Change in carbon stocks in soils

Although both organic and inorganic forms of C are found in soils, land use and management typically has a larger impact on organic C stocks. Consequently, the methods provided in these guidelines focus mostly on soil organic C. Overall, the influence of land use and management on soil organic C is dramatically different in a mineral versus an organic soil type. Organic (e.g., peat and muck) soils have a minimum of 12 to 20 percent organic matter by mass (see Chapter 3 Annex 3A.5, for the specific criteria on organic soil classification), and develop under poorly drained conditions of wetlands (Brady and Weil, 1999). All other soils are classified as mineral soil types, and typically have relatively low amounts of organic matter, occurring under moderate to well drained conditions, and predominate in most ecosystems except wetlands. Discussion about land-use and management influences on these contrasting soil types is provided in the next two sections.

MINERAL SOILS

Mineral soils are a carbon pool that is influenced by land-use and management activities. Land use can have a large effect on the size of this pool through activities such as conversion of native Grassland and Forest Land to Cropland, where 20-40% of the original soil C stocks can be lost (Mann, 1986; Davidson and Ackerman, 1993; Ogle et al., 2005). Within a land-use type, a variety of management practices can also have a significant impact on soil organic C storage, particularly in Cropland and Grassland (e.g., Paustian et al., 1997; Conant et al., 2001; Ogle et al., 2004 and 2005). In principle, soil organic C stocks can change with management or disturbance if the net balance between C inputs and C losses from soil is altered. Management activities influence organic C inputs through changes in plant production (such as fertilization or irrigation to enhance crop growth), direct additions of C in organic amendments, and the amount of carbon left after biomass removal activities, such as crop harvest, timber harvest, fire, or grazing. Decomposition largely controls C outputs and can be influenced by changes in moisture and temperature regimes as well as the level of soil disturbance resulting from the management activity. Other factors also influence decomposition, such as climate and edaphic characteristics. Specific effects of different land-use conversions and management regimes are discussed in the land-use specific chapters (Chapters 4 to 9).

Land-use change and management activity can also influence soil organic C storage by changing erosion rates and subsequent loss of C from a site; some eroded C decomposes in transport and CO2 is returned to the atmosphere, while the remainder is deposited in another location. The net effect of changing soil erosion through land management is highly uncertain, however, because an unknown portion of eroded C is stored in buried sediments of wetlands, lakes, river deltas and coastal zones (Smith et al., 2001).

ORGANIC SOILS

Inputs of organic matter can exceed decomposition losses under anaerobic conditions, which are common in undrained organic soils, and considerable amounts of organic matter can accumulate over time. The carbon dynamics of these soils are closely linked to the hydrological conditions, including available moisture, depth of the water table, and reduction-oxidation conditions (Clymo, 1984; Thormann et al., 1999). Species composition and litter chemistry can also influence those dynamics (Yavitt et al., 1997).

Carbon stored in organic soils will readily decompose when conditions become aerobic following soil drainage (Armentano and Menges, 1986; Kasimir-Klemedtsson et al., 1997). Drainage is a practice used in agriculture and forestry to improve site conditions for plant growth. Loss rates vary by climate, with drainage under warmer conditions leading to faster decomposition rates. Losses of CO2 are also influenced by drainage depth; liming; the fertility and consistency of the organic substrate; and temperature (Martikainen et al., 1995). Greenhouse gas inventories capture this effect of management.

While drainage of organic soils typically releases CO2 to the atmosphere (Armentano and Menges, 1986), there can also be a decrease in emissions of CH4 that occur in un-drained organic soils (Nykanen et al., 1995). However, CH4 emissions from un-drained organic soils are not addressed in the inventory guidelines with the exception of a few cases in which the wetlands are managed (See Chapter 7, Wetlands). Similarly, national inventories typically do not estimate the accumulation of C in the soil pool resulting from the accumulation of plant detritus in un-drained organic soils. Overall, the rates of C gain are relatively slow in wetland environments with organic soils (Gorham, 1991), and any attempt to estimate C gains, even those created through wetland restoration, would also need to address the increase in CH4 emissions. See additional guidance in Chapter 7 Wetlands.

