LUCAS 2018 soil module : presentation of dataset and results.
Date: 2022-07-26
Institutions: Joint Research Centre
Subjects: data processing, database, earth sciences, geography, land use, research report, sampling, soil analysis, soil science, soil type

LUCAS 2018 Soil Module  

Presentation of dataset and 
results  
Fernandez-Ugalde, O; Scarpa, S; Orgiazzi, A.; 
Panagos, P.; Van Liedekerke, M; Marechal A. & 
Jones, A..  

2022 

2021 

EUR 31144 EN 

 
 
 
 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and 
knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. 
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European Commission nor any person acting on behalf of the Commission is responsible for the use that might 
be  made of  this  publication. For  information on  the methodology  and quality  underlying  the  data  used  in  this 
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expression  of  any  opinion  whatsoever  on  the  part  of  the  European  Union  concerning  the  legal  status  of  any 
country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Arwyn Jones 
Address: European Commission Joint Research Centre, Sustainable Resources Directorate – Land Resources 
Unit, Via Fermi 2749, 21027 Ispra (VA), Italy 
Email: arwyn.jones@ec.europa.eu 
Tel.: +39 0332 789162 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC129926 

EUR 31144 EN 

EN PDF 

ISBN 978-92-76-54832-4 

ISSN 1831-9424 

doi:10.2760/215013 

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How to cite this report: Fernandez-Ugalde, O; Scarpa, S; Orgiazzi, A.; Panagos, P.; Van Liedekerke, M; 
Marechal A. & Jones, A. LUCAS 2018 Soil Module. Presentation of dataset and results, EUR 31144 EN, 
Publications Office of the European Union, Luxembourg. 2022, ISBN 978-92-76-54832-4, doi:10.2760/215013, 
JRC129926 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Acknowledgements ................................................................................................ 1 

Executive Summary ............................................................................................... 2 

Abstract ............................................................................................................... 3 

1  Introduction ...................................................................................................... 6 

1.1  Background................................................................................................. 6 

1.2  2018 Survey ............................................................................................... 7 

2  LUCAS sampling methodology and laboratory analysis ........................................... 9 

2.1  Sample collection ......................................................................................... 9 

2.2  Sample analysis ........................................................................................ 11 

2.2.1  Core analysis..................................................................................... 11 

2.2.2  DNA analysis ..................................................................................... 11 

2.2.3  Analysis of plant protection products .................................................... 13 

2.3  Field-based assessments ............................................................................ 13 

3  Soil data evaluation ......................................................................................... 15 

3.1  Identification and registration of samples during the field survey and in the 
laboratory ....................................................................................................... 15 

3.2  Agreement of the data format with the specifications of the call for tender for 
laboratory analysis in LUCAS 2015 ..................................................................... 17 

3.3  Evaluation of soil data produced in the laboratory .......................................... 19 

3.4  Evaluation of field-based data ..................................................................... 24 

4  The 2018 LUCAS Soil Module: structure and description ....................................... 29 

4.1  Reflections on 2018 dataset ........................................................................ 38 

4.2  Spatial representation of soil properties in the EU-28 ..................................... 39 

4.3  Assessment of gully erosion ........................................................................ 43 

4.4  Condition of organic soils ............................................................................ 45 

4.5  Bulk density and particle size distribution ..................................................... 46 

4.6  Organic carbon .......................................................................................... 46 

4.7  Nitrogen, phosphorus, and potassium .......................................................... 60 

4.7.1  Nitrogen content ................................................................................ 60 

4.7.2  Phosphorus content............................................................................ 72 

4.7.3  Potassium content ............................................................................. 82 

4.8  pH and carbonates ..................................................................................... 93 

4.8.1  pH ................................................................................................... 93 

4.8.2  Carbonates ....................................................................................... 93 

4.9  Electrical conductivity ............................................................................... 106 

4.10 

4.11 

Oxalate extractable Fe and Al .............................................................. 107 

Metals .............................................................................................. 102 

i 

5  Conclusions .................................................................................................. 103 

References ....................................................................................................... 104 

List of figures .................................................................................................... 106 

List of tables ..................................................................................................... 109 

ii 

 
 
 
Acknowledgements 

The JRC wishes to acknowledge the support provided by colleagues from AGRI, CLIMA and 
ENV, both in terms of technical contributions and financial support for the collection and 
analysis of the soil samples. 

We  would  like  to  especially  acknowledge  the  close  collaboration  with  colleagues  from 
Eurostat,  in  particular  Alessandra  Palmieri  and  Beatrice  Eiselt  to  ensure  the  successful 
outcome of the survey. 

Finally,  huge  thanks  to  colleagues  of  the  JRC  Soil  Team  for  their  invaluable  hands  on 
support during the survey preparation and in the analysis of the laboratory data. 

Best wishes to Oihane in her new career with DG AGRI – a huge thanks for all the support 
over the past six years. 

Authors  

Fernandez-Ugalde, O; Scarpa, S; Orgiazzi, A.; Panagos, P.; Van Liedekerke, M; Marechal A. & Jones, A. 

1 

 
 
 
 
Executive Summary 

This report summarises the soil dataset collected as part of the 2018 Land Use/Cover Area 
frame  statistical  Survey’  (generally  referred  to  as  LUCAS  Soil  Module).  It  presents  an 
overview  of  the  various  laboratory  analysis  and  describes  the  spatial  variability  of  soil 
properties by land cover (LC) class and a comparative analysis of the soil properties for 
NUTS 2 regions. 

The LUCAS Soil Module is the only mechanism that currently provides a harmonised and 
regular collection of soil data for the entire territory of the European Union, addressing all 
major land cover types simultaneously, in a single sampling period (April – October). 

At  the  same  time,  the  LUCAS  Soil  module  can  support  further  policy  needs  through  a 
flexibility that permits both the collection of new field data, if required, from new sampling 
sites.  In  turn,  this  can  be  complemented  with  additional  laboratory  analysis  (e.g. 
micronutrients, specific pollutants). This capacity addresses the needs of a diverse policy 
user base and an evolving policy landscape. 

Several  new  developments  were  put  in  place  for  the  2018  LUCAS  Soil  module.  These 
included: 

•  Assessment of bulk density for a subset of locations. 

•  Trial to extend sampling depth to 30 cm (only in Portugal). 

•  An assessment of different types of soil erosion (sheet, rill, gully, wind). 

•  Measuring the depth of organic soils. 

•  The collection of fresh samples in order to extract DNA from the soil fabric to assess 

soil biodiversity and detect the presence of antibiotic resistance genes. 

•  Selected measurements of metals, and a pilot to assess residues of plant protection 

products and veterinary antibiotics in a subset of samples. 

Initially, 27,069 locations were identified for soil sampling, incorporating locations visited 
in 2015. At the close of the survey, a soil related activity (i.e. assessing type of erosion, 
organic  soil  check  sample  collection)  was  made  at  19,345  locations  (i.e.  LUCAS  Grid 
Points). After the removal of samples that could not be identified or were mislabelled or 
lost in transit, the LUCAS 2018 Soil Module dataset contains data for 18,984 locations.  

As in the previous exercises, a common sampling procedure, single laboratory, standard 
analytical methods were applied. 

Additional  soil  parameters  that  were  collected  from  all  LUCAS  field  points  primarily  to 
support  soil  erosion  modelling  (e.g.  signs  of  ploughing,  presence  of  crop  residues, 
percentage  of  stones)  are  not  presented  here  but  are  included  in  the  LUCAS  2018 
microdata, which is made available by EUROSTAT. 

A parallel report will present an assessment of changes in soil properties between 2009, 
2015 and 2018. 

The  results  of  analysis  into  metals,  biodiversity  assessment,  antimicrobial  resistance 
genes,  antibiotics,  residues  of  plant  protection  products,  and  microplastics,  as  well  as 
changes between 2009 and 2018, will be presented as separate reports. 

Data can be downloaded from  

https://esdac.jrc.ec.europa.eu/content/lucas2018-topsoil-data 

2 

 
 
 
 
Abstract 

This report accompanies the release of the soil dataset collected as part of the 2018 Land 
Use/Cover Area frame statistical Survey’ (generally referred to as LUCAS Soil). It presents 
an  overview  of  the  laboratory  analysis  data  and  provides  a  detailed  description  of  the 
results for the European Union (EU) and the United Kingdom as it was still a Member State 
at the time of the survey. The report describes the spatial variability of soil properties by 
land cover (LC) class and a comparative analysis of the soil properties for NUTS 2 regions. 

Regular  monitoring  provides  a  unique  perspective  on  pressures  affecting  soils.  In  this 
respect, the soil module of LUCAS supports the specific needs of the European Commission 
by  collecting  data  that  characterises  soil  condition  and  health,  which  can  be  affected  in 
relation  to  land  use  practices  and  other  activities  that  are  driven  by  specific  policy 
instruments.  

The LUCAS Soil module is the only mechanism that currently provides a harmonised and 
regular collection of soil data for the entire territory of the EU, addressing all major land 
cover types simultaneously, in a single sampling period (April – October). 

At  the  same  time,  the  LUCAS  Soil  module  can  support  further  policy  needs  through  a 
flexibility that permits both the collection of new field data, if required, from new sampling 
sites.  In  turn,  this  can  be  complemented  with  additional  laboratory  analysis  (e.g. 
micronutrients, specific pollutants). This capacity addresses the needs of a diverse policy 
user base and an evolving policy landscape. 

The  drive  to  collect  soil  samples  under  the  umbrella  of  LUCAS  was  led  initially  by  DG 
Environment, who provided funding for the 2009 survey to collect a baseline dataset on a 
range  of  soil  characteristics  such  as  organic  matter  content,  nutrient  status,  fertility, 
acidification and soil pollution (metals). At that time, the main LUCAS survey was planned 
for 23 EU Member States (MS). Bulgaria and Romania were added in 2012 while Croatia, 
Cyprus and  Malta were formally included in 2015.  In the 2018  survey, all 28 MS at the 
time were included. 

The initial premise was developed to  collect samples from a depth  of  20 cm following a 
common sampling procedure from 10% of the sites where field visits were to be carried 
out as part of the main LUCAS survey. In 2009, this gave around 235,000 possible locations 
for a nominal target of 23,500 soil samples. At the end of the survey, about 20,000 had 
been collected. These samples were analysed according to standard analytical methods in 
a single laboratory for a range of physical and chemical properties. In addition, visible and 
near-infrared spectra were acquired for all samples. The process was repeated in 2012 for 
Bulgaria and Romania, where samples were collected from about 2,000 locations. In total, 
22,003 samples were analysed for 2009/2012. 

In  2015,  90%  of  the  locations  sampled  in  2009  and  2012  were  maintained  with  the 
remaining  10%  being  substituted  by  new  locations,  including  points  at  altitudes  above 
1,000 m, which were out of scope of the earlier surveys. In total, 21,859 samples were 
collected, of which 4,246 were at new locations compared with the 2009/2012 campaign. 
In  addition,  the  soil  module  was  extended  by  the  JRC  Enlargement  and  Integration 
Programme to Albania, Bosnia and Herzegovina, Croatia, Montenegro, North Macedonia, 
and  Serbia  (1,015  samples  were  eventually  collected).  Switzerland  also  participated 
following standard LUCAS protocols (150 samples were collected by Agroscope).  

For  2018,  27,069  locations  were  identified  for  soil  sampling.  A  soil  related  activity  (i.e. 
assessing  type  of  erosion,  organic  soil  check  sample  collection)  was  made  at  19,345 
locations (LUCAS Grid Points). After the removal of samples that could not be identified or 
were mislabelled or lost in transit, the LUCAS 2018 Soil Module dataset contains data for 
18,984 locations.  

As in the previous exercises, a common sampling procedure, single laboratory, standard 
analytical methods were applied. 

3 

 
It is worth noting that several new developments were put in place for the 2018 LUCAS 
Soil module. These included: 

•  Assessment of bulk density for a subset of locations. 

•  Trial to extend sampling depth to 30 cm (only in Portugal). 

•  An assessment of different types of soil erosion (sheet, rill, gully, wind). 

•  Measuring the depth of organic soils. 

•  The collection of fresh samples in order to extract DNA from the soil fabric to assess 

soil biodiversity and detect the presence of antibiotic resistance genes. 

•  Selected measurements of metals, and a pilot to assess residues of plant protection 

products and veterinary antibiotics in a subset of samples. 

Additional  soil  parameters  that  were  collected  from  all  LUCAS  field  points  primarily  to 
support  soil  erosion  modelling  (e.g.  signs  of  ploughing,  presence  of  crop  residues, 
percentage  of  stones)  are  not  presented  here  but  are  included  in  the  LUCAS  2018 
microdata, which is made available by EUROSTAT. 

A comparison exercise between the results of the 2018 LUCAS Soil survey and the national 
soil monitoring was carried out in Austria by AGES (LUCASA study) – this will be reported 
separately. 

A parallel report will present an assessment of changes in soil properties between 2009, 
2015 and 2018. 

A set of descriptive environmental data for the soil sampling sites is available to download 
from ESDAC. 

Some take away messages from LUCAS 2018: 

•  3rd iteration of LUCAS Soil, covering all EU Member States in a six month window. 

•  Laboratory analyses were carried out on samples collected from 18,984 locations 

•  16,556  sites  were  repeat  visits  to  those  sampled  in  2015  while  75%  were  also 
sampled in 2009/2012. In total, 13,375 sites have been visited in all three surveys 
(2009/2012, 2015, 2018). 

•  Almost 32,000 individual samples were analysed by the laboratory (standard and 

bulk density). 

•  Quality  control  checks  indicate  that  the  laboratory  analysis  was  consistent  with 

2015. 

•  The 2018 Survey saw the introduction of several new elements: 

o  6,271  samples  were  collected  using  metal  cores  to  assess  bulk  density, 

nominally for three depths. 

o  885 fresh samples were collected to assess soil biodiversity.  
o  Surveyors  were  only  able  to  ascertain  erosion  features  in  850  points, 
predominantly  correctly  identifying  gully  erosion.  The  absence  of  erosion 
features may be masked by crops at the time of the survey. 

o  The depth of the organic horizon was measured in 1,050 points, with 30% 
recording 40 cm or more. Most of the sites selected for depth assessments 
appear not to fulfil the depth criteria for Histosols. However, the assessment 
failed to assess the  reason  for  very shallow organic soils (e.g.  such as  on 
bedrock). The implication could be that many of these locations are either 

4 

 
 
 
mineral  soils  with  well-developed  organic  horizons  or  that  peatlands  have 
been eroded back to the underlying mineral base. 

o  The  elaboration  of  LUCAS  field  observations  on  gully  erosion  channels 
combined with on-screen interpretation allowed the  creation of a first  EU-
wide gully erosion inventory of 206 gully points (ca. 1% of the total surveyed 
points). 

o  Genetic metabarcoding (also known as eDNA) was used to characterise the 
complex  genetic  diversity  of  soil  bacteria,  fungi  and  eukaryotes.  Work  is 
ongoing  to  understand  the  relationships  between  environmental  drivers, 
land use and the soil microbiome. 

o  Analysis of antibiotic residues, presence  of antimicrobial resistance genes, 
and residues of active ingredients of plant protection products are ongoing 
(delayed by COVID) and will be reported separately (2022 Q2). 

o  Extractable aluminium and iron oxalate were measured for 2,510 cropland 

locations (linked to the availability of phosphorous in soils). 

•  The  concentration  of  metals  was  measured  in  997  locations,  90%  of  which  were 

resampling of sites from 2009/2012.  

•  71% of targeted points were sampled compared to 85% and 95% in LUCAS 2015 
and LUCAS 2009/12 surveys, respectively. The percentage of points sampled was 
below 50 % in Germany, Croatia, Ireland, Malta, the Netherlands, Romania and the 
United  Kingdom.  The  low  rates  of  sampling  were  attributed  to  a  change  in  the 
survey company (Romania), extreme weather and soil conditions, the presence of 
dangerous animals, poor access, rocky soils and the presence of fences in cropland 
complicated the access to the points.  

•  Legal issues related to accessing private land was the main issue in Germany for 
not taking samples while in the UK, a denial of access by land owners reflecting the 
Brexit referendum was an issue in many cases. 

•  As in the previous studies, highest organic carbon levels (EU mean = 318.1 g kg-1) 
are  found  in  the  wetlands  of  the  Boreal  and  the  Atlantic  zones  (peat).  Organic 
carbon  content  was  also  high  in  woodland  points  (EU  mean  =  88.1  g  kg-1), 
especially  in  north-western  climatic  zones  (boreal,  Atlantic,  sub-oceanic,  and 
northern sub-continental). The mean organic carbon content of grasslands was 40.2 
g kg-1 in grassland points, rising slightly to 55.2 g kg-1 in shrubland. Organic carbon 
content was the lowest in croplands (EU mean = 18.3 g kg-1) and unsurprisingly, in 
bareland locations (EU mean = 17.3 g kg-1).  

•  Phosphorous levels are higher in soils under cropland and managed grasslands. 

•  Potassium  content  tends  to  be  lower  in  northern  than  in  southern  Europe,  with 
lowest contents in boreal and northern sub-continental zones and highest contents 
in semi-arid Mediterranean and southern sub-continental zones. 

•  The  results  of  analysis  into  metals,  biodiversity  assessment,  antimicrobial 
resistance  genes,  antibiotics,  residues  of  plant  protection  products,  and 
microplastics,  as  well  as  changes  between  2009  and  2018,  will  be  presented  as 
separate reports. 

Data can be downloaded from  

https://esdac.jrc.ec.europa.eu/content/lucas2018-topsoil-data 

5 

 
 
 
 
1  Introduction 

1.1  Background 

Soils  deliver  fundamental  ecosystem  services  with  environmental,  economic,  and  social 
benefits for people. These services can be grouped into provisioning (food, feed, fuel, fibre 
and genetic resources), regulating (storage, filtration and cycling of nutrients and water), 
cultural  (aesthetic,  spiritual  and  recreational  values)  and  supporting  (essential  for  the 
provision of all other services). The provision of these ecosystem services depends on a 
sustainable management of soils aiming at maintaining/improving their health.  

Sustainable soil management is a prerequisite to meet many of the objectives of the Green 
Deal (1): preserving and restoring ecosystems and biodiversity, reducing nutrients losses 
and the use of pesticides and fertilisers, a zero pollution ambition, the mitigation of and 
adaptation to climate change, and the conservation of the rural landscape. In this context, 
a pan-European network of land and soil monitoring is fundamental. The soil assessment 
module  of  the  LUCAS  (Land  Use  and  Cover  Area  Frame  Survey)  programme  is  the  only 
mechanism  for  a  harmonised  monitoring  (common  sampling  procedure  and  standard 
analysis methods) both in space and time of topsoils in the European Union (EU). 

The  LUCAS  Programme  is  an  area  frame  statistical  survey  organised  and  managed  by 
Eurostat  (the  Statistical  Office  of  the  EU)  to  monitor  changes  in land use  (LU)  and  land 
cover (LC) over time across the EU. Since 2006, Eurostat has carried out LUCAS surveys 
every three years. The surveys are based on the visual assessment of environmental and 
structural elements of the landscape in georeferenced control points. The points belong to 
the intersections of a 2 x 2 km regular grid covering the territory of the EU. This results in 
around 1,000,000 georeferenced points. In every survey, a subsample of these points is 
selected for the collection of field-based information.  

In  LUCAS  2009,  about  235,000  points  were  visited  across  25  Member  States  (Bulgaria, 
Romania, Cyprus and Malta were not included in the formal survey but some limited soil 
samples were collected from the latter two by the JRC). In ten percent of these points, soil 
samples were taken from a depth of 20 cm and analysed for the following properties in a 
single laboratory:  coarse  fragments,  clay,  silt and  sand,  pH  (in  CaCl2  and  H2O),  organic 
carbon  (OC),  carbonates  (CaCO3),  phosphorous  (P),  total  nitrogen  (N),  extractable 
potassium (K), cation exchange capacity, multispectral properties, and metals. The details 
and outcomes of the 2009 soil survey are fully documented in Tóth et al (2013a).  

In LUCAS 2012, the soil survey was conducted in Romania and Bulgaria, whose samples 
were analysed for the same set of physical and chemical properties as in 2009. Altogether, 
the  LUCAS  soil  dataset  from  2009  and  2012  contains  data  of  physical  and  chemical 
properties of 22,003 locations. 

In  2015,  the  LUCAS  survey  was  carried  out  in  all  EU-28  Member  States  (MS).  In  the 
countries sampled in 2009 and 2012, 90% of the soil sampling locations were maintained 
while the remaining 10% of points were substituted by new sampling locations, including 
points above 1,000 m in elevation, which were out of scope of the LUCAS 2009 and LUCAS 
2012 surveys. The 2015 survey was also extended through funding provided by the JRC’s 
Enlargement and Integration Programme to Albania, Bosnia and Herzegovina, Montenegro, 
North  Macedonia,  and  Serbia.  Switzerland  also  participated  in  the  survey.  In  total, 
approximately 26,000 locations were targeted in 2015, of which just under 25,000 were in 
the  EU-28.  In  the  end,  21,859  samples  were  collected  in  the  EU-28.  A  further  1,015 
samples were eventually collected for the Western Balkan countries by the JRC while 150 
samples were collected in Switzerland by Agroscope2.  

In  2015,  as  for  LUCAS  2009  and  2012,  samples  were  collected  from  a  depth  of  20  cm 
following a common procedure and were analysed for physical and chemical properties) in 
a  single  laboratory  using  the  same  analytical  methods  (metals  were  not  analysed).  In 

(1) The European Green Deal: https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en  
2 https://www.agroscope.admin.ch/agroscope/en/home.html 

6 

 
                                           
addition,  electrical  conductivity  (EC)  was  included  for  the  first  time.  The  details  and 
outcomes of the 2015 survey are documented in Jones et al. (2020) and Fernandez Ugalde 
(2020). 

1.2  2018 Survey 

In LUCAS 2018, soil sampling was carried out in all EU MS using the same set of 25,947 
locations that were targeted in 2015. In 65% of these locations, samples were to be taken 
following the standardised sampling procedure of previous surveys, in which a spade was 
used to collect a sample from a depth of 20  cm.  In the  remaining 35%  of the locations 
(approximately 9,000 points), metallic rings were used to collect soil cores to determine 
bulk  density  (BD)  from  a  depth  of  0-10  and  10-20  cm3.  The  bulk  density  sample  was 
aggregated for further laboratory analysis. Finally, in a subset of the locations selected for 
bulk  density  determination,  1,000  fresh  soil  samples  were  also  collected  to  assess 
biodiversity. 

At the conclusion of the survey, soil samples exist for 18,984 LUCAS points. In 18,744 
locations, samples were taken from 0-20 cm depth. At a further 381 locations, surveyors 
took samples through the bulk density method only from a single depth (i.e. 0-10 cm, 10-
20 cm or 20-30 cm). For soil biodiversity assessment, samples were eventually taken in 
885 points by the end of the survey. The number of locations sampled was lower compared 
to previous surveys due to a range of issues: land ownership, meteorological conditions 
during the survey and difficulties in reaching the locations. 

As  for  LUCAS  2009/12  and  2015  surveys,  all  samples  were  analysed  in  a  single 
laboratory  for  physical  and  chemical  properties,  including  metals,  using  the  same 
analytical methods. In core samples taken from 20 to 30-cm depth in Portugal, only organic 
carbon (OC) and CaCO3 were analysed. Cation exchange capacity was not analysed in the 
2018 survey. In addition, extractable aluminium and iron oxalate were measured for 2,510 
cropland locations. 

The concentration of a series of metals (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sb, V and Zn) was 
analysed  in  997  locations,  90%  of  which  were  the  resampling  of  sites  measured  in  the 
2009/2012  surveys.  The  scope  of  this  work  is  to  assess  whether  there  has  been  any 
significant change in levels. Approximately 300 new sites were randomly chosen.  

Since its inception, the LUCAS Soil Module has demonstrated an ability to adapt to changing 
policy interests (Figure 1). For the first time, soil biodiversity was assessed through DNA 
extraction, amplification and sequencing of 885 samples from a range of land cover types, 
which were rapidly preserved after sampling through chilling and eventually freezing. The 
DNA  was  used  to  amplify  markers  (DNA  barcodes)  of  three  groups  of  soil  organisms  – 
archaea, bacteria and eukaryotes (fungi and others).  

The genetic analysis used to assess soil biodiversity was also used to measure the presence 
of antimicrobial resistance (AMR) genes. AMR refers to the ability of microorganisms 
to withstand antimicrobial treatments. The over or misuse of antibiotics has been linked to 
the  emergence  and  spread  of  microorganisms  which  are  resistant  to  them,  rendering 
treatment ineffective and posing a serious risk to public health. Within the context of the 
LUCAS Soil Module, AMR can be introduced to soil through the application of manure from 
animals that have been treated by veterinary antibiotics. In total, 630 subsamples from 
the  biodiversity  samples  taken  from  agricultural  land  were  analysed  (predominantly 
cropland). 

A  pilot  study  was  developed  (with  Wageningen  Food  Safety  Research)  to  assess  the 
presence  of  the  residues  of  selected  active ingredients of crop protection  products 
(PPP)  in  soil.  This  analysis,  of  118  substances  and  metabolites  in  approximately  3,000 
locations  from  agricultural  land,  is  currently  ongoing.  Among  pesticides,  neonicotinoids, 
conazoles, organochlorines, and organophosphorus are included. In parallel, the levels of 

3 In selected locations in Portugal (142 points), soil cores were planned to be taken also at a 20-30-cm depth to 

study the depth distribution of bulk density as affected by LC. 

7 

 
                                           
two targeted antibiotics are being measured in approximately 600 arable sites (same as 
for  AMR).  The  antibiotics  analysed  belong  to  the  groups  of  polypeptides  and 
fluoroquinolones. The results of these pilot studies will be aggregated to develop regional 
statistics.  

Finally,  a  test  was  carried  out  with  Wageningen  University  to  assess  the  presence  of 
microplastics (only 50 samples). 

Please note that results of the metals, biodiversity assessment, AMR, antibiotics and the 
PPP residues, and microplastics will be presented in separate reports. 

The  2018  survey  also  included  field-based  assessments  by  the  surveyors  of  various 
types of erosion (sheet, rill, gully, wind, mass movements, and re-deposited soil). This 
information should help to improve the modelling of soil erosion at EU level. The depth of 
organic  and  organic-rich  soils  was  assessed  to  establish  a  baseline  to  assess  the 
degradation of these soils and whether they complied with the depth criteria for Histosols. 

The following sections of this report: 

(1)  summarise the sampling protocols, 

(2)  present the dataset of the 2018 LUCAS Soil Module, 

(3)  provide a detailed description of the results of physical and chemical properties, 

including metals, 

(4)  provides an overview of the field-based measurements, 

(5)  provides  a  characterisation  of  the  spatial  variability  of  soil  properties  by  land 
cover class and a comparative analysis of the soil properties by NUTS 2 regions. 

Figure 1 Evolution of parameters analysed in LUCAS Soil module 

8 

 
 
 
 
2  LUCAS sampling methodology and laboratory analysis 

The LUCAS 2018 soil survey was set for 25,947 points. Soil samples were taken following 
different types of sampling procedures: the common sampling procedure used in 2009/12 
and  2015  surveys,  the  bulk  density  sampling  (new),  and  the  sampling  for  biodiversity 
assessment (also new).  

