New soil data processing methods to provide soil information for ecosystem service evaluation as a basis for sustainable soil management

Results from two Alpine case studies in the Aosta Valley (Italy) and in East Tyrol (Austria)

New soil data processing methods to provide soil information for ecosystem service management

Imprint

What this is about? This report provides an overview of soil data processing methods that have been developed in two case studies within the Links4Soils project. Using regionalisation as a digital soil mapping approach, point-related soil information could be transferred into continuous maps that can be used by practitioners to support a more sustainable soil management.

Project and funding Links4Soils (ASP399); EU Interreg Alpine Space

WP, Task and Deliverable WPT2 AT2.2 (D.T2.2.2)

Lead University of Innsbruck, Institute of Geography, Innrain 52f, 6020 Innsbruck, Austria

Case study contributions Aosta Valley region: Autonomous Region of Aosta Valley, Assessorato Opere pubbliche, Territorio ed Edilizia residenziale pubblica, Dipartimento programmazione, risorse idriche e territorio, Via Carlo Promis, 2, 11100, Aosta

Municipality of Prägraten:

Department of Forest Planning, Tyrol Forest Administration, Office of the Tyrolean Government, Bürgerstraße 36, 6020 Innsbruck, Austria

Authors Elisabeth Schaber1, Elena Cocuzza2, Michele D’Amico3, Emanuele Pintaldi3, Alois Simon2, Clemens Geitner1

1 University of Innsbruck, 2 Office of the Tyrolean Government, 3 Autonomous Region of Aosta Valley

1 Caring for Soils – Where Our Roots Grow

How to cite Schaber, E., Cocuzza, E., D’Amico, M., Pintaldi E., Simon, A., Geitner, C. (2019). New soil data processing methods to provide soil information ecosystem service evaluation as a basis for sustainable soil management. Results from two Alpine case studies in the Aosta Valley (Italy) and in East Tyrol (Austria). Links4Soils project report.

Acknowledgements We would like to thank the University of Turin (DISAFA), the Municipality of Prägraten, the GEOWEST Engineering Office for geology (Innsbruck), and the Laboratory of the Institute of Forest Ecology of the University of Natural Resources and Life Sciences (Vienna).

Date March 2020

Caring for Soils – Where Our Roots Grow 2 New soil data processing methods to provide soil information for ecosystem service management

Abstract

This report gives an overview of new soil data processing methods that were developed and applied in two Alpine study areas within the Interreg Alpine Space project Links4Soils. It is intended to be offered to soil-experts and technicians aiming at producing similar soil information that can be used by practitioners or administration. Many soils in the Alps are the basis to produce agricultural and silvicultural goods, they are responsible for cleaning drinking water, they play an important role in preventing floods and other hazards, and they store high amounts of carbon. These are only a few of the numerous ecosystem services that are supported by soils. However, mainly as a result of morphodynamic processes characterising the Alps but also due to land-use and climate change, Alpine soils are subject to numerous threats, such as erosion or loss of organic matter. If soils are degraded, their ability to support ecosystem services is hampered as well. In order to prevent this negative development, appropriate soil management strategies need to be developed.

Since soils are very diverse, decisions should be based on site-specific soil information. To increase the amount and to optimize the quality and applicability of data, improving also their availability, two case studies were carried out within the Links4Soils project: In the Aosta Valley (Italy) and in East Tyrol (Austria) new soil data were generated, processed and presented in the form of maps and GIS data sets. Thereby, the data processing (e.g. regionalization, classification) is a necessary step since raw soil data requires sound soil expertise to function as a basis for decision-making.

In the Aosta valley, a soil type map was created at a regional scale (1:100.000), furthermore, some derived maps have been developed covering the topics erodibility, potential erosion, land capability and carbon stock. In Tyrol, based on this newly processed soil information guidelines for forest management were developed. They are covering the topics of biomass use and compaction.

In the present report, both case studies are explained in depth enabling soil-experts to understand the soil processing methods and evaluate the possibility to apply them to other regions. The final section of the report describes the requirements for implementing the presented methods in other regions and how they could be enhanced.

3 Caring for Soils – Where Our Roots Grow

Content

Imprint ______0

Abstract ______3

Content ______4

1 Alps, Soils, Ecosystem Services and soil threats ______5

2 State of the art ______6 2.1 Evaluation of Ecosystem Services ______6 2.2 Soil threats ______6 2.3 Digital Soil Mapping ______7

3 New soil data processing methods developed in two case studies ______8 3.1 Methods and results of data processing in the Aosta Valley Region ______8 3.1.1 DATA SOURCES AND METHODS FOR THE CREATION OF A SOIL TYPE MAP ______8 3.1.2 SOIL EROSION MAPS: RUSLE METHOD APPLICATION ______12 3.2 Methods and results of data processing in the Municipality of Prägraten ______15 3.2.1 SOIL DATA SOURCE ______15 3.2.2 FOREST MANAGEMENT GUIDELINES AND TRAFFIC LIGHT SYSTEM ______17 3.2.3 SOIL DATA PROCESSING METHODS FOR FOREST MANAGEMENT GUIDELINES: BIOMASS USE ______17 3.2.4 SOIL DATA PROCESSING METHODS FOR FOREST MANAGEMENT GUIDELINES: COMPACTION RISK _____ 19

4 Outlook: Further development and transferability of the data processing methods to other regions ______21 4.1 Outlook for the Aosta Valley Region ______21 4.2 Outlook for the Municipality of Prägraten ______21

List of tables ______24

List of figures ______24

References ______25

Annex ______29

About the Links4Soils project ______32

Caring for Soils – Where Our Roots Grow 4 New soil data processing methods to provide soil information for ecosystem service management