2.3.3.1 Soil C estimation methods (land remaining in a

LAND-USE CATEGORY AND LAND CONVERSION TO A NEW LAND USE)

Soil C inventories include estimates of soil organic C stock changes for mineral soils and CO2 emissions from organic soils due to enhanced microbial decomposition caused by drainage and associated management activity. In addition, inventories can address C stock changes for soil inorganic C pools (e.g., calcareous grasslands that become acidified over time) if sufficient information is available to use a Tier 3 approach. The equation for estimating the total change in soil C stocks is given in Equation 2.24:

Equation 2.24 Annual change in carbon stocks in soils

ACSoils = ACMineral — LOrganic + AClnorganic

Where:

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

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

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

^CInorgamc = annual change in inorganic carbon stocks from soils, tonnes C yr-1 (assumed to be 0 unless using a Tier 3 approach)

For Tier 1 and 2 methods, soil organic C stocks for mineral soils are computed to a default depth of 30 cm. Greater depth can be selected and used at Tier 2 if data are available, but Tier 1 factors are based on 30 cm depth. Residue/litter C stocks are not included because they are addressed by estimating dead organic matter stocks. Stock changes in organic soils are based on emission factors that represent the annual loss of organic C throughout the profile due to drainage. No Tier 1 or 2 methods are provided for estimating the change in soil inorganic C stocks due to limited scientific data for derivation of stock change factors; thus the net flux for inorganic C stocks is assumed to be zero. Tier 3 methods can be used to refined estimates of the C stock changes in mineral and organic soils and for soil inorganic C pools.

It is possible that countries will use different tiers to prepare estimates for mineral soils, organic soils, and soil inorganic C, given availability of resources. Thus, stock changes for mineral and organic soils and for inorganic C pools (Tier 3 only) are discussed separately. A generalized decision tree in Figures 2.4 and 2.5 can be used to assist inventory compilers in determining the appropriate tier for estimating stock changes for mineral and organic soil C, respectively.

Tier 1 Approach: Default Method

Mineral soils

For mineral soils, the default method is based on changes in soil C stocks over a finite period of time. The change is computed based on C stock after the management change relative to the carbon stock in a reference condition (i.e., native vegetation that is not degraded or improved). The following assumptions are made:

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

(ii) Soil organic C stock changes during the transition to a new equilibrium SOC occurs in a linear fashion.

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

Using the default method, changes in soil C stocks are computed over an inventory time period. Inventory time periods will likely be established based on the years in which activity data are collected, such as 1990, 1995, 2000, 2005 and 2010, which would correspond to inventory time periods of 1990-1995, 1995-2000, 2000-2005, 2005-2010. For each inventory time period, the soil organic C stocks are estimated for the first (SOC0-T) and last year (SOC0) based on multiplying the reference C stocks by stock change factors. Annual rates of carbon stock change are estimated as the difference in stocks at two points in time divided by the time dependence of the stock change factors.

AC . , = annual change in carbon stocks in mineral soils, tonnes C yr-1

Where:

AC . , = annual change in carbon stocks in mineral soils, tonnes C yr-1

SOC0 = soil organic carbon stock in the last year of an inventory time period, tonnes C

SOC(0-T) = soil organic carbon stock at the beginning of the inventory time period, tonnes C

SOC0 and SOC(0-T) are calculated using the SOC equation in the box where the reference carbon stocks and stock change factors are assigned according to the land-use and management activities and corresponding areas at each of the points in time (time = 0 and time = 0-T)

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

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

c = represents the climate zones, 5 the soil types, and i the set of management systems that are present in a country.

SOCreF = the reference carbon stock, tonnes C ha-1 (Table 2.3)

FLU = stock change factor for land-use systems or sub-system for a particular land-use, dimensionless

[Note: F^d is substituted for FLU in forest soil C calculation to estimate the influence of natural disturbance regimes.

FMG = stock change factor for management regime, dimensionless

FI = stock change factor for input of organic matter, dimensionless

A = land area of the stratum being estimated, ha. All land in the stratum should have common biophysical conditions (i.e., climate and soil type) and management history over the inventory time period to be treated together for analytical purposes.

