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Biological Conservation 144 (2011) 1430–1440
Contents lists available at ScienceDirect
Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Aesthetic preferences of non-farmers and farmers for different land-use types
and proportions of ecological compensation areas in the Swiss lowlands
Xenia Junge a,b,1, Petra Lindemann-Matthies a,2, Marcel Hunziker c, Beatrice Schüpbach b,⇑
a
Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
Research Station Agroscope Reckenholz-Tänikon ART, Reckenholzstrasse 191, CH-8046 Zurich, Switzerland
c
Swiss Federal Institute for Forest, Snow and Landscape Research, Zürcherstrasse 111, CH-8903 Zurich, Switzerland
b
a r t i c l e
i n f o
Article history:
Received 26 July 2010
Received in revised form 2 January 2011
Accepted 14 January 2011
Available online 12 February 2011
Keywords:
Ecological compensation areas
Agro-environmental schemes
Survey
Photographs
Photo editing
Landscape diversity
Landscape characterisation
a b s t r a c t
Beyond its traditional function of food production, agricultural land offers public amenities such as the
protection of natural resources and landscape scenery. This study investigates the preferences of nonfarmers and farmers for nine landscape scenarios in the Swiss lowlands. The nine landscapes were the
result of a photo editing process combining three land-use types (arable crops, grassland and a mixture
of both) and three proportions of ecological compensation areas (0%, 10% and 30%). The landscape
photographs were randomly arranged on one page of a paper-based questionnaire which was sent to a
random sample of 4000 Swiss households (non-farmers) and 500 farmers. The respondents (1376 nonfarmers and 276 farmers) rated each landscape by attractiveness. Both non-farmers and farmers preferred a mixed land-use type or one dominated by arable crops over one dominated by grassland.
Non-farmers’ preference ratings were highly influenced by the proportion of ecological compensation
areas (ECAs) in the rated landscape: Non-farmers rated a landscape with a mixed land-use type and
30% ECAs highest, whereas farmers rated a landscape dominated by arable crops and 10% ECAs highest.
The results indicate that heterogeneous landscapes (mixed land use, high proportion of ECAs) influence
scenic beauty positively. Thus, farming practices and agro-environment schemes such as ECAs can have
an impact on the visual attractiveness of a landscape.
! 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Maintenance of the agricultural landscape, conservation of natural resources and recreation functions are public services which
fall within the context of a multifunctional agriculture (Potter
and Burney, 2002; Foley et al., 2005; Jongeneel et al., 2008).
Area-related direct payments which are decoupled from production payments reward public services such as the conservation of
biodiversity through agro-environment schemes (Kleijn and
Sutherland, 2003; Potter, 2006; Brady et al., 2009). Agro-environment schemes are important political instruments in European
countries (Schmid and Lehmann, 2000; Kleijn and Sutherland,
2003; European Environment Agency, 2004). However, such
schemes vary markedly among European countries. In Switzerland,
the Netherlands and the United Kingdom, for instance, they focus
mainly on wildlife and habitat conservation, whereas in Denmark
⇑ Corresponding author. Tel.: +41 44 377 7328, fax: +41 44 377 7201.
E-mail address: [email protected] (B. Schüpbach).
Present address: Swiss Federal Institute for Forest, Snow and Landscape Research,
Zürcherstrasse 111, CH-8903 Zurich, Switzerland.
2
Present address: University of Education Karlsruhe, Institute of Biology,
Bismarkstrasse 10, D-76060 Karlsruhe, Germany.
1
0006-3207/$ - see front matter ! 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2011.01.012
and Germany they focus on a reduction of agrochemical emissions
and in France on the prevention of land abandonment in agriculturally marginal areas (Kleijn and Sutherland, 2003).
Unique to Switzerland, farmers can qualify since 1998 for arearelated direct payments if they meet a number of environmental
standards (Schmid and Lehmann, 2000; Flury et al., 2005). These
standards are defined by the Proof of Ecological Performance
(PEP).3 One of the PEP-standards demands that each farmer has
to manage at least 7% of the utilised agricultural land as so-called
ecological compensation areas (ECAs). To achieve the environmental goals, in ECAs the use of fertilizers and pesticides is restricted,
and hay-meadows are not to be cut before 15 June (Günter et al.,
2002; Jeanneret et al., 2003). Farmers are free to choose the types
of ECAs for their land. Most farmers locate ECAs in areas that bear
little potential for intensification and have traditionally been
extensively managed, i.e. shaded forest edges or steep hillsides
(Herzog et al., 2005; Kampmann et al., 2008; Aviron et al., 2009).
Overall, of the 120,000 ha of ECAs (11% of Swiss farmland), three
quarters are extensively managed hay meadows. Fallows, which
are sown with seed mixtures of 20–40 herbaceous plant species
3
http://www.blw.admin.ch/themen/00006/index.html?lang=en.
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
(wildflower strips), are less extensive in area (3500 ha), but are
characteristic ECA types for arable regions in Switzerland (Aviron
et al., 2009).