Bulk density sampling was planned for 9,000 points under different LC classes. In 1,000 
out of these 9,000 points, samples were to be collected to assess soil biodiversity. In the 
remainder (16,947 points), the common sampling procedure was to be carried out. It also 
included the erosion assessment and the survey of organic soils in all points (Figure 2) ( 4). 

Figure 2 Sampling schema in the LUCAS 2018 soil survey (numbers indicate initial targets) 

1 In Portugal, soil cores were also taken from a depth of 20-30 cm. 

2.1  Sample collection 

With the common sampling procedure, a composite sample of approximately 500 g was 
taken at each LUCAS point. The composite sample consisted of five subsamples taken with 
the help of a spade. The first subsample was taken at the geo-referenced point location; 
the  other  four  subsamples  were  collected  at  a  distance  of  2  m  following  the  cardinal 
directions (North, East, South and West) (Figure 3a). Before taking the subsamples, stones 
(>6 cm) (FAO, 2006), vegetation residues, grass and litter were removed from soil surface 
by raking with the spade. As shown by Figure 3b, a V-shaped hole was dug to a depth of 
20 cm using the spade and a slice of soil (approximately 3-cm thick) was taken from the 
side of the hole with the spade. The slice was trimmed at the sides to give a 3-cm wide 
subsample.  The  subsample  was  placed  in  a  bucket.  The  procedure  was  repeated  at  the 
other four subsample sites. Finally, the five subsamples in the bucket were mixed with a 
trowel. Vegetation residues and stones were removed. Approximately 500 g of the mixed 

(4) https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-C1-Instructions.pdf  

9 

 
 
 
 
                                           
soil was taken with a trowel from the bucket, placed in a plastic bag, and labelled to derive 
the composite sample. Soil samples were allowed to air dry before the bags were sealed. 

Figure 3 (a) LUCAS sampling schema, (b) summary of the common sampling procedure, and (c) 
summary of the bulk density sampling 

(a) 

N 

subsamples 

(b) 

2 m 

W 

LUCAS 
 point location 

E 

S 

(c) 

Bag 0-10 cm 

Bag 10-20 cm 

For determining bulk density, soil cores were collected from 0 to 10 and 10 to 20 cm 
depths.  Soil  cores  were  also  taken  from  20  to  30  cm  depth  in  Portugal  to  test  the 
implications of extending the sampling depth in the LUCAS Soil Module. Before taking the 
soil cores, stones (>6 cm) (FAO, 2006), vegetation residues, grass and litter were removed 
from soil surface by raking with the spade as in the common sampling procedure. After the 
cleaning of the soil surface, five soil cores were taken from 0 to 10 cm depth with a metallic 
ring of 100 cm3 at each LUCAS point. The first soil core was taken at the geo-referenced 
point  location;  the  other  four  soil  cores  were  taken  at  a  distance  of  2  m  following  the 
cardinal directions (North, East, South and West) (Figure 3a).  

As shown in Figure 3c, a metallic ring was gently driven into soil using a wooden block to 
push the ring with a mallet. This avoided the compaction of soil. The ring was removed 
from soil with the help of a spade placed underneath the ring. The excess soil around the 
ring was removed with a knife and the soil core was pushed into a labelled plastic bag. The 
procedure was repeated in the four  cardinal directions and the soil cores  collected were 
placed in the same labelled plastic bag. In the end, five soil cores of known volume were 
taken  at  the  0  to  10-cm  depth.  After  sampling  the  0-10  cm  depth  was  completed,  the 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
sampling  of  soil  cores  in  the  10  to  20-cm  depth  was  carried  out  following  the  same 
procedure (Figures 3a and 3c).  

The soil cores were then allowed to air-dry and their weight was again recoded. The plastic 
bags were then sealed for their transportation to the laboratory.  

In Portugal, due to time restrictions, a minimum of three cores were collected from each 
depth).  

The assessment of soil biodiversity was carried out in a subset of locations sampled for 
bulk density. Field moist samples were taken from a depth of 20 cm using the standard 
approach.  A  composite  sample  of  approximately  500  g  was  taken  at  each  LUCAS  point. 
The composite sample consisted of five subsamples taken with the help of a spade. The 
first  subsample  was  taken  at  the  geo-referenced  LUCAS  point  location;  the  other  four 
subsamples were  collected at a distance of 2  m following the cardinal directions (North, 
East, South and West) (Figure 3a). The final sample was placed in a labelled jar and stored 
in  a  polystyrene  box  that  had  been  cooled  with  freezer  packs.  Samples  were  sent  by 
express courier to the JRC to preserve the biological characteristics. Samples were then 
frozen and stored at -20C at the JRC until their shipment to the laboratory for analysis. 

2.2  Sample analysis 

2.2.1  Core analysis 

Samples were analysed in a single laboratory (SGS Hungary) for each property listed in 
Table  1  according  to  standard  ISO  methods  (except  for  extractable  potassium,  oxalate 
extractable Fe and Al, and DNA analysis). Samples taken with the common procedure were 
analysed for physical and chemical properties (except for bulk density). 

Bulk  density  was  determined  with  soil  cores,  where  the  rest  of  physical  and  chemical 
properties were also analysed.  In field-moist samples, DNA and antibiotic residues were 
analysed  to  assess  soil  biodiversity.  Physical  and  chemical  properties  (except  for  bulk 
density and oxalate extractable Fe and Al) were also analysed in LUCAS 2009/12 and 2015 
following the same laboratory methods. Bulk density, oxalate extractable Fe and Al, and 
soil biodiversity were determined in the 2018 survey for the first time. 

Before the analyses, a subsample of the soil cores taken at each depth was oven-dried and 
the weight recorded to determine bulk density from 0-10, 10-20 and 20-30 cm depths (the 
latter only for samples from Portugal). The soil cores from 0-10 cm and 10-20 cm depths 
were then mixed to derive a composite sample from 0 to 20 cm depth for its analysis. The 
soil cores collected from 20 to 30 cm depth in Portugal were kept apart and analysed only 
for organic carbon content. 

2.2.2  DNA analysis 

DNA extraction, amplification and sequencing of LUCAS Soil samples were carried out by 
University  of  Tartu  (Estonia).  The  DNA  was  used  to  amplify  markers  (DNA  barcodes)  of 
three groups of soil organisms – archaea, bacteria and eukaryotes (fungi and others). The 
amplified markers were sequenced using Illumina and PacBio platforms. Most of the 885 
samples performed well in sequencing, resulting in thousands of sequences (see below). 
Only three samples performed poorly, either because of low DNA content or unexpectedly 
strong inhibition by coextracted soil chemicals. Nonetheless, DNA sequencing was carried 
out. 

DNA extraction  

DNA extraction was implemented by use of the Qiagen DNeasy PowerSoil HTP 96 
Kit Q12955-4. Three 0.2 g aliquots per sample were extracted. After extraction the 
three subsamples were pooled. A negative control and positive control were used 
during  extraction  to  locate  any  external  contamination  and  cross-contamination. 

11 

 
 
Quality check and quantification of DNA were performed with Qubit™ 1X dsDNA HS 
Assay Kit using Qubit 3 fluorometer. 

DNA amplification  

Polymerase  chain  reactions  (PCRs)  were  performed  in  three  replicates  using  5  x 
HOT FIREPol® Blend Master Mix (Solis BioDyne, Tartu, Estonia) in 25 μl volume. 
The  optimal  number  of  cycles  and  optimal  annealing  temperature  were  used  for 
each  primer  pair.  In  case  of  PCR  failure,  the  extracted  DNA  was  purified  using 
Favorgen FavorPrep Genomic DNA Clean-up Kit FAGDC001-1 and the PCR repeated. 
In  case  of  further  failure,  more  cycles  were  added  to  the  thermocycler  program. 
The PCR conditions were as follows: 

we 

For 

Archaea, 

SSU1ArF 
used 
• 
(TCCGGTTGATCCYGCBRG) + SSU1000ArR (GGCCATGCAMYWCCTCTC). The 
PCR  conditions  included  annealing  temp.  55  °C,  number  of  cycles  35, 
amount of 1.5 ng/µl DNA template 2  µl.  In  case of  PCR  failure  even  after 
DNA was purified, 40 cycles were used instead of 35 cycles. 

primers: 

the 

For 

Bacteria, 

• 
515F 
(GTGYCAGCMGCCGCGGTAA)  +  926R  (GGCCGYCAATTYMTTTRAGTTT).  The 
PCR  conditions  included:  annealing  temp.  55  °C,  number  of  cycles  26, 
amount of 1.5 ng/µl DNA template 1  µl.  In  case of  PCR  failure  even  after 
DNA was purified, 28 cycles were used instead of 26 cycles. 

primers: 

used 

the 

we 

• 
For  eukaryote  ITS  region,  we  used  the  primers:  ITS9mun 
(GTACACACCGCCCGTCG) + ITS4ngsUni (CGCCTSCSCTTANTDATATGC). The 
PCR  conditions  included  annealing  temp.  55  °C,  number  of  cycles  30, 
amount of 1.5 ng/µl DNA template 1  µl.  In  case of  PCR  failure  even  after 
DNA was purified, 33 or 35 cycles were used instead of 30 cycles. 

• 
For  eukaryote  SSU  region,  we  used  the  primers:  Euk575F 
(ASCYGYGGTAAYWCCAGC) + Euk895R (TCHNHGNATTTCACCNCT). Here, a 
4-fold  greater  amount  of  the  reverse  primer  was  used  for  optimal 
performance. The PCR conditions included annealing temp. 55 °C, number 
of cycles 30, amount of 1.5 ng/µl DNA template 1 µl. In case of PCR failure 
even after DNA was purified, 33 or 35 cycles were used instead of 30 cycles. 

Both  the  forward  and  reverse  primers  were  tagged  with  a  12-base  multiplex 
identifier (MID) tag, except in case of Archaea where only the forward primer was 
tagged with MID. The three replicates of each reaction were pooled and visualised 
on  TBE  1%  agarose  gel.  PCR  products  were  purified  using  UltraClean  96  PCR 
Cleanup Kit (Qiagen). DNA concentrations were measured with Qubit™ 1X dsDNA 
HS Assay Kit using Qubit 3 fluorometer (Invitrogen). 

Metabarcoding library preparation 

Illumina  amplicon  libraries  were  generated  using  TruSeq  DNA  PCR-Free  High 
Throughput  Library  Prep  Kit  with  TruSeq  DNA  CD  Indexes.  For  PacBio,  SMRTbell 
library  preparation  followed  precisely  the  Pacific  Biosciences  Amplicon  library 
preparation protocol. 

Metabarcode sequencing was performed using the Illumina MiSeq platform with 2 
x 300 paired-end mode or PacBio Sequel platform. Positive and negative controls 
of  extractions  as  well  as  amplifications  were  used  to  further  infer  any 
contaminations.  

Considering the expected richness in samples, the following sequencing strategies 
and sequencing depth were applied for various groups of organisms:  

12 

 
 
 
• 

• 

• 

• 

Archaea:  PacBio  Sequel  instrument  (expected  sequencing 
depth 2000 raw sequences per sample). The samples with less 
than 1000 reads were subjected to resequencing; for samples 
with less than 500 reads new tags were selected and then re-
sequenced. 

Bacteria: Illumina MiSeq (exp. sequencing depth 120,000 raw 
sequences  per  sample).  The  samples  with  less  than  50,000 
reads were subjected to resequencing. 

Eukaryote  ITS:  PacBio  Sequel  instrument  (exp.  sequencing 
depth 4000 raw sequences per sample). The samples with less 
than 3000 reads were subjected to resequencing; for samples 
with less than 1500  reads new tags were selected and then 
re-sequenced. 

Eukaryote  SSU:  Illumina  MiSeq  (exp  depth  120,000  raw 
sequences  per  sample).  The  samples  with  less  than  50,000 
reads were subjected to resequencing. 

Antibiotic resistance genes were sequenced based on shotgun metagenomic analysis of the 
DNA extracts. Metagenome sequencing was performed by using Illumina HiSeq 2 x 150 in 
a paired-end mode. Library preparation and indexing for each sample was performed using 
Nextera XT DNA Library Prep Kit in combination with Nextera XT Index kits v2. Expected 
sequencing depth was 5,000,000 reads, but was increased to 10,000,000 reads to improve 
result quality. The samples with less than 5,000,000 reads were subjected to resequencing. 

2.2.3  Analysis of plant protection products 

A set of multi-residue methods based  on Gas  Chromatography/Mass  Spectrometry (GC-
MSMS)  and  Liquid  chromatography–mass  spectrometry  (LC-MSMS)  were  developed  to 
analyse  the  presence  and  concentrations  of  120  molecules  of  active  substances  and 
metabolites  of  selected  plant  protection  products  (PPP).  In  parallel,  a  LC-MSMS  method 
was developed to quantify the presence and concentration of antibiotics.  

2.3  Field-based assessments 

In addition to the sample collection, a range of soil related assessments were carried out 
in the field by surveyors. These included: 

•  The  presence  of  rill,  gully  and  wind  erosion,  mass  movements,  and  re-deposited 
soil.  Surveyors  were  trained  and  provided  with  a  photographic  guideline  (5)  to 
identify the various erosion signs. The field form (6) included a set of questions to 
describe and detail the conditions of erosion signs.  

•  The depth of the organic horizon was measured at each LUCAS point that fulfilled 
the following conditions in the former LUCAS soil surveys: organic carbon content 
>200 g kg-1 and/or classified as wetland in the LUCAS survey. The measurement 
was  done  at  the  geo-referenced  LUCAS  point  location  and  at  a  distance  of  2  m 
following  the  four  cardinal  directions  (North,  East,  South  and  West)  giving  five 
measurements of the thickness of organic horizon per LUCAS point. 

•  The presence of stones and crop residues on the soil surface was assessed. 

•  The  presence  of  landscape  features  that  could  affect  soil  erosion  (walls,  hedges, 

grass margins, etc.). 

(5) https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-C1-Instructions.pdf 
(6) https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-C2-FieldForm-GD-Template.pdf  

13 

 
 
 
                                           
Table 1 Methods used for the analysis of physical and chemical properties in soil samples. 

Soil properties 

Method 

Description 

Bulk density 

Adapted ISO 
11272:2017 

Calculated from the mass and the volume of sole cores 
taken with rings of known volume 

Coarse fragments 

ISO 11464:2006 

Sieving  to  separate  coarse  fragments  (2-60  mm)  from 
fine earth fraction 

Laser diffraction  

Clay, silt and sand 

ISO 11277:1998 

ISO 13320:2009 

(please note that the sieving and sedimentation method 
was used in 2009 and 2012) 

pH in CaCl2 and in 
H2O 

Electrical 
Conductivity 

ISO 10390:2005 

Glass electrode in a 1:5 (V/V) suspension of soil in H20 
and CaCl2 

ISO 11265:1994 

Metal electrodes in aqueous extract of soil 

Organic carbon 

ISO 10694:1995 

Dry combustion (elementary analysis) 

Carbonates 

ISO 10693:1995 

Volumetric method 

Phosphorus 

ISO 11263:1994 

Spectrometric determination of P soluble in sodium 
hydrogen CaCO3 solution 

Total nitrogen 

ISO 11261:1995 

Modified Kjeldahl method 

Extractable 
potassium 

Oxalate extractable 
Fe and Al 

USDA−NRCS, 2004 

Atomic absorption spectrometry after extraction with 
NH4OAc 

Ross and Wang, (1993)  Acid ammonium oxalate method 

Metals 

ISO 11466: 1995 

Biodiversity* 

DNA analysis 

Orgiazzi et al (2022) 

Plant protection 
products* 

Various methodologies 

Trace elements are extracted in aqua regia. The 
resulting solution is analysed by inductively coupled 
plasma-optical emission spectrometry. 

DNA extraction, amplification and sequencing using 
Illumina and PacBio platforms 
Sample preparation: 
- Multi-residue method QuEChERS 
- Strong alkaline/acid extractions 
- McIlvainbuffer/acetonitrile;SPE cleanup 
- MeOH/0.1 M HCl, 80°C; dilution 
Instrumental analysis: 
LC-MS/MS and GC-MS/MS 

*analysed in different laboratories to physical and chemical parameters 

14 

 
 
 
 
 
 
3  Soil data evaluation 

Data  validation  processes  aim  to  provide  certain  guarantees  of  accuracy,  completeness 
and consistency of data. Based on the methodology developed by Hiederer et al. (2018) 
for other pan-European soil databases, three aspects were assessed for the LUCAS 2018 
soil data: compliance, conformity, and uniformity. Compliance concerns the data format, 
conformity involves the data content, and uniformity is related to the comparability of data 
between  different  surveys.  The  following  tests  were  applied  to  evaluate  compliance  and 
conformity: 

—  control of the identification and registration of samples during the field survey and in 

the laboratory (Conformity check, section 3.1), 

—  agreement of the data format with the specifications indicated in the call for tender of 

LUCAS 2018 (Compliance check, section 3.2), 

—  evaluation of soil data produced in the laboratory and application of pedological criteria 

(Conformity check, section 3.3), 

—  evaluation of field-based data (Conformity check, section 3.4). 

The uniformity of soil data is not addressed by this report. This aspect will be evaluated in 
the report focused on the comparison of changes in soil data between the various LUCAS 
soil surveys.  

3.1  Identification  and  registration  of  samples  during  the  field 

survey and in the laboratory 

Points in the LUCAS grid are identified by unique Point IDs. These Point IDs are used in 
each  survey  to  record  agro-environmental  data  related  to  each  point  in  the  Data 
Management Tool (DMT) managed by Eurostat. Furthermore, samples collected in LUCAS 
points are identified by Soil IDs. The JRC creates these Soil IDs. In each LUCAS survey, 
surveyors randomly assign these Soil IDs to the samples when collected. Each sample is 
double-packed with twin labels that have the same Soil ID. At each LUCAS point, surveyors 
document  agro-environmental  observations  by  filling  in  a  field  form  and  by  taking 
photographs. Surveyors have to indicate the Point ID and the Soil ID in the field form. All 
the data is then stored in the DMT. Thus, each sample has a double identification: the Soil 
ID  and  the  Point  ID.  The  Soil  ID  is  used  to  identify  the  samples  in  the  laboratory  and 
provides the soil data, while the Point ID gives the field data and is used to link information 
from different LUCAS surveys. 

Of the 18,916 samples taken at 0-20 cm depth, 139 did not have any Soil ID assigned, but 
had a Point ID assigned. This allowed us to identify the samples and link them to LUCAS 
points in the LUCAS grid. There were also 410 duplications of Soil IDs that affected 821 
samples (reasons: transcription errors in the laboratory, identification errors of surveyors 
in the field). For most of these, we were able to correctly identify them using the analysis 
groups they belonged to (see Section 3.2), the MS in which they were collected, and their 
Point  ID.  However,  there  were  67  samples  with  duplicated  Soil  ID  that  could  not  be 
assigned to unique LUCAS points and agro-environmental information either because they 
had  duplicated  Point  IDs  or  they  were  not  recorded  in  the  DMT.  As  a  result,  these  67 
samples were removed from the dataset. There were also 70 samples with duplicated Point 
IDs that could be correctly identified using their Soil IDs. Finally, there were 79 samples 
whose Point IDs were not traceable and had to be removed from the dataset. 

While 25,947 LUCAS points were planned for soil sampling, a total of 18,984 LUCAS points 
were  registered  with  unique  records,  taken  at  various  depths.  These  consist  of  18,744 
unique records with soil data from 0 to 20 cm depth (Table 2). For 141 points (all taken in 
Portugal),  the  laboratory  also  registered  samples  taken  from  20  to  30  cm  depth  and 
identified  with  unique  Soil  IDs  and  Point  IDs. For  these  points,  only  organic  carbon  and 

15 

 
 
carbonates  contents  analyses  were  performed  (Table  2).  Furthermore,  the  laboratory 
registered 232 samples taken from 0-10 cm depth only and 8 samples taken from 10-20-
cm depth only (Table 2). These 240 samples were identified with unique Soil IDs and Point 
IDs, different from the 18,744 points with samples from 0-20 cm depth. 

While  the  determination  of  bulk  density  was  planned  in  9,000  points,  the  laboratory 
registered  samples  taken  at  different  depths  in  6,846  LUCAS  points.  313  samples  had 
repeated Point IDs (in all, there were 142 repeated Point IDs). We were able to identify 
137  out  of  these  points  through  the  groups  they  belonged  to,  the  MS  where  they  were 
taken, and the Soil IDs of samples taken in these points. As detailed in section 2, samples 
were  taken  at  two  depths  (0-10  and  10-20-cm)  in  each  point.  In  points  from  Portugal, 
extra samples were also taken from 20 to 30-cm depth. All samples collected in a LUCAS 
point had the same Soil ID. In the end, bulk density data at different depths were available 
for 6,271 points identified with unique Point IDs (Table 2). Bulk density from 0 to 10 cm 
depth was determined in 6,246 points and from 10 to 20 cm depth in 5,786 points, and 
from  20  to  30  cm  depth  in  140  points.  Moreover,  bulk  density  from  0-20  points  was 
calculated in 5,761 points interpolating data from 0-10 and 10-20 cm depths (Table 2). 

The soil collected for bulk density was then analysed for physical and chemical properties. 

Metals were planned to be analysed in 1,013 points. This was based on locations where 
one  or  more  metal  element(s)  showed  a  high  content  in  the  LUCAS  2009/12  survey, 
together with a randomly selected set of points for assessing changes in locations with low 
or  no  concentrations.  Overall,  the  laboratory  registered  samples  taken  in  997  LUCAS 
points: 978 samples from 0 to 20 cm depth, 15 samples from 0 to 10 cm depth only, and 
4 samples from 10 to 20 cm depth only (Table 2). 

Table 2 Summary of LUCAS points visited and number of samples taken or observations in the 
LUCAS 2018 soil survey 

LUCAS points 

N samples 

Planned 

Sampled (1) 

0-20 cm 

0-10 cm 

10-20 cm  20-30 cm(2) 

Basic soil properties 

25,947 

19,125 

18,744 

232 

Metals 

1,013 

997 

Bulk density 

9,000 

6,271 

Biodiversity 

1,000 

885 

Organic soils 

1,470 

1,050 

Soil erosion 

25,947 

24,759 

978 

N/A 

885 

N/A 

N/A 

8 

4 

141 

-- 

15 

6,246 

5,786 

140 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

N/A 

(1)  LUCAS points sampled and identified with unique Point IDs. 
(2)  These additional samples were taken at a deeper depth on points otherwise also sampled at the standard 

0-20cm depth.  

16 

 
 
 
 
 
 
 
 
 
 
 
3.2  Agreement of the data format with the specifications of the call for tender 

for laboratory analysis in LUCAS 2015 

The technical specifications of the call for tender for the laboratory analysis in the LUCAS 
2018 survey (7) included the following conditions: 

—  Data generated in the laboratory for each soil sample shall be appropriately attributed 

to its Soil ID in the dataset. 

—  Data of physical and chemical properties shall be delivered in Excel (or 100 % Excel-

compatible) workbook. 

o  A  separate  workbook  shall  be  created  for  each  group  of  samples  identified in 

the technical specifications: 

  Group 1: samples taken in organic-rich soils with the common sampling 

procedure, 

  Group 2: samples taken in organic-rich soils with metallic rings for the 

determination of bulk density, 

  Group  3:  samples  taken  in  mineral  soils  with  metallic  rings  for  the 

determination of bulk density, 

  Group  4:  samples  taken  in  mineral  soils  with  the  common  sampling 

procedure. 

o  Each workbook shall have two sheets: the first sheet shall contain data of basic 
physical  and  chemical  properties  and  the  second  sheet  shall  contain  data  of 
metals. 

—  Data in the two sheets of each workbook shall be presented in columns following the 

order specified in the technical specifications. 

—  Units  and  number  of  decimals  for  each  property  shall  also  follow  the  technical 

specifications. 

The  laboratory  delivered  an  Excel  workbook  with  the  data  generated  for  soil  samples  in 
each  group  identified  in  the  technical  specifications.  As  requested,  each  workbook 
contained two sheets, one for basic soil properties and other for metals. Data of each soil 
sample was linked to its identifier, named Soil ID, in the workbooks. The Soil ID permitted 
to attribute the soil data to a LUCAS point in the LUCAS 2018 database of Eurostat. 

Data were ordered in columns as indicated in the technical specifications, except for silt 
and sand columns that were interchanged in the worksheet of basic soil properties. The 
laboratory added four extra columns in each of the two sheets of the workbooks with the 
following  information:  sample  identification  (an  internal  identification  of  samples  in  the 
laboratory), Point ID (unique identifier of the point in the LUCAS Survey grid), group to 
which each sample belongs, and depth at which the sample  was taken.  In addition, the 
laboratory  changed  the  name  of  the  column  that  contained  the  identifier  of  the  sample 
from Soil ID to Client ID in the workbooks. 

The technical specifications did not include indications for the coding of missing data nor 
for data outside detection limits. From the four workbooks it could be concluded that empty 
fields  or  fields  with  ‘NA’  indicated  missing  data.  This  was  the  case  of  NAs  in  Client  ID 
column, which indicated that the sample identifiers (Soil ID) were not found. For physical 
and  chemical  properties,  the  empty  fields  indicated  that  these  properties  were  not 
analysed. Regarding the limits of detection (LOD), the laboratory provided the values for 
the methods used to analyse the physical and chemical properties (Table 3). 

(7) https://etendering.ted.europa.eu/cft/cft-display.html?cftId=3200  

17 

 
 
                                           
The units and number of decimals reported for each property adjusted to the specifications 
provided to the laboratory (Table 3). 

Table 3 Limits of detection (LOD) of the various physical and chemical properties analysed. 

Physical and chemical properties 

Units 

Decimals 

LOD 

Basic properties 

Bulk density 

Coarse fragments 

Sand 

Silt 

Clay 

pH-CaCl2 

pH-H2O 

Electrical conductivity 

Organic carbon 

Carbonates 

Phosphorous 

Total nitrogen 

Extractable potassium 

Oxalate extractable Fe and Al 

g kg-1 

% 

% 

% 

% 

-- 

-- 

mS m-1 

g kg-1 

g kg-1 

mg kg-1 

g kg-1 

mg kg-1 

mg kg-1 

2 

0 

0 

0 

0 

1 

2 

2 

1 

0 

1 

1 

1 

1 

-- 

-- 

-- 

-- 

-- 

2−10 

2−10 

0.1 

2.0 

1.0 

10.0 

0.2 

10.0 

20 ppm 

Physical and chemical properties 

Units 

Decimals 

LOD 

Metals 

Arsenic 

Cadmium 

Cobalt 

Chrome 

Copper 

Mercury 

Nickel 

Lead 

Antimony 

Vanadium 

Zinc 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

mg kg-1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1.0 

0.2 

0.5 

0.5 

2.0 

0.05 

1.0 

1.0 

0.1 

0.05 

2.0 

18 

 
 
 
 
 
3.3  Evaluation of soil data produced in the laboratory  

This section presents an overview of the data of soil properties analysed. For basic physical 
and chemical properties (except bulk density) and metals, the evaluation focused on the 
data of samples from 0 to 20 cm depth (i.e. the standard sampling depth on LUCAS 2018). 
For bulk density, we evaluated data from 0-10, 10-20, 0-20 and 20-30 cm depths. 