Inventory calculations are based on land areas that are stratified by climate regions (see Chapter 3 Annex 3A.5, for default classification of climate), and default soils types as shown in Table 2.3 (see Chapter 3, Annex 3A.5, for default classification of soils). The stock change factors are very broadly defined and include: 1) a land-use factor (Flu) that reflects C stock changes associated with type of land use, 2) a management factor (FMG) representing the principal management practice specific to the land-use sector (e.g., different tillage practices in croplands), and 3) an input factor (FI) representing different levels of C input to soil. As mentioned above, FND is substituted for FLU in Forest Land to account for the influence of natural disturbance regimes (see Chapter 4, Section 4.2.3 for more discussion). The stock change factors are provided in the soil C sections of the land-use chapters. Each of these factors represents the change over a specified number of years (D), which can vary across sectors, but is typically invariant within sectors (e.g., 20 years for the cropland systems). In some inventories, the time period for inventory (T years) may exceed D, and under those cases, an annual rate of change in C stock may be obtained by dividing the product of [(SOC0 - SOC(0 -T)) • A] by T, instead of D. See the soil C sections in the land-use chapters for detailed step-by-step guidance on the application of this method.

Table 2.3

Default reference (under native vegetation) soil organic C stocks (SOCREF) for mineral soils

Table 2.3

Default reference (under native vegetation) soil organic C stocks (SOCREF) for mineral soils

Climate region

HAC soils1

LAC soils2

Sandy soils3

Spodic soils4

Volcanic soils5

Wetland soils6

Boreal

68

NA

10#

117

20#

146

Cold temperate, dry

50

33

34

NA

20#

87

Cold temperate, moist

95

85

71

115

130

Warm temperate, dry

38

24

19

NA

70#

88

Warm temperate, moist

88

63

34

NA

80

Tropical, dry

38

35

31

NA

50#

86

Tropical, moist

65

47

39

NA

70#

Tropical, wet

44

60

66

NA

130#

Tropical montane

88*

63*

34*

NA

80*

Note: Data are derived from soil databases described by Jobbagy and Jackson (2000) and Bernoux et al. (2002). Mean stocks are shown.

A nominal error estimate of ±90% (expressed as 2x standard deviations as percent of the mean) are assumed for soil-climate types. NA

denotes 'not applicable' because these soils do not normally occur in some climate zones.

# Indicates where no data were available and default values from 1996 IPCC Guidelines were retained.

* Data were not available to directly estimate reference C stocks for these soil types in the tropical montane climate so the stocks were based on estimates derived for the warm temperate, moist region, which has similar mean annual temperatures and precipitation.

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

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

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

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

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

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

Figure 2.4 Generic decision tree for identification of appropriate tier to estimate changes in carbon stocks in mineral soils by land-use category

Figure 2.4 Generic decision tree for identification of appropriate tier to estimate changes in carbon stocks in mineral soils by land-use category

Factor Tree 729

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.

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.

Figure 2.5 Generic decision tree for identification of appropriate tier to estimate changes in carbon stocks in organic soils by land-use category

Figure 2.5 Generic decision tree for identification of appropriate tier to estimate changes in carbon stocks in organic soils by land-use category

Factor Tree 729
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.

When applying the Tier 1 or even Tier 2 method using Equation 2.25, the type of land-use and management activity data has a direct influence on the formulation of the equation (See Box 2.1). Activity data collected with Approach 1 fit with Formulation A, while activity data collected with Approach 2 or 3 will fit with Formulation B (See Chapter 3 for additional discussion on the Approaches for activity data collection).

Box 2.1

Alternative formulations of Equation 2.25 for Approach 1 activity data versus Approach 2

OR 3 ACTIVITY DATA WITH TRANSITION MATRICES

Two alternative formulations are possible for Equation depending on the Approach used to collected activity data, including

Formulation A (Approach 1 for Activity Data Collection)

E (sOCREFCr!j • FLUcsi • FMGcsi • Flc,st • Ac,s,i )

E (sOCREFCr!j • FLUc,s,i • FMGcsi • Flc,si • Ac,s,i)

Mineral

Formulation B (Approaches 2 and 3 for Activity Data Collection)

!"(socreFc s p • fluc s p • fmGc s p • Flc,s,p !)

(soc

REFc

Mineral

Where:

p = parcel of land

See the description of other terms under the Equation 2.25.

Activity data may only be available using Approach 1 for data collection (Chapter 3). These data provide the total area at two points in time for climate, soil and land-use/management systems, without quantification of the specific transitions in land use and management over the inventory time period (i.e., only the aggregate or net change is known, not the gross changes in activity). With Approach 1 activity data, mineral C stock changes are computed using formulation A of Equation 2.25. In contrast, activity data may be collected based on surveys, remote sensing imagery or other data providing not only the total areas for each land management system, but also the specific transitions in land use and management over time on individual parcels of land. These are considered Approach 2 and 3 activity data in Chapter 3, and soil C stock changes are computed using formulation B of Equation 2.25. Formulation B contains a summation by land parcel (i.e., "p" represents land parcels in formulation B rather than the set of management systems "i") that allows the inventory compiler to compute the changes in C stocks on a land parcel by land parcel basis.