The Swiss agro-environment scheme has been evaluated in several studies, most of them showing positive effects on plants and
various groups of insects (Jeanneret et al., 2003; Kampmann et al.,
2008; Aviron et al., 2009). Even if effects of site conditions, landscape context, and regional location are accounted for, the ECA
management scheme still has a significant positive effect on biodiversity and added ecological value in Switzerland (Kampmann
et al., 2008; Aviron et al., 2009). However, little is known about
whether land-use changes that lead to ecological benefits also result in aesthetic benefits for the public (Lindemann-Matthies
et al., 2010). As conservation should be both about ecology and
about people and the choices they make (Balmford and Cowling,
2006), aesthetic experiences expressed by the public can provide
valuable information to policymakers in biodiversity management
(Fischer and Van der Wal, 2007; Gobster et al., 2007). Moreover,
for sustainable agro-environmental measures the perception and
values of farmers should be considered (Van der Meulen et al.,
1996; Berentsen et al., 2007; Schenk et al., 2007). It has been
pointed out that as part of an ‘ecological aesthetics’ (Gobster
et al., 2007, p. 962) decision-making strategies are needed that
bring ecological goals and human values into better alignment.
The present study investigated the aesthetic responses of nonfarmers and farmers in Switzerland to photo-realistic visualizations of different land-use scenarios in the Swiss lowlands. Our
study is one of the first that tests the hypothesis that species richness in farmland is of aesthetic value to humans. It is also one of
the first to investigate the influence of different land-use types
(arable land, grassland) on aesthetic perception. The visualized
landscapes varied in grassland to crop-ratio and abundance of
ECAs. The ECAs were typical for the Swiss lowlands and consisted
of low-intensity meadows, high stem fruit trees, hedgerows, and
wildflower strips (Jeanneret et al., 2003; Charollais et al., 2004).
In the Swiss lowlands, current land use is characterised by a
mixture of arable crops and grassland. About 11% of the agricultural utilised area is managed as ECAs (FOAG, 2010). Land-use-scenario models indicate that under a liberalization of the Swiss
agricultural market the area used for crop production would decrease, whereas grassland area would increase, leading to a simplification of agricultural landscapes (Schüpbach et al., 2008).
However, processes of agricultural intensification which simplify
landscape structure and result in less variation and complexity
may reduce the quality of the landscape experience (Dramstad
et al., 2001; Clergue et al., 2005). We therefore hypothesized that
landscapes in the Swiss lowlands which are characterised by a
mixture of grassland and arable crops will be preferred over landscapes that are either dominated by grassland or by arable crops.
Recent studies indicate that the public likes species-rich elements in agricultural land (Strumse, 1994; Junge et al., 2009).
Moreover, in a series of experiments and field studies using natural
meadows on people’s perception and appreciation of species diversity, aesthetic appreciation always increased with true species
richness (Lindemann-Matthies et al., 2010). We therefore hypothesized that an increase in the abundance of ECAs might lead to an
increase in aesthetic appreciation. Recent land-use scenario models for the Swiss lowlands predict that with decreasing proportions
of ECAs due to reduced ecological subsidies the attractiveness of
the region would severely decrease (Schüpbach et al., 2008).
Landscape preferences are not only influenced by physical characteristics such as the heterogeneity of a landscape, the species
richness of a landscape element or its spatial structure. They are
also influenced by socio-demographic factors such as age, gender,
formal knowledge and expertise of a person as well as familiarity
and experience with a certain landscape type (Strumse, 1996; Kap-
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lan and Kaplan, 1989; Nohl, 2001; Gobster et al., 2007). Moreover,
they are influenced by people’s environmental value orientations
(Kaltenborn and Bjerke, 2002; Soliva and Hunziker, 2009). Higher-educated people and members of environmental organisations
might be more in favour of ECAs, as they are likely to be more informed about the ecological benefits of biodiversity than other
people. This might also be the case for women as they have shown
a greater affinity for plant species richness than men (Strumse,
1996; Lindemann-Matthies and Bose, 2007). Moreover, people
might prefer familiar landscapes or landscape elements, i.e. those
they have experienced for some time, and regard them as typical
(Nohl, 2001). Differences between people from different parts of
Switzerland, for instance, could thus be expected.
We especially expected differences in the aesthetic perception
between non-farmers and farmers. While non-farmers’ perceptions of cultural landscapes might be driven by scenic beauty,
farmers’ perceptions might be more driven by an aesthetic of care,
i.e. perceptible cues of human stewardship and displays of order
(Gobster et al., 2007). Care, and thus good husbandry skills, could
be indicated by the regularity of crop height, regular tramlines or
fields free of weeds (Burton, 2004; Sullivan et al., 2004; Gobster
et al., 2007), resulting in neat and ordered landscapes as the visible
signs of a ‘good farmer’ (McEachern, 1992; Young et al., 1995; Burton, 2004; Benson, 2008; Burton et al., 2008). Such preferences
might be in conflict with practices to enhance farmland biodiversity as species- and structurally-rich semi-natural vegetation
might be perceived as rather messy (not neat) and disordered by
the farming population (Nassauer, 1995; Hands and Brown, 2002).
However, farmers themselves may differ in landscape preferences depending on their environmental attitudes (Vogel, 1996;
Vanslembrouck et al., 2002) or farm characteristics such as organic
or non-organic farming (Egoz et al., 2001). In addition, farm continuation and full-time or part-time farming (Visser et al., 2007) as
well as farm location (Jongeneel et al., 2008) have been found to
influence opinions on agricultural topics. We therefore hypothesized differences in aesthetic preferences for certain land-use types
and proportions of ECAs between non-farmers and farmers, but
also among farmers.
