Identification of appropriate indicators for the targets of the EU Biodiversity Strategy in relation to agro-ecosystems.
Date: 2024-05-22
Institutions: Joint Research Centre
Subjects: EU strategy, agricultural land, biodiversity, degradation of the environment, ecology, ecosystem, environmental indicator, environmental protection, nature restoration, organic farming, research report, sustainable agriculture

ISSN 1831-9424 

Identification of appropriate indicators for 
the targets of the EU Biodiversity Strategy 
in relation to agro-ecosystems 

Princé, K., Robuchon, M., Liquete, C., Guerrero, I. 

2024 

 EUR 31934 EN 

 
 
  
 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
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Contact information  
Name: Irene Guerrero 
Address: Joint Research Centre, Via E. Fermi 2749, I-21027 Ispra, Italy. 
Email: irene.guerrero-fernandez@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC137438 

EUR 31934 EN 

PDF  

ISBN 978-92-68-15928-6   ISSN 1831-9424   doi:10.2760/984931  

KJ-NA-31-934-EN-N 

Luxembourg: Publications Office of the European Union, 2024  

© European Union, 2024  

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How to cite this report: European Commission, Joint Research Centre, Princé, K., Robuchon, M., Liquete, C. and Guerrero, I., Identification of 
appropriate indicators for the targets of the EU Biodiversity Strategy in relation to agro-ecosystems, Publications Office of the European Union, 
Luxembourg, 2024, https://data.europa.eu/doi/10.2760/984931, JRC137438. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents  

Abstract ................................................................................................................................................................................................................................. 1 

Acknowledgements ...................................................................................................................................................................................................... 2 

1  Introduction ................................................................................................................................................................................................................ 3 

1.1  Context and background information ....................................................................................................................................... 3 

1.2  Purpose, objectives and scope........................................................................................................................................................ 4 

2  Indicators for target 4 ....................................................................................................................................................................................... 7 

2.1  Sub-target 4.1: restoration of degraded and carbon-rich ecosystems ........................................................ 7 

2.1.1 

Indicator(s) already in the EU BDS dashboard .................................................................................................. 7 

2.1.2  Candidate indicator(s) for the EU BDS dashboard ......................................................................................... 7 

2.2  Sub-target 4.2: no deterioration in habitats trends and status ......................................................................... 9 

2.2.1 

Indicator(s) already in the EU BDS dashboard .................................................................................................. 9 

2.2.2  Candidate indicator(s) for the EU BDS dashboard ......................................................................................... 9 

2.3  Sub-target 4.3: no deterioration in species trends and status ........................................................................ 10 

2.3.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 10 

2.3.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 10 

2.4  Sub-target 4.4: 30% of species reach favourable conservation status .................................................. 13 

2.4.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 13 

2.4.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 13 

2.5  Sub-target 4.5: 30% of habitats reach favourable conservation status ................................................ 13 

2.5.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 13 

2.5.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 13 

3  Indicators for target 7: high diversity landscape features .............................................................................................. 14 

3.1.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 14 

3.1.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 14 

4  Indicators for target 8 .................................................................................................................................................................................... 17 

4.1  Sub-target 8.1: 25% of agricultural land under organic farming ................................................................. 17 

4.1.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 17 

4.1.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 17 

4.2  Sub-target 8.2: uptake of agro-ecological practices ................................................................................................ 17 

4.2.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 17 

4.2.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 17 

5  Indicators for target 13................................................................................................................................................................................. 22 

5.1  Sub-target 13.1: 50% reduction in nutrient losses ................................................................................................... 22 

5.1.1 

Indicator(s) already in the EU BDS dashboard ............................................................................................... 22 

i 

 
 
5.1.2  Candidate indicator(s) for the EU BDS dashboard ...................................................................................... 22 

5.2  Sub-target 13.2: 20% reduction in fertilisers use ...................................................................................................... 25 

5.2.1 

Indicators already in the EU BDS dashboard .................................................................................................. 25 

5.2.2  Candidate indicators for the EU BDS dashboard ......................................................................................... 26 

6  Conclusions .............................................................................................................................................................................................................. 27 

References ....................................................................................................................................................................................................................... 28 

List of abbreviations ................................................................................................................................................................................................ 33 

List of figures ................................................................................................................................................................................................................ 34 

List of tables .................................................................................................................................................................................................................. 35 

ii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract  

The EU Biodiversity Strategy for 2030 (EU BDS) is a comprehensive plan aimed at protecting nature 
and  reversing  the  degradation  of  ecosystems.  To  support  the  implementation  of  the  EU  BDS,  the 
Knowledge Centre for Biodiversity (KCBD) has developed an online tool, the EU BDS dashboard, to 
track  progress  towards  the  strategy's  targets  using  indicators.  This  report  focuses  on  the 
identification  and  pre-selection  of  suitable  indicators  for  targets  related  to  agro-ecosystems, 
whether  they  are  on  nature  restoration,  landscape  features,  organic  farming,  agro-ecological 
practices or nutrients (i.e. targets 4, 7, 8 and 13).  

The  report  outlines  the  methodology  used  to  identify  candidate  indicators,  including  screening 
multiple  sources  of  information  and  consulting  with  experts  and  policy  makers.  The  identified 
candidate indicators for the EU  BDS targets related to agro-ecosystems are described in detail  in 
the report. 

Out of the 24 indicators presented in this report, 3 are already published in the dashboard, and 3 
are in the pipeline to be published following consultation at the policy level in the expert group on 
Monitoring  and  Assessment  of  the  EU  Biodiversity  Platform.  Additionally,  this  report  highlights 
possibilities  for  the  development  or  selection  of  various  indicators  based  on  data  that  are  either 
existing or currently being collected, as well  as those calculated for other policies and monitoring 
frameworks.  This  comprehensive  effort  in  identifying  and  proposing  indicators  represents  a 
significant step towards tracking progress and ensuring the effective implementation of the EU BDS 
in relation to agro-ecosystems. 

1 

 
 
 
 
Acknowledgements 

We thank Maria Luisa Paracchini and Raphaël d'Andrimont for providing useful feedback on targets 
7 and 8. 

Authors 

Karine Princé1, Marine Robuchon2, Camino Liquete2, Irene Guerrero2 

1  Centre  d'Ecologie  et  des  Sciences  de  la  Conservation  (CESCO),  Muséum  national  d'Histoire 
naturelle, CNRS, Sorbonne Université, Paris, France 

2 European Commission, Joint Research Centre (JRC), Ispra, Italy 

2 

 
 
 
1 

Introduction 

1.1  Context and background information 

The EU Biodiversity Strategy for 20301 (EU BDS) is a comprehensive, ambitious, and long-term plan 
to  protect  nature  and  reverse  the  degradation  of  ecosystems.  The  strategy  aims  to  put  Europe's 
biodiversity on a path to recovery by  2030 and contains specific actions and commitments.  There 
are 16 targets in the EU Biodiversity Strategy to be reached by 2030, including 4 specifically related 
to agro-ecosystems (see below). 

Reconciling  agricultural  production  and  biodiversity conservation  has  long been  a  major  challenge 
(Hence  et  al.  2008;  Brassard  et  al.  2010;  Phalan  et  al.  2011),  that  policy  tries  to  face  through  a 
number  of  measures  and  targets  in  some  major  EU  sectoral  policies,  such  as  the  Common 
Agricultural  Policy.  With  the  adoption  of  the  European  Green  Deal,  the  European  Commission 
recognises  the  importance  of  approaching  the  impacts  of  the  European  agricultural  and  food 
system  from  a  global  point  of  view.  Included  actions  as  the  Farm  to  Fork  Strategy  (F2F),  the  EU 
BDS, the European Commission proposal for a Nature Restoration Law (NRL) or a greener Common 
Agricultural Policy (CAP) for the 2023-2027 period, aim at achieving sustainable food systems with 
objectives including reversing biodiversity loss.  

Among these, the Farm to Fork Strategy is the first sustainable food policy the EU has ever adopted 
and, thus, there is no comprehensive evidence base to build upon. Among the resulting challenges is 
the  establishment  of  a  monitoring  framework  adapted  to  the  specificities  of  the  Farm  to  Fork 
Strategy. Such a monitoring framework is  a key step for future evaluations of the Strategy in the 
coming  years.  The  need  for  consistent  and  harmonised  monitoring  of  the  common  objectives 
between  the  two  strategies  (EU  BDS  and  F2F)  reinforces  the  need  for  extensive  work  to  identify 
candidate  indicators  that  can  illustrate  the  progress  made  in  the  implementation  of  measures  in 
European agro-ecosystems.   

The  parallel  work  of  the  Joint  Research  Centre  (JRC)  on  both  monitoring  schemes,  as  well  as  the 
application  of  similar  principles,  ensures  the  consistency  of  the  frameworks.  We  have  adhered  to 
the "reuse" principle, as per the European Commission's "Better Regulation" framework2, in line with 
good practices recommended by the UN SDSN for SDG monitoring, making use of existing concepts, 
terms, solutions and tools wherever possible. To contribute to interoperability, we have work as well 
on the integration of elements from established monitoring systems in this EU BDS framework3. 

The  Knowledge  Centre  for  Biodiversity4  (KCBD)  supports  policymaking  by  developing  tools  that 
support  the  implementation  of  the  EU  BDS.  Two  online  tools  track  progress  in  implementing  the 
strategy: 

—  an  actions  tracker  provides  up-to-date  information  on  the  state  of  implementation  of  the 

strategy’s many actions, 

—  a  dashboard  shows  progress  to  the    targets  set  by  the  Strategy,  at  the  EU  level  and,  when 

possible, in the Member States. 

As presented in this document, the KCBD is also mandated to propose candidate indicators for all 
the targets. After the necessary scientific and technical research and discussions, those candidate 
indicators are presented for consultation at the policy level in the expert group on Monitoring and 

1 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52020DC0380 

2https://commission.europa.eu/law/law-making-process/planning-and-proposing-law/better-regulation/better-regulation-

guidelines-and-toolbox_en 

3 https://joinup.ec.europa.eu/interoperable-europe/policy 
4 https://knowledge4policy.ec.europa.eu/biodiversity_en 

3 

 
 
                                                        
 
Assessment  (EUBP  MA)5,  EU  Biodiversity  Platform  (EUBP)6
responsible for the implementation of the EU BDS. The last step is the publication of the selected 
indicators in the dashboard (including their latest values and their documentation), which are then 
continuously updated and maintained.  

1.2  Purpose, objectives and scope 

Following  the  above-mentioned  mandate,  the  goal  of  this  work  was  to  support  the  identification 
and pre-selection of suitable indicators to track the following targets of the EU BDS related to agro-
ecosystems: 

Target  4  -  Legally  binding  EU  nature  restoration  targets  to  be  proposed  in  2021,  subject  to  an 
impact  assessment.  By  2030,  significant  areas  of  degraded  and  carbon-rich  ecosystems  are 
restored. Habitats and species show no deterioration in conservation trends and status; and at least 
30% reach favourable conservation status or at least show a positive trend. This target is further 
divided in the following sub-targets:  

Sub-target 4.1 - Significant areas of degraded and carbon-rich ecosystems are restored 

Sub-target 4.2 - Habitats show no deterioration in conservation trends and status 

Sub-target 4.3 - Species show no deterioration in conservation trends and status 

Sub-target  4.4  -  At  least  30%  of  species  currently  not  in  favourable  conservation  status 
reach favourable conservation status or at least show a positive trend  

Sub-target  4.5  -  At  least  30%  of  habitats  currently  not  in  favourable  conservation  status 
reach favourable conservation status or at least show a positive trend 

Target 7 - At least 10% of agricultural area is under high-diversity landscape features 

Target 8 - At least 25% of agricultural land is under organic farming management, and the uptake 
of  agro-ecological  practices  is  significantly  increased  [common  target  with  F2F].  This  target  is 
further divided in the following sub-targets: 

Sub-target 8.1 - At least 25% of agricultural land is under organic farming management 

Sub-target 8.2 - The uptake of agro-ecological practices is significantly increased 

Target 13 - The losses of nutrients from fertilisers are reduced by 50%, resulting in the reduction 
of the use of fertilisers by at least 20% [common target with F2F]. This target is further divided in 
the following sub-targets:  

Sub-target 13.1 - The losses of nutrients from fertilisers are reduced by 50%  

Sub-target 13.2. The use of fertilisers is reduced by at least 20% 

As mentioned before, some of these targets are closely  related to aspirational  targets of the  F2F 
and  thus,  this  work  may  help  to  establish  links  with  other  agricultural,  fisheries,  food  and 
environmental indicator frameworks and possibly aligning proposals across different policies.  

Starting with a list of indicators pre-identified by the KCBD, the identification of suitable indicators 
has  then  been  conducted  following  a  similar  methodology  as  for  systematic  literature  review,  by 

5 All records of the working group are publicly available at https://circabc.europa.eu/ui/group/03c84fa2-7896-4cb9-b743-

11646b4daf0e/library/47e2c581-c730-4f2f-9599-9bbb5f58aaf3?p=1&n=10&sort=modified_DESC 

6  All  records  of  the  working  group  are  publicly  available  at  https://circabc.europa.eu/ui/group/6f30d1d2-d6f2-4c6e-a4dc-

1feb66201929 

4 

 
 
 
 
 
 
 
 
 
 
 
 
                                                        
 
screening multiple  sources of candidate indicators. Information sources used include the scholarly 
database  of  peer-reviewed  articles  Web  of  Science,  as  well  as  grey  literature  collections  (other 
existing policies, past and current research projects, and technical reports).  

Relevant  information  was  extracted  from  these  different  sources  using  keywords  searching,  and 
also citation searching and reference list checking. Information was collected by directly contacting 
experts in the field in some cases. The candidate indicators for the EU  BDS are filtered based on 
policy relevance & coherence, data availability (including coverage) and scientific quality (Viti et al., 
2024).  KCBD  organises  extensive  discussions  with  experts  and  policy  makers  before  some  of  the 
candidate indicators are presented for consultation in the EUBP MA. 

