A model-based inventory of sinks and sources of CO 2 in agricultural soils in Switzerland:

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Model-based soil carbon inventory | March 2013

A model-based inventory of sinks and sources of CO ₂ in agricultural soils in Switzerland:

development of a concept

Autoren

Katharina Köck, Jens Leifeld, Jürg Fuhrer, Lufthygiene/Klima, Agroscope

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Impressum

Herausgeberin Forschungsanstalt Agroscope

Reckenholzstrasse 191, CH-8046 Zürich

Telefon +41 (0)44 377 71 11, Fax +41 (0)44 377 72 01 info@agroscope.ch; www.agroscope.ch

Titelbild: Katharina Köck – ART ISBN: 978-3-905733-28-0 Copyright: 2013 ART

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List of frequently used acronyms

Acronym English German

AFOLU Agriculture, Forestry and Other Land Use Landwirtschaft, Forstwirtschaft und andere Landnutzungen

ART Research Station Agroscope Reckenholz- Tänikon

Forschungsanstalt Agroscope Reckenholz- Tänikon

BDM Biodiversity Monitoring Switzerland Biodiversitätsmonitoring Schweiz

BEK Soil Suitability Map Bodeneignungskarte

C Carbon Kohlenstoff

CC Combination Category Kombinationskategorie

CO2 Carbon dioxide Kohlendioxid

FADN Swiss Farm Accountancy Data Network Zentrale Auswertung von Buchhaltungsdaten FOAG Federal Office for Agriculture Bundesamt für Landwirtschaft

FOEN Federal Office for the Environment Bundesamt für Umwelt

GHG Greenhouse Gas Treibhausgas

GHGI Greenhouse Gas Inventory Treibhausgasinventar

GIS Geographic Information System Geographisches Informationssystem GRUDAF Principles of Fertilization in Arable and

Forage Crop Production

Grundlagen für die Düngung im Acker- und Futterbau

IP Integrated Production Integrierte Produktion

IPCC Intergovernmental Panel on Climate Change

Zwischenstaatlicher Ausschuss für Klimaänderungen, Weltklimarat KABO Cantonal Soil Monitoring Network Kantonale Bodenbeobachtung

KP Kyoto Protocol Kyoto Protokoll

LULUCF Land Use, Land-Use Change and Forestry Landnutzung, Landnutzungsänderung und Forstwirtschaft

N Nitrogen Stickstoff

N2O Nitrous oxide Lachgas

NABO Swiss Soil Monitoring Network Nationale Bodenbeobachtung NABODAT National Soil Information System Nationales Bodeninformationssystem

NFI National Forest Inventory Landesforstinventar

NIR National Inventory Report Nationaler Inventarbericht

OM Organic Matter Organische Substanz

PEP Proof of Ecological Performance Ökologischer Leistungsnachweis ÖLN

SBV Swiss Farmers’ Union Schweizerischer Bauernverband

SFSO Swiss Federal Statistical Office Bundesamt für Statistik

SOC Soil Organic Carbon Organischer Bodenkohlenstoff

SOM Soil Organic Matter Organische Substanz des Bodens

UNFCCC United Nations Framework Convention on Climate Change

Rahmenübereinkommen der Vereinten Nationen über Klimaänderungen

Acknowledgments

We are grateful to Daniel Bretscher, Chloé Wüst and Armin Keller (Agroscope Reckenholz-Tänikon, ART)

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Executive summary

A concept for a Tier 3 model-based inventory of SOC in Swiss agricultural soils, i.e. Cropland and Grassland land-use categories, for GHG reporting under the UNFCCC is developed. Additional modifications needed for GHG reporting of Article 3.4 activities under the KP, i.e. cropland management and grazing land management, are presented. The basis for the development of the concept is: a review of Tier 3 model-based methods in other countries and a review of data sets available for a Swiss inventory, as well as the IPCC 2006 guidelines (IPCC, 2006). The review of methods used in other countries for inventory reporting reveals that currently only a few countries apply a Tier 3 model-based method for the inventory of soil C in agricultural soils (Denmark, Sweden, USA, Australia; status: reporting of 2011).

However, these models are only applied to mineral soils, whereas Tier 1 or Tier 2 methods are used for organic soils. Thus, most countries combine methods of different tiers in their reporting of soil C.

The inventory system suggested here is a combination of Tier 3 and lower tier methods. For Cropland, the Grassland category ‘permanent grassland’ and land-use changes between these land-use categories, a Tier 3 method will be developed. Due to the lack of appropriate data or models, SOC stocks and stock changes of all agricultural areas on organic soils, as well as of all grassland categories except for

‘permanent grassland’, will be estimated with the Tier 1 and Tier 2 methods used to date. However, the system will be developed in such a way that allows for future updates of these categories to higher Tier methods.

In the following, the steps for the development of a Tier 3 model-based inventory system for C stock changes of agricultural soils in Switzerland are summarized (Figure 1).

Model selection. From a large number of potentially suitable models, the three models RothC, Yasso07 and CCB have been chosen based on their agreement with the specified model requirements and data availability. These three models are soil models with no dynamic vegetation components. The models work on a monthly or annual time step. Relatively simple models have been chosen, as the required input data corresponds to data available at the field and regional scale in Switzerland. It would be possible to identify one model from these three as the most suitable, based on model evaluation (described below). On the other hand, it might also be suitable to use the three models in parallel as part of a model uncertainty assessment.

Identification of model inputs for evaluation at the field and plot scale. Data for model evaluation from long-term agricultural experiments and from monitoring sites have been identified; these are either i) already available, ii) will be made available by the responsible research groups, or iii) may be applied in cooperation with the responsible research groups.

Model evaluation at the field and plot scale. Several of the identified data sets are readily available, whereas for others the collection of missing data and further measurements may be needed. These data sets will be used as calibration and validation sites, according to the availability of meteorological, soil and agricultural management data of each data set. An important task is the development of a Swiss specific method for the calculation of C input to the soil from yield data, as a comparison of published allometric relations revealed that results deviated largely and that there is no readily available method. To ensure consistency in soil C data measured with different analytical techniques, the derivation of correction factors from re-measurements of archived soil samples might also be needed. Initialization of the SOC pools of the

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model will be done with the ‘inverse mode’ of the models, i.e. by performing spin-up simulations to reach measured initial SOC contents under the assumption of steady-state soil C pools. The RothC model further offers the possibility to either use, where available, ¹⁴C measurements as additional constraint or to initialize the model soil C pools with measured soil C fractions not requiring the assumption of steady-state conditions. Two methods for model evaluation are suggested; these differ in complexity and thus will be chosen depending on available time, man and computer power.