We set out to investigate the following questions: (1) Are landscapes characterised by a mixture of grassland and arable crops
preferred over landscapes dominated by either grassland or arable
crops? (2) Does an increase in the abundance of ECAs increase the
aesthetic appeal of a landscape? (3) Do non-farmers and farmers
differ in their aesthetic preferences? (4) How do they characterise
the most liked and disliked landscape? (5) Do certain farm characteristics as well as socio-demographic variables such as age, sex,
education, environmental interest and place of living influence aesthetic preferences?
2. Materials and methods
2.1. Photo editing
Landscapes can be visualized in different ways, including elaborated 3D-visualization techniques (e.g. Hehl-Lange, 2001; Lange,
2001). This study used photo-realistic visualisations as they provide natural-looking images and are relatively easy to generate
(Soliva and Hunziker, 2009). The ability of photographs to represent the dynamic multidimensionality of real landscapes has been
questioned (Scott and Canter, 1997; Daniel and Meitner, 2001).
However, despite some criticism, colour photographs and simulated colour images have been found valid to represent landscapes
in a satisfactory manner (Trent et al., 1987; Daniel, 2001).
To conduct a photo-survey among Swiss households and Swiss
farmers, images of different agricultural landscapes were pro-
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
cessed. Three variations in land-use type with three variations in
the proportion of ECAs were combined in a two-factorial design
(Fig. 1). The combination of the two factors resulted in nine landscape images, in which a mixture of arable crops and grassland
with 10% ECAs resembles the current situation in the Swiss lowlands. Future scenarios could be an intensification of production
in the Swiss lowlands (simulated by either 100% high-intensity
grassland or 100% cash crops, and abolishment of the ecological
compensation scheme). However, if ecological subsidies are
becoming more and more attractive, the abundance of ECAs might
increase (simulated by 30% ECAs).
The nine images were derived from one original photograph taken in a typical agricultural landscape of the Swiss lowlands (Rafzer Feld, canton of Zurich). The original photograph was taken in
June 2006, in sunny weather using a Canon EOS 350D with
18 mm focal length. This focal length is corresponding to about
28 mm focal length when using a 35 mm film camera. The landscape images were edited with Adobe Photoshop CS2. Both foreand middle ground were edited by removing or adding arable
crops, grassland or ECAs according to the two-factorial design
(see Fig. 1). To determine the proportion of landscape elements,
their proportion of the area of the picture was determined using
a fine grid. The background was standardized for all images. During
the editing process, anthropogenic elements such as utility poles,
power lines, agricultural infrastructure and houses were removed
to avoid their negative influence on scenic beauty ratings (Ulrich,
1986; Kaplan et al., 1998).
The arable crops displayed were ripe (yellow) wheat, maize and
beet (both green), and rapeseed (with ripe husks, thus not flowering anymore). These crops are typical in the Swiss lowlands, with
wheat and maize the most common crops (55% and 17%, respectively, of the total crop area; FOAG, 2010). The grasslands depicted
were intensively-used meadows and grass-clover leys which are
also typical for the Swiss lowlands. To show the most common
crop (wheat) in its most characteristic stage, the original photograph was taken in June, i.e. the season when wheat is ripe in
the Swiss lowlands. At this time, ECAs such as low-intensity meadows and wildflower strips are flowering, whereas intensive grassland appears uniformly green.
The ECAs displayed were of good ecological quality, and consisted of high stem fruit trees, hedgerows, wildflower strips (in
the mixed and arable crop dominated landscapes) and low-inten-
sity meadows (in the mixed and grassland dominated landscapes;
see Fig. 1). Small ECA-elements (meadows and wildflower strips)
were positioned in the foreground, while large ECA-elements (high
stem fruit trees and hedgerows) were positioned in the middle
ground.
2.2. Questionnaire and pilot test
A written questionnaire was sent to both randomly selected
Swiss households and farmers. The questionnaire for the two groups
was identical. However, farmers received some additional questions
about the structure of their farm. As it was a Swiss-wide survey,
both questionnaires were translated from German to French and
Italian. The nine landscape images were randomly distributed on
one page. A number and, at the bottom, a scale from 1 to 7 was attached to each image. Study participants (non-farmers and farmers)
were asked to rate each landscape on the scale attached, ranging
from 1: ‘totally dislike it’ to 7: ‘totally like it’. In addition, they were
asked to select the one landscape they liked most and the one they
disliked most among the landscapes presented, to write down the
respective number and to indicate how well each of 14 given adjectives characterised their most liked and their most disliked landscape (on five-step scales, ranging from 1: ‘disagree’ to 5: ‘agree’).
The adjectives referred to landscape characteristics which have
been found influential on people’s perception of scenic beauty
(Appleton, 1975; Ulrich, 1986; Kaplan and Kaplan, 1989), and have
been used in other studies (e.g. Hunziker, 1995; Hunziker and Kienast, 1999). They referred to physical characteristics of a landscape
(varied, diverse, species-rich), its conservation potential (worth preserving) and naturalness which is a strong predictor of scenic beauty
(Purcell and Lamb, 1998; Ode et al., 2009), but also to other associated thoughts and feelings (beautiful, familiar, common, comfortable, ordered, unkempt, productive, useful, and boring).