Table 4 gives an overview of the range of values for each soil property in the 2018 LUCAS 
Soil  Module.  The  values  reported  for  basic  physical  and  chemical  properties  by  the 
laboratory were within reasonable limits for soils in Europe. In fact, they were similar to 
those  reported  in  the  LUCAS  2009/12  and  2015  surveys.  The  range  of  values  for  bulk 
density at different depths were also considered acceptable for soils in Europe, with higher 
values reported in the  20 to 30 cm depth than in the 0 to 10  and 10 to 20 cm depths. 
Values of the various metals analysed were similar to those observed in the LUCAS 2009/12 
surveys. In addition, all metals showed some high values as in the 2009/12 surveys. 

The LOD of the analytical methods were used to highlight the presence of values that were 
outside  the  possible  ranges  (Table  4).  For  most  of  the  properties,  only  very  few  values 
were recorded below the LOD. Only CaCO3 and P showed a high number of values below 
their LOD. The high number of outlier values for CaCO3 was due to the use of the value “0” 
to indicate the absence of CaCO3 in soil samples with low pH (pH<7). The value “0” has 
later been substituted by “NA” in the dataset. Soil samples with the P content below the 
LOD were mainly located in woodland and shrubland (altogether 54 %), and grassland and 
cropland most likely not subject to fertiliser applications (19 % and 23 %, respectively). 

19 

 
 
 
Table 4 Range of values and number of samples below the limit of detection (LOD) for soil 
properties in LUCAS 2018 Soil Module. 

Range actual 
values 

LOD 

N samples 
<LOD 

% of the 
data 

Soil parameter 

Basic properties 

Coarse fragments (%)* 

Sand (%)* 

Silt (%)* 

Clay (%)* 

pH-CaCl2 

pH-H2O 

0−100 

2−100 

0−72 

0−66 

2.6−9.8 

3.34−10.43 

-- 

-- 

-- 

-- 

2−10 

2−10 

0.1 

2.0 

1.0 

10.0 

0.2 

10.0 

-- 

-- 

-- 

-- 

0 

0 

0 

31 

7652 

4918 

14 

38 

-- 

-- 

-- 

-- 

0 

0 

0 

0.2 

41 

22 

0.1 

0.2 

Electrical conductivity (mS m-1) 

0.24−1295.6 

Organic carbon (g kg-1) 

Carbonates (g kg-1) 

Phosphorous (mg kg-1) 

Total nitrogen (g kg-1) 

0−723.9 

0−926 

0−515 

0−46.5 

Extractable potassium (mg kg-1) 

1.4−7578.8 

Oxalate extractable Fe and Al (mg kg-1) 

Bulk density, 0-20 cm (g kg-1) 

Bulk density, 0-10 cm (g kg-1) 

Bulk density, 10-20 cm (g kg-1) 

Bulk density, 20-30 cm (g kg-1) 

Al: 0.1-34.7 

Fe: 0.1 – 35.8 

0.04−8.66 

0.03−8.47 

0.04−8.86 

0.71−1.72 

20 ppm 

2527 

13.3 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

-- 

Range accepted 
values 

Limit of 
detection 
(LOD) 

N samples 
<LOD 

% of the 
data 

Soil parameter 

Metals 

Arsenic (mg kg-1) 

Cadmium (mg kg-1) 

Cobalt (mg kg-1) 

Chrome (mg kg-1) 

Copper (mg kg-1) 

Mercury (mg kg-1) 

Nickel (mg kg-1) 

Lead (mg kg-1) 

Antimony (mg kg-1) 

Vanadium (mg kg-1) 

Zinc (mg kg-1) 

1.0−939.9 

0.2−10.4 

0.5−182.9 

0.5−1203.2 

2.0−973.9 

1.0 

0.2 

0.5 

0.5 

2.0 

0.05−4 

0.05 

1.0−3249.3 

1.0−294.7 

0.1−35.9 

1.6−320 

2.0−2385.6 

1.0 

1.0 

0.1 

0.05 

2.0 

8 

58 

1 

1 

8 

2 

11 

1 

7 

0 

2 

0.8 

5.9 

0.1 

0.1 

0.8 

0.2 

1.1 

0.1 

0.7 

- 

0.2 

20 

*PSD are reported in the 2009 and 2015 datasets 

 
The pedological coherence of the data was assessed with a range of correlations between 
soil properties. These correlations included: 

—  Correlation between OC and N. A close relationship exists between OC and N levels in 
soil.  The  higher  the  OC  concentration,  the  greater  the  N  concentration  (Figure  4). 
Moreover,  the  C-to-N  ratio  is  relatively  stable  across  different  soil  types.  Overall, 
mineral soils generally have a C-to-N ratio close to 12:1 (Table 5), while organic-rich 
soils shall have a C-to-N ratio close to 30:1 (Table 5). Soil samples with a C-to-N ratio 
greater than 40:1 need further consideration, since it is not usual for soil organic matter 
to have values higher than this. 

Figure 4 Relation between OC and N in the dataset. 

Table 5 Summary statistics of C-N ratio in mineral (<200 g kg-1) and organic (>200 g kg-1) soils 

N samples 

Range values 

Mean 

Median 

Std dev 

Mineral samples 

17,847 

2:1−142:1 

12:1 

Organic samples 

859 

10:1−67:1 

24:1 

10:1 

23:1 

6:1 

8:1 

Samples with C-to-N 
ratio >40:1 

96 (1) 

40:1−142:1 

51:1 

44:1 

19:1 

(1) Out of the 96 samples, 53 are mineral samples and 43 are organic samples. 

—  Correlation between pH and CaCO3. pH is a measure of the acidity or alkalinity in the 
soil.  Soil  pH  can  be  measured  in  H2O  and  in  CaCl2.  The  values  of  pH  in  CaCl2  are 
normally lower than pH in H2O by 0.5 to 0.9. Soils have commonly pH in H2O values 
between 3.5 and 9.0. Calcium carbonate should not be present (or the concentrations 
should be very low) in soils where pH is below 7, as its solubility is pH-dependent and 
it  does  not  form  under  acidic  conditions.  In  accordance  with  this  criterion,  Figure  5 
shows (i) that pH in H2O ranges between 3.3 and 10.4 while pH in CaCl2 ranges between 

21 

 
 
 
 
 
 
2.6  and  9.8,  and  (ii)  that  soil  samples  with  pH  around  7.0  −  8.5  have  the  greatest 
contents of CaCO3. 

Figure 5 Relation between pH and CaCO3 in the dataset: (a) pH in H2O, (b) pH in CaCl2 

(a) 

(b) 

—  Coherence of particle size distribution (PSD) data. Mineral fraction <2 mm in soil can 
be  split in  three  size  fractions:  sand  (2-0.063  mm),  silt  (0.063-0.002  mm)  and  clay 
(<0.002  mm).  In  the  LUCAS  2018  survey,  these  three  fractions  were  measured  in 
2,505 points, randomly selected, as quality check, for comparison purposes with PSD 
data from the LUCAS 2009/12 and 2015 surveys (8). The random selection considered 
the variability of sand, silt, and clay contents, and the occurrence of different textural 
classes in the dataset. In order to check the coherence of the PSD data, we checked 
that the sum of the mass of the sand, silt and clay fractions was equal to 100 %. The 
sum of the three fractions ranged between 99 % and 101 % in the dataset because the 
contents of the three fractions were rounded to the nearest whole number. 

—  Correlation between bulk density, sand, and OC contents. Bulk density depends on OC 
content,  the  combination  of  sand,  silt  and  clay  contents  (i.e.  soil  texture),  and  the 
structure/porosity  of  the  soil.  It  was  not  possible  to  establish  individual  correlations 

(8) Comparison of PSD data among surveys will be described in a separate report assessing differences on soil 

properties among the various soil surveys.  

22 

 
 
 
 
                                           
between  bulk  density  and  OC  or  sand  content  (Figure  6).  However,  there  are  some 
general rules that can be observed. In general, organic-rich soils (>120 g kg-1) tend to 
have a low bulk density (circa 0.5 g cm-3) (Figure 6a), while sandy soils (sand content 
> 80 %) have a relatively high bulk density (>1.6 g cm-3) (Figure 6b). Furthermore, 
compacted soils with low sand content (<20 %) can have very high bulk density values 
(Figure 6). 

Figure 6 Relation between bulk density, OC and sand contents in the dataset: (a) bulk density – OC 
content, (b) bulk density – sand content 

(a) 

(b) 

Overall, the actual values of the various physical and chemical properties analysed as part 
of the 2018 LUCAS Soil Module are considered to be in line with expectations for European 
soils,  and  are  similar  to  those  reported  in  the  LUCAS  2009/12  and  2015  surveys.  In 
addition, the correlations assessed between soil properties followed the expected trends 
from a pedological point of view. 

23 

 
 
 
 
 
 
 
3.4  Evaluation of field-based data 

Soil erosion and the condition of organic soils were assessed for the first time in the LUCAS 
2018 survey. This section presents an overview of the checks carried out on the information 
collected by surveyors for these two aspects. 

a)  Evaluation of soil erosion 

Surveyors were asked to record evidence of soil erosion by water or wind within a distance 
of 500 m from all the LUCAS points included in the soil module. In case of any evidence, 
surveyors  had  to  take  a  picture  of  the  area  affected  and  indicate  the  type  of  erosion 
observed  (i.e.  rills,  gullies,  wind  erosion,  mass  movements  and/or  re-deposited  soil), in 
which  cardinal  direction  signs  were  observed,  and  the  distance  at  which  signs  were 
observed from the LUCAS point. 

The DMT included the following record descriptors regarding soil erosion: 

—  erosion can do (1 yes / 2 no) [able to assess the presence of erosion], 

—  signs of erosion (1 yes / 2 no / NR not relevant), 

—  for each type of erosion observed: 

o  erosion in the LUCAS point, North, East, South, West (1 yes), 
o  distance from LUCAS points for the four cardinal directions (in m). 

Empty  fields  in  the  responses  to  ‘erosion  can  do’  and  ‘signs  of  erosion’  were  taken  as 
negative  responses.  Surveyors  also  used  the  value  “0”  or  left  fields  empty  to  give  a 
negative response or indicate absence of information in the two questions on each erosion 
type. Zero values were substituted by empty fields to harmonize information in the dataset. 

Surveyors were able to assess evidence of erosion in 65% of the assessed points, of which 
only 850 points showed signs of erosion. Most  of the points identified as having erosive 
features (687 points, 80.8%) showed only one type of erosion. In 126 points (14.8 %), 
evidence of two types of erosion were observed. The remaining 37 points showed evidence 
of three or four types of erosion (Figures 7 & 8). 

Regarding the distance at which evidence of erosion was observed from the LUCAS point, 
642 out of 850 points recorded this information (75.5 %). At four points, no evidence of 
erosion was recorded, however, information on the observation distance was provided. 

Table 6 shows the distribution of points with evidence of erosion according to the various 
erosion types assessed. In all types of erosion, more than 55 % of the points showed only 
one  type  of  erosion,  followed  by  those  points  that  showed  two  types.  As  expected,  the 
number of points with evidence of three or more types of erosion were negligible. 

The  photos  taken  by  surveyors  showed  that  sheet,  rill  and  wind  erosions,  signs  of  re-
deposited soil and mass movements were not correctly identified. Thus, the information of 
these types of erosion could not be validated and has not been included in this report. On 
the  contrary,  surveyors  were  able  to  correctly  identify  evidence  of  gully  erosion.  A 
comparison of field photos with images of remotely sensed data (Google Earth) to validate 
the data showed a high correspondence to gully erosion. As a result, this report offers a 
detailed description of gully erosion in the EU during the LUCAS 2018 survey. 

24 

 
 
 
Table 6 Distribution of points with evidence of different types of erosion as recorded by surveyors 
in the LUCAS 2018 survey. 

Erosion type 

N points with 
evidence of erosion 

% of points with 
only 1 type of 
erosion 

% of points with 
at least 2 types 
of erosion 

Sheet erosion 

Rill erosion 

Gully erosion 

Mass movement 

Re-deposited soil 

Wind erosion 

192 

290 

211 

223 

96 

40 

57.8 

58.9 

69.7 

87.4 

42.7 

55.0 

31.2 

30.3 

19.4 

7.6 

37.5 

25.0 

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7 Maps showing the locations where signs of deposition by wind (left) and mass movement 
(right) were recorded by surveyors. 

Figure 8 Maps showing the locations where signs of wind erosion (left) and rill erosion (right) were 
recorded by surveyors. 

26 

 
 
 
 
 
b)  Evaluation of organic soils 

The  condition  of  organic  soils  at  specific  LUCAS  points  was  assessed  by  measuring  the 
depth of the organic horizon in five locations (at the LUCAS point and at a distance of 2 m 
following  the  four  cardinal  directions).  Surveyors  were  provided  with  a  list  of  potential 
points located in organic soils based on their OC data and LC classification in former LUCAS 
surveys (OC content >200 g kg-1 and/or located in the Wetland LC class). Overall, 1,470 
points were identified as potentially being located in organic soils. Surveyors had to indicate 
whether they could assess the depth of the organic horizon and, if so, record the depth of 
the organic horizon in the five locations at each LUCAS point. If the depth of the organic 
horizon was less than 40 cm, surveyors had to indicate the exact depth. If the depth was 
greater than 40 cm, it was not compulsory to indicate the exact depth (they could record 
that the depth was >40 cm). In addition, they had to indicate whether the points were in 
cultivated soils. 

The DMT included the following record descriptors regarding the condition of organic soils: 

—  organic can do (1 yes / 2 no), [able to assess the presence of organic soils] 

—  organic cultivated (1 yes / 2 no / NR not relevant), 

—  depth of organic horizon in the LUCAS point, north, east, south, west (in cm), 

—  depth more than 40 cm (1 yes / 2 no) 

The responses to ‘organic can do’ and ‘organic cultivated’ included empty fields that were 
taken as negative responses. In fact, there was no NR (‘not relevant’) record in the DMT 
for the question on cultivation of organic soils. In the last two questions, surveyors used 
the  value  “0”  or  left  fields  empty  to  give  a  negative  response  or  indicate  absence  of 
information. Zero values were substituted by empty fields to harmonize information in the 
dataset. 

Surveyors were able to measure the depth of organic horizon in 1,050 out of the 1,470 
potential points located in organic soils, although depth was finally recorded only in 1,042 
points (Figure 9). From these points, the exact depth of the organic horizon was recorded 
for  731  points  (70%)  while  the  depth  recorded  was  >40  cm  in  the  remaining  30%  of 
locations  sampled.  Where  exact  depth  was  recorded,  surveyors  recorded  three 
measurements  of  depth  in  most  of  the  points  (in  583  out  of  the  731  points).  Five 
measurements of depth were recorded only in 77 points (Table 7). Regarding cultivation 
of organic soils, only 98 points out of 1,042 were located in cultivated fields. 

Most of the sites selected for depth assessments appear not to fulfil the depth criteria for 
Histosols. However, the assessment failed to provide a reason for the very shallow organic 
soils (e.g. such as on bedrock). The implication is that many of these LUCAS points are on 
either  mineral  soils  with  well-developed  organic  horizons  or  that  peatlands  have  been 
eroded back to the underlying mineral base. 

27 

 
 
 
 
 
 
 
 
 
 
Figure 9 Map showing the locations where the depth of organic soils was measured. 

Table 7 Summary of data of organic soils recorded by surveyors in the 2018 LUCAS Soil Module. 

Characteristics of 
organic soils 

N points 

Depth info recorded 

1,042 

Depth > 40 cm recorded 

311 

Exact depth recorded 

    Range of values (cm) 

731 

1−55 

s
t
n
e
m
e
r
u
s
a
e
m

N

i

t
n
o
p
r
e
p
h
t
p
e
d

f
o

1 

2 

3 

4 

5 

N points 

34 

21 

583 

16 

77 

28 

 
 
 
 
 
 
 
 
 
 
 
4  The 2018 LUCAS Soil Module: structure and description 

The 2018 LUCAS Soil Module contains various files with different soil data: 

—  Basic  soil  properties:  data  of  physical  and  chemical  properties  analysed  in  samples 

taken at various depths in 18,984 LUCAS points (9), as follows: 

o  Samples taken from 0-20 cm depth in 18,744 points (in 141 of these points, all 
taken in Portugal, OC and CaCO3 were also analysed in additional samples taken 
from 20-30-cm depth) 

o  Samples taken from 0-10 cm depth in 232 points, 
o  Samples taken from 10-20 cm depth in 8 points, 

—  Metals:    a  summary  of  descriptive  statistics  of  997  LUCAS  points  with  high  and  low 

concentrations of metals, 

—  Extractable aluminium and iron oxalate for 2,510 cropland locations, 

—  Erosion assessment: information recorded on erosion evidences for 879 LUCAS points, 

—  Assessment  of  organic  soils:  measurements  of  depth  of  organic  horizon  from  1,050 

LUCAS points, 

—  Biodiversity and plant protection products: summary statistics only, 

—  Environmental conditions: reference ancillary data describing a range of environmental 

conditions for all LUCAS points of the soil module. 

In the various files, data from each LUCAS point are identified with their Point ID, which 
serves  to  link  data  from  the  various  files  of  the  soil  dataset  with  the  field  information 
published  in  the  LUCAS  portal  of  Eurostat  (10).Table  8  shows  the  distribution  of  LUCAS 
points  by  MS  and  LC  in  the  LUCAS  2018  survey.  It  has  to  be  noted  that  not  all  LUCAS 
points have data for all properties analysed and field-based assessments.  

Data of physical and chemical properties in the file of basic soil properties are reported in 
the  fields  (columns)  as  indicated  in  Table  9a.  In  addition,  a  limited  number  of 
supplementary data have been extracted from the LUCAS portal of Eurostat to provide a 
geographical and LC/LU context to users of the soil data (Table 9b). 

The file of erosion assessment is arranged in 69 fields that contain information on erosion 
evidences (Table 10). Similarly, the file of assessment of organic soils contains information 
of the different measurements of the organic horizon depth made at  each  LUCAS point. 
The information is arranged in 14 fields (Table 11). 

Supplemental  environmental  information,  such  as  climate,  topographic  setting,  soil 
regions, and NATURA 2000 sites, for all soil sampling locations are available from ESDAC.  

The 2018 LUCAS Soil dataset is made available in two formats (CSV and SHAPE). Data can 
be downloaded through the European Soil Data Centre (ESDAC) using the following URL: 
https://esdac.jrc.ec.europa.eu/resource-type/datasets. 

(9) Note that the dataset contains 88 LUCAS points with bulk density data but no data for the rest of physical and 

chemical properties. 

(10) https://ec.europa.eu/eurostat/web/lucas/data/primary-data/2015  

29 

 
 
 
 
 
 
 
                                           
Table 8 Summary of points by MS and LC in the LUCAS 2018 survey. 

NUTS 0 

Points 

Artificial land  Bareland  Cropland  Grassland  Shrubland  Water  Wetlands  Woodland 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

FI 

FR 

HR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

449 

130 

574 

69 

445 

779 

171 

201 

598 

3867 

1143 

2735 

106 

354 

143 

1242 

386 

35 

331 

2 

99 

1376 

429 

603 

1906 

112 

186 

UK 
EU+UK 

EU-27 

513 
18984 

18471 

6 

0 

2 

0 

1 

5 

1 

2 

1 

12 

2 

9 

1 

3 

0 

1 

0 

1 

0 

0 

2 

3 

2 

5 

5 

1 

3 

3 
71 

68 

2 

7 

3 

3 

2 

13 

3 

3 

29 

338 

3 

84 

8 

24 

0 

4 

3 

1 

5 

0 

2 

18 

6 

23 

21 

0 

0 

90 

81 

274 

30 

232 

332 

125 

55 

228 

1870 

134 

1383 

9 

198 

17 

628 

196 

12 

91 

2 

47 

686 

111 

257 

120 

11 

71 

135 

14 

122 

15 

104 

213 

24 

37 

127 

599 

50 

709 

27 

67 

107 

297 

98 

12 

88 

0 

33 

344 

89 

227 

121 

27 

49 

33 
638 

605 

139 
7429 

7290 

254 
3989 

3735 

5 

0 

17 

14 

2 

3 

3 

4 

52 

339 

24 

51 

6 

2 

6 

34 

0 

0 

5 

0 

1 

6 

50 

19 

51 

1 

3 

22 
720 

698 

0 

0 

0 

0 

0 

0 

0 

0 

2 

0 

0 

2 

0 

0 

0 

0 

0 

0 

0 

0 

0 

1 

0 

0 

0 

0 

0 

0 
5 

5 

1 

2 

0 

0 

1 

1 

0 

0 

0 

0 

1 

2 

0 

1 

5 

0 

0 

0 

0 

0 

1 

2 

0 

0 

18 

0 

1 

4 
40 

36 

210 

26 

156 

7 

103 

212 

15 

100 

159 

709 

929 

495 

55 

59 

8 

278 

89 

9 

142 

0 

13 

316 

171 

72 

1570 

72 

59 

58 
6092 

6034 

30 

 
 
 
 
 
 
 
 
 
 
 
Table 9a.Table 9 Soil fields in the file of basic soil properties 2018 LUCAS Soil Module. 

Field 

Depth 

POINTID 

pH_CaCl2 

pH_H2O 

EC 

OC 

CaCO3 

P 

N 

K 

Description 

Based on sample collected can be 0-10 cm or 0-20 cm 

LUCAS Point Identifier – link to Eurostat LUCAS Microdata 

pH – measured in calcium chloride 

pH – measured in water 

Electrical conductivity (milliSiemens per meter – mS m-1) 

Organic carbon content (g kg-1) 

Calcium carbonate content (g kg-1) 

Total phosphorus (g kg-1) 

Total nitrogen (g kg-1) 

Extractable potassium (g kg-1) 

OC (20-30 cm) 

Organic carbon content (g kg-1) from different depth 

CaCO3 (20-30 cm) 

Calcium carbonate content (g kg-1) from different depth 

Ox_Al 

Ox_Fe 

Oxalate extractable Al (mg kg-1) 

Oxalate extractable Fe (mg kg-1) 

31 

 
Table 9b. Non-soil fields in the file of basic soil properties in the 2018 LUCAS Soil Module. 

Field 

NUTS_0 

NUTS_1 

NUTS_2 

NUTS_3 

TH_LAT 

TH_LONG 

SURVEY_DATE 

Elev 

LC 

LU 

LC0_Desc 

LC1_Desc 

LU1_Desc 

Description 

NUTS 0 Code 

NUTS 1 Code 

NUTS 2 Code 

NUTS 3 Code 

LUCAS POINT Theoretical Latitude 

LUCAS POINT Theoretical Longitude 

Date of Survey 

Elevation in meters from surveyor GPS 

Primary land cover 

Primary land use 

Description of primary land cover 

Description of secondary land cover 

Description of primary land use 

32 

 
 
 
 
 
Table 10 Fields in the file of soil erosion in the 2018 LUCAS Soil Module 

Field 

POINTID 

Description 

LUCAS Point Identifier – link to Eurostat LUCAS 
Microdata 

SURVEY_EROSION_SIGNS 

Signs of erosion (1=Yes,2=20) 

SURVEY_EROSION_SHEET 

Sheet Erosion visible 

SURVEY_EROSION_SHEET_P 

SURVEY_EROSION_SHEET_N 

SURVEY_EROSION_SHEET_E 

SURVEY_EROSION_SHEET_S 

SURVEY_EROSION_SHEET_W 

Sheet erosion in the LUCAS point (Within 500 m 
if visible) 

Sheet  erosion  in  N  direction  (Within  500  m  if 
visible) 

Sheet  erosion  in  E  direction  (Within  500  m  if 
visible) 

Sheet  erosion  in  S  direction  (Within  500  m  if 
visible) 

Sheet  erosion  in  W  direction  (Within  500  m  if 
visible) 

SURVEY_EROSION_SHEET_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_SHEET_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_SHEET_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_SHEET_S_DIST_M 

Distance from Point Direction S 

SURVEY_EROSION_SHEET_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_RILL 

Presence of Rill erosion 

SURVEY_EROSION_RILL_P 

Presence of Rill erosion in LUCAS point 

SURVEY_EROSION_RILL_N 

SURVEY_EROSION_RILL_E 

SURVEY_EROSION_RILL_W 

SURVEY_EROSION_RILL_S 

Rill  erosion  in  N  direction  (Within  500  m  if 
visible) 

Rill  erosion  in  E  direction  (Within  500  m  if 
visible) 

Rill  erosion  in  S  direction  (Within  500  m  if 
visible) 

Rill  erosion  in  W  direction  (Within  500  m  if 
visible) 

SURVEY_EROSION_RILL_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_RILL_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_RILL_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_RILL_S_DIST_M 

Distance from Point Direction S 

33 

 
SURVEY_EROSION_RILL_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_GULLY 

Presence of Gully erosion 

SURVEY_EROSION_GULLY_P 

Presence of Gully erosion in LUCAS point 

SURVEY_EROSION_GULLY_N 

SURVEY_EROSION_GULLY_E 

SURVEY_EROSION_GULLY_S 

SURVEY_EROSION_GULLY_W 

Gully  erosion  in  N  direction  (Within  500  m  if 
visible) 

Gully  erosion  in  E  direction  (Within  500  m  if 
visible) 

Gully  erosion  in  S  direction  (Within  500  m  if 
visible) 

Gully  erosion  in  W  direction  (Within  500  m  if 
visible) 

SURVEY_EROSION_GULLY_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_GULLY_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_GULLY_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_GULLY_S_DIST_M 

Distance from Point Direction S 

SURVEY_EROSION_GULLY_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_MASS 

Presence of Mass Movement erosion 

SURVEY_EROSION_MASS_P 

SURVEY_EROSION_MASS_N 

SURVEY_EROSION_MASS_E 

SURVEY_EROSION_MASS_S 

SURVEY_EROSION_MASS_W 

Presence  of  Mass  Movement  erosion  in  LUCAS 
point 

Mass  Movement  erosion  in  N  direction  (Within 
500 m if visible) 

Mass  Movement  erosion  in  E  direction  (Within 
500 m if visible) 

Mass  Movement  erosion  in  S  direction  (Within 
500 m if visible) 

Mass Movement erosion in W direction (Within 
500 m if visible) 

SURVEY_EROSION_MASS_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_MASS_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_MASS_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_MASS_S_DIST_M 

Distance from Point Direction S 

SURVEY_EROSION_MASS_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_DEP 

Presence of Re-deposition soil 

SURVEY_EROSION_DEP_P 

Presence of Re-deposition soil in LUCAS point 

34 

 
SURVEY_EROSION_DEP_N 

SURVEY_EROSION_DEP_E 

SURVEY_EROSION_DEP_S 

SURVEY_EROSION_DEP_W 

Re-deposition soil in N direction (Within 500 m 
if visible) 

Re-deposition soil in E direction (Within 500 m 
if visible) 

Re-deposition soil in S direction (Within 500 m 
if visible) 

Re-deposition soil in W direction (Within 500 m 
if visible) 

SURVEY_EROSION_DEP_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_DEP_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_DEP_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_DEP_S_DIST_M 

Distance from Point Direction S 

SURVEY_EROSION_DEP_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_WIND 

Presence of Wind erosion 

SURVEY_EROSION_WIND_P 

Presence of Wind erosion in LUCAS point 

SURVEY_EROSION_WIND_N 

SURVEY_EROSION_WIND_E 

SURVEY_EROSION_WIND_S 

SURVEY_EROSION_WIND_W 

Wind  erosion  in  N  direction  (Within  500  m  if 
visible) 

Wind  erosion  in  E  direction  (Within  500  m  if 
visible) 

Wind  erosion  in  S  direction  (Within  500  m  if 
visible) 

Wind  erosion  in  W  direction  (Within  500  m  if 
visible) 

SURVEY_EROSION_WIND_NR 

Always empty/not present in the Manual 

SURVEY_EROSION_WIND_N_DIST_M 

Distance from Point Direction N 

SURVEY_EROSION_WIND_E_DIST_M 

Distance from Point Direction E 

SURVEY_EROSION_WIND_S_DIST_M 

Distance from Point Direction S 

SURVEY_EROSION_WIND_W_DIST_M 

Distance from Point Direction W 

SURVEY_EROSION_RILLGULLY_N 

Number of rills or gullies 1-2-3 = <5,5-10,>10) 

SURVEY_EROSION_CANDO 

Erosion survey can be done? (1=Yes, 2=No) 

35 

 
 
Table 11 Fields in the file of organic soils in the 2018 LUCAS Soil Module. 