This report describes in detail the candidate indicators, their relationship to the objectives and their 
status  in  terms  of  selection  and  readiness  for  the  4  objectives  of  the  EU  BDS  related  to  agro-
ecosystems.  Table  1  below  provides  a  schematic  view  of  the  results,  but  for detailed  information 
and definition of the indicators mentioned, the user is encouraged to read the main text. 

Table 1. Summary of identified potential indicators to track EU BDS targets 4, 7, 8 and 13 and their status 
regarding the selection process and their readiness (published = already published in the EU BDS dashboard, 
in the pipeline to be published = to be published in the EU BDS dashboard following consultation in EUBP MA, 
candidate ready = indicator not yet presented in EUBP MA for which data is already available, and candidate 
perspective = indicator not yet presented in EUBP MA for which data is not yet available). Indicator running 
title is provided, read main text for detailed information.  

Target 

Sub-target 

Indicator title 

Target 4 

Sub-target 4.1 

Sub-target 4.2 

Sub-target 4.3 

Soil organic carbon for mineral soils and 
cropland (SOC from Sentinel-2 data) 
Topsoil Organic Carbon (from LUCAS soil 
survey) 
CAP I.20/C.37 Enhancing biodiversity protection  
- Percentage of species and habitats of 
Community interest related to agriculture with 
stable or increasing trends 
Common bird index by type of species 

Grassland butterfly index 

 Butterfly indicators based on abundances of all 
widespread species 
CAP I.20/C.37 Enhancing biodiversity protection 
- Percentage of species and habitats of 
Community interest related to agriculture with 
stable or increasing trends 
Red List Index at EU level 

Sub-target 4.4 

Birds and Habitats Directives reporting data. 

Sub-target 4.5 

Birds and Habitats Directives reporting data. 

Status 

Candidate 
perspective 
Candidate ready 

Candidate 
perspective 

Published 
(Dashboard 
Indicator 4.1.1)7 
Published 
(Dashboard 
Indicator 5.0.1)7 
Candidate 
perspective 
Candidate 
perspective 

Candidate 
perspective 
Candidate 
perspective 
Candidate 
perspective 

7 https://dopa.jrc.ec.europa.eu/kcbd/EUBDS2030-dashboard/?version=1 

5 

 
 
 
                                                        
 
Target 7 

Target 8 

Sub-target 8.1 

CAP I.21 Enhancing provision of ecosystem 
services: share of agricultural land covered with 
landscape features 
Share of agricultural area under high-diversity 
landscape features (based on LF typology) 
Area under organic farming 

Sub-target 8.2 

Trends in the uptake of agro-ecological 
practices (based on the LIFT Farm typology) 
EU Crop diversity 

High Nature Value farming (HNV) 

Target 13  Sub-target 

Nutrients in freshwater 

13.1 

Nutrients in transitional, coastal and marine 
waters in Europe 
Biochemical oxygen demand in rivers (Eurostat 
indicator SDG_06_30) 
Oxygen concentrations in coastal and marine 
waters surrounding Europe 
Marine waters affected by eutrophication 
(Eurostat indicator SDG_14_60) 
ESDAC Soil nutrients (Phosphorus, Nitrogen, 
Potassium) datasets 
Gross nutrient balance (compiled by Eurostat)  

In the pipeline to be 
published 

Candidate 
perspective 
Published 
(Dashboard 
Indicator 8.1.1)7 
In the pipeline to be 
published 
Candidate 
perspective 
Candidate 
perspective 
Published 
(Dashboard 
Indicators 13.1.1, 
13.1.2, 13.1.3, 
13.1.4)7 
Candidate ready 

Candidate ready 

Candidate ready 

Candidate ready 

Candidate 
perspective  
Candidate ready 

Sub-target 
13.2 

Consumption of inorganic fertilizers 

Candidate ready 

Share of agro-ecological farming (based on the 
LIFT Farm typology) 
Trend in fertilisers use (FaST) 

Candidate 
perspective 
Candidate 
perspective 

Source: Own elaboration. 

6 

 
 
 
 
 
2 

Indicators for target 4 

Target  4  -  Legally  binding  EU  nature  restoration  targets  to  be  proposed  in  2021,  subject  to  an 
impact  assessment.  By  2030,  significant  areas  of  degraded  and  carbon-rich  ecosystems  are 
restored. Habitats and species show no deterioration in conservation trends and status; and at least 
30% reach favourable conservation status or at least show a positive trend. 

2.1  Sub-target 4.1: restoration of degraded and carbon-rich ecosystems 

Sub-target 4.1 - Significant areas of degraded and carbon-rich ecosystems are restored 

2.1.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target. 

2.1.2  Candidate indicator(s) for the EU BDS dashboard 

While this objective is closely linked to the EU legal framework for Nature restoration, defining how 
to  measure  progress  towards  this  objective  depends  very  much  on  the  final  outcomes  of  such 
framework. At the time of writing this report, the Nature Restoration Law (NRL) has been adopted 
by the European Parliament8 but is yet to be adopted by the EU Council. Even if defining indicators 
in these circumstances can be complex,  taking into account the general formulation of the target, 
measures related to observed trends in soil carbon stocks could be of interest for this section. 

Soils are home to around 25% of the world s biodiversity and are fundamental to terrestrial nature 
restoration. In a context of climate change, better soil management can increase the carbon storage 
capacity of soils, which are the largest terrestrial carbon pool, and enhance soil ecosystem services 
that contribute to climate adaptation, including food security and hazard risk mitigation (Amelung et 
al.  2020;  Hou  2021).  Soil  organic  carbon  (SOC)  plays  a  crucial  role  in  essential  soil  functions 
including carbon storage, biodiversity,  soil  fertility,  and water storage, yet around 45% of EU soils 
have low SOC levels.  

Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change 
of carbon stocks within and across various systems (Campbell et al. 2022). As such, the Copernicus 
Sentinel-2 data offers an interesting perspective to monitor soil conditions in croplands and mineral 
soils with the mapping of Soil organic carbon for mineral soils and cropland (SOC). A recent 
study  highlighted that the spatial resolution of Copernicus Sentinel-2 is adequate to describe  SOC 
variability  both  at  field  and  regional  scale  and  the  prediction  accuracy  obtained  by  Copernicus 
Sentinel-2  data  is  very  similar  to  that  retrieved  by  airborne  hyperspectral  data  (Castaldi  et  al. 
2019). The SOC maps should be available at fine resolution, updated on a season/month basis. In 
addition, it fits into Carbon Cycle  Essential  Biodiversity  Variable9 (Skidmore et al. 2021). However, 
prior to the ability of being embedded in the EU BDS dashboard, there is a need to determine how 
to  summarise  this  information  at  EU  level  and  the  governance  to  estimate  and  maintain  this 
indicator in the long-term. 

In  addition  to  remote  sensing  data,  field-based  censuses  can  provide  useful  data  to  build  on 
relevant  indicators  for  the  EU  BDS.  The  LUCAS  topsoil  database10  provides  relevant  data  in  that 

8 https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html 
9 EBVs form a core set of complementary biological measurements for capturing considerable biodiversity change and are 

produced by integrating primary observations from in situ monitoring with remote sensing. 

10 The LUCAS soil module entails soil sampling at a large number of points across EU (more then 20,000), consecutive soil 
analysis, and sample storage. Such campaigns took place in 2009, 2015, and 2018, and were repeated in 2022.  

7 

 
 
                                                        
 
sense used for the mapping of Topsoil Soil Organic Carbon (LUCAS) for EU25 - produced by the 
European  Soil  Data  Centre  (ESDAC)11  and  based  on  paper  from  de  Brogniez  et  al.  (2015).  LUCAS 
data are updated on a regular basis, however this specific indicator does not cover the full extent of 
EU (EU-27) as expected for indicators published in the EU BDS dashboard. This may be however an 
upcoming  perspective  of  extension  -  ESDAC  may  be  able  to  provide  more  information  on  this 
matter. For example, Panagos et al. (2023) and De la Rosa et al. 2024 provided a baseline for two 
CAP  impact  indicators  (soil  erosion  and  SOC)  and  modelled  SOC  stocks  across  the  EU28  for  the 
2018 (data available in ESDAC). 

Specifically,  CAP  Performance,  Monitoring  and  Evaluation  Framework  (PMEF)  includes  the 
impact/context indicator I11/C40 Enhancing carbon sequestration: Soil organic carbon in agricultural 
land to monitor the effect of the policy on SOC. The indicator is composed of 3 specific indicators 
related  to  total  organic  carbon  content  in  soils  on  agricultural  land  of  EU  MS,  average  organic 
carbon  content  in  agricultural  land,  and  an  estimate  of  SOC  changes  over  time.  For  this  CAP 
indicator,  Member  States  are  not  required  to  submit  their  own  data.  The  information  is  instead 
gathered  through  the  LUCAS  soil  survey  (Jones  et  al., 2021)  and  processed  by  the  JRC.  The 
methodology  is  based  on  the  integration  of  ground  data,  from  the  LUCAS  soil  survey  with  an 
advanced modelling framework coupling process-based and machine learning models. However, this 
indicator  is  limited  to  agricultural  land  and,  therefore,  its  coverage  may  exclude  some  soils  of 
interest to this monitoring framework beyond these land uses. 

 Overall,  coordination  with  EU  Soil  Observatory12  is  mandatory,  as  EUSO  aims  at  establishing  a 
comprehensive dashboard containing indicators that present data on soil-related issues within and, 
in some cases, outside of the EU. One of the five objectives of the EUSO is indeed to expand the 
European  Soil  Data  Centre  (ESDAC)  and  reinforce its  data  sharing  capacity.  Moreover,  on  5th  July 
2023,  the  EU  proposed  a new  Directive  on  Soil  Monitoring  and  Resilience13  to  protect  and  restore 
soils  and  ensure  that  they  are  used  sustainably.  The  monitoring  framework  proposed  by  the  law, 
closely linked to the EUSO, will provide more accurate information for this target in the near future.  

The  restoration  of  peatlands,  wetland  ecosystems  containing  carbon-rich  peat  soils,  is  widely 
recognised  as  one  of  the  most  effective  nature-based  solutions  for  climate  and  is  coherent  with 
other  policy  objectives,  creating  win-wins  for  biodiversity  and  disaster  risk  mitigation. The  latest 
Common  Agricultural  Policy  (CAP)  regulation14  proactively  ensures  the  protection  of  wetlands  and 
peatlands  by  introducing  significant  changes  to  its  Green  Payment  Framework.  By  linking 
conditionality (setting the minimum requirements for CAP beneficiaries to access income support) to 
environmentally and climate-friendly farming practices and standards known as 'Good Agricultural 
and  Environmental  Conditions'  (GAECs).  This  reinforcement  of  the  policy  results  in  a  total  of  10 
GAECs,  notably  including  GAEC  2.  GAEC  2  explicitly  requires  each  Member  State  to  effectively 
protect wetlands and peatlands due to their role as vital carbon stores that are crucial to preventing 
the worsening of climate change. National definitions identify peatlands within the protective ambit 
of  GAEC  2  in  the  CAP.  The  prescribed  agricultural  practices  and  conservation  measures  for 
peatlands managed in agriculture are outlined in the CAP strategic plans of EU Member States. The 
identification of these areas will be explicitly detailed and reported, but the details and availability 
of this data are yet to be determined. Similarly, the forthcoming NRL could provide some metrics on 
the condition of these ecosystems, but as mentioned above, the final scope is yet to be established.  

Apart  from  peatlands  and  wetlands,  some  agricultural  practices  can  significantly  contribute  to 
carbon sequestration. It is the case of agroforestry, the integrated management of woody elements 

11 European Soil Data Centre (ESDAC), hosted by the European Commission's Joint Research Centre (JRC) 
12 https://joint-research-centre.ec.europa.eu/eu-soil-observatory-euso_en 
13 https://environment.ec.europa.eu/publications/proposal-directive-soil-monitoring-and-resilience_en 
14 https://eur-lex.europa.eu/eli/reg/2021/2115 

8 

 
 
                                                        
 
on  croplands  or  grasslands  (European  Commission,  2013),  highlighted  as  one  of  the  agricultural 
practices with the greatest potential for climate change mitigation and adaptation (Aertsens et al., 
2013; Hart  et  al.,  2017).  Kay  et  al.  (2019)  assessed  that  implementing  agroforestry  on  Priority 
Areas15  could  lead  to  a  sequestration  of  2.1  to  63.9  million  tCa 1  (7.78  and  234.85  million 
tCO2eqa 1) depending on the type of agroforestry. This corresponds to between 1.4 and 43.4% of 
European  agricultural  greenhouse  gas  (GHG)  emissions.  Monitoring  the  share  of  agroforestry  in 
European agricultural areas could therefore partially inform about progress towards sub-target 4.1.  

2.2   Sub-target 4.2: no deterioration in habitats trends and status 

Sub-target 4.2 - Habitats show no deterioration in conservation trends and status 

2.2.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target. 

2.2.2  Candidate indicator(s) for the EU BDS dashboard 

The  conservation  trends  and  status  of  EU  habitats  is  reported  under  the  Habitats  Directive  and 
regularly summarised in the State of Nature report by the European Environmental Agency (EEA)16. 
Summary  statistics  and  dashboards  are  provided  by  the  EEA,  among  others  on  the  conservation 
status of habitats of Community interest or on the trend in conservation status per habitat groups. 
These analyses are based on the number of assessments reported by EU Member States (MS). This 
information is not shown or further aggregated in the EU BDS dashboard so far.  

