposition, dimensionless (i = 1, 2, …, 12)tmax= maximum monthly air temperature for decomposition, degrees C (see Table 5.5a)tempi= monthly average air temperature (i = 1, 2, …, 12), degrees C

topt= optimum air temperature for decomposition, degrees C (see Table 5.5a)

NOTE: When the monthly average air temperature is greater than 45 °C (i.e., the maximum average airtemperature) set Ti to 0.

Calculate Water Effect on Decomposition
Estimate the water effect on soil organic matter decomposition using Equation 5.0f
EQUATION 5.0F (NEW)
WATER EFFECT ON DECOMPOSITION FOR MINERAL SOILS USING THE STEADY-STATE METHOD

 1 12 
w fac = 1.5 •  wi 
 12 i =1 

wi = 0.2129 + ( ws •mappeti ) − ( 0.2413 • mappeti 2 )precipi mappeti = min 1.25,
PETi 
Where:

wfac= annual water effect on decomposition, dimensionlesswi= monthly water effect on decomposition, dimensionlessws= modifier for mappeti , dimensionless (see Table 5.5a)mappeti= ratio of total precipitation to total potential evapotranspiration (dimensionless) for month i(i = 1, 2, …12)

precipi= total precipitation for month i, mm

PETi= total potential evapotranspiration for month i, mm

NOTE: If the mappeti is >1.25, then set the value of mappeti for the month to 1.25 for non-irrigated system (i.e.,

mappeti does not exceed 1.25). Set wi for months with irrigation to 0.775.

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Chapter 5: CroplandCalculate C Input to the Active Sub -pool
Calculate alpha value using Equation 5.0g, which is the C input to the active SOC sub-pool.
EQUATION 5.0G (NEW)C INPUT TO THE ACTIVE SOIL C SUB-POOL FOR MINERAL SOILS USING THE STEADY-STATEMETHOD=  • f1  + ( Cinput • (1 − LC ) −  ) • f 2  + ( Cinput • LC ) • f3 • ( f 7 + f 6 • f8 )
1 − ( f 4 • f 7 ) − ( f 5 • f8 ) − ( f 4 • f 6 • f8 ) LC   NC  

 = Cinput • 0.85 − 0.018 • Where:
= C input to the active soil carbon sub-pool, tonnes C ha-1



= C input to the metabolic dead organic matter C component, tonnes C ha-1 year-1

Cinput= total carbon input, tonnes C ha-1year-1

f1= fraction of metabolic dead organic matter decay products transferred to the active sub-pool,
proportion (see Table 5.5a)f2= fraction of structural dead organic matter decay products transferred to the active sub-pool,
proportion (see Table 5.5a)

f3= fraction of structural dead organic matter decay products transferred to the slow sub-pool,
proportion (see Table 5.5a)

f4= fraction of active sub-pool decay products transferred to the slow sub-pool, proportion, (seeEquation 5.0c)

f5= fraction of active sub-pool decay products transferred to the passive sub-pool, proportion(see Table 5.5a)

f6= fraction of slow sub-pool decay products transferred to the passive sub-pool, proportion(see Table 5.5a)

f7= fraction of slow sub-pool decay products transferred to the active sub-pool, proportion (seeTable 5.5a)

f8= fraction of passive sub-pool decay products transferred to the active sub-pool, proportion(see Table 5.5a)LC= lignin content of carbon input, proportion (see Tables 5.5b and 5.5c for default values,
otherwise compile country-specific values)NC= nitrogen fraction of the carbon input, proportion (see Tables 5.5b and 5.5c) for default
values, otherwise compile country-specific values)

Table 5.5A provides the default parameters, minimum and maximum values for parameters, and their associatedstandard deviation. The probability distribution functions for the parameters should be constructed as truncatednormal distributions, in which parameter values lower than the minimum value are constrained the minimum value,and parameter values greater than the maximum values are constrained to the maximum value. Uncorrelated draws
from the probability distribution functions of the parameters can be made using the data in this table, but morerobust estimates of uncertainty can be made using a truncated joint probability distribution with the parameter
covariance matrix found in Annex 2A.3
S tep-b y- Step p roc ed ure fo r imp lem en t ing t he Tie r2 st ead y -stat e me tho d fo r M in e ra l
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Volume 4: Agriculture, Forestry and Other Land Use

Steps 1 to 8 are conducted for each grid cell or region, depending on the spatial unit of the inventory. Step 9 sums
the changes across the entire spatial domain6.
Step 1. Calculate the Initial Stocks of the Active, Slow and Passive SOC sub-pools
The initial stocks are calculated based on the climatic, soil texture, management and carbon input data for a runin period7 of 5 to 20 years (more years may be used if data are available).
Step 1.1: Calculate the average annual values of t fac (Equation 5.0e) and wfac (Equation 5.0f) for the run-in
period.
Step 1.2: Calculate the C input to the active sub-pool (  ) for the run-in period (Equation 5.0g) using the following
data:a. the average annual carbon input ( Cinput ) for the run-in period, which may be estimated with Equation 5.0h
if country-specific methods are not available,b. the appropriate values for LC and NC for the crop and/or grass in place during the run-in period can befound in the Tier2 steady-state method section for cropland (see Section 5.2.3.2 for cropland default values,
otherwise compile country-specific values),c. the value of f 2 from Table 5.5a, andd. the sand content of the 0-30 cm soil layer ( sand ).
Step 1.3: Calculate the values of ka (Equation 5.0b), k s (Equation 5.0c) and k p (Equation 5.0d) using:a. the average values of t fac and wfac calculated in Step 1.1,b. the values of k faca , k facs , k fac p and the appropriate tillage factor ( till fac ) from Table 5.5A, andc. the sand content of the 0-30 cm soil layer ( sand ).
Step 1.4: Calculate the values for ACTIVEy* (Equation 5.0b), SLOWy* (Equation 5.0c) and PASSIVEy* (Equation
5.0d) for the run-in period, which become the initial SOC stocks for the ACTIVE, SLOW and PASSIVE SOC
sub-pools at the commencement of the inventory period.
Step 2. Calculate C Input to the Active Sub-pool for each year of the inventory period
Calculate value of  (the C input to the active SOC sub-pool) for each year in the inventory period using Equation
5.0g.
Step 2.1: Calculate the C input to the metabolic dead organic matter component (  ).
Step 2.2: Calculate the C input to the active soil carbon sub-pool (  ).
Step 2.3: Repeat Steps 2.1 to 2.2 for all other years in the inventory period to derive annual values for  and  .
Step 3. Calculate Water Effect on Decomposition
Estimate the water effect on soil organic matter decomposition using Equation 5.0f.
Step 3.1: For each month in a year, calculate the ratio of total precipitation to total potential evapotranspiration.
a. If the ratio is ≤1.25 then set the value of mappeti for the month to the estimated ratio.
b. If the ratio is >1.25 then set the value of mappeti for the month to 1.25.
c. Set wi for months with irrigation to 0.775.
Step 3.2: Calculate water effect on decomposition for each month ( wi ) in a year. For land area under irrigation
management, set the water effect on decomposition for the month ( wi ) to 0.775.
Step 3.3: Calculate the annual water effect on decomposition ( wfac ).

6

An example of the Tier 2 steady state method is provided in a supplementary file, V4_Ch5_Tier2_Steady_State_Method.xlsx

7 Compilers can use longer run-in periods than 20 years to establish the initial soil organic C stocks for the inventory, but 5

years is considered a minimum period of time for this method. Initial values of the active, slow and passive pools can leadto biases in results if the run-in period is not long enough to capture the trajectory of the stocks based on legacy effects
associated with historical land use and management.

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

Chapter 5: Cropland

Step 3.4: Repeat steps 3.1 to 3.3 to calculate the water effect ( wfac ) on decomposition for all years in the inventory
period.
Step 4. Calculate Temperature Effect on Decomposition
Calculate the temperature effect on soil organic matter decomposition using Equation 5.0e.
Step 4.1: For each month in a year, calculate temperature effect on decomposition ( Ti ) using the values for
maximum monthly temperature for decomposition ( tmax ), optimum temperature for decomposition ( topt ) and themonthly average temperature ( tempi ).
a. If the monthly average temperature is ≤45 °C, use the calculated value of Ti .
b. If the monthly average temperature is >45 °C, set Ti equal to 0.
Step 4.2: Calculate annual temperature effect on decomposition ( t fac ).
Step 4.3: Repeat steps 4.1 and 4.2 to calculate the annual temperature effect on decomposition for all years in theinventory.
Step 5. Calculate the size of the Passive C Sub-pool
Calculate the size of the passive sub-pool using Equation 5.0d.
Step 5.1: Calculate decay rate for the PASSIVE SOC sub-pool in the soil ( k p ).
Step 5.2: Calculate the steady state stock for the PASSIVE sub-pool SOC stock ( PASSIVEy* ).
Step 5.3: Calculate the PASSIVE sub-pool SOC stock by determining the additional increase or decrease in SOC
from the previous year in the inventory ( PASSIVEy ). Note that the initial size of the PASSIVE SOC sub-pool usedat the start of the inventory period is calculated as defined in step 1. Note also that if the estimated k p value isabove 1, then set the value of k p to 1 in the equation for calculating PASSIVEy .
Step 5.4: Repeat steps 5.1 to 5.3 to calculate the PASSIVE SOC stocks for all years in the inventory.
Step 6. Calculate the size of the SLOW SOC Sub-pool
Calculate the size of the slow sub-pool using Equation 5.0c.
Step 6.1: Calculate decay rate for SLOW SOC sub-pool in the soil ( k s ).
Step 6.2: Calculate the steady state stock for the SLOW SOC sub-pool ( SLOWy* ).
Step 6.3: Calculate the SLOW SOC stock by determining the additional increase or decrease in SOC from theprevious year in the inventory ( SLOWy ). Note that the initial size of the SLOW SOC sub-pool used at the start ofthe inventory period is calculated as defined in step 1. Note also that if the estimated k s value is above 1, then set
the value of k s to 1 in the equation for calculating SLOWy ).
Step 6.4: Repeat steps 6.1 to 6.3 to calculate the SLOW SOC sub-pool stocks for all years in the inventory.
Step 7. Calculate the size of the ACTIVE SOC Sub-pool
Calculate the size of the active sub-pool using Equation 5.0b.
Step 7.1: Calculate decay rate for the ACTIVE SOC sub-pool in the soil ( ka ).
Step 7.2: Calculate the steady state stock for the ACTIVE SOC sub-pool ( ACTIVEy* ).
Step 7.3: Calculate the ACTIVE SOC stock by determining the additional increase or decrease in SOC from theprevious year in the inventory ( ACTIVEy ). Note that the initial size of the ACTIVE SOC sub-pool used at thestart of the inventory period is calculated as defined in step 1. Also note that if the estimated ka value is above 1,
then set the value of ka to 1 in the equation for calculating ( ACTIVEy ).
Step 7.4: Repeat Steps 7.1 to 7.3 to calculate the ACTIVE SOC sub-pool stocks for all years in the inventory.
Step 8. Calculate the total annual SOC stock change

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Volume 4: Agriculture, Forestry and Other Land Use

Step 8.1: Calculate the SOC stock ( SOCy ) for each grid cell or region by summing the SOC in the ACTIVE,
SLOW and PASSIVE sub-pools ( ACTIVEy , SLOWy and PASSIVEy , respectively) using Equation 5.0a.
Step 8.2: Calculate the stock change factor (𝐹𝑆𝑂𝐶𝑖 ) for each grid cell or region using Equation 5.0a.
Step 8.3: Calculate the total change in SOC stock ( CMineral ) using Equation 5.0a by multiplying the stock change
factor (𝐹𝑆𝑂𝐶𝑖 ) by the area of the grid cell or region i ( A ), and summing the changes across all land included in the
Tier 2 steady-state method.
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 management
practices/systems for croplands; model inputs (i.e., driving variables) are compatible with the availability ofcountry-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.
For biochar C amendments to soils, Tier 3 methods can be used to address GHG sources and sinks not captured inTiers 1 or 2, such as priming effects, changes to N2O or CH4 fluxes from soils, and changes to net primary
production. More information on Tier 3 methods is provided in Section 2.3.3.1 of Chapter 2, Volume IV.

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

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).
Tie r 2Developing Country-Specific Factors for the Default Equations
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 and/or well tested model. Additional guidance is provided in Chapter 2, Section 2.3.3.1.

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

Chapter 5: Cropland

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

Factor
value
type

Level

Temperature
regimeCool
Temperate/
BorealLand
use5Longterm
cultivated

(FLU)

Warm
Temperate

Moisture
regime1

IPCC
defaults

Error

Dry

0.77

±14%

Moist

0.70

±12%

Dry

0.76

±12%

Moist

0.69

±16%

Dry

0.92

±13%

Moist/Wet

0.83

±11%

2,3

Tropical

Land
use6

Paddy
rice

(FLU)

Land
use5

Perennial/
Tree
Crop

(FLU)

Land
use

Set aside(< 20 yrs)

(FLU)

Tillage

Full

(FMG)

All

Dry and
Moist/Wet

1.35

±4%

Temperate/
BorealDry and
Moist

0.72

±22%

TropicalDry and
Moist/Wet

1.01

±25%

Temperate/
Boreal andTropical

Dry

0.93

±11%

Moist/Wet

0.82

±17%

Tropicalmontane44

n/a

0.88

±50%

All

Cool
Temperate/
BorealTillage

7(FMG)

Re-duced

Warm
Temperate

Dry and
Moist/Wet

1.00

n/a

Dry

0.98

±5%

Moist

1.04

±4%

Dry

0.99

±3%

Moist

1.05

±4%

Dry

0.99

±7%

Moist/Wet

1.04

±7%

Dry

1.03

±4%

Moist

1.09

±4%

Dry

1.04

±3%

Moist

1.10

±4%

Dry

1.04

±7%

Moist/Wet

1.10

±5%

Description

Represents area that has been converted
from native conditions and continuously
managed for predominantly annual crops
over 50 yrs. Land-use factor has been
estimated under a baseline condition offull tillage and nominal (‘medium”)
carbon input levels. Input and tillage
factors are also applied to estimate carbon
stock changes, which includes changes
from full tillage and medium input.
Long-term (> 20 year) annual cropping of
wetlands (paddy rice). Can include
double-cropping with non-flooded crops.
For paddy rice, tillage and input factors
are not used.
Long-term perennial tree crops such as
fruit and nut trees, coffee and cacao.