Field 

POINTID 

SURVEY_SOIL_ORG_CULTIVATED 

SURVEY_SOIL_ORG_DEPTH_P_CM 

SURVEY_SOIL_ORG_DEPTH_N_CM 

SURVEY_SOIL_ORG_DEPTH_E_CM 

SURVEY_SOIL_ORG_DEPTH_S_CM 

SURVEY_SOIL_ORG_DEPTH_W_CM 

SURVEY_SOIL_ORG_DEPTH_P_40_CM 

SURVEY_SOIL_ORG_DEPTH_N_40_CM 

SURVEY_SOIL_ORG_DEPTH_E_40_CM 

SURVEY_SOIL_ORG_DEPTH_S_40_CM 

SURVEY_SOIL_ORG_DEPTH_W_40_CM 

SURVEY_SOIL_ORG_TAKEN 

Description 

LUCAS Point Identifier – link to Eurostat LUCAS 
Microdata 

Is soil cultivated? (1=Yes,0=N0) 

Depth of organic horizon at the point (in cm up 
to 40 cm) 

Depth of organic horizon at the N hole (in cm 
up to 40 cm) 

Depth of organic horizon at the E hole (in cm 
up to 40 cm) 

Depth of organic horizon at the S hole (in cm 
up to 40 cm) 

Depth of organic horizon at the W hole (in cm 
up to 40 cm) 

Depth of organic horizon at the point (>40 cm) 

Depth of organic horizon at the N hole (>40 
cm) 

Depth of organic horizon at the E hole (>40 
cm) 

Depth of organic horizon at the S hole (>40 
cm) 

Depth of organic horizon at the W hole (>40 
cm) 

Soil Organic taken? (1=Yes,0=No) 

SURVEY_SOIL_ORG_DEPTH_CANDO 

Can you assess the organic layer depth? 

(1=Yes,0=No) 

36 

 
 
 
 
Table 12 Environmental descriptors at LUCAS sampling points 

Field Name 
Point_ID 

Clima_COD 

Elevation 
Slope 
Aspect 

Description 
Unique ID to be linked to other LUCAS datasets (on 
ESDAC or EUROSTAT) 
Climatic Code based on Köppen-Geiger 
classification 
Height extracted from EuDEM v1.0 
Slope gradient 
Orientation of slope 

BioGeo 
Natura2000_sitecode
1 
Natura2000_sitecode
2 
Soil_Group 
Soil_Code 
BIO1 
BIO2 
BIO3 
BIO4 
BIO5 
BIO6 
BIO7 
BIO8 
BIO9 
BIO10 
BIO11 
BIO12 
BIO13 
BIO14 
BIO15 

Biogeographic Region 
Code of Natura2000 site in which the point is 
located 
Code of second Natura2000 site in which the point 
is located 
Estimation of dominant reference soil group 
WRB Code of the first dominant soil group 
Annual Mean Temperature 
Mean Diurnal Range 

Isothermality (BIO2/BIO7) (×100) 
Temperature Seasonality (standard deviation ×100) 
Max Temperature of Warmest Month 
Min Temperature of Coldest Month 
Temperature Annual Range (BIO5-BIO6) 
Mean Temperature of Wettest Quarter 
Mean Temperature of Driest Quarter 
Mean Temperature of Warmest Quarter 
Mean Temperature of Coldest Quarter 
Annual Precipitation 
Precipitation of Wettest Month 
Precipitation of Driest Month 
Precipitation Seasonality (Coefficient of Variation) 

Unit 
- 

- 

m 
degree 
Compass 
degree 
- 
- 

- 

- 
- 
oC 
oC 
oC 
oC 
oC 
oC 
oC 
oC 
oC 
oC 
oC 
mm 
mm 
mm 
mm 

37 

 
 
 
 
4.1  Reflections on 2018 dataset 

The number of points actually sampled as part of the 2018 LUCAS survey was less than 
initially planned. In total, 71 % of points were sampled compared to 85 % and 95 % in 
LUCAS 2015 and LUCAS 2009/12 surveys, respectively. The percentage of points sampled 
was  especially  low  (below  50  %)  in  Germany,  Croatia,  Ireland,  Malta,  the  Netherlands, 
Romania  and  the  United  Kingdom.  Romania,  Malta  and  Germany  had  the  lowest 
percentages of samples taken with 17 %, 33 % and 37 %, respectively. Different reasons 
were provided by surveyors to explain the difficulties in taking soil samples: 

—  In  Romania,  most  of  the  sampling  was  carried  out  between  September  and  October 
2018 due to a change in the  organisation coordinating the survey.  Extreme weather 
and  soil  conditions,  especially  rainfalls,  floods  and  frost,  complicated  the  sampling 
collection  during  these  months.  In  addition,  surveyors  found  physical  difficulties  to 
reach points on several occasions due to the presence of dangerous animals, together 
with limited and poor road infrastructure for points in the rest of LC classes. As a result, 
alternative LUCAS points for soil sampling were selected. 

—  In  Malta  and  Croatia,  compaction  and  rocky  soils  under  cropland  and  shrubland 
complicated the sampling. Presence of fences in cropland complicated the access to the 
points. 

—  Legal issues related to accessing private land was the main issue in Germany for not 

taking samples, and this affected all LC classes. 

—  In  Ireland,  water  saturation  in  wetland  and  woodland  points  due  to  bad  weather 
complicated the sampling. In addition, the distance of the points from the roads and 
technical problems with the transmission of point IDs affected the sampling for other 
LC classes. 

—  In the United Kingdom, denial of access by land owners reflecting the upcoming Brexit 

referendum was the main difficulty for the sampling. 

38 

 
 
 
 
 
 
4.2  Spatial representation of soil properties in the EU-28 

Point data of each physical and chemical soil property is presented on four maps: one for 
the whole dataset and three for cropland, grassland and woodland points separately. The 
same ranges of values, based on pedological and agrochemical criteria, were used as in 
the maps presented in the reports of LUCAS 2009 (Tóth et al., 2013a) and LUCAS 2015 
(Jones et al., 2020) to characterise and describe these properties. 

Maps of aggregated data at NUTS 2 level are also presented for all points and for the three 
main  LC  classes  separately  (cropland,  woodland,  and  grassland).  Maps  of  bareland, 
shrubland  and  wetland are not  shown  due  to  the  low  number  of  points  at  each  NUTS  2 
region. The NUTS classification of 201611 was used for this exercise, which has 281 regions 
for the EU-28 MS 12 at NUTS level 2. The 2018 LUCAS Soil Module was carried out in 254 
of these regions, although the number of regions sampled is lower when considering each 
LC  class  separately  (Table  13).  Wetland  points,  for  example,  are  principally  present  in 
northern  regions  from  Sweden,  followed  by  Ireland  and  the  United  Kingdom.  On  the 
contrary, bareland points are mainly present in southern regions from Spain and France. 
Cropland, grassland, and woodland points are present in almost all regions (Table 8). For 
data  aggregation,  we  only  considered  NUTS  2  regions  in  which  at least  3  samples  were 
taken. This resulted in 242 (out of 253) regions when considering the whole dataset, 206 
regions for cropland, 204 regions for grassland, 181 regions for woodland, 48 regions for 
bareland, 57 regions for shrubland and 3 regions for wetland (Table 13). 

As shown in Table 13, the median values of the sampling density in NUTS 2 regions were 
below  300  km2  per  sample,  except  for  woodland  and  wetland.  The  point  density  in 
woodland was only slightly above 300 km2 per sample. These values of sampling density 
can be considered acceptable, at least for the assessment of soil organic carbon content, 
as  proposed  by  Jandl  et  al.  (2011).  These  authors  suggested  that  a  minimum  sampling 
density of  one sample for  every 300 km2 could be  enough to assess  changes on topsoil 
organic carbon content over a 10-year time lapse at European level. Similarly, Panagos et 
al.  (2013)  considered  that  a  sampling  density  of  approximately  200  km2  could  be 
reasonable  for  the  LUCAS  Soil  survey  to  measure  soil  organic  carbon.  Based  on  these 
studies, we considered a minimum density of 250 km2 per sample as being a reasonable 
preliminary indicator for the confidence level of the LUCAS 2018 Soil survey to measure 
chemical  soil  properties.  According  to  this  criterion,  the  sampling  density  was  below  or 
equal to 250 km2 per sample in at least 50% of the NUTS 2 regions in cropland, grassland, 
bareland and shrubland (Table 13, Figure 10). When considering the whole dataset (all LC 
together), the percentage of NUTS 2 regions with an acceptable point density was slightly 
lower,  at  49%  (Table  13).  However,  the  number  of  regions  with  a  minimum  sampling 
density of 250 km2 per sample was significantly low in woodland and wetland (Table 13). 

(11)  Regional  data  of  LUCAS 2018  is  presented  based  on  NUTS  2016  classification  at  the  Eurostat  webpage.  Thus,  we 

decided to use the same classification to describe LUCAS 2018 soil data in this report. 

12 i.e. the EU-27 Member States and the UK, then also an EU Member State. 

39 

 
 
 
 
                                           
Table  13  Summary  statistics  of  the  sampling  density  in  NUTS  2  regions  of  the  2018  LUCAS  Soil 
Module. 

NUTS 2 regions 
sampled 

NUTS 2 
regions with 
≥3 samples 

Sampling density 
(km2 per sample) 

Mean  
of NUTS 2 regions 

Median  
of NUTS 2 regions 

% of NUTS 2 regions 
with density <250 
km2 per sample 

Whole dataset 

Cropland 

Grassland 

Woodland 

Bareland 

Shrubland 

Wetland 

253 

238 

238 

218 

124 

109 

22 

242 

206 

204 

181 

48 

57 

3 

353.6 

915.4 

1013.7 

919.2 

5281 

3771.6 

14405 

253.1 

652.3 

1480.3 

1353.4 

3357 

5054.6 

12024 

49 

8 

1 

2 

0 

0 

0 

Both  point  data  and  aggregated  data  are  described  following  the  climatic  zones  for  soil 
quality assessment identified in Tóth et al. (2013b) (Figure 11), to match the reports of 
LUCAS  2009  and  LUCAS  2015  Surveys  (Tóth  et  al.,  2013a  and  Jones  at  al.,  2020, 
respectively). 

40 

 
 
 
 
 
 
 
Figure 10 (a) Sampling density in NUTS 2 regions for the complete dataset of the 2018 LUCAS Soil 
Module; number of points in each NUTS2 region for (b) cropland, (c) grassland, (d) woodland 

(a) 

(b) 

(c) 

(d) 

41 

 
 
 
 
 
 
Figure 11 Climatic zones identified in Tóth et al. (2013b) 

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.3  Assessment of gully erosion 

Sheet and rill erosion (Panagos et al., 2020), wind erosion (Borrelli et al., 2017) and soil 
loss due to harvesting crops (Panagos  et al., 2019) can be assessed  at a pan-European 
scale. However, gully erosion susceptibility assessments are currently limited to local and 
for 
regional  scale  applications  due  to  the 
calibration/validation purposes. Accordingly, we lack harmonized national and continental 
scale  gully  erosion  monitoring  and  assessments  to  better  understand  the  geographical 
distribution  of  gully  erosion  processes  and  to  evaluate  the  threats  to  soil  and  the 
environment. 

inventory  data 

large-scale 

lack  of 

We integrated a soil erosion component into the 2018 LUCAS Soil Module to support actions 
to prevent soil degradation. We will discuss and explore opportunities to further improve 
this  method.  Erosion  observations  were  conducted  in  around  10%  (n  =  24,759)  of  the 
238,077  LUCAS  2018  in-field  survey  sites.  Gully  erosion  channels  were  detected  for  ca. 
1% (211 sites) of the visited sites (Borrelli et al., 2022). Overall, the findings indicate that 
the approach tested in the survey is effective for detecting the incidence of gully erosion. 
The morphogenesis of the mapped gullies suggests that the approach is an effective tool 
to  map  permanent  gullies,  whereas  it  appears  less  effective  in  detecting  short-lived 
ephemeral  gullies.  Spatial  patterns  emerging  from  the  field  observations  provide  new 
insights on typical gully formation sites across the EU and UK. This can help design further 
targeted research activities. An extension of this approach to all LUCAS sites in the next 
LUCAS  Survey  would  significantly  enhance  our  understanding  of  the  geographical 
distribution of gully erosion processes across the EU. 

If repeated every three years, LUCAS soil erosion surveys could provide a unique temporal 
dimension. It will enable the monitoring of, and eventually predict, the dynamics of gully 
erosion.  Data  collected  are  publicly  available  by  downloading  the  LUCAS  Gully  Erosion 
Visual Assessment inventory (GE-LUCAS v1.0). 

https://esdac.jrc.ec.europa.eu/content/gully-erosion-based-lucas  

Out  of  24,759  locations  visited,  the  surveyors  recorded  a  total  of  211  sites  with  gully 
erosion channels (equal to ca. 1% of total) (Figure 12). The surveyors also registered the 
number of gullies observable from the visited sites (ranging in length 1–420 m from the 
LUCAS  sampling  point,  with  a  median  distance  of  50  m)  and  classified  them  into  three 
different categories based on the number of gullies present: i) < 5 gullies, ii) 5–10 gullies, 
and iii) > 10 gullies. The majority of the sites have less than 5 gullies (159 sites) while 28 
sites  have  5–10  gullies  and  19  sites  have  more  than  10  gullies.  5  points  could  not  be 
verified as being gully erosion (i.e. false positives). 

The  gully  erosion  observations  have  been  validated  using  Google  Earth  imagery  (Figure 
13).  The  procedure  of  post-survey  validation  showed  that  the  majority  of  the  field 
observations could be confirmed through on-screen visual interpretation and included in 
the GE-LUCAS inventory. Only five sites (2% of total) reported that gullies could not be 
validated  and  were  classified  as  possible  false  positives.  The  remaining  206  field 
observations were verified as follows: i) 50 sites (24% of total) based on the most recent 
Google  Earth  images,  ii)  117  sites  (56%  of  total)  with  an  in-depth  procedure  including 
interpretation of historical high-resolution satellite and aerial GE images and Street View 
terrestrial images, and iii) 30 sites (14% of total) with LUCAS 2018 terrestrial photos. The 
remaining 9 (4% of total) sites were validated using other high-resolution satellite images. 
Concerning the presence of possible false negatives, the onscreen visual assessment of the 
250 randomly selected LUCAS Topsoil sites confirmed the presence of at least one gully 
erosion channel in 14 of the observed sites (equal to 5.6% of the total). 

43 

 
 
 
 
 
Figure 12 Spatial distribution of the sites for which potential gully erosion channels were observed 
during the 2018 LUCAS Topsoil field survey. The light blue dots indicate the sites validated through 
expert-based on-screen visual interpretation, while the red ones could not be observed during this 
phase 

44 

 
 
 
 
 
 
 
 
 
Figure 13 Examples of gully erosion channels validated by reviewing Google Earth Images (Credit 
Google Earth). 

Panel (a): Spain, Andalusia, 37.73N, 4.82W, image date: October 2019; Panel (b): Romania, 
Barlad, 46.18N, 27.72E, image date: August 2012; Panel (c): Spain, Castile and Leon, 41.71N, 
4.72W, image date: September 2017; Panel (d): Spain, Zaragoza, 41.05N, 0.32W, image date: 
October 2018. 

The circle within the black dotted rectangle indicates the location of a LUCAS point (from Borrelli et 
al., 2022). 

4.4  Condition of organic soils 

A  novel  aspect  of  the  2018  survey  was  to  ascertain  the  depth  of  the  organic  horizon  in 
locations where it was determined from previous LUCAS surveys that the land cover was 
wetland or that the soil organic matter content was greater than 200 g mg-1. The depth 
(up to 40 cm) was measured in 1,050 points (measurement locations were concurrent with 
the subsampling sites of the Soil Module – Figure 3a). 

Most of the sites selected for depth assessments appear not to fulfil the depth criteria for 
Histosols  (i.e.  >40  cm  or  10  cm  above  a  hard  contact).  Approximately  30%  of  sites 
recorded organic horizons with a depth of 40 cm or more, which would seem to indicate 
the presence of Histosols.  

However, the assessment failed to record the reason for very shallow organic soils (e.g. 
such as the presence of bedrock close to the surface). The implication could be that many 
of  these  locations  are  either  mineral  soils  with  well-developed  organic  horizons  or  that 
peatlands have been eroded back to the underlying mineral base. 

45 

 
 
 
4.5  Bulk density and particle size distribution 

Bulk density is expressed as the dry weight of soil divided by its volume, which includes 
the volume of soil particles and the volume of pores among soil particles. Bulk density is 
typically expressed in g cm-3. Bulk densities in undisturbed soils tend to range from about 
1.0-1.4 g/cm3 with higher values typical for sandy soils and with increasing depth.  

An increasing bulk density implies a decrease of large pores and a corresponding increase 
in micropores. A reduction in pore space affects the flow of oxygen into the soil and carbon 
dioxide out of the soil. Soil compaction is often associated with development of crusts on 
the  surface  of  soils,  which  in  turn,  reduces  infiltration  and  thus  an  increase  in  surface 
runoff. In addition, the growth of plants in compacted soil is greatly reduced. 

It is an important parameter since it can measure whether soils are compacted in response 
to land use and soil management practices. Bulk density determines infiltration capacity of 
the  soil,  available  water  capacity,  soil  porosity,  rooting  depth  restrictions,  soil 
microorganism activity, root proliferation and nutrient availability. Bulk density is a critical 
parameter in the calculation of soil organic carbon stocks. 

4.6  Organic carbon 

The  climate,  vegetation  type  and  land  use  determine  the  spatial  variability  of  organic 
carbon  (OC)  content  across  the  EU.  Based  on  the  2018  OC  measurements,  OC  content 
increases from south-eastern to north-western climatic zones (Table 14, Figure 14). This 
trend confirms that the cooler and more humid conditions in north-western climatic zones 
prevent decomposition of organic residues and promote accumulation of OC in soil. On the 
contrary,  the  drier  and  warmer  conditions  in  south-eastern  climatic  zones  accelerate 
organic matter decomposition which explains the lower OC content. In addition, OC content 
was  higher  in  wetland,  woodland,  shrubland  and  grassland  compared  to  cropland  and 
bareland  in  most  of  the  countries  (Table  14,  Figure  15).  In  line  with  these  trends,  OC 
content was the highest in wetland points in the boreal zone (mean = 345.1 g kg-1, median 
= 436.6 g kg-1 in SE) and the Atlantic zone (mean = 380.2 g kg-1, median = 455.6 g kg-1 
in IE) and the lowest in bareland points in the Mediterranean climatic zones (EL, IT, ES). 
However,  difference  in  OC  content  among  LC  classes  differed  from  the  general  trend  in 
some  countries  due  to  specific  combinations  of  pedoclimatic  conditions  and  type  of 
vegetation.  In  Cyprus,  for  instance,  woodland  points  were  located  in  pine  forests  with 
shallow  and  coarse  textured  soils,  while  cropland,  grassland  and  shrubland  points  were 
located in soils with medium to fine textures. As a result, OC content in woodland points 
was lower than in the rest of LC classes. Organic carbon content in bareland was similar to 
that in woodland, shrubland and grassland in Portugal and Sweden (Figure 15). The land 
use recorded for most of the bareland points in the two countries can explain this condition: 
mainly forestry and (semi-)natural areas not in use. Lastly, cropland points in Finland had 
a  similar  OC  content  to  those  in  woodland,  shrubland  and  grassland  (Figure  15).  This 
observation can be related to (1) the presence of organic farming in Finland (13.5% against 
7.9% in the EU - Eurostat, 2019), (2) the association of cropland with natural vegetation 
in the landscape (present in approximately 56% of cropland in 2015, IIASA & SDSN 2020), 
and (3) the cultivation of organic soils, including peats, which accounted for 11% of the 
cropland of Finland (Myllys et al., 2019; MTK, 2020). 

Altogether, 40 points were sampled in wetlands in 22 (out of 281) NUTS 2 regions. These 
points recorded the highest levels of OC (mean = 318.1 g kg-1, Table 14, Figure 16) and 
were located mainly in the boreal and the Atlantic zones (Table 15); although a few points 
were  also  identified  in  the  sub-oceanic  (1  point),  sub-continental  (5  points),  temperate 
mountainous  (1  point),  and  temperate  to  sub-oceanic  Mediterranean  (1  point)  zones. 
According to their OC content, the soil was organic (OC >200 g kg-1) in 27 points (69 %), 
and  it  was  mineral  with  high  OC  content  (>30  g  kg-1)  in  eight  points.  Organic  carbon 
content was <30 g/kg in five points, which is considered common in mineral soils. A similar 
situation  was  also  observed  in  the  LUCAS  2009  Soil  survey  (de  Brogniez  et  al.,  2015). 

46 

 
 
These data suggest a large variability of OC content in wetland points both among climatic 
zones  and  within  each  climatic  zones  (among  NUTS  2  regions),  most  likely  due  to 
differences in management practices and local climatic conditions. The NUTS 2 region with 
the highest content of OC in wetland was IE04 (mean = 517.8 g kg-1, median = 527.2 g 
kg-1), followed by SE31 (mean = 482.1 g kg-1, median = 487.9 g kg-1) and SE33 (mean = 
309.3 g kg-1, median = 407.9 g kg-1) (Table 15). The remaining 18 regions with wetland 
points had less than three samples or low sampling densities for this LC class. 

Table 14 Summary of organic carbon content in the 2018 LUCAS Soil Module, by climatic zone and 
by land cover class 

Organic carbon (g kg-1) 

Median 

Std Dev 

Climatic zones 

Boreal to sub-boreal 

Atlantic 

Sub-continental (northern) 

Sub-oceanic 

Sub-continental (southern) 

Temperate mountainous 

Mediterranean (semi-arid) 

Mediterranean (temperate to sub-oceanic) 

Mediterranean zones 

Land cover classes 

Mean 

114.8 

41.5 

49.6 

41.3 

23.7 

58.5 

16.8 

27.4 

19.9 

44.2 

22.6 

19.5 

30.3 

19.1 

37.1 

12.7 

18.2 

13.9 

Wetland 

Woodland 

Shrubland 

Grassland 

Cropland 

Bareland 

318.1 

418.2 

88.1 

55.2 

40.2 

18.3 

17.3 

42.7 

32.6 

27.7 

14.7 

12.6 

147.3 

66.5 

88.5 

39.6 

20.1 

70.1 

15.4 

26.1 

19.7 

200.9 

118.3 

77.3 

51.6 

20.6 

25.6 

47 

 
 
 
 
 
 
Figure 14 Organic carbon content (all 2018 LUCAS Soil Module points) presented (a) as point data and (b) average aggregated at NUTS 2 level 

(a) 

(b) 

48 

 
 
 
Figure 15 Organic carbon content in the 2018 LUCAS Soil Module (mean and standard error) by land cover class in the 27 Member States and the UK * 

*the horizontal lines indicate breaks in y-axis to facilitate the viewing of the data. 

49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 15 Summary of organic carbon in wetland points in the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in wetland 
(g kg-1) (1) 

NUTS 2 with lowest OC 
content in wetland (g 
kg-1) (1) 

Boreal to sub-boreal 

20 

345.1 

436.6 

182.6 

Atlantic 

12 

380.2 

455.6 

189.2 

SE31 
N points = 5 
mean = 482.1 
median = 487.9 

SE33 
N points = 10 
mean = 309.3 
median = 407.9 

IE04 
N points = 5 
mean = 517.8 
median = 527.2 

Sub-continental 
(northern) 

3 

330.2 

444.2 

256.8 

None of the regions had at least 3 points 

(1)  only NUTS 2 regions with three or more points were considered. 

50 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 16 Summary of organic carbon in woodland points in the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in 
woodland (g kg-1) (1) 

NUTS 2 with lowest 
OC content in 
woodland (g kg-1) (1) 

Boreal to sub-boreal 

2095 

122.1 

50.1 

152.0 

Atlantic 

374 

86.9 

50.6 

106.4 

Sub-oceanic 

693 

61.2 

45.7 

53.1 

Sub-continental (northern) 

1145 

94.6 

45.1 

123.9 

Sub-continental 
(southern) 

275 

36.2 

26.6 

34.1 

Temperate mountainous 

665 

78.5 

51.1 

85.9 

Mediterranean (semi-arid) 

425 

30.0 

22.6 

25.6 

Mediterranean 
(temperate to sub-
oceanic) 

323 

43.7 

35.5 

37 

SE31 
N points = 296 
mean = 160.6 
median = 78.5 

IE05 
N points = 4 
mean = 295.2 
median = 283.0 

ITF1 
N points = 14 
mean = 184.7 
median = 179.0 

FI1B 
N points = 4 
mean = 294.2 
median = 319.9 

SI04 
N points = 11 
mean = 111.8 
median = 113.4 

AT12 
N points = 17 
mean = 172.9 
median = 147.3 

ITG1 
N points = 3 
mean = 83.9 
median = 71.6 

ES22 
N points = 11 
mean = 84.2 
median = 54.3 

LV00 
N points = 81 
mean = 87.3 
median = 41.7 

FRB0 
N points = 17 
mean = 22.9 
median = 20.0 

EL63 
N points = 3 
mean = 11.7 
median = 9.3 

PL72 
N points = 6 
mean = 9.8 
median = 10.2 

HU12 
N points = 11 
mean = 12.2 
median = 10.2 

BG32 
N points = 5 
mean = 19.0 
median = 16.6 

CY00 
N points = 7 
mean = 9.0 
median = 7.8 

PT18 
N points = 23 
mean = 16.6 
median = 9.9 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 16 Organic carbon content in wetland presented as point data 

52 

 
 
 
 
 
Organic carbon content in woodland points was also high (mean = 88.1 g kg-1,Table 14). 
It was higher in north-western climatic zones (boreal, Atlantic, sub-oceanic, and northern 
sub-continental)  than  in  the  south-eastern  climatic  zones  (Mediterranean  and  southern 
sub-continental) (Table 16, Figure 17). In the north-western climatic zones, the mean OC 
content ranged from 122.1 g kg-1 (median = 50.1 g kg-1) in the boreal to sub-boreal zone 
to  61.2  g  kg-1  (median  =  45.7  g  kg-1)  in  the  sub-oceanic  zone  (Table  16).  The  highest 
content of OC was observed in the NUTS 2 region SE31 (mean OC = 160.6 g kg-1, median 
OC = 78.5 g kg-1) and the lowest in EL63 (mean = 11.7 g kg-1, median = 9.3 g kg-1) (Table 
16).  In  the  south-eastern  zones,  mean  OC  content  ranged  from  43.7  g  kg-1  (median  = 
35.5 g kg-1) in the temperate to sub-oceanic Mediterranean zone to 30.0 g kg-1 (median = 
22.6 g kg-1) in the semi-arid Mediterranean zone (Table 16). The highest content of OC in 
woodland was observed in the NUTS 2 region SI04 (mean OC = 111.8 g kg-1, median OC 
= 113.4 g kg-1) and the lowest in CY00 (mean = 9.0 g kg-1, median = 7.8 g kg-1) (Table 
16).  The  temperate  mountainous  zone  had  woodland  OC  levels  similar  to  these  in  the 
north-western zones (Table 16, Figure 17). This variability of OC content in woodland could 
be due to differences in climatic conditions (temperature and precipitation) and to quality 
and quantity of litter that regulate, at least partially, organic matter decomposition in soil 
(Hanewinkel et al., 2013). 

