u n i ve r s i t y o f co pe n h ag e n

Development of a harmonised soil profile analytical database for Europe

a resource for supporting regional soil management

Kristensen, Jeppe Ågård; Balstrøm, Thomas; Jones, Robert J.A.; Jones, Arwyn;Montanarella, Luca; Panagos, Panos; Breuning-Madsen, Henrik

Published in:SOIL

DOI:10.5194/soil-5-289-2019

Publication date:2019

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Citation for published version (APA):Kristensen, J. Å., Balstrøm, T., Jones, R. J. A., Jones, A., Montanarella, L., Panagos, P., & Breuning-Madsen, H.(2019). Development of a harmonised soil profile analytical database for Europe: a resource for supportingregional soil management. SOIL, 5(2), 289-301. https://doi.org/10.5194/soil-5-289-2019

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SOIL

Development of a harmonised soil profile analyticaldatabase for Europe: a resource for supporting

regional soil management

Jeppe Aagaard Kristensen1,2,*, Thomas Balstrøm2, Robert J. A. Jones3, Arwyn Jones4,Luca Montanarella4, Panos Panagos4, and Henrik Breuning-Madsen2,*,†

1Department of Physical Geography and Ecosystem Science,Lund University, Sölvegatan 12, 223 62 Lund, Sweden

2Department of Geosciences and Natural Resource Management,University of Copenhagen, 1350 Copenhagen K, Denmark

3School of Energy, Environment and AgriFood, Cranfield University, College Road, Cranfield, MK43 0AL, UK4European Commission, Joint Research Centre, Via E. Fermi 2749, 21027 Ispra (VA), Italy

∗These authors contributed equally to this work.†deceased

Correspondence: Jeppe Aagaard Kristensen (jeppe.aa.kristensen@gmail.com)

Received: 30 March 2019 – Discussion started: 6 May 2019Revised: 20 August 2019 – Accepted: 28 August 2019 – Published: 7 October 2019

Abstract. Soil mapping is an essential method for obtaining a spatial overview of soil resources that are in-creasingly threatened by environmental change and population pressure. Despite recent advances in digital soil-mapping techniques based on inference, such methods are still immature for large-scale soil mapping. Duringthe 1970s, 1980s and 1990s, soil scientists constructed a harmonised soil map of Europe (1 : 1000000) basedon national soil maps. Despite this extraordinary regional overview of the spatial distribution of European soiltypes, crude assumptions about soil properties were necessary for translating the maps into thematic informa-tion relevant to management. To support modellers with analytical data connected to the soil map, the EuropeanSoil Bureau Network (ESBW) commissioned the development of the soil profile analytical database for Europe(SPADE) in the late 1980s. This database contains soil analytical data based on a standardised set of soil analyt-ical methods across the European countries. Here, we review the principles adopted for developing the SPADEdatabase during the past five decades, the work towards fulfilling the milestones of full geographic coverage fordominant soils in all the European countries (SPADE level 1) and the addition of secondary soil types (SPADElevel 2). We illustrate the application of the database by showing the distribution of the root zone capacity andby estimating the soil organic carbon (SOC) stocks at a depth of 1 m for Europe to be 60×1015 g. The increasedaccuracy, potentially obtained by including secondary soil types (level 2), is shown in a case study to estimateSOC stocks in Denmark. Until data from systematic cross-European soil-sampling programmes have sufficientspatial coverage for reliable data interpolation, integrating national soil maps and locally assessed analytical datainto a harmonised database remains a powerful resource to support soil resources management at regional andcontinental scales by providing a platform to guide sustainable soil management and food production.

Published by Copernicus Publications on behalf of the European Geosciences Union.

290 J. A. Kristensen et al.: A harmonised soil profile analytical database for Europe

1 Introduction

In a world subject to constant environmental change and in-creasing population pressure, soil becomes an increasinglyimportant but threatened resource (FAO, 2015; SustainableFood Trust, 2015). This challenge must be met at multiplemanagement levels and spatial scales; hence, accurate under-standing of the available resources at the appropriate scaleis required (e.g. Robinson et al., 2017). In spite of advancesin digital soil mapping using remote sensing and geograph-ical information systems to infer soil properties (McBratneyet al., 2003; Arrouays et al., 2014; Minasny and McBratney,2016; Zhang et al., 2017), data and standardised methods forlarge-scale mapping are still inadequate. In particular, the ex-isting methods are challenged in densely vegetated areas andfor subsoil properties (Mulder et al., 2011), which are highlyrelevant to environmental management and food production.This was recently emphasised by the suggesting that the un-certainty in soil data could potentially offset climate changeimpacts on future crop yields due to the strong climate re-sponse dependence on soil type (Folberth et al., 2016). Thisnotion calls for continued efforts to improve soil maps.