Represents temporary set aside ofannually 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 byresidues.

Primary and/or secondary tillage but with
reduced soil disturbance (usually shallow
and without full soil inversion). Normallyleaves surface with >30% coverage byresidues at planting.

Tropical
Cool
Temperate/
BorealTillage7(FMG)

No-tillWarm
Temperate

Direct seeding without primary tillage,
with only minimal soil disturbance in theseeding zone. Herbicides are typicallyused for weed control.

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Volume 4: Agriculture, Forestry and Other Land Use

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

Factor
value
type

Level

Temperature
regimeTemperate/
Boreal

Input
(FI)

Low

(FI)

Input
(FI)

Mediu
m

High

All

Temperate/
Boreal andTropicalwithout
manureTropicalmontane4

Input
(FI)

High –
withmanure

IPCC
defaults

Error

Dry

0.95

±13%

Moist

0.92

±14%

Dry

0.95

±13%

Moist/ Wet

0.92

±14%

n/a

0.94

±50%

2,3

TropicalTropicalmontane4

Input

Moisture
regime1Temperate/
Boreal andTropicalTropicalmontane4

Dry and
Moist/ Wet

1.00

n/a

Dry

1.04

±13%

Moist/ Wet

1.11

±10%

n/a

1.08

±50%

Dry

1.37

±12%

Moist/ Wet

1.44

±13%

n/a

1.41

±50%

Description

Low residue return occurs when there isremoval of residues (via collection or
burning), frequent bare-fallowing, productionof crops yielding low residues (e.g.,vegetables, tobacco, cotton), no mineral
fertilization or N-fixing crops.
Representative for annual cropping withcereals where all crop residues are returned to
the field. If residues are removed thensupplemental organic matter (e.g., manure) is
added. Also requires mineral fertilization or
N-fixing crop in rotation.
Represents significantly greater crop residue
inputs over medium C input cropping systems
due to additional practices, such as productionof high residue yielding crops, use of green
manures, cover crops, improved vegetatedfallows, irrigation, frequent use of perennial
grasses in annual crop rotations, but without
manure applied (see row below).
Represents significantly higher C input overmedium C input cropping systems due to an
additional practice of regular addition ofanimal manure.

Notes: Long-term cultivation, perennial crops paddy rice and tillage management factors were derived using methods provided in Annex
5A1.
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 to derivea default, uncertainty was assumed to be + 50% based on expert opinion. NA denotes ‘Not Applicable’, where factor values constitutedefined 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 some of the 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.
Sources:
5

The following references used for land-use factors (other than paddy rice): Aborisade and Aweto 1990; Adachi et al. 2006; Agbenin and
Goladi 1997; Aina 1979; Alcantara et al. 2004; Allen 1985; An et al. 2003; Ashagrie et al. 2005; Assad et al. 2013; Aweto 1981; Aweto
and Ayuba 1988; Aweto and Ayuba 1993; Aweto and Ishola 1994; Ayanaba et al. 1976; Banaticla and Lasco 2006; Bashkin and Binkley
1998; Batlle-Bayer et al. 2010; Bautista-Cruz and del Castillo 2005; Berhongaray et al. 2013; Bernardi et al. 2007; Bernhardreversat 1988;
Berthrong et al. 2012; Bertol and Santos 1995; Beyer 1994; Binkley et al. 2004; Binkley and Resh 1999; Bonde et al. 1992; Bowman andAnderson 2002; Brand and Pfund 1998; Brown and Lugo 1990; Bruun et al. 2006; Burke et al. 1995; Burke et al. 1995; Buschbacher et al.
1988; Buschiazzo et al. 1998; Buyanovksy et al. 1987; Cadisch et al. 1996; Cai et al. 2008; Cambardella and Elliott 1994; Cambardellaand Elliott 1992; Campos et al. 2007; Cao et al. 2004; Carvalho et al. 2009; Carvalho et al. 2009; Cerri et al. 1991; Cerri et al. 2003; Cerri
et al. 2007; Chan 1997; Chandran et al. 2009; Chen et al. 2007; Chen 2006; Chia et al. 2017; Chidumayo and Kwibisa 2003; Chiti et al.
2014; Chone et al. 1991; Cleveland et al. 2003; Collins et al. 1999; Conant et al. 2001; Conti et al. 2014; Cook et al. 2014; Corazza et al.
1999; D'Annunzio et al. 2008; da Silva-Junior et al. 2009; Dai et al. 2008a; Dai et al. 2008b; Dalal et al. 2005; Dalal and Mayer 1986;
Dawoe et al. 2014; de Blecourt et al. 2013; de Camargo et al. 1999; de Freitas et al. 2000; de Koning et al. 2003; de Moraes et al. 2002;de Moraes et al. 1996; de Neergaard et al. 2008; Dechert et al. 2004; Delelegn et al. 2017; Denef et al. 2007; Desjardins et al. 1994;
Desjardins et al. 2004; Detwiler 1986; Eaton and Lawrence 2009; Eclesia et al. 2012; Eden et al. 1990; Ekanade 1991; Elliott et al. 1991;
Elmore and Asner 2006; England et al. 2016; Epron et al. 2009; Erickson et al. 2001; Fabrizzi et al. 2009; Farley et al. 2004; Feldpauschet al. 2004; Feller et al. 2001; Fernandes et al. 2002; Fernandez et al. 2012; Fisher et al. 1994; Follett et al. 1997; Freibauer 1996; Freixo
et al. 2002; Fu et al. 2000; Fu et al. 2001; Han et al. 2004; Han et al. 2005; Harden et al. 1999; Hölscher et al. 1997; Hou et al. 2008;

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Chapter 5: Cropland

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

Hsieh 1996; Hu et al. 2007; Huang et al. 2007; Hughes et al. 2000; Hughes et al. 2002; Hughes et al. 2000; Ihori et al. 1995; Ishizuka et
al. 2005; Islam and Weil 2000; Jakelaitis et al. 2008; Janssen and Wienk 1990; Jaramillo et al. 2003; Jia et al. 2004; Jia et al. 2007; Jimenez
et al. 2007; Jun and Liqing 2007; Juo et al. 1995; Juo and Lal 1977; Juo and Lal 1979; Kainer et al. 1998; Karhu et al. 2011; Kawanabe etal. 2000; Keith et al. 2015; King and Campbell 1994; Kotto-Same et al. 1997; Koutika et al. 1997; Krishnaswamy and Richter 2002; Lal
1998; Lemenih et al. 2005; Lemenih et al. 2005; Lemma et al. 2006; Lepsch et al. 1994; Li et al. 2005; Li et al. 2007; Li et al. 2007; Li etal. 2007; Lilienfein et al. 2003; Lima et al. 2006; Lisboa et al. 2009; Lugo and Sanchez 1986; Luizao et al. 1992; Ma et al. 2006; Macedo
et al. 2008; Maia et al. 2009; Makumba et al. 2007; Manlay et al. 2002; Manlay et al. 2002; Maquere et al. 2008; Marin-Spiotta et al.
2009; Markewitz et al. 2004; Martins et al. 2009; Masto et al. 2008; Materechera and Mkhabela 2001; McGrath et al. 2001; Mendham etal. 2003; Mikhailova et al. 2000; Morris 1984; Motavalli et al. 2000; Motavalli and McConnell 1998; Muller et al. 2001; Mutuo et al.
2005; Nadal-Romero et al. 2016; Navarrete et al., 2016; Navarrete and Tsutsuki, 2008; Neill et al., 1997; Neill et al., 1997; Neufeldt etal., 2002; Ogunkunle and Eghaghara 1992; Ohta 1990; Osher et al. 2003; Parfitt et al. 1997; Paul et al. 2008; Pennock and van Kessel
1997; Perrin et al. 2014; Piccolo et al. 2008; Potter et al. 1999; Potvin et al. 2004; Powers 2004; Powers and Veldkamp 2005; Rangel etal. 2007; Rasiah et al. 2004; Reeder et al. 1998; Reiners et al. 1994; Resh et al. 2002; Rhoades et al. 2000; Richards et al. 2007; Riezebos
and Loerts 1998; Rojas et al. 2016; Roscoe and Buurman 2003; Rossi et al. 2009; Russell et al. 2007; Sa et al. 2001; Saggar et al. 2001;
Saha et al. 2009; Saha et al. 2010; Salimon et al. 2004; Sanchez et al. 1983; Saynes et al. 2005; Schedlbauer and Kavanagh 2008;
Schiffman and Johnson 1989; Schwendenmann and Pendall 2006; Shang and Tiessen 1997; Sheng et al. 2004; Siband 1974; Silva et al.
2009; Silver et al. 2004; Sitompul et al. 2000; Six et al. 1998; Six et al. 2000; Slobodian et al. 2002; Smiley and Kroschel 2008; Smith etal. 2002; Sohng et al. 2017; Solomon et al. 2002; Solomon et al. 2007; Solomon et al. 2000; Sommer et al. 2000; Sparling et al. 2000;
Srivastava and Singh 1991; Su 2007; Su et al. 2006; Su et al. 2004; Su et al. 2002; Su et al. 2004; Szott and Palm 1996; Templer et al.
2005; Tian et al. 2001; Tian et al. 2008; Tiessen et al. 1992; Tiessen et al. 1982; Tornquist et al. 1999; Townsend et al. 1995; Trouve etal. 1994; Trumbore et al. 1995; Uhl and Jordan 1984; Unger 2001; Vagen et al. 2006; van Dam et al. 1997; van Noordwijk et al. 1997;
van Straaten et al. 2015; Veldkamp 1994; Veldkamp et al. 2003; Villarino et al. 2014; Voroney et al. 1981; Wadsworth et al. 1988; Wairu
and Lal 2003; Walker and Desanker 2004; Wang et al. 2004; Wang and Zhang 2009; Wang et al. 2011; Wang et al. 2005; Wang et al.
2006; Wang et al. 2007; Wang et al. 2006; Wang et al. 2008; Weaver et al. 1987; Wick et al. 2000; Wick et al. 2005; Wu and Tiessen
2002; Wu et al. 2006; Xu et al. 2013; Yan et al. 2008; Yang et al. 2004; Yang et al. 2016; Yemefack et al. 2006; Yin et al. 2008; Yonekura
et al. 2010; Yu et al. 2007; Yue et al. 2007; Zhan et al. 2005; Zhang et al. 1988; Zhao et al. 2005; Zhou et al. 2007; Zingore et al. 2005;
Zinn et al. 2005; Zinn et al. 2002; Zou and Bashkin 1998
6

The following references were used for paddy rice land-use factor: Andreetta et al. 2016; Bi et al. 2009; Gami et al. 2001; Hao et al.
2008; Huang et al. 2015; Kölbl et al. 2014; Liu et al. 2003; Majumder et al. 2008; Mandal et al. 2007; Nayaka et al. 2012; Nayaka et al.
2009; Pampolino et al. 2008; Pan et al. 2009; Shen et al. 2007; Shirato et al. 2011; Shirato and Yokozawa 2005; Wang et al. 2011; Wu et
al. 2000; Xu et al. 2007; Zhang et al. 2006
7

