Chapter 6: Grassland

CHAPTER 6

GRASSLAND

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

6.1

Volume 4: Agriculture, Forestry and Other Land Use

Authors
Brian McConkey (Canada), Stephen Michael Ogle (USA), Ngonidzashe Chirinda (CGIAR, Zimbabwe), Ayaka
W. Mo Kishimoto (Japan), Jeffrey Baldock (Australia), Aleksandr Trunov (Russian Federation)

Contributing Authors
Cody Alsaker (USA), Johannes Lehmann (USA) Dominic Woolf (USA)6.2

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

Chapter 6: Grassland

Contents
6

Grassland .................................................................................................................................................. 6.4
6.1

Introduction ......................................................................................................................................... 6.4

6.2

Grassland Remaining Grassland ......................................................................................................... 6.4
6.2.1

Biomass.................................................................................................................................... 6.4

6.2.2

Dead organic matter ................................................................................................................. 6.4

6.2.3

Soil carbon ............................................................................................................................... 6.4

6.2.3.1

Choice of method ............................................................................................................... 6.4

6.2.3.2

Choice of stock change and emission factor ...................................................................... 6.5

6.2.3.3

Choice of activity data ........................................................................................................ 6.7

6.2.3.4

Calculation steps for Tier 1 .............................................................................................. 6.10

6.2.3.5

Uncertainty assessment .................................................................................................... 6.11

6.2.4
6.3

Non-CO2 greenhouse gas emissions from biomass burning .................................................. 6.11

Land Converted to Grassland ............................................................................................................ 6.12
6.3.1

Biomass.................................................................................................................................. 6.12

6.3.2

Dead organic matter ............................................................................................................... 6.12

6.3.3

Soil carbon ............................................................................................................................. 6.12

6.3.3.1

Choice of method ............................................................................................................. 6.12

6.3.3.2

Choice of stock change and emission factors ................................................................... 6.13

6.3.3.3

Choice of activity data ...................................................................................................... 6.15

6.3.3.4

Calculation steps for Tier 1 .............................................................................................. 6.16

6.3.3.5

Uncertainty assessment .................................................................................................... 6.17

6.3.4
6.4

Non-CO2 greenhouse gas emissions from biomass burning .................................................. 6.17

Completeness, Time series, QA/QC, and Reporting ......................................................................... 6.17

Annex 6A.1

Estimation of default stock change factors for mineral soil C emissions/removals for
Grassland............................................................................................................... 6.18

References .......................................................................................................................................................... 6.19

FiguresFigure 6.1

Classification scheme for grassland/grazing systems.. ........................................... 6.8

TablesTable 6.2 (Updated)

Relative stock change factors for grassland management ....................................... 6.6

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Volume 4: Agriculture, Forestry and Other Land Use

6

GRASSLAND

6.1

INTRODUCTION

No refinement.

6.2

GRASSLAND REMAINING GRASSLAND

No refinement.

6.2.1

Biomass

No refinement.

6.2.2

Dead organic matterNo refinement.

6.2.3

Soil carbon

This section deals with the impacts of grassland management on soil organic C stocks, primarily by influencing C
inputs to the soil, and thus soil C storage, by affecting net primary production, root turnover, and allocation of C
between roots and shoots. Soil C stocks in grassland are influenced by fire, grazing intensity, fertilizer management,
liming, irrigation, re-seeding with more or less productive grass species and mixed swards with N-fixing legumes(Conant et al., 2001; Follett et al., 2001; Ogle et al., 2004). In addition, drainage of organic soils for grassland
management causes losses of soil organic C (Armentano and Menges, 1986).
General information and guidance for estimating changes in soil C stocks are provided in Chapter 2, Section 2.3.3(including equations), and this section needs to be read before proceeding with a consideration of specificguidelines dealing with grassland soil C stocks. The total change in soil C stocks for grassland is estimated using
Equation 2.24 (Chapter 2), which combines the change in soil organic C stocks for mineral soils and organic soils;
and stock changes associated with soil inorganic C pools (if estimated at Tier 3). This section provides specificguidance for estimating soil organic C stocks. There is a general discussion in Section 2.3.3.1 on soil inorganic C
and no additional information on this is provided here.
To account for changes in soil C stocks associated with Grassland Remaining Grassland, countries need to have,at a minimum, estimates of grassland areas at the beginning and end of the inventory time period. If land-use andmanagement data are limited, aggregate data, such as Food and Agriculture Organization (FAO) statistics on
grassland, can be used as a starting point, along with knowledge of country experts about the approximate
distribution of land management systems (e.g., degraded, nominal and improved grassland/grazing systems).
Grassland management classes must be stratified according to climate regions and major soil types, which could
either be based on default or country-specific classifications. This can be accomplished with overlays of land use
on suitable climate and soil maps.