During the last century, national soil maps were estab-lished in most European countries, but they were not har-monised across borders, as they were based on specificnational soil classification systems (Morvan et al., 2008).Therefore, international classification systems were devel-oped during the 1960s and early 1970s to facilitate the com-pilation of globally standardised soil maps (FAO-Unesco,1974; SMSS/USDA/AID, 1983). The FAO maps portrayedthe soil resources for each individual country as mappingunits with a distinct set of soil types, as delineation of indi-vidual soil types was not feasible for global-scale soil map-ping. The soil types comprised three categories: dominantsoils, associated soils and inclusions. The dominant soil typecovered the largest proportion of the mapping unit; associ-ated soils occupied 20 % to 50 % of the unit, while the in-clusions accounted for less than 20 %. The maps were pub-lished with an explanatory text describing the geology, ge-omorphology and land use and a map showing the level ofknowledge behind the map construction, i.e. the level of con-fidence (King et al., 1994).

In the beginning of the 1980s, the 10 European Commu-nity (EC) member states elaborated on the approach definedby the Food and Agriculture Organization of the United Na-tions (FAO-Unesco) to make an expanded and a more de-tailed version of the FAO-Unesco (1974) system for the soiltypes present in their respective countries. Based on this, theEC published seven soil maps (scale of 1 : 1000000; Com-mission of the European Communities, 1985). The completesoil map of Europe was digitised by the end of the 1980s(Platou et al., 1989) as a part of the EC-financed CORINEprogramme (Briggs and Martin, 1988). Quickly, it becamean important dataset in the forecasting of national crop yieldsacross Europe by the European Commission’s Joint Re-

search Centre’s Monitoring Agricultural ResourceS (MARS)project (Vossen, 1993). Subsequently, the EC soil map wasused widely to underpin soil resource assessments within theEuropean Union (EU), including the mapping of carbon (C)stocks (European Commission, 2005; Jones et al., 2005; Lu-gato et al., 2014), soil erosion risks (Kirkby et al., 2008;Panagos et al., 2015), vulnerability to compaction (Jones etal., 2003; Schjønning et al., 2015) and salinity (EuropeanCommission, 2005) as well to raise awareness and provideeducation materials (e.g. European Commission, 2005).

Yet such assessments were based on assumptions aboutthe characteristics of each soil type or extrapolationsfrom limited amounts of (often) country-specific analyt-ical data. Therefore, incorporating national datasets intoone uniform European database would dramatically increasethe quality of predictions and evaluations based on theEC soil map across member state borders. A global at-tempt to meet a similar challenge has led to the devel-opment of the Harmonized World Soil Database (HWSD;FAO/IIASA/ISRIC/ISSCAS/JRC, 2012), in which data fromEurope are extracted from the European Soil Database(v.2.0), which in turn is based on the soil profile analyti-cal database for Europe (SPADE). This paper illustrates howthis cornerstone (SPADE) in the European Soil Data Cen-tre (ESDA; Panagos et al., 2012) was created based on soilphysical and chemical soil data provided for each soil type bynational expert stakeholders from each member state. Specif-ically, we go through how a database containing estimatedanalytical data for all dominant soil types within the EU withfull geographical coverage (SPADE 14) was compiled. Fur-thermore, we describe how a level-2 database was developedfor a small subset of countries to show the principles of howa full coverage level-2 database (SPADE 18) will in the yearsto come be expanded to cover the entire EU and surroundingcountries. Finally, we show how the database can be usedto obtain estimates of environmentally relevant soil proper-ties (e.g. root zone capacity and soil organic carbon – SOC –stocks).

2 Establishing the soil profile analytical databasefor Europe framework (SPADE 1)

A working group of Europe-wide soil specialists was formedto advise the Commission of the European Communityon the establishment of a soil profile analytical database(SPADE) connected to the EC soil map (Fig. 1a). By the endof the 1980s, the working group proposed that it should bebased on four levels of analytical data (Breuning-Madsen,1989): level 1 would provide analytical data from a typicalsoil profile for the dominant soil typological unit (STU) ineach soil-mapping unit (SMU), preferably on arable land,level 2 would expand the database to include a typicaldataset for all STUs, including associated soils and inclu-sions, level 3 would be a further expansion to include soil

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Figure 1. (a) Structure of the European Soil Database to which SPADE provides data (after Lambert et al., 2003). (b) Timeline of theestablishment of the soil profile analytical database for Europe (SPADE). See text for details.

analytical data for all soil types with differentiation betweenland uses, and level 4 would allow different soil analyticaldata for the same soil type (STU) that occurs in differentsubregions, e.g. based on geology or geomorphology (seeFig. 1b for a timeline).

Initially, two soil analytical databases were established:one containing estimated mean values for typical soil pro-files according to a fixed set of standardised soil analyticalprocedures provided by national stakeholders (referred to asProforma I) and another containing soil profile data measuredusing established yet not necessarily country-wide standard-ised analytical procedures (referred to as Proforma II). Thus,the Proforma I database contains data comparable acrosscountry borders, while this is not always the case for theProforma II database (Breuning-Madsen and Jones, 1995).In order to make the database functional as soon as possi-ble for the entire coverage area, each member state stake-holder was asked to deliver one full set of Proforma I (es-timated) analytical data for each dominant soil type (STU)in each of the SMUs delineated in the soil map of Europe(1 : 1000000). Providing data for the Proforma II (measured)database was made optional to smooth the data collectionprocedure. Where possible, the data should be provided foragricultural land, as the primary aim of the database was tounderpin large-scale assessments of agricultural land man-agement (Breuning-Madsen, 1989; Vossen, 1993).