The following references were used for tillage management factors: Ahl et al. 1998; Al-Kaisi et al. 2005; Al-Kaisi et al. 2005; Alvarez
et al. 2014; Alvarez et al. 1998; Alvarez et al. 1995; Alvarez et al. 1998; Alvarez et al. 1995; Alvarez et al. 1995; Alvarez et al. 1995;
Alvaro-Fuentes et al. 2009; Alvaro-Fuentes et al. 2008; Alvaro-Fuentes et al. 2014; Angers et al. 1997; Angers et al. 1995; Anken et al.
2004; Balesdent et al. 1990; Barber et al. 1996; Bayer et al. 2006; Bayer et al. 2000; Bayer et al. 2002; Beare et al. 1994; Bhattacharyya
et al. 2008; Bhattacharyya et al. 2013; Bhattacharyya et al. 2009; Black and Tanaka 1997; Blanco-Canqui et al. 2004; Blanco-Canqui etal. 2011; Boddey et al. 2010; Bordovsky et al. 1999; Borin et al. 1997; Borresen and Njos 1993; Bowman and Anderson 2002; Bowman
and Anderson 2002; Burch et al. 1986; Buschiazzo et al. 1998; Buyanovsky and Wagner 1998; Calegari et al. 2008; Campbell et al. 1999;
Campbell et al. 1996; Carter 1991; Carter et al. 1988; Carter et al. 1994; Carter et al. 2002; Cavanagh et al. 1991; Chagas et al. 1995;
Chan et al. 2002; Chan et al. 2003; Chan and Mead 1988; Chaney et al. 1985; Chen et al. 2009; Chen et al. 2009; Chen et al. 2015; ChengFang et al. 2012; Choudhary et al. 2013; Clapp et al. 2000; Corazza et al. 1999; Costantini et al. 1996; Dalal 1989; Dalal et al. 1991; Denefet al. 2007; Devine et al. 2014; Diaz-Zorita 1999; Díaz-Zorita et al. 2004; Dick and Durkalski 1997; Dikgwatlhe et al. 2014; Dimassi etal. 2014; Dolan et al. 2006; Dominguez et al. 2016; Doran et al. 1998; Dou et al. 2008; Du et al. 2010; Du et al. 2015; Duiker and Lal
1999; Edwards et al. 1992; Eghball et al. 1994; Fabrizzi et al. 2003; Fabrizzi et al. 2009; Fan et al. 2014; Feiziene et al. 2011; Ferreras etal. 2000; Fettell and Gill 1985; Fleige and Baeumer 1974; Follett and Peterson 1988; Franzleubbers et al. 1995; Franzluebbers and Arshad
1996; Franzluebbers et al. 1999; Franzluebbers and Stuedemann 2002; Freitas et al. 2000; Freixo et al. 2002; Gál et al. 2007; Galantini etal. 2006; Garcia-Prechac et al. 2004; Ghimire et al. 2012; Ghuman and Sur 2001; Grabski et al. 1997; Green et al. 2007; Gwenzi et al.
2009; Halvorson et al. 1997; Halvorson et al. 2002; Hansmeyer et al. 1997; Hao et al. 2001; Havlin and Kissel 1997; Heenan et al. 1995;
Heinze et al. 2010; Hendrix 1997; Hermle et al. 2008; Hernanz et al. 2002; Hernanz et al. 2009; Hertnanz et al. 2009; Higashi et al. 2014;
Hou et al. 2011; Huggins et al. 2007; Hulugalle 2000; Hussain et al. 1999; Ismail et al. 1994; Jagadamma and Lal 2010; Jarecki and Lal
2010; Jarvis 1996; Jemai et al. 2012; Jemai et al. 2013; Karlen et al. 1998; Karlen et al. 1994; Kruger 1996; Kumar et al. 2012; Kumar etal. 2014; Kushwaha et al. 2000; Küstermann et al. 2013; Lal 1998; Lal et al. 1994; Lammerding et al. 2010; Larney et al. 1997; Laudicina
et al. 2014; Lavado et al. 1999; Liang et al. 2011; Liang et al. 2007; Lilienfein et al. 2000; Liu et al. 2014; Lopez-Bellido et al. 2009;
Lopez-Bellido et al. 2017; Lopez-Fando et al. 2007; Lopez-Fando and Pardo 2009; Lou et al. 2012; Martin-Lammerding et al. 2013;
Martin-Rueda et al. 2007; Martinez et al. 2013; McCarty et al. 1998; McLeod et al. 2013; Melero et al. 2011; Mielke et al. 1986; Mikha
et al. 2010; Mikha et al. 2013; Mrabet et al. 2001; Munoz-Romero et al. 2017; Murage et al. 2006; Nyamadzawo et al. 2008; Nyborg etal. 1995; Olson et al. 2005; Packer et al. 1992; Page et al. 2013; Pierce and Fortin 1997; Plaza-Bonilla et al. 2011; Powlson and Jenkinson
1982; Prasad et al. 2016; Presley et al. 2011; Puget and Lal 2005; Quincke et al. 2006; Rasmussen and Albrecht 1997; Rhoton et al. 1993;
Robertson et al. 2015; Ross and Hughes 1985; Sa et al. 2014; Saffigna et al. 1989; Sainju et al. 2009; Sainju et al. 2005; Sainju et al. 2011;
Sainju et al. 2005; Sainju et al. 2008; Sainju et al. 2002; Salinas-Garcia et al. 1997; Salinas-Garcia et al. 2002; Salvo et al. 2010; Schomberg
and Jones 1998; Sheehy et al. 2013; Shi et al. 2011; Shrestha et al. 2015; Shukla et al. 2006; Singh et al. 2015; Six et al. 2000; Sombreroand de Benito 2010; Steinbach and Alvarez 2006; Studdert et al. 2017; Studdert et al. 1997; Sun et al. 2011; Taboada et al. 1998; Thomas
et al. 2007; Tian et al. 2013; Tivet et al. 2013; Ussiri and Lal 2009; van Groenigen et al. 2011; VandenBygaart et al. 2002; Varvel andWilhelm 2011; Venterea et al. 2006; Viaud et al. 2010; Wander et al. 1998; Wang and Dalal 2006; Wanniarachchi et al. 1999; Wright andHons 2004; Xu et al. 2013; Yang and Kay 2001; Yang and Wander 1999; Zhang et al. 2007; Zhang et al. 20172019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Reference C stocks can be derived from country-specific data in a Tier 2 approach. Reference values in Tier 1
correspond to non-degraded, unimproved lands under native vegetation, but other reference conditions can also bechosen for Tier 2. In addition, the depth for evaluating soil C stock changes can be different with the Tier 2 method.
The effect of tillage on soil carbon stocks can be markedly different for depths above the tillage depth compared
to below the tillage depth (Angers et al. 1997; Angers and Eriksen-Hamel, 2008; Gal et al. 2017), and including
soil C stock data below the depth of tillage is necessary to provide an accurate estimate of tillage system effect on
C stocks. However, the depth of the reference C stocks (SOCREF) and stock change factors need to the same for allland uses (i.e., FLU, FI, and FMG) to ensure consistent application of methods for determining the impact of land
use change on soil C stocks..
The carbon stock estimates may be improved when deriving country-specific factors for FLU and FMG, byexpressing carbon stocks on a soil-mass equivalent basis rather than a soil-volume equivalent (i.e. fixed depth)basis. This is because the soil mass in a certain soil depth changes with the various operations associated with land
use that affect the density of the soil, such as uprooting, land levelling, tillage, and rain compaction due to thedisappearance of the cover of tree canopy. However, it is important to realize that all soil C stocks used to derivestock change factors across all land uses must be on an equivalent mass basis if this method is applied. This will
require necessary soils data to do comprehensively for all land uses. See Box 2.2b in Chapter 2, Section 2.3.3.1
for more information.
Biochar C Amendments
The parameter 𝐹𝑝𝑒𝑟𝑚𝑝 can be based on H/Corg or O/Corg measured directly from representative samples of biochar,or from published data for biochar produced using similar process conditions as the biochar that is applied to soils
in the country. Tier 2 emission factors may be disaggregated based on variation in environmental conditions, suchas the climate and soil types, in addition to variation associated with the biochar production methods that generate
production types defined by the specific feedstock type and conversion process. See Section 2.3.3.1, Chapter 2,
Volume IV for more information.
Steady-State Method
Default parameters are provided for the three-pool steady-state C pool equations (Table 5.5a). The average lignin
and nitrogen contents of the C input is also required to estimate the size of the three C pools (See Tables 5.5b and5.5c).
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. Tier 3 methods for biochar C amendments to soils arecountry-specific and may involve empirical or process-based models to account for a broader set of impacts ofbiochar amendments. More information on Tier 3 methods is provided in Section 2.3.3.1, Chapter 2, Volume IV.

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Chapter 5: Cropland

TABLE 5.5A (NEW)GLOBALLY CALIBRATED MODEL PARAMETERS TO BE USED TO ESTIMATE SOC CHANGES FOR MINERAL SOILS WITH THE
TIER 2 STEADY-STATE METHOD
Practice

Value (min, max)Standard
Deviation

Full-till3.036 (1.4, 4.0)

0.579

Reduced-till

2.075 (1.0, 3.0)

0.569

No-till

1

ws

All

1.331 (0.8, 2.0)

0.386

k faca

All

7.4

n/a

Decay rate constant under optimal conditions
for decomposition of the active sub-pool

k facsAll

0.209 (0.058, 0.3)

0.566

Decay rate constant under optimal conditions
for decomposition of the slow sub-pool

k fac pAll

0.00689 (0.005, 0.01)

0.00125

Decay rate constant under optimal conditions
for decomposition of the passive sub-pool

f1

All

0.378 (0.01, 0.8)

0.0719Fraction of metabolic dead organic matterdecay products transferred to the active subpool

f2

Full-till0.368 (0.007, 0.5)

0.0998Fraction of structural dead organic matterdecay products transferred the active subpool

f3

All

0.455 (0.1, 0.8)

0.201

Fraction of structural dead organic matterdecay products transferred to the slow subpool

f5

All

0.0855 (0.037, 0.1)

0.0122Fraction of active sub-pool decay products
transferred to the passive sub-pool

f6

All

0.0504 (0.02, 0.19)

0.0280Fraction of slow sub-pool decay products
transferred to the passive sub-pool

f7

All

0.42

n/a

Fraction of slow sub-pool decay products
transferred to the active sub-pool

f8

All

0.45

n/a

Fraction of passive sub-pool decay products
transferred to the active sub-pool

topt

All

33.69 (30.7, 35.34)

0.66

Optimum temperature to estimate
temperature modifier on decomposition

tmax

All

45

n/a

Maximum monthly average temperature fordecomposition.

Parameter

till fac

Description

Tillage disturbance modifier for decay rates

slope parameter for mappeti term to estimate

w fac

Methods used in the Bayesian calibration process are described in Annex 5A.3.
Source: Campbell et al. 1997; Collins et al. 2000; Dick et al. 1997; Diaz-Zorita et al. 1999; Dimassi et al. 2014; e-RA 2013; Gregorich etal. 1996; Halvorson et al. 1997; Huggins and Fuchs 1997; Janzen et al. 1997; Jenkinson 1990; Jenkinson and Johnston 1977; KBS LTER
2017; Küstermann and Hülsbergen 2013; Maillard et al. 2018; Marchado 2013; Marchado et al. 2008, 2011; Pierce and Fortin 1997;
Rasmussen and Smiley 1997; Schultz 1995; Skjemstad et al. 2004; Vanotti et al. 1997; See Annex 5A.3 for more information.

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TABLE 5.5B (NEW)DEFAULT VALUES FOR NITROGEN AND LIGNIN CONTENTS IN CROPS FOR THE STEADY-STATE METHOD

N content of residues1

Crops

Lignin content of residues2

Generic value for crops not indicated below

0.0083

0.073

Generic Grains

0.00680.074

Winter Wheat

0.00690.053

Spring Wheat

0.0070

0.053

Barley0.0090

0.046

Oats

0.0073

0.047

Maize

0.0063

0.11

Rye3

0.008

0.05

Rice4

0.007

0.125

Millet4

0.007

0.062

Sorghum3

0.00650.06

Beans and Pulses

0.008

0.075

Soybeans

0.008

0.085

Potatoes and Tubers

0.01690.073

Peanuts4

0.016

0.086

N-fixing forages

0.02500.072

Alfalfa

0.02380.072

Non-N-fixing forages

0.0134

0.049

Perennial Grasses

0.01260.049

Grass-Clover Mixtures4

0.01780.061

0.01340.057

Non-legume hay
1-1

The estimates are in units of g N (g residue) on dry weight basis from a biomass-weighted average of aboveground and belowgroundfor each crop based on data in Table 11.1a in Volume IV, Chapter 11 of this report.
2

Winter wheat, spring wheat, barley, oats, millet, beans and pulses, soybeans, peanuts, values from Equi-Analytical Laboratories (2018);
maize, rice, and sorghum from Cornell University (2017); and potatoes and tubers from Zereu et al. (2014).
3

Simple average of nitrogent content of aboveground and belowground. 4 Nitrogen content of aboveground assumed to represent all
residue.
4value is an average of N fixing and non-N fixing grasses.

Notes: Uncertainty is assumed to be ±75% for the N content estimates and ±50% for the lignin content estimates, expressed as a 95%
confidence intervals.

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Chapter 5: Cropland

TABLE 5.5C (NEW)DEFAULT VALUES FOR CARBON TO NITROGEN RATIOS, NITROGEN, AND LIGNIN CONTENTS IN LIVESTOCK MANURE FOR
THE STEADY-STATE METHOD
Livestock Manure TypeC to N ratio of manure

N content of manure(% dry basis)

Lignin content ofmanure (% dry basis)

Dairy Cattle

16

2.9

13

Beef Cattle

191

2.31

91

Poultry

102

5.12

52

Swine

113

4.13

53

Horses/Mules/Asses

20

1.3

134

Sheep

11

3.3

134

Sources: Chen et al. 2003 for Dairy Cattle, Beef Cattle, Poultry and Swine.
ASAE 2005 for Horses/Mules/Asses.
MWPS 2004; Hébert et al. 1991; Sørensen and Jensen, 1995; Rees and Castle, 2002 for Sheep
1

Average of Beef and Cattle- Feedlot categories.

2

Average across four development categories.

3

Average of Nursery, Grower and Finisher categories.

4

Average of Beef and Dairy from Chen et al. 2003.

Notes: Uncertainty is assumed to be ± 50% for all of these estimates, expressed as a 95% confidence interval.

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

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 insoil 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 to estimate the default
stock change factors. Rice production, perennial croplands, and set-aside lands (i.e., lands removed fromproduction) 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 >30percent coverage by residues at planting)and full tillage (substantial soil disturbance with full inversion and/or frequent, within year tillage operations, whileleaving <30percent 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.

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Volume 4: Agriculture, Forestry and Other Land Use

Figure 5.1

Classification scheme for cropping systems

In order to classify cropland management systems, the inventory compiler should start at the top and proceed
through the diagram answering questions (move across branches if answer is yes) until reaching a terminal pointon 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

Annualcrop with residues
removed orburned1?

Yes

Organic
amendment?

No

Yes

Annualcrop with low residue2
or rotation with barefallow?

Medium C
input

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

No

Low C
input

Yes

Medium C
input

Practiceincreasing C
input4?

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.

The main types of land-use activity data are: i) aggregate statistics (Approach 1), ii) data with explicit informationon land-use conversions but without specific geo-referencing (Approach 2), or iii) data with explicit informationon 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://www.fao.org/faostat),provide annual compilations of total land area by major land-uses, select management data (e.g., irrigated vs. nonirrigated 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).