Figure 17 Organic carbon content in woodland: (a) point data and (b) average aggregated at NUTS 
2 level 

(a) 

(b) 

The mean OC content was 40.2 g kg-1 in grassland points and 55.2 g kg-1 in shrubland 
points (Table 14). In both LC classes, OC content increased from south-eastern to north-
western climatic zones (Figures 18 and 19), as also observed for woodland points. Organic 
carbon content was the highest in the boreal to sub-boreal zone, followed by the Atlantic 
zone both in grassland and shrubland (Tables 17 and 18). In these zones, OC content in 
 (median = 78.7 g kg-1) in the NUTS 2 region UKM6 in 
grassland ranged from 154.5 g kg-1
 (median = 19.0 g kg-1) in FR10 region in the 
the boreal to sub-boreal zone, to 18.2 g kg-1
Atlantic zone (Table 17). In shrubland, OC content ranged from 279.6 g kg-1
 (median = 
281.3 g kg-1) in the IE06 region in the Atlantic zone to 27.2 g kg-1
 (median = 22.2 g kg-1) 
in FI1D region in the boreal to sub-boreal zone (Table 18).  Organic carbon  content was 

53 

 
 
 
 
intermediate  in  the  sub-oceanic,  northern  sub-continental  and  temperate  mountainous 
zones in both LC classes. The temperate to sub-oceanic Mediterranean zone showed also 
intermediate values of OC content in shrubland. It ranged from 53.4 g kg-1
 (median = 42.3 
g kg-1) in ES22 region in the temperate mountainous zone to 32.2 g kg-1
 (median = 33.4 
g kg-1) in ES23 region in the sub-oceanic zone (Table 17). In shrubland, OC content ranged 
 (median = 87.8 g kg-1) in the SE12 region in the northern sub-continental 
from 127.5 g kg-1
zone to 18.7 g kg-1
 (median = 16.9 g kg-1) in ES43 region in the temperate to sub-oceanic 
Mediterranean  zone  (Table  18).  Lastly,  OC  content  was  the  lowest  in  the  southern  sub-
continental and semi-arid Mediterranean zones in both grassland and shrubland (Tables 17 
 (median = 68.0 g kg-1) in HU21 region in the southern 
and 18). It ranged from 67.0 g kg-1
 (median = 7.1 g kg-1) in ES51 region in the semi-arid 
sub-continental zone to 9.1 g kg-1
Mediterranean zone (Table 17). In shrubland, OC content ranged from 43.7 g kg-1
 (median 
 (median = 18.9 g kg-1) in ES22 region, 
= 33.8 g kg-1) in the ITG1 region to 15.1 g kg-1
both  in  the  semi-arid  Mediterranean  zone  (Table  18).  The  spatial  variability  of  OC  in 
grassland and shrubland reflects the distribution of various types of grassland and climatic 
conditions in the EU, ranging from permanent grassland and moorlands that dominate the 
north  and  north-western  cool  and  humid  regions  to  dry,  and  desert-like  grassland  and 
Mediterranean maquis that predominate in the southern and south-eastern. 

Table 17 Summary of organic carbon in grassland points of the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in 
grassland (g kg-1) (1) 

NUTS 2 with lowest 
OC content in 
grassland (g kg-1) (1) 

Boreal to sub-boreal 

167 

75.0 

31 

103.6 

Atlantic 

715 

54.7 

38.6 

60.4 

Sub-oceanic 

789 

43.6 

35.9 

32.9 

Sub-continental (northern) 

703 

40.7 

21.1 

69.1 

Sub-continental 
(southern) 

355 

26.6 

22 

20.0 

Temperate mountainous 

413 

43.6 

34.9 

43.5 

Mediterranean (semi-arid) 

537 

17.4 

13.4 

14.7 

Mediterranean 
(temperate to sub-
oceanic) 

261 

25.9 

20.7 

18.9 

UKM6 
N points = 6 
mean = 154.5 
median = 78.7 

DK05 
N points = 5 
mean = 134.7 
median = 55 

AT31 
N points = 4 
mean = 109.3 
median = 41.4 

DE80 
N points = 5 
mean = 106.8 
median = 18.8 

HU21 
N points = 5 
mean = 67.0 
median = 68 

AT31 
N points = 5 
mean = 123.7 
median = 38.6 

EL65 
N points = 8 
mean = 30.6 
median = 22.5 

ITH3 
N points = 6 
mean = 45.1 
median = 43.1 

FI1C 
N points = 10 
mean = 48.3 
median = 32.9 

FR10 
N points = 4 
mean = 18.2 
median = 19 

ITF2 
N points = 5 
mean = 12 
median = 12.9 

PL21 
N points = 16 
mean = 18.4 
median = 14.9 

RO41 
N points = 13 
mean = 13.3 
median = 9.8 

BG34 
N points = 5 
mean = 18.9 
median = 13.9 

ES51 
N points = 3 
mean = 9.1 
median = 7.1 

PT17 
N points = 4 
mean = 11.3 
median = 10.1 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 18 Summary of organic carbon in shrubland points of the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in 
shrubland (g kg-1) (1) 

NUTS 2 with lowest 
OC content in 
shrubland (g kg-1) (1) 

Boreal to sub-boreal 

76 

126.7 

56.3 

148.7 

Atlantic 

56 

117.1 

68.4 

137.7 

Sub-oceanic 

126 

51.9 

37.8 

49.6 

Sub-continental (northern) 

18 

57.5 

37.1 

67.1 

Sub-continental 
(southern) 

30 

28.63 

25.6 

15.4 

Temperate mountainous 

33 

51.0 

36.2 

41.6 

Mediterranean (semi-arid) 

223 

29.7 

24.5 

23.8 

Mediterranean 
(temperate to sub-
oceanic) 

136 

41.4 

34.1 

26.8 

SE31 
N points = 15 
mean = 231.4 
median = 161.3 

IE06 
N points = 3 
mean = 279.6 
median = 281.3 

ES12 
N points = 3 
mean = 109.3 
median = 105.2 

SE12 
N points = 4 
mean = 127.5 
median = 87.8 

RO31 
N points = 4 
mean = 29.5 
median = 21.5 

ES22 
N points = 3 
mean = 53.4 
median = 42.3 

ITG1 
N points = 5 
mean = 43.7 
median = 33.8 

ES11 
N points = 3 
mean = 53 
median = 52.1 

FI1D 
N points = 15 
mean = 27.2 
median = 22.2 

UKL1 
N points = 4 
mean = 59.5 
median = 48.0 

ES23 
N points = 4 
mean = 32.3 
median = 33.4 

SE21 
N points = 4 
mean = 62.1 
median = 54.7 

RO11 
N points = 6 
mean = 23.6 
median = 24.2 

BG41 
N points = 5 
mean = 30.3 
median = 31.3 

ES22 
N points = 3 
mean = 15.1 
median = 18.9 

ES43 
N points = 4 
mean = 18.8 
median = 16.9 

55 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 18 Organic carbon content in grassland: (a) point data and (b) average aggregated at NUTS 
2 level 

(a) 

(b) 

Figure 19 Organic carbon content in shrubland presented as point data 

56 

 
 
 
 
Organic carbon content was on average 18.3 g kg-1 in cropland points and 17.3 g kg-1 in 
bareland  points  (Table  14),  showing  an  increasing  trend  from  south-eastern  to  north-
western climatic zones (Figures 20 and 21). The lowest contents of OC in cropland were 
measured  in  points  from  the  semi-arid  Mediterranean,  ranging  from  9.0  g  kg-1  in  ES30 
region to 33.7 g kg-1 in PT15 region (Table 19, Figure 20).  

Surprisingly,  the  lowest  mean  OC  content  in  bareland  was  observed  in  the  temperate 
mountainous zone (8.6 g kg-1, Table 20); although care must be taken in interpreting this 
result  since  the  number  of  bareland  samples  for  this  climatic  zone  is  low  (8  points 
sampled). If this climatic zone is not considered, the lowest OC contents in bareland are 
found  in  the  semi-arid  Mediterranean  zone,  ranging  from  17.4  g  kg-1  in  CY00  region  to 
11.2 g kg-1 in ES41 region (Table 20, Figure 21). Dry and warm climatic conditions, and 
low agricultural potential of many soils (e.g. Calcisols, Leptosols, Arenosols) characteristic 
of the semi-arid Mediterranean zone hinder OC accumulation in this climatic zone. On the 
contrary,  OC  content  was  the  highest  in  the  boreal  to  sub-boreal  zone  both  in  cropland 
(55.1 g kg-1) and bareland (56 g kg-1), followed by the temperate mountainous and the 
sub-oceanic zones in cropland (21.8 g kg-1 and 21.4 g kg-1, respectively) (Table 19, Figure 
20) and by the sub-oceanic zone in bareland (22.4 g kg-1) (Table 20, Figure 21). The cooler 
and more humid conditions can explain the larger accumulation of OC in soil in these zones, 
in which characteristic soils such as Luvisols, Phaeozems and Cambisols are found, which 
are particularly suited to agricultural production. 

Table 19 Summary of organic carbon in cropland points of the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in 
cropland (g kg-1) (1) 

NUTS 2 with lowest 
OC content in cropland 
(g kg-1) (1) 

Boreal to sub-boreal 

210 

55.1 

26.7 

80.2 

Atlantic 

1438 

19.0 

15.6 

14.8 

Sub-oceanic 

814 

21.4 

18.4 

12.3 

Sub-continental (northern) 

1429 

17.6 

13.9 

21.7 

Sub-continental 
(southern) 

754 

17.9 

16.7 

8.2 

Temperate mountainous 

170 

21.8 

18.7 

12.2 

Mediterranean (semi-arid) 

1905 

13.0 

11.4 

8.1 

Mediterranean 
(temperate to sub-
oceanic) 

615 

17.2 

14 

13.4 

FI1D 
N points = 37 
mean = 123.9 
median = 60.2 

NL13 
N points = 4 
mean = 71.3 
median = 69.9 

ITH4 
N points = 19 
mean = 49.9 
median = 49.7 

EE00 
N points = 7 
mean = 45.0 
median = 38.4 

RO12 
N points = 11 
mean = 23.2 
median = 21.9 

ITH2 
N points = 10 
mean = 36.9 
median = 35.7 

PT15 
N points = 7 
mean = 33.7 
median = 23.6 

ES11 
N points = 6 
mean = 54.4 
median = 46.7 

LV00 
N points = 25 
mean = 17.5 
median = 16.1 

FRJ1 
N points = 3 
mean = 9.5 
median = 10.1 

EL53 
N points = 3 
mean = 9.3 
median = 8.7 

PL43 
N points = 19 
mean = 10.1 
median = 9.1 

PL81 
N points = 27 
mean = 11.4 
median = 9.8 

EL51 
N points = 10 
mean = 10.8 
median = 10.1 

ES30 
N points = 18 
mean = 9.0 
median = 8.1 

PT18 
N points = 11 
mean = 7.8 
median = 8.2 

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 20 Summary of organic carbon in bareland points of the 2018 LUCAS Topsoil survey. 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
OC content in 
bareland (g kg-1) (1) 

NUTS 2 with lowest OC 
content in bareland (g 
kg-1) (1) 

Boreal to sub-boreal 

18 

56 

20.1 

83 

Atlantic 

95 

18.8 

17.4 

8.1 

Sub-oceanic 

48 

22.4 

19.2 

17.7 

Sub-continental (northern) 

41 

34.7 

18.5 

67.7 

Sub-continental (southern) 

55 

19.1 

18.7 

8.2 

Temperate mountainous 

8 

8.6 

8.5 

3.4 

Mediterranean (semi-arid) 

309 

11.5 

10.1 

7.3 

Mediterranean 
(temperate to sub-
oceanic) 

54 

17.3 

11.6 

18.7 

SE12 
N points = 3 
mean = 130.7 
median = 25.4 

UKF2 
N points = 10 
mean = 28.2 
median = 29 

FRK1 
N points = 6 
mean = 30.2 
median = 27 

LT02 
N points = 3 
mean = 15.8 
median = 15.9 

HU31 
N points = 3 
mean = 34.6 
median = 24.3 

LV00 
N points = 4 
mean = 16.8 
median = 18.2 

FRC1 
N points = 5 
mean = 12.4 
median = 11.4 

DE73 
N points = 4 
mean = 14.9 
median = 12.9 

PL42 
N points = 3 
mean = 10.25 
median = 10.35 

RO41 
N points = 3 
mean = 14.4 
median = 17.2 

EL52 
N points = 4 
mean = 9.5 
median = 9.1 

PT11 
N points = 3 
mean = 39.6 
median = 40.7 

ES41 
N points = 3 
mean = 11.2 
median = 10.8 

CY00 
N points = 3 
mean = 17.4 
median = 15.4 

PT18 
N points = 3 
mean = 39.6 
median = 40.7 

58 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 20 Organic carbon content in cropland: (a) point data and (b) average aggregated at NUTS 
2 level 

(a) 

(b) 

Figure 21 Organic carbon content in bareland presented as point data 

59 

 
 
 
 
 
 
 
 
4.7  Nitrogen, phosphorus, and potassium 

4.7.1  Nitrogen content 

The spatial distribution of nitrogen (N) was highly correlated with that of OC, given that N 
is a main component of soil organic matter: (i) N content increased from south-eastern to 
north-western regions (Table 21, Figure 22), and (ii) it was greater in wetland, woodland, 
grassland and shrubland than in cropland and bareland (Table 21, Figure 23). Thus, as for 
OC, the main drivers of N content in soil were climate and the type of vegetation. 

Table 21 Summary of nitrogen content in the 2018 LUCAS Topsoil survey, by climatic zone and by 
land cover class 

Climatic zones 

Boreal to sub-boreal 

Atlantic 

Sub-continental (northern) 

Sub-oceanic 

Sub-continental (southern) 

Temperate mountainous 

Mediterranean (semi-arid) 

Mediterranean (temperate to sub-oceanic) 

Mediterranean zones 

Land cover classes 

Wetland 

Woodland 

Shrubland 

Grassland 

Cropland 

Bareland 

Nitrogen (g kg-1) 

Mean 

Median 

Std Dev 

5.2 

3.3 

3.2 

3.5 

2.2 

4.2 

1.6 

2.2 

1.8 

2.4 

2.3 

1.9 

2.8 

1.9 

3.4 

1.3 

1.7 

1.4 

13.8 

15.2 

4.5 

3.7 

3.6 

1.8 

1.6 

2.7 

2.7 

2.8 

1.6 

1.4 

6.3 

3.3 

4.2 

2.5 

1.4 

3.5 

1.0 

1.6 

1.3 

8.1 

5.0 

3.6 

3.5 

1.3 

1.1 

Overall,  wetland,  woodland,  grassland  and  shrubland  showed  a  relatively  large 
proportion of points with high content of N (>3 g kg-1) in the boreal to sub-boreal, Atlantic, 
temperate mountainous and sub-oceanic zones (Figures 24 to 27). The average nitrogen 
content was >3 g kg-1 for these LC in the climatic zones mentioned, except for wetland in 
the temperate mountainous zone that had a slightly lower content (2.6 g kg-1) (Table 21). 
In addition, the median was >3 g kg-1 in woodland, grassland and shrubland in the Atlantic, 
temperate mountainous and sub-oceanic zones, which indicates that N content was >3 g 
kg-1 in more than50 % of the points in these zones (Table 21). Similarly, the median of N 
content was >3 g kg-1 in the boreal to sub-boreal, Atlantic, and sub-oceanic zones (Table 
21).  However,  as  shown  in  Tables  22  to  25,  the  N  content  varied  largely  in  woodland, 
grassland and shrubland within each climatic zone mentioned. The largest variations in N 
content  in  woodland,  grassland  and  shrubland  were  observed  in  the  Atlantic  zone:  the 
average N content ranged from 1.5 g kg-1 in the NUTS 2 region FRB0 to 14.7 g kg-1 in IE05 
region  in  woodland,  from  1.9  g  kg-1  in  FR10  region  to  10.3  g  kg-1  in  DK05  region  in 
grassland, and from 4.8 g kg-1 in ES11 region to 13.5 g kg-1 in IE06 region in shrubland 
(Table 23 to 25). In wetland, the N content ranged from 13.6 g kg-1 in SE33 region to 17.4 
g kg-1 in SE31 region (Table 22). None of the NUTS 2 regions in the other climatic zones 
had enough points (>3 points) to be included in the statistics. On the contrary, the average 
N content in woodland, grassland and shrubland in the sub-continental and Mediterranean 
zones was <3 g kg-1, except for woodland (4.6 g kg-1) and grassland (3.5 g kg-1) in the 

60 

 
 
 
 
 
 
 
northern sub-continental zone (Table 21). In all climatic zones, the median N content was 
<3 g kg-1, which means that less than50 % of the points had N contents >3 g kg-1. 

Figure 22 Nitrogen content (all 2018 LUCAS Soil Module points) presented (a) as point data and (b) 
average aggregated at NUTS 2 level 

(a) 

(b) 

Figure 23 Nitrogen content (mean and standard error) by land cover class in the 27 Member States 
and the UK 

Nitrogen content was lower in cropland and bareland compared to wetland, woodland, 
grassland  and  shrubland  (Table  21).  Nitrogen  content  in  these  two  LC  classes  was 
especially low in the sub-continental and Mediterranean zones (Figures 27-29). Both the 
mean and median contents of N were >2 g kg-1 in cropland and bareland in the climatic 

61 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
zones mentioned (Table 21). In cropland, the mean N content ranged from 0.8 g kg-1 in 
ES62  region  in  the  temperate  to  sub-oceanic  Mediterranean  zone  to  4.2  g  kg-1  in  EE00 
region  in  the  northern  sub-continental  zone  (Table  26).  In  bareland,  mean  N  content 
ranged from 1 g kg-1 in ES43 region in the semi-arid Mediterranean zone to 3.6 g kg-1 in 
SE12 region in the northern-sub-continental zone (Table 27). 

The N contents observed in LUCAS 2018 are consistent with the estimations produced by 
Ballabio  et  al.  (2019)  based  on  the  LUCAS  2009/2012  data.  In  the  N  map  produced  by 
these  authors,  woodland  and  grassland  in  boreal-to  sub-boreal,  Atlantic  and  temperate 
mountainous  zones  clearly  stand  out  for  their  high  N  contents.  The  cool  and  humid 
conditions  in  these  climatic  zones  favour  organic  matter  accumulation,  which  results  in 
high  contents  of  OC  and  N  in  topsoil.  The  N  map  shows  also  low  N  contents  in  the 
Mediterranean  and  sub-continental  zones.  The  warm  and/or  dry  conditions  accelerate 
organic matter decomposition in the Mediterranean zones, which can explain the lower N 
content  in  cropland  in  these  zones  compared  to  cropland  in  other  climatic  zones.  Soil 
texture also plays a role in preserving organic matter and, thus, OC and N. For instance, 
N  content  tends  to  be  lower  in  the  coarse  textured  soils  found  in  the  northern  sub-
continental zones even if other factors, such as climate, are favourable. In addition, it must 
be noted that different fertilization practices at regional and national scales can influence 
the spatial distribution of N in soils under cropland. 

Figure 24 Nitrogen content in wetland presented as point data 

Figure 25 Nitrogen content in woodland presented (a) as point data and (b) average aggregated at 
NUTS 2 level 

62 

 
 
 
(a) 

(b) 

Figure 26 Nitrogen content in grassland presented (a) as point data and (b) average aggregated at 
NUTS 2 level 

(a) 

(b) 

Figure 27 Nitrogen content in cropland presented (a) as point data and (b) average aggregated at 
NUTS 2 level 

63 

 
 
 
 
 
 
 
(a) 

(b) 

Figure 28 Nitrogen content in bareland presented as point data 

64 

 
 
 
 
 
 
 
Figure 29 Nitrogen content in shrubland presented as point data 

65 

 
 
Table 22 Summary of nitrogen content in wetland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in wetland (g 
kg-1) (1) 

Boreal to sub-boreal 

20 

14.5 

15.9 

7.5 

SE31 
N points =5  
mean = 17.4 
median = 15.2 

NUTS 2 with lowest N 
content in wetland (g 
kg-1) (1) 
SE33 
N points =10  
mean = 13.58 
median = 15.4 

Atlantic 

12 

15.2 

16.5 

7.1 

IE04 
N points = 5 
mean = 18.9 
median = 19.4 

Sub-oceanic 

1 

7.4 

7.4 

NA 

None of the regions had at least 3 points 

Sub-continental (northern) 

3 

18.0 

16.4 

15.0 

None of the regions had at least 3 points 

Sub-continental (southern) 

2 

4.4 

4.4 

1.2 

None of the regions had at least 3 points 

Temperate mountainous 

Mediterranean 
(temperate to sub-oceanic) 

1 

1 

2.6 

2.6 

NA 

None of the regions had at least 3 points 

5.1 

5.1 

NA 

None of the regions had at least 3 points 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 23 Summary of nitrogen content in woodland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in woodland 
(g kg-1) (1) 

Boreal to sub-boreal 

2095 

5.3 

2.4 

6.5 

Atlantic 

374 

4.9 

3.6 

4.4 

Sub-oceanic 

693 

4.1 

3.3 

2.8 

Sub-continental (northern) 

1145 

4.6 

2.7 

5.2 

Sub-continental (southern) 

275 

2.8 

2.3 

2.1 

Temperate mountainous 

665 

4.8 

3.6 

4.1 

Mediterranean (semi-arid) 

427 

2.1 

1.7 

1.4 

Mediterranean 
(temperate to sub-
oceanic) 

321 

2.9 

2.4 

2.3 

EE00 
N points = 87 
mean = 8.5 
median = 4.4 

IE05 
N points = 4 
mean = 14.7 
median = 13.5 

ITF1 
N points = 4 
mean = 11.8 
median = 11.5 

FI1B 
N points = 4 
mean = 9.1 
median = 10.3 

SI04 
N points = 11 
mean = 7.2 
median = 6.8 

AT12 
N points = 17 
mean = 8.7 
median = 6.3 

ITG1 
N points = 3 
mean = 5.3 
median = 6.8 

ES22 
N points = 11 
mean = 5.9 
median = 4.3 

NUTS 2 with lowest N 
content in woodland (g 
kg-1) (1) 
SE33 
N points = 462 
mean = 4.3 
median = 1.5 

FRB0 
N points = 17 
mean = 1.5 
median = 1.4 

EL63 
N points = 3 
mean = 1 
median =0.9  

PL72 
N points = 6 
mean = 0.9 
median = 0.9 

HU12 
N points = 11 
mean = 1.2 
median = 1 

EL51 
N points = 15 
mean = 1.7 
median = 1.1 

CY00 
N points = 7 
mean = 0.7 
median = 0.7 

PT18 
N points = 23 
mean = 1.1 
median = 0.7 

67 

 
 
 
 
 
 
 
 
 
 
 
 
Table 24 Summary of nitrogen content in grassland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in grassland 
(g kg-1) (1) 

Boreal to sub-boreal 

167 

4.9 

2.7 

5.9 

Atlantic 

716 

4.8 

3.8 

3.7 

Sub-oceanic 

789 

4.3 

3.7 

2.7 

Sub-continental (northern) 

704 

3.5 

2.1 

5.1 

Sub-continental (southern) 

355 

2.6 

2.3 

1.7 

Temperate mountainous 

413 

4.1 

3.5 

3.0 

Mediterranean (semi-arid) 

538 

1.8 

1.5 

1.3 

Mediterranean 
(temperate to sub-
oceanic) 

261 

2.3 

1.9 

1.3 

UKM6 
N points = 6 
mean = 11 
median = 5.7 

DK05 
N points = 5 
mean = 10.3 
median = 5.3 

AT31 
N points = 4 
mean = 8.7 
median = 4.6 

DE80 
N points = 5 
mean = 9.1 
median = 2 

HU21 
N points = 5 
mean = 6.1 
median = 4.5 

AT33 
N points = 15 
mean = 7.9 
median = 5.9 

EL65 
N points = 8 
mean = 3.0 
median = 2.4 

ES21 
N points = 3 
mean = 3.7 
median = 4 

NUTS 2 with lowest N 
content in grassland 
(g kg-1) (1) 
FI1C 
N points = 10 
mean = 3.2 
median = 2.4 

FR10 
N points = 4 
mean = 1.9 
median = 2 

ITF2 
N points = 5 
mean = 1.3 
median = 1.2 

PL82 
N points = 11 
mean = 1.9 
median = 1.7 

RO41 
N points = 13 
mean = 1.3 
median = 1 

EL52 
N points = 4 
mean = 1.4 
median = 1.3 

EL52 
N points = 17 
mean = 1.1 
median = 1.1 

PT17 
N points = 4 
mean = 1.1 
median = 1.0 

68 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 25 Summary of nitrogen content in shrubland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in shrubland 
(g kg-1) (1) 

Boreal to sub-boreal 

76 

5.5 

2.4 

6.6 

Atlantic 

56 

7.0 

4.8 

6.3 

Sub-oceanic 

126 

4.0 

3.2 

2.5 

Sub-continental (northern) 

18 

3.1 

2.55 

2.4 

Sub-continental (southern) 

30 

2.7 

2.55 

1.4 

Temperate mountainous 

33 

4.4 

3.6 

3.0 

Mediterranean (semi-arid) 

226 

2.4 

2.1 

1.7 

Mediterranean 
(temperate to sub-
oceanic) 

136 

3.2 

2.85 

1.8 

LV00 
N points = 5 
mean = 11.4 
median = 12 

IE06 
N points = 3 
mean = 13.5 
median = 11.1 

FRC2 
N points = 3 
mean = 7 
median =6  

SE12 
N points =4  
mean = 5.6 
median = 4.5 

RO31 
N points =4  
mean = 2.7 
median = 2.1 

ES22 
N points = 3 
mean = 4.2 
median = 3.8 

ITG1 
N points = 5 
mean = 2.9 
median = 1.6 

EL54 
N points = 5 
mean = 4.3 
median = 3.8 

NUTS 2 with lowest N 
content in shrubland 
(g kg-1) (1) 
FI1D 
N points = 15 
mean = 1.3 
median = 1 

ES11 
N points = 19 
mean = 4.8 
median = 3.6 

EL54 
N points = 3 
mean = 2.8 
median = 2.3 

SE21 
N points = 4 
mean = 3.1 
median = 3.3 

BG31 
N points = 3 
mean = 2.2 
median = 1.8 

BG41 
N points = 5 
mean = 3.1 
median = 3 

ES22 
N points = 3 
mean = 1.5 
median = 1.6 

ES43 
N points = 4 
mean = 2.3 
median = 2 

69 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 26 Summary of nitrogen content in cropland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in cropland (g 
kg-1) (1) 