In 1993, Proforma I and II schemes (including guidelines)were sent to the stakeholders in order to collect data for theindividual countries; detailed guidelines for the compilationof the SPADE 1 dataset were published by Breuning-Madsenand Jones (1995).

Subsequently, the SPADE 1 database was expanded to in-clude data from the new EU member states but also fromnon-EU European nations such as Albania, Norway andSwitzerland. By the end of the 1990s, SPADE 1 was subject

to a data quality assessment and scrutinised to identify miss-ing data and evaluate overall data reliability. Based on therecommendations presented at a European Soil Bureau Net-work (ESBN) meeting in Vienna 1999, the national stake-holders were requested to update their individual datasets.Meanwhile, only a few national stakeholders engaged in thisexercise due to lack of resources or limitations on data dis-semination, which left the SPADE 1 incomplete and not well-suited for modelling at the European level.

3 An attempt to populate SPADE with measureddata (SPADE 2)

Due to the limitations of SPADE 1, SPADE 2 was devel-oped to derive appropriate soil profile data to support, forexample, higher-tier modelling of pesticide fate at the Eu-ropean level (Hollis et al., 2006). Data were supplied fromnational data archives, similar to the SPADE 1 Proforma II.Despite the analytical methods differing between countries,the raw national data were harmonised and validated to pro-vide a single data file for use in conjunction with the exist-ing Soils Geographical Database of Europe (SGDBE; Platouet al., 1989). The primary soil properties required for eachsoil were horizon nomenclature (e.g. A, E, B and C), upperand lower horizon depth (cm), particle-size distribution (clay,silt, total sand and content of at least three sand fractions),content of coarse fragments (> 2 mm), pH in water (1 : 2.5soil : water), organic carbon content (%), and dry bulk den-sity (BD; gcm−3).

The acquisition of data happened in two steps: the firstdatasets were obtained from Belgium, Luxembourg, Den-mark, England and Wales, Finland, Germany, Italy, theNetherlands, Portugal, and Scotland (Hollis et al., 2006),and next the database was expanded with data from Bul-

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292 J. A. Kristensen et al.: A harmonised soil profile analytical database for Europe

garia, Estonia, France, Hungary, Ireland, Romania, Slovakia,Spain, France and Ireland. Due to the lack of method-ological consistency between countries, the final database(SPADE2v11) was never published; hence it only exists asa beta version of collated datasets from the first and sec-ond phases of soil profile data acquisition (Hannam et al.,2009). However, it was used to estimate bulk densities formissing data in the later SPADE 14 (see Fig. 1b for timelineand overview of the SPADE versions).

4 Steps towards full geographical coverage(SPADE 8)

In an effort to obtain a functional database with full spa-tial coverage for Europe, a small specialist group fromDenmark (Henrik Breuning-Madsen, Thomas Balstrøm andMads Koue from the Institute of Geography, University ofCopenhagen) assessed the national datasets in 2008 using er-ror finding equations based on literature values, expert judge-ments and pedotransfer functions (Koue et al., 2008).

First, a quality check was conducted on all data. This pro-cess consisted of the following:

i. cross-checking of interdependent variables (e.g. pHvs. base saturation or porosity vs. saturated water con-tent),

ii. checking the plausibility of all values according to pub-lished theoretical or empirical values (e.g. for bulk den-sity – BS – or C : N values).

Examples of common questionable data were occurrences ofbulk soil C : N values < 5, mismatches between BS and pH(e.g. BS > 90 % at pH < 4.5), and volumetric water contentat saturation exceeding the porosity. Based on this exami-nation, implausible values were either adjusted to plausiblevalues or marked as unlikely based on predefined criteria.All changes and suggestions were carefully flagged to makethem obvious to national evaluators. However, in terms ofspatial extent, it was still only possible to link a soil analyti-cal dataset for a dominant soil type to approximately 70 % ofthe SMUs in the area covered by the database.

At an ESBN meeting in Paris in December 2008, the re-viewed SPADE 8 database was discussed, and following themeeting, the national evaluation reports and the country-specific databases were sent to the national stakeholders witha request to (i) review and change the existing data to plau-sible values based on the expert scrutiny and (ii) estimatenew datasets for the dominant soil types without data basedon their local expertise. The modifications received from thestakeholders were incorporated in the SPADE 8 database,which was renamed SPADE 11. However, once again thedata received from national stakeholders were inadequate,which still left the database incomplete, so SPADE 11 re-mained unpublished work in progress.

5 Establishing a SPADE for dominant soil types withfull coverage of the EU (SPADE 14)

Without further input from the national stakeholders, implau-sible data identified in SPADE 8 were estimated to make theProforma I (level 1) database more functional for modelling.Thus, starting in 2014, the SPADE 8 database was updatedby a working group consisting of the authors of the currentpaper.

Specifically, this work package had three key goals:

i. to implement the suggested improvements of the exist-ing data in the SPADE database suggested during the2008 evaluation;

ii. to estimate values for the profiles lacking data (approxi-mately 32 % of the dominant STUs) based on matchingof similar soil types in neighbouring countries, the datain SPADE 2 or other reference data sources;

iii. to update the existing SPADE database with the com-plete dataset after revision by the national stakeholders.