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Chapter 5: CroplandManagement 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 associatedwith 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 knowledge is another sourceof information for cropping practices. It is good practice to elicit expert knowledge using methods provided inVolume 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 bysampling or by referencing the location to a suitable soil map. Inventory points that are selected based on an
appropriate statistical design also enable estimates of the variability associated with activity data, which can beused 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 2Developing Country-Specific Factors for the Default Equations
Tier 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 within country subdivisions of annual cropping
input categories (i.e., low, medium, high, and high with amendment), rice cultivation, perennial cropping systems,
and set-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.
For Tier 2, the specific definitions of management and input factors are typically made to match available activity
data on how an activity affects C stocks. For example, if a country has management factors related to specifictillage practices that involve a mix of intensities over time, then the country will also need activity data on thosespecific tillage practices to apply the country-specific factors.
Biochar C Amendments
For biochar C amendments, the activity data required for the Tier 2 method includes the total quantities of biochar
distributed as amendment to mineral soils. These data must be disaggregated by production type, where production
type is defined as a process utilizing a specific feedstock type, and a specific conversion process. Changes in soil
C associated with biochar amendments are considered to occur where it is incorporated into soil. However, due tothe distributed nature of the land sector in which this can take place, inventory compilers may not have access todata on when or where biochar C amendments occur. Inventory compilers may be able to compile data on the total
amount of biochar applied to cropland mineral soils from biochar producers, exporters, importers, distributors
and/or from those applying biochar to cropland in the country. Note that exported biochar is not included in the
total amount of biochar amended to soils in the country.
Additionally, activity data on the amount of biochar amendments may be disaggregated by climate zones and/or
soil types if country-specific factors are disaggregated by these environmental variables. The additional climate
and soil activity data may be obtained with a survey of biochar distributors and land managers.
Steady-State Method
This method requires soil C input data based on the amount of biomass that is converted to dead organic matter
annually. This rate will vary depending on the crop production, management activity, and other environmental
variables. Removals or reductions in dead organic matter are subtracted from the C input amount, which could
occur with livestock grazing, grassland burning, or harvesting of grass for feed or bioenergy. Additions of C,
particularly organic amendments such as manure, are included in the estimate of C input.
It is good practice to estimate C input using country-specific factors in order to produce more accurate estimates.
If country-specific factors are not available, Equation 5.0h can be used to estimate C inputs with global factors

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Volume 4: Agriculture, Forestry and Other Land Use

provided in Table 11.1a, Chapter 11, Volume 4 or alternatively, the amount can be calculated using the methodand data in Table 11.2, Chapter 11.
EQUATION 5.0H (NEW)CROPLAND C- INPUT TO SOIL FOR THE STEADY-STATE METHOD

(

) (

) (

) (

Cinput =  AGR(T ) • C AG (T ) + BGR(T ) • CBG(T ) + FAM (T ) • CN AM (T ) + FOON (T ) • CN OON (T )T((

AGR(T ) = AGDM (T ) • Area(T ) • 1 − FracRemoval (T ) − FracBurnt (T ) • C f

(

)

))

)

BGR(T ) = Crop(T ) + AGDM (T ) • RS(T ) • Area(T ) • FracRenew(T )AGDM (T ) = Crop(T ) • RAG (T )Where:

Cinput= annual amount of C input from residues to the soil (above and below ground), kg C yr-1

AGR(T )

= annual total amount of above-ground crop residue for crop T, kg d.m. yr-1.

C AG (T )= C content of above-ground residues for crop T, kg C (kg d.m.) -1 (Default: 0.42 kg C (kgd.m.) -1)

FracRemove(T ) = fraction of above-ground residues of crop T removed annually for purposes such as feed,
bedding and construction, dimensionless. Survey of experts in country is required to obtaindata. If data for FracRemove are not available, assume no removal

FracBurnt (T )= fraction of annual harvested area of crop T burnt, dimensionlessCf= combustion factor (dimensionless) (refer to Chapter 2, Table 2.6)

BGR(T )

= annual total amount of belowground crop residue for crop T, kg d.m. yr-1

CBG(T )

= C content of below-ground residues for crop T, kg C (kg d.m.)-1, (Default: 0.42 kg C (kgd.m.) -1)

FAM (T )= N in animal manures applied to crop T, kg N yr-1 (Equation 10.34 in Section 10.5.4, Chapter
10)

CN AM (T )= C to N ratio of animal manures applied to crop T, kg C (kg N)-1 (Table 5.5c)

FOON(T)= N in other organic amendments applied to crop T, kg N yr-1 (Equation 11.3 in Section
11.2.1.3, Chapter 11; with the exclusion of FAM)

CNOON(T)= C to N ratio of other organic amendments applied to crop T, kg C (kg N)-1. It is generallycomprised between 10 and 20

AGDM (T )=Above-ground residue dry matter for crop T, kg d.m. ha-1
(Use factors for RAG(T) in Table 11.1a, Chapter 11, or alternatively, the above-groundresidue dry matter may be estimated using the method and data in Table 11.2, Chapter 11).
It is good practice to ensure consistency in the method applied to estimate AGDM(T) inequations 5.0h (New) and 11.6 (Updated)

Crop(T )= harvested annual dry matter yield for crop T, kg d.m. ha-1 (Use Equation 11.7, Chapter 11)

RAG(T )

= ratio of above-ground residues dry matter (AGDM(T)) to harvested yield for crop T (Crop(T)),
kg d.m. ha-1(kg d.m. ha-1)-1, (Table 11.1a)

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Chapter 5: CroplandArea(T )= total annual area harvested of crop T, ha yr-1

FracRenew(T ) = fraction of total area under crop T that is renewed annually 8, dimensionless. For countries
where forages are renewed on average every X years, FracRenew(T ) = 1/X. For annual crops

FracRenew(T ) = 1
RS(T )= ratio of below-ground root biomass to above-ground shoot biomass for crop T, kg d.m. ha1

T(kg d.m. ha-1)-1, (Table 11.1a)= crop or forage type

Data on crop yield statistics (yields and area harvested, by crop) may be obtained from national sources. If suchdata are not available, FAO publishes data on crop production: (http://faostat.fao.org/). Tillage management data
are also required (proportion of full tillage, reduced tillage and no-till), and irrigation data for any lands that areprovided supplement water (proportion of land). Monthly average temperature, precipitation and potential
evapotranspiration is needed for each grid cell or region. This information is available from global datasets, suchas the CRU climate dataset (https://crudata.uea.ac.uk/cru/data/hrg/), if country-specific data are not available. The
average sand content is needed for each grid cell or region, which is available from Harmonized World Soil
Database (http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/).
Tier 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed
data on the combinations of climate, soil, topographic and management data are needed, relative to the Tiers 1 and2 methods, but the exact requirements will depend on the model or measurement design.
For biochar C amendments, the additional activity data required to support a Tier 3 method will depend on which
processes are represented and which environmental variables that are required as input to the model. Priming
effects, soil GHG emissions, and plant production responses to biochar all vary with biochar type, climate, andsoil type. Furthermore, soil GHG emissions and plant production responses also vary with crop type andmanagement. Therefore, Tier 3 methods may require environmental data on climate zones, soil types, crop types
and crop management systems (such as nitrogen fertilizer application rates, and whether soils are flooded for paddy
rice production), in addition to the amount of biochar amendments in each of the individual combinations of strata
for the environmental variables. More detailed activity data specifying the process conditions for biochar
production or the physical and chemical characteristics of the biochar may also be required (such as surface area,
cation exchange capacity, pH, and ash content).

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

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 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 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 dependon 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.

8 This term is included in the equation to account for N release and the subsequent increases in N O emissions (e.g., van der2

Weerden et al., 1999; Davies et al., 2001), from renewal/cultivation of grazed grass or grass/clover pasture and other forage
crops.

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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 5.6.
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 thendividing 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 the time 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 anddefault reference C stocks (Table 2.3) and stock change 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 high activity clay soils there are 1Mha ofpermanent annual cropland. The native reference carbon stock (SOCREF) for the region is 64 tonnes
C ha-1. At the beginning of the inventory calculation period (in this example, 10 yrs earlier in 1990),
the distribution of cropland systems were 400,000 ha of annual cropland with low carbon input levels
and full tillage and 600,000 ha of annual cropland with medium input levels and full tillage. Thus,
initial soil carbon stocks for the area were:
400,000 ha ● (64 tonnes C ha-1 ● 0.75 ● 1 ● 0.92) + 600,000 ha ● (64 tonnes C ha-1 ● 0.75 ● 1 ● 1)
= 46.46 million tonnes C.
In the last year of the inventory 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 and medium C input, and 100,000 ha of annual cropping with no-till and medium C input.
Thus, total soil carbon stocks based on the inventory year are:200,000 ha ● (64 tonnes C ha-1 ● 0.75 ● 1 ● 0.92) + 700,000 ha ● (64 tonnes C ha-1 ● 0.75 ● 1.01
● 1) + 100,000 ha ● (64 tonnes C ha-1 ● 0.75 ● 1.11 ● 1) = 49.06 million tonnes C.
Thus, the average annual stock change over the period for the entire area is: 49;06 – 46.46 = 2.60
million tonnes/20 yr = 130000 tonnes C per year soil C stock increase (Note: 20 years is the timedependence of the stock change factor, i.e., factor represents annual rate of change over 20 years).

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

5.2.3.5 U NCERTAINTY ASSESSMENT
No refinement.

5.2.4

Non-CO2 greenhouse gas emissions from biomassburning

No refinement.

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Chapter 5: Cropland

5.3

LAND CONVERTED TO CROPLAND

No refinement in the Introduction.

5.3.1

Biomass

5.3.1.1 C HOICE OF METHODThis 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 pasture andgrazing 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 isplanted 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 is insufficient
information to provide a default approach with default parameters to estimate carbon stock change in dead organic
matter (DOM) pools9. 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 aboveground biomass since limited data are available on below-ground carbon stocks in perennial 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 tier possible
given national circumstances. It is good practice for countries to use a Tier 2 or Tier 3 approach if carbon emissions
and removals in Land Converted to Cropland is a key category and if the sub-category of biomass is consideredsignificant based on principles outlined in Volume 1, Chapter 4. Countries should use the decision 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 fromland-use conversion to Cropland. Average carbon stock change on a per hectare basis is estimated for each typeof conversion. The average carbon stock change is equal to the carbon stock change due to the removal of biomass
from the initial land use (i.e., carbon in biomass immediately after conversion minus the carbon in biomass prior
to conversion), plus carbon stocks from one year of growth in Cropland following conversion. It is necessary 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 landfollowing conversion into a single term. In this method, they are separated into two terms, BAFTER and CG to
increase transparency.
As described in section 5.3.1.1., 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 from annual 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
9 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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land remaining in a land-use category) and Section 5.2.1 (Change in carbon stocks in biomass in croplandremaining cropland). Thus, carbon gain of an annual crop is estimated only for the first year following a conversion,
whereas, carbon gains and losses of perennial woody crop may also occur in subsequent years up to 20 years (atmaximum).
The 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 country-specific carbon
stocks values.
Second, Tier 2 may modify the assumption that carbon stocks immediately following conversion are zero. This
enables countries to take into account land-use transitions where some, but not all, vegetation from the originalland use is removed.
Third, under Tier 2, it is good practice to apportion carbon losses to burning and decay processes if applicable.
Emissions of carbon dioxide occur as a result of burning and decay in land-use conversions. 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 disturbancematrix, which describes the retention, transfers and releases of carbon in the pools in the original ecosystemfollowing conversion to Cropland. A disturbance matrix defines for each pool the proportion that remains in thatpool 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.
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.

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 for each
initial and final land use; ii) carbon densities and soil carbon stock change are based on locally specific information,
which makes possible a dynamic link between biomass and soil; and iii) biomass volumes are based on actual
inventories. The transfer of biomass, to dead wood and litter following land-use conversion can be estimated using
Equation 2.20.

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Chapter 5: Cropland5.3.1.2 C HOICE OF EMISSION 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 cropland growth.
Tier 1
Default biomass carbon stock in initial land-use categories (BBEFORE) mainly Forest Land and Grassland areprovided in Updated Table 5.8. Initial land-use based carbon stocks should be obtained for different Forest Landor 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 (CG).
Updated Table 5.9 provides general defaults for annual and perennial crop for CG while updated Table 5.3
provides defaults for specific perennial crops. Separate defaults are provided for annual non-woody crops andperennial woody crops. For lands planted in annual crops, the default value of CG is 4.7 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 (UPDATED1).
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) in Table 4.3 consistent with what usedin forest land estimation to convert dry matter to carbon.

See Section 4.3(Land Converted
to Forest Land)

Grassland

See Chapter 6 Table 6.4 for carbon stocks in a range of grassland types
by climate regions. Multiply default carbon fraction (CF) 0.47 (forherbaceous biomass for Grassland, see page 6.29, Chapter 6 of the2006 IPCC Guidelines to convert dry matter to carbon.

+ 75%

Land-use category

1 Updates Table 5.8 from the IPCC 2006 IPCC Guidelines.
* Note that the condition of forests that are converted to grassland or cropland is not likely to be typical of the forest type in general, i.e.
the carbon stocks are probably lower than average (Carter et al. 2017; Puhlick et al. 2017). Specific values for disturbed forest may beappropriate.
# Represents a nominal estimate of error, equivalent to two times standard deviation, as a percentage of the mean.

TABLE 5.9 (UPDATED1)DEFAULT BIOMASS CARBON STOCKS PRESENT ON LAND CONVERTED TO CROPLAND IN THE YEAR FOLLOWINGCONVERSION

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

Errorrange#

4.7+ 75%

Crop type byclimate region

Ecologicalzone

Continent

Annual cropland

All

All

Annual cropland

Perennial
croplandAll

All

Agroforestry

See G in Tables 5.1 and
5.2

All

All

Monocultures

See G in Table 5.3

1

Cropping system

Update to Table 5.9 in the 2006 IPCC Guidelines# 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 and2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Useemissions 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. In general, the condition of forests thatare converted to grassland or cropland is not likely to be typical of the forest type, i.e. the carbon stocks areprobably lower than average. It is good practice for countries to evaluate country specific values for disturbed
forest under Tier 2.
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. Researchstudies suggest that the fraction is highly variable and could be as low as 0.2 (Fearnside, 2000; Barbosa andFearnside, 1996; and Fearnside, 1990). Updated default proportions of biomass 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 and burnt
is 0.35. This default takes into consideration research, which suggests the fraction should fall within the range 0.2
to 0.5 (e.g., Fearnside, 2000; Barbosa and Fearnside, 1996; and Fearnside, 1990). It is good practice for countries
to use 0.35 or another value within this range, provided that the rationale for the choice is documented. There isno default value for the amount of biomass taken off-site and burnt; countries will need to develop a proportion
based on national data sources. In Chapter 4 (Forest Land), the default proportion of biomass oxidized as a resultof 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 that inreality they occur over a 10 year period, and 2) report all emission from decay on an annual basis, estimating therate 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 to require estimation on the basisof 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 estimatesof conversions may be extrapolated over time based on the judgement of country experts. Under Tier 1 calculations,international statistics such as FAO databases, GPG-LULUCF and other sources, supplemented with soundassumptions, can be used to estimate the area of Land Converted to Cropland from each initial land use. For higher
tier calculations, country-specific data sources are used to estimate all possible transitions from initial land use to
final crop type. For perennial woody cropland, the total area of planted perennial woody crops for the age classes
within the maturing/harvesting cycle (up to 20 years) is required to estimate all biomass carbon change (CG). See
section 5.2.1.3 for details.
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 remotely sensed
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 are encouraged 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.