1 Alps, Soils, Ecosystem Services and soil threats

The European Alps (Alpine Space area as defined by the EU) are home to about 70 million people, who depend on Alpine environments (Price et al. 2011, Heimsath 2014). This dependence refers to so-called ecosystem services, which are direct or indirect benefits that ecosystems provide to humans (MEA 2005). Ecosystems consist of different components that fulfil functions, which contribute to the provision of ecosystem services. Depending on the service, the importance of an individual component varies. Soils are an essential part of Alpine ecosystems – as in all terrestrial ecosystems around the globe – and fulfil numerous vital soil functions, such as soil fertility for agricultural and silvicultural production, retention and purification of water, the provision of nutrients for plants, the storage of carbon and the cooling of local climate, as well as the provision of natural and cultural archives (Alpine Convention 1998). Ecosystem services that mainly depend on soil functions are also referred to as ‘soil-based ecosystem services’, ‘ecosystem services provided/supported by soils’ or simply ‘soil ecosystem services’ (Greiner et al. 2017). Alpine soils, which cover all soils in the Alps from the valley to the peaks, have very diverse properties and fulfil the abovementioned functions to very different degrees. Soil diversity is a result of different combinations of so-called soil-forming factors, i.e. lithology, climate, topography, organisms, land use and time (Baruck et al. 2016, Geitner et al. 2017). Therefore, soils are never in a steady state. However, if one or several soil-forming factors change abruptly or continuously, such as a change of land use or a morphodynamic process, soil properties and the soil’s ability to provide ecosystem services can be affected negatively. Such soil degrading processes are also known as soil threats. However, the soil’s vulnerability varies according to its properties and the respective soil threat. In order to manage soils in a way that the provision of soil-based ecosystem services can be sustained and soil threats can be kept to a minimum, spatial soil information is required. The following report, which is a result of the Interreg Alpine Space project Links4Soils, presents, through two case studies, how soil information can be processed in a way that the end product is useful for practitioners and thus contributes to a more sustainable soil management. The Italian case study demonstrates how a soil type map for an entire region, generated from point-related soil profile descriptions, can be used to derive important function-related properties (e.g. carbon sequestration, soil erodibility). In the Austrian case study, which is limited to forests, soil profile information was processed into two maps, the first showing the soil’s vulnerability towards compaction, the second giving suggestions about the use of biomass.

5 Caring for Soils – Where Our Roots Grow

2 State of the art 2.1 Evaluation of Ecosystem Services The concept of ecosystem services has gained importance since the late 1990s (Greiner et al. 2017, Schaber et al. 2019). Within this anthropocentric concept, nature is seen as a service provider for humans and ecosystem services are defined as “the benefits people obtain from nature” (MEA 2005). Originally this concept was introduced to strengthen the arguments for nature protection. As nature is often endangered by developments that foster economics, it was soon tried to even economically evaluate ecosystem services (e.g. Constanza et al. 1998). However, since the economic evaluation faces methodical and ethical problems, the evaluation is nowadays mostly only done in a qualitative or semi-quantitative manner (Baveye et al. 2016). For example, a service, e.g. biomass production, could be classified into 5 classes (1 (very low) - 5 (very high)). Besides those methodical details, an evaluation of ecosystem services is useful to raise awareness for the value of nature and the need for protection and to illustrate trade-offs between services induced by (possible) land-use changes. Although the provision of ecosystem services depends on all components of the ecosystem, the evaluation is often based on data that represents only a fraction of the whole, e.g. land cover or land use. A reason, therefore, is the lack of appropriate data and methods to merge all relevant and available data (Schaber et al. 2019). To consider soils in ecosystem service evaluation, only a handful of soil parameters are indispensable. This minimum dataset consists of organic carbon/matter, pH, stone content, bulk density, texture and hydromorphic properties (Greiner et al. 2017). Unfortunately, even this small set of soil parameters is often not available. As a result of the lack of data, soils are mostly underrepresented or completely neglected in ecosystem service evaluation. Thus, spatial information on soil properties would be a great benefit for soil function evaluation. This includes also soil type maps since each soil type normally has typical, specific physical and chemical properties, strictly related to its services. Therefore, more soil data processing methods are much needed. Processing includes classification, aggregation or clustering and regionalization.

2.2 Soil threats The topography of Alpine environments, characterized by steep slopes and active geomorphic processes, as well as harsh climatic conditions, exposes soils to many threats characteristic of mountain areas. In particular, erosion, driven by rainfall, snow gliding, avalanches and shallow landslides, are important threats greatly affecting soil ecosystem services (Romeo et al. 2015, Stolte et al. 2015). However, also other soil threats affect Alpine areas, such as organic carbon and nutrient loss, soil compaction and sealing (Stolte et al. 2015, Baritz et al. 2018). In particular, organic carbon and nutrient losses are obviously related to soil erosion, which usually removes the