- In the first method, a comparison of model estimates and measurements for the calibration sites is performed by quantifying model-measurement agreement with the help of a statistically based performance measure. If an improvement of model results is expected, a simple model calibration to measured data, for example in a trial-and-error fashion, will be performed. Further, a simple sensitivity analysis is recommended to gain insight in the sensitivity of model results to parameter values.

- The second method addresses model uncertainty and includes more sophisticated methods for calibration and estimation of parameter uncertainty. Several automated techniques are available.

This method has the advantage that model results are given with their corresponding uncertainty.

Further, the derived model parameter uncertainties can be transferred to the uncertainty estimation of the regional model application.

Figure 1: Scheme illustrating the development steps needed for the Tier 3 model-based inventory system. The numbers in the boxes denote the chapters in which these steps are explained.

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For both methods, the final step in model evaluation is the simulation with the calibrated model for the validation sites. This will demonstrate whether or not the model is suitable for the simulation of SOC stock changes in agricultural soils in Switzerland. In the case that model calibration and validation are not successful, either model structures could be improved, or the model would have to be rejected from the list of possible models. A comparison of model evaluation results for the three models will be used to rank the performance of the models. Based on these results, the best performing model will be chosen for the regional application; alternatively all three models will be used in parallel as an uncertainty assessment about model structures.

Identification of model inputs for the regional model application. An overview of spatio-temporal data from the fields of climate, soil, land use and agriculture for Switzerland is given and the data are evaluated for their possible use in regional model simulations. Currently available data sets largely fulfill the requirements concerning spatial and temporal coverage. However, some gaps in available data are identified, such as in detailed country-wide soil data. Thus, the available data sets allow for a stratified simulation of SOC stocks and stock changes in Switzerland, though not at high spatial resolution.

Model implementation at the regional scale. Spatial stratification of the area to be simulated is a key consideration. We suggest defining irregular polygons from a combination of production regions and soil texture classes. An intersection of agricultural production regions and NFI regions gives a maximum of 20 production regions. Soil texture classes can be derived from the soil suitability map, with a maximum of 12 classes. For each production region, a set of meteorological data, which can be obtained from MeteoSwiss gridded or station data, have to be provided; agricultural management regimes for cropland and grassland also have to be defined. Cropland management regimes will be based on a number of typical crop rotations. These can be modeled from the distribution of crop types in agricultural production regions as reported by SFSO and from crop rotation rules as published for example in the framework of PEP.

Grassland management regimes have to account for grazing, cutting and a combination of these two as well as the different grassland management intensities reported in SFSO censuses. To derive input data sets for these management regimes, management dates such as sowing and harvest dates have to be defined, as well as the C input to the soil. A range of data sources such as SFSO, SBV, FADN and GRUDAF will provide agricultural data needed for the definition of these management data sets. For the estimation of C input to the soil by organic fertilizers, the data have to be in accordance with fertilization data used in the Agriculture sector of the GHGI, which is mainly simulated with the Agrammon model. C input to the soil from plant residues will be calculated from yield data from FADN, SBV and GRUDAF using the Swiss-specific relations developed before. A sufficient number of SOC measurements from the initial year of simulation from several locations within these strata have to be gathered from sources such as the national and cantonal soil monitoring schemes, and will serve as initial SOC contents for model initialization with spin-up runs for all strata (production region intersected by soil texture class). After preparation of the input data sets and initialization of the model SOC pools for all strata, the models will be run for these strata for both cropland and grassland, as well as for areas under land-use change. For each stratum and land- use type, a number of runs for different agricultural management regimes will be performed meaning that the mean SOC stock changes and their variance due to the variability in agricultural management can be calculated. Simulated SOC stock changes can be up-scaled to a national estimate with the corresponding areas of the strata. To derive uncertainty in model estimates due to input data uncertainty, a Monte Carlo analysis is suggested. Such an analysis can also account for model parameter uncertainty, if such parameter uncertainty estimates are derived along with model evaluation at the field scale.

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Evaluation with independent data of the regional model application. A data set of repeated SOC measurements across the country which is independent from the data set used for model evaluation at the field and plot scale has to be collected and the consistency of SOC measurements ensured. Possible data sources in Switzerland are the cantonal and national soil monitoring networks. These measurements have to be georeferenced to match the sample points to the strata, and uncertainties of measured SOC stock changes must be provided to compare to differences in model data and measurements. The comparison of model results with measurements will reveal whether the system is appropriate to be used in inventory reporting or whether further improvements regarding model implementation or input data are needed.

Reporting and documentation. Inventory results need to be reported in a systematic and transparent manner and documentation needs to accompany all development steps described above.

Finally, a suggested time schedule for the implementation of the inventory system comprising a 3-year period as well as possible further improvements which may be realized in future inventory development, is given in the report.

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1. Introduction

Soils are an important storage of organic carbon (C), amounting globally to approximately 1’500 Pg C (Batjes, 1996; Post et al., 1982). The amount of C stored in soil depends on site conditions such as climate (Post et al., 1982), and can change rapidly, e.g., due to human disturbances such as land-use changes (Poeplau et al., 2011). Thus, soils under agricultural land use can become either sources or sinks for carbon dioxide (CO2) for a certain period (Guo and Gifford, 2002). Agricultural management additionally affects soil organic carbon (SOC) storage, with several measures expected to lead to a potential for C sequestration by agricultural soils (Freibauer et al., 2004).

Yearly values of C stock changes from soils, as well as from biomass and dead organic matter, under different land uses and land-use changes are submitted in national greenhouse gas (GHG) inventory reporting under the United Nations Framework Convention on Climate Change (UNFCCC) by Annex I Parties. These emissions or removals are reported within the Land Use, Land-Use Change and Forestry (LULUCF) sector, which is one of seven sectors from which emissions are reported. Under Article 3.4 of the Kyoto Protocol (KP), Parties may additionally elect human-induced activities within the LULUCF sector to account for corresponding anthropogenic GHG emissions and removals for the first commitment period (2008-2012). Such human-induced activities include forest management, cropland management, grazing land management and revegetation (UNFCCC, 2012). For the second commitment period, Parties may further elect the activity wetland drainage and rewetting (document FCCC/KP/CMP/2011/10/Add.1).