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

Mineral soils
Ti er 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 storage. After a finite transition period, one can assume a steadystate for this stock. Equation 2.25 (Chapter 2) is used to estimate change in soil organic C stocks in mineral soils

6.4

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

Chapter 6: Grasslandby subtracting the C stock in the last year of an inventory time period (SOC0) from the C stock at the beginning of
the inventory time period (SOC(0 –T)) and dividing by the time dependence of the stock change factors (D). Notethat area of exposed bedrock in grasslands are not included in the soil C stock calculation (assume a stock of 0).
In practice, country-specific data on grassland management activity should be obtained and classified into
appropriate land management systems, and then stratified by IPCC climate regions and soil types (see Chapter 3).
Soil organic C stocks (SOC) are estimated for each time period in the inventory using default reference carbon
stocks (SOCref) and default stock change factors (FLU, FMG, FI ).
Ti er 2The Tier 2 method for mineral soils also uses Equation 2.25 (Chapter 2), but the inventory approach is further
developed with country-specific information to better specify stock change factors, reference C stocks, climate
regions, soil types, and/or the land management classification system. For biochar C amendments, Tier 2 methods
utilize a top-down approach in which the total amount of biochar generated and added to mineral soil is used toestimate the change in soil organic C stocks with country-specific factors. See Section 2.3.3.1, Chapter 2, Volume
IV for more information.
Ti er 3Tier 3 approaches do not employ simple stock change factor per se, but rather use dynamic models and/or detailed
soil C inventory measurements as the basis for estimating annual stock changes.
Estimates of stock changes using model-based approaches are computed from the coupled equations that estimate
the net change of soil carbon. A variety of models designed to simulate soil carbon dynamics exist (for example,
see reviews by McGill et al., 1996; Smith et al., 1997). Key criteria in selecting an appropriate model include its
capability of representing all of the relevant management practices/systems for grasslands; model inputs (i.e.,
driving variables) are compatible with the availability of country-wide input data; and the model sufficiently
represents stock changes based on comparisons with 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. In contrast to a network associated with model
validation, a much higher density of benchmark sites will be needed to adequately represent the combination ofland-use and management systems, climate and soil types. Additional guidance is provided in Section 2.3.3.1(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 for guidance on Tier 1, 2, and 3 approaches for drained organic soils.

6.2.3.2

C HOICE OF STOCK CHANGE AND EMISSION FACTOR

Mineral soils
Ti er 1For the Tier 1 approach, default stock change factors are provided in Table 6.2, which includes values for land use
factor (FLU), input factor (FI), and management factor (FMG). The method and studies that were used to derive thedefault stock change factors are provided in Annex 6A.1. The time dependence (D) is 20 years for default stockchange factors in grasslands, and they represent the influence of management to a depth of 30cm. Default reference
soil organic C stocks are found in Table 2.3 of Chapter 2. The reference stock estimates are for the top 30cm of
the soil profile, to be consistent with the depth increment for default stock change factors.

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 6.2 (UPDATED)RELATIVE STOCK CHANGE FACTORS FOR GRASSLAND MANAGEMENT
FactorLevel

Climateregime

IPCC
default

Error 1,2

Definition

Land use
(FLU)

All

All

1.0

NA

All native and/or permanent grassland in anominal condition is assigned a land-use factorof 1.

Management(FMG)

Nominally
managed(non –
degraded)

All

1.0

NA

Represents low or medium intensity grazing
regimes, in addition to periodic cutting andremoval of above-ground vegetation, without
significant management improvements.

Management
(FMG)

High
Intensity
Grazing3±8%

Represents high intensity grazing systems (orcutting and removal of vegetation) with shifts
in vegetation composition and possibly
productivity but is not severely degraded4.

Management(FMG)

Severelydegraded

Implies major long-term loss of productivity
and vegetation cover, due to severe mechanicaldamage to the vegetation and/or severe soil
erosion.

Management(FMG)

Input (applied
only toimproved
grassland) (FI)Input (applied
only toimproved
grassland) (FI)Improved
grassland

Medium

High

All

0.90

All

0.7

±40%

Temperate/
Boreal

1.14±11%

Tropical

1.17

±9%

TropicalMontane5

1.16

±40%

All

1.0

NA

Applies to improved grassland where no
additional management inputs have been used.

±7%

Applies to improved grassland where one ormore additional management
inputs/improvements have been used (beyond
that required to be classified as improved
grassland).

All

1.11

Represents grassland which is sustainably
managed with light to moderate grazingpressure (or cutting and removal of vegetation)and that receive at least one improvement (e.g.,
fertilization, species improvement, irrigation).

Management factors were derived using methods and studies provided in Annex 6A1. The basis for the other factors is described in the2006 IPCC Guidelines.
Source:3

The bibliography for the following references used for management factor can be found in Annex 6A.1:

Cao et al., 2013; Ding et al., 2014; Du et al., 2017; Frank et al., 1995; Franzluebbers and Stuedemann, 2009; Gao et al., 2018; Gao et al.,2007; Gillard, 1969; Han et al., 2008; He et al., 2008; Ingram et al., 2008; Kioko et al., 2012; Kölbl et al., 2011; Li et al., 2008; Liu etal., 2012; Manley et al., 1995; Martinsen et al., 2011; Potter et al., 2001; Qi et al., 2010; Rutherford and Powrie, 2011; Schulz et al.,2016; Schuman et al., 1999; Segoli et al., 2015; Smoliak et al., 1972; Sun et al., 2011; Talore et al., 2016; Teague et al., 2011; Wang etal., 2017; Wei et al., 2011; Xu et al., 2014; Yanfen et al., 1998; Zhang et al., 2018; Zhou et al., 2010; Zou et al., 2015
Notes:
1+ two standard deviations, expressed as a percent of the mean; where sufficient studies were not available for a statistical analysis adefault, based on expert judgement, of + 40% is used as a measure of the error. NA denotes ‘Not Applicable’, for factor values that
constitute reference values or nominal practices for the input or management classes.
2This 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