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Chapter 5: Cropland

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.5.3.1.4

C ALCULATION STEPS FOR T IER 1 AND T IER 2
No refinement.

5.3.1.5 U NCERTAINTY ASSESSMENT
No refinement.

5.3.2

Dead organic matterNo refinement.

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 irrigationof 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 of Chapter
2 (including equations), and that section needs to be read before proceeding with a consideration of specificguidelines dealing with cropland soil C stocks. The total change in soil C stocks for Land Converted to Croplandis 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). Thissection 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 andmanagement data are limited, aggregate data, such as FAO statistics, can be used as a starting point, along withknowledge of country experts of the approximate distribution of land-use types being converted and theirassociated management. If the previous land uses and conversions are unknown, SOC stocks changes can still becomputed 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, thatthe 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 acorresponding 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 country-specific classifications. This canbe accomplished with overlays of climate and soil maps, coupled with spatially-explicit data on the location ofland 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; and stockchanges associated with soil inorganic C pools). Decision trees are provided for mineral soils (Figure 2.5) andorganic soils (Figure 2.6) in Section 2.3.3.1 (Chapter 2) to assist inventory compilers with selection of theappropriate tier for their soil C inventory.

Mineral soils

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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 lastyear 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 the difference instocks (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, but involves country-specific or region-specificreference C stocks and/or stock change factors and may include disaggregated land-use activity and environmental
data. Tier 2 methods for biochar C amendments utilize a top-down approach in which the total amount of biochar
generated and added to mineral soil is used to estimate the change in soil organic C stocks with country-specificfactors. See Section 2.3.3.1, Chapter 2, Volume IV for more information.
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 of residues(e.g., burning, site preparation) will affect C inputs to soil organic matter formation and C losses throughdecomposition and combustion. It is important that models be evaluated with independent observations fromcountry-specific or region-specific field locations that are representative of the interactions of climate, soil andcropland management on post-conversion change in soil C stocks.
Tier 3 methods for biochar C amendments can be used to address GHG sources and sinks not captured in Tiers 1
or 2, such as priming effects, changes to N2O or CH4 fluxes from soils, and changes to net primary production.
More information on Tier 3 methods is provided in Section 2.3.3.1 of Chapter 2, Volume IV.

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

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

5.44

Level

Climateregime

IPCC
default

Error#

Definition

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Chapter 5: CroplandLand use

Native forest or
grassland(non-degraded)

All

1

NA
Represents native or long-term, nondegraded and sustainably managed
forest and grasslands.

Tropical

1

NA

Shifting
cultivation –Shortened fallow

Tropical

0.64

+ 50%

Shifting
cultivation –Mature fallow

Tropical

0.8

+ 50%

Land-use,
Management,& Input

Managed forest(default value is 1)

Land-use,
Management,& Input

Managedgrassland(See default values in Table 6.2)

Land-use,
Management,& Input

Cropland(See default values in Table 5.5)

Land use

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.

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 be derived from country-specific data in a Tier 2 approach. Reference values in Tier 1
correspond to non-degraded, unimproved lands under native vegetation, but other reference conditions can also bechosen for Tier 2. In addition, the depth for evaluating soil C stock changes can be different with the Tier 2 method(see also section 6.2.3.1). However, the depth of the reference C stocks (SOCREF) and stock change factors needs
to be the same for all land uses (i.e., FLU, FI, and FMG) to ensure consistency in the application of methods for
estimating the impact of land use change on soil C stocks.
The Tier 1 method may over- or under-estimate soil C stock changes on an annual basis, particularly with land usechange (e.g., Villarino et al. 2014). Therefore, land use change, such as Cropland converted to Grassland, may
include development of factors that estimate changes over longer periods of time than the default 20 years, andmay better match the period of time over which carbon accumulates or is lost from soils due to land use change.
When C stock changes extend over periods of many decades, activity data for historical land-use change are needed
to estimate the soil C stock changes that are still occurring in the current inventory year.
The carbon stock estimates may be improved when deriving country-specific factors for FLU and FMG, byexpressing carbon stocks on a soil-mass equivalent basis rather than a soil-volume equivalent (i.e. fixed depth)basis. This is because the soil mass in a certain soil depth changes with the various operations associated with land
use that affect the density of the soil, such as uprooting, land levelling, tillage, and rain compaction due to thedisappearance of the cover of tree canopy. However, it is important to realize that all data used to derive stockchange factors across all land uses must be on an equivalent mass basis if this method is applied. This will bechallenging to do comprehensively for all land uses. See Box 2.2b in Chapter 2, Section 2.3.3.1 for more
information.
For biochar C amendments, the parameter 𝐹𝑝𝑒𝑟𝑚𝑝 can be based on H/Corg or O/Corg measured directly from
representative samples of biochar, or from published data for biochar produced using similar process conditions
as the biochar that is applied to soils in the country. Tier 2 emission factors may be disaggregated based on variationin environmental conditions, such as the climate and soil types, in addition to variation associated with the biochar
production methods that generate production types defined by the feedstock type and conversion process. See
Section 2.3.3.1, Chapter 2, Volume IV for more information.

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Country-specific emission factors (i.e., permanence factors) for biochar C for croplands may be different from thepast land use for Land Converted to Cropland, and these differences need to be addressed in the calculations. Thisrequires estimating the biochar carbon stocks from past biochar carbon additions that remain in Land Converted
to Cropland after conversion. The biochar C stocks are then subject to the conditions for cropland, which may leadsome additional loss of biochar C.
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.
Tier 3 methods for biochar C amendments are country-specific and may involve empirical or process-based models
to account for a broader set of impacts of biochar amendments. These methods will likely estimate biochar C
stocks and associated changes over time so the biochar C stocks in Land Converted to Cropland will need to be
tracked through the land use change process.
More information on Tier 3 methods is provided in Section 2.3.3.1, Chapter 2, Volume IV.

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

5.3.3.3 C HOICE OF ACTIVITY DATA
Mineral soils
Tier 1 and Tie r 2 - D efau lt Equa tion s
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
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 for discussion
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 of area in each landat the beginning and end of the inventory period (e.g., 1985 and 2005). Approach 1 data are not sufficient todetermine 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 importantto achieve coordination among all land sectors to ensure that the total land base is remaining constant over time,
given that some land area will be lost and gained within individual sectors during each inventory year due to landuse change.
For biochar C amendments, the activity data for the Tier 2 method includes the total quantities of biochar
distributed as amendment to mineral soils. These data must be disaggregated by production type, where production
type is defined as a process utilizing a specific feedstock type, and a specific conversion process. Changes in soil
C associated with biochar amendments are considered to occur where it is incorporated into soil. However, due tothe distributed nature of the land sector in which this can take place, inventory compilers may not have access todata on when or where biochar C amendments occur. Inventory compilers may be able to compile data on the total
amount of biochar applied to cropland mineral soils from biochar producers, distributors and/or from those
applying biochar to cropland in the country. Note that exported biochar is not included in the total amount ofbiochar amended to soils in the country.
Additionally, activity data on the amount of biochar amendments may be disaggregated by climate zones and/or
soil types if country-specific factors are disaggregated by these environmental variables. The additional climate
and soil activity data may be obtained with a survey of biochar distributors and land managers.
Tier 3For application of dynamic models and/or a direct measurement-based inventory in Tier 3, similar or more detailed
data on the combinations of climate, soil, topographic and management data are needed, relative to Tier 1 or 2
methods, but the exact requirements will be dependent on the model or measurement design.

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Chapter 5: Cropland

For biochar C, the additional activity data required to support a Tier 3 method will depend on which processes are
represented and environmental variables that are required as input to the model. Priming effects, soil GHG
emissions, and plant production responses to biochar all vary with biochar type, climate, and soil type. Furthermore,soil GHG emissions and plant production responses also vary with crop type and management. Therefore, Tier 3
methods may require environmental data on climate zones, soil types, crop types and crop management systems(such as nitrogen fertilizer application rates, and whether soils are flooded for paddy rice production), in addition
to the amount of biochar amendments in each of the individual combinations of strata for the environmental
variables. More detailed activity data specifying the process conditions for biochar production or the physical andchemical characteristics of the biochar may also be required (such as surface area, cation exchange capacity, pH,
and ash content).

Organic soils
No Refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

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 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 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 dependon 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 then dividing
by the time dependence of the stock change factors (i.e., 20 years using the default factors). Note: if an inventory
time period is greater than 20 years, then divide by the difference in the initial and final year of the time 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 periods
that are less than 20 years may need to refer to the previous inventory time period to evaluate if a parcel of land isconsidered 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 5.6).

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Example: For a forest on volcanic soil in a tropical moist environment: SOCRef = 70 tonnes C ha-1.
For all forest soils (and for native grasslands) default values for stock change factors (FLU , FMG , FI)
are all 1; thus SOC(0-T) is 70 tonnes C ha-1. If the land is converted into annual cropland, withintensive tillage and low residue C inputs then:
SOC0 = 70 tonnes C ha-1 ● 0.90 ● 1 ● 0.92 = 58.0 tonnes C ha-1.
Thus the average annual change in soil C stock for the area over the inventory time period is
calculated as:(58 tonnes C ha-1 – 70 tonnes C ha-1) / 20 yrs =-0.6 tonnes C ha-1 yr-1.

Organic soils
No refinement.
The 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands provides
additional guidance that updates the 2006 IPCC Guidelines for national Greenhouse Gas Inventories. See section
2.2 of the 2013 Wetlands Supplement covers Tier 1, 2, and 3 approaches for drained organic soils in cropland.

5.3.3.5 U NCERTAINTY ASSESSMENT
No refinement.

5.3.4

Non-CO2 greenhouse gas emissions from biomassburning

No refinement.

5.4

COMPLETENESS, TIME SERIES, QA/QC, AND
REPORTING

No refinement.

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Chapter 5: Cropland

5.5

METHANE EMISSIONS FROM RICECULTIVATIONNo refinement in the Introduction.

5.5.1

Choice of method

The basic equation to estimate CH4 emissions from rice cultivation is shown in Equation 5.2. CH4 emissions are
estimated by multiplying daily emission factors by cultivation period10 of rice and annual harvested areas11. In its
most simple form, this equation is implemented using national activity data (i.e., national average cultivation periodof 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 disaggregated approach, 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:

CH 4 Rice= annual methane emissions from rice cultivation, Gg CH4 yr-1

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

ti, j ,k= cultivation period of rice for i, j, and k conditions, day

Ai, j ,k= 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,and other conditions under which CH4 emissions from rice may vary

The different conditions that should be considered include rice ecosystem types, flooding pattern before and during
cultivation period, and type and amount of organic amendments. Other conditions such as soil type, and ricecultivar can be considered for the disaggregation if country-specific information about the relationship between
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 sub-divided into agro-climatic zones withdifferent production systems (e.g., flooding patterns), Equation 5.2 should be applied to each region separately.
The same applies if rice statistics or expert judgments are available to distinguish management practices or otherfactors along administrative units (district or province). In addition, if more than one crop is harvested during agiven year, emissions should be estimated for each cropping season taking into account possible differences incultivation practices (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 the availability ofactivity and emission factor data, as well as the importance of rice as a contributor to its national greenhouse gas

10 In the case of a ratoon crop, ‘cultivation period’ should be extended by the respective number of days.
11 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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Volume 4: Agriculture, Forestry and Other Land Useemissions. The specific steps and variables in this decision tree, and the logic behind it, are discussed in the textthat follows the decision tree.
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-, wetseason-, early-, single-, latecropping)No

Are there
multiple rice cropping
during the same
year?

No

Are country
specific methods including
modelling or direct measurement
approach available?

Yes

Calculate emissions using
country-specific methods forhigher level of disaggregationas basis for the Tier 3 method

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

Yes

Calculate emissions using theTier 2 methodology
Box 2: Tier 2NoIsrice production
a key category?1

Yes

No

Collect data for Tier
2 or Tier 3 method

Calculate emissions using theTier 1 default emission factor
and scaling factors together
with activity 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.