To investigate the influence of socio-demographic variables on
landscape preferences, all participants were asked to indicate their
age, sex, education and the postal code of their place of residence.
The postal code was used to distinguish between three residence
groups: urban, agglomeration and rural. Study participants were
further asked to indicate whether they belonged to an environmental organisation, whether they were a farmer or not, whether they
had farmers among their friends or relatives, and whether their profession was related to ecology or landscape planning. The last three
Fig. 1. Construction of the nine landscape images. Three variations in the proportion of ecological compensation areas (ECAs) were combined with three variations in landuse type.
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
questions were not asked in the farmers’ questionnaire. Instead,
farmers were asked some additional questions about their farm:
size of the farm and proportion of ECAs on their farm, type of management (organic, non-organic), whether they were full-time or
part-time farmers and whether the farm continuation was assured
or not. In addition, with the help of the postal code, farms were classified to different geographical regions of Switzerland: Swiss Plateau, Alps, or others (i.e. northern edge of the Alps and Jura).
The questionnaire was pilot-tested with 20 agricultural and
environmental experts. After critical discussions about its comprehensiveness, the validity of the questions, and the quality of the
images used, the improved questionnaire was pre-tested with a
random sample of 500 Swiss households drawn by the Swiss Federal Office of Statistics. The response rate was 32% and only minor
layout changes on the questionnaire were necessary.
2.3. Data collection and respondents
Data were collected in June 2007 by mail-out questionnaires. A
random sample of 4000 Swiss households was drawn by the Swiss
Federal Office of Statistics (non-farmers), and a random sample of
500 farmers was drawn by the Swiss Federal Office of Agriculture.
In the cover letter, the person living in the household or on the farm
(age 18 or older) whose date of birth was first in the calendar year
was asked to answer the questionnaire. Following Dillman (1978),
two reminders were sent to the participants after the original
mail-out. A postcard reminder was sent to everyone after one week.
Three weeks after the original mail-out a replacement questionnaire with a shorter cover letter was sent only to non-respondents.
About 38% of the questionnaires sent to the Swiss households
and 55% of the ones sent to farmers were completed and returned.
Among the household sample, some respondents were farmers.
However, they were excluded from the sample as they had not received the additional questions about the farm structure. The final
study sample consisted of 1376 non-farmers (response rate of 34%)
and 276 farmers. About 75% of the questionnaires that had been returned were in German (78% in case of the farmers), 21% (20%) in
French and 4% (2%) in Italian which reflects more or less the language group distribution in Switzerland.
The participating non-farmers (48% women) were between 18
and 91 years old (mean age = 52 years). About 60% lived in agglomerations, 12% in urban and 28% in rural areas. About 53% had visited a high school (or an equivalent), and 10% had a profession
that was related to ecology or landscape topics. About 62% had
friends or relatives who were farmers and 22% were members of
an environmental organisation.
The participating farmers were between 20 and 71 years old
(mean age = 47). As only 15% were women, the influence of sex
was not tested in the farmers’ sample. About 42% had visited a high
school (or an equivalent), and 6% were members of an environmental organisation. Farm size varied between 0.3 and 70 ha
(mean size = 20.1 ha), and the mean proportion of ECAs on a farm
was 11.9%. Organic farming was practised by 12% of the farmers
(10% in Switzerland; FOAG, 2010). Most participants were full-time
farmers (81%; 75% in Switzerland; FOAG, 2010). For 24% of the
farmers, farm continuation was assured, for 25% it was not assured
and 51% did not know it at the time of the study.
The participants were on average better educated than the
Swiss population, and members of environmental organisations
were over-represented. Such differences were also found in other
studies (Soliva and Hunziker, 2009).
2.4. Data analysis
A linear mixed model with participant and the interactions between participant and proportion of ECAs and land-use type as
random terms was used to test for influences on landscape rating.
To test for differences of the landscape preference rating between
farmers and non-farmers, the data sets of the non-farmers and the
farmers were pooled. Subsequently, the two datasets were analysed separately, in order to address the additional variables in
the farmers’ data set (farm properties). The proportion of ECAs
(0%, 10%, 30%) and land-use type (grassland, arable land, mixed),
and the interaction between the proportion of ECAs and land-use
type were treated as fixed effects. In addition, the socio-demographic variables (age, sex, education, profession (farmer yes/no),
membership in an environmental organisation (yes/no), place of
residence (urban, agglomeration or rural), and language region of
Switzerland (German, French, Italian) were included in the model
as fixed effects and tested against the variation among the study
participants. The interactions between the land-use type and socio-demographic variables were tested against the interaction between participant and land-use type, and the interactions
between the proportion of ECAs and socio-demographic variables
were tested against the interaction between participant and proportion of ECAs.
In the farmer’s data set the following socio-demographic variables were additionally tested for influences on landscape rating:
size of the farm, type of management (organic, non-organic), farm
location (Swiss Plateau, Alps or other, i.e. northern edge of the Alps
and Jura), the level of agricultural activity (full-time or part-time
farmer), farm continuation, and the proportion of ECAs on their
own farm. As this type of analysis does not allow strong correlations between the explanatory variables (Crawley, 2005), Pearson
correlations were tested first. In the farmers’ sample farm succession was negatively correlated with age (!0.31) and farm location
positively with the proportion of ECAs on the own farm (0.31).