High intensity grazing may be moderately degraded, but do not represent excessive grazing intensity that leads to severe grassland
degradation.
5

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

Ti er 2Estimation of country-specific stock change factors is an important advancement for improving an inventory thatcan be developed in the Tier 2 approach. Derivation of management factors (F MG) and input factors (FI) are basedon experimental comparisons to nominally-managed grasslands with medium input, respectively, because these

6.62019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

Chapter 6: Grasslandclasses are considered the nominal practices in the IPCC default classification scheme for management systems(see Choice of Activity Data). It is considered good practice to derive values for more detailed classification
schemes of management, climate and soil types, if there are significant differences in the stock change factors
among finer categories based on an empirical analysis.
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.
However, this will require consistency with the depth of the reference C stocks (SOC REF) and stock change factors
for all land uses (i.e., FLU, FMG, and FI) to ensure consistent application of methods for determining the impact ofland 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 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 2.2C in Chapter2, Section 2.3.3.1 for more
information.
For biochar C amendments to soils, the parameter 𝐹𝑝𝑒𝑟𝑚𝑝 can be based on H/Corg or O/Corg measured directlyfrom representative samples of biochar, or from published data for biochar produced using similar process
conditions as the biochar that is applied to soils in the country. Tier 2 emission factors may be disaggregated basedon variation in environmental conditions, such as the climate and soil types, in addition to variation associatedwith the biochar production methods that generate production types defined by a specific feedstock type andconversion process. See Section 2.3.3.1, Chapter 2, Volume IV for more information.
Ti er 3
Constant stock change rate factors per se are less likely to be estimated in favor of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 (Chapter 2) for further discussion. Tier
3 methods for biochar C amendments to soils are country-specific and may involve empirical or process-basedmodels to account for a broader set of impacts of biochar amendments. More information on Tier 3 methods isprovided 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 for guidance on Tier 1, 2, and 3 approaches for drained organic soils.

6.2.3.3 C HOICE OF ACTIVITY DA TA
Mineral soils
Ti er 1
Grassland systems are classified by practices that influence soil C storage. In general, practices that are known toincrease C input to the soil and thus soil organic C stocks, such as irrigation, fertilization, liming, organic
amendments, more productive grass varieties, are given an improved status, with medium or high inputs dependingon the level of improvement. Practices that decrease C input and soil organic C storage, such as long-term heavy
grazing, are given a degraded status relative to nominally-managed seeded pastures or native grassland that areneither improved nor degraded. These practices are used to categorize management systems and then estimate thechange in soil organic C stocks. A classification system is provided in Figure 6.1, which forms the basis for a Tier
1 inventory. Inventory compilers should use this classification to categorize management systems in a mannerconsistent with the default Tier 1 stock change factors. This classification may be further developed for Tiers 2
and 3 approaches.
The main types of land-use activity data include: i) aggregate statistics (Approach 1), ii) data with explicit
information on land-use conversions but without specific geo-referencing (Approach 2), or iii) data withinformation on land-use conversion and explicit geo-referencing (Approach 3), such as point-based land-use andmanagement inventories making up a statistically-based sample of a country’s land area. (See Chapter 3 fordiscussion of Approaches). At a minimum, globally available land-use statistics, such as FAO’s databases

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Volume 4: Agriculture, Forestry and Other Land Use(http://www.fao.org/faostat/en/#home), provide annual compilations of total land area by major land-use types. This
would be an example of aggregate data (Approach 1).
Figure 6.1

Classification scheme for grassland/grazing systems. In order to classify
grassland management systems, the inventory compiler should start at the
top and proceed through the diagram answering questions (move acrossbranches if answer is yes) until reaching a terminal point on the diagram.
The classification diagram is consistent with default stock change factors in
Table 6.2.
Start

Grassland1 has severe damage2 tovegetation and soils

Yes

Severely degraded

No

Grassland productivity
greater than native due toimprovements3

Yes

Multiple Improvements?

Yes

Improved-High
Input

No

No

High intensity grazing4?

Yes

High Intensity
Grazing

ImprovedMedium Input

No

Nominal/Native

Notes:
1: Includes continuous pasture, hay lands and rangelands
2: Large loss in vegetation cover and productivity due to continual overgrazing and/or high rates of erosion.
3: Productivity refers explicitly to C input to soil (management improvements that increase input e.g., fertilization, organic amendment,
irrigation, planting more productive varieties, liming, and seeding legumes).
4: High intensity grazing is defined as grazing that deteriorates the condition and/or long-term recovery capacity of the vegetation comparedwith the vegetation state under nominal to moderate grazing intensity. High intensity grazing does not refer to stocking rate and duration only,
but to the stocking rate and duration in relation to grassland productivity and resilience. This may be called a moderately degraded condition
but high intensity grazing does not lead to the severe degradation such as is caused by relentless overgrazing. High intensity grazing also
includes land where vegetation is frequently cut and removed equivalent to high intensity grazing and without application of any animal manure.