Guidelines for National Greenhouse Gas Inventories and Good Practice Guidance provided by the Intergovernmental Panel on Climate Change (IPCC) describe standard methods for estimating SOC stock changes through time. Currently, the 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry (IPCC, 2003) is adopted for reporting within the LULUCF sector. However, updated Guidelines have been published (IPCC, 2006), where the previously used sectors Agriculture and LULUCF are combined into one sector, the Agriculture, Forestry and Other Land Use sector (AFOLU). These 2006 Guidelines will be used as the reference for the development of the concept within this report. Concerning reporting under the KP, the reference applied here is still the 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry, as the ‘2013 Revised Supplementary Methods and Good Practice Guidance Arising from the Kyoto Protocol’ are only currently being prepared and should be published in October 2013 (IPCC, 2012a).

As anthropogenic emissions and removals have to be reported in the GHG inventory (GHGI), emissions and removals on managed land are taken as a proxy in the AFOLU sector (IPCC, 2006; IPCC, 2010).

Three methods differing in complexity, the so-called tiers are given. Tier 1 are default methods, Tier 2 methods employ some country-specific parameters, and Tier 3 methods are most complex applying measurements and/or modeling (for a more detailed description of Tier methods see Table 1). Higher tier methods (Tier 2 and 3) are regarded as the more accurate and precise methods, although requiring increased resources, e.g., considerably more activity data (IPCC, 2006; Smith et al., 2012). A combination of tiers for different pools or soil types can be used. The UNFCCC encourages countries to develop Tier 3 methods for national GHG reporting. Smith et al. (2012) also stress that the higher tier methods for the estimation of C in soils will assist future policy makers to steer land use and management activities which have an effect on C stock changes.

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Table 1: Framework of Tier structure for Agriculture, Forestry and Other Land Use (AFOLU) methods (cited literally from IPCC, 2006).

Tier 1 methods are designed to be the simplest to use, for which equations and default parameter values (e.g., emission and stock change factors) are provided in this volume [IPCC guidelines]. Country-specific activity data are needed, but for Tier 1 there are often globally available sources of activity data estimates (e.g., deforestation rates, agricultural production statistics, global land cover maps, fertilizer use, livestock population data, etc.), although these data are usually spatially coarse.

Tier 2 can use the same methodological approach as Tier 1 but applies emission and stock change factors that are based on country- or region-specific data, for the most important land-use or livestock categories.

Country-defined emission factors are more appropriate for the climatic regions, land-use systems and livestock categories in that country. Higher temporal and spatial resolution and more disaggregated activity data are typically used in Tier 2 to correspond with country-defined coefficients for specific regions and specialized land-use or livestock categories.

At Tier 3, higher order methods are used, including models and inventory measurement systems tailored to address national circumstances, repeated over time, and driven by high-resolution activity data and disaggregated at sub-national level. These higher order methods provide estimates of greater certainty than lower tiers. Such systems may include comprehensive field sampling repeated at regular time intervals and/or GIS-based systems of age, class/production data, soils data, and land-use and management activity data, integrating several types of monitoring. Pieces of land where a land-use change occurs can usually be tracked over time, at least statistically. In most cases these systems have a climate dependency, and thus provide source estimates with interannual variability. Detailed disaggregation of livestock population according to animal type, age, body weight etc., can be used. Models should undergo quality checks, audits, and validations and be thoroughly documented.

GHG reporting within the AFOLU/LULUCF sector is done for six IPCC land-use categories: Forest Land, Cropland, Grassland, Wetlands, Settlements and Other Land. Furthermore, for the estimation of changes in C stocks in soils, it is differentiated between mineral and organic soils to account for differences in the influence of land use and management on SOC. Organic soils are defined by a minimum thickness of the organic horizon of 10 cm and minimum percentages of organic C between 12 and 20 percent depending on organic horizon thickness, water saturation and clay content (IPCC, 2006). Soils not matching these criteria are defined as mineral soils.

The approach currently employed for the calculation of SOC stocks and stock changes in Cropland and Grassland soils in Switzerland combines Tier 1 and Tier 2 methods. The expert review team of the UNFCCC have repeatedly recommended developing higher-tier methods (“repeated measurements or modeling”, UNFCCC, 2011a) to estimate C stock changes in mineral soils for Cropland remaining Cropland (UNFCCC, 2009; UNFCCC, 2010; UNFCCC, 2011a). Thus, the aim of this report is to develop a concept for a Tier 3 model-based inventory of SOC in agricultural, i.e. cropland and grassland, soils in Switzerland.

The framework for the development of such a system is set by the approach applied in the current GHGI of Switzerland, the special properties of the country, and the guidelines provided by IPCC. This framework will be described as the basis for the subsequent specification of requirements for the Tier 3 inventory system to be developed.

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1.1. Current SOC inventory method for Switzerland

Currently, Switzerland employs a combination of Tier 1 and Tier 2 methods for the calculation of C emissions and removals in the LULUCF sector (FOEN, 2012). Land-use data are provided by Swiss Land Use Statistics (AREA) which derives land-use and land-cover categories from three land-use surveys using aerial photos (AREA). This land-use data set is geographically explicit with a spatial resolution of one hectare. This method corresponds to Approach 3 for representing land areas following the IPCC guidelines¹. The re-evaluation of AREA data with a new nomenclature for the three surveys is ongoing, with a spatial coverage of 72% of Switzerland in 2012 and an expected full coverage in 2013 (FOEN, 2012).

Thus, the currently used land-use data rely on a spatial extrapolation for the complete area as well as on a temporal interpolation to determine data for each year.

The Swiss GHGI differentiates the six main IPCC land-use categories (Forest Land, Cropland, Grassland, Wetlands, Settlements and Other Land). All land-use categories except Cropland and Other Land are further subdivided. For example, Grassland consists of seven combination categories (CC): CC31 Permanent Grassland; CC32 Shrub Vegetation; CC33 Vineyards, Low-Stem Orchards, Tree Nurseries;

CC34 Copse; CC35 Orchards; CC36 Stony Grassland; and, CC37 Unproductive Grassland (Figure 2). For these land-use categories and subcategories, the land-use and land-use change matrix is calculated from AREA data. There is no dominating land use in Switzerland. One third of the area is under Forest land, one third of the area is under Grassland, and 10% is Cropland. The remaining area is distributed between Wetlands, Settlements and Other Land.

Figure 2: Distribution of Swiss land area to the six land-use categories (in 2010). The total area of Switzerland is 4’128’400 ha. For Grassland, the distribution of land area among the Grassland combination-categories is also shown.

(Data from FOEN, 2012).