Caring for Soils – Where Our Roots Grow 6 New soil data processing methods to provide soil information for ecosystem service management

uppermost soil horizons, particularly rich in organic matter (SOM) and associated nutrients. Apart from erosion, the decline in SOM can also be a consequence of mineralization. Among other factors, mineralization is dependent on temperature and, thus, it can be enhanced by climate change (Prietzl et al. 2016). SOM is a key property, positively associated with water retention and filtration, aeration and resistance to compaction. The loss of SOM participates in the carbon emission and CO2 production, important for climate change. Soil compaction (reduction in porosity mainly caused by machinery moving on the soil surface) is particularly linked to forestry activities, ski run construction and management or certain agricultural practices, such as land amelioration involving soil movements and stone removal to facilitate mechanical agriculture. Soil sealing is a common problem in the Alpine Space, and it leads to a complete soil destruction and loss of all its functions and ecosystem services). One of the common factors increasing erosive processes, together with all other soil threats, is land-use change, such as forest exploitation and, thus, soil-specific threat and vulnerability characterizations are necessary for a sustainable land planning (Romeo et al. 2015, Baritz et al. 2018). 2.3 Digital Soil Mapping The main goal of a soil map, produced conventionally or digitally, is the representation of the variation in soils across the landscape. The displayed information spans from the classification of soil types to different physical or chemical soil properties. The representation could either be discrete or continuous. Conventional soil maps from legacy soil surveys mainly display discrete classes, as most commonly used for soil types. On the other hand, Digital Soil Mapping (DSM) can also display continuous values, as it is much more suitable for most of the physical or chemical soil properties. For instance, in a conventional soil map, a polygon delineates a certain class of the humus content in the topsoil (e.g. 1-5%). In contrast, a soil map produced with DSM could show a continuous value range for the same location (e.g. 1.4%, 2.5%, 3.8%, 6%) depending on the spatial resolution of the model and the available in situ values. The approach of DSM evolved in the 21st century and is nowadays a commonly used technique to display the spatial distribution of soil data, recorded at individual points. Thereby a broad variety of statistical methods is used describing quantitative relationships between field or laboratory observations at points and comprehensive spatial covariates. Most of these covariates describe the soil-forming factors of Jenny (1941) and the scorpan-model of McBratney et al. (2003), including climate, topography, biotic factors, parent material and time. However, it is important that the used covariates describe underlying processes and are ecological plausible. The benefit of DSM lies clearly in the prediction of spatially continuous soil information from legacy soil data in 2D or 3D including soil depth. As with all maps, special attention should be paid to the accuracy and uncertainty of the information. Taking into account its limitations, DSM is a promising tool to complement conventional methods. This is particularly important in a mountain environment, where soil information is rare due to poor accessibility and high sampling effort. In the end, a combination of conventional and digital approaches can foster the spread and use of soil maps and their information.

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3 New soil data processing methods developed in two case studies

Within the Interreg Alpine Space project Links4Soils, two case studies have been carried out, in which soil data have been further processed using DSM to gain spatial soil information. The thematic focus varies between the case studies. Whereas a soil type map is the result of the case study in the Aosta valley (associated with carbon stocks and erosion vulnerability derived maps), in Prägraten (Tyrol) maps that show the risk for compaction and the suggested biomass use were created.

3.1 Methods and results of data processing in the Aosta Valley Region The Aosta Valley region is one of the few regions in Italy (and in the Alps) not having an official soil type map yet. So, one of the main tasks of the Italian Links4soils project partners was the production of a soil type map (1:100.000) from which several derived maps were created, in particular: soil erosion map based on RUSLE equation (with and without humus forms), soil erosion vulnerability map (K factor of the RUSLE equation), landslide frequency map, soil carbon stock map and land capability map.

3.1.1 Data sources and methods for the creation of a Soil Type Map For this purpose, 691 soil profiles were used, obtained from many different soil sampling campaigns performed between the 1990s and summer 2018. The soil profile descriptions and analytical data were homogenized according to WRB’s (World Reference Base for Soil Resources) latest methods and nomenclature (FAO 2006, IUSS Working group WRB 2015). These profiles were then grouped according to their pedogenetic trends and, thus, ecological functioning, into 16 main soil types and finally mapped as Cartographic Units (CUs). The basis for the data extension and the production of the soil type map were six datasets, three of them available on the geoportal of Aosta Valley Region (http://geoportale.regione. vda.it), i.e. Geological Map (1:100.000), the Nature Map (1:20.000), and the Digital Elevation Model (DEM) (10 m raster definition). Two more layers have been produced from the DEM (slope steepness and absolute aspect), and one has been produced by kriging of rainfall data (rainfall map). Other usually used layers (e.g. slope convexity) were tested but, as they did not significantly change the output, they were not used to generate the final version of the soil type map. The Geological Map was reclassified in order to produce a simplified parent material map. In particular, slope sediments have been reclassified according to their main lithological composition (Table 1), according to the specific location of each polygon and the nearby rock

Caring for Soils – Where Our Roots Grow 8 New soil data processing methods to provide soil information for ecosystem service management

formations. The Nature Map was reclassified as well to create a simplified map showing vegetation types (and thus land use) associated with specific soil types (Table 2).

Table 1: Parent material classification based on the 1:100.000 Geological Map.

Parent material Lithology (Geological Map) class Calc-schists, flysch, phyllitic marbles 100 Mafic rocks (prasinites, amphibolites, metagabbros) 200 Ultramafic rocks (serpentinites) 300 Black Schists (carbon schists) 400 Landslide deposits Reclassified* Unclassified sediments Reclassified* Slope deposits Reclassified* Limestones, dolomites, carnioles, gypsum-rich rocks 600 Mixed glacial till 700 Sialic rocks (granites, gneisses, mica-schists, metamorphic conglomerates, quartzites) 800 Alluvial deposits 900 Glaciers 15000 * according to the local substrate

Table 2: Land use / vegetation classification based on the Nature Map.

