Policy recommendations for large-scale systems in Europe : preserving territorial identity and addressing pressures.
Date: 2025-10-13
Institutions: Caballero García De Arevalo, Rafael, Joint Research Centre
Subjects: adaptation to climate change, biodiversity, common agricultural policy, extensive farming, grassland, livestock farming, local government, regional and local authorities, report, rural development, utilised agricultural area

Joint Research Centre 

Policy Recommendations for 
Large-Scale Grazing Systems 
in Europe: Preserving 
Territorial Identity and 
Addressing Pressures 

Caballero García De Arevalo, R., Van Der Velde, M. 

2025 

 
 
 
 
 
 
 
 
 
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this 
publication. For information on the methodology and quality underlying the data used in this publication for which the 
source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations 
employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the 
part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or 
concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Marijn van der Velde 
Address: marijn.van-der-velde@ec.europa.eu 

The Joint Research Centre: EU Science Hub  
https://joint-research-centre.ec.europa.eu  

JRC143525 
EUR 40486 

PDF  

ISBN 978-92-68-32569-8  

ISSN 1831-9424  

doi:10.2760/7528855   KJ-01-25-514-EN-N 

Luxembourg: Publications Office of the European Union, 2025 

© European Union, 2025 

The reuse policy of the European Commission documents is implemented by the Commission Decision 2011/833/EU of 12 
December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse 
of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence 
(https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and 
any changes are indicated.  

For any use or reproduction of photos or other material that is not owned by the European Union permission must be 
sought directly from the copyright holders. 

Cover page illustration, © Pedro José Moreno 

How to cite this report: Caballero García De Arevalo, R. and Van Der Velde, M., Policy Recommendations for Large-Scale 
Grazing Systems in Europe: Preserving Territorial Identity and Addressing Pressures, Publications Office of the European 
Union, Luxembourg, 2025, https://data.europa.eu/doi/10.2760/7528855, JRC143525. 

 
 
 
 
 
 
 
 
 
Contents 

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

Acknowledgements .............................................................................................................................................................................. 4 

Authors ................................................................................................................................................................................................. 4 

1.  Introduction ....................................................................................................................................................................................... 5 

2.  Material and Methods .................................................................................................................................................................. 8 

2.1.  Questionnaire design ......................................................................................................................................................... 8 

2.2.  Gathering and refining information ....................................................................................................................... 12 

2.3.  Data analysis ...................................................................................................................................................................... 13 

3.  Presentation of the European LSGS and Study Areas ........................................................................................... 14 

3.1.  The Boreal and North Alpine region ....................................................................................................................... 14 

3.2.  The Atlantic bioregion .................................................................................................................................................... 16 

3.2.1.  The UK and Ireland ............................................................................................................................................ 17 

3.2.2.  The Atlantic corridor from France to Denmark .................................................................................. 18 

3.2.3.  The Atlantic fringe of the Iberian Peninsula ........................................................................................ 19 

3.3.  The Continental and Pannonian bioregions ....................................................................................................... 20 

3.4.  The South Alpine bioregion ......................................................................................................................................... 23 

3.5.  The Mediterranean bioregion ..................................................................................................................................... 26 

3.6.  A note on the interface to biodiversity ................................................................................................................. 27 

4.  Results ............................................................................................................................................................................................... 29 

4.1.  Study areas and the environmental and management variables ........................................................ 29 

4.2.  Criteria and global scores ............................................................................................................................................ 30 

4.3.  Current constraints .......................................................................................................................................................... 33 

4.4.  The abandonment trend ............................................................................................................................................... 34 

4.5.  Factors of variation ......................................................................................................................................................... 36 

4.6.  Correlation, regression and factorial analysis .................................................................................................. 38 

4.7.  Cluster and discriminant analysis ........................................................................................................................... 41 

4.8.  Beneficial management ................................................................................................................................................ 43 

5.  Discussion........................................................................................................................................................................................ 46 

6.  Conclusions ..................................................................................................................................................................................... 49 

References ............................................................................................................................................................................................. 50 

1 

 
 
List of abbreviations and definitions ...................................................................................................................................... 58 

List of boxes.......................................................................................................................................................................................... 59 

List of figures ....................................................................................................................................................................................... 60 

List of tables ......................................................................................................................................................................................... 61 

Annex: Identity questionnaire for Large Scale Grazing Systems (LSGS) in Europe ...................................... 62 

2 

 
 
 
 
Abstract 

Safeguarding the remaining Large Scale Grazing Systems (LSGS) in Europe and improving targeted 
territorial support can have benefits for biodiversity, wildfire prevention, rural economies, cultural 
landscapes, and food security. Large scale grazing helps maintain habitats, reduces the risk of 
large-scale fires by managing fuel loads, supports rural jobs and incomes, and preserves traditional 
farming practices and scenic vistas. Systematic research is particularly important for LSGS in 
Europe. These are cultural landscapes located in marginal areas and at high risk of farm 
abandonment. At the same time, LSGS are a main repository of cultural and environmental values, 
with many of them linked and supported by activities around grazing. The objective of this research 
was to link grazing-supportive land use and vegetation types with grazing management in a wide 
range of European environments.  

Our study is based on an expert-derived perception of change and the identification of the main 
constraints and opportunities for a wide range of European LSGS located in different bioregions. 
Through this approach, we rated LSGS for their social ecological values and formulated common 
and shared proposals to enhance resilience that can be used for policy design and implementation. 
A questionnaire was administered to 46 experts (with backgrounds in environmental, socio-
economic and grazing management disciplines) in 22 European countries to collect data on 48 
variables. The main European environmental zones are represented: Atlantic (eight study areas); 
Alpine (11), Boreal (six), Continental (seven); and Mediterranean (14). The results revealed a high 
level of correlation amongst responses, which indicates that there are areas of common ground for 
policy development covering diverse LSGS in Europe. The LSGS are assessed according to criteria 
related to their identity, environment, economics and social characteristics.  

Overall, downward trends are observed for these criteria, although LSGS score high on having 
distinct identities. Furthermore, in terms of economic activities, 96% of the experts do not see 
alternatives for grazing related activities in these areas. The main weaknesses and corresponding 
improved management alternatives voiced by the participants included: better grazing plans 
(named by 82% of respondents); policy reforms (59%); a marketing framework for local products 
(52%); support to herding labour (39%); improvement of grazing infrastructures (34%); social 
empowerment of local farm communities (32%); support to grazing institutions and governance 
(23%); and better technical support and advisory services (18%).  

The Common Agricultural Policy and the Cohesion Fund will be combined in the single National 
Regional Partnership Fund (NRPF), retaining a ring-fenced portion for direct farmer income support. 
The CAP's two-pillar structure is being replaced by a more unified framework, and Cohesion funding 
is also being adjusted with a focus on civil preparedness and climate resilience. The information 
presented here may help to improve the design of policy support for LSGS as Member States start 
to design their National and Regional Partnership Plans.  

3 

 
 
 
Acknowledgements 

The authors wish to thank the following representative experts for their contributions in filling out 
the 46 questionnaires on the territorial identity of European LSGS and further narrative comments 
and explanations to their responses: Brendan Dunford (case study Nº 1), Norbert Röder (2), Ewa 
Tyran (3), Juan Antonio Valladares (4), Niklas Labba (5), Teresa D. Soares and Augusta Costa (6), 
Nathaniel Page and Razvan Popa (7), Sally Huband (8), Geza Nagy (9), Rosario Fanlo (10), Suzana 
Kratovalieva (11), Nikos Theodoridis (12), Senija Alibegovic and Muhamed Brka (13), Anna 
Sidiropoulou and Vasilios P. Papanastasis (14), Pius Hofstetter (15), Andy Guy and Ian Condliffe 
(16), Giovanni Peratoner (17), Paride D’Ottavio (18), Lucia Sepe and Salvatore Claps (19), Tommaso 
La Mantia and Salvatore Pasta (20), Iva I. Apostolova (21), Tiiu Koff (22), Giampiero Lombardi (23), 
Anna Berg and Ulf Segerström (24), Berien Elbersen (25), Antun Alegro and Renata Sostaric (26), 
Fernando J. Pulido (27), Veronica Sarateanu (28), Rosa M. Canals (29), Rainer Luick (30), Jan Jansen 
(31), Ivica Ljubicic (32), Federico Fillat (33), Terry McCormick, Stephen Lard and Lois Mansfield (34), 
Marina Meca Ferreira de Castro (35), Bruce Forbes and Ari Laakso (36), Edgar Reisinger (37), Martin 
Stock (38), Martin Vadella (39), Marijn Nijssen (40), Jacques Lasseur (41), Davy McCracken (42), Pier 
Paolo Roggero and Simonetta Bagella (43), Marie-José Gaillard (44, 45). The author also wishes to 
thank partners of the EU-funded LACOPE project (Contract EVK2-CT-2002-00150) for discussion 
and implementation of the LSGS concept, to Laura Barrios of the Secretaría General Adjunta 
Informática (SGAI CSIC, Madrid, Spain) for performing the statistical analyses and to Lee Robertson 
of the National Museum of Natural Sciences (CSIC, Madrid, Spain) for English corrections of the 
main text. 

Authors 

Rafael Caballero García De Arevalo 

Marijn van der Velde 

4 

 
1.  Introduction 

Large Scale Grazing Systems (LSGS) are cultural landscapes emerging from the interaction of 
human intervention and natural resources. They are broadly defined as extensive systems of 
grassland management located mainly in harsh environments and marginal areas (Caballero et al., 
2009; Plachter & Hampicke, 2010). They represent a community of livestock farmers sharing 
productive forms, traditions and cultural values that, in turn, shape the environment (Caballero et 
al., 2007, 2009). LSGS have changed over time under different disturbances and pressures, but 
besides an evolving identity, their maintenance is under threat as well in several regions. 

The European Landscape Convention (Council of Europe, 2016) defines landscapes in a broad sense 
including natural and cultural landscapes, which result from the actions and interactions of natural 
and/or human factors. The pace and magnitude of change in managed rural landscapes is 
heterogeneous and dependent upon increasingly faster technological innovation and social values. 
However, the European Landscape Convention is not very clear on how to proceed for planning and 
management (Antrop, 2005), particularly for cultural landscapes located in the most marginal 
areas. 

We may question whether we still have LSGS of a recognisable structure in Europe, containing 
legible elements that can be identified not only for their natural and societal values but also as a 
way of living for farmers. Landscape change is substantial, and our baselines are shifting, and the 
pace and magnitude of changes may have driven some European LSGS beyond recognition, with a 
subsequent loss of territorial identity. If territorial identity is defined by the coherence and 
continuity of properties, we may assume that, in other cases, some essential characteristics have 
been maintained, particularly in LSGS located in less developed areas. 

The pace of change is not unique (Caballero et al., 2009). The main negative drivers of LSGS are (or 
have been) land use abandonment and/or intensification, loss of region-specific grazing practices, 
deterioration of institutional management and social cohesion and, in the long term, increased 
frequency of extreme weather events such as droughts and flooding due to climate change. These 
processes have taken place on different temporal and spatial scales and the resilience to change 
depends on the general socio-economic environment (e.g., demographic trends, off-farm income 
opportunities, agricultural support policies and even, in some cases, international conventions). 

All of these processes and events have given way to an elusive concept of ‘territorial identity’ with 
limited operational interpretations (Roca & Oliveira-Roca, 2007). Whether or not a cultural 
landscape can be valued is debatable and involves perception but also knowledge. Currently, the 
perception of cultural landscapes is the focus of regional identity (Sooväli et al., 2003), but cultural 
landscapes can also be shaped by ideology and politics (Olwig, 2002). Different stakeholders (e.g., 
farmers, environmental NGOs or citizens) attach contrasting values to components and functions of 
LSGS, and paths of change, with the focused objective of preserving identity, are frequently blurred 
or misrepresented. Therefore, we assume that factual expertise, together with perceptions of LSGS, 
can contribute to the identification of the main values and constraints experienced by LSGS and aid 
in better defining future pathways for their preservation. Any path of change is substantially linked 
to technological innovations. By aligning ourselves with the identity of LSGS, we are in a better 
position to steer technologies and make sensible choices (Kelly, 2011). 

Extensive livestock systems in Europe are recognised as harbouring the greatest potential 
environmental values as a substantial part of High Nature Value (HNV) farmland (Cooper et al., 
2009), but beneficial management should be entrenched in a concept of working land and a 
sustainable path of change. Recent research has stressed the requirement of knowledge-based 

5 

 
changes (Caballero, 2011) and the need to address changes away from a negative perspective of 
unmanaged land under the assumption of linking changes to the loss of nature values (Antrop, 
2005). Proper transition pathways and operational tools to pilot changes are required (Caballero, 
2015). An identification of LSGS as cultural landscapes is essential to assess whether the direction 
of change is contributing to the coherence and continuity of beneficial values or whether the 
changes are contributing to the loss of identity. Our study is based on an expert-derived perception 
of change, with experts identifying the  main trends, the main constraints, and any  management 
alternatives over a wide range of European LSGS located in different bioregions. The he perceptions 
of experts on disturbances and management alternatives can be an input for improved policy 
formulation. 

Extensive grazing systems harbour a repository of environmental and social values. For rural 
landscapes, the delivery of public goods is emerging as the main rationale for policy reforms 
(Cooper et al., 2009; Targetti et al., 2024). This latter is a broad concept that is suited to non-
demarcated rural territories.  

Key territorial EU policies such as the Common Agricultural Policy (CAP) recognize the multiple 
values associated with pastoralism including culture heritage, environmental management, and 
territorial cohesion (Nori, 2022). Indeed, the EU supports pastoralists with direct and indirect 
measures, including subsidies. For instance, for livestock holders whose cattle is extensively grazing 
on Areas with Natural Constraints or on High Nature Value land.  

All European rural landscapes have the potential capability of delivering some type and level of 
public goods and thus of being supported under this rationale. However, the delivery of public goods 
linked to agricultural management are in the hands of farmers controlling production factors 
(including land ownership) and farming practices within particular farming systems. Almost none of 
these potential public goods are priced in markets, and the precise rate of delivery under a 
particular land use is barely known.  

Historically, extensive grazing was more widespread in Europe, and a body of evidence exists of 
grasslands affected by abandonment (e.g., Orlandi et al., 2016; Szczęch et al., 2024). A better 
understanding of where extensive grazing occurs is desirable for a variety of reasons. This 
understanding would help to better target policy interventions to maintain extensive grazing where 
it is desirable, but it may help to inform choices where (assisted) natural revegetation would be 
preferred.  

The requirement for demarcation of the countryside is even more acute for LSGS given their 
biophysical and social heterogeneity. Approximate zoning of potential LSGS is helpful, although a 
real baseline should be designed at the farm level for eligibility of individual farmers.  In the past, 
policies have contributed to  HNV livestock systems  (Bartley et al., 2009; von Metzner et al., 2010).  

For instance, open access to boreal forests and northern alpine pastures for reindeer herding in 
Fennoscandia (Riseth et al., 2004), maintenance of mountain pastures in the Italian Alps (Corti, 
2004; Cozzi et al., 2006; Cavallero et al., 2007), maintenance of indigenous livestock breeds in the 
Rhön area of Germany (Nyenhuis et al., 2007) or blending crops, grasslands and forest in the unique 
dehesa landscape of South-West Spain (Plieninger & Pulido, 2009) are the result of a working land 
perspective with management practices for coherence and continuity. These landscapes maintain 
indigenous livestock breeds and deliver singular products such as reindeer meat, Alpine cheeses or 
Iberian ham. The delivery of public goods (e.g., plant and animal biodiversity, scenic landscapes) or 
economic side activities (e.g., tourism) are an effect of lively and working landscapes. 

6 

 
We are interested in further exploring the recognition of the identity of European LSGS as a 
separate farming category. Hence the main objectives of this research are to design and apply a 
questionnaire to identify trends in a wide range of European LSGS and to identify the main concerns 
and beneficial management practices for coherence and continuity of these systems. Our main 
argument is that the continuity and coherence of these farming systems is relevant in the European 
context, and they deserve the design and implementation of a framework scheme of agricultural 
support. More specifically, using the data collected by experts in LSGS who filled out the 
questionnaire, we tested whether the criteria and indicators are independent and whether they cut 
across or are generalised across systems to identify if there are areas of common grounds for 
further policy development. A high level of correlation amongst responses indicates the existence of 
shared identities  and a common ground for actions.  

7 

 
 
 
 
 
2.  Material and Methods 

To explore a representative sample of 46 LSGS across Europe, a relevant number of experts were 
identified for their LSGS expertise based on a questionnaire. The contacts originated from the EU 
funded LACOPE project and associated activities.    

2.1.  Questionnaire design 

The information was collected using a questionnaire that was administrated with experts in LSGS. 
The questionnaire consisted of nine sections: the first three (expert identification, location and size 
of the LSGS operation, and the biophysical and grazing conditions) were aimed at recording 
sampling frequencies for the main factors of variation across the 46 European LSGS. The locations 
of these LSGS are provided in Figure 1.  

Figure 1. Locations of extensive systems of grassland management in Europe. 

Source: Authors 

Data on the expert identification included affiliation and expertise. Data on the LSGS included 
location and size of the LSGS operation, climate and altitude, location within the environmental 
European zones and the main features of the grazing system such as dominant pastoral resources, 
land property and grazing rights, size of the holdings, the dominant grazer, the mobility of 
herds/flocks, the size of the LSGS operation or the dominant objective of production. These first 
three sections recorded 11 factors of variation (Table 1) and six environment and management 
(EM) variables (Table 2). 

8 

 
 
 
 
Table 1. Grouping 46 Large Scale Grazing Systems (LSGS) study areas by main factors of variation, levels 
and observations.  

Factor (FV) 
aBio-  
geographical 
Region (FV1) 

Levels/Categories 
Atlantic (AT) 
Alpine (AL) 
Boreal (BO) 
Continental (CO) 
Mediterranean (MED) 

Frequency   Notes 
8 
11 
6 
7 
14 

aMetzger et al., (2005) 
One Pannonian study area 
included as Continental 

bManagement includes advisory 
staff, counselling and nature 
managers 

cIncluding animal production  
 and grasslands experts  

dSemi-natural grasslands and 

more or less open forest and scrub or 
mixed resources 

ePublic under some scale of 
government (local, regional or 
national) 

fMobile including vertical mobili 
ty and short/long migrations 

gMixed cattle and sheep and in 
particular study areas also 
horses and pig. 

L (>30 LU). LU=Livestock Unit 
M (10-30 LU) 
S (<10 LU) 

L (>10,000 km2) 
M (1,001-10,000 km2) 
S (100-1,000 km2) 
VS (<100 km2) 

hNon-exclusive production 
objectives 

H (>100 inhabitants/km2) 
M (31-100 inhabitants/km2) 
L (10-30 inhabitants/km2) 
VL (<10 inhabitants/km2) 

bExperts’ 
 affiliation 
(FV2) 

University professor (U) 
Public/private research (R) 
Management (M) 

 Experts’   
 discipline(FV3) 

Environmental (ENV) 
cSocio-economic (ECON) 

dDominant type   Semi-natural grassland 
(SG)   
Wooded pastures (FW) 

 of forage 
resources (FV4)  Mixed (M) 

eOwnership  
Private (PR) 
of grazing 
Public (PU) 
resources (FV5)  Mixed (M) 

fMobility 
(FV6) 

Mobile (M) 
Sedentary (S) 

gDominant  
grazer (FV7) 

Cattle (C) 
Sheep and/or goats (S) 
Mixed (M) 
Reindeer (R) 

Size of  
holdings (FV8) 

Large (L) 
Medium (M) 
Small (S) 

Size of the  
LSGS 
operation (FV9) 

Large (L) 
Medium (M) 
Small (S) 
Very small (VS) 

hDominant  
objective (FV10)  Meat (ME) 

Milk (MI) 

Population 
in the LSGS 
area (FV11) 

High (H) 
Medium (M) 
Low (L) 
Very Low (VL) 

See Table 2 for the definitions of the superscripts. 

Source: Authors 

23 
12 
11 

29 
17 

14 

7 
25 

14 
12 
20 

28 
18 

10 
21 
12 
3 

24 
15 
7 

12 
18 
10 
6 

23 
23 

9 
16 
11 
10 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2. List of the Large Scale Grazing Systems (LSGS) case studies and the main environmental and 
management variables collected.   