1 Approach 1: total land use area, no data on conversions between land uses; Approach 2: total land-use area, including changes between categories; Approach 3: spatially explicit land-use conversion data IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change, Japan.

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Furthermore, three criteria are used for spatial stratification within the land-use categories (FOEN, 2012).

These criteria are altitude, forest production region, as well as soil type. Cropland is so far only stratified b y soil types (mineral, organic), whereas altitudinal zones are applied to Forest land and Grassland under cultivation. Forest land is further stratified according to the five National Forest Inventory (NFI) production regions which differ in growth and production conditions (Brändli, 2010): the Jura, the Central Plateau, the Pre-Alps, the Alps and the Southern Alps (Table 2 and Figure 3).

Switzerland spans a wide altitudinal range from 200 m to 4600 m a.s.l. In the current GHGI Cropland is not differentiated for the three altitude zones. However, the National Inventory Report (NIR) gives an overview of the distribution of Cropland area to the altitudinal zones (Table 3). In 1990, about two thirds of the area of Cropland (CC21) fell into the lowest altitude class (<600 m a.s.l.), one third into the altitude class 600 to 1200 m a.s.l. and less than 1% into the highest altitude class (>1200 m a.s.l.). The stratification of permanent Grassland (CC31) is different, with about 40% each in the altitude belts higher than 1200 m a.s.l. and 600 to 1200 m a.s.l. More than 70% of the Cropland area belonged to the Central Plateau, 18% to the Jura, and only 11% to the Pre-Alps and Alps regions (FOEN, 2012). For Grassland, 47% of the area was located in the Alps.

Table 2: Spatial stratification applied in the LULUCF chapter of Switzerland’s GHGI (FOEN, 2012).

Altitude zones (Forest land and Grassland)

Forest production regions (Forest land)

Soil types (Forest land, Cropland, Grassland)

1 < 601 m 1 Jura 0 Mineral soil

2 601 – 1200 m 2 Central Plateau 1 Organic soil

3 > 1200 m 3 Pre-Alps

4 Alps

5 Southern Alps

Figure 3: Production regions of Switzerland according to the National Forest Inventory (Data: Swiss National Forest Inventory, © 2012 Eidg. Forschungsanstalt WSL, CH-8903 Birmensdorf. Base information: swisstopo, © 1990).

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Table 3: Areas in kha of the Cropland (CC21) and Grassland (CC31: Permanent Grassland; CC32:

Shrub Vegetation; CC33: Vineyards, Low-stem Orchards, Tree Nurseries; CC34: Copse; CC35:

Orchards; CC36: Stony Grassland; CC37: Unproductive Grassland) combination categories in 1990 (FOEN, 2012).

Altitude (m a.s.l.)

Cropland Grassland

CC21 CC31 CC32 CC33 CC34 CC35 CC36 CC37

< 601 307.2 154.6 1.5 21.9 36.9 1.3 0.3 2.7

601-1200 135.5 368.5 5.5 4.8 38.9 0.4 1.7 1.5

> 1200 0.4 429.8 137.7 0 30.4 0 160.1 59.6

Total (kha) 443 953 144.7 26.7 106.1 1.6 162.2 63.7

Total (%) 100.0 65.4 9.9 1.8 7.3 0.1 11.1 4.4

The Tier 2 methodology for calculating C emissions and removals in the LULUCF sector employs country- specific emission factors and C stock values (FOEN, 2012). For Forest Land, these were derived from three Swiss National Forest Inventories. For the other land-use categories, particular research activities, surveys and measurements in the fields of agriculture (Cropland, Grassland) and nature conservation (Wetlands) served as sources for the derivation of country-specific values for emission factors and C stocks. IPCC default values and expert judgment are additionally applied. Total C fluxes in living biomass, dead organic matter and soil for all cells of the land-use change matrix are calculated. These C fluxes consist of emissions and removals under constant land use as well as stock changes due to conversion of land use.

For changes in soil C stocks due to a land-use conversion the default IPCC conversion time of 20 years is applied.

In the Swiss GHGI, the following agricultural categories are key categories in 2009 and 2010 (FOEN, 2011b; FOEN, 2012), i.e. categories which have a significant influence on the total GHGI (IPCC, 2006):

CO2 from Cropland remaining Cropland, CO2 from Grassland remaining Grassland and CO2 from Land converted to Grassland. Land converted to Cropland is not a key category in 2009 and 2010.

For the agricultural land-use categories (Cropland, Grassland), C stocks in living biomass are calculated from yield data. For Cropland, this value varies annually as reported in agricultural statistics, whereas for the Grassland CCs, constant values, differentiated by altitude are applied. For soil C stocks, values have been derived from Leifeld et al. (2003) and Leifeld et al. (2005). For mineral soils, specific values for Cropland and all spatial stratifications of the Grassland CCs are given, whereas for organic soils the same C stock value is used for all Cropland and Grassland CCs. For mineral soils under constant land use C stocks are assumed to be in balance and thus no changes are occurring. For organic soils under constant land use, CO2 emission factors were derived from measurements in Europe including Switzerland according to Leifeld et al. (2003), Leifeld et al. (2005) and Leifeld (2009). For cropland and managed grasslands, the same value is applied, whereas a smaller value is used for weakly managed grasslands.

Thus, for Cropland and Grassland under constant land use, C mineralization on organic soils is the highest contributor of CO2 emissions from agricultural soils, although the corresponding areas are small.

Measurements of the Swiss Soil Monitoring Network (Nationale Bodenbeobachtung, NABO) indicate that mineral soils of Cropland remaining Cropland and Grassland remaining Grassland have been neither a net source nor a sink of C during the last 20 years. This data set supports the assumption of no changes in mineral soil C stocks in Cropland and Grassland (FOEN, 2012).

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Switzerland has decided to account for forest management under Article 3.4 of the KP (FOEN, 2012), but not for cropland management, grazing land management, or revegetation. The NIR contains a supplementary part on KP LULUCF reporting, covering activities under Article 3.3 (Afforestation, Reforestation, and Deforestation) and forest management under Article 3.4 of the KP. There, general information, e.g., definitions of forest and the reported activities, as well as information on specific KP reporting methodologies for the determination of activity data and for the calculation of C stock change estimates, and GHG emission and removal estimates, are given.

A number of category-specific improvements are planned in the LULUCF sector of the Swiss GHGI. For Forest Land, the Yasso07 model will be used to simulate C stock changes in dead organic matter (dead wood and litter) and soil organic matter (SOM), with first results being reported in the 2013 GHGI submission. For Forest Land, Cropland and Grassland, it is intended to expand the use of SOC data provided by NABO in future submissions. For the Cropland, Grassland and Wetland categories, a project started in November 2011 to identify the areas of (drained) fens and raised bogs beyond the data currently available for the National GHGI. The current study is also listed as a planned improvement for the quantification of C stocks and C stock changes in agricultural soils.