Description (Nature Map) Land use code No soil: Lakes, ponds, rivers, streams, barren rocks, glaciers, urban areas, quarries 0 Riverbeds with herbaceous cover 2000 Rhododendron ferrugineum, Vaccinium ssp. and Juniperus ssp. subalpine heaths 3000 Alnus ssp. and Salix ssp. formations 4000 Steppic endoalpine prairies, xerophilous grasslands and xerophilous Pinus sylvestris and Quercus pubescens formations. 5000 Subalpine Nardus stricta and alpine Carex curvula and Sesleria varia grasslands 6000 Fertilized pastures and meadows 8000 Broadleaf mesophilous forests and woodlands 9000 Castanea sativa formations 10000 Montane Picea excelsa and Abies alba forests 11000 Subalpine Larix decidua, Pinus uncinata and Pinus cembra formations, with Ericaceae in the understory 12000 Swamps 13000 Talus slopes 14000 Mixed crops 16000 Vineyards 17000

9 Caring for Soils – Where Our Roots Grow

We used the Maximum Likelihood algorithm, a technique that allows classifying each cell of a raster by calculating the probability that each pixel belongs to a specific class (Cox et al. 1999). Given the 16 soil type classes (CUs), the algorithm calculates the probability for each cell of the raster map to belong to each of the classes, according to the six input variables (geology, vegetation, average annual precipitation, elevation, slope steepness, absolute aspect), and assigns it to the most probable class, using variance and covariance matrices. In particular, absolute aspect has been calculated according to the equation: |(180 – |(aspect – 225)|)|. This method is suitable to be applied to a combination of continuous variables (precipitation, elevation, slope steepness, absolute aspect), and discrete (soil type, geology, vegetation) (Little and Schluchter 1985, Zare et al. 2018). We used ArcGIS 10.1 to perform the analysis and produce the map (Figure 1). The result was then refined by using raster filtering techniques, merging isolated pixels and small patches (<10 hectares) with surrounding areas. A validation has been performed by checking how well each soil profile belonging to the training data set fitted in the produced map. Moreover, ca. 50 additional profiles from Valsavaranche and Val di Rhemes (Università degli Studi di Milano Bicocca, unpublished data), originally not included in the model setup, have been used to check the error (validation data set). The validation procedure showed that 22-76% of the training data set was correctly classified in the resulting map (Table 3), with different correct classifications for each soil type. Correct classification values increased to 57-96% when considering soil types belonging to the same WRB Group (e.g. Albic Podzol - CU1, included in Entic Podzol - CU2 or Umbric Entic Podzols - CU3 areas, or Kastanozems - CU6 included in Calcisols - CU7 areas). Moreover, 82% of the validation data set was correctly classified in the produced soil type map, while only ca. <18% were wrong (uncorrelated soil type, e.g. Podzol instead of Phaeozem). Soils attributed to Regosols, Fluvisols and Leptosols included in other CUs have not been considered as wrong results as they are usually found in morphologically unstable locations such as landslide areas or eroded surfaces, or on small particularly steep or flat areas, not representable at the 1:100.000 scale. The accuracy of the produced map can be considered, thus, satisfying and the production of the map successful. The greatest error was obtained in CUs produced using only a small number of soil profiles (i.e. CU3, CU11, CU14, CU16) or CUs based on specific land uses such as grasslands and pastures, which are often in small patches, weakly spatialized at the considered scale (CU5 and CU9).

Caring for Soils – Where Our Roots Grow 10 New soil data processing methods to provide soil information for ecosystem service management

Table 3: Confusion matrix for the training data set, showing the attributions of soil profiles to different CUs in the produced map, and errors. “% broadly” refers to the number of profiles which were included in CUs with associated soil types (e.g. Albic Podzols in areas dominated by Entic Podzols). RG+FL are Regosols and Fluvisols included in other UC areas, mainly because of small landforms included in others, such as eroded areas, landslides, or small flatlands.

CU Soil type (WRB 2015) N. Profiles % correct % broadly % wrong RG+FL 1 Albic Podzol 128 60.16 80.47 13.28 6.25 2 Skeletic Entic Podzol 80 47.50 81.25 15.00 3.75 3 Umbric Entic Podzol 18 27.78 94.44 5.56 0.00 4 Dystric Cambisol (Protospodic, Arenic)* 53 49.06 67.92 15.09 16.98 5 Haplic/Cambic/Gleyic Phaeozem 38 60.53 71.05 21.05 7.89 6 Haplic Kastanozem 31 61.29 80.65 12.90 6.45 7 Petric/Haplic Calcisol 59 76.27 91.53 3.39 5.08 8 Calcaric Regosol 28 39.29 67.86 21.43 10.71 9 Haplic Umbrisol 23 21.74 56.52 21.74 21.74 10 Eutric Cambisol 32 53.13 59.38 21.88 18.75 11 Hypocalcic Rhodic Cambisol 5 60.00 100.00 0.00 0.00 12 Dystric Cambisol 74 52.70 70.27 14.86 14.86 13 Hyperskeletic/Skeletic Regosol 75 73.33 80.00 20.00 8.49 (FL) 14 Skeletic Eutric Regosol (Turbic) 35 31.43 57.14 20.00 37.14 15 Fluvisol 4 50.00 75.00 25.00 25.00 16 Skeletic Dystric Leptosol 7 57.14 42.86

Figure 1: Soil type map of the Aosta Valley Region; the 16 CUs are described in Table 3.