Both farm succession and farm locations were therefore excluded
from the models. The linear mixed model analysis was carried
out with GENSTAT (version 11, VSN International 2008).
3. Results
3.1. Influence of land-use type and abundance of ECAs on preference
ratings
Non-farmers and farmers differed in their landscape preference
ratings (significant interactions in Table 1 and Fig. 2a and b). The
rating of the non-farmers was influenced by both land-use type
and abundance of ECAs (Table 2). With increasing proportion of
ECAs, mean preference scores increased in all land-use types
(Fig. 2a).
Table 1
Influence of land-use type, proportion of ECAs and professional background (nonfarmers, farmers) on the rating of the landscape images. (a) ANOVA and (b) variance
components estimated by restricted maximum likelihood (REML).
(a) ANOVA (fixed effects)
Source of variation
Non-farmer/farmer
Proportion of ECAs
Land-use type
Proportion of ECAs " Land-use type
Non-farmer/farmer " Proportion of ECAs
Farmer/non-farmer " Land-use type
df
F-value
p-value
1
1
2
2
1
2
5.33
884.00
484.12
12.92
161.65
240.80
0.021
<0.001
<0.001
<0.001
<0.001
<0.001
(b) Estimated variance components (random effects)
Source of variation
Component
Participant
Participant " Proportion of ECAs
Participant " Land-use type
Participant " Proportion of ECAs " Land-use type
0.4180
0.0018
0.5234
!0.0001
SE
0.0245
0.0001
0.0197
0.0000
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
Fig. 2. Interaction between the effects of the proportion of ecological compensation areas (ECAs) and land-use type on landscape preference ratings by (a) non-farmers and
(b) farmers. Mean scores ± 1 SE are shown.
Table 2
Influence of land-use type, proportion of ECAs and background characteristics of nonfarmers on the rating of the landscape images. (a) ANOVA and (b) variance
components estimated by restricted maximum likelihood (REML). Non-significant
interactions are not shown.
(a) ANOVA (fixed effects)
Source of variation
Age
Sex
Membership in environmental organisation
Education
Place of residence
Language region
Proportion of ECAs
Land-use type
Proportion of ECAs " Land-use type
Land-use type " Age
Proportion of ECAs " Sex
Proportion of ECAs " Membership in organisation
Land-use type " Membership in organisation
Proportion of ECAs # Education
Land-use type " Place of residence
Proportion of ECAs " Language
Land-use type " Language
df
F-value
p-value
1
1
1
1
2
2
1
2
2
2
1
1
2
1
4
2
4
3.13
0.95
22.54
0.98
1.19
12.08
1235.85
638.71
8.86
23.96
7.81
71.91
7.72
15.99
2.81
18.72
15.57
0.077
0.329
<0.001
0.323
0.304
<0.001
<0.001
<0.001
<0.001
<0.001
0.005
<0.001
<0.001
<0.001
0.024
<0.001
<0.001
(b) Estimated variance components (random effects)
Source of variation
Component
Participant
Participant " Proportion of ECAs
Participant " Land-use type
Participant " Proportion of ECAs " Land-use type
0.3952
0.0013
0.4875
0.0000
SE
0.0268
0.0001
0.0214
0.0001
Preference ratings of farmers were mainly influenced by landuse type (Table 3). Only in the land-use type dominated by grassland, mean rating scores increased with increasing proportions of
ECAs (significant interaction in Table 3 and Fig. 2b). For the other
land-use types the highest rating was reached with a proportion
of 0% ECA (mixed land-use type) or 10% ECA (landscape dominated
by arable crops).
3.2. Most liked and disliked landscapes and their characterisations
A mixed type of land-use with 30% ECAs (image 2.3) received
the highest mean preference rating and was also selected most often as most liked by the participating non-farmers (Table 4). A
Table 3
Influence of land-use type, proportion of ECAs and background characteristics of the
farmers on the rating of the landscape images. (a) ANOVA and (b) variance
components estimated by restricted maximum likelihood (REML). Non-significant
interactions are not shown.
(a) ANOVA (fixed effects)
Source of variation
Farm management (organic/non-organic)
Proportion of ECAs on own farm
Age
Membership in environmental organisation
Education
Language region
Proportion of ECAs
Land-use type
Proportion of ECAs " Land-use type
Proportion of ECAs " Farm management
Land-use type " Farm management
Proportion of ECAs " ECAs on own farm
Proportion of ECAs " Age
Land-use type " Age
Proportion of ECAs " " Membership
in organisation
Land-use type " Membership in organisation
Land-use type " Education
Land-use type " Language
df
F-value
p-value
1
1
1
1
1
2
1
2
4
2
2
2
2
2
2
1.31
1.09
3.77
0.03
0.67
1.59
0.09
100.13
17.14
16.29
4.29
4.35
4.59
5.75
9.59
0.254
0.298
0.053
0.867
0.414
0.207
0.762
<0.001
<0.001
<0.001
0.014
0.038
0.033
0.003
0.002
2
2
4
6.18
3.97
4.09
0.002
0.020
0.003
(b) Estimated variance components (random effects)
Source of variation
Component
Participant
Participant " Proportion of ECAs
Participant " Land-use type
Participant " Proportion of ECAs " Land-use type
0.412
0.003
0.376
0.000
SE
0.062
0.000
0.050
0.000
landscape dominated by arable crops with 30% ECAs (image 3.3)
received the second highest mean preference rating and was selected second most often as most liked. All other landscapes were
only rarely selected (by less than 8% of the non-farmers).