1.2. Special properties of the country

Some special properties of Switzerland, which need to be taken into account for the future inventory system to be developed, are already accounted for in the current inventory system, e.g., the large range in altitude (see preceding section). Further, steep climatic gradients and agricultural structures have to be reflected by the system.

The Swiss climate is strongly influenced by the Atlantic Ocean, with moist and maritime air transported by the predominantly westerly winds to Switzerland (MeteoSwiss, 2008). Thus, precipitation in most areas of the country is sufficient. However, the Alps have a large impact on climate, resulting in dry inner-alpine areas (e.g., Valais, Engadin) and climatic differences between northern and southern Switzerland.

The agricultural area of Switzerland comprises 40% of the area of the country, with three quarters of it belonging to the Grassland land-use category (Figure 2). Agricultural policies in Switzerland have changed considerably over the last 20 years, mainly due to financial support to national programs from the Swiss federal government since 1993 and the introduction of the ‘Proof of Ecological Performance’ (PEP, Ökologischer Leistungsnachweis ÖLN) in 1998, which favors integrated production (IP) and organic farming (Leifeld and Fuhrer, 2005). Thus, since 1993 the share of IP and organic farming has increased continuously from less than 20% of the agriculturally useful area to more than 95% in 2001 (FOAG, 2011a).

Also, a reduction of the cattle population and of the application of mineral fertilizers was observed between 1990 and 2002, as well as an increase in leys and ecological compensation areas (Leifeld and Fuhrer, 2005). The decline in the number of cattle continued until 2004, but then increased until 2010. The swine population increased between 1996 and 2006, and fluctuated around the 2006 level until 2010. The poultry population rapidly increased between 1990 and 2010 (FOEN, 2012).

1.3. Tier 3 approaches

The 2006 IPCC guidelines provide detailed descriptions of Tier 1 methods and give guidance on Tier 2 methods for the AFOLU sector (IPCC, 2006). However, for the development of Tier 3 methods, only a general overview of the steps needed is given. Thus, the particular design and documentation of country-

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Tier 3 inventories are described: measurement-based Tier 3 inventories and model-based Tier 3 inventories (IPCC, 2006).

The basis for measurement-based Tier 3 inventories is the direct measurement of C stock changes.

Even though these methods do not rely on process models, spatial and temporal scaling of plot measurements to the national scale has to be done with appropriate statistical models. The development of such methods involves six steps (IPCC, 2006):

1. Developing a sampling scheme 2. Selecting the sampling sites 3. Collecting initial samples

4. Re-sampling the monitoring network on a periodic basis

5. Analyzing data and determining C stock changes/non-CO2 emissions, and infering national emissions and removal estimates and measuring of uncertainty

6. Reporting and documentation

Advanced models, e.g., empirical or process-based models, are the basis of model-based Tier 3 inventories. Independent measurements are however utilized for model evaluation, although the measurement network required for model evaluation does not have to be as dense as for the measurement-based inventories. Here, preference will be given to a model-based Tier 3 inventory due to a number of advantages. Several benefits are associated with the use of complex models in GHGI (IPCC, 2011). The use of a model can increase the temporal resolution of model results and thus also provide annual estimates where no annual measurements exist. Furthermore, model-based Tier 3 methods have the potential to be used at a range of scales, i.e. from the national scale down to the individual landholder (Smith et al., 2012). Using a model may reduce the costs, resources and time needed, compared to intensive SOC sampling campaigns (Gärdenäs et al., 2011; IPCC, 2011; Ogle and Paustian, 2005; Smith et al., 2012). Thus, where only a small number of repeated SOC stock measurements are available, models may be an alternative (Gärdenäs et al., 2011). Changes in SOC have been shown to be non-linear, e.g., Poeplau et al. (2011). In contrast to Tier 1 and 2 methods, which assume a constant SOC change factor, process-based models which integrate several processes and factors influencing SOM dynamics can account for this non-linearity (IPCC, 2006; IPCC, 2011; Viaud et al., 2010). Thus, longer-term legacy effects of land use and management may also be reflected in a model-based Tier 3 approach (IPCC, 2006). Lastly, the use of complex models may also allow future projections and the simulation of different scenarios for mitigating GHG emissions to be carried out (Gärdenäs et al., 2011; IPCC, 2011).

A model-based Tier 3 inventory in the AFOLU sector requires the following seven development steps (IPCC (2006), Figure 4). Additional information given for Tier 3 approaches for C stocks in soils is included here:

1. Select/develop a model for calculating the stock changes and/or GHG emissions: Many models are available that could be used in a GHGI for the simulation of SOC stocks and stock changes (Smith et al., 2012). These models include processes controlling SOC stock changes, i.e.

reflecting the influence of different land uses and management options. The model should be chosen with regard to the availability of input data (Step 3) and computational resources (Step 5).

2. Evaluation with calibration data: As it is essential to test the model for a variety of conditions and to proof its capability for the planned applications, model results have to be directly compared to measurements which were used for model calibration/parameterization. Statistical tests or graphical comparisons are possible methods. In the case that the model does not

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adequately capture the measurements, the model has to be re-calibrated or the structure of the model improved. In the worst case, the selected model has to be rejected and a new model chosen.

3. Gather spatio-temporal data on activities and relevant environmental conditions that are needed as inputs to a model: Models require specific input data for the simulation of SOC development due to agricultural management. These input data stem from soil and weather data sets as well as from data sets concerning agricultural management. It is important to ensure the consistency of the spatio-temporal scales of the input data and the model. Thus, as mentioned before, available spatio-temporal input data also determine the choice of model.

4. Quantify uncertainties: There are several sources of uncertainty in modeling SOC stocks and stock changes, as in all model studies. Uncertainties can stem from model input data, model initialization, model parameters, model structure and validation data (Ogle and Paustian, 2005).

To account for uncertainties, an uncertainty analysis is needed, which can follow several approaches. Such an analysis gives a measure of variability in the SOC stock changes and thus a measure of confidence attributed to the model results.

5. Implement the model: Preparation of input data, execution of model simulations and analysis of model results are the main components of this step. One of the key considerations is the definition of the spatial and temporal extents and the resolution of the simulations. Available computational resources and personnel time are important determinants for the choices made within this development step.