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3.1.2 Soil erosion maps: RUSLE method application After creating the soil type map, the RUSLE equation was applied (Renard et al. 1997), in which soil erosion (A [t ha-1 a-1]) is the product of rainfall erosivity (R), soil erodibility (K), vegetation and land cover protective action (C), slope steepness and length (LS) and mitigation practices (P), whereby the latter is normally not considered at 1:100.000 or smaller scales: A = R*K*C*LS*P Soil erodibility (Figure 2, expressed in t ha MJ−1mm−1), in particular, is a measure of the susceptibility of soil particles to be detached and transported by rainfall and runoff of specific strength, in absence of vegetation. It is related to soil structure and aggregation, soil permeability, organic matter content and texture. Since these soil properties are reflected in the soil type, soil erodibility can be derived from the soil type map. We considered A as potential erosion. As humus types are not considered by RUSLE equation (which was specifically created for agricultural soils) and only partially included in the C factor – although it plays a significant role in the physical erosion process – we added an expert- based classification of an H factor. The factor allows reducing differentially actual erosion under natural vegetation according to the humus form, considering that OH organic horizons require many decades to form and have a highly protective effect on the underlying mineral soil (Figure 3). Moreover, the areal density of shallow landslides, mudflows and soil movements (number of events per km2 in the last ca. 50 years, available at http://catastodissesti. partout.it/) of 16 soil types was calculated, to improve the existing shallow landslide susceptibility map (Figure 4). The overall results indicate that severe erosion may occur mostly in high elevation ecosystems (above treeline) due to the lack of sufficient vegetation cover and bare soil exposure (Figure 3). However, also weakly developed soils at low elevation, located in the dry inneralpine area, are prone to erosion, especially those characterized by broadleaves, xerophilous or steppe vegetation (Figure 2-4).

Caring for Soils – Where Our Roots Grow 12 New soil data processing methods to provide soil information for ecosystem service management

Figure 2: K (erodibility) factor calculated for the 16 soil types (t ha MJ−1mm−1); the soils developed in the most xeric area are the most susceptible to erosion, followed by high elevation soils on calcschists.

Figure 3: A (soil erosion) factor calculated for the Aosta Valley region (t ha−1a−1).

13 Caring for Soils – Where Our Roots Grow

Figure 4: Shallow landslides frequency in the different soil types (n km- 250a-1).

Caring for Soils – Where Our Roots Grow 14 New soil data processing methods to provide soil information for ecosystem service management

3.2 Methods and results of data processing in the Municipality of Prägraten

3.2.1 Soil data source Since 1988 an information system of Tyrolean soils has been developed, with the primary aim to monitor soil functions and recognize possible nutrition deficiencies, erosion threats and the presence of contaminants. The so-called “Bodenzustandsinventur” (Soil State Inventory) was originally carried out in 1988 on 257 forest soil sampling points (Stöhr et al., 1989) across Tyrol and a resampling activity took place in 1996 only on few locations (14) (Moosmann et al., 1996). Another source of information for 64 additional sampling points in Tyrol is the “Waldbodenzustandsinventur” (FBVA 1992), which was carried out specifically on forest soils in 1992. We downloaded all three lists of soil properties from BORIS (2013), an online information system on soils, managed by all federal governments of Austria and used them as starting point for our Tyrolean soil data evaluation. More recently, the Public Research Center for Forest collected and analysed 139 forest soils in Austria, including 13 in Tyrol, in the frame of a European forest soil monitoring project (Project BioSoil – European Forest Soil Monitoring 2006/07, Mutsch et al. 2013). Moreover, several sampling activities have been conducted since 2014 in the Department of Forest Planning of the Office of the Tyrolean Government. From 2014 until 2019, chemical and physical soil properties of 41 plots were analysed. The sampling campaign is on-going and 13 new sites were probed in the summer season of 2019 and the analysis results will be soon integrated into our database. We started to apply the results of our data evaluation in the case study area of Prägraten, in Eastern Tyrol. Therefore, we considered it essential to investigate 5 soil profiles in the municipality, specifically on 5 sites of the 1936 ha of forested area. We conducted field work in Prägraten in October 2018 and we described and reported the following site characteristics: coordinates, elevation [m], aspect [gon], inclination, water balance, soil water dynamic, local climate, micro-, macro- and meso-reliefs and most importantly, the parent material, with a description of the layer of overburden. The profile description conducted in the field includes information on the horizon designation according to the Austrian Soil Classification, textural classes, coarse fragments, shape and size of the coarse fraction, estimated bulk density, abundance of roots and mottles and classification of the horizon boundaries (Nestroy et al. 2011, Kilian 2015). This sound characterization of each site was further integrated with data measured from volumetric soil samples in the laboratory. These have been collected at unique soil depth intervals: 0-5 cm, 5-10 cm, 10-20 cm, 20-40 cm and 40-80 cm. In this study, the humus properties were not considered, so we excluded all data recorded above the 0 cm depth, which marks the starting point of the mineral soil in Austrian soil profile descriptions. The following chemical properties were measured and processed: cation exchange capacity [mmol kg-1] calculated from the cations Al+++, Ca++, Mg++, K+, H+, Mn++, Fe+++ and Na+