The resulting SPADE14 database is publicly availablethrough JRC’s ESDAC website (https://esdac.jrc.ec.europa.eu/content/spade-14, last access: 1 October 2019).

Firstly, the questionable values identified in SPADE 8, butnot corrected by stakeholders, and were adjusted to fit the-oretical or average values according to predefined equationsor guidelines (see below and Breuning-Madsen et al., 2015).Secondly, data for profiles lacking stakeholder-estimated val-ues were assigned by copying complete datasets from identi-cal soil types in neighbouring countries. If no matching pro-files were identified, the search was extended to the entiredatabase. Thirdly, data for the remaining∼ 15 % of the dom-inant soil types (STUs for which no estimated data existedanywhere in the database) were created by adjusting existingdata from similar soil profiles, preferably from the countryitself or neighbouring countries to minimise variation due toclimate and parent material. The evaluation guidelines sent tothe stakeholders during the SPADE 14 evaluation provideda detailed description of the methodology and an overviewof all modifications made, with the suggested changes prop-erly flagged with colour coding of adjusted values dependingon the nature of the change (Breuning-Madsen et al., 2015).The entire database was quality controlled with the updatedversions of equations and guidelines used during the 2008evaluation, thus ensuring consistency across member states.Finally, the quality-controlled national data were sent to eachstakeholder for final checking and revision. The changes sug-gested by stakeholders were incorporated before publication.

5.1 Examples of correction guidelines

For some parameters, no correction guidelines were speci-fied during the 2008 evaluation, in which case they were de-veloped during the 2014–2015 evaluation. As examples, the

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Table 1. Average bulk densities calculated from the SPADE 2database. The mean, standard deviation and the number of obser-vations (n) are shown.

OM Depth Bulk density SD n

(%) (cm) (gcm−3) (gcm−3)

90–100 0.1 0.13 16580–90 0.1 0.05 8170–80 0.2 0.11 6460–70 0.2 0.13 3650–60 0.3 0.13 2540–50 0.4 0.08 2830–40 0.4 0.17 1920–30 0.8 0.31 3510–20 1.0 0.72 1765–10 1.1–1.2 n/a n/a< 5 0–25 1.3 0.18 400

25–50 1.4 0.18 72650–75 1.4 0.17 719

75–100 1.5 0.14 468> 100 1.5 0.18 714

n/a: not applicable

estimation of bulk density and volumetric water content areelaborated below.

5.1.1 Bulk density

Missing BD values were assigned the average of all mea-sured values from the SPADE 2 depending on their OM anddepth (Table 1). For soil horizons with organic matter (OM)content > 10 %, BD values were calculated from the OMcontent grouped into 10 % intervals. For soils with OM con-tents < 5 %, BD values were averaged over depth intervalsof 25 cm down to 100 cm. Deeper horizons were assigned avalue of 1.5 gcm−3 unless geomorphology or overlying hori-zons indicated a significantly different value. For soils withOM contents between 5 % and 10 %, the BD was estimated tobe a value in the range 1.1–1.2 gcm−3 based on surroundinghorizons and profiles and whether it was in the high (∼ 10 %)or low (∼ 5 %) OM range.

5.1.2 Volumetric water content (VWC)

National stakeholders were requested to specify the wa-ter content at 1, 10 (field capacity), 100 and 1500 kPasuction for each soil horizon, enabling the calculation offunctions such as root zone capacity, i.e. plant-availablewater to a specified root depth, which could be 50 cm forgrasses, 100 cm for barley and up to 200 cm for wheat(e.g. Jensen et al., 1998). In order to assign realistic datato missing estimates, we regressed (multivariate linearregression) water-retention data, i.e. VWC (volumet-ric water content) at 1, 10, 100 and 1500 kPa suction,from countries with complete datasets against multi-

ple explanatory variables: BD, particle-size fractions(TEXT – % mass; < 2 µm=TEXT2; 2–20 µm=TEXT20;20–50 µm=TEXT50; 50–200 µm=TEXT200; 200–2000 µm=TEXT2000) and organic matter content (OM– % mass). Member states with complete datasets were Bel-gium, the United Kingdom (UK) and Denmark. As data fromDenmark were used for validation, the derived equationswere based on data from Belgium and the UK. Fluvisolswere omitted, as they often have complicated water-retentionproperties due to their geomorphological origin. Only 7 %(9 of 132) of the observations from Denmark deviated morethan 10 % VWC from the 1 : 1 line between observed andcalculated values using the linear models. The adjustedcorrelation coefficients were 0.85, 0.86, 0.87 and 0.91for VWC1, VWC10, VWC100 and VWC1500, respectively(P < 0.001), and the resulting regression equations were asfollows:

VWC1 = (−27.653×BD+ 1.463×OM+ 0.208×TEXT2

+ 0.017×TEXT20+ 0.154×TEXT50+ 0.013×TEXT200+ 0.003×TEXT2000+ 57.783)×BD,

(1)

VWC10 = (−20.231×BD+ 1.110×OM+ 0.262×TEXT2

+ 0.029×TEXT20+ 0.193×TEXT50− 0.026×TEXT200− 0.072×TEXT2000+ 41.072)×BD,

(2)

VWC100 = (−4.246×BD+ 1.356×OM+ 0.335×TEXT2

+ 0.071×TEXT20+ 0.105×TEXT50− 0.002×TEXT200− 0.015×TEXT2000+ 8.380)×BD,

(3)

VWC1500 = (−0.330×BD+ 1.088×OM+ 0.358×TEXT2

+ 0.125×TEXT20+ 0.072×TEXT50+ 0.056×TEXT200+ 0.053×TEXT2000− 4.719)×BD.