6. Evaluation with independent data: Model results have to be evaluated with a data set that is completely independent from the data sets used for model calibration and parameterization (step 2). Suitable sources for a data set of benchmark sites needed for this step are measurements from a monitoring network or from research sites. Repeated measurements of SOC stocks are needed, with re-sampling every 3 to 10 years. These sites should be representative for the major climatic regions, soil types and management systems. Even though such a network is similar to a network needed for a measurement-based Tier 3 inventory, the required sampling density is lower. Again, the comparison of model results and measurements could reveal unacceptable incongruences indicating that the developed system is inappropriate.

Analysis of possible problems may reveal errors in the implementation step, poor input data, or, less likely if step 2 was successful, an inappropriate model. Thus, the inventory developer has to return to the preceding steps. If it is necessary to even return to step 2, the use of measurement data of step 6 for model re-calibration or refinement should be avoided.

7. Reporting and documentation: Inventory results have to be presented in a systematic and transparent manner for reporting. Important components of documentation are a description of the model, summary of model input data sources, model evaluation results and the interpretation of emission trends. Also, the reporting of QA/QC (quality assurance/quality control) is required (for more details see IPCC (2006), volume 1, chapter 6).

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Figure 4: Steps to develop a Tier 3 model-based inventory estimation system (after IPCC, 2006).

As the focus of the 2006 IPCC Guidelines is on lower level tiers, additional guidance for Tier 3 model-based approaches was collected in an IPCC Expert Meeting and published in IPCC (2011). There, the key issue and major concern identified was the transparency of model applications. Thus, additional guidance is given on model reporting and documentation, as well as on model suitability and evaluation including calibration and uncertainty analysis. Also, supplementary guidance is currently being developed for organic soils, peatlands and rewetted peatlands by the IPCC (IPCC, 2012b). This guidance will also include the development of higher Tier methods to estimate GHG fluxes from drained peatlands (Joosten et al., 2012).

1.4. Aim: Development of a Tier 3 model-based method for Switzerland

The aim of this report is to develop a concept for a Tier 3 model-based inventory of SOC stock changes in agricultural soils in Switzerland. Such an inventory system has to account for Swiss-specific needs and be generally usable for a national inventory of SOC stock changes due to agricultural use. Furthermore, general requirements posed by, e.g., IPCC have to be met and the system has to account for the special properties of the country as well as the preconditions in terms of available expertise, data and models. In detail, the following requirements for the inventory system have to be met:

- Results must be area-based and available for the complete agricultural area of the country as well as for all areas under land-use change.

- To be able to estimate changes in soil C through time, a temporal dimension of the data is necessary. Yearly values for changes in SOC stocks are required for the GHGI.

- Generally available and verifiable land-use and cultivation data have to be the starting point of the system. This data set also has to allow for retrospective estimations.

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- According to the basic principles of the KP, the system has to be able to distinguish between direct, anthropogenic influences on sources and sinks and natural influences. Anthropogenic emissions and removals by sinks are those that occur on managed lands (IPCC, 2006).

- The system has to consider the special properties of the country, such as climatic gradients.

Furthermore, organic soils have to be explicitly taken into account.

- Uncertainty of the data has to be assessable and quality control and assurance have to be performed.

- The calculated results have to be transparent and their quality has to be evaluated with independent measurements. According to IPCC (2006), indicators of inventory quality which should be met by using a good practice approach are transparency, completeness, consistency, comparability and accuracy.

- On the one hand, the system has to be efficient, i.e. available data and methods have to be used as far as possible. On the other hand, the system has to be flexible to allow for future changes.

Furthermore, the system must be capable of being integrated into the present GHGI.

This report comprises three main chapters. In the second chapter, Tier 3 methods used by other countries for their inventory of SOC stock changes in mainly agricultural soils are reviewed and compared. Possible available data sets for the development of a Tier 3 inventory system in Switzerland are presented in the third chapter. Considerations for the development of a model-based inventory system for Switzerland following the development steps outlined previously, whilst taking available methods and data sets into account, are found in the fourth chapter.

This report serves as the basis for the easy implementation of a model-based inventory system for SOC stock changes in agricultural soils. The inventory system should subsequently be developed and made available for routine GHG reporting.

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2. Methods and models applied in GHG reporting

2.1. Overview

Every year, 42 Annex I parties to the Convention, plus Kazakhstan, submit their NIR. From these GHGIs, the UNFCC yearly compiles a synthesis and assessment report each year. There, data reported by the countries are collected and displayed for comparison (e.g., UNFCCC, 2011b). Tables 5.2a and 5.2b of the 2011 report give an overview of methods and emission factors for CO2, methane (CH4) and nitrous oxide (N2O) emissions (or removals) from LULUCF for all land-use categories. These tables reveal that for CO2

emissions and removals, only 15 Annex I parties apply a Tier 3 method in at least one of the land-use categories Forest Land, Cropland or Grassland, for at least one of the ecosystem components (living biomass, dead organic matter or soil) in their submission (Table 4, based on GHGI 2011 submissions, does not cover the improvements implemented since then). Lokupitiya and Paustian (2006) also reported on a predominance of Tier 1 methods for agricultural soils. They attribute this predominance to two main reasons. First, the activity data needed for higher-tiered approaches are challenging to obtain. Second, approaches themselves are more complex and information from a range of sources is needed.

The focus of this current study is on Tier 3 model-based methods for the simulation of C stock changes in soils. A closer look at the methods applied in the 15 countries in the 2011 submission revealed that not all countries listed apply a Tier 3 method for their inventory of changes in C stocks in soils. Also, Tier 3 inventories applied can be measurement-based, as for example for Grassland soils in Sweden. Of the listed countries, seven developed a Tier 3 method for SOC stock changes in forest soils, four in cropland soils and two in grassland soils. For a further review of Tier 3 model-based methods, we chose the countries that developed a Tier 3 model-based inventory for at least one of the land-use categories Cropland or Grassland (Australia, Denmark, Sweden, and United States). Further, we included Finland, who uses a model-based approach for simulating SOC stock changes due to land-use changes from agricultural use to forest and vice versa, as well as Germany. In the following chapters, methods and models will be described in more detail for these countries based on their National inventory submissions of the year 2011. Table 5 gives an overview of the methods used by these countries for the calculation of SOC stock changes in forest, cropland and grassland soils. Swiss methods (from the 2011 submission) are also included in the table for comparison. Tier 3 approaches are only applied for mineral soils, with model-based approaches in Denmark (Cropland, C-TOOL model), Sweden (Cropland, ICBMregion model), USA (Grassland, Cropland, Century model) and Australia (Forest, Grassland, Cropland, FullCAM model). For organic soils, all countries compared here use either the Tier 1 or Tier 2 methodologies. For Cropland and Grassland, either a Tier 2 method with a country-specific emission factor, or the Tier 1 default methodology with the IPCC default value for the emission factor is used for organic soils. For organic soils under forests, Switzerland and partly also Finland assume that the soil C stock is in balance and no emissions occur. (Switzerland has changed the method in the 2012 submission to a Tier 1 methodology using the default emission factor.) Barthelmes et al. (2009) compared methodologies for calculations of GHG from peatlands in National Inventory Submissions 2009 of ten countries, and also found that none of the countries developed a Tier 3 methodology for organic soils. No Tier 3 methods are applied in Germany and Switzerland. For mineral soils under the three land-use categories, these countries assume that the soil C pool is in balance and report no changes in SOC stocks (Tier 1). Germany and Switzerland, as well as Denmark and Sweden, established a Tier 2 methodology for the calculation of changes in SOC stocks due to land-use changes.