Management activity data supplement the land-use data, providing information to classify management systems,
such as stocking rates, fertilizer use, irrigation, etc. These data can also be aggregate statistics (Approach 1) orprovide information on explicit management changes (Approach 2 or 3). It is good practice where possible forgrassland areas to be assigned appropriate general management activities (i.e., degraded, native, or improved) or
specific management activities (e.g., fertilization or grazing intensity). Soil degradation maps may be a usefulsource of information for stratifying grassland according to management (e.g., Conant and Paustian, 2002;
McKeon et al., 2004). Expert knowledge is another source of information for management practices. It is goodpractice to elicit expert knowledge, where appropriate, using methods provided in Volume 1, Chapter 2 (Annex
2A.1, A protocol for expert elicitation).

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

Chapter 6: Grassland

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 pastoral and land-use statistics(Approach 1). Time series data can be more readily associated with a particular grassland management system andthe soil type associated with the particular location can be determined by sampling or by referencing the location
to a suitable soil map. Inventory points that are selected based on an appropriate statistical design also enableestimates of the variability associated with activity data, which can be used as part of a formal uncertainty analysis.
An example of a survey using Approach 3 is the National Resource Inventory in the U.S. (Nusser and Goebel,1997).
Activity data require additional in-country information to stratify areas by climate and soil types. If suchinformation has not already been compiled, an initial approach would be to overlay available land cover/land-use
maps (of national origin or from global datasets such as IGBP_DIS) with soil 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, and potential
evapotranspiration.
Ti er 2Tier 2 approaches are likely to involve a more detailed stratification of management systems (Figure 6.1) than inTier 1, if sufficient data are available. This could include further subdivisions of grassland systems (i.e., moderately
degraded, severely degraded, nominal and improved), and the input classes (medium and high input). It is goodpractice to further subdivide default classes based on empirical data that demonstrates significant differences insoil organic C storage among the proposed categories. In addition, Tier 2 approaches could involve a finer
stratification of climate regions and soil types. The resolution of activity data, such as that determined by intensityof survey data, often determines the finest feasible resolutions for spatial stratification.
For Tier 2, the specific definitions of management and input factors are typically made to match available activity
data on how activities affects C stocks. For example, if a country has management factors related to levels of
grazing intensity, then the country will also need activity data on grazing intensity to apply the country-specificfactors.
For biochar C amendments, the activity data for the Tier 2 method includes the total quantities of biochar
distributed for 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 to the distributed nature of the land sector in which this can take place, inventory compilers may
not have access to data on when or where biochar C amendments occur. Inventory compilers may be able tocompile data on the total amount of biochar applied to grassland mineral soils from biochar producers, importers,
exporters or distributors, and/or from those applying biochar to grassland 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 theamount 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 obtainedwith 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 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 vegetation type andmanagement. Therefore, Tier 3 methods may require environmental data on climate zones, soil types, vegetation
type and grazing management systems, 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.

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

6.9

Volume 4: Agriculture, Forestry and Other Land Use

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 for guidance on Tier 1, 2, and 3 approaches for drained organic soils.

6.2.3.4

C ALCULATION STEPS FOR T IER 1Mineral soils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change from Grassland Remaining Grassland are
as follows:
Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g.,
1990 and 1995, 1995 and 2000, etc.)
Step 2: Determine the land-use and management by mineral soil type and climate region for land at the beginningof the inventory period, which can vary depending on the time step of the activity data (0-T; e.g., 5, 10 or 20 years
ago).
Step 3: Select the native reference C stock value (SOCREF), based on climate and soil type from Table 2.3, foreach area of land being inventoried. The reference C stocks are the same for all land-use categories to ensure thaterroneous changes in the C stocks are not computed due to differences in reference stock values among sectors.
Step 4: Select the land-use factor (FLU), management factor (FMG) and C input levels (FI) representing the landuse and management system present at the beginning of the inventory period. Values for F LU, FMG and FI areprovided in Table 6.2.
Step 5: Multiply these values by the reference soil C stock to estimate the ‘initial’ soil organic C stock (SOC(0-T))
for the inventory time period.
Step 6: Estimate SOC0 by repeating Step 1 to 4 using the same native reference C stock (SOC REF), but with landuse, management and input factors that represent conditions in the last (year 0) inventory year.
Step 7: Estimate the average annual change in soil organic C stock for the area over the inventory time period(∆CMineral).
Step 8: Repeat Steps 1 to 6 if there are additional inventory time periods (e.g., 1995 to 2000, 2001 to 2005, etc.).
A case example is given below for computing a change in grassland soil organic C stocks using Equation 2.25
(Chapter 2), default stock change factors and reference C stocks.
Updated Example: The following example shows calculations for aggregate areas of grassland soil
carbon stock change to a 30 cm depth. In a tropical moist climate on Ultisol soils, there are 1Mha ofpermanent grassland. The native reference carbon stock (SOCREF) for the climate/soil type is 47
tonnes C ha-1. At the beginning of the inventory time period (1990 in this example) the distributionof grassland systems was 500,000 ha of unmanaged native grassland; 400,000 ha of unimproved,moderately degraded grazing land; and 100,000 ha of heavily degraded grassland. Thus, initial soil
carbon stocks for the area were:
500,000 ha ● (47 tonnes C ha-1 ● 1 ● 1 ● 1) + 400,000 ha ● (47 tonnes C ha-1 ● 1 ● 0.97 ● 1) +100,000 ● (47 tonnes C ha-1 ● 1 ● 0.7 ● 1) = 45,026,000 tonnes C.
In the last year of inventory time period (2010 in this example), there are: 300,000 ha of unmanaged
native grassland; 300,000 ha of unimproved, moderately degraded grazing land; 200,000 ha ofheavily degraded grassland; 100,000 ha of improved pasture receiving fertilizer; and 100,000 ofhighly improved pasture receiving fertiliser together with irrigation. Thus, total soil carbon stocks
in the inventory year are:300,000 ha ● (47 tonnes C ha -1 ● 1 ● 1 ● 1) + 300,000 ha ● (47 tonnes C ha-1 ● 1 ● 0.97 ● 1) +200,000 ● (47 tonnes C ha-1 1 ● 0.7 ● 1) + 100,000 ● (47 tonnes C ha-1 1 ● 1.17 ● 1) + 100,000 ●(47 tonnes C ha-1 ● 1 ● 1.17 ● 1.11) = 45,959,890 tonnes C.
The average annual stock change over the period for the entire area is: 45,959,890 – 45,026,000 =
933,890 tonnes/20 yr = 46,694.5 tonnes 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).