More specifically related to agro-ecosystems, the previous CAP Common Monitoring and Evaluation 
Framework (CMEF) context indicator C.36  Conservation status of agricultural habitats (grassland)  
was  based  on  the  information  reported  under  the  Habitats  Directive.  At  present,  the  CAP 
Performance,  Monitoring  and  Evaluation  Framework  (PMEF)  includes  the  impact/context  indicator 
I.20/C.37  Enhancing  biodiversity  protection  -  Percentage  of  species  and  habitats  of 
Community interest related to agriculture with stable or increasing trends17. This indicator 
assesses trends in the conservation of specific habitats and species listed in the Habitats Directive 
Annexes that have a strong link with agro-ecosystems. The persistence and sustainable survival of 
these  particular  habitats and  species is  strongly  linked  to  the presence  and optimal  conditions  of 
particular  agricultural  systems.  Their  conservation  status  is  particularly  affected  by  agricultural 
management practices and land use changes such as abandonment, intensification and conversion. 
While  the  Habitats  Directive  does  not  explicitly  outline  the  species  and  habitats  that  depend  on 
agro-ecosystems,  a  list  for  each  Member  State  specific  to  its  biogeographical  regions  was 
stablished, based on studies such as Halada et al. (2011) and the European Commission's guidance 
on Farming for Natura 2000' (2018). The base National lists were subjected to consultation by MS 
authorities and finalised by expert review to ensure consistency. These lists, regularly updated and 
confirmed  by  Member  States,  show  variation  between  regions  and  countries,  with  only  those 
habitats  and  species  most  dependent  on  farming  practices  and  associated  management  being 
included.  This  newly  proposed  indicator  builds  on  the  Member  States  regularly  reporting  in 
accordance with Article 17 of the Habitats Directive. Data are reported to the European Environment 
Agency  (EEA)  by  MS  and  the  EEA  carries  out  the  necessary  calculations.  Due  to  the  fact  that 
Member  States  apply  different  approaches  to  collect  the  data  (complete  inventories,  sampling, 

15 Areas where environmental pressures accumulate. See Kay et al 2019 for more details. 
16 https://www.eea.europa.eu/ims/conservation-status-of-habitats-under 
17https://agriculture.ec.europa.eu/document/download/b7b8a856-e6d5-48fc-abc2-acdbda887e34_en?filename=pmef-

context-impact-indicators_en.pdf 

9 

 
 
                                                        
 
expert  opinion),  the  level  of  accuracy  of  the  data  set  provided  by  Member  States  may  vary. 
Nevertheless, this indicator  can  give  an  idea  on  the  status  of  agricultural  habitats in  Europe  and, 
thus, can be a candidate indicator for sub-target 4.2. 

Grasslands are important agro-ecosystems with significant environmental value. Their role ranges 
from  conserving  biodiversity,  supporting  food  production  and  influencing  key  ecological  processes 
such as pollination, water regulation, carbon storage and climate moderation, operating from local 
landscapes to global scales. The Copernicus for Natura 2000 (COP4N2K) project, including the EU 
Grassland Watch, focuses on monitoring and assessing the status of grasslands within Natura 2000 
sites. Launched in 2019 as part of the 'EP Pilot Projects', it aims to establish consistent monitoring 
services for grasslands in Natura 2000 sites. Using satellite data such as Landsat and Copernicus 
Sentinel 1 and 2 imagery, EU Grassland Watch provides detailed insights into grassland dynamics in 
3,689 Natura 2000 sites across 27 EU Member States, covering changes from 1994 to the present. 
The main objective is to develop practical indicators to track the status and changes of grasslands 
over  time.  These  indicators  are  crucial  for  assessing  whether  Natura  2000  sites  are  effectively 
protecting  their  grasslands  in  line  with  nature  conservation  policy  to  prevent  habitat  degradation. 
The project aims to make these indicators visible to both experts and the general public, highlighting 
trends  such  as  grassland  loss  and  improvement.  It aims in  particular  to  distinguish  between  high 
biodiversity  value  (unmanaged  or  extensively  used)  and  low  biodiversity  value  (intensively 
managed) grasslands, providing a detailed understanding of the performance of Natura 2000 sites. 
The  service  focuses  on  developing  key  indicators  of  grassland  status  and  change,  such  as  land 
cover, class diversity and permanence, derived from earth observation data. These indicators, from 
site to European scales, can provide comprehensive insights into status and change within Natura 
2000 sites. If successful, these indicators could be a valuable addition to the dashboard. 

2.3  Sub-target 4.3: no deterioration in species trends and status 

Sub-target 4.3 - Species show no deterioration in conservation trends and status 

2.3.1  Indicator(s) already in the EU BDS dashboard 

To  monitor  sub-target  4.3.,  the  Common  bird  index  by  type  of  species  has  already  been 
published in the EU BDS dashboard, which includes values since 1990 and a full documentation18. 
This indicator is a multi-species index measuring changes in population abundances at EU level of 
all  common  bird  species  (n=168)  as  well  as  those  associated  with  specific  habitats:  common 
farmland  bird  species  (n=39)  and  common  forest  bird  species  (n=34).  The  three  indices  give 
estimated bird numbers relative to the reference year 1990, when their value is set at 100. Values 
are calculated every year by the European Bird Census Council through the Pan-European Common 
Bird  Monitoring  Scheme  programme,  based  on  data  from  citizen  science,  and  further  provided  by 
Eurostat. 

At  present,  the  grassland  butterfly  index  is  included  in  the  EU  BDS  dashboard  under  Target  5 

dashboard19). Still, we discuss here the relevance and variants of this indicator due to its relevance 
for agro-ecosystems. 

2.3.2  Candidate indicator(s) for the EU BDS dashboard 

The  Common  bird  index  by  type  of  species  that  is  already  in  the  EU  BDS  dashboard  could  be 
completed  by  another  indicator  that  would  monitor  the  state  and  trends  of  the  overall  native 

18 https://dopa.jrc.ec.europa.eu/kcbd/dashboard/#Target%204 
19 https://dopa.jrc.ec.europa.eu/kcbd/dashboard/#Target%205 

10 

 
 
                                                        
 
breeding  bird  populations  in  the  EU  (n=445).  For  this  purpose,  population  trends  (long-term 
~1980 2018  and  short-term  ~2007 2018)  and  population  estimates  (~2013 2018)  are 
available for bird species in the EU at a national level (Burns et al., 2021; Eionet, 2020). These two 
metrics could be considered as complementary to the Common bird index. Corresponding data are 
collated  every  six  years  as  part  of  mandatory  reporting  by  EU  Member  States  to  the  European 
Commission under Article 12 of the EU Birds Directive (2009/147/EC) (European Union, 2009). For 
the  last  two  reporting  rounds  (2013  and  2019),  BirdLife  International  was  contracted  by  the 
European Commission to collate and validate these data, in close consultation with national experts. 
Following detailed guidance (DG Environment, 2017), each Member State must report an estimate 
of the population size of each regularly occurring native breeding bird species and where possible 
associated  trend  estimates.  Metadata  are  collected  on  the  time  period  of  each  estimate;  the 
method  used,  which  varies  from  full  or  sampled  surveys  to  expert  opinion;  and  as  a  full  set  of 
references  as  possible.  Instead  of  the  Common  Bird  Index  by  group  of  species  which  concerns  a 
specific list of species (167 species total), the data for population trends and estimates cover  all 
445  native  breeding  species  in  the  EU.  Both  trends  and  population  estimates,  and  the  time 
periods  they  cover,  are  most  commonly  represented  by  minimum  and  maximum  estimates.  On 
some occasions, a best single value is given in addition or instead of these. 

Apart  from  birds,  butterflies  are  also  ideal  biological  indicators:  they  are  well-documented, 
measurable,  and  sensitive  to  environmental  change,  occur  in  a  wide  range  of  habitat  types, 
represent  many  other  insects,  and  are  popular  with  the  public  because  of  their  beauty.  Field 
monitoring  is  essential  to  assess  changes  in  their  abundance.  Indicators  based  on  butterfly 
monitoring data are valuable to understand the state of the environment and help evaluate policy 
and  implementation.  In  this  perspective,  building  on  the  existing  EU  Grassland  butterfly 
indicator  (see van Swaay et al.,  2019), the European Pilot Project ABLE (Assessing ButterfLies in 
Europe)20  has  recently  been  very  successful  in  promoting and  improving  butterfly  monitoring  in 
Europe and producing high-quality butterfly indicators. 

All  indicators  are  produced  for  pan-Europe  (all  participating  countries in  Europe,  including  the UK, 
which was formerly included in data and indicators covering the EU28 Member States) as well as 
for  the  European  Union  (EU27).  Two  types  of  indicators  could  be  particularly  relevant  as  agri-
indicators of the EU Biodiversity Strategy. Of all the derived indicators, only those of interest for this 
report are mentioned below:  

—  Butterfly indicators based on abundances of defined species sets  

Grassland butterfly indicator: this is a continuation of the indicator which was produced 
for the first time by Van Swaay & Van Strien (2005). This means that the same set of 17 
widespread and specialist species is used, and that weighting is done at the MS-level. This 
makes  it  possible  to  compare  this  indicator  with  all  previous  versions.  For  each  of  the 
previous grassland butterfly indicators the weights per species per BMS have changed when 
new countries join the European Butterfly Monitoring Scheme (eBMS). 

—  Butterfly  indicators  based  on  abundances  of  all  widespread  species  occurring  in 

defined areas  

The widespread butterfly indicator, includes all species for which reliable trends can be 
calculated.  Robust  trends  require  several  sites  to  be  monitored  regularly  (within  and 
between years) across biogeographical regions in which they occur 
 at least ten transects 
within  each  biogeographical  region  within  a  Butterfly  Monitoring  Scheme  is  a  minimum 

20 Assessing ButterfLies in Europe (ABLE): an EU project aiming at capacity building for butterfly monitoring, collecting 
butterfly monitoring data into the eBMS, producing tools for analysis of the data and produce trends and indicators. 

11 

 
 
                                                        
 
requirement.  Several  filters  were  applied  to  the  species  indices  which  resulted  in  the 
exclusion of rarer species   full details of the filters applied are given in Annex II. Almost all 
species meeting the data requirements are therefore relatively widespread.  

The Natura 2000 butterfly indicator, based on all species for which reliable trends can 
be calculated from transects for which the centroid lies inside or within a 1 km radius of a 
Natura 2000 site. For this classification, we used the map Natura 2000 End 2019 released 
by  the  EEA  on  the  21st  of  April  2020  and  overlaid  the  BMS  transects  to  classify  them 
Natura 2000 or non-Natura 2000. 

Butterfly  Conservation  Europe,  the  partnership  organisation  responsible  for  establishing  the 
European  Butterfly  Monitoring  Scheme  (eBMS),  should  be  consulted  to  explore  the  possibility  of 
regularly sharing up-to-date butterfly indicators at the EU27/European level. 

As in the previous sub-target, the conservation trends and status of species is reported under the 
Habitats  and  the  Birds  Directives  and  regularly  summarised  in  the  State  of  Nature  report  (EEA, 
2020).  Summary  statistics  and  dashboards  are  provided  by  the  EEA,  among  others  on  the 
conservation status of species21 on the trend in conservation status per non-bird species groups22. 
As  for  bird  species,  there  is  no  assessment  of  status  but  national  and  EU  information  about 
species/populations and some population and distribution trends23. These analyses are based on the 
number  of  assessments  reported  by  EU  Member  States.  This  information  is  not  shown  or  further 
aggregated in the EU BDS dashboard so far.  

Again,  the  previously  mentioned  PMEF  indicator  I.20/C.37  could  provide  illustrative  information 
on  the  status  of  species  of  Community  interest  specifically  depending  on  the  state  of  European 
agro-ecosystems.  It  also  observes  the  inclusion  of  a  breakdown  of  the  percentage  for  wild 
lture with stable or increasing trends24. 
For pollinators, the indicator will build on a subset of the same species lists, but extracted from the 
38 butterflies listed in the Habitats Directive under Annex II and Annex IV. Thus, this process mirrors 
the  selection  approach  for  habitats  and  general  species.  Nevertheless,  the  breakdown  remains 
subject to test and verification.  

Finally, as a way to characterise conservation trends over a wide-range of species and taxonomic 
groups,  the  Red  List  Index  (RLI)  at  EU  and/or  Pan-European  level  is  considered  as  a  candidate 
indicator to further inform this sub-target in the EU BDS dashboard (Robuchon et al., 2023a, b). The 
RLI measures change in aggregate extinction risk across groups of species. It is based on genuine 
changes in the number of species in each category of extinction risk on the Red List of Threatened 
Species25 and is expressed as changes in an index  ranging from 0 to 1 (Butchart et al., 2006). As 
part  of  a  service  contract  between  the  International  Union  for  the  Conservation  of  Nature  (IUCN) 
and 
-General for Environment (DG ENV), IUCN is currently re-assessing 
the  status  of  more  than  9.000  species  from  11  taxonomic  groups,  and  will  use  these  data  to 
calculate RLI at both EU and Pan-European level. 

21 https://www.eea.europa.eu/ims/conservation-status-of-species-under 
22https://tableau-

public.discomap.eea.europa.eu/views/SONConservationstatusandtrend/Story1?%3Adisplay_count=n&%3Aembed=y&
%3AisGuestRedirectFromVizportal=y&%3Aorigin=viz_share_link&%3AshowAppBanner=false&%3AshowVizHome=n 

23 https://www.eea.europa.eu/en/topics/at-a-glance/nature/state-of-nature-in-europe-a-health-check/explore-nature-

reporting-data/national-summary-dashboards-birds-directive-art.12 

24 https://agriculture.ec.europa.eu/system/files/2023-02/pmef-context-impact-indicators_en.pdf 
25 www.iucnredlist.org 

12 

 
 
                                                        
 
2.4  Sub-target 4.4: 30% of species reach favourable conservation status 

Sub-target 4.4 - At least 30% of species currently not in favourable conservation status reach 
favourable conservation status or at least show a positive trend  

2.4.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target. 

2.4.2  Candidate indicator(s) for the EU BDS dashboard 

The  NRL  adopted  by  the  European  Parliament  reinforces  the  monitoring  and  reporting  of  the 
condition  and  trend  in  condition  of  the  habitats  and  the  quality  and  the  trend  in  quality  of  the 
habitats of the species. It calls Member States to put in place by 2030 restoration measures on at 
least 30 % of the area of each Habitats Directive Annex I habitat types group that is not in good 
condition  and  on  areas  representing  at  least  30  % of  the  additional  surface needed  to  reach  the 
favourable  reference  area  of  each  group  of  habitat  types  listed  in  Annex  I.  However,  the  final 
content of the NRL, including indicators that could be re-used for EU BDS monitoring of this sub-
target, depends on the position of the EU Council on such law. Meanwhile, data reported under the 
Birds and Habitats directives described in the previous section can also be used to have indicators 
on this sub-target. 