The farmers rated a landscape dominated by arable crops with
10% ECAs (image 3.2) highest. However, in line with the non-farmers they most often selected a mixed land-use type with 30% ECAs
(image 2.3) as most liked (see Table 4). A mixed land-use type
without ECAs (image 2.1) received the second highest mean preference rating and was also selected second most often as most
liked. All other landscapes were selected by less than 11% of the
farmers.
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A landscape dominated by grassland without ECAs (image 1.1)
received the lowest mean preference rating and was also selected
most often as most disliked by the participating non-farmers (see
Table 4). A landscape dominated by grassland with 10% ECAs (image 1.2) received the second lowest mean rating, whereas a landscape dominated by arable crops without ECAs (image 3.1) was
selected second most often as most disliked. All other landscapes
were selected by only few of the non-farmers.
The farmers rated a landscape dominated by grassland without
ECAs (image 1.1) lowest and selected it most often as most disliked
(see Table 4). A landscape dominated by grassland with 10% ECAs
(image 1.2) received the second lowest mean preference rating.
However, a landscape with a mixed land-use type with 30% ECAs
(image 2.3) was selected second most often (by 16% of the participants) as most disliked, although it was selected by 30% of the
farmers as the most liked one. All other landscapes were selected
by less than 8% as most disliked.
Non-farmers and farmers characterised the most liked landscape (image 2.3) as diverse, varied, species-rich and worth preserving (Fig. 3a and b). Non-farmers characterised the most
disliked landscape (image 1.1) as rather common and boring but
as ordered and not unkempt (Fig. 3a).
Farmers who selected image 1.1 as their most disliked
landscape characterised it as productive, but not diverse and species-rich, whereas farmers who selected image 2.3 as their most
disliked landscape characterised it as diverse and species-rich
but rather unkempt and rather not productive (see Fig. 3b).
3.3. Influence of farm characteristics and socio-demographic variables
on preference ratings
Similar to non-farmers, organic farmers rated landscapes without ECAs lower and landscapes with ECAs higher than non-organic
farmers (organic farmers: 0% ECAs: mean score 4.0 ± 0.17 on the
Table 4
Aesthetic preferences of Swiss non-farmers and farmers for nine landscape images that differed in land-use and proportion of ECAs. Preference rating was measured on 7-step
scales ranging from 1: totally dislike it to 7: totally like it. In bold-face: highest mean rating; most and second most liked and disliked landscapes.
Land-use type and proportion of ECAs
Landscape image
Mean rating scores
Non-farmers
Farmers
Proportion of participants who selected the landscape as
Most liked (%)
Most disliked (%)
Non-farmers
Farmers
Non-farmers
Farmers
Mixed, 30% ECAs
6.0
5.0
54.3
30.0
4.1
15.8
Arable crops, 30% ECAs
5.7
5.0
16.3
9.5
2.7
5.1
Mixed, 10% ECAs
5.5
5.0
5.1
10.6
1.6
2.6
Arable crops, 10% ECAs
5.2
5.3
3.3
5.5
2.0
1.5
Grassland, 30% ECAs
4.8
4.4
7.3
4.4
2.5
6.6
Mixed, 0% ECAs
4.6
5.2
6.3
23.1
3.2
1.8
Arable crops, 0% ECAs
4.4
5.0
3.3
10.3
11.2
6.6
Grassland, 10% ECAs
4.2
4.1
2.1
2.2
6.1
6.6
Grassland, 0% ECAs
3.7
4.0
2.1
4.4
66.7
53.5
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Fig. 3. Characterisation of the most liked and most disliked landscape by (a) non-farmers and (b) farmers. Participants were asked to characterise their most liked and most
disliked landscape using 14 pre-given adjectives on 5-step scales (from 1: disagree to 5: agree). In brackets: percentage of study participants who selected the respective
landscape as most liked/disliked.
seven-step scale, 10% ECAs: 4.8 ± 0.16, 30% ECAs: 5.2 ± 0.15; nonorganic farmers: 0% ECAs: 4.8 ± 0.06, 10% ECAs: 4.8 ± 0.06, 30%
ECAs: 4.8 ± 0.06). With increasing proportion of ECAs on their
own farm, the rating of landscapes without ECAs decreased
(b = !0.03, F1,751 = 21.02, p < 0.001), whereas the rating of
landscapes with 30% ECAs slightly increased (b = 0.01,
F1,751 = 3.67, p = 0.056). The rating of landscapes with 10% ECAs
was not influenced by the proportion of ECAs on their own farm.
Older farmers rated landscapes with 10% ECAs (b = 0.01,
F1,798 = 5.96, p = 0.015) and with 30% ECAs (b = 0.02, F1,803 = 14.56,
p < 0.001) higher than did younger farmers.