15 Caring for Soils – Where Our Roots Grow

-1 [mmol kg ] (ÖNORM L 1086-1) extracted in 0,1 M BaCl2 solution and base saturation [%]. Total nitrogen [%] (ÖNORM L 1082), total organic carbon [%] (ÖNORM L 1080), pH in CaCl2 solution (ÖNORM L 1083) were also included. As the volumetric sampling allowed us to calculate the bulk density, it was possible to retrieve the short-term available stocks of K, Ca and Mg down to 80 cm depth and the long-term available stocks [kg ha-1] of C, N and P extracted with hot acids (ÖNORM L 1085). Except for the BioSoils data, the stock values were not available in the previously conducted inventories. We also grouped the available data on texture for different horizons on the upper 30 cm of the soil profile, which was recorded for 121 of the 389 sampling locations in Tyrol. We retrieved information on soil coarse fragments from 1272 soil profiles descriptions from the project “Forest Site Classification Tyrol” (Forest Site Classification Tyrol 2018), which were sampled between 2003 and 2017. The georeferenced sampling sites (GPS recorded and post corrected with a Digital Terrain Model) range from 485 to 2120 m a.s.l. and are evenly distributed over the Tyrolean forest. At each sampling site, a soil pit was excavated and described in line with the Austrian soil taxonomy (Nestroy et al. 2011). In the frame of the project Links4Soils, the geological map of the forest area of Prägraten was revised and improved, subdividing the solid bedrock and the quaternary deposits into surficial geological units. These units provide information on the lithogenetic entity and the petrological composition of the geological substrate material. The units (definitions and abbreviations) present in the Prägraten area are summarized in Table 4.

Table 4: Surficial geological units in Prägraten Abbr. Surficial geological units BlB- Boulders, mafic rocks, poor in clay minerals FeB0 Solid rock, mafic rocks, intermediate clay minerals FeC0 Solid rock, siliceous-carbonate rocks, intermediate clay minerals FeM0 Solid rock, carbonate-siliceous rocks, intermediate clay minerals MoC0 Moraine, siliceous-carbonate rocks, intermediate clay minerals

MoI0 Moraine, intermediate siliceous rocks, intermediate clay minerals MoM0 Moraine, carbonate-siliceous rocks, intermediate clay minerals HaB0 Debris, mafic rocks, intermediate clay minerals HaI0 Debris, intermediate siliceous rocks, intermediate clay minerals HaM0 Debris, carbonate-siliceous rocks, intermediate clay minerals KiB0 Gravel, mafic rocks, intermediate clay minerals KiC0 Gravel, siliceous-carbonate rocks, intermediate clay minerals KiM0 Gravel, carbonate-siliceous rocks, intermediate clay minerals

A model-based substrate map with all 91 different geological units is available for the entire region of Tyrol and we used this map to assign a unit to each of the 389 soil sampling points. As a result, we averaged soil chemical and physical parameters at specific soil profile depth steps for each geological unit. An example of a description for the unit FeM0 can be found in

Caring for Soils – Where Our Roots Grow 16 New soil data processing methods to provide soil information for ecosystem service management

Annex 1. Except for the geological unit BlB-, we collected enough data to provide averaged soil property values for the whole case study area. Following the “Forest site field mapping” of 2003 (AK Standortskartierung 2003), we assigned specific colours to different ranges of properties in the chemistry tables of the geological unit’s description: from red to blue the ranges cover very low to very high values and they give a first impression on how rich in nutrients the soils of a specific geological unit are (see Annex 1).

3.2.2 Forest management guidelines and traffic light system The previously described properties categorization sets the baseline to classify the potential tree harvesting practices and the vulnerability towards compaction of the area of Prägraten, establishing the first improved forest management guidelines in Tyrol based on soil data. The classification applied in the form of a traffic light system with different risk categories (green, orange and red) informs forest management planners and practitioners on site- related risks of soil compaction and biomass use. Guidelines for specific measure implementations are elaborated for each category, following the principle that sites which are rich in nutrients will sustain the “whole tree” harvesting practice (green colour), while it is suggested to harvest less or no biomass from nutrient-poor areas (orange and red). The risk of compaction is reduced by regulating the transit of heavy machinery: possible transit with green colour, transit with specific adaptions with orange and no transit with red. The tool enables forestry experts and land owners to improve forest management, ensure long term site productivity and minimize soil compaction.

3.2.3 Soil data processing methods for forest management guidelines: biomass use Concerning the forest biomass use guidelines, the first step consisted in isolating properties affecting and accurately representing the actual nutrient availability, cycling and retention in forest soils. We subdivided intervals of base saturation, cation exchange capacity, pH and carbon to nitrogen ratio, measured until 40 cm profile depth, into 3 categories, assigning one preliminary traffic light colour to each individual property (Table 5).

Table 5: Preliminary traffic light categories assigned to value ranges of base saturation, cation exchange capacity, pH, and C/N Properties BS [%] CEC [mmol kg-1] pH C/N 0 – 25 < 60 < 4.2 > 25 Intervals 25 – 70 60 – 200 4.2 – 6.2 25 - 12 70 – 100 > 200 > 6.2 < 12

17 Caring for Soils – Where Our Roots Grow

The final category for biomass use is obtained combining the four preliminary assigned colours, taking into account several priorities and conditions, as shown in Figure 5. The category assigned to the base saturation should be used as a reference for the final category unless the following combinations occur: if the base saturation value is in the range of the green category and either the pH or the CEC are red, the outcome is orange. A red colour for the pH and the C/N ratio can downgrade the biomass use colour to red when the base saturation is orange. A green pH and C/N ratio can upgrade the biomass colour to orange when the base saturation is red.