2.5  Sub-target 4.5: 30% of habitats reach favourable conservation status 

Sub-target 4.5 - At least 30% of habitats currently not in favourable conservation status reach 
favourable conservation status or at least show a positive trend  

2.5.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target. 

2.5.2  Candidate indicator(s) for the EU BDS dashboard 

The  NRL  adopted  by  the  European  Parliament  reinforces  the  monitoring  and  reporting  of  the 
condition  and  trend  in  condition  of  the  habitats  and  the  quality  and  the  trend  in  quality  of  the 
habitats of the species. It calls Member States to put in place by 2030 restoration measures on at 
least 30 % of the area of each Habitats Directive Annex I habitat types group that is not in good 
condition  and  on  areas  representing  at  least  30  % of  the  additional  surface needed  to  reach  the 
favourable  reference  area  of  each  group  of  habitat  types  listed  in  Annex  I.  However,  the  final 
content of the NRL, including indicators that could be re-used for EU BDS monitoring of this sub-
target, depends on the position of the EU Council on such law. Meanwhile, data reported under the 
Birds and Habitats directives described in the previous section can also be used to have indicators 
on this sub-target. 

13 

 
 
3 

Indicators for target 7: high diversity landscape features 

Target 7 - At least 10% of agricultural area is under high-diversity landscape features 

3.1.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target, but the KCBD proposed 
in  the  4th  meeting  of  the  EUBP  MA  on  27  September  2023  to  use  as  a  proxy  the  CAP  indicator 
Share of agricultural land covered with landscape features 26 (not necessarily high-diversity 
landscape  features),  as  explained  in  section  3.1.2  below.  Following  consultation  in  EUBP  MA,  the 
indicator  is  now  in  the  pipeline  to  be  published  in  the  EU  BDS  dashboard.  A  factsheet  further 
describing this indicator was published in the annex of the science for policy brief prepared ahead 
of the last EUBP MA (Robuchon et al., 2023b), and a more detailed version was later published by 
the experts developing this indicator (D

.  

3.1.2  Candidate indicator(s) for the EU BDS dashboard 

As  defined  under  the  proposal  for  a  regulation  on  nature  restoration,  high-diversity  landscape 
features  are  elements  of permanent  natural  or  semi-natural  vegetation  present  in  an  agricultural 
context  which  provide  ecosystem  services  and  support  for  biodiversity27.  However,  Czucz  el  al. 
(2022)  identify  as  one  of the  critical  difficulties  for protecting,  restoring,  or monitoring  landscape 
features  in  agricultural  areas  the  lack  of  a  harmonised  understanding  on  its  definition  and  main 
types.  In  this  document,  the  JRC  proposed  a  functional  typology  that  helps  to  distinguish  broad 
landscape  features (LF) types based on their key functional  characteristics. The authors identified 
four Functional Landscape Feature classes for which data sources exist and can be relevant in the 
context of an EU-level LF monitoring: woody, grassy, wet and stony features (Table 2). 

Table 2. Functional Landscape Feature Classes crosswalked to examples of LF types commonly recognised in 
policy documents and considered as core elements to be included in the indicator PMEF I21/C21  Share of 

Functional LF class 

Examples of commonly recognised subtypes 

Woody features 

hedges, wooded strips, isolated trees, trees in line, and grouped trees 

Grassy features 

field margins, buffer strips, and grassed thalwegs 

Wet features 

ditches, ponds, seepage areas, and small wetlands 

Stony features 

stone walls, terraces, clearance cairns, and rock outcrops 

Source: own elaboration from Czucz et al. 2022. 

However,  while  this  general  definition  and  the  proposed  functional  typologies  could  fit  into  any 
generic  formulation  of  landscape  elements,  varying  interpretations  in  the  definitions  for  different 
policies make it difficult to arrive at a metric that accurately accounts for them. Variations related 
to  size  ranges,  time  span  or  location  in  the  agricultural  matrix,  inter  alia,  make  it  difficult  to 
categorise these elements in a common way that meets the needs of all policies.  

In  any  case,  the  importance  of  landscape  features  in  European  agro-ecosystems  has  been 
recognised  by  several  major  European  sectoral  policies.  The  two  main  pieces  of  policy  currently 

26See https://publications.jrc.ec.europa.eu/repository/handle/JRC135650 and factsheets. 
27 https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52022PC0304 

14 

 
 
  
                                                        
 
addressing these features are the CAP and the EU BDS. The CAP in its latest regulation now in force, 

main tool to achieve this protection, GAEC standard 8 has been included to support the maintenance 
of non-productive features and area to improve on-farm biodiversity, prescribing a minimum share 
of agricultural area devoted to them. Consequently, it includes in its PMEF monitoring framework a 
specific indicator to measure success in implementing the measures proposed by member states to 
improve  the  status  of  these  elements.  This  is  impact/context  indicator  I.21/C.21-  Enhancing 
provision of ecosystem services: Share of agricultural land covered with landscape features. 

For  EU  BDS  and  NRL,  the  concept  of  high-diversity  landscape  elements  has  a  broader  approach, 
capturing more typologies and certain management conditions related to the policy's own vocation. 
According to the proposal for the NRL regulation, in order to provide ecosystem services and support 
for biodiversity, landscape features need to be subject to as little external disturbances as possible 
to  provide  safe  habitats  for  various  taxa,  and  therefore  need  to  comply  with  the  following 
conditions:  a)  they  cannot  be  under  productive  agricultural  use  (including  grazing  or  fodder 
production), and b) they should not receive fertilizer or pesticide treatment. Land lying fallow can be 
considered  as  high  diversity  landscape  features  if  it  complies  with  criteria  (a)  and  (b)  above. 
Productive  trees  part  of  arable  land  agroforestry  systems  and  productive  elements  in  non-
productive hedges can also be considered as high diversity landscape features, if they comply with 
criterion (b) above, and if harvests take place only at moments where it would not compromise high 
biodiversity levels. 

High-diversity landscape features (ponds, ditches, hedges, isolated or aligned trees, groves, etc.) and 
other woody formations are reportedly under-declared by farmers (Guyomard, Bureau et al. 2020), 
requiring the use of external sources of data (e.g. Earth observations, or field surveys) to monitor 
these  landscape  features.  However,  currently  there  is  no  single,  comprehensive  database  which 
could be used to get a consistent overview of LF at the EU level (Czucz et al. 2022).  

Two main options are theoretically available for creating a suitable overview at the EU level, both of 
which come with their own advantages, technical challenges, and limitations: LF can be mapped on 
the  basis  of  very  high  resolution  remote  sensing  data;  or  the  area  of  LF  can  be  estimated  from 
detailed  field  observations  in  an  area-frame  statistical  survey.  These  two  main  data  types  are 
currently  somewhat  covered  by  the  existing  Copernicus28  products  (mapping  based  on  remote 
sensing  data),  and  the  LUCAS  transect  data  (statistical  survey).  However,  these  existing  data 
sources  do  not  provide  yet  comprehensive  information  on  all  of  the  main  LF  types.  To  overcome 
some  of  the  shortcomings  and  to  provide  data  for  the  Performance  and  Monitoring  Evaluation 
Framework  of  the  new  CAP,  there  has  been  a  dedicated  LUCAS  LF  module  for  the  surveying 
campaign  2022.  There  are  also  further  developments  in  the  frame  of  the  Copernicus  program to 
better capture LF (methodological improvements of the Small Woody Features (SWF) products and 
use of eXtremely High Resolution (XHR) satellite data). 

LUCAS datasets, COPERNICUS remote-sensing products as well as EMBAL monitoring  29 data could 
provide valuable sources of data (with their own limitations) to measure the different categories of 
landscape  features.  However,  as  pointed  out  by  Czucz  et  al.  (2022),  these  existing  data  sources 
come  with  serious  issues,  which  need  to  be  addressed  for  an  efficient  and  reliable  EU-level 
quantification.  From  the  two  existing  EU-level  data  sources,  the  LUCAS  Transect  module  was 
critically  assessed  for  its  capabilities  for  capturing  all  LF  types,  and  has  been  redesigned  into  a 

28  Copernicus 

is 
https://www.copernicus.eu/en 

the  Ea

29 EMBAL is steered by DG Environment since 2018 in order to develop a sound methodology to assess (i) agricultural 
landscape structure, and (ii) the state of farmland biodiversity. Landscape features (which are called “landscape 

 the EMBAL parlance) are one of the main focusses of the EMBAL survey.  

15 

 
 
                                                        
 
point survey combining photo-interpretation and field survey for the LUCAS 2022 landscape feature 
module. Similarly, Copernicus products (including the existing and upcoming Small Woody Features 
(SWF)  and  Small  Landscape  Feature  (SLF)  products)  also  need  to  be  assessed  by  an  entity 
independent from the Copernicus consortium for their fitness to ensure they can answer the needs 
in terms of policy assessment.  

Once  elements  to  precisely  define  high-diversity  LF  are  established,  it  is  relevant  to  define  a 
standardised  metric  of  the  total  area  of  these  specific  features  (woody,  grassy,  wet  and  stony 
features),  to  further  monitor  the  share  of  agricultural  area  under  high-diversity  landscape 
features. There are several robust statistical methods that can be used to combine data from wall-
to-wall maps and statistical surveys in a meaningful way, leading to an unbiased estimation for any 
region of interest (GEOSS, 2009; Olofsson et al., 2014). These unbiased estimations also come with 
mathematically quantifiable statistical properties (accuracy, error rates), which outperform those of 
the individual data sources (maps, surveys) taken alone (e.g. Olofsson et al., 2014). With a careful 
attention to the underlying semantic inconsistencies, Copernicus SWF and LUCAS LF could be tested 
for  the  computation  of  such  an  estimator  (Czucz  et  al.,  2022).  EMBAL  can  also  provide  further 
important details to enrich this picture, because it is the only data source that will give information 
on the quality of the surveyed features. 

Under the premise that these developments  are not yet  fully operational,  and taking into account 
the extreme difficulty in detecting certain nuances associated with the definition of high-diversity 
landscape  elements  under  the  EU  BDS  and  the  NRL,  we  have  closely  followed  the  technical 
development  of  the  impact/context  indicator  of  the  PMEF  I.21/C.21  Enhancing  provision  of 
ecosystem  services:  Share  of  agricultural  land  covered  with  landscape  features,  and 
proposed it as a proxy for the EU BDS target. For this development, the JRC has based its work on 
the data provided by the above mentioned LUCAS LF 2022 in-situ survey. The indicator collects data 
on  the  core  elements  considered  more  narrowly  under  the  policy  definition  that  the  framework 
serves  (CAP,  Table  2).  However,  we  consider  it  to  be  a  reliable  metric,  with  significant 
representativeness,  making  it  a  good  candidate  as  a  proxy  for  the  target  under  discussion.  Other 
elements included in the broader BDS definition, such as fallow land, could be explored and added 
to the framework in the future.  

Beyond  agricultural  land  and  the  specific  metric  of  landscape  features  for  Target  7,  landscape 
heterogeneity  has  long  been  considered  a  key  determinant  of  biodiversity  (Tews  et  al.  2004; 
Fahrig et al. 2011). Metrics of landscape heterogeneity can also be regarded as good surrogates of 
species  diversity  because,  in  ecology,  habitat  diversity  is  associated  with  an  increase  in  niche 
availability  for  species  (Fahrig  et  al.  2011;  Kisel  et  al.  2011).  Landscape  heterogeneity  is  defined 
as the  number  and  proportions  of  different  cover  types  (compositional  heterogeneity)  and  their 
complex  spatial  arrangement  (configurational  heterogeneity)  in  the  landscape (Fahrig  and  Nuttle 
2005).  Based  on  the  set  of  high-diversity  LF  that  will  be  defined,  and  of  the  corresponding  data 
sources that will be chosen, it could be relevant to integrate both  landscape compositional and 
configurational heterogeneity metrics as additional indicators to monitor not only the share of 
agricultural area under high-diversity landscapes, but also the diversity and the spatial distribution 
of these elements. 

16 

 
 
4 

Indicators for target 8 

Target 8 - At least 25% of agricultural land is under organic farming management, and the uptake 
of agro-ecological practices is significantly increased [common target with F2F] 

4.1  Sub-target 8.1: 25% of agricultural land under organic farming 

Sub-target 8.1 - At least 25% of agricultural land is under organic farming management 

4.1.1  Indicator(s) already in the EU BDS dashboard 

Regarding the monitoring of the agricultural land under organic farming management, the indicator 
Area  under  organic  farming  is  already  published  in  the  EU  BDS  dashboard,  including  its  full 
documentation30.  This  indicator  measures  the  percentage  of  total  utilised  agricultural  area,  by 
Member  State  and  at  EU  level,  occupied  by  organic  farming.  It  includes  both existing  organically-
farmed areas and areas in the process of conversion. Values are calculated and provided every year 
by Eurostat based on data provided by the Member States. 

4.1.2  Candidate indicator(s) for the EU BDS dashboard 

There is no need of further indicator in addition to the one already published. 

4.2   Sub-target 8.2: uptake of agro-ecological practices 

Sub-target 8.2 - The uptake of agro-ecological practices is significantly increased 

4.2.1  Indicator(s) already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target, but the KCBD proposed 
in the 4th meeting of the EUBP MA on 27 September 2023 to use the forthcoming indicator  Trends 
in  the  uptake  of  agro-ecological  practices 31,  as  explained  in  section  4.2.2  below.  Following 
consultation  in  EUBP  MA,  the  indicator  is  now  in  the  pipeline  to  be  published  in  the  EU  BDS 
dashboard. A factsheet further describing this indicator was published in the annex of the science 
for policy brief prepared ahead of the last EUBP MA (Robuchon et al., 2023b) 

4.2.2  Candidate indicator(s) for the EU BDS dashboard 

Agroecology is increasingly recognised as an important vehicle of transformation for food systems, 
entering  both  the  discourse  and  agendas  of  various  international  organisations,  such  as  iPES-
Food (International  Panel  of  Experts  on  Sustainable  Food  Systems.  Gliessman  et  al  2018)  of  the 
Food  and  Agriculture  Organization  of  the  United  Nations  (FAO)32.  Agroecology  optimises  and 
maintains  biodiversity  above  and  below  ground  (a  wide  range  of  species  and  varieties,  genetic 
resources, locally adapted varieties/breeds, etc.) over time and space (at plot, farm, and landscape 
levels, CSIDE 2018). Agroecology is defined as a process of transformation. It is in fact a paradigm 
shift in which various production practices and techniques are integrated in a holistic manner, and 
coupled with transformations in all realms of food system, all aiming to reach a higher degree of 
respect for nature and enabling more equitable systems of human interaction (Peteers et al. 2021). 