Boreal to sub-boreal 

210 

3.7 

2.3 

4.1 

Atlantic 

1438 

2.0 

1.8 

1.2 

Sub-oceanic 

814 

2.2 

2 

1.0 

Sub-continental (northern) 

1428 

1.8 

1.5 

1.6 

Sub-continental (southern) 

754 

1.8 

1.7 

0.6 

Temperate mountainous 

170 

2.3 

1.9 

1.1 

Mediterranean (semi-arid) 

1916 

1.4 

1.3 

0.7 

Mediterranean 
(temperate to sub-
oceanic) 

615 

1.7 

1.5 

1.0 

FI1D 
N points = 37 
mean = 6.5 
median = 3.9 

UKN0 
N points = 7 
mean = 4.4 
median = 3.9 

BE34 
N points = 3 
mean = 4.3 
median = 4.3 

EE00 
N points = 7 
mean = 4.2 
median = 3.6 

SK04 
N points = 5 
mean = 2.7 
median = 2.7 

AT22 
N points = 3 
mean = 3.4 
median = 3.5 

PT15 
N points = 7 
mean = 2.4 
median = 2.3 

ES11 
N points = 6 
mean = 4.2 
median = 3.5 

NUTS 2 with lowest N 
content in cropland (g 
kg-1) (1) 
LV00 
N points = 25 
mean = 1.7 
median = 1.7 

FRJ1 
N points = 3 
mean = 1.1 
median = 1.2 

EL53 
N points = 3 
mean = 1 
median =1  

PL91 
N points =8  
mean = 1.1 
median = 1.05 

PL81 
N points = 27 
mean = 1.3 
median = 1.2 

EL51 
N points = 10 
mean = 1.4 
median = 1.2 

PT16 
N points = 4 
mean = 0.9 
median = 0.9 

ES62 
N points = 6 
mean = 0.8 
median = 0.8 

70 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 27 Summary of nitrogen content in bareland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest N 
content in bareland (g 
kg-1) (1) 

Boreal to sub-boreal 

18 

2.8 

1.8 

2.7 

Atlantic 

95 

2.1 

1.9 

0.8 

Sub-oceanic 

48 

2.3 

2.0 

1.2 

Sub-continental (northern) 

41 

2.4 

2 

2.2 

Sub-continental (southern) 

55 

1.9 

1.9 

0.7 

Temperate mountainous 

8 

1.1 

1.1 

0.3 

Mediterranean (semi-arid) 

312 

1.2 

1.2 

0.5 

Mediterranean 
(temperate to sub-
oceanic) 

54 

1.5 

1.2 

1.2 

SE12 
N points = 3 
mean = 5.6 
median = 2.9 

UKF3 
N points = 3 
mean = 3.1 
median = 3.4 

FRF2 
N points = 4 
mean = 3.2 
median = 2.8 

SE12 
N points = 8 
mean = 3.6 
median = 2.6 

HU31 
N points = 3 
mean = 3.2 
median = 2.1 

EL52 
N points = 4 
mean = 1.1 
median = 1.2 

CY00 
N points = 3 
mean = 1.9 
median = 1.8 

PT11 
N points = 3 
mean = 3.1 
median = 3.1 

NUTS 2 with lowest N 
content in bareland (g 
kg-1) (1) 
LV00 
N points = 4 
mean = 1.7 
median = 1.7 

FRC1 
N points = 5 
mean = 1.4 
median = 1.4 

DE73 
N points =4  
mean = 1.8 
median = 1.6 

PL42 
N points = 3 
mean = 1.2 
median = 1.2 

HR04 
N points = 8 
mean = 1.4 
median = 1.3 

EL52 
N points = 4 
mean = 1.1 
median = 1.2 

ES43 
N points = 11 
mean = 1 
median =1  

ES24 
N points =22  
mean = 1.3 
median = 1.1 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.7.2  Phosphorus content 

Phosphorus  (P)  content  in  soil  across  the  various  climatic  zones  appears  to  reflect  land 
cover type. The highest contents were measured in cropland, followed by grassland and 
bareland in the Atlantic, sub-oceanic and northern sub-continental zones, while the lowest 
contents  were  observed  in  woodland  and  shrubland  in  the  Mediterranean  and  sub-
continental  zones  (Table  28,  Figure  30).  This  reflects  the  application  of  phosphorous 
fertilzers on croplands and managed grasslands. 

Table 28 Summary of phosphorus content by climatic zone and by land cover class 

Climatic zones 

Boreal to sub-boreal 

Atlantic 

Sub-continental (northern) 

Sub-oceanic 

Sub-continental (southern) 

Temperate mountainous 

Mediterranean (semi-arid) 

Mediterranean (temperate to sub-oceanic) 

Mediterranean zones 

Land cover classes 

Wetland 

Woodland 

Shrubland 

Grassland 

Cropland 

Bareland 

Phosphorus (mg kg-1) 

Mean 

Median 

Std Dev 

30.3 

44.9 

34.2 

37.3 

27.4 

27.0 

30.9 

32.3 

31.3 

25.4 

28.2 

28.8 

34.5 

39.1 

34.3 

24.3 

37.2 

26.2 

30.2 

19.3 

19.7 

22.0 

22.2 

22 

20.1 

21.8 

20.2 

24.8 

31.2 

25.9 

20.7 

30.6 

24.9 

26.3 

25.4 

23.6 

31.2 

28.6 

30.5 

16.2 

21.0 

24.3 

30.2 

29.2 

25.2 

72 

 
 
 
 
 
 
 
 
 
Figure 30 Phosphorus content (all 2018 LUCAS Soil Module points) presented (a) as point data 
(mg/kg) and (b) average aggregated at NUTS 2 level 

(a) 

(b) 

Average P content in woodland ranged from 23.6 mg kg-1 (median = 18.7 mg kg-1) in the 
temperate mountainous region to 34.2 mg kg-1 (median = 23.3 mg kg-1) in the Atlantic 
regions  (Table  29,  Figure  31).  A  similar  range  of  values  was  observed  in  shrubland, 
varying from 19.8 mg kg-1 (median = 14.1 mg kg-1) in the semi-arid Mediterranean zone 
to 42.1 mg kg-1 (median = 29.9 mg kg-1) in the Atlantic zones (Table 30, Figure 32). These 
values  indicate  that  P  content  was  low  (<40  mg  kg-1)  in  most  of  the  woodland  and 
shrubland points. In woodland, 89% of the points had <40 mg kg-1. In shrubland, P content 
was <40 mg kg-1 in 91 % of the points. 

Grassland and bareland had higher P contents than woodland and shrubland. Average P 
content in grassland ranged from 23.8 mg kg-1 (median = 17.4 mg kg-1) in the southern 
sub-continental zone to 44.2 mg kg-1 (median = 36.1 mg kg-1) in the Atlantic zone (Table 
31, Figures 33 & 34). Average P content in bareland ranged from 14.4 mg kg-1 (median = 
13 mg kg-1) in the temperate mountainous zone to 45.3 mg kg-1 (median = 38.9 mg kg-1) 
in the sub continental (northern) zone (Table 32). Thus, the percentage of points with low 
P content was lower than in woodland and shrubland. Both in grassland and bareland, 78% 
of the points had <40 mg kg-1 of P. 

Cropland showed the highest contents of P, ranging from 28.9 mg kg-1 (median = 21.7 
mg kg-1) in the southern sub-continental zone to 47.3 mg kg-1 (median = 40.5 mg kg-1) in 
the  Atlantic  zone  (Table  33,  Figure  35).  Sixty-eight  percent  of  cropland  points  had  P 
contents <40 mg kg-1. 

This trend among land cover classes reflects the effect of fertilization in soil. Woodland and 
shrubland  are  normally  not  subjected  to  fertilization  practices,  which  explains  the  low 
content of P in these LC classes. Grassland and bareland classes include managed soils, 
that most likely are fertilized, and (semi-)natural soils. 

73 

 
 
 
 
 
Table 29 Summary of phosphorus content in woodland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest P 
content in woodland 
(mg kg-1) (1) 

Boreal to sub-boreal 

1471 

27.8 

22.9 

17.9 

Atlantic 

252 

34.2 

23.3 

31.7 

Sub-oceanic 

392 

25.8 

19.4 

18.3 

Sub-continental (northern) 

925 

30.8 

25.3 

20.7 

Sub-continental (southern) 

140 

24.9 

16.1 

25.4 

Temperate mountainous 

350 

23.6 

18.7 

15.5 

Mediterranean (semi-arid) 

107 

25.6 

17.0 

36.7 

Mediterranean 
(temperate to sub-
oceanic) 

82 

25.8 

16.2 

25.4 

UKM6 
N points = 3 
mean = 42.0 
median = 18.5 

BE21 
N points = 4 
mean = 108.4 
median = 90.2 

DE14 
N points = 4 
mean = 57.5 
median = 30.3 

AT31 
N points = 5 
mean = 64.8 
median = 33.2 

RO41 
N points = 6 
mean = 48.4 
median = 18.7 

AT32 
N points = 13 
mean = 34.8 
median = 27.3 

EL65 
N points = 8 
mean = 66.3 
median = 24.1 

ES11 
N points = 2 
mean = 55.7 
median = 15.2 

NUTS 2 with lowest P 
content in woodland 
(mg kg-1) (1) 
SE12 
N points = 20 
mean = 20.9 
median = 18.4 

FRC1 
N points = 3 
mean = 11.2 
median = 10.6 

FRI1 
N points = 6 
mean = 13.1 
median = 12.3 

CZ02 
N points = 7 
mean = 15.8 
median = 14.9 

RO42 
N points = 5 
mean = 10.3 
median = 8.2 

RO12 
N points = 3 
mean = 11.4 
median = 12.4 

EL53 
N points = 8 
mean = 15.6 
median = 15.7 

ITI1 
N points = 3 
mean = 11.4 
median = 11.4 

74 

 
 
 
 
Figure 31 Phosphorous content in woodland presented (a) as point data (mg/kg) and (b) average 
aggregated at NUTS 2 level 

(a) 

(b) 

Figure 32 Phosphorous content in shrubland presented as point data (mg/kg) 

75 

 
 
 
 
 
 
Table 30 Summary of phosphorus content in shrubland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest P 
content in shrubland 
(mg kg-1) (1) 

NUTS 2 with lowest P 
content in shrubland 
(mg kg-1) (1) 

Boreal to sub-boreal 

59 

36.4 

26.9 

24.2 

Atlantic 

35 

42.1 

29.9 

34.5 

Sub-oceanic 

67 

24.5 

20.0 

16.7 

Sub-continental (northern) 

14 

27.9 

14.5 

24.1 

Sub-continental (southern) 

20 

24.3 

16.6 

18.0 

Temperate mountainous 

14 

27.0 

19.2 

29.7 

Mediterranean (semi-arid) 

49 

19.8 

14.1 

12.9 

Mediterranean 
(temperate to sub-
oceanic) 

47 

27.4 

15.8 

28.7 

FI19 
N points = 6 
mean = 61.4 
median =59.5 

UKM9 
N points = 3 
mean = 86.7 
median =59.1 

FRK1 
N points = 11 
mean = 28.4 
median =22 

UKM6 
N points = 3 
mean = 17.4 
median = 18.3 

UKM5 
N points = 3 
mean = 16.8 
median = 17.8 

ES11 
N points = 6 
mean = 16.7 
median = 14.4 

SE12 
N points = 4 
mean = 37.9 
median =24.2 

RO31 
N points = 4 
mean = 24.0 
median =14.7 

RO11 
N points = 5 
mean = 20.2 
median = 11.8 

NA 

NA 

ES61 
N points = 4 
mean = 17.2 
median =15.4 

PT11 
N points = 17 
mean = 34.8 
median =21.4 

ES42 
N points = 6 
mean = 14.2 
median = 12.55 

ES24 
N points = 10 
mean = 13.1 
median = 12.15 

76 

 
 
 
 
 
Table 31 Summary of phosphorus content in grassland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest P 
content in grassland 
(mg kg-1) (1) 

Boreal to sub-boreal 

142 

35.6 

28.3 

30.5 

Atlantic 

670 

44.2 

36.1 

32.7 

Sub-oceanic 

644 

31.4 

23.7 

22.4 

Sub-continental (northern) 

595 

36.2 

27.4 

29.2 

Sub-continental (southern) 

210 

23.8 

17.4 

22.1 

Temperate mountainous 

281 

27.3 

19.1 

25.0 

Mediterranean (semi-arid) 

299 

31.8 

19.8 

43.3 

Mediterranean 
(temperate to sub-
oceanic) 

140 

30.5 

21.1 

24.3 

UKM6 
N points =6  
mean = 52.5 
median = 36.8 

UKF1 
N points = 6 
mean = 106.5 
median = 84.1 

DE22 
N points = 3 
mean = 76 
median = 86.7 

PL91 
N points = 8 
mean = 72.4 
median = 29.0 

BG32 
N points = 3 
mean = 59.8 
median = 44.4 

ITH3 
N points = 4 
mean = 65.6 
median = 37.7 

EL64 
N points = 6 
mean = 109 
median = 27.3 

PT11 
N points = 15 
mean = 54.8 
median = 62 

NUTS 2 with lowest P 
content in grassland 
(mg kg-1) (1) 
FI1B 
N points =3  
mean = 25.5 
median = 19 

FRI2 
N points = 3 
mean = 15.8 
median = 16.9 

ITI1 
N points = 3 
mean = 15 
median = 13 

RO22 
N points = 3 
mean = 18.2 
median = 19.5 

RO22 
N points = 8 
mean = 13.0 
median = 6.5 

RO42 
N points = 3 
mean = 9.5 
median = 9 

ITG2 
N points =4  
mean = 16.8 
median = 14 

FRJ1 
N points = 6 
mean = 15.3 
median = 13.9 

77 

 
 
 
 
Figure 33 Phosphorous content in grassland presented (a) as point data (mg/kg) and (b) average 
aggregated at NUTS 2 level 

(a) 

(b) 

Figure 34 Phosphorous content in bareland presented as point data (mg/kg) 

78 

 
 
 
 
 
 
 
 
Table 32 Summary of phosphorus content in bareland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest P 
content in bareland 
(mg kg-1) (1) 

Boreal to sub-boreal 

16 

34.8 

28.6 

14.6 

Atlantic 

93 

44.5 

35.6 

27.1 

Sub-oceanic 

43 

38.8 

29.4 

27.2 

Sub-continental (northern) 

37 

45.3 

38.9 

26.8 

Sub-continental (southern) 

48 

33..8 

26.1 

23.1 

Temperate mountainous 

3 

14.4 

13.0 

7.0 

Mediterranean (semi-arid) 

221 

28.5 

20.2 

23.1 

Mediterranean 
(temperate to sub-
oceanic) 

38 

29.7 

21.4 

23.7 

NUTS 2 with lowest P 
content in bareland 
(mg kg-1) (1) 
SE12 
N points = 3 
mean = 22.8 
median = 21.5 

UKE1 
N points = 6 
mean = 23.9 
median = 23.3 

FRC1 
N points = 14 
mean = 31.5 
median = 26.5 

SE12 
N points =7  
mean = 34.3 
median = 23.2 

RO41 
N points = 3 
mean = 19.2 
median = 5.1 

EE00 
N points = 3 
mean = 41.7 
median = 51.2 

FRH0 
N points = 3 
mean = 67.8 
median = 78.1 

FRK1 
N points = 6 
mean = 56.9 
median = 46.4 

PL42 
N points = 3 
mean = 41 
median = 38.9 

HR04 
N points = 8 
mean = 54.3 
median = 54.7 

NA 

NA 

CY00 
N points = 3 
mean = 89.2 
median = 101.5 

ES52 
N points = 4 
mean = 47.8 
median = 37 

ES43 
N points = 8 
mean = 16.7 
median = 17.0 

PT11 
N points = 3 
mean = 18.5 
median = 17.5 

79 

 
 
 
 
 
Table 33 Summary of phosphorus content in cropland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest P 
content in cropland 
(mg kg-1) (1) 

Boreal to sub-boreal 

203 

43.6 

40.7 

25.3 

Atlantic 

1385 

47.3 

40.5 

29.0 

Sub-oceanic 

707 

42.0 

35.5 

28.2 

Sub-continental (northern) 

1360 

42.1 

36.0 

27.5 

Sub-continental (southern) 

587 

28.9 

21.7 

26.8 

Temperate mountainous 

129 

35.6 

28.4 

34.1 

Mediterranean (semi-arid) 

1379 

32.0 

23.9 

29.1 

Mediterranean 
(temperate to sub-
oceanic) 

460 

34.8 

24.8 

30.5 

FI19 
N points = 46 
mean = 56.4 
median = 47.6 

NL21 
N points = 4 
mean = 113.4 
median = 77.1 

BE34 
N points = 3 
mean = 84.0 
median = 83.5 

DE23 
N points = 5 
mean = 105.7 
median = 100.8 

SI03 
N points = 6 
mean = 52.1 
median = 46.1 

SK02 
N points = 8 
mean = 58.3 
median = 59.1 

EL30 
N points = 3 
mean = 83.8 
median = 38.1 

ES11 
N points = 6 
mean = 61.2 
median = 52.7 

NUTS 2 with lowest P 
content in cropland 
(mg kg-1) (1) 
SE31 
N points = 7 
mean = 22.4 
median = 19.4 

SE23 
N points = 3 
mean = 23.5 
median = 23 

ITI1 
N points = 7 
mean = 14.4 
median = 13.3 

RO22 
N points = 24 
mean = 12.8 
median = 9.7 

RO22 
N points = 21 
mean = 14.7 
median = 9.6 

FRK2 
N points = 3 
mean = 12.8 
median = 13.7 

PT18 
N points = 20 
mean = 22.4 
median = 17.1 

ITI2 
N points = 3 
mean = 14.3 
median = 15.2 

80 

 
Figure 35 Phosphorous content in cropland presented (a) as point data (mg/kg) and (b) average aggregated at NUTS 2 level 

(a) 

(b) 

81 

 
 
 
4.7.3  Potassium content 

Potassium  (K)  content  in  soil  across  the  various  climatic  zones  appears  to  reflect  the 
influence of land cover. Highest levels were  observed in croplands of the Mediterranean 
Region while the lowest levels were observed in woodlands of the Boreal Zones (Table 34, 
Figure 36).  

Overall, woodland points tend to display low-middle levels of K. Around half of the points 
show K content <100 mg kg-1 while about three-quarters of points had K levels <150 mg 
kg-1.   

Potassium levels in cropland and grassland points were both higher than in woodland points 
(Table 36. Figures 37-40). In croplands, mean K content ranged from around 163 mg kg-
1  in both the Boreal and the Northern subcontinental zones to around 315 mg kg-1 in the 
temperate  Mediterranean  zone  (Table  29).  In  grassland,  mean  K  content  ranged  from 
121.3 mg kg-1 (median = 88.8 mg kg-1) in the boreal zone to 275.9 mg kg-1 (median = 
161.0 mg kg-1) in both of the Mediterranean zones (Table 35). Higher K contents in these 
zones and land cover classes are linked to the application of mineral fertiliser. In addition, 
texture also played a key role in the distribution of K with lower contents in the northern 
sub-continental  and  boreal  zones,  where  soils  with  sandy  textures  are  more  prone  to 
leaching and thus the removal of K. Soils with higher clay and silt contents, such as found 
in the Atlantic, oceanic and Mediterranean zones, manifest themselves with higher K levels. 
K levels in grasslands tend to be lower than croplands.  

Potassium content was lowest in the woodlands of the boreal zone (mean = 100.6 mg kg-
1, median = 61.3 mg kg-1) where soils are where relatively young, coarse textured and 
highly leached soils (Figure 39 and Table 37). Woodland points in Portugal had also quite 
low  contents  of  K,  probably  reflecting  their  relatively  high  proportion  of  sand  particles. 
Finally,  points  in  the  Atlantic  and  sub-oceanic  zones  in  northern  Spain  had  also  low  to 
middle K contents most likely due to the leaching (Figure 39).  

Potassium contents in shrubland were also relatively low, with more than half of the points 
analysed recording <150 mg kg-1. Lowest values were observed in the Boreal Zones, as 
was the case for other land cover classes (mean = 125.9 mg kg-1). In the southern sub-
continental zones, in Bulgaria and Romania, K contents in shrubland were relatively high 
(mean= 276.5 mg kg-1) (Table 38, Figure 40). 

82 

 
 
 
 
Table 34 Summary of potassium content by climatic zone and by land cover class 

Climatic zones 

Boreal to sub-boreal 

Atlantic 

Sub-continental (northern) 

Sub-oceanic 

Sub-continental (southern) 

Temperate mountainous 

Mediterranean (semi-arid) 

Mediterranean (temperate to sub-oceanic) 

Mediterranean zones 

Land cover classes 

Wetland 

Woodland 

Shrubland 

Grassland 

Cropland 

Bareland 

Potassium (mg kg-1) 

Mean 

Median 

Std Dev 

109.0 

206.9 

228.4 

135.1 

238.9 

182.8 

283.3 

282.7 

283.1 

206.9 

138.7 

218.6 

206.9 

250.9 

242.6 

70.2 

167.5 

177.3 

103.8 

197.6 

144.5 

220.2 

206.7 

215.4 

141.2 

93.3 

165.3 

146.7 

200.8 

204.6 

113.2 

166.4 

177.8 

121.7 

217.4 

151.1 

229.9 

391.1 

285.9 

256.8 

152.0 

178.4 

257.2 

207.2 

176.1 

83 

 
 
 
 
 
 
 
 
 
 
Figure 36 Potassium content (all 2018 LUCAS Soil Module points) presented (a) as point data and 
(b) average aggregated at NUTS 2 level 

(a) 

(b) 

Figure 37 Potassium content in cropland presented (a) as point data and (b) average aggregated at 
NUTS 2 level 

(a) 

(b) 

84 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 35 Summary of potassium content in cropland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in cropland (g 
kg-1) (1) 

Boreal to sub-boreal 

210 

163.2 

131.5 

109.9 

Atlantic 

1438 

223.2 

188.1 

156.0 

Sub-oceanic 

814 

276.7 

228.6 

190.4 

Sub-continental (northern) 

1428 

164.2 

133.0 

134.2 

Sub-continental (southern) 

754 

251.2 

217.8 

147.8 

Temperate mountainous 

170 

223.9 

188.7 

122.7 

Mediterranean (semi-arid) 

1916 

316.4 

254.9 

237.8 

Mediterranean 
(temperate to sub-
oceanic) 

615 

315.5 

243.2 

338.9 

FI19 
N points = 46 
mean = 56.4 
median = 47.6 

NL21 
N points = 4 
mean = 113.4 
median = 77.1 

BE34 
N points = 3 
mean = 84.0 
median = 83.5 

DE23 
N points = 5 
mean = 105.7 
median = 100.8 

SI03 
N points = 6 
mean = 52.0 
median = 46.1 

SK02 
N points = 8 
mean = 58.5 
median = 59.1 

EL30 
N points = 3 
mean = 83.8 
median = 38.1 

ES11 
N points = 6 
mean = 61.2 
median = 52.7 

NUTS 2 with lowest K 
content in cropland (g 
kg-1) (1) 
SE31 
N points = 7 
mean = 22.4 
median = 19.4 

SE23 
N points = 3 
mean = 23.5 
median = 23 

ITI1 
N points = 7 
mean = 14.4 
median = 13.3 

RO22 
N points = 24 
mean = 12.8 
median = 9.7 

RO22 
N points = 3 
mean = 12.8 
median = 13.7 

FRK2 
N points = 3 
mean = 12.8 
median = 13.7 

PT18 
N points = 20 
mean = 22.4 
median = 17.1 

ITI2 
N points = 3 
mean = 14.3 
median = 15.2 

85 

 
 
 
 
Table 36 Summary of potassium content in grassland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in grassland 
(g kg-1) (1) 

Boreal to sub-boreal 

167 

133.1 

102.0 

111.3 

Atlantic 

716 

200.2 

153.5 

175.2 

Sub-oceanic 

789 

217.6 

166.7 

181.5 

Sub-continental (northern) 

700 

126.2 

91.5 

109.7 

Sub-continental (southern) 

355 

244.4 

181.3 

239.4 

Temperate mountainous 

413 

188.5 

139.0 

200.6 

Mediterranean (semi-arid) 

539 

259.0 

177.6 

252.2 

Mediterranean 
(temperate to sub-
oceanic) 

261 

326.9 

195.2 

678.2 

UKM6 
N points = 6 
mean = 52.5 
median = 36.8 

UKF1 
N points = 6 
mean = 106.5 
median = 84.1 

DE22 
N points = 3 
mean = 76 
median = 86.7 

PL91 
N points = 8 
mean = 72.4 
median = 29.0 

BG32 
N points = 3 
mean = 59.8 
median = 44.4 

ITH3 
N points = 4 
mean = 65.6 
median = 37.7 

EL64 
N points = 6 
mean = 109 
median = 27.3 

PT11 
N points = 15 
mean = 54.7 
median = 62 

NUTS 2 with lowest K 
content in grassland 
(g kg-1) (1) 
FI1B 
N points = 3 
mean = 25.5 
median = 19 

FRI2 
N points = 3 
mean = 15.8 
median = 16.9 

ITI1 
N points = 3 
mean = 15 
median = 13 

RO22 
N points = 3 
mean = 18.1 
median = 19.5 

RO22 
N points = 8 
mean = 13.0 
median = 6.5 

RO42 
N points = 3 
mean = 9.5 
median = 9 

ITG2 
N points = 4 
mean = 16.8 
median = 14 

FRJ1 
N points = 6 
mean = 15.3 
median = 13.9 

86 

 
 
 
 
Figure 38 Potassium content in grassland presented (a) as point data and (b) average aggregated 
at NUTS 2 level 

(a) 

(b) 

Figure 39 Potassium content in woodland presented (a) as point data and (b) average aggregated at 
NUTS 2 level 

(a) 

(b) 

87 

 
 
 
 
 
 
 
 
 
 
Table 37 Summary of potassium content in woodland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in woodland 
(g kg-1) (1) 

Boreal to sub-boreal 

2076 

100.6 

61.3 

111.8 

Atlantic 

374 

153.1 

93.9 

174.2 

Sub-oceanic 

693 

183.8 

147.8 

136.4 

Sub-continental (northern) 

1137 

101.8 

72.1 

100.2 

Sub-continental (southern) 

275 

186.7 

130.4 

328.4 

Temperate mountainous 

664 

167.9 

136.0 

116.4 

Mediterranean (semi-arid) 

427 

206.3 

144.6 

179.4 

Mediterranean 
(temperate to sub-
oceanic) 

322 

209.1 

139.6 

202.9 

UKM6 
N points = 3 
mean = 42.0 
median = 18.5 

BE21 
N points = 4 
mean = 108.4 
median = 90.2 

DE14 
N points = 4 
mean = 57.5 
median = 30.3 

AT31 
N points = 5 
mean = 64.8 
median = 33.2 

RO41 
N points = 6 
mean = 48.4 
median = 18.7 

AT32 
N points = 13 
mean = 34.8 
median = 27.3 

EL65 
N points = 8 
mean = 66.3 
median = 24.1 

ES11 
N points = 3 
mean = 55.7 
median = 15.2 

NUTS 2 with lowest K 
content in woodland 
(g kg-1) (1) 
SE12 
N points =20  
mean = 20.9 
median = 18.4 