Tier 1

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Chapter 5: Cropland

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 for atleast three baseline water regimes including irrigated, rainfed, and upland. It is encouraged to incorporate as manyof 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 pre-season flooding forless than 180 days prior to rice cultivation and continuously flooded fields without organic amendments, EFc) byvarious scaling factors as shown in Equation 5.2. The calculations are carried out for each water regime and organic
amendment separately as shown in Equation 5.3.
EQUATION 5.2 (UPDATED)ADJUSTED DAILY EMISSION FACTOR (TIER 1)

EFi = EFc • SFw• SFp • SFo
Where:EFi= adjusted daily emission factor for a particular harvested areaEFc

= baseline emission factor for continuously flooded fields without organic amendmentsSFw= scaling factor to account for the differences in water regime during the cultivation period(from Table 5.12)

SFp= scaling factor to account for the differences in water regime in the pre-season before thecultivation period (from Table 5.13)

SFo= scaling factor should vary for both type and amount of organic amendment applied (fromEquation 5.3 and Table 5.14)

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 (e.g. effects ofsoil type and rice cultivar). As for Tier 1 approach, it is encouraged to implement the method at the mostdisaggregated level and to incorporate the multitude of conditions (i, j, k, etc.) that influence CH4 emissions.
EQUATION 5.2A (NEW)ADJUSTED DAILY EMISSION FACTOR (TIER 2)

EFi = EFc • SFw• SFp • SFo • SFs • SFr
Where:

SFs= scaling factor for soil typeSFr

= scaling factor for rice cultivar

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 (e.g. satellite-based and in-situ measurement) anddisaggregated at sub-national level. Models can be empirical or mechanistic, but in either case need to be validatedwith independent observations from country or region-specific studies (Cai et al. 2003b; Li et al. 2004; Huang etal. 2004; and Pathak et al. 2005). Tier 3 methodologies may also take into account inter-annual variability triggeredby typhoon, flooding, drought, etc. A few countries have used Tier 3 method in their national communications to2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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UNFCCC12 [e.g. China and Japan used CH4MOD (Huang et al. 2004) and DNDC-Rice models (Katayanagi et al.
2017), and USA used DayCent (Cheng et al. 2013)].
BOX 5.2 (UPDATED)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 rice crop is harvested on a given area of land during the year, andthe growing 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 et al.
1992; Yagi et al. 1996; Wassmann et al. 2000; Pathak and Wassmann 2007; Pathak et al. 2003). Riceecosystems can further be distinguished into 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 CH4 emissions(Schütz et al. 1989; Yagi and Minami 1990; Sass et al. 1991; Pathak and Wassmann 2007; Pathak et
al. 2003). The impact of organic amendments on CH4 emissions depends on type and amount of theapplied 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 et al.
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., cansignificantly influence CH4 emissions. Inventory agencies are encouraged to make every effort toconsider these conditions if country-specific information about the relationship between these
conditions and CH4 emissions is available.

5.5.2

Choice of emission and scaling factors

Tier 1
Scaling factors are used to adjust the baseline emission factor (EFc), as provided in Table 5.11, to account for thevarious conditions discussed in Box 5.2, which result in adjusted daily emission factors (EFi) for a particular subunit of disaggregated harvested area according to Equation 5.3. Default cultivation period is provided in Table
5.11A which can be used for Equation 5.1.
The most important scaling factors, namely water regime during and before cultivation period and organic
amendments, are represented in Tables 5.12, 5.13 and 5.14, respectively, through default values. Country-specific
12 https://unfccc.int/

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Chapter 5: Croplandscaling 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.
TABLE 5.11 (UPDATED)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
World

Regional

Emission factor

Error range(kg CH4 ha-1 d-1)

(kg CH4 ha-1 d-1)

0.80 – 1.76

1.19

RegionEmission factor

Error range(kg CH4 ha-1 d-1)

(kg CH4 ha-1 d-1)

Africa 1

1.19

0.80 – 1.76

East Asia

1.32

0.89 – 1.96

Southeast Asia

1.22

0.83 – 1.81

South Asia

0.85

0.58 – 1.26

Europe1.56

1.06 – 2.31

North America

0.65

0.44 – 0.96

South America

1.27

0.86 – 1.88

Note: Emission factors and error ranges were estimated based on 95% confidence interval, using statistical model with updated database;
See Annex 5A.2 for more information.
1

For Africa, the global estimate is used due to lack of data.

TABLE 5.11A (NEW)DEFAULT CULTIVATION PERIOD OF RICEWorld

Regional

Cultivation Period

Error range

(day)

(day)

113

74– 152

RegionCultivation Period

Error Range

(day)

(day)

Africa 1

113

74 – 152

East Asia

112

73 – 147

Southeast Asia

102

78 – 150

South Asia112

90 – 140

Europe123

111 – 153

North America

139

110 – 165

South America

124

110 – 146

Note: Cultivation period was calculated from updated database, and the error range or uncertainty was based on the 2.5th percentile to97.5th percentile of the distribution of ratios; See Annex 5A.2 for more information.
1

For Africa, the global estimate is used due to lack of data.

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 good practice to collect moredisaggregated activity data and apply disaggregated case SFw whenever possible.

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TABLE 5.12 (UPDATED)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

–

1.00

0.73 – 1.27

0.71

0.53 – 0.94

0.55

0.41 – 0.72

0.54

0.39 – 0.74

0.16

0.11 – 0.24

0.06

0.03 – 0.12

Continuously flooded
Irrigated b

Single drainage period

0.60

0.44 – 0.78

Multiple drainage periods
Regular rainfed
Rainfed anddeep water cDisaggregated case

0.45

0.32 – 0.62

Drought prone
Deep water

0.06

0.03 – 0.12

Source: Scaling factors and error ranges (based on 95% confidential interval) were determined using statistical model and updated
database; see Annex 5A.2 for more information.
Notes:
aFields are never flooded for a significant period of time.

b

Fields are flooded for a significant period of time and the 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).
• Single drainage period: Fields have a single drainage event and period during the cropping season at any growth stage, in addition to
the end of season drainage.
• Multiple drainage periods: Fields have more than one drainage event and period of time without flooded conditions during the
cropping season, in addition to an end of season drainage, including alternate wetting and drying (AWD).
c

Fields are flooded for a significant period of time with water regimes that depend 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: Water level rises to more than 50 cm above the soil for a significant period of time during the cropping season.

Other rice ecosystem categories, like swamps and inland, saline or tidal wetlands may be discriminated within each sub-category.

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 four 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;

3.

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; and4.

Non-flooded pre-season in which the rice fields were not flooded for > 365 days such as upland crop ̶ paddy
rotation.

When activity data for the pre-season water status are not available, aggregated case factors can be used. It is goodpractice 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. Note that the scaling factor SFp
indicates the water management condition of a rice field before planting, which consequently affects the seasonalCH4 emission. SFp, however, is only used to estimate CH4 emission during the rice growing period, and cannot be
used to quantify CH4 emissions that occurred before the cultivation period or after harvest (i.e. outside of rice
growing season, such as CH4 emission during winter flooding period).

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Chapter 5: Cropland

TABLE 5.13 (UPDATED)DEFAULT CH4 EMISSION SCALING FACTORS FOR WATER REGIMES BEFORE THE CULTIVATION PERIOD
Water regime prior to rice cultivation (schematicpresentation showing flooded periods as shaded)
Non flooded preseason <180 d
Non flooded preseason >180 d

Aggregated case
Scalingfactor (SFp)

Disaggregated case
Scalingfactor (SFp)

Error
range

CROP

1.00

0.88 – 1.12

CROP

0.89

0.80 – 0.99

2.41

2.13 – 2.73

0.59

0.41 – 0.84

Error
range

< 180 d> 180 dFlooded pre-season(>30 d)a,b> 30 dCROP

1.08 – 1.37

1.22

Non-flooded preseason >365 d c

Source: Scaling factors and error ranges (based on 95% confidential interval) were determined using statistical model and updated
database; see Annex 5A.2 for more information.
a

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)c

Refers to "upland crop - paddy rotation" or fallow without flooding in previous year.

Organic amendments (SFo): It is good practice to develop scaling factors that incorporate information on the typeand amount of organic amendment applied (compost, farmyard manure, green manure, and rice straw). On an equalmass basis, more CH4 is emitted from amendments containing higher amounts of easily decomposable carbon andemissions also increase as more of each organic amendment is applied. Equation 5.3 and Table 5.14 present anapproach to vary the scaling factor according to the amount of different types of amendment applied. Rice straw isoften incorporated into the soil after harvest. In the case of a long fallow after rice straw incorporation, CH4emissions in the ensuing rice-growing season will be less than the case that rice straw is incorporated 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 tostraw applied shortly before cultivation) as shown in Table 5.14.

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TABLE 5.14 (UPDATED)DEFAULT CONVERSION FACTORS FOR DIFFERENT TYPES OF ORGANIC AMENDMENTS
Organic amendment

Conversion factor(CFOA)

Error range

Straw incorporated shortly (<30 days) before cultivationa

1.00

0.85 – 1.17

Straw incorporated long (>30 days) before cultivationa

0.19

0.11 – 0.28

Compost0.17

0.09 – 0.29

Farm yard manure

0.21

0.15 – 0.28

Green manure

0.45

0.36 – 0.57

Source: Conversion factors and error ranges (based on 95% confidential interval) were determined using statistical model and updated
database; see Annex 5A.2 for more information.
aStraw application means that straws are incorporated into the soil. It does not include cases where straws are just placed on soil surface,and straws that were burnt on the field.

Tier 2Inventory agencies can use country-specific emission factors from field measurements that cover the conditions ofrice cultivation in their respective country. Box 5.2a provides information about measuring methane emissions fordeveloping a baseline emission factor for rice cultivation. It is good practice to compile country-specific data bases
on available field measurements which supplement the Emission Factor database 13 by other measurement
programs (e.g., national) not yet included in this data base. However, certain standard QA/QC requirements apply
to these field 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 fromthe baseline.
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, for Tier 2 method. Both experiments andmechanistic knowledge confirm the importance of these factors, but large variations within the available data do
not allow one to define reasonably accurate default values for Tier 1 method.
Tier 3Tier 3 approaches do not require choice of emission factors, but are instead based on a thorough understanding ofdrivers and parameters (see above).

13 https://www.ipcc-nggip.iges.or.jp/EFDB/main.php

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Chapter 5: Cropland

BOX 5.2A (NEW)GOOD PRACTICE GUIDANCE FOR DEVELOPING BASELINE EMISSION FACTORS (EF) FOR CH4 EMISSIONS FROMRICE CULTIVATION

The following information provides good practices in performing manual measurement of methane
emissions using the closed-chamber technique for continuously flooded rice fields withrecommended fertilizer application and no organic amendment. The data can be used to develop
country- and region-specific EFc.
Chamber Design: It is good practice to use lightweight material that is break resistant and inert toreactions with CH4 (e.g., acrylic and PVC). It may be a rectangular or cylindrical chamber, covering
at least two rice hills. The chamber height must be higher than the rice plant. If necessary, use a basewith a grove that can be filled with water to ensure a gas-tight closure. The chamber is equippedwith a small fan, a thermometer, a vent hole with a stopper, and a gas sampling port (e.g., a flexible
tube connected to a valve).
Field Set up and Experimental Design: Select a field that is homogeneous with respect to soil
properties. Use an appropriate experimental design with at least 3 replications.
Sampling Strategies: Sampling can be done 1 or 2 times per day between mid-morning and latemorning period, and at least once a week for the whole growing period. More frequent measurements
are needed during agricultural management events (e.g., irrigation, drainage, and N fertilization).
All treatments would have to be measured at the same time. At each sampling time, it is goodpractice to obtain 3 to 4 gas samples within 30 minutes after closure of the chamber.
For gas sampling, the use of a syringe or a pump is recommended depending on the required sample
volume. Plastic or glass containers can be used for collecting samples and should be transferred to alaboratory and analyzed within the allowable storage period.
Gas Analysis: Use gas chromatograph (GC) equipped with a flame ionization detector (FID) foranalysis. Calibrate the GC before every analysis, using certified standard gases.
Data Processing: Use a linear regression of the gas concentration inside the chamber against time tocalculate the hourly flux. Identify the reasons of non-linearity (if exists) for the validation andcorrection of calculated flux. Use trapezoidal integration to calculate cumulative gas emissions fromthe hourly flux data.
Deriving Emission Factor: Flux data from several sites, regions, or environmental conditions that
conform to the requirements for a continuously flooded rice system with no organic amendments,
can be used to derive region- or country-specific EFs based on a simple average and standard
deviation. The compiler could also derive disaggregated EFs using regression models to predict thevalues for different regions and/or environmental conditions.
For more details refer to Minamikawa et al. (2015) and Sanders and Wassmann (2014).

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 to complete a survey ofcropping 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 through accepted
methods of reporting. The use of locally verified areas would be most valuable when they are correlated withavailable 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., theWorld Rice Statistics on the website of International Rice Research Institute (IRRI14), which include harvest areaof rice by ecosystem type for major rice producing counties, a rice crop calendar for each country, and other useful
information, and the FAOSTAT on the website of FAO15, where data of rice area harvested can be obtained. The
use of locally verified areas would be most valuable when they are correlated with available data for emission
14 http://www.irri.org/science/ricestat/
15 www.fao.org/faostat/

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factors under differing conditions such as climate, agronomic practices, and soil properties. It may be necessary toconsult 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.