As  for  agroecological  practices,  they  are  defined  as  agricultural  practices  aiming  to  produce 
significant  amounts  of  food,  which  valorise  in  the  best  way  ecological  processes  and  ecosystem 

30 https://dopa.jrc.ec.europa.eu/kcbd/dashboard/#Target%208 
31 See https://publications.jrc.ec.europa.eu/repository/handle/JRC135650 and factsheets. 
32 https://www.fao.org/agroecology/overview/overview10elements/en/ 

17 

 
 
                                                        
 
services in integrating them as fundamental elements in the development of the practices, and not 
simply relying on ordinary techniques, such as chemical fertiliser and synthetic pesticide application 
or technological solutions, such as genetically modified organisms (Wezel et al., 2014). Examples of 
agroecological practices mentioned in literature are: use of biofertilisers or natural pesticides, crop 
choice  and  rotations,  intercropping,  agroforestry,  reduced  tillage,  biological  control,  resource  and 
biodiversity  conservation  practices,  or  livestock  integration,  integration  of  semi-natural  landscape 
elements at field and farm or their management at landscape scale (Altieri 1995, 2002; Arrignon 
1987; Gliessman 1997, 2015; Wojtkowski 2006; Wezel et al. 2014). 

Although a clear definition of agro-ecological practices in the framework of the BDS is still under 
discussion,  interesting  perspectives  arise  from  recently  developed  frameworks,  to  monitor  the 
uptake of agro-ecological practices globally. 

The  LIFT  project33  recently  built  a  Farm  Typology  on  the  concept  of  agroecological  farming 
(Gliessman, 2015), considered by the authors of the typology  as the archetypical description of a 
fully  sustainable  farming  system  able  to  preserve  natural  resources  in  the  long  run  (Rega  et  al. 
2021). Farm typologies centred on ecological criteria can be a useful tool to inform and monitor EU 
policies,  including  the  EU  Biodiversity  Strategy  for  2030  (Rega  et  al.  2021).  To  this  end,  the  LIFT 
Farm  Typology  was  developed  with  the  aim  of  classifying  EU  farms  and  covering  the  entire 
spectrum  of  environmental  management,  from 
least 
environmentally friendly (Fig. 1).  

the  most  agroecological 

the 

to 

Figure 1. Schematic representation of the farming approaches of the LIFT farm typology. 

Source: Rega et al. 2021 

33 The LIFT: “Low-Input Farming and Territories   Integrating knowledge for improving ecosystem-based farming (ID: 
770747) is a research project funded under the European Union’s Horizon 2020 research programme “H2020-EU.3.2.1.1. 

topic “SFS-29-2017   Socio-eco-economics   socio-economics in ecological approaches  

18 

 
 
 
 
                                                        
 
 
The LIFT Farm typology was developed to classify farms according to their ecological performance, 
based on farm-level variables from the EU-wide Farm Accountancy Data Network (FADN)34. The use 
of FADN data to apply the typology guarantees that its application is not dependent on ad-hoc data 
collection  efforts,  but  on  a  survey  that  is  regularly  carried  out  at  the  EU  level,  following  a 
standardised  approach.  Selected  variables  are  used  to  assess  three  key  ecological  dimensions  of 
farming:  total  input  intensity;  degree  of  circularity  (reliance  on  own-produced  versus  external 
inputs); and avoidance of the use of specific inputs of concern for the environment and consumers. 
The  combination  of  these  aspects  is  considered  as  a  measure  of  the  farm  proximity  to  a  full 
agroecological approach. The proposed method is very flexible and allows calculation of the share 
of  farms  per  farming  approach  at  different  levels  (EU,  national,  biogeographic  region,  and  farm 
type). This enables comparisons across countries and/or accounting years, and provides a suitable 
tool  for  policy  evaluation.  The  application  of  the  typology  at  different  points  in  time  can  provide 
information on the extent to which established policy objectives have been achieved. 

After careful consideration, we have found this to be a most appropriate approach, both in terms of 
its sound scientific basis and the availability and reliability of the data on which it is based. Thus, an 
indicator  built  on  this  LIFT  methodology  has  been  selected  to  account  for  the  progress  made  by 
member states on sub-target 8.2: Trends in the uptake of agro-ecological  practices. The indicator 
will  present  a  dimensionless  score  of  the  degree  of  farms  uptaking  agroecological  practices  as 
defined by the LIFT farm typology. Calculations will be  made based on data from FADN now and 
FSDN (Farm Sustainability Data Network) when they become available, pending the outcome of the 
legislative process underway.  

Agroecology  Europe  (AEEU)35  has  recently  developed  a  new  system  of  agroecological  indicators 
named  OASIS,  the  Original  Agroecological  Survey  and  Indicator  System.  The  principles  of 
agroecology  defined  by  the  High-Level  Panel  of  Experts  (HLPE  2019),  were  chosen  as  the 
theoretical  foundation  of  the  OASIS  framework  (Peeters  et  al.  2021).  The  general  goal  of  OASIS 
consists in scaling agroecology 
 specific objective is assessing a farm s level 
of transition to agroecology (Peeters et al. 2021). OASIS is set up so that all the information can be 
gathered through interviews with farmers and farm workers, followed by a transect walk - without 
the need to apply expensive tests and analyses. OASIS is comprised of 5 dimensions among which 
op  rotation  design,  maximum  soil 
cover,  minimum  soil  tillage,  grass-based  livestock  systems,  crop  /  livestock  /  tree  integration,  and 
nutrient  cycle  optimization,  are  the  backbone  of  the  criteria  in  OASIS  used  for  assessing  level  of 
transition  considering  farming  practices.  Each  of  the  OASIS  dimensions  is  further  divided  into 
themes, which are then divided into criteria, each of which is comprised of one or several indicators. 
Value  5  represents  a  fully  developed  agroecological  stage  while  value  1  represents  the  opposite. 
The  mean  value  of  each  theme  is  calculated,  and  from there the  mean  value  of  each dimension. 
Since the data is collected via interviews, a large proportion of the indicators fall under the category 
of practice-based indicators (also called prescriptive/process indicators)36. This also means that each 
criterion  has  a  list  of  defined  best  (agroecological)  practices,  which  are  organized  in  hierarchical 
levels, and which form the reference values for the semi-quantitative interpretative scales, that are 
used by the assessors for the evaluation. 

OASIS can be  used to help  farmers in evaluating their progress through yearly evaluations. It can 
also be useful for other stakeholders such as decision-makers, advisors, researchers, students, and 

34 The European FADN is a pan-European statistical survey conducted each year on a sample of around 80,000 farms. It is 
the only survey that uses homogeneous and harmonised bookkeeping principles across the entire EU. It allows 
comparisons of farms across Member States, and over time. 
35 Agroecology Europe was supported by the FAO in defining a survey method and initiating a mapping of agroecological 
initiatives with the goal of capacity building, knowledge exchange, and promoting agroecology in Europe and Central Asia 
region to find new ways to assess agriculture from a more holistic perspective, beyond productivity. 

19 

 
 
                                                        
 
 
citizens.  It  may  be  used  for  mapping  the  state  of  development  of  agroecological  practices  in  a 
geographical  area  and  collecting  statistical  data.  As  such,  OASIS  outputs,  and  more  particularly 
 to monitor the uptake of at 
EU level. Further consideration will be needed to compare the capabilities of the LIFT farm typology 
and the OASIS framework to provide singular and/or complementary indicators for tracking progress 
toward Target 8.  

In  previous  policy  cycles,  High  Nature  Value  (HNV)  farmland  areas  and  features  have  been 
widely recognised as a valuable asset of European agricultural landscapes, providing highly varied 
living conditions for a wide range of species and thereby contributing to biodiversity. Although the 
HNV  farming  concept  lacks  some  scientific  stringency  in  its  definition,  it  is  well  established  with 
policy makers and conservationists in the EU (Strohbach et al., 2015). There are strong overlaps with 
the land sharing conservation concept and there are many policy instruments already in place for 
supporting  HNV  farming.   
areas  in  Europe  where  agriculture  is  a  major 
(usually the dominant) land use and where that agriculture supports, or is associated with, either a 
high species and habitat diversity or the presence of species of European conservation concern, or 
both (Beaufoy  et al.,  1994, EEA, 2004, Pedroli et al., 2007). HNV farmland assists to create highly 
permeable  landscape  to  species  individuals,  since  it  contains  a  high  proportion  of  semi-natural 
vegetation and/ or a mosaic of low-intensity farming plots. It is mostly commonly found in marginal 
land,  and  with  traditional  farming  practices.  Typical  HNV  farmland  areas  are  extensively  grazed 
uplands, alpine meadows and pasture, steppic areas in eastern and southern Europe,  and dehesas 
and  montados  in  Spain  and  Portugal.  Certain  more  intensively  farmed  areas  in  lowland  Western 
Europe  can  also  host  concentrations  of  species  of  particular  conservation  interest,  such  as 
migratory  waterfowl.  Notably,  a  wide  variety  of  approaches  and  combinations  of  methods  are 
currently being used across the EU to assess the extent of HNV farming, and the assessment of its 
condition  presents  a  considerable  challenge.  Due  to  the  variation  in  data  availability  across  the 
Member  States  and  regions  of  the  EU  and  the  range  of  physical  situations  (territory  size,  farm 
structure  and  systems,  predominant  land  and  habitat  types),  it  is  not  appropriate  to  impose  a 
common  methodology  for  the  assessment  of  HNV  farming.  Therefore,  a  unique  precise  definition 
embracing all types of HNV farming areas across Europe is not possible. Nor it is possible to derive 
an  aggregate  value  for  the  EU-27  of  the  extent  in  ha  of  the  HNV  area.  Data  on  the  percentage 
share of HNV farming in total UAA have been submitted by Member States to DG AGRI in the Rural 
Development  Programmes  (RDPs)  2014-2020.  Unfortunately,  the  RDPs  do  not  include  the 
methodologies used by MSs to define the values submitted. Thus, these data need a careful revision 
and  check  before  they  can  be  considered.  Nevertheless,  the  share  of  estimated high  nature 
value  (HNV)  farmland  in utilised  agricultural  area (UAA)  is  part  of  the  28  selected  agri-
environmental  indicators,  published  by  Eurostat37,  and  the  EEA  is  revising  the  HNV  farmland 
methodology by including information on grazing pressure, and an updated time series for the years 
2000  to  2018  is  expected38.  In  any  case,  due  to  the  nature  more  related  to  the  conservation  of 
farming systems than to the actual spread in the adoption of concrete practices, this indicator could 
potentially be seen as a valuable complementary indicator (or sub-indicator), but not as an indicator 
to properly monitor Target 8.  

Another  type  of  data  that  could  provide  partial  information  on  the  adoption  of  agro-ecological 
measures, in particular concerning diversification and alternative crop choice, is that concerning crop 

37 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmental_indicator_-
_High_Nature_Value_farmland 
38 https://www.eea.europa.eu/data-and-maps/data/high-nature-value-farmland-1 

20 

 
 
                                                        
 
diversity in agricultural landscapes. Crop diversification   the increase of crop diversity from field 
to national scale   is considered as an essential linchpin of the agroecological transition (Kremen et 
al.,  2012;  Lechenet  et  al.,  2016;  Renard  and  Tilman,  2021;  Wezel  et  al.,  2014).  Also,  it  has  been 
suggested that diversifying food crops would be an effective agricultural management strategy to 
aid  in  preserving  local  biodiversity  (Sirami  et  al.  2019;  Strobl  2021).  Sirami  et  al.  (2021)  have 
examined the effect of field size and crop diversity on biodiversity in agricultural landscapes. Their 
work shows that increasing the complexity of the crop mosaic produces a considerable (and largely 
under-exploited)  benefit  in  conserving  and  restoring  the  biodiversity  of  farm  landscapes,  while 
maintaining areas of land under agricultural production. As such, monitoring trend in crop diversity 
could  be  relevant to  monitor  the  uptake  of  such  agro-ecological  practices  knowing  their  expected 
positive impact on biodiversity.  

The JRC has recently published the EU crop map39, the first continental map of crops grown in the 
European Union at 10-
 and Verheggen et al. 2021). This map  combines 
Copernicus Sentinel-1 satellite observations and in-situ LUCAS 2018 Copernicus data using machine 
learning  and  cloud  computing.  This  could  replace  data  on  cropping  systems  from  EUROSTAT  and 
would  allow  to  have  a  more  up-to-date  indicator.  The  EU  crop  map  is  a  product  of  the  current 
momentum  towards  combining  Copernicus  Sentinel  fleet  observations,  extensive  in-situ  data  and 
cloud computing. The EU crop map provides a coherent and unique continental view with a spatial 
detail  that  is  relevant  for  crop  management  interventions  at  the  parcel  level.  As  such,  the 
information can be relevant for EU wide policy monitoring, e.g. by deriving indicators such as crop 
diversity  (Merlos  &  Hijmans,  2020).  The  EU  crop  map  product  is  expected  to  be  updated  every  3 
years, from 2018. More scientific and methodological development is needed to transform the first 
demonstration into an operational  in-season mapping service and to translate spatial information 
into sound environmental indicators.  Such perspectives also rely on the availability of high-quality 
(in-situ  and  satellite-based)  European  data  (such  as  those  provided  by  Copernicus)  and  ad-hoc 
European  cloud  computing  infrastructure.  However,  when  fully  operational,  this  product  could  be 
further  used  for  a  spatial  assessment  of  trend  in  arable  crop  diversity  relying  on  common 
measures of diversity such as the  true  diversity which quantifies the effective number of types of 
entities (Jost 2006; see Merlos & Hijmans, 2020 for an example) or to more ecological approaches 
targeting the provision of ecosystem services in agro-ecosystems as the functional crop diversity as 
measured by Shaak et al. (2023) for instance. 