Special consideration is needed if using Approach 1 activity data (see Chapter 3) as the basis for estimating land-use and management effects on soil C stocks, using Equation 2.25. Approach 1 data do not track individual land transitions, and so SOC stock changes are computed for inventory time periods equivalent to D years, or as close as possible to D, which is 20 years in the Tier 1 method. For example, Cropland may be converted from full tillage to no-till management between 1990 and 1995, and Formulation A (see Box 2.1) would estimate a gain in soil C for that inventory time period. However, assuming that the same parcel of land remains in no-till between 1995 and 2000, no additional gain in C would be computed (i.e., the stock for 1995 would be based on no-till management and it would not differ from the stock in 2000 (SOC0), which is also based on no-till management).

If using the default approach, there would be an error in this estimation because the change in soil C stocks occurs over 20 years (i.e., D = 20 years). Therefore, SOC(0 -T) is estimated for the most distant time that is used in the inventory calculations up to D years before the last year in the inventory time periods (SOC0). For example, assuming D is 20 and the inventory is based on activity data from 1990, 1995, 2000, 2005 and 2010, SOC(0 _T) will be computed for 1990 to estimate the change in soil organic C for each of the other years, (i.e., 1995, 2000, 2005 and 2010). The year for estimating SOC(0 _T) in this example will not change until activity data are gathered at 2011 or later (e.g., computing the C stock change for 2011 would be based on the most distant year up to, but not exceeding D, which in this example would be 1995).

If transition matrices are available (i.e., Approach 2 or 3 activity data), the changes can be estimated between each successive year. From the example above, some no-till land may be returned to full tillage management between 1995 and 2000. In this case, the gain in C storage between 1990 and 1995 for the land base returned to full tillage would need to be discounted between 1995 and 2000. Further, no additional change in the C stocks would be necessary for land returned to full tillage after 2000 (assuming tillage management remained the same). Only land remaining in no-till would continue to gain C up to 2010 (i.e., assuming D is 20 years). Hence, inventories using transition matrices from Approach 2 and 3 activity data will need to be more careful in dealing with the time periods over which gains or losses of SOC are computed. See Box 2.2 for additional details. The application of the soil C estimation approach is much simpler if only using aggregated statistics with Approach 1 activity data. However, it is good practice for countries to use transition matrices from Approach 2 and 3 activity data if that information is available because the more detailed statistics will provide an improved estimate of annual changes in soil organic C stocks.

There may be some cases in which activity data are collected over time spans longer than the time dependence of the stock change factors (D), such as every 30 years with a D of 20. For those cases, the annual stock changes can be estimated directly between each successive year of activity data collection (e.g., 1990, 2020 and 2050) without over- or under-estimating the annual change rate, as long as T is substituted for D in Equation 2.25.

Organic soils

The basic methodology for estimating C emissions from organic (e.g., peat-derived) soils is to assign an annual emission factor that estimates the losses of C following drainage. Drainage stimulates oxidation of organic matter previously built up under a largely anoxic environment. Specifically, the area of drained and managed organic soils under each climate type is multiplied by the associated emission factor to derive an estimate of annual CO2 emissions (source), as presented in Equation 2.26:

Equation 2.26 Annual carbon loss from drained organic soils (CO2)

Where:

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

A = land area of drained organic soils in climate type c, ha

Note: A is the same area (Fos) used to estimate N2O emissions in Chapter 11, Equations 11.1 and 11.2

EF = emission factor for climate type c, tonnes C ha-1 yr-1

See the soil C sections in the land-use chapters for a detailed step-by-step guidance on the application of this method.