Example Calculation for Tier 1

5.5.4

An example is provided for estimating methane emission from rice cultivation, with the following backgroundinformation.
A country in Southeast Asia has rice area of 3 million hectares, with 50percent of the area classified as irrigated,
30percent rainfed, 15percent upland, and 5percent deep water. Irrigated areas are planted for 2 growing seasons
annually. Rice growing periods are 102 days, except for deep water rice which has 220 days. For irrigated areas,
50percent is continuously flooded and 50percent is managed with multiple drainage periods. All irrigated areas are
not flooded for less than 180 days prior to cultivation, while rainfed and upland areas are not flooded for more than
180 days prior to cultivation. Deepwater rice areas are flooded for 30 days prior to cultivation. For irrigated areas,
2 tonnes/ha of straw residues are incorporated shortly before cultivation (less than 30 days).
Table 5.14a shows the calculation for total rice harvested area in a given year. Cropping season refers to the numberof times rice is harvested per year. The calculation for adjusted daily emission factor is presented in Table 5.14b
using Equation 5.2. The scaling factor for organic amendment (SFo), for irrigated rice field, is computed using
Equation 5.3 for rice straw application rate of 2 tonnes/ha and conversion factor (CFOA) of 1.0 as provided in Table
5.14. Based on Equation 5.1, the total methane emission is 410.47 Gg CH4/yr, as shown in Table 5.14c.
TABLE 5.14A (NEW)CALCULATION FOR TOTAL HARVESTED AREA
Rice Area(ha)% of Total
Area

Cropping Season

Harvested Area(yr-1)(ha yr-1)

A

BC

D = (A x C)

- Irrigated, continuously flooded

750,000

25

21,500,000- Irrigated, with multiple drainage
periods

750,000

25

21,500,000

Rainfed

900,00030

1

900,000

Upland450,000

15

1

450,000

Deepwater

150,000

5

1

150,000

Total

3,000,000

100

Rice Ecosystem

Irrigated

5.58

4,500,000

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Chapter 5: Cropland

TABLE 5.14B (NEW)CALCULATION FOR ADJUSTED DAILY EMISSION FACTOR
Scaling
Factor for
Water
Regimeduring
Cultivation(SFw)

Scaling
Factor for
Pre-season
Water
Regime(SFp)[from Table
5.12(Updated)][from Table
5.13(Updated)][usingEquation 5.3
and Table
5.14(Updated]

E

F

G

H

I= (E x F x G x
H)- Irrigated, continuously
flooded

1.22

1.00

1.00

1.21

1.48

- Irrigated, with multiple
drainage periods

1.22

0.55

1.00

1.21

0.81

Rainfed

1.22

0.54

0.89

1.00

0.59

Upland

1.22

0

0.89

1.00

0.00

Deepwater

1.22

0.06

2.41

1.00

0.18

Baseline
Emission
Factor (EFc)(kg CH4 ha-1
d-1)
Rice Ecosystem[from Table
5.11(Updated)]

Scaling
Factor for
Organic
Amendment(SFo)

Adjusted Daily
Emission Factor(EFi)[kg CH4 ha-1 d-1]

Irrigated

TABLE 5.14C (NEW)CALCULATION FOR TOTAL METHANE EMISSIONS FROM RICE CULTIVATION
HarvestedArea(ha yr-1)

Adjusted Daily
Emission
Factor (EFi)

Cultivation
Period(days)

Methane Emissions[from
Table 5.14a(New)][kg CH4 ha-1 d1][from Table
5.14b (New)]

D

I

J

K= [(D x I x J)/106]- Irrigated, continuously flooded

1,500,000

1.48

102

226.44- Irrigated, with multiple drainage periods

1,500,000

0.81

102

123.93Rainfed

900,000

0.59

102

54.16

Upland450,000

0.00

102

-

Deepwater

150,000

0.18

220

5.94

Total

4,500,000

Rice Ecosystem(Gg CH4 y-1)

Irrigated

5.5.5

410.47

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 climatic, temporal,
and spatial heterogeneity. Reducing the uncertainty depends on a better understanding of the spatial heterogeneityand correlation among these variables and the complexity of the mechanisms driving methane emission (Zhang etal. 2017).

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For this source category, good practice should permit determination of uncertainties using standard statisticalmethods 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 in such 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 the estimated fraction
should also be based on expert judgement. As a default value for the uncertainty in the fraction 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 generalprinciples of statistical analysis as outlined in Volume 1, Chapter 3 as well as model approaches as outlined inVolume 4, Chapter 3, Section 3.5.

5.5.6

Completeness, time series, QA/QC, and reporting

No Refinement.

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Chapter 5: CroplandAnnex 5A.1

Estimation of default stock change factors formineral soil C emissions/removals for croplandLong-Term Cultivation, Perennial Crops and Tillage Management Factors:
Default stock change factors have been updated in Table 5.5 based on an analysis of a global dataset ofexperimental results for tillage long-term cultivation, and perennial crops to a 30cm depth. The land-use factor for
long-term cultivation and perennial crops represents the change in carbon that occurs after 20 or more years ofcontinuous cultivation or perennial crop production, respectively. Tillage factors represent the effect on C stocks
at 20 years following the management change. Data were compiled from published literature based on thefollowing criteria: a) must be an experiment with a control and treatment; b) provide soil organic C stocks or thedata needed to compute soil organic C stocks (bulk density, OC content, gravel content); c) provide depth ofmeasurements; d) provide the number of years from the beginning of the experiment to C stock sample collection;and c) provide location information.
There were 303 published studies with 2383 observations for long-term cultivation and perennial tree/woody crops,and 212 published studies with 2046 observations for reduced tillage and no-tillage (References provided at bottomof Table 5.5). The histograms below provide summaries of the distribution of published studies for climate regions.

50

Long-Term Cultivation
40

30

20

10

0
t
ist
Dry
Dry
ois
Mo
le-M
erea
al
rat
t
o
e
e
a
r
B
er
mp
Bo
te/
mp
Te
te/
era
Te
rm
era
a
m
mp
p
r
e
a
m
W
T
W
Te
ol
ol
Co
Co

ist
Dry
Mo
al
et/
pic
o
W
r
T
al
pic
Tro

30

25

Perennial Tree/
Woody Crops

20

15

10

5

0
Temperate Dry/Moist

Tropical Dry/Moist/Wet

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60

Tillage Management
50

40

30

20

10

0
Reduced Tillage

No-Tillage

Cool Temperate/Boreal - Dry
Cool Temperate/Boreal - Moist
Warm Temperate - Dry
Warm Temperate - Moist
Tropical Dry
Tropical Wet/Moist

Semi-parametric mixed effect models were developed to estimate the new factors (Breidt et al. 2007). Several
variables were tested including depth, number of years since the management change, climate, the type ofmanagement change (e.g., reduced tillage vs. no-till), and the first-order interactions among the variables.
Variables and interactions terms were retained in the model if they met an alpha level of 0.05 and decreased the
Akiake Information Criterion by two. For depth, data were not aggregated to a standardized set of depths but rather
each of the original depth increments were used in the analysis (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) as separate
observations of stock changes. Similarly, time series data were not aggregated, even though those measurements
are taken from the same plots. Consequently, random effects were included to account for the dependencies intimes series data and among data points representing different depths from the same study.
Special consideration was given to representing depth increments in order to avoid aggregating data acrossincrements from the original experiments. Data are collected by researchers at various depths that do not matchamong studies. We created a custom set of covariates, which are functions of the increment endpoints. These
functions come from integrating the underlying quadratic function over the increments. This approach was neededin order to make statistically valid inferences with the semi-parametric mixed effect model techniques, and to
avoid errors associated with aggregating data into a uniform set of depth increments.
Using this customized approach, we estimated land use and management factors to a 30 cm depth. Uncertainty
was quantified based on the prediction error for the model, and represents a 95percent confidence interval for eachof the factor values. The resulting confidence intervals can be used to construct probability distribution functions
with a normal density for propagating error through the inventory calculations.
Paddy Rice Land-Use Factors:
Evidence from chronosequences with up to 2000 years of rice cultivation history show rice paddy production
accumulates soil organic carbon at a fast rate during the first few decades, and then continues to accumulate carbon
at a slower rate until a steady-state is reached at about 300 years (Huang et al. 2015; Kölbl et al. 2014). To update
this land use factor for paddy rice, we conducted a literature review and collected the field experiment data of soil
carbon stock changes in paddy rice fields that are available in peer-reviewed journals (References provided atbottom of Table 5.5). For each long-term experiment site, data were compiled for conventional management (e.g.,
normal levels for N, P, K chemical fertilizer applications, rice straw residue management and organic amendments).
We calculated the ratio of soil organic carbon (tonne C ha-1 for 0-30 cm soil depth) between survey years for thepaired comparisons between paddy rice and corresponding native vegetation. The length of time ranged from 15
to 25 years. The resulting estimates capture the large increase in carbon in the first few decades after rice cultivation,and therefore, are considered conservative because carbon can still increase at a slower rate for several more years(Huang et al. 2015; Kölbl et al. 2014). The land use factor for paddy rice is estimated as the average of these ratios,
and uncertainty is based on the 2.5 percentile to 97.5 percentile of the distribution of ratios.

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Chapter 5: CroplandAnnex 5A.2

Background for developing emission factors andscaling factors for methane emission from paddy
field, using scientific literature

1. Collection of data
• Since 2004, there exists a large body of field measurements of CH4 emission from rice fields across theworld. The data set of Yan et al. 2005 (which is the data set used in developing the default emission factor
and scaling factors in the IPCC 2006 IPCC Guidelines) was updated with all studies conducted through 30
June 2017, expanding the dataset with observations of CH4 emission from rice fields around the world.
• A comprehensive search was performed of published literature, which report field measurements of CH4,
as described previously in the paper by Yan et al. 2005. This included a keyword search for topics such as
rice or paddy*; methane or CH4 or greenhouse gas*; and flux* or emission*, in the ISI Web of Science(Thomson Reuters, New York, NY, USA) and Google Scholar (Google, Mountain View, CA, USA).
• From this comprehensive search, the following information was compiled: (i) the average CH4 flux in the
rice-growing season; (ii) integrated seasonal emission; (iii) water regime during and before the rice-growing
season; (iv) the timing, type and amount of organic amendment; (v) soil properties (i.e., SOC and soil pH);(vi) location, agroecological zone, and year of experiment or studies; and (viii) duration and season of
measurement.
• The following information describes the criteria for selecting data that were included in the data set:(i) As suggested previously by Yan et al. 2005, hourly or daily flux is used in the compilation because
it has a better index of emission strength than the integrated seasonal emission. When the average
daily CH4 flux was not directly reported, the value is estimated using integrated seasonal emissions
divided by the measurement period.
(ii) Water regimes were categorized into following conditions: (i) continuous flooding; (ii) single
drainage; (iii) multiple drainage; (iv) rainfed; and (v) deep water. The pre-season water regime wasclassified as: (i) non flooded pre-season for less than 180 days; (ii) non flooded pre-season for morethan 180 days; (iii) flooded pre-season for more than 30 days; and (iv) non-flooded pre-season for
more than 365 days. See Table 5.15 for the illustration of the water regimes before the cultivation
period.
(iii) For organic amendments, the data were classified as (i) straw incorporated shortly (i.e. less than 30days) before cultivation; (ii) straw incorporated long (i.e. more than 30 days) before cultivation; (iii)compost; (iv) farmyard manure; and (v) green manure. Data for rice straw are expressed in dry weight,
while for other organic materials data are expressed in fresh weight.
(iv) To account for the spatial variability of CH4 emissions at the global scale, experimental sites wereclassified into different zones based on their climatic conditions. Using IRRI’s climatic classification(IRRI, 2002), Asian rice fields were categorized into six agro-ecological zone: (i) warm arid and semiarid tropics; (ii) warm sub-humid tropics; (iii) warm humid tropics; (iv) warm arid and semi-arid subtropics with summer rainfall; (v) warm sub-humid sub-tropics with summer rainfall; and (vi)warm/cool humid sub-tropics with summer rainfall. Rice fields in the other region of the world weregrouped into three regions, i.e., Latin America, Europe and United States.
(v) For soil properties, because of the limited availability of information, only soil organic carbon (SOC)and soil pH (as continuous variables) were included in the data set. If soil organic matter content
rather than SOC was reported, it was converted to SOC using a Bemmelen index value of 0.58. To
meet the requirement of the statistical model, measurements without information for threecontinuous variables (i.e. SOC data, soil pH and the amount of organic amendment) were excluded.
The final dataset used in the analysis included 1089 measurements, from 122 rice fields across theworld. In this data set, measurements from Asian rice fields increased from 554 (Yan et al. 2005) to2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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942. In addition, 147 measurements from other regions of the world were added to the datasets(dataset provided in Wang et al. 2018).
2. Processing and compilation of data
Consistent with previous study by Yan et al. (2005), the following linear mixed model, suitable for analyzing
unbalanced data (Speed et al. 2013), was used to determine the effect of controlling variables on CH4 flux fromrice fields:
EQUATION 5A.2.1 (NEW)EFFECT OF CONTROLLING VARIABLES ON CH4 FLUX FROM RICE FIELDS

ln ( flux ) = constant+a • ln ( SOC ) + pH h + PWi + WR j + CLk + OM l • ln (1 + AOM l )Where:ln ( flux )= natural logarithm of average CH4 flux (mg CH4 m-2 h-1) during the rice-growing season

SOC= soil organic carbon content, %constant= the intercept of the mixed linear model, dimensionless"a"= represents the effect on soil organic carbon, dimensionlesspH h= soil pH, dimensionless

PWi= pre-season water regime (e.g. continuous flooding; single drainage; multiple drainage;
rainfed; and deep water), dimensionless

WR j= water regime in the rice-growing season (e.g. non flooded pre-season for less than 180
days; non flooded pre-season for more than 180 days; flooded pre-season for more than 30days; and non-flooded pre-season for more than 365 days), dimensionlessCLk= climate type expressed using IRRI’s agro-ecological zone for Asia; other regions werecategorized into Europe, Latin America and United States, dimensionless

OM l= organic amendment (straw incorporated shortly (<30 days) before cultivation, straw
incorporated long ( >30 days) before cultivation, compost, farmyard manure, and green
manure), dimensionless

AOM l= amount of organic amendment, tonne ha-1

In this model soil pH was treated as a categorical variable and grouped into the following “h” classes: <4.5, 4.5-5.0,
5.0-5.5, 5.5-6.0, 6.0-6.5, 6.5-7.0, 7.0-7.5, 7.5- 8.0 and >8.0. For other categorical variables, their corresponding
sublevels (i, j, k, l) and descriptions are shown in Tables 5A.2-1.
The last part of Equation 5A.2-1 reflects the effect of the application of organic amendment on CH4 flux. This effectis an interaction of the type and amount of organic material. In cases where the amount of organic amendment iszero, it is assumed that there is zero application rate for each type of organic material. Obviously, this assumption
will result in more data points in the analysis than there are in real observations of organic amendments. To
ameliorate this problem, the residuals of observations are weighted with organic amendment as 1 and those without
as 0.2 (as the observational result was repeated five times for the five types of organic materials. All the variables
were treated as fixed effect, and experimental site was treated as a random effect to address dependencies in datacollected from the same experiment.
The effects of the controlling variables on CH4 flux were computed by fitting Equation 5A.2.1 to field observations
using the SPSS Mixed Model procedure (V24.0, SPSS Inc., Chicago, IL, USA).
3. Developing of global and regional emission factors and scaling factors• The estimated effects of various variables were used to derive a default EF. In the model, the CH4 emissions
from rice fields are a combination of the effects of SOC and pH values, pre-season water status, water