39 EU Crop Diversity. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/f2c60a95-
d7dd-44c9-9b86-6eb2787190f7 

21 

 
 
                                                        
 
5 

Indicators for target 13 

Target 13 - The losses of nutrients from fertilisers are reduced by 50%, resulting in the reduction of 
the use of fertilisers by at least 20% [common target with F2F] 

5.1   Sub-target 13.1: 50% reduction in nutrient losses 

Sub-target 13.1 - The losses of nutrients from fertilisers are reduced by 50%  

5.1.1  Indicator(s) already in the EU BDS dashboard 

The indicator Nutrients in freshwater in Europe40  is already published in the EU BDS dashboard, 
but  it  in  a  fine-tuned  way  to  better  fit  the  reporting  needs.  Because  the  target  is  to  have  50  % 
reduction in nutrient losses and because this may vary according to nutrients and ecosystems, we 
have published 4 indicators stemming from Nutrients in freshwater, all expressed in % of change 
between 2000 and 2021. Values and full documentation can be found in the corresponding pages 
of the dashboard.  

Change in the concentration of nitrate in groundwater41 

Change in the concentration of nitrate in rivers42 

Change in the concentration of phosphate in rivers43 

Change in the concentration of total phosphorus in lakes44 

5.1.2  Candidate indicator(s) for the EU BDS dashboard 

Nutrients such as nitrogen (N) and phosphorus (P) are essential elements for the plants. They are 
often used as fertilisers in agriculture to guarantee higher yields and quality products. However, the 
increasing demand in food production has resulted in an increased production and use of fertilisers 
associated  with  considerable  inefficiencies,  leading  to  pollution  of  water,  air  and  soil,  affecting 
human  health  and  the  environment.  At  a  global  level,  N  and  P  surplus  into  the  environment  are 
already exceeding safe planetary boundaries (Steffen et al. 2015), representing a severe threat to 
nature as well as to the climate. 

According to a recent report from the European Commission concerning the water pollution caused 
by nitrates, about 81% of agricultural nitrogen leaks into water systems. Large inputs of nitrogen 
and  phosphorus  to  water  bodies  from  urban  areas,  industry  and  agricultural  areas  can  lead  to 
eutrophication (Torrent et al. 2007; EEA 2019). This causes ecological changes that can result in a 
loss of plant and animal species (reduction in ecological status) and can have negative impacts on 
the use of water for human consumption and other purposes.  

As  such,  the  monitoring  of  concentrations  of  nitrates  and  phosphates  in  European  aquatic 
ecosystems  (freshwaters,  coastal  and  marine  waters)  seems  mandatory  to  effectively  track  this 
sub-target  related  to  the  reduction  of  the  losses  of  nutrients  from  fertilisers.  The  indicator 
Nutrients  in  freshwater  in  Europe  fits the  target  and  is  immediately  available  from  EEA.  This 
indicator  shows  concentrations  of  phosphate  and  nitrate  in  European  rivers,  total  phosphorus  in 
lakes and nitrate in groundwater bodies, on an annual basis. The indicator can be used to illustrate 
geographical  variations  in  current  nutrient  concentrations  and  temporal  trends.  Annual  mean 

40 https://www.eea.europa.eu/ims/nutrients-in-freshwater-in-europe 
41 https://dopa.jrc.ec.europa.eu/kcbd/EUBDS2030-dashboard/1.2.8.13.1.1/?version=1 
42 https://dopa.jrc.ec.europa.eu/kcbd/EUBDS2030-dashboard/1.2.8.13.1.2/?version=1 
43 https://dopa.jrc.ec.europa.eu/kcbd/EUBDS2030-dashboard/1.2.8.13.1.3/?version=1 
44 https://dopa.jrc.ec.europa.eu/kcbd/EUBDS2030-dashboard/1.2.8.13.1.4/?version=1 

22 

 
 
                                                        
 
concentrations are used as a basis in the indicator analyses. Unless the country reports aggregated 
data, the aggregation to annual mean concentrations is done by the EEA. The indicator was selected 
following  consultation  in  EUBP  MA  in  April  2023  (Robuchon  et  al.,  2023a),  and  was  consequently 
published in the EU BDS dashboard as explained above.  

Another indicator, on the Nutrients in transitional, coastal and marine waters in Europe45, is 
also available from EEA. It reports the trends in dissolved inorganic nitrogen and orthophosphate in 
transitional,  coastal  and  marine  waters  in  Europe,  since  1980.  The  indicator  covers  the  different 
-East  Atlantic  Ocean),  with  trends 
updated  annually.  According  to  EEA,  the  availability  of  time  series  data  for  the  assessment  of 
nutrient  levels  varies  across  regional  seas,  although  the  availability  of  long  time  series  data  is 
clearly  important  for  assessing  the  effectiveness  of  measures  taken  to  achieve  good  status  for 
Europe s  seas.  Therefore,  EEA  highlights  that  efforts  should  be  made  to  increase  the  spatial 
coverage of the assessment and fill the gaps in some regional seas (e.g. Mediterranean and Black 
Seas).  These  two  state  indicators,  from  EEA,  can  help  to  track  the  results  of  nutrient  policies  in 
rivers, lakes, groundwater, and coastal waters. 

As  mentioned  above,  elevated  N  and  P  concentrations  in  surface  waters,  especially  rivers,  and  in 
coastal  and  marine  waters  contribute  to  the  phenomenon  of  eutrophication.  The  two  most  acute 
symptoms of eutrophication in waters are oxygen depletion and extreme growth of algal blooms, 
which can be harmful or noxious; they can destroy aquatic life in affected areas and affect human 
and  animal  health.  The  analysis  of  oxygen  concentrations  and  their  change  over  time  is  key  to 
assessing  progress  towards  better  marine  and  coastal  water  quality  in  line  with  EU  policy 
objectives46.  Reduced  oxygen  concentrations is  one  of  the  indicators  used  to measure  the indirect 
effects  of  nutrient  enrichment  and,  consequently,  eutrophication  (chlorophyll-a  concentrations  in 
surface  waters  is  another).  In  this  regard,  the  indicator  Biochemical  oxygen  demand  in  rivers 
(SDG_06_30),  available  from  Eurostat  (and  based  on  data  from  EEA  Waterbase  database  on  the 
status  and  quality  of  Europe's  rivers)  could  fit  the  target.  The  indicator  is  meant  to  give  a 
representative  overview  of  oxygenation  availability  conditions  in  European  rivers.  Biochemical 
oxygen demand (BOD) is used to measure water quality. High values of BOD5 are usually a sign of 
organic pollution, which affects the water quality. The full interpretation of this indicator should be 
taken with caution though, as the organic pollution causing the loss of oxygen in rivers may come 
from  diffuse  runoff  from  agriculture,  as  well  as  from  wastewater,  both  municipal  and  industrial. 
Annual  mean  concentrations  are  used  as  a  basis  in  the  indicator  analyses.  Unless  the  country 
reports aggregated  data, the aggregation to annual  mean concentrations is done by the EEA. The 
datasets for rivers include almost all countries within the EEA, but the time coverage varies from 
country to country, both through the analysed period and within the year for which the aggregated 
mean value is provided. Based on the Eurostat metadata description, for time series analyses, only 
complete  series  after  inter/extrapolation  are  used. As  measuring  stations  change  over  time,  this 
leads  to  a  recalculation  of  the  whole  time  series  with  each  update. Complete  time  series  2000 
2020 are based on a total of 764 measuring stations (717 within 18 EU Member States) distributed 
over  the  following  countries  (number  of  stations  in  parentheses):  Belgium  (36),  Bulgaria  (74), 
Czechia (22), Estonia (35), Ireland (14), Spain (179), Croatia (23), Italy (44), Cyprus (4), Latvia (16), 
Lithuania  (22),  Austria  (46),  Poland  (90),  Romania  (88),  Slovenia  (10),  Slovakia  (8),  Finland  (5), 
Sweden (1), North Macedonia (8), Albania (5) and Serbia (34). The aggregate for EU_V includes data 
for 18 EU Member States (Belgium, Bulgaria, Czechia, Estonia, Ireland, Spain, Croatia, Italy, Cyprus, 
Latvia, Lithuania, Austria, Poland, Romania, Slovenia, Slovakia, Finland and Sweden). 

45 https://www.eea.europa.eu/ims/nutrients-in-transitional-coastal-and 
46 The Water Framework Directive and the Marine Water Framework Directive aim to achieve ‘good ecological status ’and 
‘good environmental status ’of Europe’s waters respectively  

23 

 
 
 
                                                        
 
In  complement  to  this  indicator  of  BOD  in  rivers,  EEA  is  producing  an  indicator  on  Oxygen 
concentrations in coastal and marine waters surrounding Europe47, which may also fit the 
target.  This  indicator  illustrates  the  geographical  distribution  and  trends  in  summer-autumn 
concentrations  of  oxygen  in  the  near-bottom  waters  of  Europe s  regional  seas.  Reduced  oxygen 
concentrations  are  used  as  an  indicator  of  the  indirect  effects  of  nutrient  enrichment  and, 
consequently, eutrophication. The Occurrence of reduced oxygen concentrations is provided as 
the average over the time period since 2009; whereas the Trends in oxygen concentrations by 
concentration group, which aims to inform if the state of Europe s seas is improving, is provided 
over a time period starting in 1989. As for the Nutrients indicator, it covers the different Europe s 
seas (Baltic Sea, Black Sea, Mediterranean Sea, North-East Atlantic Ocean), and is updated once a 
year. The Trends indicator by itself may be sufficient to inform on the eutrophication trend. 

Regarding  the  monitoring  of  algae  blooms,  the  indicator  Marine  waters  affected  by 
eutrophication48  (also  part  of  the  EU  Sustainable Development Goals indicator  set;  SDG  14)  fits 
the  target  and  is  available  from  Eurostat.  The  data  are  from  the  Copernicus  Marine  Environment 
Monitoring  Service  (CMEMS)  which  calculates  the  indicator  from  satellite  imagery.  The  indicator 
shows  the  share  of  eutrophic  marine  waters  in  the  Exclusive  Economic  Zone  (EEZ).  An  area  is 
classified  as  eutrophic  if  for  more  than  25  %  of  the  observation  days  of  a  given  year  the 
chlorophyll concentrations as a proxy for eutrophication are above the 90th percentile of the 1998
2017 reference base line.  

As  a  complement  to  the  marine  eutrophication  indicator,  an  indicator  of  eutrophication  in 
freshwater  ecosystems  could  be  relevant.  In  this  regard,  the  global  component  of  the  Copernicus 
Land  Monitoring  Service  provides  up-to-date,  reliable  and  accurate  data  on  medium  and  large 
freshwater lakes around the world. More particularly, among the three water quality parameters for 
which the Lake Water Quality product provide semi-continuous observations, is the trophic state 
index  that indicates  the lake’s  productivity  in  terms  of  phytoplankton  and  indirectly,  over  a  long-
time  scale,  reflects  the  eutrophication  status  of  a  water  body  (Dodds  2007).  Discussions  with 
Copernicus LM service is necessary to determine the availability (or perspectives) of a similar index 
to the marine eutrophication indicator, applied to freshwater lakes.  

Also,  other  interesting  perspectives  may  come  from  the  CyanoLakes  RealTime  service49,  a 
satellite-data  based  online  service  providing  early  warnings  of  the  health  risks  caused  by  toxic 
cyanobacteria  blooms (distinguishing  them  from  algae  blooms)  developed by Mark  Matthews,  the 
2014 Copernicus Masters Ideas Challenge winner. 

Apart from the above mentioned specific indicators related to either oxygen concentration or algae 
bloom in marine and coastal waters, the EEA has produced a synthetic indicator on Eutrophication 
status as part of the recent EEA assessment  Nutrient enrichment and eutrophication in Europe s 
seas  (EEA Report No 14/2019). This assessment represented a first attempt to map eutrophication 
'problem areas' and 'non-problem areas' at the scale of Europe's seas, while also exploring whether 
Europe has reversed trends with respect to eutrophication. Eutrophication status was evaluated by 
indicators within three criteria: nutrient levels, direct effects and indirect effects of eutrophication. It 
is based on HEAT+ classification: The HELCOM Eutrophication Assessment Tool (Developed for the 
pan-European  assessment).  The  temporal  coverage  of  the  published  results  is  2006-2016.  Data 
coverage  varies  considerably  between  regions,  sometimes  due  to issues  with  data  accessibility.  A 
direct comparison between regions is therefore complex. At the end of the assessment report50, EEA 
updated at regular intervals with more and new 

47 https://www.eea.europa.eu/ims/oxygen-concentrations-in-coastal-and 
48 https://ec.europa.eu/eurostat/databrowser/view/sdg_14_60/default/table?lang=en 
49 https://www.cyanolakes.com/ 
50 https://www.eea.europa.eu/publications/nutrient-enrichment-and-eutrophication-in 

24 

 
 
                                                        
 
data,  leading  to  a  better  understanding  of  how  to  achieve  more  non-problem  areas  in  terms  of 

the perspectives that will be given to this work.  

It was mentioned above that the use of mineral fertilisers is affecting not only aquatic ecosystems, 
but  also  soil  ecosystems.  In  this  regard,  ESDAC  (Panagos  et  al.  2022)  has  produced  European 
datasets  on  chemical  properties  of  soils 
including  nutrients  (Phosphorus,  Nitrogen, 
Potassium).  In  September  2019,  ESDAC  has  concluded  the  development  of  the  LUCAS  Chemical 
parameters  [pH,  pH  CaCl,  Cation  Exchange  Capacity  (CEC),  Calcium  carbonates  (CaCO3),  C:N  ratio, 
Nitrogen  (N),  Phosphorus 
indeed 
produced chemical soil properties datasets using LUCAS topsoil database and advanced digital soil 
mapping methodologies. In detail, the authors proposed a soil mapping model for interpolating the 
22,000  surveyed  points  in  LUCAS  for  chemical  soil  properties  at  continental  scale.  They  also 
developed  datasets  for soil  chemical  properties,  among  which  total  phosphorus,  potassium  and 
nitrogen, plus derived products based on soil organic carbon and nitrogen (e.g. C:N ratio), covering 
EU-26 (not including Croatia and Cyprus). The LUCAS survey collects harmonised data on changes in 
land  cover  and  the  state  of  land  use  for  the  European  Union  (EU),  at  repeated  locations,  every  3 
years (since 2009).  