Comparison between use of Approach 1 aggregate statistics and Approach 2 or 3 activity data WITH TRANSITION MATRICES

Assume a country where a fraction of the land is subjected to land-use changes, as shown in the following table, where each line represents one land unit with an area of 1 Mha (F = Forest Land; C = Cropland; G = Grassland):

Land Unit ID

1990

1995

2000

2005

2010

2015

2020

1

F

C

C

C

C

C

C

2

F

C

C

C

G

G

G

3

G

C

C

C

C

G

G

4

G

G

F

F

F

F

F

5

C

C

C

C

G

G

G

6

C

C

G

G

G

C

C

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

1990

1995

2000

2005

2010

2015

2020

F (Mha)

2

0

1

1

1

1

1

G (Mha)

2

1

1

1

3

3

3

C (Mha)

2

5

4

4

2

2

2

SOC0 (Mt C)

458

436

442

442

462

462

462

SOC(0.T) (Mt C)

458

458

458

458

458

436

442

Mineral v J '

0

-1.1

-0.8

-

0.8

0.2

1.3

1.0

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

1990

1995

2000

2005

2010

2015

2020

SOC0 (Mt C) for unit 1

ll.0

l5.5

l4.0

l2.5

li.0

l1.0

li.0

SOC0 (Mt C) for unit 2

ll.0

l5.5

l4.0

l2.5

l5.0

ll.5

80.0

SOC0 (Mt C) for unit 3

81.0

l8.5

l6.0

l3.5

li.0

l3.5

l6.0

SOC0 (Mt C) for unit 4

81.0

81.0

80.0

l9.0

l8.0

ll.0

ll.0

SOC0 (Mt C) for unit 5

l1.0

li.0

li.0

li.0

l3.5

l6.0

l8.5

SOC0 (Mt C) for unit 6

l1.0

li.0

l3.5

l6.0

l8.5

l6.0

l3.5

SOC0 (Mt C)

458

453

449

445

447

451

456

SOC(0.T) (Mt C)

458

458

453

449

445

44l

451

Mineral v J '

0

-1.1

-0.8

-

0.8

0.5

0.8

1.0

Both methods yield different estimates of carbon stocks, and use of Approach 2 or 3 data with transition matrices would be more accurate than use of Approach 1 aggregate statistics. However, estimates of annual changes of carbon stocks would generally not be very different, as shown in this example. The effect of underlying data approaches on the estimates differ more when there are multiple changes in land-use on the same piece of land (as in land units 2, 3 and 6 in the example above). It is noteworthy that Approach 1, 2 and 3 activity data produce the same changes in C stocks if the systems reach a new equilibrium, which occurs with no change in land-use and management for a 20 year time period using the Tier 1 method. Consequently, no carbon stock increases or losses are inadvertently lost when applying the methods for Approach 1, 2 or 3 activity data, but the temporal dynamics do vary somewhat as demonstrated above.

Soil inorganic C

The effects of land-use and management activities on soil inorganic C stocks and fluxes are linked to site hydrology and depend on specific mineralogy of the soil. Further, accurate estimation of the effects requires following the fate of discharged dissolved inorganic C and base cations from the managed land, at least until they are fully captured in the oceanic inorganic C cycle. Thus, a comprehensive hydrogeochemical analysis that tracks the fate of dissolved CO2, carbonate and bicarbonate species and base cations (e.g., Ca and Mg) applied to, within, and discharged from, managed land over the long term is needed to accurately estimate net stock changes. Such an analysis requires a Tier 3 approach.

Tier 2 Approach: Incorporating country-specific data

A Tier 2 approach is a natural extension of the Tier 1 method that allows an inventory to incorporate country-specific data, while using the default equations given for mineral and organic soils. It is good practice for countries to use a Tier 2 approach, if possible, even if they are only able to better specify certain components of the Tier 1 default approach. For example, a country may only have data to derive country-specific reference C stocks, which would then be used with default stock change factors to estimate changes in soil organic C stocks for mineral soils.

Mineral soils

Country-specific data can be used to improve four components of the Tier 1 inventory approach for estimating stock changes in mineral soils, including derivation of region or country-specific stock change factors and/or reference C stocks, in addition to improving the specification of management systems, climate, or soil categories (e.g., Ogle et al., 2003; Vanden Bygaart et al., 2004; Tate et al., 2005). Inventory compilers can choose to derive specific values for all of these components, or any subset, which would be combined with default values provided in the Tier 1 method to complete the inventory calculations using Equation 2.25. Also, Tier 2 uses the same procedural steps for calculations as provided for Tier 1.

1) Defining management systems. Although the same management systems may be used in a Tier 2 inventory as found in the Tier 1 method, the default systems can be disaggregated into a finer categorization that better represents management impacts on soil organic C stocks in a particular country based on empirical data (i.e., stock change factors vary significantly for the proposed management systems). Such an undertaking, however, is only possible if there is sufficient detail in the underlying data to classify the land area into the finer, more detailed set of management systems.

