Preferences for European agrarian landscapes

13 Pages·2014·0.89 MB·English

LandscapeandUrbanPlanning132(2014)89–101

ContentslistsavailableatScienceDirect

Landscape and Urban Planning

journal homepage: www.elsevier.com/locate/landurbplan

Preferences for European agrarian landscapes:

A meta-analysis of case studies

BorisT.vanZanten∗,PeterH.Verburg,MarkJ.Koetse,PieterJ.H.vanBeukering

InstituteforEnvironmentalStudies,VUUniversity,DeBoelelaan1087,1081HVAmsterdam,TheNetherlands

h i g h l i g h t s

•
WecomparelandscapepreferencesacrossadiversesetofEuropeancasestudies.
•
Wefindgenericpreferencesforlivestock,mosaiclandandhistoricbuildings.
•
Preferencesforlandscapeattributesarerelatedtopopulationdensityandincome.

a r t i c l e i n f o a b s t r a c t

Articlehistory: StatedpreferencestudiesareincreasinglyemployedtoestimatethevalueofattributesofEuropeanagrar-
Receiv ed8February2014 ianlan dscapesan dchang est herein.Desp itethevas ta mounto fca sestu die s,prefere nc esforland scape
RAeccceepivteedd i2n 0 rAevuigsuedst f 2o0rm14 19 August 2014 attr ibutes are c onte xt speci fic, whic h inhibit s cr oss-c ase com pa rison and up -scaling. In t his study, we
addressthisproblembyapplyingameta-analysisofstatedpreferencestudiesthatfocusonattributesof

Europeanagrarianlandscapes(n=345).Themainobjectiveofthisstudyistoidentifygenericpreferences
Keywords: forparticu lartype soflandsca pe a ttribu tes acros scasestu die s.In addit io n, landscap econte xtvariables
Meta-analysis
thatexplainpreferenceheterogeneitybetweendifferentcasesthataddresssimilarlandscapeattributes
Landscapevalues
areidentified.Wefindthatlandscapeattributesthatdescribemosaiclandcover,historicbuildingsorthe
Landscapepreferences
LLaannddssccaappee fveaalutuarteiosn prerleasteionnces boef tliwveesetnocpkr egfeenreenraclelys froercepiavret itchuel ahrigahttersitb suttaetsedan pdrecfoenretenxctesv aarciraobslse sca–sessu.c Fhuartshpeormpuolraeti, owned fiennd-
Culturalec osystemservices sityandG DPperc apita–using am eta-regre ssionanal ysis. Theresu ltsofthe p resen ts tudyprovid ethe

firstcross-disciplinaryandcross-caseevidenceonrelationsbetweenpreferencesforlandscapeattributes

andsocio-economicandlandscapecontextconditions.Thestudyisafirststeptowardup-scalingofland-

scapepreferencesandthedevelopmentsociallandscapeindicatorsthatreflecttheperceivedvalueof

landscapesatregionalandpan-regionalscales,whichisincreasinglyimportantaslandscapepoliciesare

progressivelyimplementedatEuropeanlevel.

©2014ElsevierB.V.Allrightsreserved.

1. Introduction various scientific disciplines have made contributions to the

landscape preference literature. Many of these research efforts

Agricultural landscapes provide multiple ecosystem services were driven by changes in landscapes due to processes such as

besidetheproductionoffood,feedandfibers(VanZantenetal., intensification, scale enlargement and agricultural abandonment

2014). Amongst the most common services are recreation and (Howley,Hynes,&Donoghue,2012;Hunziker&Kienast,1999;Van

tourism as well as cultural heritage and aesthetic functions, Berkel&Verburg,2014).Theseprocesseshavedrasticallychanged

often summarized as cultural services (Chan et al., 2012; Daniel landscape structure and composition and, therefore, the visual

et al., 2012). A common way to obtain insight into these cul- appearanceandqualityofmanypost-warEuropeanagrarianland-

turalservicesistostudystatedlandscapepreferences.InEurope, scapes(Klijn,2004;VanderZanden,Levers,Verburg,&Kuemmerle,

inreview).