(4)

5.2 Traceability and quality check

In order to ensure traceability of all proposed changes, wedeveloped a colour-coding system for the Excel spreadsheetssubmitted to stakeholders that allowed them to identify thekind of changes that had been applied to each data element(Breuning-Madsen et al., 2015; Koue et al., 2008). More-over, a tracing document kept track of whether the domi-nating STUs contained original stakeholder-estimated data,a dataset copied from another profile in the database or adataset modified by the working group. For this dataset, aseparate tracing document kept track of profiles and parame-ters modified to anticipate potential criticism and controversyby national stakeholders, who were, however, always encour-aged to change and improve their national datasets. Finally,the data quality was evaluated prior to the modifications, anda new cross-database check was introduced to make sure

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294 J. A. Kristensen et al.: A harmonised soil profile analytical database for Europe

that the topsoil texture class specified in the estimated profiledatabase matched the actual topsoil texture class specified inthe estimated horizon database. When inconsistencies wereidentified, the topsoil texture class in the estimated horizondatabase was adjusted accordingly (Breuning-Madsen et al.,2015).

5.3 Evaluating, updating and publishing the SPADE 14database

Table 2 provides an overview of the origin of the data foreach country. The first column (original SPADE 8) showshow many profiles were available from both SPADE 1 and 8.The second column (SPADE 14 – profiles from other coun-tries) shows how many profiles were copied from other coun-tries, and the third column (SPADE 14 – modified profiles)shows how many profiles were created by the working groupby adjusting existing profiles in order to complete the na-tional datasets with suggested values.

Overall, the SPADE 18 (level 2) database contains soil an-alytical data from 1820 profiles, which is about 40 % morethan the number of profiles in SPADE 14 (level 1) contain-ing soil analytical data from 1078 profiles, almost double thenumber of profiles available in SPADE 1 and 8. Most of theprofiles originally lacking data had allocated datasets fromcomplete profiles from other countries. Yet ∼ 15 % of thedominant profiles specified by soil type and texture were nei-ther present in SPADE 1 nor 8 and had to be constructed bymodifying other existing profile datasets to fit the requiredsoil classification. Eight countries neither delivered data toSPADE 1 nor 8. Thus, datasets for these countries were ex-clusively based on imported or modified datasets. Stakehold-ers have been notified throughout this project that they mayupdate their national datasets at any time by contacting theresponsible ESDAC office.

6 Creating a pilot version of the SPADE 18 level-2database (SPADE 18)

As described previously, the SPADE framework has four lev-els. The level-2 database contains the same type of analyticaldata as the level-1 database, but in addition to the dominat-ing soil types, the inclusions and associations have been as-signed a set of estimated analytical data. This improves theuse of the SGDBE to predict soil characteristics (e.g. irriga-tion need or carbon stocks), as users can assign values for allsoil types within each SMU.

In 2017, a working group from the European Soil Bureauand University of Copenhagen discussed the methodologyfor creating a level-2 SPADE database (SPADE 18). Giventhat it took about 20 years to create the level-1 database, itwas decided to speed up the process by following the routeused to finalise SPADE 14 to have a complete dataset thatcould be subsequently improved by national stakeholders.The following concepts were developed based on the work

on finalising level-2 datasets from two member states, Den-mark and the UK.

1. For each country, unique combinations of all soil typesand topsoil textures present as dominant, associated orincluded STUs were listed. For the UK 79 new soiltypes had to be added to the 62 at level 1, and for Den-mark this left 29 unique combinations compared to 13at level 1, where only dominant soil types were consid-ered. Thus, 16 new soil types had to be added to theDanish database.

2. For each missing soil type, the entire level-1 databasewas scrutinised for the particular soil type. If multiplecountries contained the soil type, profiles from neigh-bouring countries had preference. If more than oneneighbouring country had the desired soil type, agricul-tural land use had preference, according to the originalaim of MARS and SPADE (Breuning-Madsen, 1989;Vossen, 1993).

3. In cases where the soil type did not exist as a dominatingsoil type for any other country in the database, the soiltypes were taken from a database containing modifiedsoil profile data. This database was created by compil-ing a list of all combinations of soil type and topsoiltexture in the entire SPADE database that did not ex-ist as dominating in any country and therefore had noestimated data assigned at level 1 (129 unique combina-tions in total). In the same way as described for the dom-inating soil types, data were estimated for these profilesby making minor modifications to existing profiles. Forexample, a Podzol with a topsoil texture class 2 (Po-2) could be created from a slight modification of thetopsoil particle-size distribution for a Po-1 and a subse-quent adjustment of other characteristics affected by thechange in soil texture.

4. After completion, the level-2 database will be sharedwith national stakeholders for evaluation, and changescan be made to any data not found to be valid or mean-ingful.

5. The final version will be published through JRC’s ES-DAC website (http://esdac.jrc.ec.europa.eu/, last access:1 October 2019).