Figure 5: Decision process behind the definition of a final category for biomass use, considering four soil properties (BS, CEC, pH and C/N)

Another property influences the definition of the final category: the sum of the cations calcium and magnesium over the cation exchange capacity ((Mg+Ca)/CEC), not calculated as the average on the first 40 cm but only at depth 20 to 40 cm. We excluded the top surface because the concentrations of basic cations might be biased by historical land use impacts such as pasturing and litter removal. A condition which categorizes the geological unit as red in terms of biomass use, independently on the other properties, is the ratio of the cations magnesium and calcium over the cation exchange capacity being equal to 1. This condition expresses a high deficiency of all other cations in the soil profile, including potassium and iron, and it determines strong negative effects in case the biomass is harvested. Additionally, when the ratio (Mg+Ca)/CEC is equal to 0.99, the following conditions are applied depending on the CEC: the final category is set to green in case the cation exchange capacity is higher

Caring for Soils – Where Our Roots Grow 18 New soil data processing methods to provide soil information for ecosystem service management

than 400 mmol kg-1. A lower CEC (200-400 mmol kg-1) downgrades the biomass use colour to orange and at values lower than 200 mmol kg-1, the deficiency of other nutrients than magnesium and calcium suggests harvesting only tree logs (red traffic light). Below the threshold of 0.99, the ratio (Mg+Ca)/CEC is not considered for the definition of the final category and all other previously mentioned conditions are applied.

3.2.4 Soil data processing methods for forest management guidelines: compaction risk The most valuable soil properties in this second phase of our analysis for defining the compaction risk category were the soil grain size fractions of the upper 30 cm of the soil profile. In line with the method applied for the biomass use categories, we defined specific intervals of coarse fragments fraction, as a percentage on the total soil amount, and clay and sand fractions, as percentages on the total soil fine fraction (< 2mm). We assigned to each textural class a preliminary traffic light category (Table 6), interpreting the effect that these ranges have on the risk of compaction of each geological unit.

Table 6: Preliminary traffic light categories of compaction risk assigned to value ranges of coarse fragments fraction, clay and sand Properties Coarse fragments [%] Clay [%] Sand [%] 0 – 25 > 30 < 25 Intervals 25 – 50 15 – 30 25 – 45 50 – 100 < 15 > 45

Following the principle that fine-textured soil is more prone to compaction than the coarse- textured one (Wästerlund 1985), we set some conditions to obtain a final category for compaction risk (see Figure 6). Specific conditions are applied to select one unique resulting category, prescribing the measures to adopt to avoid compaction. When coarse fragments fraction is in the green range, the fine texture becomes secondary and the resulting category will be assigned to green. When the coarse fragments fraction is below 50%, clay content becomes critical and it defines the resulting category, ranging to all chromatic spectrum, depending on its weight on the fine texture. When either coarse fragments fraction or fine texture classification are not available, only one is taken into account for categorizing the surficial geological unit.

19 Caring for Soils – Where Our Roots Grow

Figure 6: Decision process behind the definition of a final category for compaction risk, considering soil grain size fractions

Caring for Soils – Where Our Roots Grow 20 New soil data processing methods to provide soil information for ecosystem service management

4 Outlook: Further development and transferability of the data processing methods to other regions

In this section, the aspect of the transferability of the described methods to other regions is discussed for both case studies. Regarding the Aosta Valley case study, it is further presented how the RUSEL method could be considerably improved by adding a Humus factor. For the Prägraten case study, an outlook regarding future developments of the described method is provided. 4.1 Outlook for the Aosta Valley Region The soil type map approach, with derived maps about erosion, vulnerability, etc., is applicable to any context in mountain environments, even if it should be adapted to the environmental specificity, the availability of pre-existent pedological information and base maps (e.g. geology, land use, rainfall etc..). In particular, considering the high variability of mountain soils, detailed soil information (e.g. soil profiles) are required. However, the specific environmental conditions of Alpine environments do not always allow to obtain this information easily. The RUSLE method is widely applicable only for the areas where soil type maps exist and the basic data for soil types are available. However, besides the standard input required for the RUSLE method, the introduction of Humus factor (H), although expert-based, could represent in a better way the real mountain environmental susceptibility to water erosion. Indeed humus forms may represent a synthetic index combining soil biological activity and interaction between organic matter and mineral phases. Therefore, they can give important information on soil vulnerability to losses of aggregates and erosion. However, available data on humus forms for a specific region is usually quite scarce, therefore their description during the pedological field activities is recommended. Such maps are an important basis for better decision making at the local scale and thus can improve land planning and agro-forestry heritage management, allowing better soil protection which is especially important in mountain areas.

4.2 Outlook for the Municipality of Prägraten The model, which assigns typical average soil properties (e.g. base saturation, pH, C, N, cation exchange capacity, coarse fraction) to the surficial geological unit, does not take into account other important soil-forming factors. Climate, topography, vegetation, human disturbances and time available for soil formation all play a role (Jenny 1941) and partially contribute to the soil weathering and erosion of specific areas, further defining their vulnerability towards tree harvesting and compaction. Site-related characteristics are included in the Forest Type descriptions of Tyrol: by means of GIS modelling and field work, areas with similar potential natural vegetation, soil, climatic

21 Caring for Soils – Where Our Roots Grow

conditions and topography were grouped into Forest Type units. Forest planners, practitioners and the general public can access a descriptive Forest Type report including the above-mentioned characteristics, as a tool for retrieving relevant information regarding tree growth and forest management. As output for the “Links4Soils” project, a section was recently added with soil-related data, including a graphical representation of a typical soil type profile and a site-based categorization of biomass use and compaction risk (see Annex 2). The integration of these short Forest Type descriptions, including soil information and management to the so-called “Waldwirtschaftspläne” (forest management plans) of Tyrol is on-going. These plans are developed every 20 years for each Tyrolean community and serve as an official document, by setting limits of felling, while guaranteeing the wood production and maintaining the forest functions. Ideally, the biomass use and compaction risk categorizations based on soil properties that have been developed in Prägraten can be implemented to the whole Tyrolean forest surface. In order to provide an overview of the two classifications of biomass use and compaction risk in Prägraten, we show in Table 7 how the area surrounding the sampling points has been categorized, based on its Forest Type and its surficial geological unit. A complete unification of the two systems resulting in one category comprising multiple factors is on-going for all Tyrolean forest cover.