2) Climate regions and soil types. Countries that have detailed soil classifications and climatic data have the option of developing country-specific classifications. Moreover, it is considered good practice to specify better climate regions and soil types during the development of a Tier 2 inventory if the new classification improves the specification of reference C stocks and/or stock change factors. In practice, reference C stocks and/or stock change factors should differ significantly among the proposed climate regions and soil types based on an empirical analysis. Note that specifying new climate regions and/or soil types requires the derivation of country-specific reference C stocks and stock change factors. The default reference C stocks and stock change factors are only appropriate for inventories using the default climate and soil types.

3) Reference C stocks. Deriving country-specific reference C stocks (SOCRef) is another possibility for improving an inventory using a Tier 2 approach (Bernoux et al., 2002). Using country-specific data for estimating reference stocks will likely produce more accurate and representative values. The derivation of country-specific reference soil C stocks can be done from measurements of soils, for example, as part of a country's soil survey. It is important that reliable taxonomic descriptions be used to group soils into categories. There are three additional considerations in deriving the country-specific values, including possible specification of country-specific soil categories and climate regions (i.e., instead of using the IPCC default classification), choice of reference condition, and depth increment over which the stocks are estimated. Stocks are computed by multiplying the proportion of organic carbon (i.e., %C divided by 100) by the depth increment (default is 30 cm), bulk density, and the proportion of coarse-fragment free soil (i.e., < 2mm fragments) in the depth increment (Ogle et al., 2003). The coarse fragment-free proportion is on a mass basis (i.e., mass of coarse fragment-free soil/total mass of the soil).

The reference condition is the land-use/cover category that is used for evaluating the relative effect of land-use change on the amount of soil C storage (e.g., relative difference in C storage between a reference condition, such as native lands, and another land use, such as croplands, forming the basis for FLU in Equation 2.25). In the Tier 1 method, the reference condition is native lands (i.e., non-degraded, unimproved lands under native vegetation), and it is likely that many countries will use this same reference in a Tier 2 approach. However, another land use can be selected for the reference, and this would be considered good practice if it allows for a more robust assessment of country-specific reference stock values. Reference stocks should be consistent across the land uses (i.e., Forest Land, Cropland, Grassland, Settlements, and Other Land), requiring coordination among the various teams conducting soil C inventories for the AFOLU Sector.

Another consideration in deriving country-specific reference C stocks is the possibility of estimating C storage to a greater depth in the soil (i.e., lower in the profile). Default stocks given in Table 2.3 account for soil organic C in the top 30 cm of a soil profile. It is good practice to derive reference C stocks to a greater depth if there is sufficient data, and if it is clear that land-use change and management have a significant impact over the proposed depth increment. Any change in the depth for reference C stocks will require derivation of new stock change factors, given that the defaults are also based on impacts to a 30 cm depth.

4) Stock change factors. An important advancement for a Tier 2 approach is the estimation of country-specific stock change factors (FLU, FMG and FI). The derivation of country-specific factors can be accomplished using experimental/measurement data and computer model simulation. In practice, deriving stock change factors involves estimating a response ratio for each study or observation (i.e., the C stocks in different input or management classes are divided by the value for the nominal practice, respectively).

Optimally, stock change factors are based on experimental/measurement data in the country or surrounding region, by estimating the response ratios from each study and then analyzing those values using an appropriate statistical technique (e.g., Ogle et al., 2003 and 2004; VandenBygaart et al., 2004). Studies may be found in published literature, reports and other sources, or inventory compilers may choose to conduct new experiments. Regardless of the data source, it is good practice that the plots being compared have similar histories and management as well as similar topographic position, soil physical properties and be located in close proximity. Studies should provide C stocks (i.e., mass per unit area to a specified depth) or the information needed to estimate SOC stocks (i.e., percent organic matter together with bulk density; proportion of rock in soil, which is often measured as the greater than 2mm fraction and by definition contains no soil organic C). If percent organic matter is available instead of percent organic carbon, a conversion factor of 0.58 can be used to estimate the C content. Moreover, it is good practice that the measurements of soil C stocks are taken on an equivalent mass basis (e.g., Ellert et al., 2001; Gifford and Roderick, 2003). In order to use this method, the inventory compiler will need to determine a depth to measure the C stock for the nominal land use or practice, such as native lands or conventional tillage. This depth will need to be consistent with the depth for the reference C stocks. The soil C stock for the land-use or management change is then measured to a depth with the equivalent mass of soil.