Landscape preferences have been addressed by numerous

empirical studies. These studies have applied different method-
∗ Correspondingauthor.Tel.:[phone].
ologies originating from different disciplines, among others
E-mailaddresses:[email protected],[email protected]
environmentalpsychology,landscapeecology,environmentaleco-
((BM.T.J.. vKaone tZsaen),tePnie)t, ePre.vtearn.v.beerubkuerrgi@[email protected] .(nPl.H(P. .VJ.Her.bvuarng)B, eMukarekri.knoge)[email protected] nomics and ge ography. D espite add ressing a similar prob lem,

http://dx.doi.org/10.1016/j.landurbplan.2014.08.012
0169-2046/©2014ElsevierB.V.Allrightsreserved.
90 B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101

methodologicalheterogeneityconstrainsthecomparisonofland- intheanalysisastheyinhibitquantitativemeta-analysesofcase

scapepreferencesacrossempiricalstudiesand,therefore,inhibits studyresults.

theadvancementofcross-caseevidence.Animportantconceptual Section2ofthispaperdescribesthemethodsthatwereapplied;

distinctioncanbemadebetweenmonetaryandnon-monetaryval- section3describestheresultsofadescriptivecross-casecompar-

uationoflandscapepreferences,wheremonetaryvaluationstudies ativeanalysisandameta-regressionanalysis;section4discusses

presentbeneficiarieswithlandscapealternativesthatalsoinclude theresultsanddrawsconclusions.

afinancialtradeoff,whilenon-monetarystudiesuserankingmeth-

odstomeasurethelandscapepreferencesofrelevantbeneficiaries. 2. Methods

Anotherimportantconceptualdistinctionbetweenempiricalpref-

erencestudiesarisesfromdifferencesbetweenexpert-basedand 2.1. Overviewofmethodology

stakeholder-based assessments of landscape quality. The former

typeofstudiesregardlandscapequalitytobeanintrinsicattribute Thispaperusesmeta-analysistosynthesizefindingsofempiri-

ofthelandscape,whereasthelattertyperegardslandscapequality callandscapepreferencestudiesinEurope.Meta-analysesofcase

asasubjectivevaluethatisderivedthroughtheeyesofthebeholder studiesareappliedtoprovideahigherlevelofgeneralizationof

(Lothian,1999;Tveit,2009). specificcasestudyknowledgeandaddressthescalesensitivityof

Instakeholder-basedlandscapeassessments,researchershave causalmechanismsandeffects(Young,Lambin,&Alcock,2006).

appliedbothcognitive(e.g.Sevenant&Antrop,2009)andphysi- Toconstructadatabasewithcomparativecases,thisstudyfollows

callandscapeattributeapproaches(e.g.Arnberger&Eder,2011; themethodologicalrecommendationsformeta-analysesproposed

Dachary-Bernard & Rambonilaza, 2012) to measure visual pre- by Rudel (2008). First, empirical studies were selected based on

ferences for landscapes. Cognitive attributes, such as landscape a predefined set of criteria. Second, a typology of agricultural

coherence, disturbance, and naturalness, often measure aspects landscapeattributeswasdesignedtoenablecross-casecompari-

of landscape preference based on evolutionary theories that sonandfrequencyanalysisofsimilarattributes.Third,preference

emerged in environmental psychology (Appleton, 1975; Kaplan scores for specific landscape attributes in the individual studies

& Kaplan, 1989). This category of attributes does not address werenormalizedtoenablecross-casecomparisonofpreferences.

preferences for a specific physical component of a landscape, Fourth, a number of potential explanatory variables were coded

but provides a holistic assessment of landscape character (Tveit, foreachcase.Thedatabasewasanalyzedusingfrequencyanalysis,

Ode, & Fry, 2006). Physical attributes address preferences for cross-casecomparisonofmeanpreferencesforspecificlandscape

tangible and quantifiable landscape components, such as the attributes,andmeta-regressionanalysis.

presence of hedges or a land cover type. Studies that address

physical attributes often estimate a change in preferences as 2.2. Searchprotocolandselectioncriteria

a result of (potential) landscape change. Hunziker and Kienast

(1999), for example, examined stakeholder preferences for dif- Thisstudyanalyzedempiricalstudies(n=51;seeS1intheSup-

ferent stages of afforestation in Switzerland. Campbell (2007) plementarymaterial)thatfocusonstatedlandscapepreferences

estimated the economic value of landscape attributes, such as forasetoflandscapeattributes.Everypreferenceestimatefora

hedgerows and stone walls in Ireland, using stated preferen- landscapeattributestatedbyadefinedgroupofbeneficiariesina

ces. definedcasestudyareawastreatedasauniquecaseinthedatabase,

Inadditiontotheirconceptualandmethodologicalheterogene- resultingin345cases.Thecasestudyareasinthedatabaserange

ity,studiesthataddresspreferencesforlandscapeattributestend fromlocaltonationalscaleandallstudieswerepublishedbetween

to be context specific and thus lack external validity (Bateman, 1993and2013.Thestudieswereretrievedbykeywordsearchusing