Table 7: Traffic light categories for biomass use and compaction risk applied to the five sampling points in Prägraten, based on forest type and surficial geological unit. Sampling Category of Category of Category of Category of points biomass use biomass use compaction risk compaction risk assigned to assigned to the assigned to forest assigned to the forest type geological unit type geological unit

L4S1 Fi6 FeC0 Fi6 FeC0 L4S2 Fs7 FeB0 Fs7 FeB0 L4S3 Fi5 HaB0 Fi5 HaB0 L4S4 Fs1 FeM0 Fs1 FeM0 L4S5 Fi5 HaM0 Fi5 HaM0

Furthermore, the model can be easily extended to all Alpine regions that have a similar amount of characterized substrates and collected soil samples. For example, the region of South Tyrol (Italy) has the same forest stand type characterization as Bavaria (Germany) and as a small part of the province of Salzburg (Austria). Until the end of the year 2021, the Department of Agriculture and Forest Management of the Provincial Government of Styria is planning to conclude the complete characterisation of the forest area of Styria (Austria), retrieving data on topography, climate, geology and historical land use. Detailed information on soil, vegetation and tree stands is being collected on 1800 sites and 360 soil sampling points have been selected for analytical measurements. Classification of parent material and substrates is being conducted, based on 240 site physical and mineralogical surveys (Englisch

Caring for Soils – Where Our Roots Grow 22 New soil data processing methods to provide soil information for ecosystem service management

et al., 2019). Therefore, it can be said that the processing method developed in Prägraten is generally transferable to other regions. However, a sound soil data base must exist or an extensive soil survey needs to be conducted, as it is being done in Styria. Additionally, a detailed quaternary map is required. Regarding the definition of the traffic light categories, some adaptions for soil property intervals might be needed in regions, where the typical soil characteristics differ much from those in Tyrol (Austria). In any case, the digitalisation of soil data is a prerequisite to create multiple thematic maps. Those can, if new or updated data is available, be adapted, especially in sight of the changing climate conditions and the needs to maintain healthy soils for the provision of multiple ecosystem services.

23 Caring for Soils – Where Our Roots Grow

List of tables

Table 1: Parent material classification based on the 1:100.000 Geological Map...... 9 Table 2: Land use / vegetation classification based on the Nature Map...... 9 Table 3: Confusion matrix for the training data set, showing the attributions of soil profiles to different CUs in the produced map, and errors. “% broadly” refers to the number of profiles which were included in CUs with associated soil types (e.g. Albic Podzols in areas dominated by Entic Podzols). RG+FL are Regosols and Fluvisols included in other UC areas, mainly because of small landforms included in others, such as eroded areas, landslides, or small flatlands...... 11 Table 4: Surficial geological units in Prägraten ...... 16 Table 5: Preliminary traffic light categories assigned to value ranges of base saturation, cation exchange capacity, pH, and C/N...... 17 Table 6: Preliminary traffic light categories of compaction risk assigned to value ranges of coarse fragments fraction, clay and sand ...... 19 Table 7: Traffic light categories for biomass use and compaction risk applied to the five sampling points in Prägraten, based on forest type and surficial geological unit...... 22

List of figures

Figure 1: Soil type map of the Aosta Valley Region; the 16 CUs are described in Table 3...... 11 Figure 2: K (erodibility) factor calculated for the 16 soil types (t ha MJ−1mm−1); the soils developed in the most xeric area are the most susceptible to erosion, followed by high elevation soils on calcschists...... 13 Figure 3: A (soil erosion) factor calculated for the Aosta Valley region (t ha−1a−1)...... 13 Figure 4: Shallow landslides frequency in the different soil types (n km- 250a-1)...... 14 Figure 5: Decision process behind the definition of a final category for biomass use, considering four soil properties (BS, CEC, pH and C/N) ...... 18 Figure 6: Decision process behind the definition of a final category for compaction risk, considering soil grain size fractions ...... 20

Caring for Soils – Where Our Roots Grow 24 New soil data processing methods to provide soil information for ecosystem service management

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Caring for Soils – Where Our Roots Grow 28 New soil data processing methods to provide soil information for ecosystem service management

Annex

Annex 1: Example for the description of a geological unit (FeM0)

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Caring for Soils – Where Our Roots Grow 30 New soil data processing methods to provide soil information for ecosystem service management

Annex 2: Descriptive forest type report including a site-based categorization of biomass and compaction risk

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About the Links4Soils project

Web links Links4Soils results web page: Alpine Soil Platform – www.alpinesoils.eu Links4Soils Interreg Alpine Space project web page: www.alpine-space.eu/projects/links4soils

Links4Soils project partners

Agricultural Institute of Slovenia, SI (project leader) Kmetijski inštitut Slovenije

Slovenian Forest Service, SI Zavod za gozdove Slovenije

Office of the Tyrolean Government, AT Amt der Tiroler Landesregierung

Climate Alliance Tirol, AT Klimabündnis Tirol

Institute of Geography, University of Innsbruck, AT Institut für Geographie, Universität Innsbruck

University of Turin, Department of Agricultural, Forest and Food Sciences, IT Università degli Studi di Torino, Dipartimento di Scienze Agrarie, Forestali e Alimentari Autonomous Region of Aosta Valley, IT Regione autonoma Valle d´Aosta

National Research Institute of Science and Technology for the Environment and Agriculture, Grenoble Regional Centre, FR Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Grenoble

Municipality of Kaufering, DE Markt Kaufering

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