FRC1 
N points = 3 
mean = 11.2 
median = 10.6 

FRI1 
N points =6  
mean = 13.1 
median = 12.3 

CZ02 
N points = 7 
mean = 15.8 
median = 14.9 

RO42 
N points = 5 
mean = 10.3 
median = 9.2 

RO12 
N points = 3 
mean = 11.4 
median = 12.4 

EL53 
N points = 8 
mean = 15.6 
median = 15.7 

ITI1 
N points = 3 
mean = 11.4 
median = 11.4 

88 

 
 
 
 
Table 38 Summary of potassium content in shrubland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in shrubland 
(g kg-1) (1) 

Boreal to sub-boreal 

76 

125.9 

82.2 

126.4 

Atlantic 

56 

162.7 

114.2 

120.0 

Sub-oceanic 

126 

207.6 

150.3 

170.6 

Sub-continental (northern) 

18 

139.1 

115.9 

120.5 

Sub-continental (southern) 

30 

276.5 

256.4 

204.0 

FI19 
N points =6  
mean = 61.4 
median = 59.6 

UKM9 
N points = 3 
mean = 86.7 
median = 59.1 

FRK1 
N points = 11 
mean = 28.4 
median = 22 

SE12 
N points = 4 
mean = 37.9 
median = 24.2 

RO31 
N points = 4 
mean = 24.0 
median = 14.7 

NUTS 2 with lowest K 
content in shrubland 
(g kg-1) (1) 
UKM6 
N points =3  
mean = 17.4 
median = 18.3 

UKM53 
N points = 3 
mean = 16.8 
median = 17.8 

ES11 
N points = 6 
mean = 16.7 
median = 14.4 

SE12 
N points = 4 
mean = 38 
median = 24.2 

RO11 
N points = 5 
mean = 20.2 
median = 11.8 

Temperate mountainous 

33 

222.4 

180.1 

157.9 

NA 

NA 

Mediterranean (semi-arid) 

227 

248.7 

195.4 

187.4 

Mediterranean 
(temperate to sub-
oceanic) 

136 

250.3 

205.8 

194.2 

ES61 
N points = 4 
mean = 17.2 
median = 15.4 

PT11 
N points = 17 
mean = 34.8 
median = 21.4 

ES42 
N points = 6 
mean = 14.2 
median = 12.5 

ES24 
N points = 10 
mean = 13.1 
median = 12.1 

89 

 
 
 
 
Figure 40 Potassium content in shrubland presented as point data 

Table 39 Summary of potassium content in wetland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in wetland (g 
kg-1) (1) 

Boreal to sub-boreal 

20 

136.6 

100.6 

109.1 

SE31 
N points =5  
mean = 19.7 
median = 7.7 

NUTS 2 with lowest K 
content in wetland (g 
kg-1) (1) 
SE33 
N points =9  
mean = 16.8 
median = 12.6 

Atlantic 

12 

305.7 

213.1 

424.9 

IE04 
N points =4  
mean = 19.5 
median = 18.6 

Sub-oceanic 

1 

82.1 

82.1 

NA 

None of the regions had at least 3 points 

Sub-continental (northern) 

3 

225.5 

281.2 

100.2 

None of the regions had at least 3 points 

Sub-continental (southern) 

2 

294.1 

294.1 

136.3 

None of the regions had at least 3 points 

Temperate mountainous 

Mediterranean 
(temperate to sub-oceanic) 

1 

1 

81.9 

81.9 

NA 

None of the regions had at least 3 points 

447.3 

447.3 

NA 

None of the regions had at least 3 points 

90 

 
 
 
 
 
 
 
 
Figure 41 Potassium content in wetland presented as point data 

Figure 42 Potassium content in bareland presented as point data 

91 

 
 
 
 
Table 40 Summary of potassium content in bareland in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest K 
content in bareland (g 
kg-1) (1) 

Boreal to sub-boreal 

19 

114.1 

91.0 

64.2 

Atlantic 

96 

235.7 

205.8 

134.3 

Sub-oceanic 

48 

288.9 

231.4 

233.5 

Sub-continental (northern) 

42 

186.3 

129.8 

148.5 

Sub-continental (southern) 

55 

265.0 

242.2 

147.4 

EE00 
N points =3  
mean = 41.6 
median = 51.2 

FRH0 
N points = 3 
mean = 67.7 
median =  

FRK1 
N points = 6 
mean = 56.9 
median = 46.4 

PL42 
N points = 3 
mean = 41 
median = 38.9 

HR04 
N points = 8 
mean = 54.3 
median = 54.7 

NUTS 2 with lowest K 
content in bareland (g 
kg-1) (1) 
SE12 
N points =3  
mean = 22.8 
median = 21.5 

UKE1 
N points = 6 
mean = 23.9 
median = 23.3 

FRC1 
N points = 14 
mean = 31.5 
median = 26.5 

SE12 
N points = 7 
mean = 34.4 
median = 23.2 

RO41 
N points = 3 
mean = 19.2 
median = 5.1 

Temperate mountainous 

8 

229.1 

203.1 

131.4 

None of the regions had at least 3 points 

Mediterranean (semi-arid) 

313 

253.9 

212.2 

190.6 

Mediterranean 
(temperate to sub-
oceanic) 

54 

216.9 

168.6 

145.9 

CY00 
N points = 3 
mean = 89.2 
median = 101.5 

ES52 
N points = 4 
mean = 47.85 
median = 37 

ES43 
N points = 8 
mean = 16.7 
median = 17.0 

PT11 
N points = 3 
mean = 18.5 
median = 17.5 

92 

 
 
 
 
 
4.8  pH and carbonates 

4.8.1  pH 

Soil pH (also known as the soil reaction) is an indication of the acidity or alkalinity of the 
soil  and  is  measured  in  pH  units.  Soil  pH  is  defined  as  the  negative  logarithm  of  the 
hydrogen ion concentration. The pH scale goes from 0 to 14 with pH 7 as the neutral point. 
A  very  acid  soil  will  exhibit  a  lower  pH  as  hydrogen  ion  concentrations  are  higher. 
Conversely,  alkaline  soils,  with  low  hydrogen  ion  concentrations,  will  display  higher  pH 
values. As for most soil characteristics, the natural pH of the soil generally depends on its 
parent materials, the climate, vegetation, topography and time. Most soils have pH values 
between 3.5 and 10. Where rainfall is higher, the natural pH of most soils typically ranges 
from 5 to 7 as primary carbonates are leached from the soil while temperatures are too 
low for secondary carbonates to develop. In drier climates, the range tends to be 6.5 to 9. 
Soils with pH values of 6.5 to 7.5 are broadly considered as neutral.  

pH affects the solubility of nutrients and chemicals and therefore their availability to plants. 
Some nutrients are more available under low pH conditions while others are more available 
under  more  alkaline  conditions.  However,  most  nutrients  are  readily  available  to  plants 
when soil pH is near neutral. Increasing acidity can result in poor plant growth due to either 
aluminium  or  manganese  toxicity  or  a  deficiency  in  calcium  or  magnesium.  Increased 
alkalinity  may  result  in  deficiencies  of  nutrients  such  as  zinc,  copper,  boron  and 
manganese. Soils with a very high pH (>8.5) are likely to contain high levels of sodium. 
Soil pH can be changed through the application of lime (i.e. calcium carbonate raises pH) 
or fertilisers (those containing sulphur or are ammonium-based can lower pH).    

The LUCAS data on pH (Tables 41-46; Figures 43-46) clearly show a trend of increasing 
pH from northern to southern regions. Lowest  values are recorded  for woodlands in the 
Boreal zone, corresponding to the large expanses of podzolic soils that are characteristics 
of this region. Most landcover types display higher pH levels in the Mediterranean region.  

Cropland points have generally neutral pH in almost all climatic zones. Slightly lower and 
higher  values  were  recorded  by  pH  in  H2O  the  Boreal  zone  (6.2)  and  Semi-arid 
Mediterranean zone (7.7), respectively (Tables 41 and 42, Figure 44).   

A slightly higher pH is generally observed in cropland (mean and median pH in H2O were 
6.2 to 7.7) compared to grassland (mean and median pH in H2O were 5.8 to 7.0; Tables 
43 and 44, Figure 45). Woodland points in the Boreal and Atlantic Zones had very acid pH 
(<  5)  (Figure  46,  Tables  45  and  46).  The  iron-humus  accumulation  on  the  topsoil  of 
podzols,  predominant  soil  type  in  the  boreal  zone,  can  explain  these  very  low  values. 
Woodland points in the Mediterranean zones had close to neutral pH (>6.5). This slightly 
lower  than  expected  value  is  most  likely  due  to  the  averaging  of  points  under 
Mediterranean conifers (mostly pines) with acidic soils with those with soils derived from 
calcareous parent materials or with the presence of secondary carbonates in the soil.   

4.8.2  Carbonates 

⁻

Carbonates are minerals containing the carbonate ion, CO²
₃. The most common carbonate 
mineral  in  soils  is  calcium  carbonate  (CaCO3).  Carbonates  in  soil  solution  are  highly 
soluble,  especially  at  lower  pH  values,  and  are  often  leached  from  the  soil  where 
precipitation is  high.  In  contrast,  soils  where  evaporation  rates  exceed  precipitation  can 
often  see  the  formation  of  secondary  carbonates,  especially  if  irrigated  with  water  with 
high mineral content. Free calcium carbonates typically do not occur in soils with pH values 
below  5.0.    As  shown  by  the  data  (Tables  47-49;  Figure  47),  the  presence  and 
concentrations  of  carbonates  reflect  the  main  environmental  and  climatic  controls 
described above. Levels are highest in the Mediterranean region and lowest in the more 
acidic  conditions  of  Scandinavia  (especially  under  conifer  forests).  Trends  for  individual 
land cover  classes mirror those described for  pH, with lowest values  in the Boreal Zone 
and  highest  in  the  Semi-arid  Zone  with  croplands  ranging  from  9.7  to  227  g  kg-1, 
grasslands 13 to 179 g kg-1 and woodlands from 2 to 97 g kg-1, respectively.  

93 

 
 
Figure 43 pH in H2O (all 2018 LUCAS Soil Module points) presented (a) as point data and (b) 
average aggregated at NUTS 2 level 

(a) 

(b) 

Figure 44 pH in H2O in cropland presented (a) as point data and (b) average aggregated at NUTS 2 
level 

(a) 

(b) 

94 

 
 
 
 
 
 
Figure 45 pH in H2O in grassland presented (a) as point data and (b) average aggregated at NUTS 
2 level 

(a) 

(b) 

Figure 46 pH in H2O in woodland presented (a) as point data and (b) average aggregated at NUTS 
2 level 

(a) 

(b) 

95 

 
 
 
 
 
 
 
Table 41 Summary of pH in H2O in cropland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in H2O 

NUTS 2 with lowest pH 
in H2O 

Boreal to sub-boreal 

210 

6.2 

6.2 

0.7 

Atlantic 

1438 

6.8 

6.8 

0.8 

Sub-oceanic 

814 

7.0 

7.2 

0.8 

Sub-continental (northern) 

1430 

6.4 

6.5 

0.9 

Sub-continental (southern) 

754 

6.9 

6.9 

0.9 

Temperate mountainous 

170 

6.8 

6.8 

0.9 

Mediterranean (semi-arid) 

1916 

7.7 

7.7 

0.7 

Mediterranean 
(temperate to sub-
oceanic) 

615 

7.6 

7.6 

0.9 

EE00 
N points = 48 
mean = 6.7 
median = 6.5 

FRJ1 
N points = 3 
mean = 8.1 
median = 8.1 

EL53 
N points = 3 
mean = 8.3 
median = 8.4 

DEG0 
N points = 13 
mean = 7.3 
median = 7.5 

HU12 
N points = 15 
mean = 7.9 
median = 8 

FRL0 
N points = 6 
mean = 8.0 
median = 7.8 

EL30 
N points =5  
mean = 8.2 
median = 8.2 

ES62 
N points = 6 
mean = 8.3 
median = 8.2 

FI1D 
N points = 37 
mean = 5.7 
median = 5.8 

SE23 
N points = 3 
mean = 5.3 
median = 5.4 

ES43 
N points = 5 
mean = 5.7 
median = 5.7 

PL82 
N points = 7 
mean = 6.1 
median = 5.9 

PL82 
N points = 7 
mean = 6.1 
median = 5.9 

RO12 
N points = 14 
mean = 5.8 
median = 5.6 

PT16 
N points = 4 
mean = 5.6 
median = 5.5 

ES11 
N points = 6 
mean = 4.8 
median = 4.6 

96 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 42 Summary of pH in CaCl2 in cropland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in CaCl2 

NUTS 2 with lowest pH 
in CaCl2 

Boreal to sub-boreal 

218 

5.6 

5.6 

0.7 

Atlantic 

1438 

6.3 

6.4 

0.8 

Sub-oceanic 

814 

6.6 

6.8 

0.8 

Sub-continental (northern) 

1430 

5.9 

6.0 

0.9 

Sub-continental (southern) 

754 

6.3 

6.3 

0.9 

Temperate mountainous 

170 

6.3 

6.4 

0.9 

Mediterranean (semi-arid) 

1916 

7.1 

7.4 

0.8 

Mediterranean 
(temperate to sub-
oceanic) 

615 

7.0 

7.4 

0.9 

EE00 
N points =48  
mean = 6.2 
median = 6.1 

FRJ1 
N points = 3 
mean = 7.5 
median = 7.5 

EL53 
N points = 3 
mean = 7.6 
median = 7.8 

DEG0 
N points = 13 
mean = 6.8 
median = 7.0 

AT12 
N points =33  
mean = 7.2 
median = 7.3 

FRL0 
N points = 3  
mean = 7.3 
median = 7.4 

EL30 
N points = 5 
mean = 7.6 
median = 7.7 

ES62 
N points = 6 
mean = 7.6 
median = 7.6 

SE31 
N points = 7 
mean = 5.1 
median = 5.1 

SE23 
N points = 3 
mean = 4.8 
median = 4.8 

ES43 
N points = 5 
mean = 5 
median =4.9  

PL82 
N points = 5 
mean = 4.7 
median = 4.8 

RO32 
N points = 5 
mean = 5.4 
median = 5.4 

RO12 
N points = 14 
mean = 5.4 
median = 5.4 

PT16 
N points = 4 
mean = 4.8 
median = 4.7 

ES11 
N points = 6 
mean = 4.2 
median = 4.2 

97 

 
 
 
 
 
 
 
 
 
 
 
Table 43. Summary of pH in H2O in grassland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in H2O 

NUTS 2 with lowest pH 
in H2O 

Boreal to sub-boreal 

167 

5.8 

5.9 

0.9 

Atlantic 

716 

5.9 

5.8 

0.9 

Sub-oceanic 

789 

6.2 

6.1 

0.9 

Sub-continental (northern) 

704 

6.0 

5.9 

0.8 

Sub-continental (southern) 

355 

6.6 

6.5 

0.9 

Temperate mountainous 

413 

6.2 

6.2 

1.0 

Mediterranean (semi-arid) 

539 

7.1 

7.4 

1.0 

Mediterranean 
(temperate to sub-
oceanic) 

261 

7.0 

7.5 

1.1 

EE00 
N points = 28 
mean = 6.5 
median = 6.4 

UKF3 
N points = 6 
mean = 7.2 
median = 7.3 

ES61 
N points = 3 
mean = 7.9 
median = 8.0 

RO22 
N points = 6 
mean = 7.3 
median = 7.5 

HU33 
N points = 20 
mean = 7.7 
median = 7.7 

EL52 
N points = 4 
mean = 8.0 
median = 8.0 

ES62 
N points = 4 
mean = 8.0 
median = 8.0 

ITI1 
N points = 3 
mean = 8.0 
median = 7.9 

UKM7 
N points = 3 
mean = 4.5 
median = 4.5 

UKL1 
N points = 21 
mean = 4.9 
median = 4.9 

ES11 
N points = 10 
mean = 4.8 
median = 4.7 

SE31 
N points = 4 
mean = 5.3 
median = 5.4 

PL82 
N points = 16 
mean = 5.8 
median = 5.4 

PL21 
N points = 11 
mean = 5.0 
median = 4.8 

ES43 
N points = 17 
mean = 6.0 
median = 6.0 

ES11 
N points = 3 
mean = 5.0 
median = 4.8 

98 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 44 Summary of pH in CaCl2 in grassland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in CaCl2 

NUTS 2 with lowest pH 
in CaCl2 

Boreal to sub-boreal 

167 

5.3 

5.4 

1.0 

Atlantic 

716 

5.4 

5.3 

0.9 

Sub-oceanic 

789 

5.8 

5.6 

0.9 

Sub-continental (northern) 

704 

5.5 

5.4 

0.9 

Sub-continental (southern) 

355 

6.0 

6.0 

1.0 

Temperate mountainous 

413 

5.7 

5.7 

1.0 

Mediterranean (semi-arid) 

539 

6.5 

7.0 

1.1 

Mediterranean 
(temperate to sub-
oceanic) 

261 

6.5 

7.1 

1.1 

E00 
N points = 28 
mean = 6.1 
median = 5.8 

UKF3 
N points = 6 
mean = 6.8 
median = 7.0 

ES61 
N points = 3 
mean = 7.5 
median = 7.5 

RO22 
N points = 6 
mean = 6.8 
median = 7.1 

RO22 
N points = 8 
mean = 7.2 
median = 7.3 

EL52 
N points = 4 
mean = 7.3 
median = 7.4 

ES22 
N points = 4 
mean = 7.6 
median = 7.6 

ES24 
N points = 19 
mean = 7.4 
median = 7.5 

SE33 
N points = 9 
mean = 4.1 
median = 4.3 

UKL1 
N points = 21 
mean = 4.5 
median = 4.5 

ES11 
N points = 10 
mean = 4.4 
median = 4.4 

SE22 
N points = 12 
mean = 4.7 
median = 4.8 

HU23 
N points = 4 
mean = 5.3 
median = 5.3 

PL21 
N points = 11 
mean = 4.7 
median = 4.5 

PT11 
N points = 7  
mean = 5.3 
median = 5.3 

ES11 
N points = 10 
mean = 4.4 
median = 4.4 

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 45 Summary of pH in H2O in woodland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in H2O 

NUTS 2 with lowest pH 
in H2O 

Boreal to sub-boreal 

2096 

4.6 

4.5 

0.6 

Atlantic 

374 

4.9 

4.6 

0.9 

Sub-oceanic 

693 

5.6 

5.4 

1.2 

Sub-continental (northern) 

1146 

4.7 

4.5 

0.8 

Sub-continental (southern) 

275 

5.9 

5.8 

1.2 

Temperate mountainous 

665 

5.8 

5.7 

1.1 

Mediterranean (semi-arid) 

427 

6.6 

6.5 

1.0 

Mediterranean 
(temperate to sub-
oceanic) 

324 

6.2 

6.0 

1.3 

EE00 
N points = 87 
mean = 5.6 
median = 5.8 

NL23 
N points = 4 
mean = 7.0 
median = 7.2 

ES62 
N points = 4 
mean = 7.9 
median = 8.0 

LT02 
N points = 76 
mean = 5.6 
median = 5.7 

HU33 
N points = 6 
mean = 7.5 
median = 7.5 

ES21 
N points = 3 
mean = 7.3 
median = 7.2 

EL30 
N points = 10 
mean = 7.9 
median = 8.0 

ES24 
N points = 26 
mean = 7.7 
median = 7.8 

SE23 
N points = 5 
mean = 4.3 
median = 4.2 

UKM6 
N points = 3 
mean = 4.1 
median = 3.9 

BE34 
N points = 5 
mean = 4.3 
median = 4.2 

FI1B 
N points =4  
mean = 4.0 
median = 4.0 

AT22 
N points = 55 
mean = 4.6 
median = 4.3 

AT22 
N points = 55 
mean = 4.6 
median = 4.3 

PT11 
N points = 6 
mean = 5.6 
median = 5.6 

ES11 
N points = 7 
mean = 4.6 
median = 4.7 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 46 Summary of pH in CaCl2 in woodland points 

Climatic zone 

N points 

Mean         Median      Std dev     

NUTS 2 with highest 
pH in CaCl2 

NUTS 2 with lowest pH 
in CaCl2 

Boreal to sub-boreal 

2096 

3.9 

3.8 

0.7 

Atlantic 

374 

4.3 

4.0 

1.0 

Sub-oceanic 

693 

5.1 

4.8 

1.3 

Sub-continental (northern) 

1146 

4.1 

3.8 

0.9 

Sub-continental (southern) 

275 

5.3 

5.1 

1.3 

Temperate mountainous 

665 

5.2 

5.0 

1.2 

Mediterranean (semi-arid) 

427 

6.0 

5.9 

1.1 

Mediterranean 
(temperate to sub-
oceanic) 

324 

5.7 

5.5 

1.4 

EE00 
N points = 28 
mean = 6.1 
median = 5.8 

UKF3 
N points = 6 
mean = 6.8 
median = 7.0 

ES61 
N points = 3 
mean = 7.5 
median = 7.5 

RO22 
N points = 8 
mean = 7.2 
median = 7.3 

RO22 
N points =6  
mean = 6.9 
median = 7.1 

EL52 
N points = 4 
mean = 7.3 
median = 7.4 

ES22 
N points = 4 
mean = 7.6 
median = 7.5 

ES24 
N points = 19 
mean = 7.4 
median = 7.5 

SE23 
N points = 5 
mean = 3.4 
median = 3.2 

UKM6 
N points = 3 
mean = 3.3 
median = 3.3 

BE34 
N points = 5 
mean = 3.7 
median = 3.5 

FI1B 
N points = 4 
mean = 3.2 
median = 3.2 

AT22 
N points = 8 
mean = 3.8 
median = 3.7 

AT22 
N points = 55 
mean = 3.9 
median = 3.7 

PT11 
N points = 6 
mean = 4.7 
median = 4.6 

ES11 
N points = 7 
mean = 4.0 
median = 4.0 

101 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 47 Carbonates content (all 2018 LUCAS Soil Module points) presented (a) as point data and 
(b) average aggregated at NUTS 2 level 

(a) 

(b) 

102 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 47 Summary of CaCO3 content in cropland points 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
CaCO3 content in 
cropland (g kg-1) (1) 

Boreal to sub-boreal 

41 

9.7 

2.0 

17.4 

Atlantic 

663 

110.0 

10.0 

194.7 

Sub-oceanic 

488 

112.5 

52.5 

150.4 

Sub-continental (northern) 

521 

16.4 

4.0 

34.0 

Sub-continental (southern) 

403 

38.2 

12.0 

58.7 

Temperate mountainous 

90 

64.5 

16.5 

104.1 

Mediterranean (semi-arid) 

1658 

222.4 

192.5 

198.1 

Mediterranean 
(temperate to sub-
oceanic) 

508 

227.7 

191.0 

184.8 

FI19 
N points = 46 
mean = 56.4 
median = 47.6 

NL21 
N points = 4 
mean = 113.4 
median = 77.1 

BE34 
N points = 3 
mean = 84.0 
median = 83.5 

DE23 
N points = 5 
mean = 105.7 
median = 100.8 

SI03 
N points = 6 
mean = 52.0 
median = 46.1 

SK02 
N points = 8 
mean = 58.5 
median = 59.1 

EL30 
N points = 3 
mean = 83.8 
median = 38.1 

ES11 
N points = 6 
mean = 61.2 
median = 52.7 

NUTS 2 with lowest 
CaCO3 content in 
cropland (g kg-1) (1) 
SE31 
N points =7  
mean = 22.4 
median = 19.4 

SE23 
N points = 3 
mean = 23.5 
median = 23 

ITI1 
N points = 7 
mean = 14.4 
median = 13.3 

RO22 
N points = 24 
mean = 12.8 
median = 9.7 

RO22 
N points = 21 
mean = 14.7 
median = 9.6 

FRK2 
N points = 3 
mean = 12.8 
median = 13.7 

PT18 
N points = 20 
mean = 22.3 
median = 17.1 

ITI2 
N points = 3 
mean = 14.3 
median = 15.2 

103 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 48 Summary of CaCO3 content in grassland points 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
CaCO3 content in 
grassland (g kg-1) (1) 

Boreal to sub-boreal 

33 

13.2 

9.0 

14.4 

Atlantic 

209 

52.6 

3.0 

113.6 

Sub-oceanic 

308 

64.5 

5.0 

111.9 

Sub-continental (northern) 

171 

19.3 

3.0 

52.6 

Sub-continental (southern) 

156 

44.4 

11.0 

84.8 

Temperate mountainous 

197 

62.9 

7.0 

122.2 

Mediterranean (semi-arid) 

352 

174.9 

106.0 

197.6 

Mediterranean 
(temperate to sub-
oceanic) 

171 

179.3 

138.0 

168.5 

UKM6 
N points = 6 
mean = 52.5 
median = 36.8 

UKF1 
N points = 6 
mean = 106.5 
median = 84.1 

DE22 
N points = 3 
mean = 76 
median = 86.7 

PL91 
N points = 8 
mean = 72.4 
median = 29.0 

BG32 
N points = 3 
mean = 59.8 
median = 44.4 

ITH3 
N points = 4 
mean = 65.6 
median = 37.7 

EL64 
N points = 6 
mean = 109 
median = 27.3 

PT11 
N points = 15 
mean = 54.8 
median = 62 

NUTS 2 with lowest 
CaCO3 content in 
grassland (g kg-1) (1) 
FI1B 
N points = 3 
mean = 25.5 
median = 19 

FRI2 
N points = 3 
mean = 15.8 
median = 16.9 

ITI1 
N points = 3 
mean = 15 
median = 13 

RO22 
N points = 3 
mean = 18.2 
median = 19.5 

RO22 
N points = 8 
mean = 13.0 
median = 6.5 

RO42 
N points = 3 
mean = 9.5 
median = 9 

ITG2 
N points = 4 
mean = 16.8 
median = 14 

FRJ1 
N points = 6 
mean = 15.3 
median = 13.9 

104 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 49 Summary of CaCO3 content in woodland points 

Climatic zone 

N points 

Mean        

(g kg-1) 

Median     
(g kg-1) 

Std dev    
(g kg-1) 

NUTS 2 with highest 
CaCO3 content in 
woodland (g kg-1) (1) 

Boreal to sub-boreal 

1400 

2.0 

1.0 

5.6 

Atlantic 

239 

15.7 

1.0 

78.7 

Sub-oceanic 

548 

37.2 

2.0 

97.7 

Sub-continental (northern) 

719 

2.1 

1.0 

6.5 

Sub-continental (southern) 

197 

24.2 

2.0 

64.4 

Temperate mountainous 

511 

36.6 

2.0 

93.7 

Mediterranean (semi-arid) 

351 

78.7 

2.0 

161.7 

Mediterranean 
(temperate to sub-
oceanic) 

264 

97.4 

3.0 

169.2 

UKM6 
N points = 3 
mean = 42.0 
median = 18.5 

BE21 
N points = 4 
mean = 108.4 
median = 90.2 

DE14 
N points = 4 
mean = 57.5 
median = 30.3 

AT31 
N points = 5 
mean = 64.8 
median = 33.2 

RO41 
N points = 6 
mean = 48.4 
median = 18.7 

AT32 
N points = 13 
mean = 34.8 
median = 27.3 

EL65 
N points = 8 
mean = 66.3 
median = 24.1 

ES11 
N points = 3 
mean = 55.7 
median = 15.2 

NUTS 2 with lowest 
CaCO3 content in 
woodland (g kg-1) (1) 
SE12 
N points = 20 
mean = 20.9 
median = 18.4 

FRC1 
N points = 3 
mean = 11.2 
median = 10.6 

FRI11 
N points =6  
mean = 13.1 
median = 12.3 

CZ02 
N points =7  
mean = 15.8 
median = 14.9 

RO42 
N points = 5 
mean = 10.3 
median = 8.2 

RO12 
N points = 3 
mean = 11.4 
median = 12.4 

EL53 
N points = 8 
mean = 15.6 
median = 15.7 

ITI1 
N points = 3 
mean = 11.4 
median = 11.4 

105 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.9  Electrical conductivity 

Soil electrical conductivity (EC) is a measure of the amount of salts in soil (salinity). It is 
also an excellent indicator of nutrient availability and loss, soil texture, and available water 
capacity. Excessive salt levels hinder plant growth by affecting the soil and water balance.  