7 SPADE applications: root zone capacity (RZC) andSOC stocks in Europe

Earlier versions of the SPADE have been used to estimateSOC stocks (European Commission, 2005). More recently,SPADE was used to map the distribution of wheel load-carrying capacity in Europe (Schjønning et al., 2015).

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Table 2. The origin of SPADE data at the national level. “Original” shows the soil profiles for which the stakeholders originally provideddata, “Profiles from other countries” show the soil profiles for which data were copied and pasted from a similar country, “Modified profiles”show the soil profiles to which slight adjustments were made, “Level-1 total” shows the total number of dominating soil profiles, whichare available in the current database (SPADE 14), and “Level-2 total” shows the total number of profiles when associated soil types wereincluded. The datasets for associated soils will be available when the level-2 database (SPADE 18) is fully developed.

Country Country Original Profiles from other Modified profiles Level-1 total Level-2 totalcode (SPADE 8) countries (SPADE 14) (SPADE 14) (SPADE 14) (SPADE 18)

AL Albania 14 13 3 30 49AT Austria 0 23 4 27 35BE Belgium 42 14 0 56 74BG Bulgaria 0 16 7 23 40CH Switzerland 28 2 7 37 51CZ Czech Rep. 0 19 7 26 73DE Germany 60 15 2 77 149DK Denmark 13 0 0 13 29EE Estonia 11 2 4 17 26ES Spain 26 15 8 49 65FI Finland 6 1 0 7 12FR France 118 35 22 175 230GB United Kingdom 41 15 6 62 141GR Greece 10 15 4 29 66HU Hungary 40 10 11 61 92IE Ireland 18 4 3 25 44IT Italy 21 11 9 41 91LT Lithuania 0 20 8 28 52LU Luxembourg 0 10 2 12 26LV Latvia 26 0 0 26 39NL The Netherlands 20 12 0 32 42NO Norway 15 0 1 16 23PL Poland 0 28 12 40 63PT Portugal 18 10 4 32 66RO Romania 28 28 21 77 115SE Sweden 0 9 3 12 23SK Slovakia 17 6 1 24 73SL Slovenia 0 15 9 24 31

Total 572 (31 %) 348 (19 %) 158 (9 %) 1078 (59 %) 1820 (100 %)

7.1 Root zone capacity to 100 cm

As an example of the use of the complete SPADE level-1database for a relevant soil property, we calculated the plant-available water for crops having an effective root depth of100 cm (e.g. barley), also called root zone capacity (RZC100)(Fig. 2). Crop production on soils with RZC100 < 50 mm innorthern Europe and < 100 mm in southern Europe is highlydependent on irrigation. RZC was estimated from the follow-ing equation:

RZC100 =∑

i=100(VWC10i −VWC1500i)×Di, (5)

where RZC100 is the cumulated root zone capacity (mm)within the upper 100 cm, VWC1500i is the volumetric wa-ter content at −1500 kPa for horizon i (%), VWC10i is thevolumetric water content at −10 kPa for horizon i (%) andDi is the depth of horizon i (mm).

Areas with very high RZC100 (> 300 mm) relate mainly tothe occurrence of Histosols, Gleysols and Fluvisols, whichare affected by shallow groundwater tables and few well-drained soils with high silt and fine sand content (Fig. 2).Soils with high RZC100 (200–300 mm) are common in theloess belt, just south of the ice margin from the previous iceages, e.g. Belgium and Germany. The medium RZC100, 100–200 mm, corresponds mainly to loamy soils, for instance,those dominating in eastern Denmark, England and Poland,while sandy soils and some shallow loamy soils have a lowRZC100 of 50–100 mm, e.g. western Denmark and Sweden.Very shallow soils (Leptosols) have a very low RZC100 of0–50 mm, which are found primarily in mountainous regionssuch as the Alps, coastal Norway and large parts of Greece.

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296 J. A. Kristensen et al.: A harmonised soil profile analytical database for Europe

Figure 2. Plant available water content (in mm) within the up-permost 1 m of the soil: very low – 0–50 mm, low – 50–100 mm,medium – 100–200 mm, high – 200–300 mm – and very high –> 300.

7.2 SOC stock to 100 cm for Europe

We estimated the SOC stock for Europe from the followingequation:

SOC100 =∑i=1

(1− gi)piSOCiDiA, (6)

where SOC100 is the cumulated SOC stock to 100 cm depth,gi is the gravimetric coarse-particle fraction (> 2 mm) ofhorizon i (coarse material correction; Hobley et al., 2018),pi is the fine-earth (< 2 mm) bulk density of horizon i, SOCi

is the SOC concentration for horizon i, Di is the depth ofhorizon i and A is the area of the particular STU, i.e. thearea of the SMU multiplied by the proportional area coveredby the STU (Fig. 3). The regional distribution of soil organicC stocks is similar to what was found previously (EuropeanEnvironment Agency, 2012; Panagos et al., 2013). The high-est stocks are concentrated in areas dominated by Histosols(e.g. northwestern British Isles and Finland; Fig. 3). Inter-mediate stocks are situated in the wet northwestern IberianPeninsula, in the Massif Central region in France and in theinterior parts of the Scandinavian Peninsula, while soils withrelatively low SOC stocks are situated in mountainous areas(e.g. coastal Norway), dry Mediterranean areas and areas un-der intensive cultivation (e.g. northern France, Germany andDenmark).