The remaining three countries (Australia, United States, and Finland) combine such methods with Tier 3 model-based methods for land-use changes.

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Table 4: Methods for CO2 emissions from soils in countries employing a Tier 3 approach in the estimation of C stock changes in biomass, litter or soil in at least one of the land use classes Forest Land, Cropland and Grassland (UNFCCC, 2011b, and information from NIRs).

Forest Land Cropland Grassland

Method

all Method soil Method

all Method soil Australia T1,T2,T3 Tier 1/ Tier 2;

Tier 3: FullCAM model (RothC) T3 Tier 3: FullCAM model (RothC) T2,T3 Tier 2;

Tier 3: FullCAM model (RothC) Austria T1,T2,T3 Tier 1: Soil C pool in balance

Canada T3 Tier 3: CBM-CFS3 CS,T1,

T2,T3

Tier 2: Century model to determine country-specific SOC change factors for management changes

Denmark T1,T3 Tier 3: C-TOOL

Finland T2,T3 Tier 3: Yasso, Yasso07 for land-use

change D,T1,T3 Tier 3: Yasso07 for land-use change CS,T1, T3 Tier 3: Yasso07 for land-use change Iceland T2,T3 FL remaining FL: Not estimated

Tier 2: for land-use change T1,T2, T3 GL remaining GL: Not estimated

Tier 2: for land-use change Ireland D,T1,T3 Tier 1: mineral soils

Tier 2: organic soils

Italy T1,T2,T3 Tier 3: For-est model T1,T2,T3

Tier 3: Linear relationships between aboveground C and soil C from measurements

T1,T2, T3

Tier 1 for grazing land

Tier 3: Linear relationships between aboveground C and soil C from measurements for other wooded land

Japan T1,T2,T3 Tier 3: CENTURY-jfos

Norway T1,T3 Tier 3: Yasso, Yasso07 for land-use

change to Forest Land T1,T2,T3 Tier 3: Yasso07 for land-use change to Forest Land

Russian

Federation CS,T1, T3 -- (NIR written in Russian)

Slovenia CS,D,T1, T2,T3

Tier 1: Soil C pool in balance Tier 2: for land-use change

Sweden T1,T2,T3 Tier 3: Pedotransfer functions T1,T2,T3 Tier 3: ICBMregion model T1,T2, T3 Tier 3: Pedotransfer functions United

Kingdom CS,D,T3 Tier 3: C-Flow model CS,D,T3

Tier 3: dynamic model for land-use change matrices, based on a database of soil C density for the UK

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Tier 3: C-Flow model for afforestation United

States T3 Tier 2 T1,T2,T3 Tier 3: Century model T2,T3 Tier 3: Century model

Table 5: Methods applied in the GHGIs (based on GHGI 2011 submissions, does not cover the improvements implemented since then) for emissions of CO2 from soils for the 6 selected countries compared to the Swiss methods. Tier 3 methods are shaded in blue. (Sources of information: NIRs).

Country Mineral soils Organic soils Land-use

change

Forest Land Grassland Cropland Forest Land Grassland Cropland

Switzerland

Tier 1:

Soil C pool in balance

Tier 1:

Tier 2:

Emission factor according to measurements in Europe and Switzerland

Tier 2:

Emission factor according to measurements in Europe and Switzerland

Tier 2:

C stock-change:

country-specific C stocks and transition period

Denmark

Tier 1:

Soil C pool in balance, except for minor conversion between grazing land and other grassland

Tier 3:

C-TOOL model

Tier 1:

Emission factor:

IPCC default value

Tier 2:

Emission factor according to measurements in Denmark

Tier 2:

C stock-change:

country-specific C stocks and transition period

Sweden

Tier 3:

Repeated soil sampling, pedotransfer functions

Tier 3:

Repeated soil sampling, pedotransfer functions

Tier 3:

ICBMregion model

Tier 2:

Emission factors and area estimates for sub-categories (well and poorly drained soils)

Tier 2:

Emission factors and area estimates for sub-categories (well and poorly drained soils)

Tier 2:

Emission factors calculated from subsidence data for different crop types

Tier 2:

Estimated using an emission/removal factor in

combination with the areal change in land use

Germany

Tier 1:

Soil C pool in balance No changes reported

Tier 1:

No changes reported

Tier 2:

Only land-use changes between annual and perennial crops, emission factor according to literature

Tier 1:

Emission factor:

IPCC default value

Tier 2:

Emission factor according to literature based primarily on data collected in Germany

Tier 2:

Emission factor according to literature based primarily on data collected in Germany

Tier 2:

Estimated using an emission/removal factor in

combination with the areal change in land use

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USA

Tier 2:*

Mean SOC densities stratified by region and forest type group.

Stock change:

Difference between two successive years

Tier 3:

Century model For majority of soils Tier 2:

For gravelly, cobbly or shaley soils, and sewage sludge amendments;

country-specific stock change factors

Tier 3:

Century model for majority of annual crops Tier 2:

For remaining crops, very gravelly, cobbly or shaley soils, country-specific stock change factors from literature

Tier 2:*

Mean SOC densities stratified by region and forest type group.