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Chapter 5: Croplandregime in the rice-growing season, organic amendment and climate. An assumption was made to provide a
default EF, that is, all observations in the data set to have a water regime of continuous flooding, a preseason
water status of non flooded pre-season <180 d and no organic amendments, while keeping other conditions
constant, as stated in the original papers (Yan et al. 2005). Using Equation 5A.2.2, the default EF is derived
for continuously flooded rice fields, with a pre-season water status of non flooded pre-season <180 days,
and without organic amendment:
EQUATION 5A.2.2 (NEW)DEFAULT EMISSION FACTOR FOR CONTINUOUSLY FLOODED RICE FIELDS
WR
1 n
 PW
EF = econstant •   SOCia • e pHi • eCLi  • e short _ drainage • e continuous _ flooding
 n i =1


Where:

EF= default emission factor derived for continuously flooded rice fields, with a pre-season water
status of non-flooded pre-season <180 days, and without organic amendment, mg CH4 m-2 h1(Note: EF was converted to “kg CH4 ha-1 day-1” in Table 5.11)

constant"a" ‘constant’ and ‘a’ = values estimated in Equation 5A.2.1

n= total number of observations in the data set

SOCi= soil organic carbon content for the ith observation, %pHi= soil pH for the ith observation, dimensionlessCLi= climate type for the ith observation, (expressed using IRRI’s agro-ecological zone for Asia,
other regions were categorized into Europe, Latin America and United States), dimensionless

PWshort _ drainage = pre-season water regime (i.e. as ‘non flooded pre-season <180 days), dimensionless

WRcontinuous _ flooding = water regime in the rice-growing season (i.e. as continuous flooding), dimensionless
The values of scaling factors from the aggregated and disaggregated cases are assumed to be referenced as global
and regional scaling factors, respectively. The scaling factors of the disaggregated case for water regime during therice season and preseason are estimated using the modelling results in Equation 5A.2.1. Firstly, the fluxes of CH4
for ‘continuously flooding’ during the rice season and ‘non flooded pre-season <180 d’ in preseason were assumed
to be 1. Then, the corresponding relative fluxes for different water regimes were calculated by the ratios of backtransformed estimates (i.e., exponential function) of different water regimes to back-transformed estimates (i.e.,
exponential function) of ‘continuously flooding’ during the rice season and ‘non flooded pre-season <180 d’ in preseason. Given the different sizes of observations for various water regimes in the data set, the calculations of thescaling factors for the aggregated case were weighted accordingly. For organic amendment, the fluxes of CH4 fromvarious form of organic materials were calculated, first with an application amount of 6 t/ha. The CH4 flux fromstraw applied shortly (<30 days) before cultivation (6 t/ha) is assumed to be 1, the relative fluxes for other organic
materials are then calculated.
See Wang et al. (2018) for more information and datasets used for the analysis.

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TABLE 5A.2.1 (NEW)DESCRIPTION OF THE SELECTED VARIABLES THAT CONTROL CH4 EMISSIONS FROM RICE FIELDS
Variables

Description

Preseason water status
Flooded pre-season

Permanently flooded rice fields are assumed to have a preseason water regime of‘flooded pre-season’. Late rice (e.g., in China) is usually planted immediately after
early rice on the same field and is therefore regarded as having a preseason water
regime of ‘flooded pre-season’.

Non flooded pre-season >180d

If rice is planted once a year and the field is not flooded in the non-rice growing
season, the preseason water regime is classified as ‘non flooded pre-season >180 d’.

Non flooded pre-season <180d

Rice is planted more than once a year, but there is more than one month of fallow
time between the two seasons, ‘non-flooded pre-season <180 d’ usually implies
preseason drainage.

Non-flooded pre-season >365d

For measurements conducted on rice fields that are preceded by two upland crops oran upland crop and a drained fallow season, the preseason water regime of such
experiments is classified as ‘non-flooded pre-season >365 d’.

Water regime in the rice-growing season
Continuous flooding

Rice is cultivated under continuously flooded condintion but sometimes an endseason drainage before rice harvest included.

Single drainage

One mid-season drainage and an end-season drainage are adopted over the entire ricegrowing season.

Multiple drainage

Multiple drainge refers to the management water regime, also called 'intermittent
irrigation', in which the number of drainage events was not clear, but there are morethan one events during the growing season.

Rainfed, wet season (regular
rainfed)Rice cultivation that relies on rainfall for water, in this case the field is flood prone
during the rice-growing season.

Rainfed, dry season (droughtprone)

Rice cultivation that relies on rainfall for water, in this case the field is drought prone
during the rice-growing season.

Deep water

Rice grown in flooded conditions with water depth more than 50 cm deep.

Organic amendment
Straw incorporated shortly(<30 days) before cultivation

Straw applied just before rice transplanting as on-season; straw that is left on the soil
surface in the fallow season and incorporated into the soil before the next ricetransplanting is also categorized as ‘straw incorporated shortly (<30 days) before
cultivation’. The amount of straw return is expressed in dry weight (t ha-1).

Straw incorporated long (>30days) before cultivation

Straw incorporated into soils in the previous season (upland crop or fallow) is
categorized as ‘straw incorporated long (>30 days) before cultivation’. The amount ofstraw return is expressed in dry weight (t ha-1).

Compost, farmyard manure,green manure

The amount of organic materials is expressed in fresh weight (t ha-1).

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Chapter 5: CroplandAnnex 5A.3

Parameterisation of the Tier 2 – Steady State
Method for Mineral Soils

The Tier 2 steady state method was parameterised using Bayesian methods after evaluating the sensitivity of themodel parameters. The studies that were used to evaluate model sensitivities and parameterise the model are given
in Table 5A.3.1.
TABLE 5A.3.1 (NEW)STUDIES THAT WERE USED TO EVALUATE THE MODEL SENSITIVITIES AND PARAMETERISE THE TIER 2 STEADY-STATEMETHOD FOR MINERAL SOILS
References

Site Location

Length of Study(years)Treatments

Halvorson et al. 1997

Akron, CO, USA

25

Till

Vanotti et al. 1997

Arlington, WI, USA

34

MN

Dimassi et al. 2013

Boigneville, France

41

Till

Juma et al. 1997

Breton, AB, Canada62

MN, ONe-RA 2013; Jenkinson 1990

Broadbalk, Rothamsted, UK

153

MN, ON

Pierce and Fortin 1997

East Lansing, MI, USA

12

Till, CCe-RA 2013; Jenkinson andJohnston 1977

Hoosefield, Rothamsted,
UK

146

Dick et al. 1997

Hoytville, OH, USA

42

CR, Till

Campbell et al. 1997

Indianhead, SK, Canada35

MN, CRKBS LTER 2017; Collins et al.
2000

Hickory Corners, MI, USA

Díaz-Zorita et al. 2004

General Villegas, Argentina

25

Till

Huggins and Fuchs 1997

Lamberton, MN, USA

32

MN

Janzen et al. 1997

Lethbridge, AB, Canada41

MN, CR

Janzen et al. 1997

Lethbridge, AB, Canada

80

CR

Machado et al. 2008; Marchado
2011; Rasmussen and Smiley
1997

Pendleton, OR, USA

Machado et al. 2008; Marchado
2011; Rasmussen and Smiley
1997

Pendleton, OR, USA

Dick et al. 1997

South Charleston, OH, USA

29

Till

Küstermann et al. 2013

Scheyern, Germany

12

Till

Maillard et al. 2018

Swift Current, SK, Canada

30

Till, CR

Skjemstad et al. 2004; Schultz
1995

Tarlee, Australia

Gregorich et al. 1996

Woodslee, ON, Canada

36

MN

Dick et al. 1997

Wooster, OH, USA

31

CR, Till

7

MN, ONTill

MN, ON64
MN, Till
55

20

CR

MN = Mineral nitrogen additions; ON = organic nitrogen additions; Till = Tillage change; CR = Crop Rotations; CC = Cover Crops

The sensitivity analysis was based on a method developed by Sobol (2001). We evaluated all parameters except
for the temperate effect on decomposition (Equation 5.0e) and moisture effects on decomposition (Equation 5.0F).
The parameters in these functions were highly correlated so we only evaluated one parameter from each function(𝑡𝑜𝑝𝑡 for Equation 5.0e and 𝑤1 for Equation 5.0f). A bootstrap sampling method was used to evaluate the total
global sensitivity index of the parameters given the log-likelihood value of the mismatch between the model output
and the observed data. This information was used to determine if the sample size was sufficient for ranking thesensitivity of the parameters (i.e., minimising the variance enough on the index values to avoid Type 1 error). The

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sensitivity analysis was conducted in R using the Sensitivity Package (Pujol, Iooss, & Janon, 2017). The results
are given in the Table 5A.3.2.
TABLE 5A.3.2 (NEW)SENSITIVITY OF MODEL PARAMETERS, PARAMETER VALUES AND MINIMUM AND MAXIMUM VALUES FOR THE TIER 2
STEADY-STATE METHOD FOR MINERAL SOILS
Parameter

Practice

Sensitivity

Value (min, max)

Full-till

0.001

3.036 (1.4, 4.0)

Reduced-till<0.0012.075 (1.0, 3.0)

No-till

n/a1

1

ws

All

0.003

1.331 (0.8, 2.0)

k facaAll<0.001

7.4

k facsAll

0.005

0.209 (0.058, 0.3)

k fac p

All

0.015

0.00689 (0.005, 0.01)

f1

All

0.032

0.378 (0.01, 0.8)

f2

All

0.016

0.368 (0.007, 0.5)

f3

All

0.003

0.455 (0.1, 0.8)

f5

All

0.020

0.0855 (0.037, 0.1)

f6

All

0.040

0.0504 (0.02, 0.19)

f7

All

<0.001

0.42

f8

All

<0.001

0.45

topt

All

0.960

33.69 (30.7, 35.34)

tmax

All

n/a2

45

till fac

1

No-till cultivation factor is fixed at a value of 1 based on the model formulation.

2

The maximum temperature for decomposition was not evaluated because it was highly correlated with the temperature optimum fordecomposition.

Bayesian parameterisation techniques were used to determine the probability distributions of the most sensitive
parameters, which included parameters with a sensitivity greater than 0.001 (Table 5A.3-2). However, the 𝑡𝑖𝑙𝑙𝑓𝑎𝑐
parameter for reduced-till is included because the parameter for full-till was included. Sampling-importanceresampling was used to generate a joint posterior distribution (Rubin, 1998). This approach includes two steps, a)drawing independent random samples from a known prior distribution, and b) resampling the initial draws fromstep (a) based on importance sampling weights for individual parameter sets. Samples are more likely to be
maintained in the posterior distribution with higher likelihoods (Smith & Gelfand, 1992). Uniform priors wereselected with an initial sample size 𝑛 = 1,000,000 and a re-sample size 𝑚 = √𝑛, i.e., 1000, which allows for
distributional convergence in the posterior distribution (Givens & Hoeting, 2005). The final posterior distribution
was estimated as a truncated multivariate distribution under the assumption that parameter values should notexceed the minimum and maximum values in the posterior distribution. The resulting parameters are given in
Table 5A.3-2 and the covariance matrix is given Table 5A.3-3.

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Chapter 5: Clopland

TABLE 5A.3.3
COVARIANCE MATRIX FOR THE THREE-POOL STEADY-STATE METHOD FOR MINERAL SOILS

till fac − CTtill fac − RTw park facsk fac p

f1

f2

f3

f5

f6

topt

till fac − CT

0.3353436-0.0007128

0.0124072

0.0077939

0.0000277

0.0007889-0.0010958-0.0024497

0.0001000

0.0015558

0.0387919

till fac − RT

-0.0007128

0.3239992-0.0167975

0.0008191-0.0000013

0.0041484

0.0020256

0.0068887

0.0000775-0.0017836

0.0047429

w par

0.0124072-0.0167975

0.1486482-0.0005654-0.0001156

0.0084023

0.0055629-0.0033270

0.0004484

0.0011228-0.0389749

k facs0.0077939

0.0008191-0.0005654

0.0032024

0.0000244

0.0022843

0.0015645

0.0008130-0.0001062-0.0002235

0.0051276

k fac p0.0000277-0.0000013-0.0001156

0.0000244

0.0000016

0.0000217

0.0000186

0.0000116

0.0000033

0.0000077

0.0002567

f1

0.0007889

0.0041484

0.0084023

0.0022843

0.0000217

0.0051767

0.0021790

0.0023559-0.0001210-0.0004680-0.0086628

f2-0.0010958

0.0020256

0.0055629

0.0015645

0.0000186

0.0021790

0.0099681-0.0049865

0.0000755-0.0005823-0.0139913

f3-0.0024497

0.0068887-0.0033270

0.0008130

0.0000116

0.0023559-0.0049865

0.0405470-0.0001415

0.0001638-0.0274010

f50.0001000

0.0000775

0.0004484-0.0001062

0.0000033-0.0001210

0.0000755-0.0001415

0.0001479-0.0000365-0.0009000

f6

0.0015558-0.0017836

0.0011228-0.0002235

0.0000077-0.0004680-0.0005823

0.0001638-0.0000365

0.0007861-0.0057748

topt

0.0387919

0.0047429-0.0389749

0.0051276

0.0002567-0.0086628-0.0139913-0.0274010-0.0009000-0.0057748

0.4347643

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Collins, H. P., Blevins, R. L., Bundy, L. G., Christenson, D. R., Dick, W. A., Huggins, D. R. & Paul, E. A. (1999)
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Gami, S. K., Ladha, J. K., Pathak, H., Shah, M. P., Pasuquin, E., Pandey, S. P., Hobbs, P. R., Joshy, D. & Mishra,
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