Another option for deriving country-specific values is to simulate stock change factors from advanced models (Bhatti et al., 2001). To demonstrate the use of advanced models, simulated stock change factors can be compared to with measured changes in C stocks from experiments. It is good practice to provide the results of model evaluation, citing published papers in the literature and/or placing the results in the inventory report. This method is considered a Tier 2 approach because it relies on the stock change factor concept and the C estimation method elaborated in the Tier 1 approach.

Derivation of country-specific management factors (FMG) and input factors (FI), either with empirical data or advanced models, will need to be consistent with the management system classification. If more systems are specified for the inventory, unique factors will need to be derived representing the finer categories for a particular land use.

Another consideration in deriving country-specific stock change factors is their associated time dependence (D in Equation 2.25), which determines the number of years over which the majority of a soil organic C stock change occurs, following a management change. It is possible to use the default time dependence (D) for the land-use sector (e.g., 20 years for cropland), but the dependence can be changed if sufficient data are available to justify a different time period. In addition, the method is designed to use the same time dependence (D) for all stock change factors as presented in Equation 2.25. If different periods are selected for FLU, FMG and FI, it will be necessary to compute the influence of land use, management and inputs separately and divide the associated stock change dependence. This can be accomplished by modifying Equation 2.25 so that SOC at time T and 0-T is computed individually for each of the stock change factors (i.e., SOC is computed with FLU only, then computed with FMG, and finally computed with FI). The differences are computed for the stocks associated with land use, management, and input, dividing by their respective D values, and then the changes are summed.

Changes in C stocks normally occur in a non-linear fashion, and it is possible to further develop the time dependence of stock change factors to reflect this pattern. For changes in land use or management that cause a decrease in soil C content, the rate of change is highest during the first few years, and progressively declines with time. In contrast, when soil C is increasing due to land-use or management change, the rate of accumulation tends to follow a sigmoidal curve, with rates of change being slow at the beginning, then increasing and finally decreasing with time. If historical changes in land-use or management practices are explicitly tracked by re-surveying the same locations (i.e., Approach 2 or 3 activity data, see Chapter 3), it may be possible to implement a Tier 2 method that incorporates the non-linearity of changes in soil C stock.

Similar to time dependence, the depth over which impacts are measured may vary from the default approach. However, it is important that the reference C stocks (SOCRef) and stock change factors (FLU, FMG, FI) be determined to a common depth, and that they are consistent across each land-use sector in order to deal with conversions among uses without artificially inflating or deflating the soil C stock change estimates. It is good practice to document the source of information and underlying basis for the new factors in the reporting process.

Organic soils

A Tier 2 approach for CO2 emissions associated with drainage of organic soils incorporates country-specific information into the inventory to estimate the emissions using Equation 2.26 (see the previous Tier 1 section for additional discussion on the general equations and application of this method). Also, Tier 2 uses the same procedural steps for calculations as provided for Tier 1. Potential improvements to the Tier 1 approach may include: 1) a derivation of country-specific emission factors, 2) specification of climate regions considered more suitable for the country, or 3) a finer, more detailed classification of management systems attributed to a land-use category.

Derivation of country-specific emission factors is good practice if experimental data are available. Moreover, it is good practice to use a finer classification for climate and management systems if there are significant differences in measured C loss rates among the proposed classes. Note that any derivation must be accompanied with sufficient land-use/management activity and environmental data to represent the proposed climate regions and management systems at the national scale. Developing the Tier 2 inventory for organic soils has similar considerations as mineral soils discussed in previous section.

Country-specific emission factors for organic soils can be based on measurements of annual declines in C stocks for the whole soil profile. Another alternative is to use land subsidence as a surrogate measure for C loss following drainage (e.g., Armentano and Menges, 1986). C loss is computed as a the fraction of the annual subsidence attributed to oxidation of organic matter, C content of the mineralized organic matter, and bulk density of the soil (Ogle et al., 2003).