Day, Georgiou, & Lake, 2006). Local case studies are valuable to thesearchenginesISIwebofScience,ScopusandGoogleScholar.

gainunderstandingonlocalcausalmechanisms(i.e.howdoesone’s Searchstringswere:(ruralORagricultural)ANDlandscapeAND(pre-

occupationasafarmeraffectone’slandscapepreferences?),butthe ferencesORvaluation).Inaddition,snowballsearchwasappliedto

strengthandmagnitudeofcausaleffectscoulddifferfromplaceto selectedstudies.

place(Gerring,2007;Rudel,2008).Asaresult,ithasbeenproven Theselectioncriteriaforempiricalstudieswerethefollowing:

difficult to upscale locally measured landscape preferences and (1) studies measured landscape preferences for particular visual

tousethesepreferenceestimatesfordevelopingsociallandscape attributesoflandscapes;(2)beneficiarieswhostatedthepreferen-

indicatorsoftheperceivedvalueoflandscapestosupportlandscape cesweredefined;(3)casestudiesaddressedlandscapepreferences

planningonregionalorpan-regionalscales(Paracchini&Capitani, inagrarianlandscapes;(4)studieswereconductedinEurope.The

2011). searchprotocolandselectionprocedureofthismeta-analysiswere

Toaddressthisproblem,thispaperaimstoreviewthefindings performedinaccordancetothePreferredReportingItemsforSys-

of existing empirical stated landscape preference studies and to tematic Reviews and Meta-Analyses protocol (PRISMA; checklist

examineiftherearegenericpreferencesacrossEuropeforpartic- seeS2oftheSI).

ulartypesoflandscapeattributes.Statedpreferencestudiesassess

thegeneralpublic’spreferencesbyaskingrespondentstorank,rate 2.3. Landscapeattributetypology

orstateawillingnesstopayforanenvironmentalgoodorservice;

contrastingtorevealedpreferencestudiesthatderiveenvironmen- Toenablecross-casecomparisonofpreferencesforlandscape

talqualitypreferencesfromobservedbehavior.Weaimtoanalyze attributes,aclassificationoftheattributesusedinthedifferentcase

and interpret preference heterogeneity between different con- studiesintogenericcategoriesisrequired.Asthereisagreatdiver-

textsbyincorporatingspatiallyexplicitsocio-economicandland sityinEuropeanagriculturallandscapes,thereisalsoawidevariety

use/landcoverrelatedproxyvariablesinameta-regressionanaly- ofdifferentattributesthatpotentiallycontributetothequalityand

sis.Toenableacomparisonofpreferenceestimatesacrosscases,we valueoftheselandscapes(Gobster,Nassauer,Daniel,&Fry,2007).

havecollectedalargesetofcasestudiesthatmeasurestakeholder’s Thetypesoflandscapeattributesthatareaddressedintheempir-

landscape preferences for physical landscape attributes. We use icalstudiesdependontheobjectivesofthestudy,theperspective

thissubsetofthestatedlandscapepreferenceliteraturetoconduct oftheresearcherandtheresearchdesign.Inmanystudies,expert

ourmeta-analysis.Hence,holisticlandscapecharacterassessments knowledgeorfocusgroupsareusedtoidentifythemostimpor-

andexpertevaluationsoflandscapepreferencesarenotincluded tantlandscapeattributesthatcontributetothequalityorthevalue
B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101 91

of the landscape (Howley et al., 2012; Moran, McVittie, Allcroft, Toenablethecomparisonoflandscapepreferencesacrosscase

&Elston,2007).Otherstudiesrelatecognitiveattributestophys- studiesandtesttherobustnessoftheresults,preferencescoresare

icallandscapeattributes(e.g.landcoverdiversityorcomplexity) normalizedinthreedifferentways.

orusemetricsoflandscapestructureandcomposition(Ode,Fry,

Tveit,Messager,&Miller,2009;Ode,Tveit,&Fry,2008).
2.4.1. Preferencedirection(positive-neutral-negative)