Members of environmental organisations (non-farmers and
farmers) rated landscapes without ECAs lower than non-members.
In case of the farmers, landscapes with ECAs were rated higher by
members of environmental organisations (Fig. 4a and b). Italianspeaking participants (non-farmers and farmers) rated grassland
Fig. 4. Interactions between the effects of the proportion of ecological compensation areas (ECAs) and organisation membership on landscape preference ratings by (a) nonfarmers and (b) farmers. Mean scores ± 1 SE are shown.
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
1437
Fig. 5. Interactions between the effects of the land-use type and language region on landscape preference ratings by (a) non-farmers and (b) farmers. Mean scores ± 1 SE are
shown.
dominated landscapes higher than mixed landscapes or landscapes
dominated by arable crops (Fig. 5a and b). Non-farmers who had
visited a high school rated landscapes without ECAs lower than
did those with a lower school education (4.3 ± 0.04 and
4.09 ± 0.04, respectively).
4. Discussion
Both non-farmers and farmers liked a mixed land-use type with
30% ECAs most, and characterised it as diverse and varied. A mixture of arable crops and grassland is currently typical for cultural
landscapes in the Swiss lowlands (Schüpbach et al., 2008), and
might thus be perceived as more fitting and familiar than a landscape dominated either by arable crops or by grassland. Perceived
fittingness and familiarity are important predictors for a positive
landscape evaluation (Hammit, 1981; Kaplan and Kaplan, 1989;
Nohl, 2001). This could explain why both non-farmers and farmers
from the Italian-speaking part of Switzerland were more in favour
of landscapes dominated by grassland as this is typical for that region. However, the high aesthetic appreciation of a mixed land-use
type with abundant ECAs could also be explained by visual complexity. It has been found that humans prefer moderate to high levels of visual complexity in landscape scenes, measured as the
number of independently perceived elements in the scene (Ulrich,
1986; Kaplan and Kaplan, 1989; Kaplan et al., 1998). Spatial and
structural heterogeneity, which are combined in a mixed landuse type with abundant ECAs, are good predictors of a positive
landscape evaluation (Hunziker, 1995; Strumse, 1996; Hunziker
and Kienast, 1999; Egoz et al., 2001; Soini and Aakkula, 2007). In
contrast, less structured, homogeneous landscapes are less appealing due to a lack of complexity and mystery (Kaplan and Kaplan,
1989; Kaplan et al., 1998), which could also be seen in the present
study. A homogeneous landscape dominated by grassland without
ECAs was most disliked and also characterised as rather boring,
though productive.
Visual complexity could also explain the higher aesthetic appreciation of a landscape dominated by arable crops in comparison to
one dominated by grassland. Arable crops offer a lot of different
textures, forms, and colours (Hendriks et al., 1996), e.g. in our
study the yellow ripe grain and the green maize or beet. In contrast, intensive grassland appears to be rather without structures
and uniformly green due to heavy fertilization, frequent mowing,
and its low species richness (von Arx et al., 2002). However, the
largest complexity in our visualizations was provided by wildflower strips and low-intensity meadows. Their presence was
clearly liked by non-farmers and by parts of the farmers. As they
offer, in contrast to most crops and intensive grassland, flowering
species from spring to fall, they contribute to a positive landscape
evaluation throughout the year.
For non-farmers, the presence of ECAs always boosted preference ratings and a landscape with 30% ECAs was characterised as
diverse, species-rich, beautiful and worth preserving. Other studies
have shown similar results. Species-rich wildflower meadows in
agrarian landscapes in Norway were clearly preferred over other
landscape types (Strumse, 1994), and plant communities which
were perceived as species-rich were liked best by the public in
Switzerland (Junge et al., 2009; Lindemann-Matthies et al., 2010).
Moreover, when people were asked to create their own favourite
meadow patch by selecting local wild plants that were displayed
in flower pots, species richness and structural diversity (different
height of plants) were explicitly stated as main assemblage criteria
(Lindemann-Matthies and Bose, 2007).
Our results corroborate findings from other studies which have
shown a growing nature-friendliness of the public in western
countries in general (Grendstad and Wollebaek, 1998; Widegren,
1998; Van den Born et al., 2001), and positive reactions towards
biodiversity in agricultural land in particular (Soini and Aakkula,
2007; Junge et al., 2009). Even when the less appealing winter aspects of field margins in Switzerland were shown to the public,
they were still liked because of their natural appearance and diversity (Junge et al., 2009). As cultural landscapes in Europe are
increasingly seen as leisure-time commodities and less regarded
as mere production areas (Buijs et al., 2006), the underlying values
attached to landscapes may change. Perceived scenic beauty may
be more influenced by landscape characteristics such as biodiversity than by expectations of its productiveness. However, it should
be noted that the best-liked landscape (heterogeneous land-use,
30% ECAs) was also regarded as rather productive by our non-farming sample. Moreover, it was perceived as rather ordered, indicating that cues of care were also apparent to the public. It has been
suggested that laypeople are more positive towards measures that
enhance ecological quality if they detect signs of care (Nassauer,
1995).