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

Chapter 6: Grassland

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 for guidance on Tier 1, 2, and 3 approaches for drained organic soils.

6.2.3.5 U NCERTAINTY ASSESSMEN T
No refinement.

6.2.4

Non-CO2 greenhouse gas emissions from biomassburning

No refinement.

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

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Volume 4: Agriculture, Forestry and Other Land Use

6.3

LAND CONVERTED TO GRASSLAND

No refinement.

6.3.1

Biomass

No refinement.

6.3.2

Dead organic matterNo refinement.

6.3.3

Soil carbon

Grassland management involving drainage will generate emissions from organic soil, regardless of the previous
land use. However, the impact on mineral soils is less clear-cut for lands converted to Grassland. Literature on
one of the dominant conversion types globally (from Forest Land to Grassland in the tropics) provides evidence
for net gains as well as net losses in soil C, and it is known that the specific management of the grassland afterconversion is critical (e.g., Veldkamp, 2001).
General information and guidance for estimating changes in soil C stocks are provided in Chapter 2, Section 2.3.3(including equations), and this section needs to be read before proceeding with a consideration of specificguidelines dealing with grassland soil C stocks. The total change in soil C stocks for Land Converted to Grasslandis estimated using Equation 2.24 for the change in soil organic C stocks for mineral soils and organic soils; andstock changes associated with soil inorganic C pools (if estimated at Tier 3). This section provides specificguidance for estimating soil organic C stock changes. There is a general discussion in Section 2.3.3 in Chapter 2
on soil inorganic C and no additional information is provided here.
To account for changes in soil C stocks associated with Land Converted to Grassland, countries need to have, ata minimum, estimates of the areas of Land Converted to Grassland during the inventory time period, stratified byclimate region and soil type. If land-use and management data are limited, aggregate data, such as FAO statistics,
can be used as a starting point, along with country expert knowledge of the approximate distribution of land-use
types being converted and the management of those lands. If the previous land uses and conversions are unknown,
SOC stocks changes can still be estimated using the methods provided in Grassland Remaining Grassland, but theland base area will likely be different for grasslands in the current year relative to the initial year in the inventory.
It is critical, however, that the total land area accounted across all land-use sectors be equal over the inventory
time period (e.g., if 3 Million ha of Forest Land and Cropland are converted to Grassland during the inventory
time period, then Grassland will have an additional 3 Million ha in the last year of the inventory, while Croplandand Forest Land will have a corresponding loss of 3 Million ha in the last year). Land Converted to Grassland is
stratified according to climate regions, management, and major soil types, which could either be based on default
or country-specific classifications. This can be accomplished with overlays of suitable climate and soil maps,
coupled with spatially-explicit data on the location of land conversions.

6.3.3.1 C HOICE OF METH OD
Inventories can be developed using a Tier 1, 2 or 3 method, with each successive Tier requiring more details andresources than the previous one. It is possible that countries will use different tiers to prepare estimates for the
separate sub-categories 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 2.4) andorganic soils (Figure 2.5) in Section 2.3.3.1 Chapter 2 to assist inventory compilers with selection of theappropriate tier for their soil C inventory.

Mineral soils
Ti er 1
Using Equation 2.25 (Chapter 2), the change in soil organic C stocks can be estimated for mineral soils accounting
for the impact of land-use conversion to Grassland. The method is fundamentally the same as the one used for
Grassland Remaining Grassland, except pre-conversion C stocks are dependent on stock change factors foranother land use. Specifically, the initial (pre-conversion) soil organic C stock (SOC(0-T)) and stock in the last yearof inventory time period (SOC0) are computed from the default reference soil organic C stocks (SOCREF) stockchange factors (FLU, FMG, FI). Note that area of exposed bedrock in Forest Land or the previous land use are not

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Chapter 6: Grassland

included in the soil C stock calculation (assume a stock of 0). Annual rates of stock changes are estimated basedon the difference in stocks (over time) for the first and last year in the inventory time period divided by the timedependence of the stock change factors (D, default is 20 years).
Ti er 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 more disaggregated land-use activity and environmental data.
For biochar C amendments, Tier 2 methods 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.
Ti er 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. It is good practice that Tier 3 approaches,
estimating soil C change from land-use conversions to Grassland, employ models, data sets and/or monitoring
networks that are capable of representing transitions over time from other land uses, including Forest Land,
Cropland, and possibly Settlements or other lands. If possible, it is also recommended for Tier 3 methods to 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 through decomposition and combustion. It is important thatmodels be evaluated with independent observations from country-specific or region-specific field locations thatare representative of the interactions of climate, soil, and grassland 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
2.3 of the 2013 Wetlands Supplement for guidance on Tier 1, 2, and 3 approaches for land use conversions
associated with drained organic soil.