(K)].  Ballabio  et  al. 

(P)  and  Potassium 

(2019),  have 

Apart from nutrient concentrations, nutrient balances also provide information about environmental 
pressures.  A  nutrient  deficit  (negative  value)  indicates  declining  soil  fertility.  A  nutrient  surplus 
(positive data) indicates a risk of polluting soil, water and air. The nutrient balance is defined as 
the difference between the nutrient inputs entering a farming system (mainly livestock manure and 
fertilisers) and the nutrient outputs leaving the system (the uptake of nutrients for crop and pasture 
production).  An  indicator  on  nutrient  balance  can  help  track  whether  agriculture  has  balanced  its 
inputs and outputs of nutrients, which can be relevant in the frame of the sub-target B10.1 which 
aims at reducing losses of nutrients from fertilisers by 50%. 

In this regard, the indicator  Gross nutrient balance on agricultural land by nutrient, fits the 
target  and  is  available  from  Eurostat.  The  indicator  measures  the  potential  surplus  or  deficit  of 
nitrogen  and  phosphorous  in  agricultural  soils.  A  lack  of  nitrogen  or  phosphorous  may  lead  to 
degradation in soil fertility, while an excess may cause surface and groundwater (including drinking 
water)  pollution  and  eutrophication.  Ideally,  the  input/output  of  nutrition  to  the  soil  should  be 
balanced. The presented time series (including EU aggregates) starts in 2004, and are updated very 
two  year,  but  results  are  disseminated  every  year  (calendar  year).  Data  are  presented  for  all  EU 
Member States plus the United Kingdom, Norway and Switzerland. 

The Organisation for Economic Co-operation and Development (OECD) is also producing a Nutrient 
Balance indicator (OECD 2023), not solely focusing on agricultural soils. This dataset provides the 

indicator  is  presented  for  the  two  main  nutrients,  nitrogen  and  phosphorus,  and  is  measured  in 
tonnes of nutrient and in kilograms of nutrient per hectare of agricultural land. However, temporal 
coverage is uncertain. Temporal period differ among countries, with reference year starting in the 
nineties for most countries to 2004 for the most recent (Estonia). Publication year for latest data 
available  varies  too.  As  for  the  indicator  at  EU  level,  time  period  only  covers  2000-2014.  No 
additional information is provided on the update of this indicator. 

5.2   Sub-target 13.2: 20% reduction in fertilisers use 

Sub-target 13.2 - The use of fertilisers is reduced by at least 20%  

5.2.1  Indicators already in the EU BDS dashboard 

There is no indicator in the EU BDS dashboard yet to inform this sub-target. 

25 

 
 
5.2.2  Candidate indicators for the EU BDS dashboard 

To  monitor  this  specific  sub-target,  EUROSTAT  is  already  producing  an  indicator  reporting  the 
Consumption  of  inorganic  fertilizers51  at  EU  level,  ready  to  be  published  in  the  EU  BDS 
dashboard  (which  is  also  part  of  the  28  Agri-environmental  indicators  -  mineral  fertiliser 
consumption).  This  data  set  contains  data  on  the  total  consumption  of  inorganic  (or  mineral) 
fertilisers  -  in  tonnes  of nitrogen  (N)  and  phosphorus  (P)  -  reported  by  countries  or  estimated  by 
Eurostat (data for some MS have been estimated in some cases up to 2018). Most countries use 
data based on sales, production and trade statistics or farm surveys. They report annual data on the 
use  of  inorganic  fertilisers  in  agriculture  at  NUTS0 level  (country  level). Some  countries further 
report  data  at NUTS  2  level  and  one 
  Poland  -  at  NUTS  1  level.  This  indicator  has  already  been 
discussed in EUBP MA (Robuchon et al., 2023a), but it was not selected following the comment of 
one Member State representative arguing that an indicator representing organic fertilizer (manure) 
would be a better fit. A factsheet further describing this indicator was published as an annex to the 
science for policy brief prepared ahead of the 3rd EUBP MA meeting (Robuchon et al., 2023a). 

The LIFT Farm Typology, presented above, may also be useful to track progress in achieving this 
sub-target. Indeed, one potential application of the typology is the identification and evaluation of 
spatial and temporal trends in overall input intensity of farms. The LIFT Farm typology could thus be 
used as a monitoring tool to assess to what extent the ambitions of EU-BDS (and F2F) strategies - 
namely  to  reduce  the  use  of  mineral  fertilisers  and  pesticides  -  are  being  achieved.  More 
specifically,  if  a  reduction  target  of  20%  in  the  use  of  mineral  fertilisers  and  50%  in  the  use  of 
pesticides are achieved, an increase in the share of low‐input and integrated/circular farms would 
be captured by the LIFT typology. 

As  part  of  the new  common  agricultural  policy  (CAP)  proposals for  2021-27,  a  new  tool  is  being 
developed to  help  farmers  manage  the  use  of  nutrients  on  their  farm.  The  Farm  Sustainability 
Tool  for  Nutrients  (FaST)52,  proposed  in  the  framework  of  the  Good  Agricultural  and 
Environmental Conditions (GAECs), aims to facilitate a sustainable use of fertilisers for all farmers 
in  the  EU  while  boosting  the  digitisation  of  the  agricultural  sector.  FaST  will  illustrate  trends  and 
generate  information  that  will  offer  new  insights  for  public  actors,  which  can  help  in  policy 
development and decision making. 

51 https://ec.europa.eu/eurostat/cache/metadata/en/aei_fm_usefert_esms.htm 
52 https://fastplatform.eu/ 

26 

 
 
 
 
                                                        
 
6  Conclusions  

This  work  focused  on  identifying  indicators  for  the  4  targets  of  the  EU  Biodiversity  Strategy  for 
2030  related  to  agro-ecosystems.  It  already  led  to  concrete  outputs  as,  out  of  the  24  indicators 
presented in this report, 4 are already published in the dashboard, and  2 are in the pipeline to be 
published  following  consultation  of  the  expert  group  on  Monitoring  and  Assessment  of  the  EU 
Biodiversity  Platform,  these  6  indicators  feeding  the  monitoring  of  5  out  of  10  sub-targets.  This 
report also paves the way to further fill the gaps in the dashboard by highlighting possibilities for 
the development or selection of further indicators - including those used in other policy monitoring 
frameworks  -  based  on  data  that  are  either  existing  or  currently  being  collected.  Therefore,  it 
represents  a  significant  step  towards  tracking  progress  of  the  EU  BDS  in  relation  to  agro-
ecosystems,  while  ensuring  consistency  in  indicator  use  with  other  related  policy  monitoring 
frameworks such as those of the CAP and the F2F.  

Yet, to track progress towards the EU BDS (sub-)targets related to the restoration of degraded and 
carbon-rich ecosystems, the improvement of species and habitats trends and status, as well as the 
reduction  of  fertilisers  use,  the  candidate  indicators  identified  in  this  report  still  need  to  be 
operationalised.  Before  these  indicators  can  be  proposed  to  the  expert  group  on  Monitoring  and 
Assessment  of  the  EU  Biodiversity  Platform,  and  eventually  selected  for  publication  in  the 
dashboard, they must first be  fully  developed. Over the coming months and years,  efforts should 
therefore  focus  on  such  development  and  operationalisation  to  ensure  that  the  dashboard 
comprises  an  indicator  set  able  to  track  progress  for  all  EU  BDS  (sub-)targets  related  to  agro-
ecosystems. 

27 

 
 
 
 
References  

Aertsens,  J.,  De  Nocker,  L.,  &  Gobin,  A.  (2013).  Valuing  the  carbon  sequestration  potential  for 
European agriculture. Land Use Policy, 31, 584-594. 

Altieri MA (1995) Agroecology: the science of sustainable agriculture. Westview Press, Boulder 

Altieri  MA  (2002)  Agroecology:  the  science  of  natural  resource  management  for  poor  farmers  in 
marginal environments. Agric Ecosyst Environ 93:1 24 

Altieri, M. A., & Nicholls, C. I. (2012). Agroecology scaling up for food sovereignty and resiliency. In 
Sustainable agriculture reviews, 1-29. Springer, Dordrecht. 

Amelung, W., Bossio, D., de Vries, W. et al. Towards a global-scale soil climate mitigation strategy. 
Nat Commun 11, 5427 (2020). https://doi.org/10.1038/s41467-020-18887-7 

Andersen,  E.,  Baldock,  D.,  Brouwer,  F.  M.,  Elbersen,  B.  S.,  Godeschalk,  F.  E.,  Nieuwenhuizen,  W.,  & 
Hennekens, S. M. (2004) Developing a high nature value farming area indicator. 

Ballabio, C., Lugato, E., Fernández-Ugalde, O., Orgiazzi, A., Jones, A., Borrelli, P., Montanarella, L. and 
Panagos, P., (2019). Mapping LUCAS topsoil chemical properties at European scale using Gaussian 
process regression. Geoderma, 355: 113912. 

Beaufoy, G., Baldock, D., Clarke, J. (1994) The Nature of Farming 
in Nine European Countries. IEEP, London 

 Low Intensity Farming Systems 

Ben Phalan et al., Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land 
Sparing Compared. Science 333, 1289-1291(2011). DOI:10.1126/science.1208742 

Brussaard,  L.,  Caron,  P.,  Campbell,  B.,  Lipper,  L.,  Mainka,  S.,  Rabbinge,  R.,  &  Pulleman,  M.  (2010). 
Reconciling biodiversity conservation and food security: scientific challenges for a new agriculture. 
Current opinion in Environmental sustainability, 2(1-2), 34-42. 

Brussaard,  Lijbert,  et  al.  "Reconciling  biodiversity  conservation  and  food  security:  scientific 
challenges for a new agriculture." Current opinion in Environmental sustainability 2.1-2 (2010): 34-
42. 

Butchart  S.  H.  M.,  Akcakaya,  H.  R.,  Kennedy,  E.,  &  Hilton-Taylor,  C.  (2006).  Biodiversity  indicators 
based on trends in conservation status: strengths of the IUCN Red List Index. Conservation Biology, 
20(2), 579-581. 

Campbell,  A.  et  al.  (2022).  A  review  of  carbon  monitoring  in  wet  carbon  systems  using  remote 
sensing. Environmental Research Letters. 17. 10.1088/1748-9326/ac4d4d.  

Castaldi,  F.,  Hueni,  A.,  Chabrillat,  S.,  Ward,  K.,  Buttafuoco,  G.,  Bomans,  B.,  Vreys,  K.,  Brell,  M.,  van 
Wesemael,  B.  (2019).  Evaluating  the  capability  of  the  Sentinel  2  data  for  soil  organic  carbon 
prediction in croplands. ISPRS J. Photogramm. Remote Sens. 147, 267 282. 

CIDSE (2018). The Principles of Agroecology. (2018). 

Czúcz  B,  Baruth  B,  Terres  JM,  Hagyó  A,  Gallego  J,  Angileri  V,  Nocita  M,  Perez  Soba  M,  Koeble  R, 
Paracchini  ML.  (2022).  Classification  and  quantification  of  Landscape  Features  across  the  EU:  A 
brief review of existing definitions, typologies, and data sources for quantification. EUR 30997 EN, 
Publications Office of the European Union, Luxembourg, ISBN 978-92-76-47818-8 

D`andrimont,  R.,  Verhegghen,  A.,  Lemoine,  G.,  Kempeneers,  P.,  Meroni,  M.  and  Van  Der  Velde,  M., 
(2021) From parcel to continental scale - A first European crop type map based on Sentinel-1 and 
LUCAS Copernicus in-situ observations, Remote Sensing Of Environment, ISSN 0034-4257 (online), 
112708 (266), 2021, p. 112708, JRC125369. 

D`andrimont,  R.,  Skoien,  J.,  Koble,  R.,  Yordanov,  M.  and  Terres,  J.,  EU  Landscape  Feature  indicator 
fact sheet, European Commission, 2023, JRC136069. 

28 

 
de Brogniez, D., Ballabio, C., Stevens, A., Jones, R.J.A., Montanarella, L. and van Wesemael, B. (2015). 
A map of the topsoil organic carbon content of Europe generated by a generalized additive model. 
Eur J Soil Sci, 66: 121-134. https://doi.org/10.1111/ejss.12193  

De Rosa, D., Ballabio, C., Lugato, E., Fasiolo, M., Jones, A., & Panagos, P. (2024). Soil organic carbon 
stocks in European croplands and grasslands: How much have we lost in the past decade?.  Global 
Change Biology, 30(1), e16992. 

DG  Environment  (2017).  Reporting  under  Article  12  of  the  Birds  Directive:  Explanatory  notes  and 
guidelines for the period 2013 2018, Brussles. http://cdr.eionet.europa.eu/help/birds_art12 

Dodds, W. K. (2007). Trophic state, eutrophication and nutrient criteria in streams. Trends in ecology 
& evolution, 22(12), 669-676. 

EEA  (2004)  High  Nature  Value  Farmland 
European Environment Agency, Copenhagen, pp. 32. 

  Characteristics,  Trends  and  Policy  Challenges.  EEA, 

EEA,  (2019),  'Nutr
Agency', 
accessed January 12, 2023. 

  European  Environment 
(https://www.eea.europa.eu/publications/nutrient-enrichment-and-eutrophication-in) 

EEA, 2020. State of nature in the EU. Results from reporting under the nature directives 2013-2018. 
EEA Report No 10/2020. Available at https://www.eea.europa.eu/publications/state-of-nature-in-the-
eu-2020/at_download/file 

Eionet  (2020).  Article  12  web  tool:  population  status  and  trends  of  birds  under  Article  12  of  the 
Birds Directive. European Environment Agency. https://nature-art12.eionet.europa.eu/article12/ 

European Commission (2013) Regulation (EU) No 1305/2013 on support for rural development by 
the  European  Agricultural  Fund  for  Rural  Development  (EAFRD)  and  repealing  Council  Regulation 
(EC) No 1698/2005. Off. J. Eur. Union, 1698, pp. 487-548 

 Guidance 
European Commission, Directorate-General  for Environment, Farming for Natura 2000 
on  how  to  support  Natura  2000  farming  systems  to  achieve  conservation  objectives,  based  on 
Member 
2018, 
practice 
https://data.europa.eu/doi/10.2779/85823 

experiences, 

Publications 

Office, 

States 

good 

European  Union  (2009).  Directive  2009/147/EC  of  the  European  parliament  and  of  the  council. 
http://eurlex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009L0147&from=EN 

Fahrig L, Baudry J, Brotons L, Burel FG, Crist TO, et al. (2011)  Functional  landscape  heterogeneity 
and animal biodiversity in agricultural landscapes. Ecol Lett 14: 101 112. 