Unlike the general public, farmers varied strongly in their preferences. While a third of all farmers also preferred a mixed land-
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X. Junge et al. / Biological Conservation 144 (2011) 1430–1440
use type with 30% ECAs, there was another group (16%) who selected this landscape as most disliked and characterised it as not
useful, rather unproductive and unkempt, displaying a strong preference for neat, clean and ordered landscapes (see Burton, 2004;
Sullivan et al., 2004). This group might consist of farmers with a
strong internalized sense of ‘stewardship and care’ (Gobster
et al., 2007) and, not mutually exclusive, farmers who consider
production the only function of agriculture and do not regard ecological functions as important. Farmers with a preference for farmland diversity, however, characterised the mixed land-use type
with 30% ECAs, like the non-farmers, as diverse, worth preserving,
species-rich, useful and productive. These were farmers who practised organic farming, who were members of environmental organisations or had particularly high proportions of ECAs on their farm.
Other studies have also found that farmers with an environmental
interest, e.g. in wildlife, were more in favour of measures that aim
to increase ecological quality of agricultural land (Vanslembrouck
et al., 2002; Berentsen et al., 2007; Herzon and Mikk, 2007).
Compared to the effects of land-use type and abundance of
ECAs, socio-demographic variables such as age, sex or membership
in an environmental organisation had only small effects on aesthetic preferences. Other studies have also found a strong influence
of landscape characteristics such as species richness and structural
diversity on people’s aesthetic preferences and comparably little
influence of socio-demographic variables (Junge et al., 2009; Lindemann-Matthies et al., 2010). However, both higher education and
membership in an environmental organisation were predictors of
a dislike for landscapes without ECAs, probably due to more
knowledge about the ecological benefits of agro-biodiversity (see
also Hunziker et al., 2008).
Great caution should be exercised in generalising the results of
the present study. In our land-use visualizations, all ECAs were of
good ecological quality, i.e. contained attractive flowers and not
only grasses or weeds. In reality, however, the ecological and visual
quality of ECAs depends on physical conditions (e.g. location, soil
type) and on farmers’ management skills. In consequence, ECAs
in the Swiss lowlands may not always be perceived as attractive.
Moreover, our participants were on average better educated than
the Swiss population, and members of environmental organisations were over-represented. In consequence, our study sample
might have been more in favour of agro-environment measures.
In addition, the images used in the present study depicted both
grassland and arable land in just one seasonal stage. Future studies
should include images of different seasonal stages of agricultural
land.
5. Conclusions
The present study shows that agricultural practices influence
the aesthetic value of a landscape. Heterogeneous agricultural land
use and agro-environment schemes, in particular, can enhance
landscape aesthetics. This is an important finding for nature conservation as a positive relation of ecology and aesthetics can help
to enhance the acceptance of conservation programmes (Daniel,
2001; Gobster et al., 2007). Studies from Europe (e.g. Kleijn et al.,
2006), and from Switzerland (e.g. Aviron et al., 2009) show that
agro-environment schemes such as ECAs can promote biodiversity.
The present findings show that such schemes are aesthetically
widely accepted by the general public as well as by parts of the
farming community. This may help to increase the understanding
of the factors that influence the acceptance of agro-environmental
schemes in Switzerland and elsewhere.
Furthermore, the widespread concern that ecological sustainability and farmers’ aesthetic preferences are incompatible (Van
den Berg et al., 1998) could not be corroborated in general. In the
present study, many farmers liked ECAs in a landscape, and landscape preferences of organic farmers, for example, were closely related to preferences of the non-farming public. Burton et al. (2008)
found that for organic farmers the naturalness of the production is
more important than the symbolic meaning of care and intensive
production. The opinions of neighbouring farmers (Vogel, 1996;
Vanslembrouck et al., 2002) as well as the opinion of the non-farming public can positively influence the attitudes of farmers towards
environmental schemes (Berentsen et al., 2007; Herzon and Mikk,
2007). Moreover, the farmers’ skills could also be reflected by the
ecological (and visual) quality of ECAs. It is important that farmers
realize that they can show their skills and performance not only by
neat and orderly-farmed landscapes but also with care and management of ECAs (Burton et al., 2008). ECAs of high ecological quality are not dominated by weeds, but by wild flowers and herbs.
This is in line with findings from Nassauer (1992) in the United
States, where conservation measures in farmland were adopted because of aesthetical reasons.
The present results can also give inputs to landscape planning,
as they show that heterogeneity in the agricultural landscape is
liked by the public. This is particularly important as today’s agricultural landscapes are everyday landscapes for many people in
densely populated countries or regions such as the Swiss lowlands
(Dramstad et al., 2001). These landscapes have to provide ecological as well as recreation functions (Buijs et al., 2006).
Our results lead to the conclusions that (1) agro-environment
schemes are not only important for conservation, but also for landscape aesthetics, and that (2) agricultural practices (land use and
proportion of ECAs) can strongly influence the aesthetic value of
agricultural landscapes. These findings highlight the importance
of visual landscape aspects in agriculture and the integration of
landscape aesthetics into agricultural policy.
Acknowledgements
We would like to thank the Swiss Federal Office of Agriculture
for financial support and Reinhold Briegel for picture editing.
Moreover, we would like to thank Bernhard Schmid for statistical
advice and Felix Herzog as well as two anonymous reviewers for
valuable comments on the manuscript.
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