Nº 

aC 

bLocation 

cBR 

dR 

eT 

fE 

gP 

hGD 

iRS 

Sheep, SE Transylvania 
Sheep, S Carpathians 

CO 
MED 
AL 
AT 
AL 
CO 
MED 
MED 
CO 
BO 
AL 
BO 
AT 

Cattle, S Tyrol, E Alps 
Sheep, Marche, C Apennine 
Mixed, S Apennine, Podolica 
Mixed, Nebrodi, NE Sicily 
Sheep, C Balkans, Bulgaria 

Suckler beef, Burren, Ireland 
IE 
Dairy cattle, Bavarian Alps 
DE 
PL 
Dairy sheep, Tatra 
ES  Mixed grazing, Asturias 
Reindeer, N Fennoscandia 
NO 
PT  Mixed grazing, Montados 
RO 
RO 
HU  Mixed, Hungarian Plain 

AT 
1 
AL 
2 
AL 
3 
AT 
4 
BO 
5 
MED 
6 
CO 
7 
AL 
8 
CO 
9 
Cattle, Andorra, W Pyrenees 
AL 
10  AD 
Sheep, Republic of Macedonia  AL 
11  MK 
Goats/sheep, Rhodes, Greece  MED 
12  EL 
Sheep, C Bosnia & Herzegovi. 
13  BA 
Goats & sheep, C Macedonia 
14  EL 
15  CH 
Cattle, Swiss Alps, Entlebuch 
16  UK  Mixed, Dartmoor,SW England 
IT 
17 
IT 
18 
IT 
19 
20 
IT 
21  BG 
22  EE  Mixed, Estonian Alvars 
23 
IT 
24  SE 
25  NL 
26  HR 
27  ES  Mixed, dehesa, SW Spain 
28  RO  Wallachian sheep, W Romania  CO 
AL 
29  ES  Mixed, Navarra, W Pyrenees 
AL 
30  DE 
31  PT 
32  HR 
33  ES  Mixed, Aragon, C Pyrenees 
34  UK 
35  PT 
36 
FI 
37  DE  Mixed, wetlands, Thuringia 
38  DE 
39 
40  NL 
41 
FR 
42  UK 
IT 
43 
44  SE 
45  SE 
46  ES 

FR  Mixed, Corsican mountains 
Cattle, Atlantic dunes 
Sheep, Luberon Natural Park 
Sheep, Uplands, SW Scotland 
Dairy sheep, Sardinia 
Cattle, woodlands, Öland 
Cattle, alvars, Island of Öland 
Sheep, Castile-La Mancha 

Cattle, Piedmont alpeggio 
Reindeer, N Sweden 
Sheep, nat. conserv., Drenthe 
Sheep, S Dinaric Alps, Croatia  MED 
MED 

Cattle, Black Forest, Baden-W 
Sheep/goats, Serra da Estrela  MED 
Sheep, Island of Pag, Croatia  MED 

Sheep, Cumbria, NW England 
Sheep, Tras-os-Montes 
Reindeer, Upper Lapland 

AL 
AT 
MED 
BO 
CO 
AT 
MED 
AT 
MED 
AT 
MED 
BO 
BO 
MED 

Sheep, Wadden Sea Nat. Park 

1500 
1500 
950 
1000 
800 
650 
680 
740 
550 
1200 
492 
735 
800 
500 
1800 
1900 
1000 
900 
890 
900 
1100 
509 
1250 
600 
790 
1800 
500 
750 
1550 
1500 
1000 
1062 
900 
3200 
740 
500 
550 
810 
800 
793 
700 
2500 
650 
475 
475 
425 

10 
6 
6.5 
12 
-3 
16 
7.5 
5 
10.5 
11 
8 
19 
9 
16 
9 
8.3 
3 
13 
13.5 
11 
5 
5.6 
6 
-2 
13 
10 
17 
8 
9 
7.5 
11 
16 
11 
8.3 
13.1 
-0.8 
8 
8.5 
18 
9.8 
15 
8 
15 
7 
7 
14 

150 
1000 
800 
1500 
300 
150 
540 
1000 
150 
1200 
900 
450 
950 
600 
1500 
350 
2000 
1200 
750 
900 
800 
28 
1300 
1000 
50 
800 
400 
550 
1220 
900 
1000 
170 
900 
978 
640 
300 
550 
0 
1000 
5 
650 
200 
350 
30 
30 
600 

7 
101 
175 
20 
5 
15 
42 
30 
50 
150 
15 
70 
18 
120 
50 
35 
62 
60 
76 
60 
0.1 
13 
60 
0.9 
184 
1 
20 
32 
6 
150 
22 
30 
7 
40 
27 
0.3 
115 
101 
35 
800 
50 
20 
50 
5 
1 
20 

355 
150 
170 
200 
360 
360 
300 
210 
210 
210 
155 
300 
180 
210 
200 
360 
150 
360 
360 
210 
120 
160 
100 
360 
360 
200 
360 
150 
250 
140 
180 
365 
180 
140 
NA 
365 
180 
245 
200 
300 
300 
365 
365 
90 
210 
180 

0.6 
0.9 
0.5 
0.15 
0.02 
0.25 
0.5 
NA 
0.3 
0.25 
0.25 
0.38 
0.25 
1.1 
1.4 
0.2 
0.28 
0.25 
0.5 
NA 
0.1 
0.5 
0.26 
0.02 
0.5 
0.1 
0.4 
1.2 
0.5 
1 
0.9 
1.7 
1.1 
1.67 
NA 
0.06 
0.5 
NA 
1 
0.19 
0.3 
1.2 
0.3 
0.5 
0.5 
0.15 

aTwo-letter ISO code (ISO 3166 alpha-2) for countries, except Greece (EL). bLocation and main grazer of Large Scale Grazing System 
(LSGS). 
cBR= Biogeographical Region. AT=Atlantic; AL=Alpine; CO=Continental; BO=Boreal; MED=Mediterranean. dR=Annual rainfall (mm); eT=Mean 
annual temperature (ºC); fE=Mean elevation a.s.l. (m). gP=Population density (inhabitants/km2); hGD=Grazing days per year 
iRS=Regional stocking (LU/ha). LU=Livestock Unit. NA=Not available 
R, T, E, P, GD and RS are Environment and Management (EM) variables across the paper in this order.  

Source: Authors 

10 

 
 
 
Section four contained 24 qualitative-linguistic variables grouped under four criteria: general 
identity (A), environment (B), economics (C) and social variables (D). Under each criterion, six 
questions (variables) were submitted for consideration. Experts provided the answers on a five-point 
Likert-type scale ranging from strongly agree (five points) to strongly disagree (one point), with 
undecided in the middle (three points). The questions were drafted in such a way that a high score 
represents higher sustainability. At the end of the questionnaire, feedback was requested on 
whether the experts were confident in their responses or expressed doubt in their understanding. 
The main objective of this section was to transform the answers to a quantitative scale, thereby 
rating the LSGS by potential sustainability criteria. 

Section five collected information on the main environmental assets and trends of LSGS regarding 
the main environmental functions, economic side assets and regional environmental targets in EU 
Directives (vegetation types and plant and animal species of conservation concern). This latter 
information contributed to section three of this research. Experts were also asked about potential 
conflicts between current practices and environmental assets and whether they envisaged some 
alternative management practices with beneficial effects. This section was linked to section nine 
where potential beneficial management alternatives were requested as comments. 

The main objective of section six was to reveal the main and current constraints (CC variables) for 
continuity and coherence on a quantitative scale. Up to ten worded constraints were submitted for 
consideration, and an ordinal response was requested from the first and most important constraint 
(1) to the least important (10). Responses were normalized to a scale from 0 to 1.  

Sections seven and eight recorded qualitative and quantitative data on past and future trends of 
grazing operations. Experts provided their perceptions on past transitions from markets and 
environmental perspectives using the five-point Likert scale. Quantitative data were requested on 
changes in the absolute numbers of active livestock farmers on a time frame ranging from 30 and 
60 years before the year 2010. The yearly rates of change were then calculated as the percentage 
of farmers entering/exiting the operation.  

In section nine, experts provided comments on up to five management alternatives that may 
produce beneficial effects based on their own expertise and any available evidence. They rated 
these alternatives as environmental, socio-economical or both (win-win situation) and on the type 
of available evidence (knowledge-based, common sense or farmers’ practices). The main objective 
of this section was to test whether these management alternatives can be embedded in a 
framework of policy options for a wide array of LSGS and thus may be used for funding allocation 
and as a rationale for policy reform. 

The entire set of variables (48) included six environmental and management variables; 24 criteria-
variables where four were aggregate scores by criteria and one was the total score; ten current 
constraint variables; and three variables rating the abandonment trend. There were two main 
objectives to the questionnaire. First, it was used to locate European LSGS in a transition pathway 
ranging from semi-subsistence pastoralism to a more market-oriented operation; from a 
production-oriented operation to a management tool in pursuit of environmental functions; and 
from a semi-abandoned operation to a lively working landscape. Secondly, it was used to highlight 
European LSGS by revealing their territorial identities and potential assets while simultaneously 
disclosing potential synergies across systems that could justify the design and implementation of 
one general policy framework for their continuity and coherence. 

11 

 
2.2.  Gathering and refining information 

The first objective was to record information on a wide range of European LSGS across bioregions 
and countries. All European countries showed representation of LSGS at different spatial scales and 
levels of development although the real population of European LSGS is currently incomplete. We 
still lack an inventory of European LSGS despite previous attempts in this direction (Caballero et al., 
2009). Databases on European LSGS, and corresponding potential experts, have been developed 
within several recent EU-funded research projects such as the European Network for Livestock 
Systems and Integrated Rural Development (LSIRD) network, the European Livestock Policy 
Evaluation Network (ELPEN), the Landscape Development, Biodiversity and Co-operative Livestock 
Systems in Europe (LACOPE) project and the Thematic Network on the Cultural Landscapes and their 
Ecosystems (PAN) project. The European Forum on Nature Conservation and Pastoralism (EFNCP), 
an NGO interested in pastoral development and nature conservation, is also contributing. The main 
reason for this vacuum is that European LSGS have only recently been the focus of pan-European 
research (Caballero et al., 2008; Kleijn et al., 2008; Sheate et al., 2008; Partidário et al., 2009; 
Metzger et al., 2010). In an inventory on the perception of livestock ecosystem services in grazing 
areas (Leroy et al., 2018), despite the recognition in EU legislation, the long-term sustainability of 
related policies and incentives to provide such services is viewed as a major issue by the 
respondents. In addition to EU projects, institutional support was also requested from the FAO-
CIHEAM Network on Mountain Pastures, the FAO-CIHEAM Network on Pasture and Fodder Crops, the 
EFNCP, the German Federal Agency for Nature Conservation (BfN), the Italian Society for Agronomy 
(SIAGR) and the Spanish Grassland Society (SEP). The selection of European LSGS was mainly taken 
from the sample provided by the LACOPE project (Holz et al., 2005) in different bioregions, while the 
selection of corresponding experts was mainly sourced from the aforementioned national 
institutions and organisations, where a large majority of European countries showed representation 
of LSGS.  

We were not able to select the sample of study areas based on the sample-to-population ratio (as 
the size of the population is not well known) so instead we aimed for 60 experts from 24 European 
countries covering both socio-economic and environmental disciplines. These 60 European experts 
were first contacted by e-mail. Together with the questionnaire, a cover letter was sent explaining 
the objectives of the research. A few experts declined while a few others recommended an 
alternative with expertise in particular systems. At the end of this phase of information gathering, a 
new network of 46 experts in 22 European countries was initiated, with 29 environmental experts 
and 17 socio-economic experts providing valid questionnaires (77% of responses), where the latter 
included animal production and grassland experts.  

This phase ran from October 2010 until April 2011. A large majority of questionnaires (90%) were 
then checked to clarify some responses or to fill in unreported variables. The experts also provided 
additional information and rationale, remarks and comments on responses to different sections of 
the questionnaire. 

The European countries and the number of study areas represented in the sample were as follows: 
Portugal (3); The Netherlands (2); Sweden (3); Estonia (1); Spain (5); Andorra (1); Bulgaria (1); 
Croatia (2); Ireland (1); Norway (1); Switzerland (1); Germany (4); Poland (1); Romania (3); Italy (6); 
United Kingdom (3); Hungary (1); Republic of Macedonia (1); Greece (2); Bosnia and Herzegovina (1); 
Finland (1); and France (2). Of those experts contacted in different countries, only Austria and Serbia 
were unrepresented. A map of the environmental zones/bioregions (Metzger et al., 2005) was used 
in previous research (Caballero, 2015) to locate a sample of study areas with geographical data 

12 

 
supported by the experts, and the structure of the sample by the main factors of variation is 
provided in Table 1. 

2.3.  Data analysis 

The factors of variation were grouped by levels and frequencies of representation in the 46 study 
areas (Table 1). Environmental and management variables, criteria-variables, aggregate scores by 
criteria, total scores, current constraint variables and abandonment variables underwent descriptive 
statistical analysis and variance analysis using SPSS software. For the former, the mean ± SD, the 
median and the Wilck test of normality (Shapiro & Wilk, 1965) were calculated and applied. W-
values near 1 indicate a symmetrical (normal) distribution. For the latter, the case-study systems 
were grouped by levels under each factor of variation, and one-way ANOVA was performed. The 
Levene test was used for testing the homogeneity of the variances, and the significance of mean 
differences between pairs of groups was tested using the Bonferroni (homogeneity) or the Tamhane 
(heterogeneity) tests.   

Single inter-correlation of the 48 variables was performed by Factorial Analysis of Principal 
Components. We wanted to test whether the grouping of variables in the questionnaire and the 
grouping of variables in the principal components of the factor analysis were dependent or not. In 
the rotated component matrix, a loading score higher than ±0.1 was set for inclusion of particular 
variables within factors and eigenvalues > 1 for the number of factors (Hair et al., 2010). The 
grouping of case-study systems by clusters was performed using K-means, where the clusters were 
chosen to maximize the differences among the case studies and the significance of the clustering 
factor was tested using ANOVA and discriminant analysis. 

The inter-dependency of the criteria-variables (24) was tested by estimating the Squared Multiple 
Correlation (SMC) of each criteria-variable with all other criteria-variables and using a stepwise 
regression analysis taking the identity variables (Criterion A) as dependent variables. We wanted to 
test whether the distribution of criteria-variables in the questionnaire was consistent with the 
aggregation by criteria or whether the aggregate scores were really a pool of inter-dependent 
variables.  

Of the 46 experts, 29 were able to record the absolute numbers of farmers entering/exiting the 
operation in the requested periods (section seven). Yearly rates of change were also dependent 
variables in a stepwise regression analysis. The objective was to identify the main variables that 
better explain the changes in the number of operative farmers either as variables grouped by 
criteria in the questionnaire (our own choice) or as variables grouped within the factors-
components. The same analysis was applied to the cardinal responses of the ten constraints 
(independent variables) in section six. In this case, most experts (45) provided valid responses. 

A large majority of experts (96%) proposed at least one management alternative for the 
restructuring of the LSGS (section nine). Extended explanations for these proposals were provided in 
the process of refining the information. We tested the coherence of the responses to disturbances 
and constraints in previous sections of the questionnaire with the management alternatives 
(regenerative processes) proposed by the experts. If this was the case, a framework of policy 
incentives to spur change will be sensible. This analysis assumes that the resilience of LSGS as 
cultural landscapes is largely dependent on socio-economic variables and that the possibility of 
engaging in the planning of sustainable LSGS at the inter-regional scale depends on European LSGS 
sharing some constraints and resilience proposals (Wascher, 2005). 

13 

 
3.  Presentation of the European LSGS and Study Areas 

The main objective of this section is to identify and describe the relevant European LSGS and the 
study areas selected in our sample. Our listing is contingent and not inclusive, but this justifies our 
selected sample and may allow a link between land cover and land management in our 
identification exercise. Land cover is represented by vegetation types of potential grazing use and is 
included in the EU Birds and Habitats Directive (EC DG-ENV et al., 2014). Land management is 
characterised by the main use of grazing land, represented by stakeholders and institutional 
grazing. 

The spatial distribution of European LSGS was addressed by a regionalisation of systems across 
countries and biogeographical regions (Metzger et al., 2005). Regional types are justified by 
distinctive vegetation types of grazing resources and distinctive practices of grazing management. 
As for the empirical evidence recorded in the next section, we hypothesized that notwithstanding 
the wide range of variation in social-ecological factors, the European LSGS may share some 
common properties and trends, and some common pressures and options, for development. 

Sources of information for land cover are the EU Birds and Habitats Directive (EC DG-ENV et al., 
2014), vegetation types of potential grazing use (IEEP, 2007) and information provided by the 
experts in the questionnaire. These three sources were cross-checked with information provided in 
the LACOPE project (Holz et al., 2005). Here, up to 119 European LSGS were identified and their 
vegetation types recorded in the EUNIS code-system. This system allows an equivalent 
transformation to Flora, Fauna and Habitat (FFH) types as represented in the EU Birds and Habitats 
Directive. 

Sources of information for grazing land management, the main pressures and options, and 
dynamic/downward trends were derived from pastoral European networks referenced in the 
previous sections, including a refined version of the information provided in the LACOPE project 
(Fernandez-Santos, 2010). For European LSGS not well represented in the LACOPE study (e.g., 
eastern European countries) or in our selected sample (e.g., French LSGS), we looked for additional 
information (e.g., Gibon & Mihina, 2003; APCA, 2005). For case studies in countries well represented 
in our sample (e.g., Spain, Italy and Greece) and for which structured information is available 
(Caballero et al., 2009), we have summarized the descriptive information in the sections that follow. 

3.1.  The Boreal and North Alpine region 

This bioregion includes the three Fennoscandian countries (Norway, Sweden and Finland) and the 
three Baltic States (Estonia, Latvia and Lituania). The North Alpine region stretches along the border 
of Norway with Sweden and Finland (in the north). Along the coastline of Norway, the Atlantic (in 
the south) and the Artic (in the north) bioregions are also represented. The main vegetation types 
are listed in Box 1. 

F1.1 and F1.2 (in the EUNIS code) are vegetation types related to shrub tundra, and moss and lichen 
tundra, respectively (e.g., tundra fjell on dolomitic soils, sub-artic/sub-alpine fjell). These types are 
included for their grazing relevance in particular LSGS in this bioregion (e.g., stationary taiga and 
tundra reindeer system). 

14 

 
 
 
Box 1. The main vegetation types with grazing systems in the Boreal and North Alpine bioregion 

1630. Boreal baltic coastal meadows 

2180. Wooded dunes of the Atlantic, Continental and Boreal regions 

2320. Dry sand heaths with Calluna and Empetrum nigrum 

4060. Alpine and Boreal heaths 

5130. Juniperus communis formations on heaths or calcareous grasslands 

6120. Xeric and calcareous grasslands 

6150. Siliceous alpine and boreal grasslands 

6170. Alpine and subalpine calcareous grasslands 

6270. Fennoscandian lowland species-rich dry to mesic grasslands 

6280. Nordic alvar and precambrian calcareous flatrocks 

6440. Alluvial meadows of river valleys of the Cnidion dubii 

6450. Northern boreal alluvial meadows 

6510. Lowland hay meadows 

6520. Mountain hay meadows 

6530. Fennoscandian wooded meadows 

7120. Degraded raised bogs still capable of natural regeneration 

7130. Blanket bogs 

7140. Transition mires and quaking bogs 

7240. Alpine pioneer formations of Caricion bicoloris-atrofuscae 

9010. Western Taïga 

9020. Fennoscandian hemiboreal natural old broad-leaved deciduous forests (Quercus, Tilia, Acer, Fraxinus,or Ulmus) 
rich in epiphytes 

9050. Fennoscandian herb-rich forests with Picea abies 

9070. Fennoscandian wooded pastures 

The location and the main types of LSGS in this bioregion are the following: 

—  The Sami reindeer system is located in the north of Norway, Sweden and Finland, for the 

latter in a small alpine region. The system is based on the long migration of reindeer (Rangifer 
tarandus) from summer grazing grounds in alpine pastures to winter feeding on ground-
growing mat-forming and also on arboreal lichens and complementary forage. The Northern 
Sami region (some 150,000 km2) is dominated by boreal forests, natural alpine grasslands and 
open tundra with very limited cultivated areas in the valleys and fjords. Reindeer herders 
currently use motorised vehicles for taking care of the herds. They receive payment by head of 
animal to the slaughterhouse and thus have an incentive to increase the stocking densities. 
Overgrazing may result in particular areas, while in others, abandonment of potential grazing 
resources is the result of cross border restrictions between the countries (Riseth, 2006; 
Caballero et al., 2007). 

The system is represented in our sample by case studies Nº 5 and 24. The experts stressed the 
importance of reindeer herding in maintaining the cultural traditions and social cohesion of the 
Sámi people and in maintaining their way of living by combining productive and economic side 
activities such as tourism. For this, they relate social initiatives for teaching these values to 
young farmers and assurances on the continuity of the grazing operation. On issues of concern, 
they were critical of the current support schemes, on the requirement for adaptation to climate 
change (Aanes et al., 2000; Kumpula & Colpaert, 2003; Moen, 2008; Roturier & Roué, 2009; 
AMAP, 2011); and adaptive and congenial practices for forest and reindeer management (Berg, 
2010). 

15 

 
—  The stationary taiga and tundra reindeer system. This system is dominant in northern 

Finland under limited access to summer alpine pastures and shorter displacements of reindeer 
herds. In Upper Lapland, the tundra (sub-artic/sub-alpine fjell zone) on dolomitic soils is 
dominant throughout the boreal forest. Sámi reindeer herders are allocated to reindeer districts 
and reindeer groupings (siidas) within districts. The large number of animals and seasonal 
availability of grazing resources prevent a sensible use of resources on a yearly cycle. Each 
reindeer district and their siidas have a designated pasture area and a Total Allowable Quota 
(TAQ) of animals set by government planning. 

The system is represented in our sample by case study Nº 36. The expert expressed concerns 
regarding difficulties for the implementation of grazing plans without taking into account the 
herders’ views and the heterogeneous patterns of grazing distribution in the districts. 
Frequently, the real number of animals is higher than that allowed by the TAQ as herders have 
an incentive to own more animals with the present scheme of support based on the number of 
winter reindeer or animals left living after slaughter. In particular districts, reindeer herders 
have implemented a voluntary quota to divide the TAQ among herders, thereby reducing 
conflicts in access to pastoral resources and subsidies. Adaptive co-management of learning 
through options and the refinement of actions is encouraged and requires the herders’ views, 
but a general and independent herders’ institution is still lacking. 

—  The alvars and wooded pasture systems are much more small-scale, and the production 
objective is much less relevant. The Nordic Alvar (vegetation type 6280) is a semi-natural 
pasture type in poor soils and bedrocks, and Fennoscandian wooded meadows (vegetation type 
6530) is of low forage capacity but among species-rich plant communities. Estonian and 
Swedish alvars differ but are rare, and endemic plant species are present. In our sample, these 
systems are represented by case studies Nº 22 (Estonian alvars), Nº 44 (wooded meadows of 
the southern Öland Island) and Nº 45 (Great Alvar of the Öland Island). In the latter, several 
endemics (e.g., Artemisia oelandica and Helianthemun oelandicum) are represented as well as 
some rare insect species of coleopteran and butterflies. Experts stressed the environmental 
objectives of these small-scale systems, where grazing animals (mainly cattle and sheep) are a 
management tool for alternating grazing and hay making, pollarding or cutting hazel trees to 
avoid overgrazing or restoring areas invaded by Juniperous or Potentilla fruticosa in the Great 
Alvar. 

—  A sheep grazing system is present in the southern Atlantic fringe of Norway, not far from the 
coastline. Sheep are the dominant grazer with small or mixed herds of cattle and reindeers. 
Short migrations are frequent between pastures near the coastline and nearby mountains 
during the summer, frequently under free-ranging or only occasional surveillance. This system is 
not represented in our sample. 

3.2.  The Atlantic bioregion 

We will discriminate between three geographical areas for the description of the LSGS and the 
sampling: the UK and Ireland; the Atlantic fringe from France to Denmark; and the Atlantic fringe of 
the Iberian Peninsula. Traditional LSGS still persist in the less favoured areas of the UK and Ireland 
such as southwestern Scotland, the Cumbria Uplands or western Ireland (the Burren), in France 
(French Normandie and Marais de l’Ouest) and in the Atlantic fringe of the Iberian Peninsula 
(Galician and Cantabrian mountains). In contrast, in most of the Atlantic corridor stretching from 
Belgium to Denmark, most grazing systems are of small scale and mainly orientated to nature 

16 

 
conservation functions. The dominant grazing resources in this bioregion represented by vegetation 
types in the EU Birds and Habitats Directive are listed in Box 2.  

Box 2. The main vegetation types with grazing systems in the Atlantic bioregion. 

—  1330. Atlantic salt meadows 

—  2130. Fixed coastal dunes with herbaceous vegetation 

—  2150. Atlantic decalcified fixed dunes (Calluno-Ulicetea) 

—  2180. Wooded dunes of the Atlantic, Continental and Boreal regions 

—  21A0. Machairs 

—  2310. Dry sand heaths with Calluna and Genista 

—  4010. Northern Atlantic wet heaths with Erica tetralis 

—  4020. Temperate Atlantic wet heaths with Erica ciliaris and Erica tetralis 

—  4030. European dry heaths 

—  4040. Dry Atlantic coastal heath with Erica vagans 

—  4060. Alpine and Boreal heaths 

—  5130. Juniperus communis formations on heaths or calcareous grasslands 

—  6110. Rupicolous calcareous or basophilic grasslands of the Alysso-Sedion albi 

—  6120. Xeric and calcareous grasslands 

—  6130. Calaminarian grasslands of the Violetalia calaminariae 

—  6210. Semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) 

—  6230. Species rich Nardus grasslands on silicious substrates in mountain areas 

—  6410. Molinia meadows on calcareous, peaty or clayey-silt-loam soils (Molinion caeruleae) 

—  6430. Hydrophilous tall herbs fringe communities of plains and of the montane to alpine levels 

—  6440. Alluvial meadows of river valleys of the Cnidion dubii 

—  6510. Lowland hay meadows 

—  7130. Blanket bogs 

—  7140. Transition mires and quaking bogs 

—  8240. Limestone pavements 

—  9140. Medio-European subalpine beech woods with Acer and Rumex arifolius 

—  9150. Medio-European limestone beech forests of the Cefalanthero-Fagion 

—  91C0. Caledonian forests 

—  91E0. Alluvial forests with Alnus glutinosa and Fraxinus excelsior 

—  9230. Galicio-Portuguese oak woods with Quercus robur and Quercus pyrenaica 

3.2.1.  The UK and Ireland 

In the UK and Ireland, upland and moorland grazing systems are represented in the less favoured 
areas with variable proportions of cattle and sheep, frequently of indigenous breeds and mixed 
herds. Extensive hill and mountain sheep systems are a variant represented in the southern uplands 
of Scotland, the Cumbrian uplands and the Welsh mountains. In the former two, Scottish Blackface 
ewes are frequently crossed with Blue-faced Leicester rams to provide Scotch Mule lambs. 
Agricultural land is interspersed with semi-natural grasslands, moorland/heathland areas and 
forests, where the latter are rarely grazed. Sheep owners under land renting agreement (the 
crofters) are common. In the Cumbrian uplands, indigenous Herdwicks, Swaledales and Rough Fell 
sheep are represented with small herds of cattle using unfenced common land. 

Moorland systems are represented in England in Dartmoor, Exmoor and Bodmin Moor and also in 
the North Yorkshire Moors. Rough grazing covers the majority of grazing areas (65-80%), and lambs 

17 

 
are usually finished under grazing. For the minority of cattle herds, winter feeding supplementation 
is provided with forage conserves and some industrial protein feed. Sheep mobility is common from 
winter pastures near the farmyard to summer pastures in the uplands.  

The British uplands, after the Second World War, were affected by changes in EU policy support 
schemes and the impact of the Foot and Mouth disease epidemic of 2001. Currently, the 
recognition of the landscape and recreational values probably outweigh production from agriculture 
and forestry (RGS, 2002). 

In the west of Ireland, the Burren system is relevant. In this case, cattle are dominant, and the 
animals move to winter pastures (winterage) and stay near the farmyard during the summer in 
more productive pastures. A mild climate and limestone-rich grasslands allow winter feeding. Orchid 
rich calcareous grasslands (vegetation type 6110), semi-natural dry grasslands (6210), limestone 
heaths (4020), turlougs (3180), limestone pavements (8240) and fens are represented. The Burren 
system is basically a suckler beef based pastoral system with representation of foreign breeds 
(Charolais, Limousin and Simmental), a strong involvement and approval of local communities for 
economic side lines and recreational activities in the area, and the main objective of maintaining 
species-rich grasslands. 

In the UK and Ireland, LSGS are represented in our sample by four case studies: the Burren region of 
the Ireland Mid-West (case study Nº 1); Dartmoor in SW England (Nº 16); the Cumbrian uplands of 
northwest England (Nº 34); and the uplands of southwest Scotland (Nº 42). These LSGS have 
distinctive features in terms of geology, ecosystems, plant communities and economies, but also 
share some themes such as cultural landscapes in terms of past misuse, fragility and current needs 
for adaptation. They have developed social networks and supporting institutions for greater 
recognition of their social and ecological values to society (e.g., the Burrembeo Trust in Ireland, 
Action with Communities in Cumbria and Scottish Natural Heritage in Scotland). 

Experts recognise the current dominant environmental and recreational functions in their systems 
but also the interdependency of these functions with livestock grazing. They do not see an 
acceptable alternative out of the LSGS operation for managing these traditional and agricultural 
landscapes. They also provided comments and supporting evidence on the need to change the 
current support scheme from an income foregone basis to stocking levels attached to the delivery 
of public goods and environmental values (Nisbet & Glaves, 2010), and they provided some 
successful implementations of social schemes for luring young farmers to livestock operations (in 
the Cumbrian uplands). For Scottish farmlands, the expert provided evidence of livestock farmers 
and crofters withdrawing from sheep farming and the negative influence of grazing cessation on 
natural values as a case for additional financial support to HNV livestock farming (Marrs et al., 
2011; Pakeman, 2011). 

3.2.2.  The Atlantic corridor from France to Denmark 

The mainland Atlantic corridor stretches from France to the west of the Jutland Peninsula in 
Denmark. This is a densely populated region with intensive agriculture, but in the less favoured 
areas of heathlands, marshes, wetlands and coastal dunes, lowland cattle and sheep systems are 
generally represented on a small scale and generally under nature conservation and restoration 
objectives.  

Cattle and sheep herding in lowland Atlantic areas is represented in Germany (e.g., the Lüneburger 
heath), Belgium and The Netherland (e.g., the Ooijpolder area near Nijmegen) where grazing is only 
a tool for environmental management. These systems include protected areas, nature reserves, 

18 

 
national parks and land restoration areas, which are under pressure of being overgrown by woods 
and shrubs. There is no real focus on production objectives, and a market approach barely exists. 
Contractual agreements for environmental management usually involve different stakeholders such 
as landowners (usually local governments), managers (usually nature conservation agencies) and 
the livestock farmers who rent their animals and receive a payment for environmental grazing 
management of the resources as programmed by managers. 

Within this region, the French Atlantic LSGS are more production-oriented, although the 
environmental function is also relevant particularly when the LSGS operation is under an agri-
environmental scheme (e.g., Operations Locales Agro-Environnementales) under the assumption of 
synergies between extensive livestock rearing and wetland management. LSGS operations are 
represented in Normandie (e.g., Pays d’Auge, Cotentin and Bessin regions) and in the Marais de 
l’Ouest system between Vilain and Gironde. It encompasses resources dominated by semi-natural 
pastures associated with hedges (bocages), salt marshes (pre-salés), and wetlands, and is used by 
indigenous dairy cattle (e.g., the Normande breed) and meat-oriented cattle and sheep (e.g., the 
Charollais breeds). More than 100,000 ha of semi-natural wet grasslands and salt marshes in the 
Atlantic fringe are managed under the collective use of communes (municipalities) or under private 
landowners’ associations (Associations Foncières). Both rent the pasture to livestock owners 
(éleveurs), who are sometimes organised under pastoral groups (Groupement Pastoraux) (FMA, 
2003). 

The Atlantic coastal systems are represented in our sample by case studies Nº 25 (Drenthe, The 
Netherlands), Nº 38 (Wadden Sea National Park, Germany), and Nº 40 (the coastal dune region of 
The Netherlands including Wadden Islands). Grazing land is frequently owned by regional or local 
governments, and environmental managers are conservation organisations or water management 
boards. Grazing animals are owned by the managers or sometimes hired from neighbouring 
farmers under contractual agreements for grazing management and grazing fees. Coastal dunes, 
Atlantic salt marshes and heath vegetation types are prevalent in this area, always under the 
pressure of a high population density (e.g., reverting heaths to cultivated grasslands) and the 
requirement of contributing to nature services (e.g., water quality). 

Experts expressed concerns on adapting grazing plans to environmental conservation and provided 
evidence on this theme. Management plans can differ between mainland calcium-rich dunes and 
calcium-poor dune islands, and mixed and seasonal grazing should be adapted to particular 
conservation objectives. In general, grazing restricts the domination by tall grasses and shrubs that 
decrease biodiversity and favour smaller plants of xero-thermic conditions (Bonte & Maes, 2008; 
Plassmann et al., 2010). 

3.2.3.  The Atlantic fringe of the Iberian Peninsula 

In the Atlantic fringe of the Iberian Peninsula, the Cantabrian mountains act as a filter for moist sea 
air with frequent mists on the north-facing slopes. Forests and hedges with remnants of climax 
oaks (Quercus robur) and ash (Fraxinus excelsior) are common in the northwest Galician range 
under siliceous substrates. The presence of mixed grazing is recommended to maintain herbaceous 
communities of Agrostis-Festuca and control secondary succession towards shrubby communities 
dominated by Calluna vulgaris and Ullex gallii. There is also evidence of indigenous cattle (Rubia 
Gallega breed) checking the turf-spreading Nardus stricta not used by previous sheep grazing 
(Osoro et al., 2000). 

In the central part of the Cantabrian mountains (Picos de Europa), mesophytic basiphilous beech 
forest (Carici sylvaticae-Fagetum) and mixed forests (Corylo-Fraxinetum cantabricum) are 

19 

 
dominant. The upland pastures of the Picos de Europa (brañas) used to be sequentially grazed by 
cattle (indigenous Casina breed), sheep/goats and finally by horses (free-roaming Asturcones), 
where the latter have the function of cleaning mature refuses of tall herbs. Currently, a few farmers 
bring their animals to the communal brañas due to the fear of wolf attacks and have unsatisfactory 
infrastructure for milking and cheese-making facilities. Indigenous tree-milk cheeses 
(cattle/sheep/goats) based on upland pastures (e.g., Gamoneu de Puerto) is, thus, rare today 
although very much appreciated. In more hilly and lowland areas, heaths are the dominant 
vegetation type and under the risk of wildfires due to deforestation or abandonment of agricultural 
and livestock practices. Dairy cattle are currently a mainly indoor-feeding operation with foreign 
breeds (American Hollstein) in intensive farms near the coast. Some type of vertical mobility for the 
use of semi-natural grasslands is apparent by alternating hay making and grazing on the hills and 
using lowland grasslands near the coast for hay making during summer and for grazing of meat-
oriented cattle (Asturiana de Valle breed) during winter-early spring. 

This system is the only one represented in our sample from this region (case study Nº 4). There are 
others represented in Galicia and the border with Portugal as well as in the region of Cantabria 
(Valleys of Liébana and Cabuérniga) and in the Basque Country (Aralar Natural Park). The expert 
related management options and evidence is to maintain interspersed parcels of heathlands, 
forests and managed grasslands and combining mixed grazing and haymaking (Osoro et al., 2000). 
In other more populated European Atlantic areas, the heathland flora is more at risk (Piessens & 
Hermy, 2006; Sundseth, 2009) as heathlands are replaced by grasslands. At the same time, the 
expert related some pressures on the LSGS operation such as the lack of family business turnover 
and the hardship of the herding operation, particularly in the upland pastures because of poor 
grazing infrastructures, unresolved surveillance and compensation due to losses by predators. Thus, 
the expert suggested support for the herding operation on upland pastures and better marketing to 
support local products (e.g., local cheeses for which this region is well known). 

3.3.  The Continental and Pannonian bioregions 

This bioregion included a large part of Central Europe with large tracts in France, Germany, the 
Czech Republic and Poland, and also substantial areas of Italy, the Balkan countries, Romania and 
Bulgaria. For the purpose of this study, the Pannonian region of Hungary and Serbia is also included 
here. Relevant vegetation types are listed in Box 3. 

The bioregion includes some small scale grazing systems in the valleys, pre-alpine or wetland areas 
of central Europe that in some cases represent a remnant of past traditional and transhumant 
systems, and they are currently managed mainly under environmental conservation. This is the case 
of cattle and sheep grazing in the sub-continental lowlands of northern and central Germany such 
as Müritzhof or Klein Schorfheide management units. In areas of contact with the Alpine bioregion, 
more production-oriented systems are still operative such as sheep grazing of Merino Landschaf 
along the Lech River between the cities of Landsburg and Augsburg, the sheep grazing in the 
Swabian Jura region where an important transhumant system worked in the past (Luick, 2008) or 
the pre-alpine Allmende of Upper Bavaria rearing heifers of the lowland dairy farms in summer 
alpine pastures. 

20 

 
 
Box 3. The main vegetation types with grazing systems in the Continental and Pannonian bioregions 

—  1530. Pannonic salt steppes and salt marshes 

—  2180. Wooded dunes of the Continental region 

—  2310. Dry sand heaths with Calluna and Genista 

—  4030. European dry heaths 

—  4070. Bushes with Pinus musgo and Rododendron hirsutum 

—  40A0. Subcontinental peri-Pannonic scrub 

—  5130. Juniperus communis formations on calcareous grasslands 

—  6120. Xeric and calcareous grasslands 

—  6210. Semi-natural dry grasslands and scrubland facies on calcareous substrates 

—  6220. Pseudo-steppes with grasses and annuals of the Thero-Brachipodietea 

—  6240. Sub-Pannonic steppic grasslands 

—  6250. Pannonic loess steppic grasslands 

—  6260. Pannonic sand steppes 

—  6410. Molinia meadows on calcareous, peaty or clayey-silt-loam soils (Molinion caeruleae) 

—  6430. Hydrophilous tall herbs fringe communities of plains and of the montane to alpine levels 

—  6440. Alluvial meadows of river valleys 

—  6510. Lowland hay meadows 

—  6520. Mountain hay meadows 

—  7140. Transition mires and quaking bogs 

—  7230. Alkaline fens 

—  9110. Luzulo-Fagetum beech forests 

—  9130. Asperulo-Fagetum beech forests 

—  9140. Medio-European subalpine beech woods with Acer and Rumex arifolius 

—  9160. Sub-Atlantic and medio-European oak and oak-hornbeam forests of Carpinion betulii 

—  9170. Galio-Carpinetum oak-hornbeam forests 

—  91E0. Alluvial forests with Alnus glutinosa and Fraxinus excelsior 

—  91K0. Illyrian Fagus sylvatica forests (Aremonio-Fagion) 

—  91Y0. Dacian oak-hornbeam forests 

—  9530. (Sub-) Mediterranean pine forests with endemic black pines 

More large-scale grazing and production-oriented systems are found in other areas of continental 
Europe although under very different transition paths to sustainable systems, ranging from extreme 
forms of extensification (abandonment trend of the grazing operation) to semi-subsistence forms 
of pastoralism. The LSGS of the former type are found in the lowland areas of Hungary (the 
Hungarian plain), the Bohemia-Moravia region of the Czechia (Zlinsky kraj system), and of the latter 
type in the less favoured and plain areas of Romania and Bulgaria. Here, a semi-subsistence system 
of small herds of cattle and sheep and small holders sharing communal grazing is still common. 

In continental regions of Italy and France, more developed and market-oriented LSGS are operative 
such as in the Central Apennine region of Italy or the Massif Central in France, a region of transition 
to Atlantic and Mediterranean influence or even the Alpine regions of southeast France, and they 
are represented in the Grand Causses and the Cévennes National Park. For the southern Causses 
(e.g., Causse de Blandas or Causse de Larzac) connected with the Mediterranean bioregion, 
extensive stock breeding is a relevant and productive activity connected with transhumant sheep 
flocks of the Mediterranean region of Languedoc-Rousillon. 

21 

 
Seven LSGS of the Continental bioregion are represented in our sample. They include a small scale 
nature-management system of wetland continental areas such as Thuringia in Germany (case study 
Nº 37) and a large representation of LSGS in the less developed areas of central and eastern 
Europe. The plain areas of Hungary (case study Nº 9), Bulgaria (case study Nº 21) and the 
Transylvania and Wallachia regions of Romania (case studies Nº 7 and 28) are represented. The 
other two sampled systems are also located in the Continental bioregion although connected with 
pre-alpine or mountain regions of less developed and depopulated areas such as the Mount Vlasic 
area of central Bosnia & Herzegovina (case study Nº 13) or the developed and populated Marche 
region in the central Apennines (case study Nº 18). Frequently, these LSGS are in the fringe of the 
Continental and Alpine regions, benefiting from the movement of flocks between lowland and 
upland pastures. 

The LSGS of the Hungarian Plain are in a transition towards more indoor-feeding and consolidated 
systems while small-scale family farms are still dominant in the less favoured and plain areas of 
Romania and Bulgaria. In the short-to-medium term, experts showed interest in maintaining 
improved forms of peasant pastoralism as a way of maintaining indigenous breeds, plant-rich hay 
meadows and communal pastoralism in the mountain pastures under the aegis of the sheep 
master. In more favoured plain areas of Romania and Bulgaria, where land-based summer and 
winter feeding is allowed, the development of commercial dairy farms and industries seem a 
requirement (Draganescu, 1997 and personal communication, 2011). 

Common pressures related by experts in Romania and Bulgaria are powerless grazing regulations 
and ineffective support policies. Traditional systems are still operative while new EU regulation is 
somewhat uncongenial with traditional uses of land and animals. They also stressed the 
heterogeneous pattern of grazing distribution with indigenous cattle near the farmyard (returning 
daily to the stable for milking and supplementary feeding) while communal and mountain sheep 
grazing is poorly organised.  Family milking- or cheese-making in the farmyard or in the summer 
grazing camp require better facilities to take EU sanitary and marketing rules into account. 

The experts from these two countries provided some management options for future development, 
changing some current peasant farming for co-operative milking and/or cheese-making as well as 
social and financial support for herding operations in the figure of the herd/flock master, who 
gathers the cows of individual owners for daily grazing in communal land or gathers and moves 
small lowland sheep flocks to hill and mountain pastures away from the farmyard during most of 
the year. The Hungarian expert, on the other hand, stressed the requirement of explaining the social 
and ecological values of the LSGS to society, facing trends in international food markets and quality 
products (Karsten & Baer, 2009), but recognised a main trend toward indoor feeding and more 
intensive operations in the plain area. 

For the two sampled systems widely connected with the mountain and alpine areas, vertical 
mobility of dairy sheep flocks is common. In the Mount Vlasic area, mountain and hill stables are 
common, with mobility of Pramenka flocks in the mountain pastures during summer and winter 
transhumance to lowland snow-free pastures or crop residues after harvest. This type of inverse 
mobility is currently under threat, since an agreement and co-operative interest between lowland 
crop farmers and some grazing institutions, as well as normative rules, are required. In the Marche 
region, however, summer grazing in communal pastures is organised with some grazing and milking 
facilities in the mountains, as well as milk-collection and cheese-making organised by local 
industrial firms. Notwithstanding this differential status in the transition path to development, both 
experts indicated a trend of sheep farmers leaving farming and a consolidation of flocks to few 
units of larger size, which is more apparent in the Marche region. 

22 

 
3.4.  The South Alpine bioregion 

In Europe, the South Alpine bioregion encompasses LSGS located in the main mountain ranges of 
southern and eastern Europe including the Pyrenees (France and Spain), the Alps (France, Italy, 
Slovenia, Switzerland, Austria and Germany) the Carpathians (southern Poland, Slovakia and 
Romania), and also the LSGS of the Dinarides, Balkans and Rhodope mountain ranges in their 
corresponding countries. As in other European bioregions, there is a long history of grazing in the 
mountains with common use of pastoral resources and associated cultural traditions and local 
products. The LSGS of this bioregion are mainly large scale and production-oriented but 
environmental functions have become more apparent in recent years, particularly in the most 
developed countries. Mainly located in remote areas, LSGS are facing pressures of abandonment in 
the whole region while the transition path has varied widely across the region with more pressures 
and less options for conservation management and technological innovations in the mountain LSGS 
of non-EU countries or countries of southern and eastern Europe that joined more recently. 
Vegetation types in the EU Birds and Habitats Directive of potential grazing use are listed in Box 4. 

The LSGS in the Alpine bioregion of France are in the Alps and the Pyrenees. There is a long history 
of collective pastoralism and mobility of herds/flocks within these mountain regions and also 
between the lowland Mediterranean regions and the southern Alps. Collective pastoralism in France 
is highly regulated at the commune (municipality), regional and even the EU level. The LSGS 
operation has undergone a certain social revival after the constitution of pastoral groups 
(Groupement pastoraux) of livestock farmers (eleveurs) and landowners’ associations (Associations 
foncières pastorales) in 1972. The livestock sector under extensive pastoralism accounts for some 
20% of the total value, and large numbers of grazing animals are moved to summer grazing 
pastures (estives). Since the year 2000, summer grazing is having a revival with economic side 
activities such as tourism and second residences, with sports or food festivals on the increase. 
Market organisation of local products and social support go hand in hand, as France has a tradition 
of supporting rural life as compared to other EU countries (APCA, 2005). A review of 21 extensive 
systems of grassland management in France and their main grazing practices (Pointereau et al., 
2007) included 12 and 22 local breeds of cattle and sheep, respectively. 

For the French Alps, two mountain areas are differentiated (Alpes du Nord and Alpes du Sud) but in 
both, livestock production has turned towards product quality with Protected Designation of Origin 
and prestigious mountain labelling schemes (e.g., Sisteron and Adret lambs). In the Northern Alps, 
dairy (Montbeliarde and Tarantaise breeds) and meat-oriented cattle herds are tended by more than 
7,000 livestock farmers, the majority practising vertical mobility to summer pastures. In the 
Southern Alps, ovine meat breeding (Merino d’Arles is the dominant breed) is more common, with 
some 3,000 local and transhumant farmers. In this area, long-distance transhumance is still 
practised for the sheep flocks of the Crau Plain in the Mediterranean Provence, moving to mountain 
pastures in the southern Alps during summer (Wolff, 2002). Restrictive pastoral management in the 
Alps and the Drome is hardly compatible with homogeneous patterns of grazing distribution and 
finishing lambs on pastures due to increasing numbers of wolves. Private and public regimes of 
land ownership are present in the two regions, but common land is prevalent (58%) in the southern 
Alps. 

23 

 
 
 
Box 4. The main vegetation types of potential grazing use in the South Alpine bioregion 

—  4060. Alpine and Boreal heaths 

—  4070. Bushes with Pinus mugo and Rododendron hirsutum 

—  5110. Stable xerothermophilus formations with Buxus sempervirens on rock slopes 

—  5130. Juniperous communis formations on calcareous grasslands 

—  6120. Xeric and calcareous grasslands 

—  6130. Calaminarian grasslands of the Violetalia calaminariae 

—  6140. Siliceous Pyrenean Festuca eskia grasslands 

—  6150. Siliceous alpine and boreal grasslands 

—  6170. Alpine and subalpine calcareous grasslands 

—  6210. Semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) 

—  6230. Species-rich Nardus grasslands on siliceous substrates in mountain areas 

—  6410. Molinia meadows on calcareous, peaty or clayey-silt-loam soils (Molinion caeruleae) 

—  6430. Hydrophilous tall herb fringe communities of plains and of the montane to alpine levels 

—  6510. Lowland hay meadows 

—  6520. Mountain hay meadows 

—  7140. Transition mires and quaking bogs 

—  7230. Basiphilous active peats 

—  7240. Alpine pioneer formations of Caricion bicoloris-atrofuscae 

—  9130. Asperulo-Fagetum beech forests 

—  9140. Medio-European subalpine beech woods with Acer and Rumex arifolius 

—  9150. Medio-European limestone beech forests of the Cephalantero-Fagion 

—  9180. Tilio-Acerion forests of slopes, screes and ravines 

—  91K0. Illyrian Fagus sylvatica forests (Aremonio-Fagion) 

—  91R0. Dinaric dolomite Scots pine forests (Genisto januensis-Pinetum) 

—  9420. Alpine Larix decidua and/or Pinus cembra forests 

—  9430. Subalpine and montane Pinus uncinata forests 

—  9560. Endemic forests with Juniperous spp 

Summer pastures in the French Pyrenees (estives) are mainly controlled by local governments 
(communes), and the collective management of pastoral groups and the mobility of herds/flocks is 
mainly intra-regional. Livestock farmers (eleveurs) usually hire labour (patre de haute montagne) to 
take care of the animals in the estives (vacher or berger) with the help of pastoral guard dogs (the 
patou). In three districts (departments of Ariege, Haute Garone and Haute Pyrenees), cattle farming 
is dominant in the valleys while sheep farming is more evenly distributed between lowland and 
upland pastures. Suckler cattle farms with hardy breeds (Gasconne) are suited to management 
conservation objectives (Gibon et al., 1999; Theau & Gibon, 1999; Mottet et al., 2006). These 
authors observed pressures on the traditional management of an ageing population, a trend 
towards sedentary herds/flocks in the valleys, abandonment of local breeds, lack of winter-feeding 
programmes and finally, adaptation of summer grazing infrastructure to flock consolidation. 

In the Alpine region of Italy, three areas are also commonly differentiated (eastern, central and 
western Alps) with a small alpine region located in the central Apennines around Monte Sibilini and 
Monti della Laga-Gran Sasso National Parks. Distinctive physical conditions, vegetation types and 
the main features of LSGS in these alpine areas are structured in a recent source of information 
(Caballero et al., 2009). This source also includes information of the main Spanish, Italian and Greek 
LSGS in the Atlantic, Alpine and Mediterranean bioregions. These LSGS are well represented in our 
sample. For these reasons, we will not discuss them further in this section. 

24 

 
The Romanian, Polish and Slovakian Carpathians have a strong tradition of mountain dairy sheep 
grazing. In the past, long distance transhumance of Wallachian flocks was organised in mountain 
locations (e.g., the Sibiu area) to move animals during winter to the lowlands of the Black Sea area 
and beyond (Draganescu, 2003). These large movements have largely disappeared, and currently 
the traditional LSGS operation can be divided into two grazing units in the mountains: the lowland 
and upland pastures, and two stakeholders: the small landholder and the sheep owner of the 
lowland stable, and the sheep master and organiser of the summer grazing camp. The latter 
combines the small flocks of several owners to gather some 100-200 sheep and organises the 
summer grazing season usually with help of paid labour. The sheep master pays the grazing fee 
and the labour and shares the cheese production of the combined flock on the summer pastures 
with the other sheep owners (e.g., Polish Oscypek, smoked hard cheese), usually under stressful 
milking- and cheese-making conditions. While flocks graze in summer camps, hay from the 
lowlands meadows is produced and barn-stored for feeding during harsh winters. 

Although of HNV, the Wallachian mountain systems share some de-stabilising factors and 
pressures for their continuity. The semi-subsistence nature of most of these systems makes the 
intensification of management a less likely scenario than abandonment unless the means to 
support the social viability of these systems is urgently designed and implemented. In doing that, 
the policy schemes cannot be alien to the system structure. For example, proper claims to land 
property or the small size of land holdings may hinder access to policy support, and the 
homogeneous pattern of grazing distribution is hindered by the poor mobility of flocks and 
inoperative drove paths to far-reaching clearings. Moreover, support is required to improve grazing 
infrastructure in lowland stables as well as in the summer camp, where most of the milking and 
cheese-making is carried out. In this case, the facilities should be adapted to EU hygiene and 
storage standards for proper quality assurance and marketing. 

Other examples of LSGS in a derelict state can be found in the Dinarides and the Balkans. The 
Western Stara Planina, a LSGS not recorded in this study, is a region of some 4000 km2 sharing the 
Balkan border of Bulgaria and Serbia and in the Alpine to Continental fringe. The main land uses are 
mountain and birch-dominant forests (60%), sub-alpine and semi-natural pastures (25%) and 
farmland (15%). Semi-subsistence farming is prevalent with smallholdings (some 2 ha) and an 
ageing population living in poor conditions with little scope to set up viable farming operations. As in 
other eastern European countries and marginal areas, the design and implementation of EU 
schemes of support should take into account that large tracts of land are under communal use, 
claims to land property is not always apparent and the small size of land holdings. In the year 2005 
and for the Bulgarian side of the region, only 8% of arable land and 18% of grasslands were 
registered in the EU Land Parcel Identification System and thus allowed claims for EU support. 

From other sources, we know that historical events since the Roman times in the Carpathians and 
Balkans countries have given way to interchanges in pastoral life. Walachian sheep breeds have 
been farmed for generations in 14 countries of central and southeastern Europe and continue to 
support low-input systems with environmental values in the less favoured areas (Huband, 2007; 
Bachev, 2009; Draganescu & Grosu, 2010). 

The southern Alpine bioregion is represented in our sample by 11 study areas including the Spanish 
Pyrenees (case studies Nº 29 and Nº 33); Andorra (case study Nº 10); the eastern and western 
Italian Alps (case studies Nº 17 and Nº 23); the German and Swiss Alps (case studies Nº 2, Nº 30 
and Nº 15); the northern (Polish) and southern (Romanian) Carpathians (case studies Nº 3 and Nº 8); 
and the western Balkans in the Republic of Macedonia (case study Nº 11). 

25 

 
The experts stated that there are some common and differentiating pressures, largely related to the 
socioeconomic development in the surrounding area. In the Alpine areas of western and central 
Europe, LSGS are organised for grazing management and marketing institutions with management 
options implemented at a higher level. They cited particular and common pressures such as the 
compatibility and implementation of social and ecological functions without some type of support 
and were doubtful about the effectiveness of the current schemes, concerns about the social and 
professional status of the herding operation in need of support, and the need to preventing losses 
due to predators in the mountains and better compensation schemes. Scarce and costly paid labour 
is a frequent concern in these developed areas.  

On the other hand, LSGS in the mountain regions of southeastern Europe are in a very early phase 
in the transition path to sustainable development. In fact, some experts on LSGS expressed doubts 
on a true path to development although they agree on co-adaptive management and learning from 
experiences in other systems. Institutional grazing management, the legal status of land-based 
pastoral resources, the status of grazing infrastructure, the social and professional recognition of 
the herders’ job, and the quality assurance and marketing status of local products are indeed 
milestones for LSGS in its earlier phases to sustainable development. Policy options and technical 
innovations should be aligned with the types of LSGS in this bioregion. 

3.5.  The Mediterranean bioregion 

In Europe, the Mediterranean bioregion encompasses a large part of the Iberian Peninsula, most of 
central and southern Italy, the whole of Greece, the Adriatic coastline of the Balkans countries, the 
southeast of France and the Mediterranean islands. All these areas are a pool of European social-
ecological diversity and cultural landscapes shaped by a long history of combined forest, arable and 
grazing management with alternative pulses. As in other bioregions, livestock grazing is one of the 
few tools for avoiding encroachment of the natural vegetation, particularly in marginal and remote 
areas. Probably more than in other bioregions, here we find an impressive variety of habitats along 
relatively small spatial scales, but also under threat of wildfires. 

For a large and representative part of this bioregion, we already have a structured source of 
information (Caballero et al., 2009). This source provided a framework for typology analysis of 
LSGS, vegetation succession trajectories under grazing or post-grazing effects and a link between 
LSGS and vegetation types listed in the EU Birds and Habitats Directive. For Spain, Italy and Greece, 
15, 14 and 10 relevant LSGS are represented and described, respectively with common pressures 
and main options for development. A majority of these LSGS in Spain and Italy and the ten Greek 
systems are Mediterranean LSGS. The rest are Spanish and Italian LSGS in other bioregions 
(Atlantic, Alpine and Continental). For our sample, 33%, 43% and 20% of the former LSGS are 
represented in Spain, Italy and Greece, respectively. Following the structure of this section, we will 
redirect our attention to the vegetation types of potential pastoral use as represented in the EU 
Birds and Habitats Directive and further to the rationale of the experts in their responses to the 
questionnaire. 

In selecting vegetation types linked to grazing use and HNV farmland, it is important to recognise 
that permanent pasture or semi-natural grasslands are not the only source of forage. Herbaceous 
and non-herbaceous resources of other habitat types (e.g., heathland, scrubs, forests and their 
related grasslands) are also used in reality, which represents a large tract of marginal areas and 
important pools of European biodiversity. Particular LSGS of relevance in the Mediterranean 
bioregion (e.g., the cereal-sheep system) are supported on low-intensity use of arable land (e.g., 
green fallows and crop residues of cereals and legumes and also low-intensity olive orchards and 

26 

 
their related grasslands and crop residues). The inventory of European HNV areas and related LSGS 
cannot withstand a mismatch with the real world, and the EU cannot ignore this reality in designing 
schemes for conservation management and policy support in the next CAP reform. This has been 
our main rationale in selecting vegetation types of potential grazing use for all the bioregions, and 
the list (probably not inclusive) for the Mediterranean is provided in Box 5. 

In the Mediterranean bioregion, 14 LSGS are included in our sample: three case studies of the 
Iberian plains (Montados in Portugal (Nº 6) and dehesas and cereal-sheep in Spain (Nº 27 and Nº 
46)); six case studies of the mountain areas (Central Macedonia (Nº 11), the southern Apennines in 
Italy (Nº 19), the Dinarids in Croatia (Nº 26), Serra da Estrella and Tras-os-Montes in Portugal (Nº 
31 and Nº 35), and the Provence in France (Nº 41); and finally five LSGS of Mediterranean islands 
(island of Rhodes in Greece (Nº 12), the island of Pag in Croatia (Nº 32), the island of Corsica in 
France (Nº 39) and the islands of Sicily and Sardinia in Italy (Nº 20 and Nº 43). 

The experts related different management trajectories from almost abandoned LSGS in the 
surroundings of Mount Velebit in the Dinarids of Croatia to managed and market-driven systems in 
the Iberian plains (montados and dehesas). In general, island LSGS are less prone to abandonment 
due to less mobility of labour and combining farming and off-farming income sources such as 
tourism (e.g., LSGS on the islands of Rhodes and Pag). In fact, these two LSGS were the only ones 
(see next section) showing increasing rates of farmers entering the LSGS operation out of the 46 
sampled systems. 

Although system-specific analyses of management practices are required, experts indicated a 
common set of socio-economic pressures for the continuity of Mediterranean LSGS such as low or 
negative profitability without support, harsh working conditions on marginal or isolated areas, and 
young farmers discontinuing the family operation. They also rate common options for improvement 
such as better grazing plans supported by targeted policy schemes, better grazing infrastructures 
and the improvement of quality assurance and marketing of local products, which are particularly 
rich from Mediterranean LSGS (Caballero et al., 2009). 

3.6.  A note on the interface to biodiversity 

In the five bioregions, we have recorded 87 single habitat types of those included in the EU Birds 
and Habitats Directive. Of these 87 types, 53 were exclusive to one bioregion, 18 were replicated in 
two bioregions, eight types were replicated in three bioregions, five types were replicated in four, 
and finally, three of the recorded vegetation types were present in the five bioregions. The large 
number and proportion (61%) of exclusive habitats is an indication that planning for conservation 
management at a European scale should take actions in different bioregions and their 
corresponding LSGS into account. Within bioregions, 23, 29, 29, 27 and 38 vegetation types of 
differential pastoral relevance were recorded for the Northern Alpine and Boreal, the Atlantic, the 
Continental-Pannonian, the South Alpine, and the Mediterranean bioregions, respectively. 

27 

 
 
 
 
Box 5. The main vegetation types of with grazing systems in the Mediterranean bioregion 

—  1340. Inland salt meadows 

—  1410. Mediterranean salt meadows (Juncetalia maritimi) 

—  1510. Mediterranean salt steppes (Limonietalia) 

—  1520. Iberian gypsum steppes (Gypsophiletalia) 

—  4030. European dry heaths 

—  4090. Endemic Oro-Mediterranean heaths with gorse 

—  5120. Mountain Cytisus purgans formations 

—  5130. Juniperus communis formations on heath or calcareous grasslands 

—  5210. Arborescent matorral with Juniperus spp 

—  5330. Thermo-Mediterranean and pre-desert scrub 

—  5420. Sarcopoterium spinosum phryganas 

—  5430. Endemic phryganas of the Euphorbio-Verbascion 

—  6110. Rupicolous calcareous or basophilic grasslands of the Alysso-Sedion albi 

—  6120. Xeric and calcareous grasslands 

—  6160. Oro-Iberian Festuca indigesta grasslands 

—  6170. Alpine and subalpine calcareous grasslands 

—  6210. Semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) 

—  6220. Pseudo-steppe with grasses and annuals of the Thero-Brachypodietea  

—  6230. Species-rich Nardus grasslands on siliceous substrates in mountain areas 

—  62A0. Eastern sub-Mediterranean dry grasslands (Scorzoneratalia villosae) 

—  6310. Dehesas with evergreen Quercus spp 

—  6420. Mediterranean tall humid grasslands of the Molinio Holoschoenion 

—  6430. Hydrophilous tall herbs 

—  6440. Alluvial meadows of river valleys of the Cnidion dubii 

—  6510. Lowland hay meadows 

—  6520. Mountain hay meadows 

—  8240. Limestone pavements 

—  91B0. Thermophilous Fraxinus angustifolia woods 

—  91E0. Alluvial forests with Alnus glutinosa and Fraxinus excelsior 

—  9210. Apennine beech forests with Taxus and Ilex 

—  9230. Galicio-Portuguese oak woods with Quercus robur and Quercus pyrenaica 

—  9240. Quercus faginea and Quercus canariensis Iberian 

—  9250. Quercus trojana woods 

—  9330. Quercus suber forests 

—  9340. Quercus ilex and Quercus rotundifolia forests 

—  9530. (Sub-) Mediterranean pine forests with endemic black pines 

—  9540. Mediterranean pine forests with endemic Mesogean pines 

—  9560. Endemic forests with Juniperus spp 

28 

 
 
 
4.  Results  

4.1.  Study areas and the environmental and management variables 

The location of the study areas and the main environmental and management (EM) descriptors are 
presented in Table 2. In agreement with the wide range of environments in the study areas, these 
variables showed a high range of variation with coefficients of variation (CVs) of 56.5%, 50.8%, 
69.7%, 188.2%, 37.4% and 81.9% for annual rainfall (EM1), mean temperature (EM2), mean 
elevation a.s.l. (EM3), population density (EM4), grazing days (EM5) and regional stocking (EM6), 
respectively. Only variables EM2 and EM3 showed a symmetrical distribution (based on the Shapiro-
Wilk test of normality). 

If rated by the regional stocking, all systems have a medium-to-very low stocking density in 
common, with a mean of 0.54±0.44 LU/ha, a characteristic of low-intensity grazing systems. In 
addition, LSGS encompass large expanses and are at a landscape scale (Opdam et al., 2003) under 
the LACOPE definition (LACOPE, 2002; Caballero et al., 2009). In our study sample, 65% of LSGS 
have an area of influence larger than 1,000 km2 and 24% larger than 10,000 km2. The latter 
subsample usually coincides with areas of low population density (subsample mean of 27±23 
inhabitants per km2). The minority of small-scale systems usually coincides with areas of high 
population density near the Atlantic coastline of The Netherlands and Germany (e.g., the Atlantic 
dunes and the Wadden Sea National Park). In Scandinavian and Baltic countries, small-scale 
systems (e.g., island of Öland, Sweden; island of Ösel, Estonia) and very large-scale systems (e.g., 
reindeer herding systems in Fennoscandia) are represented, both with low-to-very low population 
density. In all cases, small-scale systems are usually orientated towards nature conservation. 

In Eastern European countries, most sampled systems, except for Tatra (Poland), are of landscape 
spatial scale ranging from very large (Hungary Plains, western Romania) to a medium area of 
influence (southeast Transylvania, southern Carpathians in Romania) with a medium population 
density. The Tatra system is not really an exception as the corresponding expert only selected the 
area under the influence of the Tatras National Park and the main city of Zakopane. The whole area 
of the Tatra mountains and the Podhale encompasses more than 1,000 km2 of much lower 
population density. 

In the Mediterranean region, our sample included systems of large grazing operation size in Spain 
(dehesa, cereal-sheep), Portugal (Montados, Tras-os-Montes) and Italy (Podolica, Sardinia) in areas 
of low-to-medium population density and others of medium size, usually in mountainous areas of 
Macedonia and Rhodes (Greece), Corsica and the Provence (France) and the Nebrodi mountains of 
Sicily (Italy). In the South Alpine bioregion of the Pyrenees and the Alps, we sampled systems of 
medium size in areas of low (Spanish Pyrenees)-to-medium (Swiss and Italian Alps) or high 
population density (German Alps and Andorra). 

The whole sample included LSGS located from the sea level to plains and mountainous areas (EM3). 
The elevation mean (670.5±467 m a.s.l.) conceals a wide range of variation. Twelve systems are 
located in mountainous areas with mean elevation of the LSGS area of influence above 1,000 m in 
the Alps, Carpathians, Pyrenees and other mountain ranges. A large majority of sampled LSGS (28) 
are located in areas with a mean elevation between 100 and 1,000 m a.s.l., and a small minority (6) 
are located near the coastline of the Atlantic and the Baltic Sea. 

The number of available grazing days (variable EM5) showed the lowest CV (37%) and a range of 
90-365 days. The LSGS were rated as mobile or sedentary. The former were represented more 

29 

 
frequently in the sample (28 LSGS) and encompass large or short displacements of herds/flocks or 
vertical mobility between upland and lowland pastures. The latter are in the minority (18 LSGS) and 
entail herds/flocks rotating seasonal grazing units. However, EM5 did not depend on mobile 
(231.4±91.6 grazing days) or sedentary (258.5±86.5 grazing days) systems. Even a large minority 
of 17 sampled systems, either mobile or sedentary, showed year-long co-ordinated grazing across 
different environmental zones and environmental variables. It seems that land pastoral uses and 
connectivity between different grazing units are more determinant factors. The alpeggio system 
(southwestern Italian Alps), for example, does not connect with pastures of the lowland valleys and 
is largely circumscribed to upland pastures with only some 100 grazing days. Overall, all sampled 
LSGS adhere to the precondition of seasonal grazing for some animal lots all year around.  

4.2.  Criteria and global scores 

The results in Table 3 show the partial scores of individual case studies by the criteria of identity 
(A), environment (B), economics (C) and social (D) character and the aggregate total score of the 
four criteria. These partial scores are also the aggregate responses of the experts to the six 
variables by criteria in the questionnaire. All individual criteria-variables showed asymmetrical 
distribution while the four aggregate variables by criteria and the total score showed a normal 
distribution based on the Shapiro-Wilck test of normality. 

As individual variables showing asymmetrical distribution, the means of the smallest variable by 
criteria across study areas, the median range and the variables showing the lowest and highest 
values are more representative than the variable means. The mean of the smallest variable across 
study areas were 1.96±1.01, 1.59±0.54, 1.20±0.40 and 1.56±0.69 for identity, environment, 
economic and social criteria, respectively. The median ranged from 2.5 in A4 and A6 to 5 in A5 for the 
identity variables; from 2 in B2, B4 and B5 to 4 in B1 for the environment variables; from 1 in C1 to 5 
in C3 and C5 for economic variables; and from 2 in D1, D2 and D4 to 4 in D5 for the social variables. 

For the identity criteria, variables with the highest scores were A1 and A5. For the former, 91% of 
experts agree or strongly agree that LSGS are still a recognisable farming operation with regional 
identity notwithstanding potential disturbances. For the latter, 93% of experts agreed or strongly 
agreed that external help for LSGS in the form of technical and institutional support or effective 
agricultural policy schemes is still required. On the opposite side of the identity criteria, we found 
variables A4 and A6. For the former, 50% of experts disagreed or strongly disagreed that the 
community of livestock farmers is well-organised for lobbying capabilities and transmitting values 
to society. For the latter, a similar 50% of experts disagreed or strongly disagreed that external help 
provided to LSGS is well-organised and effective. 

For variables on the environment, B1 reached the highest score and 80% of experts agreed or 
strongly agreed that the LSGS is still a grazing-dominant and herding operation. This descriptor, 
although included in the environment criterion for its implication on natural values, also represents 
a strong sign of identity. On the other part of the scoring scale, we found variables B2, B4 and B5. For 
the first, 76% of respondents disagreed or strongly disagreed that the spatio-temporal pattern of 
grazing distribution is homogeneous across the potential grazing area. Most frequently, differences 
occurred between grazing units near the farmyard and far-reaching grazing grounds or differences 
between lowland and upland pastures. This disturbance is unrelated to LSGS located in specific 
environmental zones, but it provides information about certain issues in different LSGS. For 
example, in the reindeer systems of northern Sweden or northern Finland, a particular issue is the 
compatibility between reindeer grazing and forest management. The state, forest companies and 
private owners must recognize the legal grazing rights of reindeer herders. Some environmental 

30 

 
goals (e.g., the preservation of old forest) may not conflict with reindeer grazing (Berg, 2010), but 
the stationary reindeer management in Upper Lapland (northern Finland) with unseasonal use of 
grazing resources (tundra and boreal forest) can be a conflicting issue (Forbes, 2006; Kitti et al., 
2009). In other LSGS, the ineffective distribution of grazing infrastructure (e.g., water points) may 
produce poor grazing distribution in certain grazing units (e.g., central Macedonia, Greece) or 
problems with mobility or access to far-reaching grazing grounds can occur (central Bosnia & 
Herzegovina, Tatra). Yet in other case studies, this disturbance is the result of harsh-working grazing 
practices and poor institutional management and the progressive abandonment of the grazing 
operation (e.g., in Castile-La Mancha, Spain). For the environment variables B4 and B5, 72% of the 
experts disagreed or strongly disagreed on the effectiveness of current policy support schemes 
(particularly agri-environment schemes) and 56% on the cooperative work of NGOs in support of 
beneficial management, respectively. 

For the economic criteria, variable C1 showed the lowest score (1.41±0.5) of all criteria-variables. It 
was the only variable with median=1 and 100% of respondents showing disagreement or strong 
disagreement that the current LSGS operation can be profitable without some type of policy 
support. A large majority of experts (91%) agreed or strongly agreed that the delivery of local 
products with marketing capabilities is or can be an important sign of identity (variable C3). For the 
few case studies showing disagreement, the experts observed that their LSGS are mainly orientated 
to economic side activities (e.g., tourism), to nature conservation or even research (case studies of 
Burren, Ireland; Bavarian Alps, Germany; Atlantic dunes, The Netherlands; and woodland meadows 
on the island of Öland, Sweden) and not production-oriented LSGS. On this issue, however, a very 
large majority of experts (96%) agreed or strongly agreed, with only two experts rating this as 
undecided, that the continuity of the grazing operation is essential for the maintenance of economic 
side activities in the area of influence (variable C5). It is clear that experts do not see a future for 
LSGS areas besides the grazing operations. On the other hand, variable C6 was inversely correlated 
to variable C3 (r=-0.35) and a large majority of experts (72%) disagreed or strongly disagreed on a 
proper consumer awareness towards production practices and products of LSGS (variable C6). 

The social criteria showed the lowest aggregate score (Table 3) and the weaker consensus was 
concentrated in three low-scoring variables (D1, D2 and D4) and a higher-scoring one (D5). About half 
of the experts (52%) disagreed or strongly disagreed on the proper functioning of the current legal 
framework and grazing institutions regulating grazing management (D1), with a higher percentage 
(59%) on the affection of young farmers towards the grazing operation and assurance of family 
business turnover (D2). We also found that proper connectivity of LSGS with society (D3) was half-
supported (41% agreed or strongly agreed, with 9% undecided), and experts perceived that the 
delivery of public goods as a rationale for public support was not well-entrenched in farmers’ minds 
(D4). On this issue, 59% disagreed or strongly disagreed, with 13% undecided. About half of the 
experts (52%) agreed or strongly agreed on the external disturbances (e.g., insensitive schemes of 
support) as being more important than internal ones (e.g., lack of internal social cohesion) for the 
continuity of the LSGS operation (D5). For the last social variable (D6), a large minority of experts 
(43% disagreed or strongly disagreed, with 22% undecided) had the perception that grazing 
management is properly governed by normative rules and not by informal rules and unclear claims 
of land property. 

31 

 
 
 
Table 3. Partial score by criteria and total score of case-study grazing systems.   

aCase Nº 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 
31 
32 
33 
34 
35 
36 
37 
38 
39 
40 
41 
42 
43 
44 
45 
46 
dMean±SD 

bIdentity 
(ΣA) 
24 
26 
24 
19 
22 
26 
20 
15 
11 
25 
17 
18 
18 
18 
27 
28 
23 
17 
18 
24 
18 
20 
21 
23 
23 
16 
26 
19 
17 
19 
21 
18 
22 
21 
20 
18 
20 
21 
20 
15 
24 
20 
24 
22 
28 
18 
20.74±3.70 

bEnvironmental 
(ΣB) 
19 
19 
24 
16 
15 
18 
19 
18 
17 
24 
16 
13 
15 
12 
22 
16 
21 
13 
16 
13 
16 
15 
12 
21 
20 
15 
21 
11 
16 
22 
18 
11 
19 
18 
18 
15 
23 
16 
10 
11 
24 
21 
18 
14 
22 
10 
17.02±3.92 

bEconomic 
(ΣC) 
16 
18 
19 
18 
20 
21 
21 
15 
17 
22 
20 
17 
19 
19 
20 
14 
18 
19 
18 
19 
18 
20 
20 
18 
16 
20 
18 
18 
19 
20 
21 
18 
21 
16 
22 
20 
17 
17 
19 
15 
15 
19 
17 
10 
17 
18 
18.24±2.26 

bSocial 
(ΣD) 
16 
17 
17 
13 
12 
22 
15 
17 
12 
22 
13 
13 
10 
11 
26 
17 
23 
14 
12 
15 
16 
19 
17 
20 
16 
16 
17 
12 
18 
20 
21 
13 
18 
18 
14 
21 
15 
18 
12 
19 
22 
20 
21 
19 
18 
12 
16.72±3.69 

cTotal score 

75 
80 
84 
66 
69 
87 
75 
65 
57 
93 
66 
61 
62 
60 
95 
75 
85 
63 
64 
71 
68 
74 
70 
82 
75 
67 
82 
60 
70 
81 
81 
60 
80 
73 
74 
74 
75 
72 
61 
60 
85 
80 
80 
65 
85 
58 
72.72±9.67 

aLocation of case studies (Large Scale Grazing Systems) is in Table 2. bSix variables within each criterion rated in the Lykert-type scale 
ranging from one to five points. cMinimum score by criterion is 6 and maximum is 30. Total score range is 24-120. 
dThe four individual criteria and the total score are normally distributed (Shapiro-Wilk test). Only mean of identity criterion is significant 
(P<0.01) with the other three criteria (Bonferroni test). The criteria source accounts for 29% of the total variance (test of within-subjects 
effects).  

Source: Authors 

32 

 
The whole the set of criteria-variables showed a high level of correlation amongst responses. Out of 
276 single inter-correlations of the 24 variables, 106 cases showed correlation coefficients higher 
than ±0.4. For the aggregate criteria scoring (Table 3), only the means differences of the identity 
criteria with that of the other three criteria were significant (P<0.01), and the criteria source of 
variation only accounted for 29% of the total variance. 

All of this suggests that criteria-variables are not independent, and grouping case studies by 
aggregate criteria scores gives little insight. It seems that the consensus reached by experts on their 
response to certain variables is more illuminating for amending purposes. If we consider the 12 
lower-scoring variables (A3, A4, B2, B3, B4, B5, C1, C4, C6, D2, D3 and D4) and the subsample of nine 
lower-scoring LSGS by their total score (case studies Nº 9, 12, 13, 14, 28, 32, 39, 40 and 46 and 
total score ranging from 57-62), a concerning consensus (disagreed or strongly disagreed) of 100% 
was reached for variables A3, B2, C1 and D3, 89% (eight out of nine study areas) for variables A4, B3, 
B5, C4, C6 and D2, and 78% (seven out of nine study areas) for variables B4 and D4. If case study 
area Nº 40 (Atlantic dunes in The Netherlands) is disregarded as being mainly a small-scale nature-
conservation area, then a 100% consensus was reached for variables A3, A4, B2, B5, C1, C4, C6, D2, D3 
and D4. It is clear that a lower total score is a first sign of a downward trend, concentrated in a set 
of variables that address different criteria. 

4.3.  Current constraints 

The 10 current constraints (CC) and their scoring are presented in Table 4. They were rated on a 
scale ranging from 0 (less important) to 1 (top constraint). The CC were also inter-correlated. Out of 
45 single cases, 16 showed correlation coefficients higher than ±0.4. The rating of certain CC was 
more illustrative. Across the whole set of case study LSGS, four constraints reached scores higher 
than 0.6 (CC4, CC5, CC7 and CC10). Three of them were related to grazing labour problems, and 
concern about the harsh working practices of the grazing operation (CC10), restrictions on paid 
labour (CC4) or family labour continuity and supply (CC7). The other one (CC5) represented the 
perception of experts on the insensitive policy support schemes.  

Table 4. Global rating of potential current constraints in 45 European Large Scale Grazing Systems (LSGS).    

aNº  Current constraints (CC) 
Lack of potential grazing resources 
CC1 
Disturbances for mobility or accessibility to grazing resources 
CC2 
Unsatisfactory grazing infrastructures (e.g. water points, barns, fences, milking) 
CC3 
Costly and scarce paid labour 
CC4 
Insensitive agricultural support policies 
CC5 
Lack of livestock farmers’ affection and tradition towards the farming operation 
CC6 
Lack of family business turnover (uninterested young farmers) 
CC7 
Deficient or inoperative advisory and training services 
CC8 
Constraints in access to capital for sustainable development 
CC9 
CC10  Current grazing practices represent a hard-working operation 

bMean±SD 
0.21±0.31 
0.43±0.30 
0.54±0.32 
0.60±0.31 
0.62±0.28 
0.29±0.28 
0.61±0.26 
0.51±0.28 
0.48±0.27 
0.68±0.27 
aRandom order of potential constraints in the questionnaire. Experts provided individual ordinal responses from 1 (most important 
constraint) to 10 (lest important). This response was transformed by regression to a scale ranging from 1 (most important) to 0 (least 
important) for statistical analysis. 
bAll constraints showed asymmetrical distribution (Shaphiro-Wilk test).  

Source: Authors 

The restriction on paid labour (CC4) was the constraint that had high inter-correlations with some 
criteria-variables. Out of 24 single cases, 11 showed correlation coefficients higher than ±0.4. It 
seems that experts in better organised systems, where external help is functioning (A6) and current 

33 

 
policy support schemes are more effective (B4), rated the paid labour constraint highly. Examples of 
this trend are case studies located in the developed areas of the southern Alps such as the Bavarian 
Alps (Germany), Entlebuch (Switzerland) and the malga system in the eastern Italian Alps (south 
Tyrol) and also the UK case studies of the Atlantic bioregion such as the Cumbrian uplands. Some 
experts provided relevant comments on initiatives to raise the social and professional status of the 
herding operation (e.g., implementation of herder schools). 

On the other side of the scoring scale, we found CC1 as the least important constraint (rating of 
0.21±0.31). The lack of potential grazing resources was not a constraint for most areas except 
those of the reindeer systems. In these cases, experts provided comments on border restrictions to 
long migrations of reindeer or unseasonal use of available resources in more stationary reindeer 
systems. 

The whole rating of constraints suggests that one important issue for reversing the current 
downward trend is the design and implementation of policy schemes in support of the herding 
operation. Dynamic LSGS are in need of raising the social and professional status of herders and 
the institutions governing grazing management. This is, however, one more issue within a broader 
and general policy framework. 

4.4.  The abandonment trend 

Section seven of the questionnaire elicited data on the number of active farmers within a time 
frame of 60 years before 2010 with two periods of 30 years. With these data, the rates of 
abandonment (AB) as the percentage of livestock farmers exiting the LSGS operation by year were 
calculated for each of the three periods (AB60-30, AB30-0 and AB60-0). For this set of variables, experts 
provided data in 28, 31 and 29 case studies, respectively, and the rest indicated only trends or data 
not available from official records. 

The descriptive statistics showed a normal distribution of the data for AB60-30 and AB30-0 and an 
asymmetrical distribution for AB60-0 with overall means of 1.11±0.79%, 1.62±1.27% and 
0.90±0.78%, respectively. Positive numbers indicated a declining rate in the number of farmers 
exiting the operation. Increasing rates were only found in one (island of Pag, Croatia), two (island of 
Pag, Croatia and island of Rhodes, Greece) and three (the two islands plus western Romania) case 
studies for the three AB variables, respectively. 

A common and general trend of farmers exiting the grazing operation is clear although the main 
causes of abandonment are not. AB60-30 and AB60-0 were highly correlated (r=0.75) suggesting that 
the rate of abandonment in the last 60 years is better explained by the rate of the 60-30 year 
period. However, the rate of abandonment was higher in the 30-0 years period and yielded a 
correlation of r=0.36 with the whole period analysed.  

As expected, lower values in regional stocking (EM6) were related to the abandonment rate with r=-
0.35 (with AB60-30) and r=-0.52 (with AB60-0). Similarly, AB30-0 was positively correlated with CC9 
(r=0.5) and negatively (r=-0.36) with CC1. None of the three abandonment variables showed 
significant dependency with the 11 factors of variation (Table 5).  

For the study cases of eastern Europe in areas under previous communist rule (up to around 1989), 
the data provided are somewhat blurred. Some comments by experts on these areas suggest that 
by the time of political change, the number of animals had decreased (a good number to the 
slaughterhouse), but the number of farmers probably increased as animals from communal farms 
were redistributed to small-scale farm owners mainly under semi-subsistence pastoralism. Yet by 

34 

 
1969, out of the 14.3 million sheep in Romania, 7.2 million were still privately owned (Matley, 
1970). In the whole sample grouped by wide geographical areas (northern and central Europe, the 
Mediterranean area and eastern Europe), the AB60-0 rate of abandonment showed values of 
1.04±0.33, 1.06±0.59 and 0.55±1.17, respectively. The rate of abandonment in eastern Europe 
seems to be lower than in the other two geographical areas but with a high rate of variation. 

Table 5. Significance of 11 factors of variation on 48 variables recorded in 46 European Large Scale Grazing 
Systems.    

FV8 
FV9 

cCases 
15 

aFactor 
FV1 

2 
3 
4 
2 
4 
10 

FV2 
FV3 
FV4 
FV5 
FV6 
FV7 

bVariables with significance and level of significance 
EM1(P=0.001); EM2(P=0.000); EM3(P=0.000); EM5(P=0.016); A1(P=0.039); 
A4(P=0.045); A5(P=0.040); B4(P=0.019); C2(P=0.017); D1(P=0.044); D2(P=0.015); 
D5(P=0.040); ΣD(P=0.016); Total score (P=0.048); CC1(P=0.003)  
CC3(P=0.031); CC7(P=0.006) 
EM5(P=0.042); D3(P=0.024); CC2(P=0.013) 
B4(P=0.014); C1(P=0.046); C2(P=0.021); C3(P=0.032) 
B3(P=0.022); C1(P=0.002) 
EM1(P=0.039); EM3(P=0.000); C1(P=0.006); CC6(P=0.021) 
EM2(P=0.000); A4(P=0.011); A6(P=0.016); C3(P=0.001); D2(P=0.000); ΣD 
(P=0.005); CC1(P=0.000); CC2(P=0.027); CC7(P=0.045); CC10(P=0.003) 
D4(P=0.010); ΣD(P=0.022) 
EM1(P=0.009); EM3(P=0.000); EM4(P=0.016); A2(P=0.018); A6(P=0.032); 
ΣC(P=0.036); CC6(P=0.035) 
EM3(P=0.006); EM5(P=0.019); C1(P=0.037) 
EM2(P=0.010); EM4(P=0.000); B6(P=0.006); C4(P=0.005); C5(P=0.013); 
CC10(P=0.004) 
aFactors of variation, see Table 1. 
bVariables EM (6) are Environment and Management variables (Table 2) 
bVariables A(6), B(6), C(6) and D(6) are within criteria variables (Table 3) 
bVariables Σ (4) are aggregates within criteria A (identity), B(environmental), C (economic) and D (social). Total score (1) is aggregate 
across criteria (Table 3). 
bVariables CC (10) are current constraints (Table 4). 
bVariables (AB) rating the abandonment trend (3) are not included in this table because no one showed significance across the 11 factors 
of variation. 
cTotal number of variables in parenthesis is 48. Total cases with significance in the last column are 58. Total cases analysed is 
48x11=528.  

FV10 
FV11 

2 
7 

3 
6 

Source: Authors 

For the two island case studies showing increasing rates, the experts explained that there were few 
off-farm opportunities in isolated places or complementary farming and off-farming activities. For 
example, in the island of Pag (Croatia), the famous Pag cheese (Pramenka x Merino breeds) is 
promoted as a tourism attraction. Conversely, just inland in Croatia in the southern Dinaric Alps (the 
county of Licko-Senjska), only a small number of sheep farmers remains active in a potential area 
of some 2,300 km2, and mountain pastures are practically unused. 

In some case studies (e.g., reindeer systems), a low stocking is the consequence of environmental 
restrictions or hindering at border crossing and not of abandonment by farmers. However, for the 
few cases of reindeer systems providing data, the trend is of declining numbers of reindeer herders. 
Lower stocking is not self-explanatory as many underlying causes are implicated. For AB60-0, a 
relationship with B6 was found (r=0.47) although a proper explanation is not consistent. Similarly, 
the relationship of AB60-0 with CC1 and CC9 suggests that the process of farmers exiting the 
operation and the process of wide areas of potential pastoral resources out of use are consequent, 
with the rate of abandonment higher where access to capital for investment is of concern. The 
abandonment process is a similar and final phenomenon everywhere, with a pool of causes acting 

35 

 
as drivers, but their large-scale environmental effects are largely deleterious (McCracken, 2008; 
Caballero et al., 2009). 

4.5.  Factors of variation 

The effects of the 11 factors of variation recorded in the three first sections of the questionnaire 
(Table 1) on 48 dependent variables are presented in Table 5. Six Environment and Management 
(EM) variables, 24 criteria-variables (six of each A, B, C and D criteria), 10 current constraints (CC), 
three abandonment (AB) variables, four of aggregate score by criteria and one of total score across 
the four criteria were tested. The number of total cases analysed was 528 (48 variables x 11 
factors). Out of these cases, 58 showed global significance (P<0.05) and only 48 showed at least 
one difference of means with significance (P<0.05). Significance of means was tested with the 
Bonferroni test in the case of homogeneity in the error variances (37 cases) or with the Tamhane 
test in the case of heterogeneity (21 cases). Of the 58 cases with significance, 15 corresponded to 
EM variables, 27 to criteria-variables, 11 to CC variables and five to aggregate score by criteria and 
total score. The most influential factors of variation were FV1 (biogeographical region), FV7 
(dominant grazer), FV9 (size of the LSGS operation) and FV11 (population density in the LSGS area of 
influence). For the other seven factors, only four or fewer variables showed significance (Table 5). 

The significance of the EM variables only showed the wide range of environmental and 
management situations included in the sample of LSGS and were mostly dependent on factors FV1, 
FV6, FV9 FV10 and FV11. The mean differences in annual rainfall (EM1) were of up to 1000mm 
between samples of the AT and the BO bioregions, up to 12.3 ºC in mean temperatures (EM2) 
between samples of the MED and BO bioregions and of up to 875 m a.s.l. in the mean elevation of 
the influential area (EM3) between samples of the AL and BO bioregions (P<0.05). As expected, the 
mobile systems were more frequently located in areas of higher elevation and reindeer systems in 
areas of the lowest mean temperatures (P<0.05). Grazing days (EM5) was only dependent on FV3 
and the regional stocking (EM6) was unrelated to the 11 factors. Although reindeer systems showed 
clearly lower regional stocking, the mean differences were not significant, probably due to the small 
number of reindeer samples and higher stocking in non-reindeer and BO samples. 

Relevant criteria-variables showed dependency with some factors of variation. On the identity 
criterion, the response to A1 was influenced by the bioregion, with experts on BO systems expressing 
strong attachment to the regional identity of their LSGS compared to those of the CO bioregion 
(difference of means of 1.26, P<0.05). The response to A2 significantly depends on the size of the 
LSGS operation (FV9). Experts of large (L), medium (M) and small sized (S) LSGS expressed strong 
agreement that the community of livestock farmers still makes a living from the LSGS operations 
compared to those experts from very small-sized LSGS (mean differences of 1.50, 1.39 and 1.50, 
for L, M and S, respectively, P<0.05). It seems that experts on very small LSGS do not agree on a 
production-oriented LSGS, and livestock farmers on these systems are mainly supported, under 
contractual agreements, through policy schemes orientated to nature conservation. The response to 
A4 (well organised livestock farmers with lobbying capabilities) was dependent on the dominant 
grazer (FV7). Experts on cattle-dominant systems showed stronger agreement than those of 
sheep/goat-dominant systems (mean difference of 1.60, P<0.05). Variable A6 (external help well-
organised and effective) also showed dependency with FV7 and FV9. Similarly, experts on cattle-
dominant systems scored higher than those of sheep-dominant ones (mean difference of 1.11, 
P<0.05), and experts of very small sized LSGS scored higher than those of large sized ones (mean 
difference of 1.0, P<0.05). 

36 

 
Regarding the environmental criterion, variables B3, B4 and B6 were the variables showing at least 
one mean difference with significance. Variable B3 (current farming practices congenial with the 
delivery of environmental values) was dependent on FV5 (ownership regime of grazing resources). 
Experts on LSGS under a mixed regime scored higher than those under a public-dominant regime 
(mean difference of 1.22, P<0.05). The effectiveness of agricultural support policies (B4) showed 
dependency on the bioregion (FV1) and on the dominant type of resources (FV4). Only the mean 
difference between the AL and MED bioregions (1.49, P<0.05) and the mean difference between 
semi-natural grassland and woodland dominant types (1.21, P<0.05) were significant. Social 
awareness of the environmental significance of production practices (B6) was dependent on 
population density in the LSGS area of influence, with the mean difference between high and 
medium densities showing significance (1.23, P<0.05). 

Although all experts, without exception, disagreed or strongly disagreed on the economic 
sustainability of the LSGS operation without some type of support (C1), this variable showed 
dependency with FV4, FV5, FV6 and FV10. The experts in areas of semi-natural grasslands or mixed 
dominant resources scored higher than those in LSGS with woodland-dominant resources (mean 
differences of 0.52 and 0.45, respectively, P<0.05). Similarly, the mean difference between mixed 
and private dominant resources (0.58, P<0.05), the mean difference between sedentary and mobile 
systems or between milk- and meat-oriented LSGS were significant. Variables C4 and C5 were 
dependent on FV11. Experts in LSGS located in areas of high population density scored higher on C4 
than those of medium density (mean difference of 1.31, P<0.05), and those experts in areas of very 
low population density scored higher on C5 (significance of the continuity of the grazing operation 
for side-economic activities) than those of high density (mean difference of 0.90, P<0.05). In this 
case, the aggregate response for the economic criteria (ΣC) was significant (P=0.036) and showed 
dependency with FV9. The mean difference between LSGS of medium and very low size was 
significant (3.0, P<0.05). 

The relevant variables of the social criterion showing dependency on certain factors were D2 (family 
business turnover assured), D3 (local products as focus of regional identity) and D4 (the delivery of 
public goods as a well-entrenched rationale in farmers’ minds for the design of public schemes of 
support). Experts on cattle-dominant LSGS scored higher on D2 than those of sheep/goats or mixed 
systems (mean differences of 1.70 and 1.52, respectively, P<0.05), and experts in the environment 
discipline (FV3) scored higher on D3 than those in the fields of socio-economics and grazing 
management (mean difference of 0.75, P<0.05). In addition, the mean scores of D4 showed 
significance between holdings of medium and large size (mean difference of 1.03, P<0.05). In this 
case, the aggregate social score (ΣD) was dependent on FV1, FV7 and FV8. Experts in the AL and BO 
bioregions scored higher than those in the CO bioregion (mean differences of 5.48 and 4.74, 
respectively, P<0.05), showing a strong social cohesion within their systems (Riseth, 2006); experts 
in cattle-dominant LSGS scored higher than those in mixed or sheep-dominant systems (mean 
differences of 4.60 and 4.40, respectively, P<0.05); and experts in LSGS with medium sized holdings 
rated higher the aggregate social score than those with small holdings (mean difference of 4.0, 
P<0.05). 

Finally, the aggregate total score of the four criteria only showed dependency on FV1, with experts 
in systems of the AL bioregion scoring higher than those in the CO bioregion (mean difference of 
13.3, P<0.05). Overall, individual factors of variation explained only a small part of the variability of 
the criteria-variables. For those cases that showed significance in the difference of means, the 
coefficients of determination (adjusted R2) never reached 40% of the variance explained and 
ranged from 0.07 to 0.37. The latter and higher value was found for the dependency of D2 on 
dominant grazer (FV7), with cattle-dominant LSGS reaching the higher score. 

37 

 
Regarding the influence of factors of variation on the current constraints (CC) variables (also in 
Table 5), we only found 10 cases with significance for the mean differences out of 110 cases 
analysed (10 constraints x 11 factors). 

For the whole sample of case studies, CC1 (limitation of grazing resources for the LSGS operation) 
was the less concerning constraint. However, the response of experts to this constraint was 
influenced by FV1 (bioregion) and FV7 (dominant grazer). Experts on the BO bioregion and on 
reindeer as the dominant grazer rated this constraint highly. For FV1, the mean differences in 
scoring with the experts in the AL, AT, CO and MED bioregions were 0.48, 0.57, 0.63 and 0.53, 
respectively (P<0.05), and FV1 explained up to 25% of the variance. For FV7, the mean differences in 
scoring with the experts in cattle, sheep/goats or mixed-dominant LSGS were of 0.56, 0.73, and 
0.71, respectively (P<0.05), and FV7 explained up to 30% of the variance. In addition, the response 
to CC2 (disturbances for mobility or access to grazing resources) was influenced by FV7 and again, 
experts in reindeer systems rated higher than those in sheep/goats or mixed systems (mean 
differences of 0.49 and 0.55, respectively, P<0.05). It is clear that seasonal availability and 
accessibility to grazing resources is a particular concern of reindeer systems. Experts in economic 
and grassland management disciplines also rated CC2 higher than those in an environmental 
discipline, with a mean difference of 0.22 (P<0.05). 

Unsatisfactory grazing infrastructures (CC3) showed dependency on the expert affiliation (FV2) and 
the lack of farmers affection and tradition towards the LSGS operation (CC6) on the size of the 
LSGS operation (FV9). As expected, experts on very small-sized systems disregard the affection of 
farmers and traditional operations as most of these systems are small-scale operations mainly 
orientated to nature conservation with engaged farmers under contractual agreements in some 
cases (e.g., case studies of the Thuringia wetlands in Germany, the Atlantic dunes in The 
Netherlands or the alvars on the island of Öland, Sweden). In the whole sample, this latter 
constraint showed little significance and the second lowest value (0.29±0.28). For most sampled 
LSGS, farmers’ affection and tradition towards the LSGS operation is confirmed. 

Constraints CC7 (lack of assurance in family business turnover) and CC10 (harsh-working character 
of the grazing operation) are labour-related constraints. CC7 showed dependency on FV2, with 
managers of LSGS scoring higher than research-oriented experts (mean difference of 0.29, P<0.05), 
and CC10 showed dependency on FV11, with experts on LSGS located in areas of high and medium 
population density scoring higher than those of very low density (mean differences of 0.33 and 
0.37, respectively, P<0.05). In the whole sample, these two variables were rated within the three top 
constraints (Table 4). 

4.6.  Correlation, regression and factorial analysis 

The single inter-correlation matrix of 43 variables, excluding the four aggregate criteria and the 
total score, showed 903 cases. Out of these cases, 197 showed single inter-correlation coefficients 
higher than ±0.4. Variables showing nine or more cases with this limit were A3, A4, A6, B6, C6, D1, D2 
and CC4. This high level of inter-correlation was relevant in the factor component analysis. 

For more detailed inter-relations between variables, the Squared Multiple Correlation (SMC) of each 
variable with all other criteria-variables was performed (Table 6). Each variable was related to the 
rest, and the regression effect was significant in one-third of cases (A2, A4, A6, B4, B6, C4, D1 and D2). 
However, the SMC (R2 adjusted) of one single variable never reached 50% of variance explained on 
the variation of all other variables. The best predictors of significant variables, as chosen by the 
significant standardized coefficients (β), also appear in Table 6.   

38 

 
Table 6. Squared Multiple Correlation (SMC) of each within-criteria variable with all other within-criteria 
variables and significance of the regression.  

a(Ai) 
A1 
A2 
A3 
A4 
A5 
A6 

bSMC 
0.30 
0.47 
0.15 
0.45 
0.33 
0.46 

cSignif. 
(Bi)  SMC 
0.17 
B1 
NS 
0.12 
P=0.011  B2 
0.02 
NS 
B3 
0.43 
P=0.014  B4 
0.28 
NS 
B5 
0.39 
P=0.012  B6 

(Ci) 
Signif. 
C1 
NS 
C2 
NS 
NS 
C3 
P=0.018  C4 
NS 
C5 
P=0.030  C6 

SMC 
0.15 
0.20 
0.25 
0.37 
-0.09  NS 
NS 
0.12 

(Di) 
Signif 
D1 
NS 
D2 
NS 
NS 
D3 
P=0.040  D4 
D5 
D6 

Signif. 
SMC 
P=0.014 
0.45 
P=0.029 
0.39 
0.21 
NS 
-0.11  NS 
NS 
0.09 
NS 
0.23 

aA, B, C and D are criteria of identity, environmental, economics and social, respectively. bSMC is R2 adjusted.cBest predictors of significant 
variables as rated by the significance of the standardized coefficients (β) : A2 (A1, B1, C3); A4 (A6); A6 (C4, D2); B4 (D1, D6); B6 (B5, C4); C4 (A5, 
A6, B6); D1 (B4); D2(A6, C1, C2). 

Source: Authors 

Similar results were found where the identity variables (A) were selected as dependent variables in 
a stepwise regression analysis upon the rest of the B, C and D variables (Table 7). In this case, the 
adjusted R2 varied from 0.2 (A3) to 0.56 (A6), and the standardized coefficient (β) with significance 
varied between 0.5 (A2-B1) and 0.27 (A1-B1). A1 (LSGS is still a recognisable land-based operation 
with regional identity) was better explained by variations in D3 and B1, and A2 was also dependent 
on B1, C3 and B3 (adjusted R2 = 0.34). If we add the single inter-correlation between A1 and A2 
(r=0.68), we may conclude that A1, A2 and B1 are the three single and most potent inter-correlated 
variables of regional identity. For the few experts that showed disagreement, the comments 
suggest a current trend towards more indoor-feeding operations (Hungarian Plain, cereal-sheep in 
Castile-La Mancha, Spain), the dominance of part-time labour (common in the Black Forest, 
Germany) or small scale and nature conservation systems (the Atlantic dunes in The Netherlands 
and wooded hay meadows on the island of Öland, Sweden). 

The best predictors of the identity variables A3 and A4 were the variables included in the social 
criteria (D3 and D1). A good part of a positive perception and regard for society towards the LSGS 
operation and its values (A3) is related to a strong social organisation of LSGS for the delivery of 
indigenous products and services (D3, R2 adjusted of 0.2 and β=0.47). In addition, the institutional 
framework with technical and managerial capabilities (D1) influenced the lobbying capabilities and 
the capacity to transmit its values to society (A4, β=0.42).  

The requirement of external help to LSGS (A5) was the identity variable reaching highest level of 
consensus (mean of 4.39±0.80), and 93% of experts showed agreement or strong agreement. This 
variable was negatively related to C4 (β=-0.48) and most experts had the perception that farmers in 
LSGS are in need of support to improve their social, financial and technical capabilities. The opposite 
occurred with variable A6 (mean response of 2.63±1.08) where 50% of experts showed 
disagreement or strong disagreement, with 22% undecided. It seems that a very large majority of 
experts perceived external help as a necessity, but only a minority would rate the current support as 
effective. The best predictors of A6 were variables of an economic or social character, with a 
positive (D2 and C4) or negative (C2 and C1) relationship (Table 7). All these results show the high 
level of inter-relationship amongst the criteria-variables and the futility of separating variables by 
criterion from the questionnaire. The territorial identity of LSGS seems to be a composite of inter-
related land, environmental and socio-economic variables. 

39 

 
 
 
Table 7. Stepwise regression of identity variables (Ai) upon the rest of within-criteria variables (Bi, Ci and Di).  

Dependent 
variable 
A1 

A2 

A3 
A4 

A5 
A6 

R2 Adjusted 

0.23 

0.34 

0.20 
0.33 

0.21 
0.56 

aVariables in 
equation 
D3 
B1 
B1 
C3 
B3 
D3 
D1 
B6 
C4 
D2 
C2 
C4 
C1 

Standardized 
coefficients (β) 
0.37 
0.27 
0.50 
0.30 
-0.30 
0.47 
0.42 
0.36 
-0.48 
0.35 
-0.29 
0.35 
-0.29 

Significance of β 

P=0.009 
P=0.048 
P=0.001 
P=0.022 
P=0.029 
P=0.001 
P=0.002 
P=0.005 
P=0.001 
P=0.003 
P=0.01 
P=0.003 
P=0.01 

aVariables in equation with probability of F to enter ≤0.05 for the standardized coefficients. 

Source: Authors 

This was also evident from the analysis of the factor components (FC). In this case, EM, CC and 
abandonment variables were also included (Table 8). The rotated sums of squared loadings 
required 14 iterations (14 factor components) to reach 90% of the variance explained. Each FC 
explained only a small part of the total variance from 15% in Factor 1 to 3% in Factor 14. Each FC 
includes a very high number of variables from 19 in FC 8 and FC 12 to 34 in FC 1. The variables 
with loading scores higher than ±0.5 and first entering each FC were of a different character from 
EM, criteria-variables, CC or even abandonment variables, although the criteria-variables and the CC 
variables were dominant (Table 8). With this structure, the FC analysis is difficult to interpret and 
basically reflects the high level of inter-correlation in the correlation matrix. 

Table 8. Rotated sums of squared loadings and variance explained by 14 principal components.  

aFactor component 

bTotal eigenvalue 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 

6.53 
3.25 
3.09 
2.75 
2.72 
2.67 
2.64 
2.57 
2.35 
2.31 
2.20 
2.01 
1.72 
1.47 

c,dNº of variables 
included 
34 
24 
21 
23 
24 
20 
25 
19 
26 
20 
24 
19 
20 
21 

Variance explained 
(%) 
15.19 
7.55 
7.19 
6.40 
6.33 
6.22 
6.14 
5.97 
5.47 
5.37 
5.12 
4.68 
4.01 
3.42 

Cumulative variance 
(%) 
15.19 
22.74 
29.93 
36.33 
42.66 
48.88 
55.02 
60.99 
66.46 
71.83 
76.95 
81.64 
85.64 
89.07 

aRotation converged in 14 iterations. Rotation method: Varimax with Kaiser normalisation. bSums of squared loadings. cNumber of 
variables with loading score over ±0.1. Total number of variables is 43. dVariables with loading score over ±0.5 and in decreasing order 
are in parenthesis:Component 1 (A6, A4, D1, CC4, EM1, B4, C2, D2 and C1); Component 2 (A1, A2, B1 and A3); Component 3 (B5 and B6); 
Component 4 (CC9, CC1 and AB30-0); Component 5 (B2 and D4); Component 6 (AB60-30, AB60-0 and EM6); Component 7 (CC5, CC7 and D3); 
Component 8 (CC2, B3 and CC8); Component 9 (C5, EM2 and EM4); Component 10 (EM3 and EM5); Component 11 (CC6, CC3 and CC10); 
Component 12 (A5); Component 13 (D5); Component 14 (D6). 

Source: Authors 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.7.  Cluster and discriminant analysis 

As the identity criteria are largely dependent on variables in the B, C and D criteria, and the 
aggregate mean differences of the three latter criteria were not significant and showed a normal 
distribution, we chose the aggregate environment (B), economics (C) and social (D) criteria for the 
clustering analysis of case studies as well as an additional clustering for relevant current 
constraints (CC4, CC5, CC7 and CC10). Both clustering analyses showed three group centroids for the 
case studies; the cluster group statistics are presented in Table 9. 

Table 9. Cluster group statistics for three criteria and for three current constraint variables in the sample of 
case studies.   

a,cCluster Nº 
1(14) 

2(15) 

3(17) 

Variable 
B 
C 
D 
B 
C 
D 
B 
C 
D 

dMean±SD 
16.50±2.38 
16.50±2.62 
18.00±1.66 
21.27±2.05 
19.13±1.96 
19.73±3.08 
13.71±2.49 
18.88±1.27 
13.00±1.77 

b,cCluster Nº 
1(11) 

2(19) 

3(15) 

Variable 
CC4 
CC5 
CC7 
CC4 
CC5 
CC7 
CC4 
CC5 
CC7 

dMean±SD 
0.67±0.31 
0.27±0.18 
0.33±0.19 
0.34±0.17 
0.77±0.21 
0.81±0.14 
0.89±0.12 
0.67±0.18 
0.57±0.21 

aCriteria-variables B, C and D and general group scores is in Table 3 for the 46 case studies. Clustering of case studies is in Fig. 1. 
bCurrent constraints variables CC4, CC5 and CC7 and general group scores is in Table 4 for 45 case studies. Clustering of case studies is in 
Fig.2. cIn parenthesis, number of case studies within clusters (one missing case-study for CC). dANOVA of any variable between clusters 
(P<0.001). F test for CC10 not significant. 

Source: Authors 

For the aggregate criteria analysis, variables ΣB and ΣD showed larger discriminant capacity than 
variable ΣC. The clustering group statistic showed the Cluster 1 grouping LSGS of experts scoring 
higher for the aggregate D, Cluster 2 higher scoring for aggregate B and Cluster 3 lower scoring for 
aggregate B and D. Although the F test is only descriptive, the mean differences of the clustering 
groups were significant (P<0.001). Grouping the case studies around the three centroids is 
presented in Figure 3. The relevant point is that each of the three clusters grouped case studies of 
undifferentiated relationships with EM variables or factors of variation. For example, two LSGS near 
the centroid of Cluster 1 are in the Italian Apennines (Marche and southern Apennines), but many 
other unrelated ones shared Cluster 1, such as case studies in the Swiss Alps, southwest England, 
The Netherlands or the Estonian alvars. Similarly, the three nearest LSGS to the centroid of Cluster 
2 are located in the southern Carpathians, the eastern Alps in Italy and the central Balkans in 
Bulgaria. Although they shared few environmental or management conditions, the experts on LSGS 
of very different structures and locations may share similar perceptions regarding the aggregate 
criteria. 

A similar analysis was derived from the clustering exercise of the relevant constraints. In this case, 
three constraints (CC4, CC5 and CC7) showed similar discriminant power and CC10 was not relevant. 
Cluster 1 grouped the LSGS of experts scoring lower CC5 and CC7 than in the whole sample and 
Cluster 2 grouped highly scored LSGS for these two constraints. Cluster 3 grouped case studies of 
experts scoring higher for CC4 (0.89±0.11 in the cluster and 0.60±0.31 in the whole sample). Cluster 
1 grouped LSGS with a higher mean of CC10, but in this case, the mean differences were not 
significant. For this reason, clustering statistics for this constraint are not included in Table 9. The 
grouping case studies around the three centroids is presented in  

41 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4. Again, unrelated LSGS shared similar constraints. For example, case studies such as the 
montados and dehesa of Portugal and Spain are included in Cluster 2 but many other unrelated 
LSGS such as reindeer herding in northern Fennoscandia, the Estonian alvars or cattle grazing in the 
Piedmont alpeggio in the southwestern Italian Alps were also included. 

Figure 2. Clustering of case studies by criteria B (environment), C (economic) and D (social). The location of 
the case studies is in Table 2. 

Cluster 1. Case studies Nº 6, 7, 11, 13, 14, 15, 16, 18, 19, 22, 23, 25, 26, 38. 
Cluster 2. Case studies Nº 2, 4, 5, 8, 9, 10, 17, 21, 24, 30, 31, 36, 37, 40, 42. 
Cluster 3. Case studies Nº 1, 3, 12, 20, 27, 28, 29, 32, 33, 34, 35, 39, 41, 43, 44, 45, 46. 

Source: Authors 

Thus, the clustering analysis suggests that experts of systems that differ in environmental and/or 
management may, nevertheless, share similar perceptions and concerns. The common and 
differentiating properties are a sign of regional identity. One the one hand, this may allow for the 
design of a European-wide policy framework in support of LSGS, but on the other hand, beneficial 
management alternatives should be system-specific (Caballero, 2011). 

42 

 
 
 
 
 
Figure 3. Clustering of case study LSGS by constraint-variables CC4, CC5, CC7 and CC10. The location of the 
case studies is in Table 2. 

Cluster 1. Case studies Nº 1, 3, 4, 9, 13, 15, 24, 32, 36, 39 and 41. 
Cluster 2. Case studies Nº 5, 6, 12, 16, 20, 22, 23, 27, 28, 30, 31, 33, 34, 35, 38, 40, 43, 45 and 46. 
Cluster 3. case studies Nº 2, 7, 8, 10, 11, 14, 17, 18, 19, 21, 26, 29, 37, 42, and 44. 

Source: Authors 

4.8.  Beneficial management 

The experts were asked to provide up to five management alternatives that they perceive of 
relevance to improve their systems. Forty-four of them (96%) provided suggestions, with the mean 
number of proposals of 4.1±1.1. We rated the management alternatives within nine tiers: Grazing 
Management Plans (GMP), Marketing Capabilities and quality assurance for the indigenous products 
(MC), Policy Reforms (PR), Herding Labour support measures (HL), Grazing Infrastructures (GI), Social 
Empowerment of local communities (SE), Environmental Services and economic side activities (ES), 
Grazing Governance and Institutions (GGI) and Technical Support (TS). The response of the experts 
to the nine types of measures across three horizontal dimensions is presented in Figure 4. 

43 

 
 
 
 
Figure 4. Beneficial management alternatives for European Large Scale Grazing Systems (LSGS). Frequency 
of response (%) by experts in 44 LSGS. 

Source: Authors 

A large majority of the experts (82%) cited the design of a GMP or measures linked to grazing 
management as their first option. In addition, a large majority of those citing this option (82%) 
grounded this alternative in the available scientific knowledge, looking towards environmental 
(42%), social-ecological (47%) or socio-economic effects (11%). However, the real effects and the 
level of implementation of the GMPs were not recorded in our sample. Some GMPs are designed as 
agro-environmental de-stocking schemes with a good level of implementation (Nisbet & Glaves, 
2010) although these are contested by farmers and breeder associations. In other cases, the level 
of implementation of agri-environmental measures is poor or even incompatible with the grazing 
operation (Caballero & Fernández-Santos, 2009; Jansen, 2011). The comments of some experts 
suggest a GMP only in the research or design phase while others explained that GMPs are not a 
panacea. Moreover, other experts indicated that GMP should be embedded in socio-economic 
sustainability, with proposals that are attractive to farmers. 

Other specific measures were less cited but also of relevance. PR, MC, HL, GI, SE, GGI, and TS types 
of measures were cited by 59%, 55%, 41%, 34%, 32%, 30% and 25% of experts, respectively. For 
those citing these reforms, the ground-based knowledge was less prevalent than that for GMP 
(35%, 30%, 18%, 20%, 36%, 40% and 37%, respectively). The rest of the experts based their 
attachment to these measures on common sense or on the common practice of farmers. It seems 
that unavailable or insufficient empirical evidence is common for these tiers in many study areas. 
For the type-measure of ES, all experts (100%) indicated the potential delivery of environmental 
services and economic side activities. In section nine, 43% cited this type of measure for 
amendment and of those citing this, 42% based their response on ground-based knowledge. 

44 

 
 
When we select a subsample of lower scoring LSGS (eight study areas), a differential response was 
found. In this subsample, GMP and MC measures were cited by 87% of the respondents; HL related 
measures by 62%, and PR and GI reforms by 50%. It seems that these types of measures can be 
considered as relevant objectives for alleviating LSGS of relevant concerns. In both the whole 
sample and the subsample, the important point of policy relevance is the concordance between 
responses to concerns in previous sections (criteria-variables and constraints) and proposals for 
reform suggested by the experts in section nine of the questionnaire. For example, the delivery of 
local products was considered as a sign of identity (C3 with 91% of consensus). For the few experts 
not in agreement such as for the Burren (Moran et al., 2008) or the Atlantic dunes (Van Duinen et 
al., 2006), they indicated their systems have a non-productive primary orientation (e.g., economic 
side activities, nature conservation, etc.). Correspondingly, a large majority of experts (55% in the 
whole sample and 87% in the lower scoring subsample) suggested market-related measures for 
their products in section nine. Once again, a large majority of LSGS are represented, 
notwithstanding their current trend. Experts on higher scoring systems such as Entlebuch, 
Switzerland, promoted conversion to organic and improvement of marketing channels (Hofstetter, 
2010). Conversion to organic was also suggested by experts of study areas 12 and 45. What this 
research suggests is that LSGS are in need of one specific labelling system, not one designed as a 
reaction to intensive methods of production. On the other side of the criteria-aggregate scale, lower 
rated LSGS such as dairy sheep in the Dinaric Alps, Croatia, required milking- and milk-storing 
facilities as assurance of quality for their products and working conditions for the shepherds. 

45 

 
5.  Discussion 

Humans and nature have interacted for centuries to reach the current state of landscapes across 
Europe. The same drivers continue to be in force and European LSGS continue to be a repository of 
natural values and services. Most LSGS represent a way of life for communities of livestock 
farmers, except for a few systems orientated towards nature conservation. Socioeconomics, cultural 
values and traditions need to be considered as part of the regional identity of these landscapes and 
systems. At the same time, environmental assets depend significantly on the availability of 
landscapes that are not intensively farmed. It is, therefore, important to identify grazing systems 
for livestock that require relatively little land management. 

This research study has been an exercise in the development of social-ecological indicators, yet the 
main objective is not the design of one more sustainability index or the ranking of sampled case 
studies. Instead, we are seeking common ground and shared perceptions and concerns as a 
rationale for policy development in a wide range of environments. 

The large number of social-ecological indicators currently available only provides an unwieldy view 
of system sustainability (Mayer, 2008). Indicators are aggregated into indices or presented in the 
context of a conceptual framework. The aggregation of different dimensions is frequently blurred 
by system-wide implications of human actions and the nonlinear or non-intuitive relationships of 
indicators in different dimensions (Shastri et al., 2008). In this research, we found a high level of 
inter-relationships between indicators of different dimensions, and that the aggregate scores do not 
properly segregate LSGS with varying properties. Low total scores, however, were found to indicate 
the first signs of downward trends and the existence of common concerns. 

Our main approach has been to develop one conceptual framework that allows for the collection of 
cumulative knowledge (Caballero, 2011), a basic understanding of what is and is not possible in 
certain systems and a sense of the time scale of possible future events. With this understanding, 
systematic approaches can then be used to develop policy guidelines. This approach requires the 
support of experts in different LSGS systems. In this study, experts provided data on their 
perception to social-ecological indicators, current constraints and the alternatives for more 
beneficial management required to change the systems in the future. 

We tested the consistency of the proposed measures by comparing the responses to social-
ecological indicators and constraints, particularly those reaching a higher consensus, and the 
objective of management alternatives. We found that the main perceived weaknesses and 
constraints were highly related to proposals of reform. For example, a large majority of experts 
(72%) were unhappy with the effectiveness of current support schemes, and correspondingly, 59% 
included this as an alternative in section nine of the questionnaire. However, the rationale of the 
social-ecological indicators and aggregating the scores of inter-dependent indicators was not 
consistent. 

In the last few years, relevant sources have stressed the declining trend of European LSGS and the 
necessity of effective public support schemes (Krzywinski et al., 2009; Veen et al., 2009; Plachter & 
Hampicke, 2010). A broad consensus has been reached on the ground of supporting cultural 
landscapes and natural values, and the delivery of public goods has emerged as the main rationale 
for public support (Cooper et al., 2009; IEEP, 2010; WWF et al., 2010). We found that based on the 
expert responses to the questionnaire, this rationale is not entrenched very well in farmers’ minds 
(59% of experts disagreed or strongly disagreed and 13% were undecided), although our 
respondents fully agreed that LSGS cannot survive without some type of support. These insights 

46 

 
strengthen the continued need to develop a sound rationale for putting public support of LSGS into 
practice, including the evolution of the CAP in this direction. 

The European Commission has proposed that the CAP and the Cohesion Fund (supporting regional 
policies) will be combined in the single National Regional Partnership Fund (NRPF), retaining a ring-
fenced portion for direct farmer income support. The CAP's two-pillar structure is being replaced by 
a more unified framework, and Cohesion funding is also being adjusted with a focus on civil 
preparedness and climate resilience. The information presented here may help to improve the 
design of policy support for LSGS as Member States start to design their National and Regional 
Partnership Plans for 2028-2035.  

Weaknesses and strengths are shared by systems in different locations and even in nearby 
locations, which may allow differentiating properties and trends to be identified (e.g., the cases of 
the island of Pag and the southern Dinaric Alps in Croatia). These weaknesses and strengths are 
part of their identity. But LSGS are not immovable structures, and we found dynamic or downward 
trends, although the latter are dominant. We found a complete consensus was reached by experts 
in response to the requirement for public support (100%) as well as a large consensus on the poor 
effectiveness of the current schemes (72% of responses). Once again, a fundamental question is 
how to focus the required changes for better use of public funds. 

Our response to this question is grounded in the analysis of the data provided by the experts. The 
high level of inter-correlation amongst the recorded variables was considered through a factor 
component analysis that had up to 14 components for explaining up to 90% of the variance. Each 
component only explained a small part of the variance and the higher loading variables within 
components were of a different character. The response to the recorded variables is of use to 
detect weaknesses and strengths in particular systems, but it is of little use for global policy 
guidelines. However, an agreement in the responses to the proposed alternatives for beneficial 
management are useful. For example, if scarce and costly herding labour is perceived as a 
disturbance in the response to criteria-variables and the constraints, this would relate to a 
corresponding response as beneficial management alternative. We grouped the proposed 
alternatives for reform around three social-ecological axes or dimensions, where each one of the 
LSGS can be recognised.  

This proposal may act as a rationale for policy reforms. However, the perception of the experts was 
not devoid of some contradictions. For example, a large majority of experts suggested a grazing 
plan or grazing management measures for achieving change, but this does not seem to be enough. 
All experts agreed that European LSGS cannot survive without some type of policy support and 
there was consensus on the high rate of farmers exiting the operation. This suggests that either 
grazing plans are in the research or design phase and not implemented or the socioeconomic 
effects of this planning are not well addressed. In the first case, recommendations should be 
attractive to farmers. In the second, no unprofitable measure is attractive. Our results suggest that 
the socioeconomics of grazing planning should attract more attention, and other related measures 
such as improvements to the herding labour operation, marketing capabilities, grazing 
infrastructures, grazing governance and institutions, and the promotion of economic side activities 
are particularly relevant to most systems.   

Our proposal also cuts across social, ecological and management disciplines and across European 
LSGS with very different pastoral resources. On these two points, some considerations are relevant. 
First, we addressed the common assumption that work between different disciplines is hampered or 
limited by basic concepts, objectives or even scientific languages (Ostrom, 2009), referred to as the 
conceptually inaccessible hypothesis. However, in this research, we found that the discipline of the 

47 

 
expert (FV3) barely influenced their response to the criteria-variables and constraints (only one 
criteria-variable out of 24 and only one constraint out of 10). In this case, the conceptually 
inaccessible hypothesis between disciplines is not supported by this research.  

Even though the interviews were taken more than a decade ago, their insights still provide valuable 
information in the scare landscape of systematic knowledge on extensive grazing in the European 
Union. Many of the downward trends that were observed have likely continued as is evidenced for 
instance by Nori, M. (2022). A repeat of the questionnaire – if possible, with the same respondents 
could lead to very useful insights, and a reflection on whether the general degradation of LSGS 
observed in Europe shows any regional brights spots of maintenance or even restoration.  

48 

 
 
6.  Conclusions 

This research may open the way for future research and further refinement of policy options in 
LSGS. We argue that European LSGS need specific policy support and provide empirical facts to 
support this notion. We found a general declining trend in LSGS as well as socio-economic concerns, 
but also common and differentiating properties that may allow a general policy framework to be 
developed. European experts agree that LSGS provide environmental services, unique grassland 
biodiversity, as well as economic side activities and that they need support. An aggregate social-
ecological score can be of use to characterize European LSGS. However, pressures and options for 
development in particular systems can only be derived from analysis of single variables and 
concordance of management alternatives. The experts related up to nine options for improvement, 
stressing social cohesion and empowerment of local communities as well as harnessing technology 
without losing their territorial identity. Our research showed that most European LSGS need 
sustainable intensification and targeted support. The conventional strategy of generalised and low 
levels of support is a recipe for further degradation of LSGS in Europe and result in a loss of social 
and ecological values.  

Safeguarding the remaining LSGS in Europe and improving targeted territorial support can have 
benefits for biodiversity, wildfire prevention, rural economies, cultural landscapes, and food security. 
Large scale grazing helps maintain habitats, reduces the risk of large-scale fires by managing fuel 
loads, supports rural jobs and incomes, and preserves traditional farming practices and scenic 
vistas. 

49 

 
 
 
References 

Aanes, R., Sæther, B.-E., & Øritsland, N. A. (2000). Fluctuations of an introduced population of 

Svalbard reindeer: The effects of density dependence and climatic variation. Ecography, 23(4), 437–

443. https://doi.org/10.1111/j.1600-0587.2000.tb00300.x 

AMAP. (2011). Artic Monitoring and Assessment Programme. Snow, Water, Ice and Permafrost in the 

Artic, SWIPA, Executive Summary. Oslo, Norway. 

Antrop, M. (2005). Why landscapes of the past are important for the future. Landscape and Urban 

Planning, 70(1), 21–34. https://doi.org/10.1016/j.landurbplan.2003.10.002 

APCA. (2005). Le pastoralism collectif en France. Assemblée Permanente des Chambres 

d’Agriculture. Chambres d’Agriculture No 940: 7-39. 

Bachev, H. (2009). Bachev H, 2009. Governing of agro-ecosystem services. MPRA Paper No. 15492. 

Munich. 

Bartley, J., Hart, K., & Swales, V. (2009). Exploring policy options for more sustainable livestock and 

feed production. Final Report for Friends of the Earth. Institute for European Environmental Policy 

(IEEP). London. 

Berg, A. (2010). Reindeer herding and modern forestry-the historical impact on forests of two main 

land users in Northern Sweden [Doctoral Thesis No 2010:45]. Swedish University of Agricultural 

Sciences. 

Bonte, D., & Maes, D. (2008). Trampling affects the distribution of specialised coastal dune 

arthropods. Basic and Applied Ecology, 9(6), 726–734. https://doi.org/10.1016/j.baae.2007.09.008 

Caballero, R. (2011). The Common Agricultural Policy (CAP) towards 2020: How can fit farming in 

the marginal areas of the EU. In V. Vasek, Y. Shmaliy, D. Trcek, N. P. Kobayashi, R. S. Choras, & Z. 

Klos (Eds.), Recent Researches in Energy, Environment, Entrepreneurship and Innovation (pp. 88–

102). WSEAS Press. 

Caballero, R. (2015). Transition pathways to sustainable pastoral systems in Europe. The Open 

Agriculture Journal, 9, 6–20. 

Caballero, R., Fernández-González, F., Pérez-Badia, R., Molle, G., Roggero, P. P., Bagella, S., D’Ottavio, 

P., Papanastasis, V., Fotiadis, G., Sidiropoulou, A., & Ispikoudis, I. (2009). Grazing systems and 

biodiversity in Mediterranean areas: Spain, Italy and Greece. Pastos, 39, 9–154. 

50 

 
Caballero, R., & Fernández-Santos, X. (2009). Grazing institutions in Castilla-La Mancha, dynamic or 

downward trend in the Spanish cereal–sheep system. Agricultural Systems, 101(1), 69–79. 

https://doi.org/10.1016/j.agsy.2009.03.004 

Caballero, R., Gil, A., & Fernández-Santos, X. (2008). An experts survey on sustainability across 

twenty-seven extensive European systems of grassland management. Environmental Management, 

42(2), 190–199. https://doi.org/10.1007/s00267-008-9134-2 

Caballero, R., Riseth, J. Å., Labba, N., Tyran, E., Musial, W., Molik, E., Boltshauser, A., Hofstetter, P., 

Gueydon, A., Roeder, N., Hoffmann, H., Moreira, M. B., Coelho, I. S., Brito, O., & Gil, Á. (2007). 

Comparative typology in six European low‐intensity systems of grassland management. In Advances 

in Agronomy (Vol. 96, pp. 351–420). Academic Press. https://doi.org/10.1016/S0065-

2113(07)96001-0 

Cavallero, A., Aceto, P., Gorlier, A., Lombardi, G., Donati, M., Martinasso, B., & Tagliatori, C. (2007). I 

tipi pastorali delle Alpi piemontesi. E. Alberto Perdisa Editor. 

Cooper, T., Hart, K., & Baldock, D. (2009). Provision of public goods through agriculture in the 

European Union. Report prepared for DG Agriculture and Rural Development, Contract 30-CE-

0233091/00-28. Institute for European Environmental Policy (IEEP). London. 

Corti, M. (2004). L’alpegio nelle Alpi lombarde tra pasato e presente. SM Annalli Di San Michele, 17, 

31–155. 

Council of Europe. (2016). Council of Europe Landscape Convention as amended by the 2016 

Protocol. 

Cozzi, G., Bazzotto, M., & Rigoni Stern, G. (2006). Uso del territorio, impatto ambientale, benessere 

degli animali e sostenibilita economica dei sistemi di allevamento della vacca da latte presenti in 

montagna. Il caso studio dell’Altopiano di Asiago. Quaderno Sozooalp, 3, 7–25. 

Draganescu, C. (1997). Romanian sheep production spectacular past, decline and uncertain future. S. 

Kukovic Ed. Proceedings of the 2nd sheep and goat production in Central and Eastern Europe. 

Pp179-187. FAO, REU Technical Series, 50. Rome, Italy. 

Draganescu, C. (2003). Romanian strategy for the management of farm animal genetic resources. 

Country Report for SoW-An-GR. Bucharest. 

Draganescu, C., & Grosu, H. (2010). Valachian (Zackel) heritage philetic sheep group-a taxonomic 

problem. Scientific Papers: Romanian Academy, DAGENE, 1-7. 

51 

 
EC DG-ENV, Ecosystems Ltd., & Sundseth, K. (2014). The EU birds and habitats directives :for nature 

and people in Europe. Publications Office. https://data.europa.eu/doi/10.2779/49288 

Elbersen, B. S., & Andersen, E. (2008). Low-inputs farming systems: Their general characteristics, 

identification and quantification. In B. Katarzyna, J. M. Terres, & P. Pointereau (Eds.), Low input 

farming systems: An opportunity to develop sustainable agriculture (pp. 12–21). JRC. 

Fernandez-Santos, X. (2010). Sustainability in extensive European systems of grassland 

management. A case study of the cereal-sheep system in Castilla-La Mancha [Doctoral Thesis]. 

Politechnic University. 

FMA. (2003). Eleveurs de bovines sur prairies des marais atlantiques. Forum des Marais Atlantiques. 

Poitou-Charente. France. 

Forbes, B. C. (2006). The challenges of modernity for reindeer management in Northernmost Europe. 

In B. C. Forbes, M. Bölter, L. Müller-Wille, J. Hukkinen, F. Müller, N. Gunslay, & Y. Konstantinov (Eds.), 

Reindeer Management in Northernmost Europe: Linking Practical and Scientific Knowledge in Social-

Ecological Systems (pp. 11–25). Springer. https://doi.org/10.1007/3-540-31392-3_2 

Gibon, A., Balent, G., Olaizola, A., & Di Prieto, F. (1999). Approache des variations communales des 

dynamiques rurales au moyen d’une typology: Cas du versant nord des Pyrenees Centrales. Options 

Mediterranéennes Serie B27: 15-34. 

Gibon, A., & Mihina, S. (2003). Livestock farming systems in Central and Eastern Europe. European 

Association of Animal Production (EAAP), Technical Publication Series No 3. Wageningen, The 

Netherlands. 

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7. ed). 

Pearson Prentice Hall. 

Hofstetter, P. (2010). Structural analysis of the dairy industry and its evolution in Central 

Switzerland. Grassland Science in Europe, 15, 109–111. 

Holz, B., Kaule, G., Lederbogen, D., & etc. (2005). Common and differentiating properties of LSGS in 

Europe. Deliverable 10.1. LACOPE Contract EVK2-CT-2002-00150. Stuttgart, Germany. 

Huband, S. (2007). The role of Romanian pastoralists in conserving agricultural  biodiversity [PhD 

Thesis]. University of Edinburgh. 

52 

 
IEEP. (2007). Final Report for the study of HNV indicators and evaluation. Institute for European 

Environmental Policy. Contract Notice 2006-G4-04. London. 

IEEP. (2010). Public goods emerging as a central rationale for future CAP support. Institute for 

European Environmental Policy (IEEP). CAP Policy Briefing No 7. London. 

Jansen, J. (2011). Managing Natura 2000 in a changing world. The case of the Serra da Estrela [PhD 

Thesis]. Radboud University. 

Jones, G. (2011). HNV farming and permanent pasture—The gap between EU-rules and reality. La 

Cañada, 26, 1–3. 

Karsten, H. D., & Baer, D. J. (2009). Grass and human nutrition. In W. Wedin & S. L. Fales (Eds.), 

Grassland: Quietness and Strength for a New American Agriculture (pp. 189–204). American Society 

of Agronomy. https://www.wiley.com/en-

us/Grassland%3A+Quietness+and+Strength+for+a+New+American+Agriculture-p-9780891181941 

Kelly, K. (2011). What Technology Wants. Penguin Books. 

Kitti, H., Forbes, B. C., & Oksanen, J. (2009). Long- and short-term effects of reindeer grazing on 

tundra wetland vegetation. Polar Biology, 32(2), 253–261. https://doi.org/10.1007/s00300-008-

0526-9 

Kleijn, D., Kohler, F., Báldi, A., Batáry, P., Concepción, E. d, Clough, Y., Díaz, M., Gabriel, D., Holzschuh, 

A., Knop, E., Kovács, A., Marshall, E. j. p, Tscharntke, T., & Verhulst, J. (2008). On the relationship 

between farmland biodiversity and land-use intensity in Europe. Proceedings of the Royal Society B: 

Biological Sciences, 276(1658), 903–909. https://doi.org/10.1098/rspb.2008.1509 

Krzywinski, K., O’Connell, M., & Küster, H. (2009). Cultural Landscapes of Europe: Fields of Demeter, 

haunts of Pan. Aschenbeck Media. 

Kumpula, J., & Colpaert, A. (2003). Effects of weather and snow conditions on reproduction and 

survival of semi-domesticated reindeer (R. t. Tarandus). Polar Research, 22(2), 225–233. 

https://doi.org/10.3402/polar.v22i2.6457 

LACOPE. (2002). Landscape Development and Co-operative Livestock Systems in Europe. Document 

of Work (DoW). Contract EVK2-CT-2002-00150. Stuttgart, Germany. 

53 

 
Leroy, G., Hoffmann, I., From, T., Hiemstra, S. J., & Gandini, G. (2018). Perception of livestock 

ecosystem services in grazing areas. Animal, 12(12), 2627–2638. 

https://doi.org/10.1017/S1751731118001027 

Luick, R. (2008). Transhumance in the Swabian-Franconian region of Germany. La Cañada, 22, 4–7. 

Marrs, S. J., Foster, S., Hendrie, C., Mackey, E. C., & Thompson, D. B. A. (2011). The Changing Nature 

of Scotland. TSO Scotland, Edinburgh, Scotland. 

Matley, I. M. (1970). Traditional pastoral life In Romania. The Professional Geographer, 22, 311–316. 

Mayer, A. L. (2008). Strengths and weaknesses of common sustainability indices for 

multidimensional systems. Environment International, 34(2), 277–291. 

https://doi.org/10.1016/j.envint.2007.09.004 

McCracken, D. (2008). The farmland biodiversity implications of land abandonment. Defra Project 

FFG 0902. Ayr, Scotland. 

Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A., & Watkins, J. W. (2005). A climatic 

stratification of the environment of Europe. Global Ecology and Biogeography, 14(6), 549–563. 

Metzger, M. J., Bunce, R. G. H., van Eupen, M., & Mirtl, M. (2010). An assessment of long term 

ecosystem research activities across European socio-ecological gradients. Journal of Environmental 

Management, 91(6), 1357–1365. https://doi.org/10.1016/j.jenvman.2010.02.017 

Moen, J. (2008). Climate change: Effects on the ecological basis for reindeer husbandry in Sweden. 

Ambio, 37(4), 304–311. 

Moran, J., Parr, S., Dunford, B., & Ó’Conchuir, R. (2008). Species rich limestone grasslands of the 

Burren, Ireland: Feed value and sustainable grazing systems. Grassland Science in Europe, 13, 150–

152. 

Mottet, A., Ladet, S., Coqué, N., & Gibon, A. (2006). Agricultural land-use change and its drivers in 

mountain landscapes: A case study in the Pyrenees. Agriculture, Ecosystems & Environment, 114(2), 

296–310. https://doi.org/10.1016/j.agee.2005.11.017 

Nisbet, A., & Glaves, D. (2010). Moorland management in Higher Level Stewardship: The evidence 

base for sustainable stocking rates. Aspects of Applied Biology, 100, 141–149. 

54 

 
Nori, M. (2022). Assessing the policy frame in pastoral areas of Europe. Robert Schuman Centre for 

Advanced Studies. RSC Working Paper 2022/03. Published by the European University Institute, San 

Domenico di Fiesole (FI), Italy. 

Nyenhuis, U., Liebing, A., Goebel, M., & Robinet, K. (2007). Nature: Our future-how rural regions 

benefit from environmental protection and conservation and how EU agricultural policy could 

contribute. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. 

Olwig, K. (2002). Landscape Nature and Body Politic: From Britain’s Renaissance to America’s New 

World. University of Wisconsin Press. 

Opdam, P., Verboom, J., & Pouwels, R. (2003). Landscape cohesion: An index for the conservation 

potential of landscapes for biodiversity. Landscape Ecology, 18(2), 113–126. 

https://doi.org/10.1023/A:1024429715253 

Orlandi, S., Probo, M., Sitzia, T., Trentanovi, G., Garbarino, M., Lombardi, G., & Lonati, M. (2016). 

Environmental and land use determinants of grassland patch diversity in the western and eastern 

Alps under agro-pastoral abandonment. Biodiversity and Conservation, 25(2), 275–293. 

https://doi.org/10.1007/s10531-016-1046-5 

Osoro, K., Celaya, R., Martinez, A., & Zorita, E. (2000). Pastoreo de las comunidades vegetales de 

montaña por rumiantes domésticos: Producción animal y dinámica vegetal. Pastos, 30, 3–50. 

Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. 

Science, 325(5939), 419–422. https://doi.org/10.1126/science.1172133 

Pakeman, R. J. (Ed.). (2011). Biodiversity and Farming. A summary of research outputs from the 

Scottish Government’s “Environment-Land Use and Rural Stewardship” research programme. 

Macaulay Land Use Research Institute. 

Partidário, M. R., Sheate, W. R., Bina, O., Byron, H., & Augusto, B. (2009). Sustainability assessment 

for agriculture scenarios in Europe’s mountain areas: Lessons from six study areas. Environmental 

Management, 43(1), 144–165. https://doi.org/10.1007/s00267-008-9206-3 

Piessens, K., & Hermy, M. (2006). Does the heathland flora in north-western Belgium show an 

extinction debt? Biological Conservation, 132(3), 382–394. 

https://doi.org/10.1016/j.biocon.2006.04.032 

Plachter, H., & Hampicke, U. (Eds.). (2010). Large-scale Livestock Grazing: A Management Tool for 

Nature Conservation. Springer. https://doi.org/10.1007/978-3-540-68667-5 

55 

 
Plassmann, K., Jones, M. L. M., & Edwards-Jones, G. (2010). Effects of long-term grazing 

management on sand dune vegetation of high conservation interest. Applied Vegetation Science, 

13(1), 100–112. https://doi.org/10.1111/j.1654-109X.2009.01052.x 

Plieninger, T., & Pulido, F. J. (2009). Dehesa, Spain: Blending grassland, crops, and forests. In P. Veen, 

R. Jefferson, J. De Smidt, & J. Van Der Straaten (Eds.), Grasslands in Europe: Of High Nature Value 

(pp. 275–282). KNNV Publishing. https://doi.org/10.1163/9789004278103 

Pointereau, P., Paracchini, M.-L., Terres, J., Jiguet, F., Le Bas, Y., & Biala, K. (2007). Identification of 

High Nature Value Farmland in France through Statistical Information and Farm Practices Surveys. 

Publications Office of the European Union. 

https://publications.jrc.ec.europa.eu/repository/handle/JRC37553 

RGS. (2002). Sustainable future for the British uplands. Summary statement. London. 

Riseth, J. Å. (2006). Sámi reindeer herd managers: Why do they stay in a low‐profit business? British 

Food Journal, 108(7), 541–559. https://doi.org/10.1108/00070700610676361 

Riseth, J. Å., Johansen, B., & Vatn, A. (2004). Aspects of a two-pasture—Herbivore model. Rangifer, 

15, 65–81. https://doi.org/10.7557/2.24.4.1725 

Roca, Z., & Oliveira-Roca, M. de N. (2007). Affirmation of territorial identity: A development policy 

issue. Land Use Policy, 24(2), 434–442. https://doi.org/10.1016/j.landusepol.2006.05.007 

Roturier, S., & Roué, M. (2009). Of forest, snow and lichen: Sámi reindeer herders’ knowledge of 

winter pastures in northern Sweden. Forest Ecology and Management, 258(9), 1960–1967. 

https://doi.org/10.1016/j.foreco.2009.07.045 

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). 

Biometrika, 52(3/4), 591–611. https://doi.org/10.2307/2333709 

Shastri, Y., Diwekar, U., Cabezas, H., & Williamson, J. (2008). Is sustainability achievable? Exploring 

the limits of sustainability with model systems. Environmental Science & Technology, 42(17), 6710–

6716. https://doi.org/10.1021/es800661x 

Sheate, W. R., Partidário, M. R. D., Byron, H., Bina, O., & Dagg, S. (2008). Sustainability assessment of 

future scenarios: Methodology and application to mountain areas of Europe. Environmental 

Management, 41(2), 282–299. https://doi.org/10.1007/s00267-007-9051-9 

56 

 
Sooväli, H. E., Palang, H., & Külvik, M. (2003). The role of rural landscapes in shaping Estonian 

national identity. In T. Unwin & T. Spek (Eds.), European Landscapes: From Mountain to sea. 

Proceedings of the Permanent European Conference for the Study of Rural Landscapes (pp. 114–

121). HUMA. https://research.wur.nl/en/publications/european-landscapes-from-mountain-to-sea-

proceedings-of-the-19th- 

Sundseth, K. (2009). Natura 2000 in the Atlantic region. European Commission. 

Szczęch, M., Kania, M., Loch, J., Ostapowicz, K., & Struś, P. (2024). Mapping grasslands’ preservation 

potential: A case study from the northern Carpathians. Land Degradation & Development, 35(2), 

633–646. https://doi.org/10.1002/ldr.4941 

Targetti, S., Niedermayr, A., Häfner, K., & Schaller, L. (2024). New pathways for improved delivery of 

public goods from agriculture and forestry. Bio-Based and Applied Economics, 13(1), Article 1. 

https://doi.org/10.36253/bae-15862 

Theau, J. P., & Gibon, A. (1999). Choix techniques et resultats economiques dans l’elevage bovin 

allaitant du haut Couserans (Central Pyrenees). Options Mediterranéennes, Serie B27, 95–110. 

Van Duinen, G. J., Brouwer, E., Nijssen, M., & Esselink, H. (2006). Dissemination of ecological 

knowledge and practical experiences for sound planning and management in raised bogs and sea 

dunes. LIFE Co-op Project. Radboud University. 

Veen, P., Jefferson, R., De Smidt, J., & Van Der Straaten, J. (Eds.). (2009). Grassland in Europe: Of 

high nature value. KNNV Publishing. https://doi.org/10.1163/9789004278103 

von Metzner, J., Jedicke, E., Luick, R., Reisinger, E., & Tischew, S. (2010). Extensive grazing and 

requirements for the new agricultural policy (in German, English summary). 42: 357-66. Naturschutz 

Und Landschaftsplanung (NUL), 42, 357–366. 

Wascher, D. M. (2005). European Landscape Character Areas—Typologies, Cartography and 

Indicators for the Assessment of Sustainable Landscapes. Final Report. European Landscape 

Character Assessment Initiative (ELCAI) (No. Alterra Report No 1254). Alterra. 

Wolff, A. (2002). Ecological consequences of pastoral practices in the Crau. La Cañada, 15, 6–7. 

WWF, BirdLife International, EEB, EFNCP, & IFOAM. (2010). Proposals for a new EU Common 

Agricultural Policy. 

Https://assets.wwf.org.uk/downloads/proposal_new_common_agricultural_policy.pdf. 

57 

 
List of abbreviations and definitions 

Abbreviations 

Definitions 

BfN 

CAP 

CC 

ELPEN 

EUNIS 

FFH 

HNV 

LSIRD 

LSGS 

SEP 

SIAGR 

SMC 

TAQ 

German Federal Agency for Nature Conservation 

Common Agricultural Policy 

Current Constraints 

European Livestock Policy Evaluation Network 

European Nature Information System 

Flora Fauna Habitat 

High Nature Value  

European Network for Livestock Systems and 
Integrated Rural Development network 

Large Scale Grazing Systems  

Spanish Grassland Society 

Italian Society for Agronomy 

Squared Multiple Correlation 

Total Allowable Quota 

58 

 
 
List of boxes  

Box 1. The main vegetation types with grazing systems in the Boreal and North Alpine bioregion ....................... 15 

Box 2. The main vegetation types with grazing systems in the Atlantic bioregion. ........................................................... 17 

Box 3. The main vegetation types with grazing systems in the Continental & Pannonian bioregions .................. 21 

Box 4. The main vegetation types of potential grazing use in the South Alpine bioregion ........................................... 24 

Box 5. The main vegetation types of with grazing systems in the Mediterranean bioregion ...................................... 28 

59 

 
 
List of figures 

Figure 1. Locations of extensive systems of grassland management in Europe. .......................................... 8 

Figure 2. Clustering of case studies by criteria B (environment), C (economic) and D (social). The 
location of the case studies is in Table 2.............................................................................................................................. 42 

Figure 3. Clustering of case study LSGS by constraint-variables CC4, CC5, CC7 and CC10. The 
location of the case studies is in Table 2.............................................................................................................................. 43 

Figure 4. Beneficial management alternatives for European Large Scale Grazing Systems (LSGS). 
Frequency of response (%) by experts in 44 LSGS. ........................................................................................................ 44 

60 

 
 
List of tables 

Table 1. Grouping 46 Large Scale Grazing Systems (LSGS) study areas by main factors of variation, 
levels and observations. ..................................................................................................................................................................... 9 

Table 2. List of the Large Scale Grazing Systems (LSGS) case studies and the main environmental 
and management variables collected. .................................................................................................................................... 10 

Table 3. Partial score by criteria and total score of case-study grazing systems. ...................................... 32 

Table 4. Global rating of potential current constraints in 45 European Large Scale Grazing Systems 
(LSGS). ....................................................................................................................................................................................................... 33 

Table 5. Significance of 11 factors of variation on 48 variables recorded in 46 European Large 
Scale Grazing Systems. ................................................................................................................................................................... 35 

Table 6. Squared Multiple Correlation (SMC) of each within-criteria variable with all other within-
criteria variables and significance of the regression. ..................................................................................................... 39 

Table 7. Stepwise regression of identity variables (Ai) upon the rest of within-criteria variables (Bi, 
Ci and Di). ................................................................................................................................................................................................ 40 

Table 8. Rotated sums of squared loadings and variance explained by 14 principal components. .. 40 

Table 9. Cluster group statistics for three criteria and for three current constraint variables in the 
sample of case studies. ................................................................................................................................................................... 41 

61 

 
 
 
Annex:  Identity  questionnaire  for  Large  Scale  Grazing  Systems  (LSGS)  in 
Europe 

1. Expert´s identification 

Name  

Affiliation institution 

Address 

Main job. If other, please, indicate 

Research 
Other  

  Management 

  Advisory services 

Counselling 

Main expertise. If other, please, indicate 

Environmental    
Other  

Animal production 

Socio-economics 

Phone (including international code) 

Fax 

E-Mail 

Available to follow up 
Responses and comments to remain anonymous 

Yes 
Yes 

No 
No 

2. Location and size of the LSGS operation. 
Please, indicate one administrative province, district or region and one geographical feature for 
proper location of the LSGS. Provide an estimate of land extent covered by the system 

Province, district or region 

Geographical feature 

Potential area (approximate zoning) of the LSGS operation (square km) 

Do you have a character name for this LSGS 

Mean population density in the potential area (inhabitants /square km): 

Climate and altitude 
Mean  annual  rainfall 
(mm) 

Mean annual tempera-
ture (ºC) 

Mean elevation in 
the area (m) 

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3. Biophysical and grazing conditions 

Biogeographical region 
  Atlantic 
Alpine 

  Boreal 

  Continen-

tal 

  Mediter-
ranean 

  Pannonic 

Main type (s) of land based resources. If other, please, indicate 
Semi-natural grasslands 

Cultivated forages and pastures 

Forests 

Agricultural residues 

Other 

Main ownership regime(s) of grazing resources. If others, please, indicate 
Private 
Other 

Local communities 

Government 

Mixed 

Mobility of herds/flocks through the year If other, please, wording 
Sedentary 
Long migrations 

Vertical mobility from lowland to upland pastures 

Short migrations 

  Other 

Dominant livestock operation. If other or mixed, please, indicate 
Cattle 
Goats 
Mixed 

Horses 
Other 

Sheep 

Pigs 

Dominant livestock breeds 
Indigenous 
Name of indigenous 

Foreign 

Crosses 

Name of foreign 

Dominant objective of production. If other or mixed, please, indicate 
Milk 
Mixed 
If relevant, please provide the name of the cheese 

Cheese-making 

Other 

Meat 

Heifers’ rearing 

Mean size of the livestock operation in Livestock Units (LU) 
Small (<10LU) 

Medium (10-30LU) 

Large (>30LU) 

Average regional stocking (LU/ha of total pasture land) 

Average number of potential grazing days through the year 

4. Experts’ view/perception on trends of change in the LSGS 
The objective of this section is to transform your choose of conceptual-scored variables to a 
rating of 1 (strongly disagree) to 5 (strongly agree) on the Likert-type scale for statistical anal-
ysis. Please, make a mark (x) on the appropriate box 

A. General 
A1. Although influenced by external and internal potential disturbances (ie, technological inno-
vations, demographic trends, off-farm income opportunities, agricultural support policies), the 
LSGS is still a recognisable structure with regional identity. 
  Agree 

Strongly disagree 

Strongly agree 

  Undecided 

  Disagree 

63 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A2. Under a rationale of working land, one community of livestock farmers still make a living 
from the LSGS. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

A3. The general view of the whole society towards the LSGS is of well knowledge and percep-
tion of its potential values and not of lack of attention or disregard. 
  Disagree 

Strongly disagree 

Strongly agree 

  Undecided 

  Agree 

A4.  The  community  of  livestock  farmers  is  well-organised,  having  lobbying  capabilities  for 
influencing on governments’ actions and for transmitting values to the whole society. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

A5. External help to the LSGS (ie, advisory and training services, financial support services, 
agricultural support policies) is required. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

A6. External help to the LSGS is well-organised and effective 
  Agree 

Strongly agree 

  Undecided 

  Disagree 

Strongly disagree 

B. Environmental 
B1. The LSGS is still a grazing-dominant and herding operation with land based pasture and 
forages as the main feeding source. 
  Agree 

Strongly disagree 

Strongly agree 

  Undecided 

  Disagree 

B2. Spatiotemporal distribution of grazing is homogeneous across the area of the whole grazing 
system (ie, no large differences between far-reaching grazing grounds and pasture and forage 
units near the farmyard or differences between lowland and upland pastures). 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

B3. Current land use and production practices implemented by livestock farmers (ie, stocking 
rate and spatial distribution, manure management) are congenial with the delivery of environ-
mental values (ie, plant and animal biodiversity, soil and water management, landscape scen-
ery). 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

B4. Current agricultural policies (ie, agri-environment schemes) are effective in the pursuit of 
beneficial environmental values. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

B5.  Environmental  NGOs  are  working  congenially  with  farmers  in  the  pursuit  of  beneficial 
management. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

B6. Livestock farmers, regulatory institutions, and industrial firms do not disguise the environ-
mental management attached to production practices. 
  Undecided 

Strongly disagree 

Strongly agree 

  Disagree 

  Agree 

C. Economics 

64 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
C1. The current grazing operation is profitable without subsidies. Most frequently, the value of 
production is above total costs. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

C2. Beneficial management alternatives can be implemented for improving economic results 
and environmental values (win-win situation). 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

C3. The delivery of indigenous products (ie, meat, cheeses) with marketing capabilities is or 
can be a main characteristic for the identity of the system. 
  Agree 

Strongly disagree 

Strongly agree 

  Undecided 

  Disagree 

C4. Livestock farmers in the LSGS have social, financial and technical capabilities and are well-
organised for access to markets. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

C5. The continuity of the grazing operation is essential for side-economic activities in the area 
(ie, agro-tourism, hunting, landscape scenery). 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

C6. The general consumer outside the system is well aware of production practices and their 
effects and thus consumer awareness is assured. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

D. Social 
D1. Legal and institutional frameworks with proper functions and technical and managerial sup-
port are operative. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

D2. Young farmers are interested in the livestock operation and the family business turnover is 
assured. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

D3. The social organisation of grazing practices and indigenous products are a focus of regional 
identity that attracts the attention of the whole society. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

D4. The delivery of public goods (ie, biodiversity of plants and animals, landscape conservation, 
soil and water management) is a well-entrenched rationale for livestock farmers when devising 
and implementing their production practices. 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

D5. Potential external disturbances (ie, demographic trends, insensitive schemes of agricultural 
support, off-farm income opportunities) are more important than internal ones (lack of social 
cohesion, small farm size, lack of assurance of family business turnover) for the continuity of 
the grazing operation. 
Strongly agree 

Strongly disagree 

  Undecided 

  Disagree 

  Agree 

65 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
D6. Grazing-rights allocation is based on normative rules allowing grazing rights interchange 
and not in informal regulations and diffuse claims of land property 
  Disagree 

Strongly disagree 

Strongly agree 

  Undecided 

  Agree 

5. Environmental assets 
The objective of this section is to provide some environmental functions, side-effects assets and 
regional environmental targets, linked to the LSGS, as contributors of their regional identity. 
You may rate all of those applicable. If other(s), please, relate 

Environmental functions 

Some vegetation types rated in the EU Habitat Directive are represented 
Some rare or endemic plant species of conservation concern are represented 
Animal species (ie, mammals, birds, insects) of conservation concern represented 
Contribution to soil conservation and properties (ie, soil biota and organic matter) 
Contribution to water management and watershed stewardship 
Contribution to erosion control 
Control of fires’ risk 
Maintenance of indigenous livestock breed (s) 
Other 

Main side-effects 

Agro-tourism and ecotourism 
Hunting 
Landscape scenery 
Cultural traditions (ie, animals’ round-up or transhumance, food festivals) 
Other (s) 

Main environmental regional targets. Please, relate the most relevant vegetation types (as rated 
in the EU Habitat Directive) and target species of conservation concern. 
Vegetation types 
Plant species 
Animal species 

Environmental trends. Please, make a mark (x) in the appropriate box 
The  above  environmental  assets  (environmental  functions,  side-effects  and  targets)  can  be 
maintained under current operational trends 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

The above environmental assets can be improved by introducing some management alternatives 
(ie, land use changes, changes in grazing practices) on current operations 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

The delivery of the above environmental assets do not conflicts with current human activities 
related to the grazing operation 

Strongly agree 

  Agree 

  Undecided 

  Disagree 

Strongly disagree 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6. Main constraints 
The objective of this  section is  to  unveil the main  constraints  hampering  the continuity and 
coherence of the grazing operation. An ordinal response is requested. Please, rate the following 
potential constraints from 1 (most important) to 10 (less important). 

Lack of potential grazing resources 
Disturbances for mobility or accessibility to grazing resources 
Unsatisfactory grazing infrastructures (ie, water points, barns, housing, milking) 
Costly and scarce waged labour 
Insensitive agricultural support policies 
Lack of livestock farmers’ affection and tradition towards the farming operation 
Lack of family business turnover (uninterested young farmers) 
Deficient or inoperative advisory and training services 
Constraints in access to capital for sustainable investments 
Current grazing practices represent a hard-working operation 

Other (s) not indicated constraint (s). Please, wording  

7. Main trends in past years 

Environment and marketing criteria in the past 30 years 
Under the environment criteria, it will be rated as YES if traditional grazing practices (ie, mo-
bility of herds/flocks, homogeneous distribution of grazing over the area) and area under semi-
natural grasslands have not varied much. Under the marketing criteria, it will be rated as YES 
if external marketing of products is dominant over family consumption and local markets. 
Environment 

Markets 

Rate of livestock farmers entering/exiting the operation 
Number of active livestock farmers in the area some 60 years ago 
Number of active livestock farmers in the area some 30 years ago 
Number of active livestock farmers in the area in 2010 

If the transition year to a declining rate occurred early, please indicate a year or short period 

8. Main trends in the next 10-15 years 
Please, indicate the most probable outcome from a farmer perspective (left column of boxes) 
and your own preferences (right column). 

Stable situation from environment, markets and farmer population criteria 
Intensification of production practices irrespective of policy support schemes 
Endogenous development and marketing capabilities with few external help 

  Mixture of endogenous development, managerial support and policy schemes 

Abandonment irrespective of policy support schemes 
Surviving as a low-intensity operation with agricultural support 
Surviving as a small-scale park-like operation under public/NGOs support 

Others outcomes. Please, wording and marks 

67 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9. Beneficial management 
The objective of this section is to identify up to five management practices and alternatives that, 
under your own expertise or available knowledge can contribute to the continuity and coherence 
of  the  LSGS.  Each  alternative  will  be  rated  by  its  ground-base  (knowledge-based,  common 
sense and farmer practice) and potential effects (environmental, socio-economic and win-win 
outcome). Please, wording and marks. 

Alternative 1  

Knowledge-based 
Environmental 

Common sense 
Socio-economic 

Farmer practice 
  Win-win outcome 

Alternative 2  

Knowledge-based 
Environmental 

Common sense 
Socio-economic 

Farmer practice 
  Win-win outcome 

Alternative 3  

Knowledge-based 
Environmental 

Common sense 
Socio-economic 

Farmer practice 
  Win-win outcome 

Alternative 4  

Knowledge-based 
Environmental 

Common sense 
Socio-economic 

Farmer practice 
  Win-win outcome 

Alternative 5  

Knowledge-based 
Environmental 

Common sense 
Socio-economic 

Farmer practice 
  Win-win outcome 

10. Remittance of the filled questionnaire 
Once filled, the questionnaire will be sent by e-mail to the address below. A complete address 
and affiliation of the requester is also provided for further contacts, comments and suggestions: 
Prof. Rafael Caballero García de Arévalo 
Main scientist (Investigador principal) 
Environmental Science Centre (Centro de Ciencias Medioambientales) 
Spanish  Council  for  Scientific  Research  (Consejo  Superior  de  Investigaciones  Científicas, 
CSIC) 
Finca Experimental La Poveda, CSIC 
Ctra. de Campo Real km 1 
28500 Arganda del Rey 
Madrid, Spain 
Phone: ++34-91-8700941 

68 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fax: ++34-91-8714655 
E-mail: rcaballero@ccma.csic.es 

11. Expanding the reach of the survey sample 
The objective of this section is to reach additional and relevant extensive livestock systems and 
corresponding experts within your country. You may include up to four by citing the area, the 
name of the corresponding expert and his/her e-mail address 

Area  

Name of expert 

E-mail 

12. Comments and suggestions 
Your comments and suggestions to the questionnaire or to some of your responses would be 
welcome. Please, wording and indicate the related section in the questionnaire. We are partic-
ularly interested in two sections. In section 2, please indicate if you are not confident with your 
response to some variable(s) (ie, A1, C4 etc). No feedback would indicate that you are confident 
with your responses. In section 5 tier Environmental trends, you may envisage some type of 
conflict between the delivery of environmental assets and current grazing practices (3rd ques-
tion). In this case, we would appreciate a comment on the type of conflict (ie, between conser-
vation of biodiversity and current grazing practices) and some initiatives for solving. 

Thanks so much for your time and willingness to provide feedback. 

69 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Getting in touch with the EU 

In person 

All over the European Union there are hundreds of Europe Direct centres. You can find the address of the cen-
tre nearest you online (european-union.europa.eu/contact-eu/meet-us_en). 

On the phone or in writing 

Europe Direct is a service that answers your questions about the European Union. You can contact this service: 

—  by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls), 
—  at the following standard number: +32 22999696, 
—  via the following form: european-union.europa.eu/contact-eu/write-us_en. 

Finding information about the EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa web-
site (european-union.europa.eu). 

EU publications 

You can view or order EU publications at op.europa.eu/en/publications. Multiple copies of free publications can 
be obtained by contacting Europe Direct or your local documentation centre (european-
union.europa.eu/contact-eu/meet-us_en). 

EU law and related documents 

For access to legal information from the EU, including all EU law since 1951 in all the official language ver-
sions, go to EUR-Lex (eur-lex.europa.eu). 

EU open data 

The portal data.europa.eu provides access to open datasets from the EU institutions, bodies and agencies. 
These can be downloaded and reused for free, for both commercial and non-commercial purposes. The portal 
also provides access to a wealth of datasets from European countries. 

 
 
 
The Joint Research Centre (JRC) provides independent, 
evidence-based knowledge and science, supporting 
EU policies to positively impact society 

Scan the QR code to visit: 
The Joint Research Centre: EU Science Hub 
https://joint-research-centre.ec.europa.eu