6.3.3.2 C HOICE OF STOCK CHANG E AND EMISSION FACTO RS
Mineral soils
Ti er 1For unmanaged land, as well as for managed Forest Land, Settlements and nominally managed Grassland withlow disturbance regimes, soil C stocks are assumed equal to the reference values (i.e., land use, disturbance (forestsonly), management and input factors equal 1), while it will be necessary to apply the appropriate stock change
factors to represent other systems such as improved and degraded grasslands, as well as all cropland systems.
Default reference C stocks are given in Chapter 2, Table 2.3. See the Choice of Stock Change and Emission Factors
in the appropriate land-use chapter for default stock change factors (Forest Land in Section 4.2.3.2, Cropland in
5.2.3.2, Grassland in 6.2.3.2, Settlements in 8.2.3.2, and Other land in 9.3.3.2).
Note that it is good practice to use the management factor (FLU) for set-asides (Table 5.5) if dealing with cultivatedannual Cropland converted into Grassland (i.e., until the land is re-classified as Grassland Remaining Grassland)because recently converted annual cropland systems will typically gain C at a rate similar to set-aside lands.
Moreover, the Tier 1 set-aside factors were derived from empirical data to explicitly represent the expected gain
during the first 20 years for lands removed from cultivation. If countries decide to assume a faster increase in C
that raises levels to native conditions within 20 years, a justification should be provided in the documentation.
Ti er 2Estimation of country-specific stock change factors is probably the most important development for the Tier 2
approach. Differences in soil organic C stocks among land uses are computed relative to a reference condition,using land-use factor (FLU). Input factor (FI) and management factor (FMG) are then used to further refine the C
stocks of the new grassland system. Additional guidance on how to derive these stock change factors is given in
Grassland Remaining Grassland, Section 6.2.3.2 as well as other general guidance in Section 2.3.3.1 (Chapter 2).
See the appropriate section for specific information regarding the derivation of stock change factors for other land-2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Useuse sectors (Forest Land in Section 4.2.3.2, Cropland in 5.2.3.2, Wetlands in 7.2.3.3, Settlements in 8.2.3.2, andOther 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 general, reference C stocks should be consistent across the land uses (i.e., Forest Land,
Cropland, Grassland, Settlements, Other Land) (see section 2.3.3.1). Therefore, the same reference stock should
be used for each climate zone and soil type, regardless of the land use. The reference stock is then multiplied byland use, input and management factors to estimate the stock for each land use based on the set of management
systems that are present in a country. In addition, the depth for evaluating soil C stock changes can be different
with the Tier 2 method. However, this will require consistency with the depth of the reference C stocks (SOCREF)
and stock change factors for all land uses (i.e., FLU, FI, and FMG) to ensure consistency in the application of methods
for estimating the impact of land use change on soil carbon stocks.
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 Croplands converted to Grasslands, may
include development of factors that estimate changes over longer periods of time than the default of 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 2.2C in Chapter2, 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 generates production types defined by the specific feedstock type and conversion process,where production type is defined as a process utilizing a specific feedstock type, and a specific conversion process.
See Section 2.3.3.1, Chapter 2, Volume IV for more information.
Country-specific emission factors (i.e., permanence factors) for biochar C for grassland may be different from thepast land use for Land Converted to Grassland, and these differences need to be addressed in the calculations.
This requires estimating the biochar carbon stocks from past biochar carbon additions that remain in Land
Converted to Grassland after conversion. The biochar C stocks are then subject to the conditions for grassland,
which may lead some additional loss of biochar C.
Ti er 3
Constant stock change rate factors per se are less likely to be estimated in favour of variable rates that moreaccurately capture land-use and management effects. See Section 2.3.3.1 in Chapter 2 for further discussion.
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 Grassland will need to be
tracked through the land use change process.

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
2.3 of the 2013 Wetlands Supplement for guidance on Tier 1, 2, and 3 approaches for land use conversions
associated with drained organic soil.