Soil inorganic C

See discussion for this sub-category under Tier 1. Tier 3: Advanced estimation systems

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

Mineral soils

Model-based approaches can use mechanistic simulation models that capture the underlying processes driving carbon gains and losses from soils in a quantitative framework, such as the influence of land use and management on processes controlling carbon input resulting from plant production and litter fall as well as microbial decomposition (e.g., McGill, 1996; Smith et al., 1997b; Smith et al., 2000; Falloon and Smith, 2002; and Tate et al., 2005). Note that Tier 3 methods provide the only current opportunity to explicitly estimate the impact of soil erosion on C fluxes. In addition, Tier 3 model-based approaches may represent C transfers between biomass, dead biomass and soils, which are advantageous for ensuring conservation of mass in predictions of C stock changes in these pools relative to CO2 removals and emissions to the atmosphere.

Tier 3 modelling approaches are capable of addressing the influence of land use and management with a dynamic representation of environmental conditions that affect the processes controlling soil C stocks, such as weather, edaphic characteristics, and other variables. The impact of land use and management on soil C stocks can vary as environmental conditions change, and such changes are not captured in lower Tiers, which may create biases in those results. Consequently, Tier 3 approaches are capable of providing a more accurate estimation of C stock changes associated with land-use and management activity.

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

In addition to model-based approaches, Tier 3 methods afford the opportunity to develop a measurement-based inventory using a similar monitoring network as needed for model evaluation. However, measurement networks, which serve as the basis for a complete inventory, will have a considerably larger sampling density to minimize uncertainty, and to represent all management systems and associated land-use changes, across all climatic regions and major soil types (Sleutel et al., 2003; Lettens et al., 2004). Measurement networks can be based on soil sampling at benchmark sites or flux tower networks. Flux towers, such as those using eddy covariance systems (Baldocchi et al., 2001), constitute a unique case in that they measure the net exchange of CO2 between the atmosphere and land surface. Thus, with respect to changes in C stocks for the soil pool, flux tower measurement networks are subject to the following caveats: 1) towers need to occur at a sufficient density to represent fluxes for the entire country; 2) flux estimates need to be attributed to individual land-use sectors and specific land-use and management activities; and 3) CO2 fluxes need to be further attributed to individual pools including stock changes in soils (also biomass and dead organic matter). Additional considerations about soil measurements are given in the previous section on Tier 2 methods for mineral soils (See stock change factor discussion).

It is important to note that measurement based inventories represent full C estimation approaches, addressing all influences on soil C stocks. Partial estimation of only land-use and management effects may be difficult.

Organic soils

Similar to mineral soils, CO2 emissions attributed to land use and management of organic soils can be estimated with a model or measurement based approach. Dynamic, mechanistic-based models will typically be used to simulate underlying processes, while capturing the influence of land use and management, particularly the effect of variable levels of drainage on decomposition. The same considerations that were mentioned for mineral soils are also important for model- and measurement-based approaches addressing soil C stock changes attributed to management of organic soils.

Soil inorganic C

A Tier 3 approach may be further developed to estimate fluxes associated with management impacts on soil inorganic C pools. For example, irrigation can have an impact on soil inorganic C stocks and fluxes, but the direction and magnitude depends on the source and nature of irrigation water and the source, amount, and fate of discharged dissolved inorganic C. In arid and semi-arid regions, gypsum (CaSO4 ' 2H2O) amendments can lead to an increase in soil inorganic C stocks depending on the amount of Ca2+ that replaces Na+ on soil colloids, relative to reaction with bicarbonate and precipitation of calcite (CaCO3). Other land-use and management activities, such as deforestation/afforestation and soil acidifying management practices can also affect soil inorganic C stocks. However, these changes can cause gains or losses of C in this pool depending on site-specific conditions and the amount attributable to the activity can be small.

Few models currently exist for estimating changes in soil inorganic C due to land use and management, and so a Tier 3 approach may require considerable time and resources to implement. Where data and knowledge are sufficient and activities that significantly change soil inorganic C stocks are prevalent, it is good practice for countries to do a comprehensive hydro-geochemical analysis that includes all important land-use and management activities to estimate their effect on soil inorganic C stocks. A modelling approach would need to isolate the land-use and management activities from non-anthropogenic effects. Alternatively, a measurement-based approach can be used by periodically sampling benchmark sites in managed lands for determining inorganic C stocks in situ, or possibly CO2 fluxes, in combination with a monitoring network for soil organic C as discussed above for mineral soils. However, the amount and fate of dissolved inorganic C would require further measurements, modelling, or simplifying assumptions, such as all leaching losses of inorganic C are assumed to be emitted as CO2 to the atmosphere.

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