Fahrig,  L.  &  Nuttle,  W.K.  (2005).  Population  ecology  in  spatially  heterogeneous  environments.  In: 
Ecosystem  Function  in  Heterogeneous  Landscapes  (eds  Lovett,  G.M.,  Jones,  C.G.,  Turner,  M.G.  & 
Weathers, K.C.). Springer-Verlag, New York, pp. 95 118. 

Gliessman S. (2015). Agroecology: the Ecology of Sustainable Food Systems. 3rd Edn. Boca Raton, 
FL: CRC Press, Taylor & Francis Group. doi: 10.1201/b17881. 

Gliessman SR (1997) Agroecology: ecological processes in sustainable agriculture. CRC Press, Boca 
Raton. 

Gliessman  S.,  Jacobs  N.,  Clément  C.  &  Grabs  J  (2018).  Breaking  away  from  industrial  food  and 
farming systems: Seven case studies of agroecological transition. International Panel of Experts on 
Sustainable Food Systems (IPES-Food): Brussels, Belgium. 94-5. 

Guyomard, H.,  Bureau  J.-C.  et  al.  (2020),  Research  for  AGRI  Committee 

  The Green Deal  and  the 

European Parliament, Policy Department for Structural and Cohesion Policies, Brussels.  

Halada,  L.,  Evans,  D.,  Romão,  C.,  &  Petersen,  J.  E.  (2011).  Which  habitats  of  European  importance 
depend on agricultural practices?. Biodiversity and Conservation, 20, 2365-2378. 

29 

 
Hart, K., Allen, B., Keenleyside, C., Nanni, S., Maréchal, A., Paquel, K., Nesbit, M., Ziemann, J.,  (2017). 
Research for agri committee - the consequences of climate change for EU agriculture. Follow-Up To 
the COP21 - Un Paris Climate Change Conference. https://doi.org/10.2861/295025 

Henle,  K.,  Alard,  D.,  Clitherow,  J.,  Cobb,  P.,  Firbank,  L.,  Kull,  T.,  &  Young,  J.  (2008).  Identifying  and 
managing  the  conflicts  between  agriculture  and  biodiversity  conservation  in  Europe A  review. 
Agriculture, ecosystems & environment, 124(1-2), 60-71. 

Henle,  Klaus,  et  al.  "Identifying  and  managing  the  conflicts  between  agriculture  and  biodiversity 
conservation in Europe A review." Agriculture, ecosystems & environment 124.1-2 (2008): 60-71. 

High  Level  Panel  of  Experts  on  Food  Security  and  Nutrition  (HLPE).  Agroecological  and  other 
innovative approaches. (2019). 

HLPE (. 2019). Agroecological and other innovative approaches for sustainable agriculture and food 
systems that enhance  food  security  and  nutrition.  A  report  by  the  High  Level Panel  of  Experts  on 
Food Security and Nutrition of the Committee on World Food Security, Rome. 

Hou,  D.  Sustainable  soil  management  and  climate  change  mitigation.  Soil  Use  Manage.  2021;  37: 
220 223. https://doi.org/10.1111/sum.12718 

Jones,  A.,  Fernandez  Ugalde,  O.,  Scarpa,  S.  and  Eiselt,  B.,  LUCAS  Soil  2022,  EUR  30331  EN, 
Publications  Office  of  the  European  Union,  Luxembourg,  2021,  ISBN  978-92-76-21079-5, 
doi:10.2760/74624, JRC121253. 

Jost, L. (2006) Entropy and diversity Oikos 113 363e375 

Kisel  Y,  McInnes  L,  Toomey  NH,  Orme  CDL  (2011)  How  diversification  rates  and  diversity  limits 
combine to create large-scale species-area relationships. Philos Trans R Soc B 366: 2514 2525. 

Merlos, F.A.  & Hijmans, R.J. (2020). The scale dependency of spatial crop species diversity and its 
relation to temporal diversity Proc. Natl. Acad. Sci. U.S.A., 117, pp. 26176-26182 

OECD  (2023),  Nutrient  balance  (indicator).  doi:  10.1787/82add6a9-en  (Accessed  on  12  December 
2022) 

Olofsson,  P.,  Foody,  G.M.,  Herold,  M.,  Stehman,  S.V.,  Woodcock,  C.E.,  &  Wulder,  M.A.  (2014).  Good 
practices  for  estimating  area  and  assessing  accuracy  of  land  change.  Remote  Sensing  of 
Environment 148: 42 - 57 

Panagos, P. and De Rosa, D., Indicators to support the soil perspectives of the Common Agricultural 
Policy  (CAP),  EUR  31410  EN,  Publications  Office  of  the  European  Union,  Luxembourg,  2023,  ISBN 
978-92-76-99010-9, doi:10.2760/235604, JRC132234. 

Panagos, P., Van Liedekerke, M., Borrelli, P., Köninger, J., Ballabio, C., Orgiazzi, A., Lugato, E., Liakos, L., 
Hervas,  J.,  Jones,  A.    Montanarella,  L.  (2022).  European  Soil  Data  Centre  2.0:  Soil  data  and 
knowledge  in  support  of  the  EU  policies.  European  Journal  of  Soil  Science,  73(6),  e13315.  DOI: 
10.1111/ejss.13315 

Pedroli,  B.,  Van  Doorn,  A.,  De  Blust,  G.,  Paracchini,  M.  L.,  Wascher,  D.,  &  Bunce,  F.  (2007)  E
living landscapes. Essays on exploring our identity in the countryside. Landscape Europe/KNNV, The 
Netherlands. 

Indicator  System.  A  simple  and  comprehensive  system  for  agroecological  transition  assessment. 
Agroecology Europe, Brussels: 82 pages 

Phalan, B., Onial, M., Balmford, A., & Green, R. E. (2011). Reconciling food production and biodiversity 
conservation: land sharing and land sparing compared. Science, 333(6047), 1289-1291. 

and  recommendations  on  data  needs.  LIFT  Project  Deliverable  D1.4.  Available  online  at: 
https://zenodo.org/record/5508222#.Y3IIZoSZPIU 

30 

 
 
Robuchon, M., Liquete Garcia, M.D.C., Dubois, G., Petri, E., Braz, P., Dalla Costa, S., Borg, J., Grizzetti, B., 
Paracchini, M. and Visconti, P., State of play  and future steps for the EU Biodiversity Strategy (EU 
BDS) 
JRC133059, 
https://publications.jrc.ec.europa.eu/repository/handle/JRC133059.  

Commission, 

dashboard, 

European 

2023a, 

Robuchon, M., Liquete Garcia, M.D.C., Neuville, A., Borg, J., Dalla Costa, S., Osipeva, E., Olsen, J., Baritz, 
R.W.,  Gebers,  K.,        Schoen,  I.,  Gras,  M., 
Addamo, A.M., Costello, M.J. State of play and future steps for the EU Biodiversity Strategy (EU BDS) 
JRC135650, 
  November  2023  update,  European  Commission,  2023b, 
dashboard 
https://publications.jrc.ec.europa.eu/repository/handle/JRC135650. 

Schaak, H., Bommarco, R., Hansson, H., Kuns, B., & Nilsson, P. (2023). Long-term trends in functional 
crop diversity across Swedish farms. Agriculture, Ecosystems & Environment, 343, 108269. 

Skidmore, A.K., Coops, N.C., Neinavaz, E. et al. (2021). Priority list of biodiversity metrics to observe 
from space. Nat Ecol Evol 5, 896 906 

Steffen, W., Richardson, K., Rockström, J., Cornell, S. E., Fetzer, I., Bennett, E. M., & Sörlin, S. (2015). 
Planetary  boundaries:  Guiding  human  development  on  a  changing  planet.  Science,  347(6223), 
1259855. 

Strohbach,  M.  W.,  Kohler,  M.  L.,  Dauber,  J.,  &  Klimek,  S.  (2015)  High  Nature  Value  farming:  From 
557 563. 
Ecological 
indication 
https://doi.org/10.1016/j.ecolind.2015.05.021 

conservation. 

Indicators, 

57, 

to 

Tews JU, Grimm BV, Tielbörger K, Wichmann MC, Schwager M, et al. (2004) Animal species diversity 
driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31: 79
92. 

Tews JU, Grimm BV, Tielbörger K, Wichmann MC, Schwager M, et al. (2004) Animal species diversity 
driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31: 79
92. 

Torralba, M., Fagerholm, N., Burgess, P.J., Moreno, G., Plieninger, T., 2016. Do European agroforestry 
systems enhance biodiversity and ecosystem services? A meta-analysis. Agric. Ecosyst. Environ. 230, 
150 161. https://doi.org/10.1016/j.agee.2016.06.002.  

Torrent,  J.,  Barberis,  E.  and  Gil-Sotres,  F.  (2007),  Agriculture  as  a  source  of  phosphorus  for 
eutrophication in southern Europe. Soil Use and Management, 23: 25-35. 

Uthes, S., Kelly E., König, H. J. (2020). Farm-level indicators for crop and landscape diversity derived 
from agricultural beneficiaries data. Ecological Indicators, 108. 

Van Swaay, C. A. M., & van Strien, A. (2005). Using butterfly monitoring data to develop a European 
grassland butterfly indicator. Studies on the ecology and conservation of Butterflies in Europe. Vol 1: 
General concepts and case studies, 106-108. 

Van Swaay, C., Warren, M., & Loïs, G. (2006). Biotope use and trends of European butterflies. Journal 
of Insect Conservation, 10(2), 189-209. 

Van  Swaay,  C.A.M.,  Dennis,  E.B.,  Schmucki,  R.,  Sevilleja,  C.G.,  Balalaikins,  M.,  Botham,  M.,  Bourn,  N., 
Brereton, T., Cancela, J.P., Carlisle, B., Chambers, P., Collins, S., Dopagne, C., Escobés, R., Feldmann, R., 
Fernández-García,  J.  M.,  Fontaine,  B.,  Gracianteparaluceta,  A.,  Harrower,  C.,  Harpke,  A.,  Heliölä,  J., 
Komac, B., Kühn, E., Lang, A., Maes, D., Mestdagh, X., Middlebrook, I., Monasterio, Y., Munguira, M.L., 
Murray, T.E., Musche, M., Õunap, E., Paramo, F., Pettersson, L.B,, Piqueray, J., Settele, J., Stefanescu, 
ly 
Indicator  for  Grassland  species:  1990-2017:  Technical  Report.  Butterfly  Conservation  Europe  & 
ABLE/eBMS (www.butterfly-monitoring.net) 

31 

 
Viti  MM,  Gkimtsas  G,  Liquete  C,  Dubois  G,  Dalla  Costa  S,  Borg  J,  Teller  A,  Hauser  R,  Robuchon  M 
(2024). Introducing the monitoring tools of the EU Biodiversity Strategy for 2030. Preprint available 
at SSRN: https://ssrn.com/abstract=4777389 or http://dx.doi.org/10.2139/ssrn.4777389   

Wezel,  A.,  Casagrande,  M.,  Celette,  F.,  Vian,  J.-F.,  Ferrer,  A.,  &  Peigné,  J.  (2014).  Agroecological 
practices for sustainable agriculture. A review. Agronomy for Sustainable Development, 34(1), 1 20. 
https://doi.org/10.1007/s13593-013-0180-7 

Wojtkowski  PA  (2006).  Introduction  to  agroecology.  Principles  and  practices.  Haworth  Press, 
Binghampton 

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List of abbreviations  

BOD 

CAP 

Biochemical oxygen demand 

EU Common Agricultural Policy 

CMEF 

CAP Common Monitoring and Evaluation Framework 

CMEMS  Copernicus Marine Environment Monitoring Service 

DG ENV 

-General for Environment 

eBMS 

European Butterfly Monitoring Scheme 

EBV 

EC 

EEA 

EEZ 

Essential Biodiversity Variable 

European Commission 

European Environmental Agency 

Exclusive Economic Zone 

EMBAL  European Monitoring of Biodiversity in Agricultural Landscapes 

ESDAC  European Soil Data Centre 

EU BDS  EU Biodiversity Strategy for 2030 

EU 

European Union 

EUBP MA Expert group on Monitoring and Assessment 

EUBP 

EU Biodiversity Platform 

EUROSTAT 

European Statistical Office 

EUSO 

EU Soil Observatory 

F2F 

EU Farm to Fork Strategy 

FADN 

Farm Accountancy Data Network 

FSDN 

Farm Sustainability Data Network 

GAECs  Good Agricultural and Environmental Conditions 

GHG 

Greenhouse gas 

IUCN 

International Union for the Conservation of Nature 

KCBD 

Knowledge Centre for Biodiversity 

LF 

Landscape Feature 

LUCAS  Land use and land cover survey 

MS 

NRL 

Member State 

European Commission proposal for a Nature Restoration Law 

OECD 

Organisation for Economic Co-operation and Development 

PMEF 

CAP Performance, Monitoring and Evaluation Framework 

RLI 

SDG 

SOC 

Red List Index 

Sustainable Development Goal 

Soil organic carbon 

33 

 
 
 
List of figures  

Figure 1. Schematic representation of the farming approaches of the LIFT farm typology.  .................................... 18 

34 

 
 
List of tables  

Table 1. Summary of identified potential indicators to track EU BDS targets 4, 7, 8 and 13 and their 
status regarding the selection process and their readiness  ........................................................................................ 5 

Table 2. Functional Landscape Feature Classes crosswalked to examples of LF types commonly 
recognised in policy documents .................................................................................................................................................. 14 

35 

 
 
 
 
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