Stock change:

Difference between two successive years

Tier 2:

Country specific C loss rates

Tier 2:

Country specific C loss rates

Tier 3/Tier 2

Finland Tier 3:

Yasso model

Tier 1:

Default C stocks and C stock change factors

Tier 1:

Reference C stock values for each soil type multiplied by the IPCC default management and input factors

Tier 1:

Undrained peatlands:

Soil C pool in balance Tier 2:

Drained peatlands:

Site-specific emission factor

Tier 2:

National emission factor

Tier 2:

National emission factor

Tier 3:

Yasso07 model for land-use change involving forest on mineral soils Tier 1:

Other land-use changes

Australia

Tier 3:

Post-1990 plantations:

FullCAM (RothC) Tier 2:

Pre-1990 plantations Tier 1:

Harvested native forests: soil C pool in balance

Tier 3:

FullCAM (RothC) For grass only areas

Tier 2:

Shrub-land areas

Tier 3:

FullCAM (RothC)

No distinction made between organic and mineral soils

Tier 3:

FullCAM (RothC) Forest Land to Cropland, Forest Land to Grassland, Grassland to Forest Land

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The countries selected for a further review of methods differ in geography and climate, therefore also in land-use distributions (Figure 5). In Switzerland, none of the land-use categories are dominating.

Largest contributions are Forest Land and Grassland, with one third of the land area each. Cropland covers 10% of the country. The land-use category Other Land (areas without soil and vegetation, such as glaciers or rocks) has a relatively large contribution of 14% compared to the other countries.

With a share of two thirds, Denmark has the largest fraction of Cropland, and Australia of Grassland.

In the northern European countries Finland and Sweden, Forest Land is the dominating land use, and wetland fractions are largest from the countries considered. In Germany and the USA, Forest Land, Grassland and Cropland constitute about 90% of the land area, with an almost even distribution of these land-use categories.

Switzerland Germany Denmark

Sweden Finland

Australia USA

Figure 5: Land-use distributions in 2009 as reported in the Synthesis Assessment Report (UNFCCC, 2011b) or the 2011 NIRs (Denmark excluding Greenland and the Faroe Islands, USA).

30%

10%

33%

5%

8%

14%

Switzerland

Forest land Cropland Grassland Wetlands Settlements Other land

32%

37%

19%

2% 10%

0%

Germany

13%

65%

4%

0% 11%

7%

Denmark

7% 65%

1%

14%

4% 9%

Sweden

Forest land Cropland Grassland Wetlands Settlements Other land 7% 65%

1%

19%

4%

Finland

14% 3%

58%

2%

0% 23%

Australia

35%

21%

33%

3%

6%

2%

United States NIR

Forest land Cropland Grassland Wetlands Settlements Other land 43%

27%

28%

0% 2% 0%

United States Synthesis Assessment

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2.2. Methods and models applied in certain countries

2.2.1. Denmark

2.2.1.1. Responsible persons

Responsible persons for the Danish LULUCF inventory and the corresponding chapters and annexes in the NIR:

Vivian Kvist Johannsen Faculty of Life Sciences University of Copenhagen Forest & Landscape Denmark/

Forestry and wood products Rolighedsvej 23

1958 Frederiksberg C Denmark

vkj@life.ku.dk Tel: 0045-353-31699 www.sl.life.ku.dk/

Lars Vesterdal, Faculty of Life Sciences University of Copenhagen Forest & Landscape Denmark/

Applied ecology Rolighedsvej 23 1958 Frederiksberg C Denmark

lv@life.ku.dk Tel:0045-353-31672 www.sl.life.ku.dk

Steen Gyldenkærne

National Environmental Research Institute

University of Aarhus

Frederiksborgvej 399, Box 358 4000 Roskilde

Denmark sgy@dmu.dk Tel: 0045-4630-1223 http://dmu.au.dk/

2.2.1.2. Available data as basis for the national inventory

The data utilized within the LULUCF sector of Denmark’s National Inventory comprise the following (Nielsen et al., 2011):

- Land use/land cover maps depicting the six major IPCC classes (Forest, Cropland, Grassland, Wetlands, Settlements and Other) that were derived for the years 1990 and 2005 from earth-observation data (e.g., Landsat imagery), other data and National Forest Inventory (NFI) in situ data (for 2005 only).

- Within the NFI, a continuous sample-based inventory, data about the stands and trees are based on a 2 km x 2 km grid; the first inventory was conducted in 2002 and is repeated for each plot in a 5-year cycle.

- Soil samples based on a 7 km x 7 km grid, located on more than 600 agricultural plots and 108 forest plots (Figure 6); this ‘Kvadratnet’ monitoring was initiated in the 1980s, partly resampled in the 1990s and fully resampled in 2008/2009. The database provides soil C distribution to 1 m depth (Krogh et al., 2003).

- The distribution of organic soils, which was recently mapped for the inventory based on soil samples and several maps.

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Figure 6: The Danish soil grid (7 km x 7 km), 830 points sampled in 1987, partly 1998 and 2009 (Red: agriculture, blue: forest, from Gyldenkærne and Petersen, 2011).

2.2.1.3. Description of methodologies Soil C stocks:

In the Danish GHGI no changes are assumed in soil C pools in Forests remaining Forest. Therefore these C pools are not reported (Nielsen et al., 2011). This assumption is supported by results from the SINKS project that was initiated in 2007. For arable land converted to forest land, only changes due to C sequestration in the organic layer on top of the mineral soil is reported, whereas possible changes in the mineral soil are not accounted for. These decisions were made with reference to several literature studies (e.g.,Vesterdal et al., 2002) and preliminary data from the SINKS project, which did not show any consistent significant changes in the mineral soil C pool following afforestation.

A Tier 3 approach was implemented for the simulation of C stock changes in mineral soils for Cropland remaining Cropland, using the C-TOOL model (see section 2.2.1.4). For mineral soils in the category Grassland remaining Grassland, no changes are assumed. Emissions of CO₂ from organic soils for Cropland remaining Cropland and Grassland remaining Grassland are calculated via emission factors. A country specific emission factor was derived from a Danish research program for organic soils in croplands, whereas the default IPCC value was applied for Grassland (Table 6).

Changes in C stocks due to conversions to Cropland or Grassland are estimated from the difference in mineral soil C contents of the land-use types before and after land-use conversion, assuming a country-specific transition period of 50 years (Table 7).

Table 6: Organic soils: emission factors (EF) t C ha^-1 yr^-1 (from Table 7.25 from Nielsen et al., 2011).

Land use Land-use specification EF References Cropland Annual crops and grass in rotation 8.7

Maljanen et al. (2010) Fertilized permanent grass 5.17

Permanent grass 1.25 Default (IPCC, 2003)

A model-based inventory of sinks and sources of CO 2 in agricultural soils in Switzerland: