research project rosa assessing preferences · pdf filede estudos em ecossistemas e paisagens...

UNIVERSIDADEDEÉVORAINSTITUTODECIÊNCIASAGRÁRIASEMEDITERRÂNICAS

GRUPODEESTUDOSEMECOSSISTEMASEPAISAGENSMEDITERRÂNICAS

RESEARCHPROJECTROSA

ASSESSINGPREFERENCESFORDIFFERENTLANDSCAPEPATTERNSBYDIFFERENTLANDSCAPEUSERS,INTHEREGIONOFALENTEJO

Instituto de Ciências Agrárias Mediterrânicas - Grupo de Investigação em Ecossistemas e Paisagens Mediterrânicas

Institute of Mediterranean Agricultural Sciences - Research Group on Mediterranean Ecosystems and Landscapes

RESEARCH PROJECT ROSA: Assessing preferences for different landscape patterns

by different landscape users, in the region of Alentejo Page 1

I. SEAMLESS INTEGRATED PROJECT AND THE LANDSCAPE AMENITIES MODEL Page 1

II. RESEARCH PROJECT ROSA Page 4

II.1 METHODOLOGY

II.1.1 Study area Page 5

II.1.2 Land cover classes Page 6

II.1.3 Sample area Page 7

II.1.4 Groups of landscape users Page 11

II.1.5 Visual tools

Photos Page 12

Block Diagrams Page 13

II.1.6 Structure of the inquiries Page 14

II.2. POSSIBLE USES OF THE DATA COLLECTED

II.2.1 Proportion of land cover classes (Q methodology) Page 15

II.2.2 Relation with the output indicators from SEAMLESS Page 15

II.3. METHODOLOGY TRIAL: THE INQUIRIES STRUCTURE

II.3.1 First trial Page 16

Test A – squared grid Page 16

Test B – line grid Page 17

Observations Page 18

II.3.2 Second trial Page 18

Test C – Grid and block diagram Page 19

Observations 0

References Page 21

2



INDEX OF FIGURES

Figure 1 - Organigram of the Landscape Amenities model Page 2

Figure 2 - Study area Page 5

Figure 3 - Maps of clusters results Page 9

Figure 4 - Sample area municipalities Page 10

Figure 5 - Photo manipulation Page 12

Figure 6 - Block diagram Page 13

Figure 7 - Comparing scenarios Page 16

Figure 8 - 1st test grid and photos Page 17

Figure 9 - 2nd test grid and photos Page 18

INDEX OF TABLES

Table 1 - Approaches to achieve the optimum values

Table 2 - Land cover classes in the study area Page 7

Table 3 - Clusters’ classification Page 8

Table 4 - Sample area municipalities Page 10

Table 5 - Landscape users Page 11

Table 6 - Photos’ numeration Page 14

Table 7 - Main steps of the Q methodology (Barry & Proops, 1999), and it

application in the project ROSA Page 15

INDEX OF ANNEXES

Annex 1 – List of meetings

Annex 2 – CORINE Land Cover 2000 classes (1st stage) and sub-classes of extensification /

intensification

Annex 3 – Landscape Units Maps and crossing Landscape Units / CORINE Land Cover Maps

Annex 4 - PROT 2006 Maps

Annex 5 – Land cover photos, to include in the inquiry

Annex 6 - ROSA - inquiry

3



1



RESEARCH PROJECT ROSA: Assessing preferences for different landscape patterns by different

landscape users, in the region of Alentejo

This document presents the methodology of the Research Project ROSA, starting presently (Autumn 2008)

in Alentejo, in the southern region of Portugal. This project is developed, in part, in a close relation with the

SEAMLESS integrated project, carried out by a consortium of 30 European partners and led by the

Wageningem University (NL)1. This relation is based on the fact the project ROSA is expected to produce

data that will permit to test the landscape amenities model, developed in the SEAMLESS context. ROSA will

produce data based on the assessment of the suitability of different landscape patterns (compositions of land

cover patches) to a selection of amenity and cultural functions provided by the landscape, through inquiries

to different groups of users. For this reason, the SEAMLESS project and its landscape amenities model are

succinctly presented here.

I. SEAMLESS INTEGRATED PROJECT AND THE LANDSCAPE AMENITIES MODEL.

The SEAMLESS project aims at developing a model integrated framework that allows ex-ante assessment of

the impact of agricultural and environmental policies and technological innovations, on several dimensions of

the agricultural sector and the rural areas. The framework has multi-scale capabilities ranging from field and

farm levels to the EU25 and globe; it is generic, modular, open and uses state-of-the-art software1.

Within SEAMLESS, and increasing during the length of the project, there has been a concern for achieving a

comprehensive evaluation of the social impacts of agricultural change, both in what concerns more common

social dimensions (labor, quality of life), as the dimensions related with the social demand of the landscape

in the present.

The work developed until now by the Portuguese team, under the social indicators framework, is based on

the construction of the Landscape Amenities Model (fig. 1). The model express what kind of amenities

(which non-commodity functions and at what level of intensity) are supported by the landscape, according to

different land cover combinations, evaluating the display of functions in relation of the land cover pattern,

resulting from agricultural activities. It permits to evaluate scenarios for the future, as the change in the land

cover data considering possible variations according to scenarios, will correspond to values of functions, i.e.,

what is going to happen to a function: which is going to decrease, which is going to increase.

The functions considered are those that correspond to a social demand in the today society: a demand for

different activities in the rural landscape, or the demand for the countryside in general, if this concerns

characteristics that people consider important. Examples of such functions are the hunting, the ecoturism or

the search for life quality.

1 www.seamless-ip.org/

2



This land cover pattern is expressed in cartography, as the CORINE Land Cover. But it can also be

calculated from the data used in Seamless, even if in a more rough way: the model outputs about the crop

diversity, specialisation, and intensity of agriculture.

If the land cover pattern more valuated by users for a specific function is identified, using specific indicators,

then it is possible to calculate a specific value or set of values, to that type of landscape that would be the

optimum values for each function. If, for alternative scenarios are applied the same indicators, then would be

possible to compare the similarity between the optimum landscape type for a function and the several

scenarios proposed.

The optimum values, based on people’s preferences, can be obtained by extrapolating the results achieved

mainly through four different approaches (Table 1):

Fig. 1 - Organigram of the Landscape Amenities model - in orange is indicated the shorter way to approach proposed, where land cover data is in straight relation with functions. Starting by surveys or expert panels, which gives the information on preferences, it is possible to establish a relation between land cover data and possible functions in the landscape through the index of function suitability (IFS). By this way, it is possible to evaluate scenarios for the future, as the change in the land cover data considering possible variations according to scenarios, will correspond to values of functions, i.e., what is going to happen to a function: which is going to decrease, which is going to increase.

3



Approach What is obtained What is

extrapolated

1

Regional study about landscape preferences, based on a set of photos

Based on surveys aiming to assess users preferences from a set of photos representative of different land cover combinations at regional level.

A set of chosen photos

2

Regional study about landscape preferences, based on virtual scenarios

Based on surveys aiming to get users to create favourite virtual scenarios for each function at regional level.

A set of virtual photos

3 Local studies about landscape preferences

Based on surveys that aim to assess users preferences from a set of photos representative of different scenarios at local level.

A set of chosen photos

4 Regional study by expert panel

To be defined with the French team of Clermont-Ferrand.

Description / pattern for chosen landscapes

Optimum values for the

landscape amenities

model outputs indicators

The landscape amenities model has been tested within an approach 3, extrapolating the optimum values

from a local study in a municipality in the Alentejo region (within a National Research project, MURAL, 2005-

2008), which aims at evaluating the preferences of different groups of landscape users, between different

land cover types and, within these, different intensities.

At the moment, the SEAMLESS team in Portugal works to extrapolate the optimum values by an approach 1,

through the starting Regional Research Project ROSA (see point II), and also the approach 4, this one with

the French team in Clermont-Ferrand, for the Auvergne region.

Table 1 – Approaches to achieve the optimum values - People’s preferences can be identified mainly through 4 different approaches, based on surveys or expert panels. Even if the information obtain is different between it, all permit to extrapolate the optimum values needed to apply the landscape amenities model outputs indicators and calculate the index of function suitability.

4



II. RESEARCH PROJECT ROSA

The sustainability of the European rural landscapes should be based in aspects as the social demand for

multifunctional landscapes, which can be provided by farmers, if economically profitable, and supported by

national and local authorities for an ecological management (Soliva et al., 2008; Vos and Meekes, 1999).

Thus, it is important to understand which are the social preferences concerning landscape, allowing then the

politicians and management authorities to develop sustainable management plans, based on users demands

and preferences (Dramstad et al., 2006).

Considering such aspects, and taking in account the straight relation between landscape preferences and

the land cover pattern, has been developed several studies to understand the interests and expectations of

local stakeholders in the future landscapes (Lewis, 2008; Tress and Tress, 2003; Petrosillo et al., 2007), to

investigate the public preferences for the effects of proposed changes in land use (Swaffield and

Fairweather,1996) or how such preferences may vary between different groups of users (Fairweather and

Swaffield, 2002).

In Portugal, some reference projects are the IMPAZA Project (Rosário and Carvalho, 2007; Antunes et al,.

2007), where it was studied the impact of the agricultural abandon in the landscape structure in sensitive

areas of the interior of Continental Portugal, the MURAL project (Pinto-Correia et al.2), based on the social

preferences for landscape types at local level, and the ABANDONO project (Pinto-Correia et al., 2006),

where were identified and assessed the rural and agricultural abandonment in continental Portugal.

The methodology proposed in the project ROSA, is based in the straight relation that can be established

between the land cover distribution and the landscape pattern. The landscape is a dynamic and complex

system where natural and cultural factors interact permanently; here, the component related to the land

cover is the most dynamic and the one that more closely depends on management of agricultural uses, at

farm level. Further, the land cover pattern is a highly dynamic component, and therefore has a strong role in

the change of the landscape appearance and character and, as Bell (2001) referrers, often the visual

aspects of a landscape tend to have a bigger influence on people’s judgements than knowledge.

Under this framework, the project ROSA aims:

- To assess, for the region of Alentejo, the land cover types and patterns that better suit the different cultural

and amenity functions, e.g., the functions which value depend solely on social demand;

- To contribute, through design and testing, to the development of methodological tools for the assessment

of the social demand of rural landscapes, for a set of differentiated functions, and of the factors that

determine this demand.

Thus, the land cover pattern, and its changes will be used for the study of the landscape value for various

cultural and amenity functions. Meaning, through the land cover pattern can be possible to assess the

preferences for specific functions and, apply indicators, related to databases, using it in modeling operations.

2 Pinto-Correia, T, Barroso, F., Menezes, H., The changing role of farming in a peripheric South European area: the challenge of the landscape amenities demand. This text is a chapter of an INNOLAND book, not published yet.

5



Starting by surveys to users, or expert panels, which gives the information on people’s preferences, it is

possible to establish a relation between land cover data and possible functions in the landscape through the

index of function suitability (see point I). This index (IFS) is calculated identifying the optimum value, or

optimum range of values, of the preferred land cover pattern, for each group of users, e.g., for each of the

cultural and amenity function selected. This permits to compare and evaluate scenarios for the future, as

how the change in the land cover data, considering possible variations according to scenarios, will

correspond to values of functions, i. e., what is expected to happen to a function: which is going to decrease,

which is going to increase.

II.1 METHODOLOGY

II.1.1 Study area

The research project ROSA aims at assessing preferences for different landscape patterns by different

landscape users, in the region of Alentejo. For this reason it is developed in the south region of Portugal,

Alentejo, a NUTS II region, which integrates 5 NUTS III sub-regions and 58 municipalities (fig. 2). Although,

the most traditional and better-known definition of Alentejo counts only with 47 municipalities, the other 11,

now belonging to the sub-region of Alentejo Litoral, belonged previously to the region of Ribatejo. Despite

the definition with the 58 municipalities be the official one, the 47 municipalities define a more coherent

territory, concerning the landscape and the social aspects.

The criteria to define the study area still in discussion, but have been considered until now the NUTS II

definition.

Fig. 2 – Study area – The project ROSA has as study area the region of Alentejo, here represented in it larger definition with 58 municipalities, distributed in 5 NUTS III sub-regions.

PORTUGAL

6



II.1.2 Land cover classes

Concerning the land cover pattern, the first step was to identify the land cover classes concerning the

agricultural and forestry areas, in the region of Alentejo, using the CORINE Land Cover (CLC) 2000 data. To

better characterized the land cover pattern of the region were defined levels of extensification / intensification

for each one of the CLC classes identified. However, from such procedure resulted 34 sub-classes (see

annex 2), which, due to the character of this project and the need to integrate such data in inquiries, were

considered as excessive.

In order to reduce it, the CLC classes and it sub-classes were analysed and discussed between the team

work, in meetings (see annex 1) within the research group DYNAMO (Dynamics and Management of Rural

Landscapes), and also with agents of the Ministry of Agriculture, taking in consideration the expert

knowledge and previous projects developed in the region.

In the end was decided to maintain the 15 main classes (based on the level 3 of the CLC 2000), with two of

those divided in levels of extensification / intensification (table 2).

Due to the scale of the CLC data, was decided to define for each class some specific features of this region:

• Class 2.1.1 – Non irrigated arable land – mainly concerning the non irrigated wheat cultures;

• Class 2.1.2 – Irrigated arable land – mostly related to the irrigated cultures of wheat, corn or tomato;

• Class 2.1.3 – Rice fields – despite this culture won’t occupy a high proportion of land cover in Alentejo, it is

very characteristic of certain areas;

• Class 2.2.2 – Fruit trees and plantations – mainly considering orchards, for its frequency in Alentejo;

• Class 2.2.3 – Olive groves – the division in two levels of extensification / intensification of the olive groves

(group 2 in the table 2) were based in expert knowledge. Despite this division do not be take into account in

the CLC data, it was considered important to define it here, due the frequent transition observed in certain

areas of the region, where the traditional, non-irrigated and irregular groves are being replaced by those

more intensive, regular and irrigated;

• Class 2.3 – Pastures – related to the areas of seeded pastures;

• Class 2.4.2 – Complex cultivation pattern – usually areas near villages, and with parcels of different

cultivations as vineyards, orchards or olive groves, among others;

• Class 2.4.4 – Agro-forestry areas – concerns the agro-silvo-pastoral areas of montado (Portuguese). This

class was divided in four levels of extensification / intensification, considering the different crown covers and

shrub covers possible (van Doorn and Pinto-Correia, 2007);

• Class 3.1.1 – Broad leaved forest – corresponds to the Eucalyptus spp. Areas;

• Class 3.1.2 – Coniferous forest – Refers the forestry areas of Pinus pinea, once other species of Pinus

species do not appear significantly in Alentejo;

• Class 3.1.3 – Mixed forest – was considered the mixed forest with Pinus spp. and Quercus spp.;

• Class 3.2.1 – Natural grasslands – non irrigated grassland areas;

• Class 3.2.2 – Moors and heathland – In Alentejo is mainly areas with tall shrub vegetation in non agricultural

areas;

• Class 3.2.3 – Sclerophyllus vegetation – mainly small shrub vegetation in agriculture areas.

7



II.1.3 Sample area

Due to the dimension of the study area and the need to establish personal contacts in order to identify the

users and select a sample, a approach has been defined to select 10 municipalities, representative of the

region in what concerns the distribution of the land cover pattern and dynamic. The distribution of the land

cover pattern is the characteristic going to be evaluated in relation to preferences, once the land cover

pattern directly changes according to changes in the agricultural activity and the relating land use systems.

The choice of the municipalities to compose the sample area was based on two different approaches, an

automatic one and an expert one.

The first approach, more automatic and quantitative, was developed through a clusters’ classification, using

the data from the CORINE Land Cover (CLC) 2000 identified within the boundaries of the municipalities

(group 1, table 2), and two indicators of the land cover dynamic: the SWAP, showing the total area of a class

that was lost in a place but gained in another place, and the Net Change, which gives the total change a land

cover class suffered related to the total area of the municipality, for 2000-1990 (Pinto-Correia et al. 2006).

It was made three different tests with the clusters’ classification, considering not only different data

combination but also different number of municipalities and classes of clusters (table 3).

GROUP 1 Corine Land Cover nomenclature

GROUP 2 Extensification / Intensification Level

2.1.1 - Non irrigated-arable land

2.1.2 - Permanently irrigated land

2.1

Ara

ble

land

2.1.3 - Rice fields

2.2.1 – Vineyards

2.2.2 -Fruit trees and plantations Groves with less regular pattern and pastures under cover

2.2

Perm

anen

t cro

ps

2.2.3 - Olive groves Irrigated and mechanized groves with clean ground under cover

2.3 Pastures 2.3 - Pastures (seeded)

2.4.2 - Complex cultivation pattern

crown cover < 10%, shrub cover < 20%

crown cover < 10%, shrub cover > 20%

crown cover 10% - 30%, shrub cover < 20%

2. A

GR

ICU

LTU

RA

L A

REA

S

2.4

Het

erog

eneo

us

agric

ultu

ral a

reas

2.4.4 - Agro-forestry areas (Montado)

crown cover 10% - 30%, shrub cover > 20%

3.1.1 - Broad leaved forest (Eucalyptus spp.)

3.1.2 - Coniferous forest (Pinus pinea)

3.1

Fore

sts

3.1.3 - Mixed forest (Pinus pinea + Quercus spp.)

3.2.1 - Natural grassland

3.2.2 - Moors and heathland (Tall shrub vegetation in non agriculture areas) 3.

FO

RES

T A

ND

SE

MI-N

ATU

RA

L A

REA

S

3.2

Scru

b an

d/or

he

rbac

eous

ve

geta

tion

asso

ciat

ions

3.2.3 - Sclerophyllus vegetation (Small shrub vegetation in agriculture areas)

Table 2 – Land cover classes in the study area – The classes in the group 1 are those coming out from the CLC cartography to the region of Alentejo. The group 2 is related to a sub-division of some group 1 classes, when considered necessary to better define the land cover.

8



In the fig. 3 are showed the maps with the municipalities distribution achieved with the clusters classification.

Only the distribution in 6 classes is presented, due to the higher level of detail. Comparing such maps it is

possible to understand that take in consideration the dynamic indicators, SWAP and Net Change, almost

doesn’t affect the results, once only 1 municipality in 58 changed its class of distribution (maps A and B, fig.

3). However, if the 11 municipalities belonging to the NUTS III, Lezíria do Tejo, are not considered, the

results changed strongly, with only 14 of the 47 municipalities remaining in the same classes (map B and C,

fig. 3).

Comparing the results achieved through the clusters classification with the data coming out of previous

projects also developed in this region (as the Pinto-Correia et al., 2006, concerning the abandon tendencies

in Portugal), the clusters seemed little coherent with the regional reality, as known by the various experts

involved in the project. Nevertheless, it is the distribution that considers only 47 municipalities that seems to

be closer to the regional reality.

The second approach, more analytical, was based in the work developed by the CCDRA3 for the PROT

Alentejo4, in 2007, where was defined the Technical and Economical Orientation for the region Alentejo, and

in the project where were defined the landscape units for Portugal, by Abreu et al. (2004), see annexes 3

and 4. PROT was based not only in the present land cover pattern and expert knowledge but also in several

meetings with expert panels where the criteria for the classification and the map output were defined.

Comparing the results of both approaches and taking into account also the knowledge of the work team in

the region, it was decided to work with the results developed for the PROT Alentejo, by its coherence.

Considering all the results, 10 municipalities were selected for the sample group (fig. 4, table 4), where the

survey should be applied (besides a survey to the urban population, to be undertaken in Lisbon). The

selection was made considering the need to have, with these 10 municipalities, a good representation of the

regional reality and also taking into account the availability of agents of the DRAAL (Direcção Regional de

Agricultura do Alentejo / Regional Direction of Agriculture of Alentejo), since they could be a major help in

the establish of first contacts to apply the inquiries.

3 CCDRA - Commission of Co-ordination and Development of Alentejo. 4 PROT Alentejo - Regional Plan of Territorial Management of Alentejo

Number of municipalities considered

Data used in the clusters classification

Number of clusters’ classes tested

58 CLC 2000, Net Change and SWAP 4, 5 and 6

58 CLC 2000 4, 5 and 6

47 CLC 2000 4, 5 and 6

Table 3 – Clusters’ classification – Different combinations used in the three clusters’ tests for the study area, considering different data combinations and the number of municipalities and clusters classes.

9



Fig. 3 – Maps of clusters results – Results of the clusters classification, in 6 classes. The map A shows the 58 municipalities distribution, considering the CLC 2000, the SWAP and Net change values. The maps B and C are referred to the distribution of the 58 and 47 municipalities, considering only the CLC 2000 values.

A

B

C

BROAD LEAVED FOREST

ARABLE LAND

COMPLEX CULTIVATION PATTERN

AGRO FORESTRY AREAS

AGRO FORESTRY AREAS AND ARABLE LANDS

FORESTS

10



NU

TS II

R

egio

n N

UTS

III

Sub-

Reg

ion

NUTS IV Municipality MAIN FEATURES

Technical and Economical Orientation*

Elvas Irrigated arable land, with a diversified pattern. Mainly maintained by an irrigation system, the Caia system. Proximity of the boarder and presence of a relatively large town, Elvas.

Beef and annual cultures

Ponte de Sor Montado: dense silvo-pastoral system, diversified and rich, where the forest component is very important, with important production of cork oak, of high quality.

Herbivorous in mixed livestock

Alto

Ale

ntej

o

Castelo de Vide

Diversified pattern. Combined large and small-scale farming. Marginal and poor, sloppy areas, covered mostly with shrub. Importance of 2nd home settlements or neo-rurals. NOTE: Possible comparison with previous studies on landscape preferences

Herbivorous in mixed livestock

Reguengos de Monsaraz

Under the influence of the Alqueva dam and it irrigation sub-systems. Strong agricultural character importance of the vineyards, dominating the landscape in many parts of the municipality. Economic dynamisms related with agriculture. Importance of 2nd home settlements or neo-rurals.


Alen

tejo

Cen

tral

Montemor-o-Novo

Rich agriculture: Montado, good cork production, good pastures, and livestock production. Presence of large and small-scale property. Innovation capacities. Importance of 2nd home settlements or neo-rurals. Central location, in the Lisbon-Madrid axis, close to Évora and relatively close to Lisbon.

Specialization in beef

Vidigueira Intensive olive groves. Diversified agricultural mosaic. Importance of 2nd home settlements or neo-rurals.

Specialization in annual cultures

Ferreira do Alentejo Specialization in

annual cultures

Baix

o Al

ente

jo

Almodôvar Hills and plain, poor pastures and in general poor extensive agricultural systems. Peripheric location.


Alcácer do Sal Rice fields. Montado. Montado mixed with pine. Beef and annual cultures

ALEN

TEJO

Alen

tejo

Lito

ral

Grândola

Forestry systems: pine trees, mixed Montado with pine trees, dense Montado, dense oak forest, eucalyptus, shrub. High level of urban occupation, including 2nd homes and neo-rurals, comparing with other municipalities in Alentejo. Atractivity due to proximity of the coast and beaches.

Mixed livestock

Table 4 – Sample area municipalities – Each municipality has specific characteristics of the study area, concerning social aspects and dynamics as well as land cover patterns.

Fig. 4 – Sample area municipalities – Selection of the 10 sample municipalities (in yellow) for in the region of Alentejo

* P

into

-Cor

reia

et a

l., 2

006.

11



II.1.4 Groups of landscape users

To understand the type of land cover pattern preferred for a specific function, the method proposed relies on

surveys to specific groups of users, being each group related to a function (table 5). According to the

definition given by de Groot (1992) in de Groot (2002), an ecosystem function is “the capacity of natural

processes and components to provide goods and services that satisfy human needs, directly or indirectly”. In

this project are considered those functions resulting from the social demand on landscapes.

Is important to point out that in the users group of farmers is considered the person that manage the farm,

meaning, the person that takes the decisions on the way how to manage the land. In the group of landscape

users connected to the life quality function, the main difference between the second homeowners and the

weekend visitors is that these last are linked with continuity of the visits to the countryside.

It was expected to defined a numbers of inquiries that could allow to be a representative sampling of these

various groups for the region, permitting a confident generalization to the population represented.

Thus, for the groups where the total population is known, as the local inhabitants, the application of a

minimum rate for representativeness will lead to a too large number of inquiries. From another side, the total

population of hunters who practice hunting in the region, or the total population of eco-tourists or weekend

visitors, are impossible to define. As such, the total of 200 for most groups and 400 for the local population

has been defined, since it is a minimum sample size for achieving conclusions as to the behaviour of the

group (Patton, 2002). This number was also discussed with a statistical expert, according to whom the

number of inquiries should be, at least, 380, to allow an extrapolation with 5% error maxim and assure that

99% of the cases will be in the group. With a total of 1200 inquiries the representativeness of the universe

(Alentejo) is guarantee, as well as the representativeness of each class.

Although, it is important to keep in mind that inside each group the inquiries should be distributed in an even

way between the 10 municipalities of the sample area, meaning, it should be applied approximately 20

inquiries per group per municipality.

Function Users Number of inquiries

Hunting Hunters ± 200

Recreation Ecotourists and tourists ± 200

Life quality Second homeowners, weekend visitors ± 200

Identity Inhabitants and neo-rurals (insiders) ± 200

Identity Urbans (outsiders) ± 200

Production Farmers ± 200 (Stratified per farm type)

Table 5 – Landscape users – To identify the social landscape preferences concerning specific functions, were distinguished six groups of users of the rural areas.

12



II.1.5 Visual tools

Photos

To assess the social landscape preferences for specific functions, the project ROSA will use inquiries based

on visual support, using photographs. Visual tools have been used in several studies to understand people’s

opinion concerning landscape (Dramstad et al., 2006; Kaplan et al., 1998; Lewis, 2007). Once it creates a

common language between interviewed and interviewees, helping to overcome the technical vs. non

technical speech obstacle (Al-Kodmany, 1999; Luz, 2000), meaning, it can work as a discussion facilitator,

helping them to explain perceptions and concepts in an easier way (Lewis, 2008; Soliva et al., 2008).

Other two important aspects of using photographs in landscape inquiries is the fact of help to focus the

discussion in the specific aspects as a land cover type, and also, be economic and easily reproduced.

The project ROSA is based on surveys supported in photos corresponding to each land cover class or sub-

class identified previously for the region (table 2). To assure the coherence between all the set of photos

used and that each photo is clear in the land cover type represented, all the photos were taken at an

approximately 4 meters high, to permit a good perspective over the land cover.

The photo manipulation obeyed to certain aspects:

• Each photo should have a first plan well defined, only with the land cover type pretended, and a

second plan, more distant, of reference, equal in all the photos;

• Half of the photo should be occupied with soil and the other half with sky (equal in all the photos);

• All the elements present that not correspond the land cover class itself should be minimized;

• All the water surfaces should be erased.

In the fig. 5 is showed an example of the manipulation made to the photos to be used in the inquiries. In the

annex 5 are presented all the final photos used.

+ MANIPULATION:

Fig. 5 – Photo manipulation – Example of the photo manipulation for the class 6 – Olive grove. The two photos got after the manipulation are the representatives of the different levels of extensification / extensification of the olive groves in Alentejo.

Original photo - extensive olive grove manipulated

Final photo - extensive olive grove

Original photo - intensive olive grove Final photo - intensive olive grove

Original photo for the sky and 2nd plan

13



All the photos were taken in the same week, with identical weather conditions (minimizing the influence of

different light exposure), in the end of the summer (when the difference between the irrigated and non-

irrigated cultures is well understandable) and with the same camera (keeping the same zoom and focal

length). The experience in previous projects showed that the aesthetic differences between photos, such as

brighter colours or clean sky, may have an important influence in the choices made answering the inquiries.

The exact coordinates of the points where each photo was taken must be registered. This procedure permits

to map it, and, in future, repeat the photo in the exact point, creating a comparable photo base.

Block Diagrams

The block diagrams are visual support tools that permit to illustrate, in a simplified way, a portion of territory

(CETE, 2001).

According to the authors, this tool has as main disadvantage the dependence of who draw it, once it results

of a landscape interpretation, for example, in the selection of the elements to include.

Although, the advantages provided by the block diagram permit to consider it as a useful tool in the

discussion concerning the landscape, once it allows expressing and comparing the different users points of

view, also because it is easily understandable and permits a direct visualisation of a territory portion.

Considering the fact of each block diagram should be made taking in account the final goal, was drawn one

specifically for this project. Due to the block diagram should be used in a large number of inquiries and with

several groups of people (table 7), the design of the block is simple, to be easily understandable. Thus, the

relief represented is generic but still related to the smooth morphology that characterizes the region of

Alentejo.

The portion of territory represented has already the limit of 5 plots, with similar dimensions. This feature

allows simplifying the following statistical analysis.

Fig. 6 – Block diagram – Block diagram used in the inquiries where each person should define which land cover types are the preferred and how to distribute it, allowing to understand the preferred land cover pattern.

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II.1.6 Structure of the inquiries

The survey is based on the demand to compose a photo arrangement to understand how people group the

land uses (see annex 6). Taking in account the land cover types identified previously (table 2), the total of

photos should be divided in two sets, being the one related to the 15 land cover classes (group 1 in table 2),

and other, related to the sub-divisions of those classes with sub-levels of extensification / intensification

(group 2 in table 2).

The inquiry should be divided in three steps:

• First step – Grouping the photos – The first set of photos is showed and the interviewed is asked,

considering the function he/she is representing, to choose the three that represent the preferred land

cover types, and three that represent the land cover types less wanted, explaining the reason of the

choice for each photo;

• Second step – Refining the preferred land cover types – If in the group of photos identified as the most

preferred for a specific function, are some referring to classes with sub-divisions (olive groves and

montado), the interviewed should have the opportunity to replace it, with photos considering such sub-

divisions, explaining why he/she took that decision.

For example, if in the first step was chosen a photo of Montado, in this second stage such photo

should be replaced by one of the four representing the possible sub-divisions Montado.

• Third step – Distributing the land cover types in the block diagram – Using any of the initial 15 photos,

each person should identify the land cover pattern, preferred for the specific function represented,

indicating in each of the 5 parcels of the block the number of the photo wanted (table 8). The number

can be repeated if wished.

GROUP 1 GROUP 2

Land Cover type showed Photo number Land Cover type showed Photo

number

Non irrigated-arable land 1 Permanently irrigated land 2

Rice fields 3 Vineyards 4

Fruit trees and plantations 5 Groves with less regular pattern and pastures under cover 6

Olive groves 6 Irrigated and mechanized groves with clean ground under cover 61

Pastures (seeded) 7 Complex cultivation pattern 8

crown cover < 10%, shrub cover < 20% 91 crown cover < 10%, shrub cover > 20% 92 crown cover 10% - 30%, shrub cover < 20% 9 Agro-forestry areas (Montado) 9

crown cover 10% - 30%, shrub cover > 20% 93 Broad leaved forest (Eucalyptus spp.) 10

Coniferous forest (Pinus pinea) 11 Mixed forest (Pinus pinea + Quercus spp.) 12

Natural grassland 13 Moors and heathland

(Tall shrub vegetation in non agriculture areas) 14

Sclerophyllus vegetation (Small shrub vegetation in agriculture areas) 15

Table 6 – Photos’ numeration – To each photo must correspond a number, allowing to easily identify each one.

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II.2. POSSIBLE USES OF THE DATA COLLECTED

II.2.1 Proportion of land cover classes (Q methodolody)

The Q methodology was developed in the social sciences framework in the 60 years and as been applied in

several different contexts concerning public opinion (Klooster et al., 2008). One of the main features of the Q

method is it attend to obtain patterns of answers across individuals, rather than variables, and allows

describing and understanding the patterns of preferences about specific traits, as the environmental issues

(Barry and Proops, 1999) and the landscape planning and rural research (Swaffield and Fairweather, 1996;

Fairweather and Swaffield, 2002). This two last authors developed several studies associating the Q

methodology with the use of landscape photos, to access people’s preferences land cover changes, showing

the efficiency of this method in landscape issues.

Since the Q-sort method use a data analysis where the matrix is inverted, with the respondents are the

variables and the items are the cases (Klooster et al., 2008), if applied to the inquiries’ answers of the project

ROSA, should permit to identify patterns of answers, according to the different groups of users.

Consequently it should generate a range of values (proportion of class) that will match to the most preferred,

the worst case and the neutral situation, per function. The main steps of the Q methodology and how it can

be used in the project ROSA are described in the table 9.

II.2.2 Relation with the output indicators from SEAMLESS

Understanding the landscape perceptions of specific groups of users can produce important information in

the development of models and possible scenarios to achieve an integrated landscape planning (Folke et al.,

2002).

So, if to each photo is calculated the values of the coefficient for the intensity, as well as the diversity and the

specialisation indicators5 then could be possible to calculate the IFS index to the data collected in this

5 www.seamless-if.org

General steps of the Q methodology In the project ROSA

1 Collection of relevant data concerning the research object CORINE Land Cover 2000 data

2 Selection and formulations of a set of meaningful statements - Q sample – each numbered randomly and printed on separated cards.

Main Land cover classes photos

3 Each inquired person should distribute all the cards through the pre-structured grid (Q-sort).

3 groups of preferences: most preferred, neutral and not wanted

4 Data analysis, with the correlation matrix of all the Q-sorts, submitted to a by-person factor analysis (attitudinal groupings) - 1 representative Q-sort per group.

1 land cover pattern per group of landscape users.

Table 7 – Main steps of the Q methodology (Barry & Proops, 1999), and it application in the project ROSA.

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project, may allow developing different aspects as to evaluate scenarios or the actual land cover pattern in

each municipality or to compare it with the possible scenarios built (fig. 7).

II.3. METHODOLOGY TRIAL: THE INQUIRIES STRUCTURE

II.3.1 – FIRST TRIAL

In an early state of the methodology development, the structure of the inquiry was tested, in order to

understand the reaction of the interviewed to different ways of selecting photos of land cover classes and a

composition of the same.

Two different types of grid and two different ways to present the photos were tested.

The photos of the CLC classes used, were from previous projects and without any kind of manipulation,

Test A – Squared grid

Inquired people: 5 (2 farmers; 1 hunter, 2 tourists).

Time of each inquiry: ± 45 minutes.

Type of grid: Squared grid of 3 x 3 spaces (fig. 8).

Photos presentation: The 19 photos, matching the CLC classes and sub-classes were printed in a sheet,

meaning all the land cover types were seen at once (fig. 8). Each photo had a legend, permitting to clearly

Fig. 7 – Comparing scenarios – Attributing the indicators values to calculate the Index Function Suitability to the ideal and worst land cover patterns (based on the surveys) as well as to the possible scenarios, permits a comparison between it. Such comparison allows inferring about the acceptance of a possible change in the land cover pattern by the landscape users.

Ideal land cover pattern IFS range of values

Worst-case land cover pattern IFS range of values

Scenario 2 (IFS value)



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understand the land cover showed, and a number, to easily identify it. The grid was filled with detachable

photos equals to those showed in this sheet.

Question proposed: “Can you fill the grid using the photos available, to show the type of place you

considered as good to develop your activity?”

If wished, the photos can be all different or all equal and disposed as preferred.

Main comments made during the inquiries:

• Confuse;

• Pleasant but difficult to imagine the final image in the reality;

• The result seems 3 different places and not 1;

• After understand, is nice to play;

• The photos are too many and a little confuse.

Test B – Line grid

Inquired people: 6 (2 farmers; 2 hunter, 2 locals).


Type of grid: Line grid of 9 spaces (fig. 9).

Photos presentation: In a first stage were presented photos only of the 15 main CLC classes (group 1 in the

table 2). After the first composition of 9 photos were made, was showed, for each chosen class with possible

sub-classes a second set of photos, that could be used to substituted the first choice (fig. 9).

Question proposed: “Imagining the path you take during your activity, can you fill the grid, with the available

photos, to should the type of place you consider better to do it?”

If wished, the photos can be all different or all equal and disposed as preferred.

Fig. 8 – 1st test grid and photos – In the right is showed the squared grid used. Each one of the 9 spaces should be filled with detachable photos, similar to those presented in the photos sheet (in the left).

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Main comments made during the inquiries:

• Original and pleasant;

• Photos should be clearer;

• Is a good way to talk about territory;

• It misses the cities and the urban areas.

Observations

In a general way both tests showed to be a good method to talk about the land cover pattern but not so good

as a method to build a true land cover mosaic.

The first test seemed to cause confusion by the grid and the try to represent a patch. Also the amount of

photos available were clearly too large. Also the final result, after fill the grid, didn’t seem to be a true

reflection of people’s opinion.

In the second test the line grid seemed to be clearer to understand. The fact of the number of photos has

been reduced was also positive. The difference between each land cover class showed was better defined,

turning the choice of photos faster and with more certain.

II.3.2 – SECOND TRIAL

After several meetings between the teamwork, the DYNAMO research group, the Agriculture Ministry and a

statistical advisor, was decided to test again the structure of the inquiries, retrying the grid (test A of the first

trial) and a new tool, a block diagram.

Fig. 9 – 2nd test grid and photos – In the 2nd test was used a grid of 9 photos in line (top of the figure). The photos presented first referred only to the CLC classes. When some of those classes were sub-divided in sub-classes, was showed a second set of photos, with the sub-division of that class. The red arrow indicates this second set of photos for the case of class of montado, which is sub-divided in four.

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The decision to retry the grid was based on the fact that, in the first trial, the grid had spaces between the

photo placeholders, which seemed to work as a barrier, not letting people to imagine it as a land cover

pattern representation. Thus, in this second trial was tested a grid with all the columns and rows contiguous.

On other hand, retry it using new photos, these ones already from the study area and manipulated to better

represent each land cover type (see point II.1.5), permitted to understand if the photos it selves were a

negative aspect in the lack of success of the first trial.

Each person answered the inquiry, through both methods and was asked to comment each as well as the

general method as a mean to understand the landscape preferences.

In both tests were used the final photos, already from the study area and manipulated. This permitted to

understand if each photo was clear in the land cover type represented.

TEST C – Grid and block diagram

QUESTIONS:

Inquired people: 5 (2 farmers; 1 hunter, 2 inhabitants).


Photos presentation: To answer the question 1 were showed just the 15 photos corresponding the main CLC

classes (group 1, table 2). Only in the question 2 were presented the photos of sub-levels of olive groves and

montado (group 2, table 2).

Questions proposed:

Question 1 - “Considering your activity chose, from the 15 photos available, those four that show the land

cover type you prefer and those four that show what you dislike more.”

Question 2 (In case of the olive groves and / or montado has been chosen as preferred) – “Being able to

change the photo related to the olive groves and / or montado, to another one more specific, do you prefer to

keep your first choice or change it?”

Question 3 – “Considering the land cover types chosen as preferred:”

Question 3.1 – “Fill the grid with the photos chosen, repeating it as many times as necessary, to better

represent the land cover pattern you prefer.”

Question 3.2 – “Using the block diagram, draw the distribution areas of each land cover type preferred.”

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Observations

The people who answered it considered the inquiry pleasant and easy to understand.

In the question 3, concerning the composition of the land cover pattern, the grid seemed to be easier to

explain than the bock diagram. The fact of, with the block, each person had to draw the distribution of the

different classes, caused some confusion, due to details of scale and landforms. However, was also referred

the block permitted to better represent the proportion of each class.

It was also said that, in the classes of olive groves and montado, the photos showed were not the best, once

in both photos the trees seemed to be little productive.

It is important to referred that the land cover patterns, defined by each person through both methods (grid

and block), were always much different. When asked about this fact and about which method was more

representative of their opinion on the distribution and proportion of the land cover classes, the answer was

clearly that the block was represented better the distribution but the proportion was less reliable.

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