Soils containing excessive salts generally occur naturally in dry and warm climates. Salt 
levels can increase as a result of cropping and irrigation. 

Factors affecting EC include soil texture and parent material, climate, and soil management 
practices (in particular poor use of irrigation, fertilization).  

Most of the points across the EU showed low EC, which indicates that they are not saline 
and that their salt contents are at a minimum (Figure 48). However, Figure 48 shows some 
salinity  hotspots  (>4  dS/m)  in  the  Ebro  Valley  in  Spain,  Sicily,  Cyprus  and  Greece.  The 
points on the coasts of Ireland and France reflect coastal salt marshes.  

Figure 48 Electrical conductivity. Levels > 4 dS/m are considered to have saline conditions. 

106 

 
 
  
 
 
4.10  Oxalate extractable Fe and Al 

The  dissolution  methods  for  extracting  Al,  Fe,  Mn,  and  Si  are  valuable  tools  to  help 
determine the chemical forms of these elements in soils. The results are useful in studies 
of metal mobility or bioavailability in soils.  

Figures 49-50 show the locations of the 2,523 samples that were analysed for the presence 
of organic-complexed Fe and Al in cropland soils through extraction with a 0.1M sodium 
pyrophosphate solution (Ross & Wang 1993). The extraction solution only slightly dissolves 
non-crystalline inorganic forms and does not attack silicate minerals and crystalline Fe and 
Al  oxides  and  hydroxides.  Fe  and  Al levels  are  quantified  by  inductively  coupled  plasma 
optical emission spectrometry (ICP-OES).  

Summaries are presented in Tables 50 and 51. 

17 samples were not present in the DMT but could be identified through the POINTID code.  

-  1 sample with a POINTID not present in the DMT was removed 

-  5 samples were identified with comment “Low amount sample” and removed 

-  2 samples with null values were removed 

-  5 samples with duplicate POINTID and wrong SOIL-ID were removed 

2,510 samples have both oxalates values available at the end: 

-  5 samples are related to 0-10 cm and not considered 

-  2505 samples are related to 0-20 cm (as all the other attributes) and used in the 

following statistics. 

-  92% of the samples are from cropland points.  

Figure 49 Point map of Oxalate Extractable 
Aluminium (all points) 

Figure  50  Point  map  of  Oxalate  Extractable 
Iron (all points) 

107 

 
 
 
 
 
 
Table 50 Summary of oxalate extractable Aluminum content in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest 
Ox Al content (mg kg-
1) (1) 

Boreal to sub-boreal 

101 

2.1 

1.8 

1.6 

Atlantic 

513 

1.3 

1.0 

1.2 

Sub-oceanic 

296 

1.6 

1.2 

2.3 

Sub-continental (northern) 

489 

1.1 

0.9 

0.7 

Sub-continental (southern) 

205 

1.0 

1.0 

0.4 

Temperate mountainous 

56 

1.3 

1.0 

1.2 

Mediterranean (semi-arid) 

637 

0.8 

0.7 

0.6 

Mediterranean 
(temperate to sub-
oceanic) 

208 

1.3 

0.8 

1.5 

SE33 
N points = 7  
mean = 3.3 
median = 1.8 

ES11 
N points = 6  
mean = 7 
median =6.1  

ITF6 
N points = 5  
mean = 8.5 
median = 2.1 

CZ05 
N points = 3 
mean = 2.1 
median = 2.1 

SK04 
N points = 4 
mean = 1.4 
median = 1.1 

ITH2 
N points = 5  
mean = 1.2 
median = 1.5 

ITF4 
N points = 24 
mean = 2.1 
median = 2.1 

ES11 
N points = 4 
mean = 5.4 
median = 5.2 

NUTS 2 with lowest Ox 
Al content (mg kg-1) 
(1) 
LV00 
N points = 5 
mean = 0.78 
median = 0.8 

NL23 
N points = 7  
mean = 0.3 
median = 0.2 

ES42 
N points = 3  
mean = 0.6 
median = 0.6 

DK02 
N points = 4 
mean = 0.6 
median = 0.7 

AT12 
N points = 10 
mean = 0.7 
median = 0.7 

SK02 
N points = 3 
mean = 0.8 
median = 0.8 

ES51 
N points = 9 
mean = 0.4 
median = 0.4 

FRL0 
N points = 3 
mean = 0.4 
median = 0.4 

100 

 
 
 
 
 
 
 
 Table 51 Summary of oxalate extractable Iron content in the 2018 LUCAS Topsoil survey 

Climatic zone 

N points 

(mg kg-
1) 

Mean        

Median     
(mg kg-
1) 

Std dev    
(mg kg-
1) 

NUTS 2 with highest 
Ox Fe content (mg kg-
1) (1) 

Boreal to sub-boreal 

101 

4.9 

3.5 

4.6 

Atlantic 

513 

3.4 

2.8 

2.5 

Sub-oceanic 

296 

3.6 

2.9 

2.7 

Sub-continental (northern) 

489 

2.5 

1.9 

1.8 

Sub-continental (southern) 

205 

2.0 

1.4 

1.8 

Temperate mountainous 

56 

3.0 

2.2 

2.2 

Mediterranean (semi-arid) 

637 

1.2 

0.8 

1.2 

Mediterranean 
(temperate to sub-
oceanic) 

208 

2.3 

1.5 

2.6 

FI1D 
N points = 16  
mean = 7.1 
median = 4.5 

UKF3 
N points = 5 
mean = 8.5 
median = 8.1 

FRJ2 
N points = 6 
mean = 6.7 
median = 6 

SE21 
N points = 3 
mean = 4.4 
median = 3.7 

SK04 
N points = 4 
mean = 7.3 
median = 7.5 

ITH1 
N points = 5 
mean = 3.5 
median = 2.5 

ITF6 
N points = 6 
mean = 4.6 
median = 3.8 

ITF3 
N points = 4 
mean = 9.5 
median = 7.8 

NUTS 2 with lowest Ox 
Fe content (mg kg-1) 
(1) 
LV00 
N points = 5 
mean = 1.4 
median = 1.5 

FRF2 
N points = 12 
mean = 1.1 
median = 0.7 

ES42 
N points = 3 
mean = 0.6 
median = 0.6 

DE80 
N points = 7 
mean = 1.4 
median = 1.6 

AT12 
N points = 10 
mean = 0.7 
median = 0.7 

SK02 
N points = 3 
mean = 1.3 
median = 1.4 

ES62 
N points = 5 
mean = 0.7 
median = 0.8 

ES41 
N points = 7 
mean = 0.6 
median = 0.6 

101 

 
 
 
 
4.11  Metals 

The  concentration  of  metals  in  soils  is  an  important  criterion  for  the  assessment  of 
environmental quality. In many cases, the concentration of metals in soil simply reflects 
the  mineral  characteristics  of  their  parent  material.  This  is  known  as  background 
concentrations.  In  most  cases,  the  natural  environment  has  adapted  itself  to  exist  with 
such  levels.  However,  contamination  of  soil  by  high  concentrations  of  metals,  often 
introduced through transport, waste streams, or emissions from industrial or agricultural 
activities, represent a potential threat to environmental well-being, food safety, and human 
and animal health.  

Some metals, at low concentration levels, are essential for living organisms. These include 
the  trace  elements  cobalt,  copper,  iron,  manganese,  molybdenum,  selenium  and  zinc. 
However, they can become toxic at elevated concentrations. A second group, consisting of 
arsenic,  cadmium,  lead,  mercury  and  vanadium  (also  plutonium,  and  tungsten  but  not 
considered here) do not have any essential function in living organisms and are highly toxic 
even at low exposure levels. 

In  2018,  LUCAS  points  with  relatively  high  concentrations  of  metals  in  the  2009/2012 
sample were re-analysed, together with an additional set of random points in all MS. In 
total, 997 samples were analysed for metal concentrations. Some summary statistics are 
presented in Table 52 (Figure 51). A separate report on changes in metal concentrations 
is  being  prepared.  Figures  52-62  show  the  locations  of  sites  according  to  the  elements 
analysed. 

Table 52 Summary of metal content in selected samples (g/mg) 

Element 
As 
Cd 
Co 
Cr 
Cu 
Hg 
Ni 
Pb 
Sb 
V 
Zn 

<LOD 
8 
58 
1 
1 
8 
155* 
11 
1 
7 
0 
2 

Min 
1.1 
0.2 
0.7 
0.9 
2.3 
0.1 
1 
2 
0.1 
1.6 
3.1 

Max 
939 
10.4 
182.9 
1203.2 
973.9 
4 
3249.3 
294.7 
35.9 
320 
2385 

Mean  Median 
33.14 
0.51 
10.87 
63.13 
40.47 
0.33 
87.65 
26.06 
1.35 
42.99 
73.37 

10 
0.4 
5.4 
20.15 
13 
0.1 
21.4 
17.9 
0.5 
20.6 
39.3 

StDev 
79.99 
0.89 
22.68 
133.62 
89.35 
0.72 
258.92 
36.8 
3.9 
51.33 
186.45 

102 

 
 
 
 
 
 
 
 
 
 
 
 
Figure 51 Map of the 997 points for which metal levels were analysed. 

103 

 
 
 
 
 
 
 
 
 
Figure 52 Arsenic analyses 

Figure 54 Cobalt analyses 

Figure 53 Cadmium analyses 

Figure 55 Copper analyses 

100 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 56 Chromium analyses 

Figure 58 Mercury analyses 

Figure 57 Nickel analyses 

Figure 59 Antimony analyses 

101 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 60 Lead analyses 

Figure 61 Vanadium analysis 

Figure 62 Zinc analyses 

102 

 
 
 
 
 
 
 
 
5  Conclusions 

Regular  monitoring  provides  a  unique  perspective  on  pressures  affecting  soils.  In  this 
respect,  the  soil  module  of  the  Land  Use/Cover  Area  frame  statistical Survey  (generally 
referred  to  as  LUCAS  Soil)  collects  data  that  characterises  soil  conditions  and  health  in 
relation  to  land  use  practices  and  other  activities  that  are  driven  by  specific  policy 
instruments. 

In 2018, the survey was carried out for all twenty-eight EU MS. Of the locations sampled 
in 2009/2012 and 2015, 90% were maintained. The remaining 10% were substituted by 
new locations, including new points at altitudes above 1,000 m, which were out of scope 
of the LUCAS 2009 and LUCAS 2012 surveys. 

The LUCAS 2018 Soil dataset has 18,984 unique records with chemical, physical and agro-
environmental data covering the 28 Member States of the EU (as in 2018). Samples are 
identified with their Point ID, which serves to link the soil data with the field data published 
in the LUCAS portal of Eurostat. The soil dataset is made available in a variety of formats 
(e.g. CSV, Excel and SHAPE) through the EU Soil Observatory’s European Soil Data Centre 
(ESDAC).  A  supplementary  dataset  containing  ancillary  environmental  information  (e.g. 
climate,  topographic  setting,  soil  regions,  NATURA  2000  sites,  etc.)  is  also  available  to 
download from ESDAC. 

The  results  of  topsoil  properties  for  LUCAS  2018  are  consistent  with  those  observed  in 
LUCAS 2009 and 2015, showing similar trends across the EU and the various land cover 
classes: 

— 
Organic carbon, N and P show a decreasing north-western to south-eastern trend, 
with higher contents in the boreal, Atlantic and northern sub-continental zones and lower 
contents 
in  the  Mediterranean  and  southern  sub-continental  zones.  Temperate 
mountainous and sub-oceanic zones have intermediate contents of OC, N and P. 

— 
Contents  of  OC  and  N  are  greater  in  wetland,  woodland  and  grassland  than  in 
cropland and bareland. On the contrary, P content is greater in cropland than in woodland 
and grassland. In wetland, P content is relatively high. 

— 
Potassium  content  tends  to  be  lower  in  northern  than  in  southern  Europe,  with 
lowest contents in boreal and northern sub-continental zones and highest contents in semi-
arid Mediterranean and southern-sub-continental zones. 

— 

K content is higher in cropland and grassland than in woodland. 

— 
Topsoil pH (both in H20 and CaCl2) tends to be highly acid in boreal, northern sub-
continental and some Atlantic zones, while pH is alkaline in the Mediterranean zones. In 
accordance with the spatial distribution of pH, high contents of carbonates are observed in 
the Mediterranean zones. 

Higher values of pH in cropland than in grassland and woodland are observed across 

— 
the EU. 

— 
Most  of  the  points  have  low  electrical  conductivity  values  (<4  dS  m-1), 
demonstrating  limited  salinity  problems.  However,  this  may  also  be  attributed  to 
insufficient sampling on affected areas. 

Some salinity hotspots are observed in the Ebro Valley in Spain, in the Adriatic coast 

— 
in Italy and in the Atlantic coast in Ireland and France. 

Data can be downloaded from  

https://esdac.jrc.ec.europa.eu/content/lucas2018-topsoil-data 

103 

 
 
 
 
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de Brogniez, D., Ballabio, C., Stevens, A., Jones, R.J.A., Montanarella, L., van Wesemael, 
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https://ec.europa.eu/eurostat/databrowser/view/t2020_rn120/default/table?lang=en  

Fernández-Ugalde,  O.,  Jones,  A.,  Meuli,  R.,  ‘Comparison  of  sampling  with  a  spade  and 
gouge  auger  for  topsoil  monitoring  at  the  continental  scale’,  European  Journal  of  Soil 
Science, Vol. 71, 2020, pp. 137–150. 

Fernández-Ugalde,  O.,  Ballabio,  C.,  Lugato,  E.,  Scarpa,  S.,  Jones,  A.,  Assessment  of 
changes  in  topsoil  properties  in  LUCAS  samples  between  2009/2012  and  2015  surveys, 
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92-76-17430-1, doi:10.2760/5503, JRC120138. 

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‘Climate  change  may  cause  severe  loss  in  the  economic  value  of  European  forest  land’, 
Nature Climate Change, 2012, doi: 10.1038/NCLIMATE1687. 

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Luxembourg. 2020, ISBN 978-92-76-21080-1, doi:10.2760/616084, JRC121325  

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key factor for resource efficiency – seventy-five per cent of greenhouse gas emissions from 
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research and policy development. European Journal of Soil Science, under review 

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content for European NUTS 2 regions based on LUCAS data collection’, Science of the Total 
Environment, Vol 442, 2013, pp. 235-246. 

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policies in the European Union. Remote Sens. 12, 1365 

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results, EUR 26102, Publications Office of the European Union, Luxembourg, 2013a, doi: 
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Tóth, G., Gardi, C., Bódis, K., Ivits, E., Aksoy, E., Jones, A., Jeffrey, S., Petursdottir, T., 
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105 

 
 
 
 
List of figures 

Figure 1 Evolution of parameters analysed in LUCAS Soil module ................................ 8 

Figure 2 Sampling schema in the LUCAS 2018 soil survey (numbers indicate initial 
targets) ................................................................................................................ 9 

Figure 3 (a) LUCAS sampling schema, (b) summary of the common sampling procedure, 
and (c) summary of the bulk density sampling .........................................................10 

Figure 4 Relation between OC and N in the dataset. ..................................................21 

Figure 5 Relation between pH and CaCO3 in the dataset: (a) pH in H2O, (b) pH in CaCl2 22 

Figure 6 Relation between bulk density, OC and sand contents in the dataset: (a) bulk 
density – OC content, (b) bulk density – sand content ..............................................23 

Figure 7 Maps showing the locations where signs of deposition by wind (left) and mass 
movement (right) were recorded by surveyors. ........................................................26 

Figure 8 Maps showing the locations where signs of wind erosion (left) and rill erosion 
(right) were recorded by surveyors. ........................................................................26 

Figure 9 Map showing the locations where the depth of organic soils was measured. ....28 

Figure 10 (a) Sampling density in NUTS 2 regions for the complete dataset of the 2018 
LUCAS Soil Module; number of points in each NUTS2 region for (b) cropland, (c) 
grassland, (d) woodland ........................................................................................41 

Figure 11 Climatic zones identified in Tóth et al. (2013b)...........................................42 

Figure 12 Spatial distribution of the sites for which potential gully erosion channels were 
observed during the 2018 LUCAS Topsoil field survey. The light blue dots indicate the 
sites validated through expert-based on-screen visual interpretation, while the red ones 
could not be observed during this phase ..................................................................44 

Figure 13 Examples of gully erosion channels validated by reviewing Google Earth 
Images (Credit Google Earth). ...............................................................................45 

Figure 14 Organic carbon content (all 2018 LUCAS Soil Module points) presented (a) as 
point data and (b) average aggregated at NUTS 2 level .............................................48 

Figure 15 Organic carbon content in the 2018 LUCAS Soil Module (mean and standard 
error) by land cover class in the 27 Member States and the UK * ...............................49 

Figure 16 Organic carbon content in wetland presented as point data .........................52 

Figure 17 Organic carbon content in woodland: (a) point data and (b) average 
aggregated at NUTS 2 level ...................................................................................53 

Figure 18 Organic carbon content in grassland: (a) point data and (b) average 
aggregated at NUTS 2 level ...................................................................................56 

Figure 19 Organic carbon content in shrubland presented as point data .......................56 

Figure 20 Organic carbon content in cropland: (a) point data and (b) average aggregated 
at NUTS 2 level ....................................................................................................59 

Figure 21 Organic carbon content in bareland presented as point data ........................59 

Figure 22 Nitrogen content (all 2018 LUCAS Soil Module points) presented (a) as point 
data and (b) average aggregated at NUTS 2 level .....................................................61 

Figure 23 Nitrogen content (mean and standard error) by land cover class in the 27 
Member States and the UK ....................................................................................61 

Figure 24 Nitrogen content in wetland presented as point data ...................................62 

Figure 25 Nitrogen content in woodland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................62 

106 

 
Figure 26 Nitrogen content in grassland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................63 

Figure 27 Nitrogen content in cropland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................63 

Figure 28 Nitrogen content in bareland presented as point data .................................64 

Figure 29 Nitrogen content in shrubland presented as point data ................................65 

Figure 30 Phosphorus content (all 2018 LUCAS Soil Module points) presented (a) as point 
data (mg/kg) and (b) average aggregated at NUTS 2 level ........................................73 

Figure 31 Phosphorous content in woodland presented (a) as point data (mg/kg) and (b) 
average aggregated at NUTS 2 level .......................................................................75 

Figure 32 Phosphorous content in shrubland presented as point data (mg/kg) .............75 

Figure 33 Phosphorous content in grassland presented (a) as point data (mg/kg) and (b) 
average aggregated at NUTS 2 level .......................................................................78 

Figure 34 Phosphorous content in bareland presented as point data (mg/kg) ...............78 

Figure 35 Phosphorous content in cropland presented (a) as point data (mg/kg) and (b) 
average aggregated at NUTS 2 level .......................................................................81 

Figure 36 Potassium content (all 2018 LUCAS Soil Module points) presented (a) as point 
data and (b) average aggregated at NUTS 2 level .....................................................84 

Figure 37 Potassium content in cropland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................84 

Figure 38 Potassium content in grassland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................87 

Figure 39 Potassium content in woodland presented (a) as point data and (b) average 
aggregated at NUTS 2 level ...................................................................................87 

Figure 40 Potassium content in shrubland presented as point data..............................90 

Figure 41 Potassium content in wetland presented as point data ................................91 

Figure 42 Potassium content in bareland presented as point data ...............................91 

Figure 43 pH in H2O (all 2018 LUCAS Soil Module points) presented (a) as point data and 
(b) average aggregated at NUTS 2 level ..................................................................94 

Figure 44 pH in H2O in cropland presented (a) as point data and (b) average aggregated 
at NUTS 2 level ....................................................................................................94 

Figure 45 pH in H2O in grassland presented (a) as point data and (b) average aggregated 
at NUTS 2 level ....................................................................................................95 

Figure 46 pH in H2O in woodland presented (a) as point data and (b) average aggregated 
at NUTS 2 level ....................................................................................................95 

Figure 47 Carbonates content (all 2018 LUCAS Soil Module points) presented (a) as point 
data and (b) average aggregated at NUTS 2 level ................................................... 102 

Figure 48 Electrical conductivity. Levels > 4 dS/m are considered to have saline 
conditions. ......................................................................................................... 106 

Figure 49 Point map of Oxalate Extractable Aluminium (all points) ............................ 107 

Figure 50 Point map of Oxalate Extractable Iron (all points) ..................................... 107 

Figure 51 Map of the 997 points for which metal levels were analysed. ..................... 103 

Figure 52 Arsenic analyses .................................................................................. 100 

Figure 53 Cadmium analyses ............................................................................... 100 

107 

 
Figure 54 Cobalt analyses .................................................................................... 100 

Figure 55 Copper analyses ................................................................................... 100 

Figure 56 Chromium analyses .............................................................................. 101 

Figure 57 Nickel analyses .................................................................................... 101 

Figure 58 Mercury analyses ................................................................................. 101 

Figure 59 Antimony analyses ............................................................................... 101 

Figure 60 Lead analyses ...................................................................................... 102 

Figure 61 Vanadium analysis ................................................................................ 102 

Figure 62 Zinc analyses ....................................................................................... 102 

108 

 
 
List of tables 

Table 1 Methods used for the analysis of physical and chemical properties in soil samples.
 ..........................................................................................................................14 

Table 2 Summary of LUCAS points visited and number of samples taken or observations 
in the LUCAS 2018 soil survey................................................................................16 

Table 3 Limits of detection (LOD) of the various physical and chemical properties 
analysed. ............................................................................................................18 

Table 4 Range of values and number of samples below the limit of detection (LOD) for 
soil properties in LUCAS 2018 Soil Module. ..............................................................20 

Table 5 Summary statistics of C-N ratio in mineral (<200 g kg-1) and organic (>200 g 
kg-1) soils ...........................................................................................................21 

Table 6 Distribution of points with evidence of different types of erosion as recorded by 
surveyors in the LUCAS 2018 survey.......................................................................25 

Table 7 Summary of data of organic soils recorded by surveyors in the 2018 LUCAS Soil 
Module. ...............................................................................................................28 

Table 8 Summary of points by MS and LC in the LUCAS 2018 survey. .........................30 

Table 9a.Table 9 Soil fields in the file of basic soil properties 2018 LUCAS Soil Module. .31 

Table 10 Fields in the file of soil erosion in the 2018 LUCAS Soil Module ......................33 

Table 11 Fields in the file of organic soils in the 2018 LUCAS Soil Module. ...................36 

Table 12 Environmental descriptors at LUCAS sampling points ...................................37 

Table 13 Summary statistics of the sampling density in NUTS 2 regions of the 2018 
LUCAS Soil Module. ..............................................................................................40 

Table 14 Summary of organic carbon content in the 2018 LUCAS Soil Module, by climatic 
zone and by land cover class ..................................................................................47 

Table 15 Summary of organic carbon in wetland points in the 2018 LUCAS Topsoil 
survey. ...............................................................................................................50 

Table 16 Summary of organic carbon in woodland points in the 2018 LUCAS Topsoil 
survey. ...............................................................................................................51 

Table 17 Summary of organic carbon in grassland points of the 2018 LUCAS Topsoil 
survey. ...............................................................................................................54 

Table 18 Summary of organic carbon in shrubland points of the 2018 LUCAS Topsoil 
survey. ...............................................................................................................55 

Table 19 Summary of organic carbon in cropland points of the 2018 LUCAS Topsoil 
survey. ...............................................................................................................57 

Table 20 Summary of organic carbon in bareland points of the 2018 LUCAS Topsoil 
survey. ...............................................................................................................58 

Table 21 Summary of nitrogen content in the 2018 LUCAS Topsoil survey, by climatic 
zone and by land cover class ..................................................................................60 

Table 22 Summary of nitrogen content in wetland in the 2018 LUCAS Topsoil survey ....66 

Table 23 Summary of nitrogen content in woodland in the 2018 LUCAS Topsoil survey .67 

Table 24 Summary of nitrogen content in grassland in the 2018 LUCAS Topsoil survey .68 

Table 25 Summary of nitrogen content in shrubland in the 2018 LUCAS Topsoil survey .69 

Table 26 Summary of nitrogen content in cropland in the 2018 LUCAS Topsoil survey ...70 

Table 27 Summary of nitrogen content in bareland in the 2018 LUCAS Topsoil survey ..71 

109 

 
Table 28 Summary of phosphorus content by climatic zone and by land cover class ......72 

Table 29 Summary of phosphorus content in woodland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................74 

Table 30 Summary of phosphorus content in shrubland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................76 

Table 31 Summary of phosphorus content in grassland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................77 

Table 32 Summary of phosphorus content in bareland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................79 

Table 33 Summary of phosphorus content in cropland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................80 

Table 34 Summary of potassium content by climatic zone and by land cover class ........83 

Table 35 Summary of potassium content in cropland in the 2018 LUCAS Topsoil survey 85 

Table 36 Summary of potassium content in grassland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................86 

Table 37 Summary of potassium content in woodland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................88 

Table 38 Summary of potassium content in shrubland in the 2018 LUCAS Topsoil survey
 ..........................................................................................................................89 

Table 39 Summary of potassium content in wetland in the 2018 LUCAS Topsoil survey .90 

Table 40 Summary of potassium content in bareland in the 2018 LUCAS Topsoil survey 92 

Table 41 Summary of pH in H2O in cropland points ...................................................96 

Table 42 Summary of pH in CaCl2 in cropland points .................................................97 

Table 43. Summary of pH in H2O in grassland points .................................................98 

Table 44 Summary of pH in CaCl2 in grassland points ...............................................99 

Table 45 Summary of pH in H2O in woodland points ............................................... 100 

Table 46 Summary of pH in CaCl2 in woodland points ............................................. 101 

Table 47 Summary of CaCO3 content in cropland points .......................................... 103 

Table 48 Summary of CaCO3 content in grassland points ......................................... 104 

Table 49 Summary of CaCO3 content in woodland points ......................................... 105 

Table 50 Summary of oxalate extractable Aluminum content in the 2018 LUCAS Topsoil 
survey .............................................................................................................. 100 

Table 51 Summary of oxalate extractable Iron content in the 2018 LUCAS Topsoil survey
 ........................................................................................................................ 101 

Table 52 Summary of metal content in selected samples (g/mg) .............................. 102 

110 

 
 
 
 
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doi:10.2760/215013 

ISBN 978-92-76-54832-4