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Chapter 6: Grassland

6.3.3.3

C HOICE OF ACTIVITY DA TA

Mineral soils
Ti er 1 a n d Ti er 2 – Def a ult E q ua t io n s
For purposes of estimating soil carbon stock change, area estimates of Land Converted to Grassland 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. A detailed description of the default
climate and soil classification schemes is provided in Chapter 3. See corresponding sections dealing with eachland-use category for sector-specific information regarding the representation of land-use/management activity
data (Forest Land in Section 4.2.3.3, Cropland in 5.2.3.3, Grassland in 6.2.3.3, Wetlands in 7.2.3.3, Settlements in
8.2.3.3 and Other land in 9.3.3.3).
An important issue in evaluating the impact of Land Converted to Grassland on soil organic C stocks is the typeof land-use and management activity data. Activity data gathered using Approach 2 or 3 (see Chapter 3 fordiscussion about Approaches) provide the underlying basis for determining the previous land use for land
categorized as Land Converted to Grassland. In contrast, aggregate data (Approach 1) only provide the total
amount of area in each land use at the beginning and end of the inventory period (e.g., 1985 and 2005). Thus,
unless supplementary information can be gathered to infer the pattern of land-use change (as suggested in Chapter
3) Approach 1 data are insufficient to determine specific transitions between land-use categories. Therefore, theprevious land use before conversion to grasslands will be unknown. Fortunately, this is not problematic using a
Tier 1 or 2 method because the calculation is not dynamic and assumes a step change from one equilibrium state
to another. Therefore, with aggregated data (Approach 1), changes in soil organic C stocks may be computed
separately for each land-use category and then combined to obtain the total stock change for all land uses combined.
The soil C stock change estimate will be equivalent to results using Approach 2 (or 3) activity data (i.e., a fullland-use change matrix), but evaluation of C stock trends will only be relevant after combining the stock estimates
for all land uses (i.e., stocks will increase or decrease with the changes in land area within individual land uses,
but this will offset by gains or losses in other land uses, and thus not an actual stock change in the soil pool for acountry. Thus, with aggregate (Approach 1 data) it is important to achieve coordination among all land sector toensure 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 land-use change.
Note that it will not be possible to determine the amount of cultivated annual croplands converted to grasslands
with aggregated activity data (Approach 1). Therefore, grassland stock change factors will be applied, without
consideration for the slower rate of C gain in recently converted annual croplands, which may lead to an overestimation of C gain over a 20-year time period, particularly using the Tier 1 method (see Choice of Stock Change
and Emission Factors for additional discussion). This caveat should be acknowledged in the reporting
documentation, and it is good practice for future inventories to gather additional information needed to estimate
the area of grassland recently converted from croplands, particularly if soil C is a key source category.
For biochar C amendments, the activity data for the Tier 2 method includes the total quantities of biochar
distributed for 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 is considered to occur where it is incorporated into soil.
However, due to the distributed nature of the land sector in which this can take place, inventory compilers may
not have access to data on when or where biochar C amendments occur. Inventory compilers may be able tocompile data on the total amount of biochar applied to grassland mineral soils from biochar producers, importers,
exporters, distributors, and/or from those applying biochar to grassland 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 theamount 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 obtainedwith a survey of biochar distributors and land managers.
Ti er 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.
For biochar C amendments, 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, 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, grassland vegetation and management
systems (such as nitrogen fertilizer application rates, and whether soils are flooded for paddy rice production), in2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

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Volume 4: Agriculture, Forestry and Other Land Use

addition to the amount of biochar amendments in each of the individual combinations of strata for theenvironmental 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
2.3 of the 2013 Wetlands Supplement for guidance on Tier 1, 2, and 3 approaches for land use conversions
associated with drained organic soil.

6.3.3.4 C ALCULATION STEPS FOR T IER 1Mineral soils
The steps for estimating SOC0 and SOC(0-T) and net soil C stock change of Land Converted to Grassland are as
follows:
Step 1: Organize data into inventory time periods based on the years in which activity data were collected (e.g.,
1990 and 1995, 1995 and 2000, etc.)
Step 2: Determine the land-use and management by mineral soil types and climate regions for land at the
beginning of the inventory period, which can vary depending on the time step of the activity data (0-T; e.g., 5, 10
or 20 years ago).
Step 3: Select the native reference C stock value (SOCREF), based on climate and soil type from Table 2.3, foreach area of land being inventoried. The reference C stocks are the same for all land-use categories to ensure thaterroneous changes in the C stocks are not computed due to differences in reference stock values among sectors.
Step 4: Select the land-use factor (FLU), management factor (FMG) and C input levels (FI) representing the landuse and management system present before conversion to grassland. Values for F LU, FMG and FI are given in therespective section for the land-use sector (Cropland in Chapter 5, Grassland in Chapter 6, Settlements in Chapter
8, and Other land in Chapter 9).
Step 5: Multiply these values by the reference soil C stock to estimate ‘initial’ soil organic C stock (SOC(0-T)) for
the inventory time period.
Step 6: Estimate SOC0 by repeating Steps 1 to 4 using the same native reference C stock (SOC REF), but with landuse, management and input factors that represent conditions (after conversion to grassland) in the last (year 0)
inventory year.
Step 7: Estimate the average annual change in soil organic C stock for the area over the inventory time period(∆CMineral)
Step 8: Repeat Steps 1 to 6 if there are additional inventory time periods (e.g., 1995 to 2000, 2001 to 2005, etc.).
A numerical example is given below for land conversion of cropland.
Using Equation 2.25 (Chapter 2), default stock change factors and reference C stocks, a case example is given
below for estimating changes in soil organic C stocks associated with Land Converted to Grassland.

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Chapter 6: Grassland

Example: For tropical moist, volcanic soil that has been under long-term annual Cropland, withintensive tillage and where crop residues are removed from the field, carbon stocks at the beginningof the inventory time period (1990 in this example), SOC(0-T) are:70 tonnes C ha-1 ● 0.90 ● 1 ● 0.92 = 58.0 tonnes C ha-1.
Following conversion to improved (e.g., fertilised) pasture, carbon stocks in the last year of
inventory (2010 in this example) (SOC0) are:70 tonnes C ha-1 ● 1 ● 1.17 ● 1 = 81.9 tonnes C ha-1.
Thus the average annual change in soil C stock for the area over the inventory time period is
calculated as:(81.9 tonnes C ha-1 – 58.0tonnes C ha-1) / 20 yrs =1.2 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
2.3 of the 2013 Wetlands Supplement for guidance on Tier 1, 2, and 3 approaches for land use conversions
associated with drained organic soil.