In this analysis, we distinguish four groups of landscape
Thisnormalizationmethodassessesthepreferencedirectionof

attributes (Fig. 1). First we distinguish a group of attributes that
beneficiariesforaparticularlandscapeattribute.Allcases,except

describe direct anthropogenic influences in agrarian landscapes,
thoseapplyingalatentclasschoicemodel,arenormalizedonthis

mainly encompassing visual agricultural management practices
qualitativescale.Forstudiesthatelicitedpreferencesusingthe%

such as irrigation, farm stewardship, presence of livestock and
respondent choice method, preference rating or economic valu-

sustainable field margins. The second and third group consist
ation,preferencescoresforattributeswereranked.Forinstance,

of attributes that describe landscape structure and composition,
whenastudyyieldsascoreof5forhedges,2forpresenceoflivestock

which are often regarded as a result of both anthropogenic and
and1formixedforests,therankingwillbe(1)hedges,(2)presence

(bioticandabiotic)biophysicalprocesses(Mücher,Klijn,Wascher,
oflivestockand(3)mixedforests.Inthiscase,hedgesareconsidered

&Schaminée,2010).Landscapeattributesreferringtolandcover
positive,presenceoflivestockneutralandmixedforestnegative.For

patternsandcroptypesarecategorizedaslandcovercomposition;
thesestudies,thepreferencedirectionreflectsrelativepreferences

landscapeattributesreferringtospecificelementsthataffectthe
fortheattributesinthestudy.

spatial structure of agrarian landscapes such as hedgerows, tree
Thenormalizationmethoddescribedaboveisonlyappliedto

lines, ditches or historic buildings, are categorized as landscape
case studies that yield preferences for at least three attributes.

elements. The fourth group consists of attributes that describe
Withrespecttocasestudiesthataddresslessthanthreeattributes

visual aspects of biophysical features of landscapes, i.e. pres-
and case studies that provide qualitative preference scores, the

ence of water in a landscape, hills or mountains. This first level
preference direction of landscape attributes is derived from the

of landscape attribute grouping enables a comparison of the
interpretationsoftheauthorsoftheparticularcasestudy.

observedlandscapeattributesintheprimarystudiesonanominal

level.
2.4.2. Normalized-ranktransformation
Thesecondleveloflandscapeattributetypologybreaksdown
This normalization method provides a normalized preference
the first level attribute groups into more refined categories
scorebasedontherankoftheattribute.Allcaseswithpreference
(for a detailed description of the landscape attribute typol-
scoresexpressedatordinal,intervalorratiolevelswereconverted
ogy see S3 of the SI). This level enables the comparison of
torankings.Caseswererankedbytheirrelativepreferencewithin
landscape preferences between attributes at ordinal or inter-
thecasestudy.Inanidealsituation,allcasestudieswouldconsist
val level. The second level typology is designed by an iterative
ofthesameamountofattributesandtheranksofattributeswould
coding routine, adapting and adjusting the classification and
bedirectlycomparableacrosscasestudies.However,somestudies
research design to interpretations and preliminary results from
includeeightattributes,whereasotherstudiesincludeonlyone.
a small subset (n=40) of the database as proposed by Rudel
Therefore,thecasesaretransformedtoanormalizedrank(0–1),
(2008).
using:
Somestudiesincludeanumberofattributesthatcannotbecate-

gorizedineitherthefirstorthesecondlevelattributetypology(e.g. (r−1)
K&aOltlesnebn o,r2n 0 1&0 B).jeS rukceh , 2cl0a0s 2si; fi Mcaot riaonn eptr oa lb.,l e2m0 0s7a;r Wiseesetie thrbeerrfgr,o Lmif rtahne, rnorm= 1 −(R − 1), (1)
fa ctthat notalla ttrib utesinthestu dyreferto lands capec harac ter- whererrepresentstherankoftheattributewithinthecasestudies,
istic sdir ectl y( e.g.public pr efer ences forth e promotion oflocally Rthel o westrank (and the ref ore thenum berofa ttri bute sinthe
grow nfood,M oran etal., 2007),orthe yoc cur whenthep ref erence ca se s tudy) a nd rn orm t he normal ized rank. Fo r instance, w he n a
thatis meas uredca nn ot beassi gn edto onea ttribu tet ypespecif- stud yyields asc oreof5fo rhedges,2 forpre sen ceoflivest ockan d
icall y( forexamp le‘summ er farm,log b uildi ngs,moun tains inthe 1for mixed fo rests,t he r ank ingwill b e(1 )hedges ,(2 )presenc eof
backg roun d’,Kalte nborn&B jerke ,20 02).Inthe formercas e, the liv est ockand (3)mix edf orests.In this cas e,h edgesre ceiv eascore of
particularlan dscapeattri bu tesare leftout of the databa se;in the 0,presen ceo fliv estock ascore of 0.5 andm ixedfo restas co reof 1.
lattercase landscape attributes are inc lude d int hedatabas e, but Th isnorma liz ationme th odis on lya pplic ablet ocase s tudies th at
marke das uncategori zed. prov idepreference estimate sf orat leastthree at tribu tes.