Our estimated cumulated SOC stock for Europe (0–100 cm) based on SPADE 14 (level 1) is 60× 1015 g. Thiscompares to the estimate of 75× 1015 g obtained by the Eu-ropean Environment Agency (2012) and the EC Joint Re-search Centre (Panagos et al., 2013) based on an earlier ver-

Figure 3. The soil organic carbon stocks (t ha−1) in Europe withinthe upper 100 cm of soil calculated based on level-1 data (dominat-ing soil types only).

sion of the database, showing that our approach produces asomewhat lower result. We did not find other estimates ofEuropean SOC stocks across landscape types in the scien-tific literature. However, as an approximation we may sum upthe recent estimates of SOC stocks in agricultural and forestsoils. The forest SOC stock in Europe (0–100 cm) was esti-mated to be 22×1015 g (De Vos et al., 2015), while the agri-cultural SOC stock (0–30 cm) was estimated to 18× 1015 g(Lugato et al., 2014). As an attempt to roughly correct forthe agricultural estimate only covering the upper 30 cm ofthe soil profile, we assumed that the topsoil (0–30 cm) con-tained about 60 % of the SOC stock in the top 100 cm (DeVos et al., 2015). Using this correction the estimate for theagricultural soils to 100 cm increased to 30× 1015 g, so theestimates sum up to 52× 1015 g SOC, which is quite similarto our SPADE 14 (level 1) estimate. Particularly consideringthat over- or underestimation of ∼ 40–100 % when compar-ing to other studies is common (De Vos et al., 2015; Guevaraet al., 2018; Lugato et al., 2014). Nonetheless, work still re-mains on elucidating the underlying sources of variation tofind the best approach, as estimates of SOC are considered tobe an important indicator of environmental health (EuropeanEnvironment Agency, 2012; Panagos et al., 2013).

7.3 Better estimates with SPADE level 2: the SOC stockin Denmark

The application of SPADE level-2 (SPADE 18) data has beentested in a pilot study calculating the RZC for wheat in Den-mark (Jensen et al., 1998). They found a substantial differ-ence of up to∼ 50 % in estimated national RZC values whencomparing level-1 to level-2 data. To show the added value

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from including the associations and inclusions in another ex-ample, we calculated the SOC to 1 m depth for Denmarkbased on SPADE 14 (level 1; Fig. 4a) and SPADE 18 (level 2;Fig. 4b) data.

Overall, the comparison shows that the estimated totalSOC stock in the upper metre of Danish soils increases by12 % from 332× 1012 to 378× 1012 gC when using level-2 data instead of level-1 data. This number is higher, yetit is not quite as high as the most recent estimate obtainedfrom digital soil mapping of about 570× 1012 gC (Adhikariet al., 2014) and previous estimates ranging from 563 to598× 1012 C (Krogh et al., 2003), but it suggests that us-ing level-2 data yields more comparable results than usinglevel-1 data. The increase in SOC stock using level-2 datacompared to level-1 data is mostly due to SOC-rich soilssuch as Histosols, Gleysols and Fluvisols, which are primar-ily present as associations or inclusions. The spatial distribu-tion of the changes reveals that particularly in northern Jut-land on the raised seabeds, the inclusion of subordinate soiltypes increased the SOC stock substantially (Fig. 4c), occa-sionally by more than 30 % (red areas). For sandy soils (west-ern Jutland), the carbon gain was modest, typically less than20 %. Only in small loamy SMUs in western Jutland did thecarbon content decrease by using the level-2 database, whichis probably due to the inclusion of sandy soils with relativelylow organic matter content. This study highlights the addedaccuracy of estimating an environmentally relevant soil prop-erty like SOC stock by the more detailed level-2 database,which yielded estimates more similar to the estimates ob-tained with pedometric (Krogh et al., 2003) and advancedinterpolation approaches (Adhikari et al., 2014) than resultsbased on SPADE level 1.

8 Limitations of our approach

Digital soil mapping (DSM; reviewed in Mulder et al., 2011;Minasny and McBratney, 2016; Zhang et al., 2017) is thefuture of soil mapping and is constantly developing and im-proving (e.g. Hengl et al., 2017; Møller et al., 2019; Pouladiet al., 2019; Stockmann et al., 2015; Zeraatpisheh et al.,2019). The great advantage of these formalised approachesis their reproducibility and ability to estimate the accuracyof their predictions. However, as mentioned earlier, chal-lenges to such inference techniques persist (Mulder et al.,2011; Zhang et al., 2017); particularly data scarcity is a ma-jor challenge. Similar conclusions underlie data harmoni-sation initiatives at the global scale led by ISRIC, whichhas led to the construction of the GlobalSoilMap (Arrouayset al., 2014), the SoilGrids (Hengl et al., 2014, 2017), theHWSD (Nachtergaele et al., 2014) and the WISE30sec (Bat-jes, 2016). To overcome this, the EU recently launched theLUCAS 2018 – SOIL COMPONENT (Fernández-Ugalde etal., 2017), which is a soil-sampling programme that will pro-

vide measured soil data from ∼ 27000 profiles covering theEuropean area.