6.3.3.5 U NCERTAINTY ASSESSMEN T
No refinement.

6.3.4

Non-CO2 greenhouse gas emissions from biomassburning

No refinement.

6.4

COMPLETENESS, TIME SERIES, QA/QC, AND
REPORTING

No refinement.

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 6A.1

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

Default stock change factors have been updated in Table 6.2 based on an analysis of a global dataset ofexperimental results for grazing intensity to a 30cm depth. Management change was defined as high-intensity
grazing from low to moderate grazing intensity. The grazing intensity categories were those used by the authorsof the published studies and so are their interpretation of the relative livestock grazing stocking density in relation
to the grassland productivity and resilience. Management factors represent the effect on C stocks after 20 years
following the management change. Data were compiled from published literature based on the following criteria:a) must be an experiment with a control and treatment; b) provide soil organic C stocks or the data needed tocompute soil organic C stocks (bulk density, OC content, gravel content); c) provide depth of measurements; d)provide the number of years from the beginning of the experiment to C stock sample collection; and c) provide
location information. There were 31 published studies with 176 observations of grassland management (i.e., highintensity grazing versus low to moderate intensity grazing). There was insufficient data to develop reliable factors
by climate or soil.
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, and the first-orderinteractions among the variables. Variables and interactions terms were retained in the model if they met an alphalevel 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 toaccount for the dependencies in times series data and among data points representing different depths from thesame 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 grassland management factors to a 30 cm depth. Uncertainty isquantified based on the prediction error for the model, and represents a 95 percent confidence interval for each of
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.

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Chapter 6: Grassland

References
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102(479): 803-812.
Cao, J., Wang, X., Sun, X., Zhang, L. & Tian, Y. (2013) Effects of grazing intensity on soil labile organic carbon
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Ding, W., Sun, X., Jia, H., Qin, P., Jia, Y. & Jin, J. (2014) Effect of grazing intensity on soil organic carbon innorth slope of Tianshan mountain meadow grassland. Southwest China Journal of Agricultural Sciences 27(4):1596-1600.
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Frank, A., Tanaka, D., Hofmann, L. & Follett, R. (1995) Soil carbon and nitrogen of Northern Great Plains
grasslands as influenced by long-term grazing. Journal of Range Management: 470-474.
Franzluebbers, A. & Stuedemann, J. (2009) Soil-profile organic carbon and total nitrogen during 12 years of
pasture management in the Southern Piedmont USA. Agriculture, Ecosystems & Environment 129(1-3): 2836.
Gao, X., Thomas, B. W., Beck, R., Thompson, D. J., Zhao, M., Willms, W. D. & Hao, X. (2018) Long-Term
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He, N., Yu, Q., Wu, L., Wang, Y. & Han, X. (2008) Carbon and nitrogen store and storage potential as affectedby land-use in a Leymus chinensis grassland of northern China. Soil Biology and Biochemistry 40(12): 29522959.
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J. D. (2008) Grazing impacts on soil carbon and microbial communities in a mixed-grass ecosystem. Soil
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Kioko, J., Kiringe, J. W. & Seno, S. O. (2012) Impacts of livestock grazing on a savanna grassland in Kenya.
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Kölbl, A., Steffens, M., Wiesmeier, M., Hoffmann, C., Funk, R., Krümmelbein, J., Reszkowska, A., Zhao, Y.,
Peth, S. & Horn, R. (2011) Grazing changes topography-controlled topsoil properties and their interaction ondifferent spatial scales in a semi-arid grassland of Inner Mongolia, PR China. Plant and Soil 340(1-2): 35-58.
Li, C., Hao, X., Zhao, M., Han, G. & Willms, W. D. (2008) Influence of historic sheep grazing on vegetation andsoil properties of a Desert Steppe in Inner Mongolia. Agriculture, Ecosystems & Environment 128(1-2): 109116.
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Martinsen, V., Mulder, J., Austrheim, G. & Mysterud, A. (2011) Carbon storage in low-alpine grassland soils:
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Potter, K. N., Daniel, J. A., Altom, W. & Torbert, H. A. (2001) Stocking rate effect on soil carbon and nitrogen indegraded soils. Journal of Soil and Water Conservation 56(3): 233-236.

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Qi, S., Zheng, H., Lin, Q., Li, G., Xi, Z. & Zhao, X. (2010) Effects of livestock grazing intensity on soil biota in asemiarid steppe of Inner Mongolia. Plant and Soil 340(1-2): 117-126.
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Schulz, K., Voigt, K., Beusch, C., Almeida-Cortez, J. S., Kowarik, I., Walz, A. & Cierjacks, A. (2016) Grazing
deteriorates the soil carbon stocks of Caatinga forest ecosystems in Brazil. Forest Ecology and Management367: 62-70.
Schuman, G. E., Reeder, J. D., Manley, J. T., Hart, R. H. & Manley, W. A. (1999) Impact of grazing managementon the carbon and nitrogen balance of a mixed-grass rangeland. Ecological Applications 9(1): 65-71.
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