However, to supplement such approaches until data avail-ability increases, databases with analytical soil properties es-timated or evaluated by local expert stakeholders are still afeasible way of assessing large-scale soil property patterns,which are substantiated by our ability to estimate similar dis-tributions and stocks as previous studies. Yet our voluntaryapproach is vulnerable to inadequate stakeholder engage-ment, which has been a challenge throughout this process.This adds to the justifications of the LUCAS 2018 – SOILCOMPONENT.

A consideration with respect to the interpretation of out-puts from bottom-up harmonised databases, like SPADE, ishow well the mapping units actually reflect real soil and land-scape delineations (Fig. 1a). Efforts have been made by theESDAC to let mapping units overlap arbitrary administra-tive limits, such as national borders, to best fit the SMU de-lineations on both sides (e.g. European Commission, 2005).However, the inherent variation in the level of detail from thenational datasets is still evident in certain areas (see for in-stance the Danish–German border in maps – European Com-mission, 2005). Therefore, the predictions based on the cur-rent dataset might be improved by modern downscaling tech-niques (see Møller et al., 2019, for an example), but it mightbe appropriate to consider a cell-based data representationif further disaggregation were to be implemented. However,considering the scale of the EU soil map (1 : 1000000), it isnot feasible to delineate single STUs, so working with SMUswith a set of STUs is still feasible for this purpose.

9 Concluding remarks

We document the development of a full-covered EU-widesoil database, containing analytical data connected to the soilmap of Europe at a scale of 1 : 1000000. We show the bene-fits of careful analysis of legacy data, wherever possible, withthe help of national soil experts.

The application of the current soil analytical database atlevel 1 was illustrated by calculating the root zone capacityto 100 cm for the Europe and associated countries, mappingout areas where severe need of irrigation for crop productionmight occur. Moreover, we estimate the SOC stock at 100 cmfor Europe to be 60×1015 g, which is comparable to previousestimates. The increased accuracy obtained by including as-sociated and included soil types in the SPADE database waspresented by comparing the SOC stock of Denmark calcu-lated from level-1 and level-2 data, showing an increase of12 % from 332× 1012 to 378× 1012 gC, which is closer toliterature estimates obtained with other methods. This exer-cise highlights the need for a level-2 database for the entireEuropean area.

Perhaps the greatest contribution of this research to themanagement and protection of Europe’s soils is the harmoni-

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298 J. A. Kristensen et al.: A harmonised soil profile analytical database for Europe

Figure 4. Soil organic carbon stocks (t ha−1) in Denmark within the upper 100 cm of the soil calculated based on (a) SPADE 14 level-1 dataand (b) SPADE 18 level-2 data. (c) Shows the relative change from level 1 to level 2 (in %).

sation of detailed soil profile data, hitherto unavailable acrossregions but now connected to the latest soil mapping. Theseconsiderations are driving initiatives such as the soil compo-nent of the LUCAS survey, which by generating harmonisedand comparable data on topsoil characteristics across the EU(Orgiazzi et al., 2014), increases the predictive capability andaccuracy of digital soil-mapping approaches. In time, soilmapping will need to accommodate high data streams thatwill be driven by precision farming, proximal sensing andthe internet of things (Carolan, 2017), but until sufficient dataamounts exist, databases with expert estimated data like thecurrent SPADE are a good supplement.

Finally, while soils are often on land under private owner-ship, there is the increasing recognition of soil as a “publicgood” that provides society with key ecosystem services. Insuch a paradigm, there is a strong case to be made for pro-

viding unrestricted access to soil data. Many national soil in-stitutions regard soil profiles as “primary data sources” thatunderpin revenue earning systems. However, there is a strongcase for inherent soil data (i.e. texture, carbon, pH, nutrientcontent, cation exchange capacity, EC, etc.) that reflect pe-dogenic processes and basic land management practices tobe publicly available (with appropriate attribution or data-sharing licence). Such an approach, possibly driven by theaims of the Global Soil Partnership to enhance the quantityand quality of soil data and data collection, could lead to amore rapid completion of the higher-level orders of SPADEwhile at the same time providing new understanding in pe-dogenesis and the need for further research.

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Data availability. The data are available at the ESDAC website:https://esdac.jrc.ec.europa.eu/content/spade-14 (last access: 2 Oc-tober 2019).

Author contributions. JAK and HBM framed the work and ledthe paper writing. HBM and JAK scrutinised and updated thedatabases. TB did the GIS analyses and created the maps. RJAJ,AJ, LM and PP made substantial contributions during revisions.

Competing interests. The authors declare that they have no con-flict of interest.

Acknowledgements. We want to warmly thank our late col-league and friend, Professor Henrik Breuning-Madsen, who passedaway during the preparation of this paper. He was a key figurein moving European soil science forward over more than threedecades. We thank all national stakeholders for their contribu-tions to the development of the SPADE database. For a full list ofstakeholders, we refer to ESDAC’s home page: http://esdac.jrc.ec.europa.eu/ (last access: 1 October 2019).

Financial support. This research has been supported by the ECJoint Research Centre.

Review statement. This paper was edited by Bas van Wesemaeland reviewed by two anonymous referees.

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