2.4.3. Min–maxnormalization
2.4. Normalizationofthedependentvariable Th is normali zation method provides a normalized preference

score based on the continuous preference score of the attribute
Landscape preferences are measured using a wide variety of (for o ther a ppl icati ons of min –max norm alizat ion see : Stürck,
methods.Asa result,thede pen dentvaria bleise xp resse dindiffe r- Poo rtinga, &Verburg,20 14 ;Tian,Bai, Sun,&Zhao,20 13).A llcases
entunits thr ou ghout the casestudie s.Econo m icvaluatio ns tudies thatexpre ss landscap eprefe renc esby aW T Pestim ateor by pref-
pro videa nestimateo fwi lling nesstop ay(WTP)i nmoneta ryunits eren cerating aresubje ctedtoamin –m a xnor malizatio nt oa ssess
for land sca pe attrib ut es. In lands ca pe r esearch , preference s for therela tivepr efe renceofth ep a rticularlan dscapeattribu te within
lan dscapeattr ibutesarege ne rallyexpres sedusing apreference rat- the casestu dy.Inthec as eof min–max normaliza tion,all prefer-
ing(forex ampleona 5p ointLike rt-scale),a prefe re ncerankin gor enc esco resare as sum edto b eatcontin uousscale.Inc on trastto
by inter preting p os it iv e or n egative corre la tions betw een stat ed then ormali zed ranktran sfo rm atio n,min–ma xnorm al izational so
pre ferencesand thepres enc eofalan dscapeattrib ute.Inad dition, acco untsforthe relat ivedifferencesb etweenth epreferencesc ores
some studie s ex pre ss prefere nc e s as the p ercentage of respon- withinth eca ses tudy.Th eexpressio nforthe nor malization proce-
dents that ch ose the landscape at trib ute as the mos t p referred dureis give nby :
attrib ute (f rom h ere o n referred to as % r esp ond ent ch oice) (e.g.
Blimarorons, o1, 9 P9i9n)to-C orrei a, R amos, Su ro vá, & Menezes, 20 12; Gom ez- snorm= (sm(sa −x −sm simn)in), (2)
92 B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101

All Cases
1st Level
Agricultural Land Cover Landscape Biophysical

Management Composition Elements Features

2nd Level
Intensive agricPurletsuerence of livestock Farm stewardshDioFpi emlidn anmcaer gaignris cultural LDC ominMaonscaie cf oLrC estG/rneaetn ulrialn eLaC r eleGrmeeyn ltis near elementHs istoric buildings Point elePrmeesnetns ce of water Hills/mountains
Fig.1. Overviewofthetypologyoflandscapeattributes.

wheresrepresentstheinitialpreferencescoreandsnormthenor- members,especiallywhencombinedwithahighlevelofeducation,

malized preference score. For instance, a study yields a score of have a relatively strong preference for more ecological compen-

5forhedges,2forpresenceoflivestockand1formixedforests.In sation areas (e.g. sustainable field margins) in Swiss agrarian

thiscase,thenormalizedpreferencescoreforhedgesis1,presence landscapes.Otherstudieshavecapturedenvironmentalattitudes

oflivestockis0.25andmixedforestis0.Similartotheprocedure usingindicatorssuchastheecologicalparadigmscore(Sevenant

in the normalized rank transformation, only case studies with a &Antrop,2010)orbydistinguishingbetweenanthropocentricand

minimumofthreeattributesareincludedinthemin–maxnormal- ecocentric beneficiaries (Kaltenborn & Bjerke, 2002). Kaltenborn

izationsample. andBjerke(2002)findastrongrelationbetweenecocentricbene-

ficiariesandpreferencesforwildlands,whereasanthropocentrists

2.5. Explanatoryvariablesintheempiricalstudies expressarelativelystrongpreferenceforfarmlandscapes.

The most common type of explanatory variables addressed

refers to characteristics of beneficiaries. Especially studies that
2.5.3. Familiaritywiththelandscape
focusontheimplicationsoflandscapepreferencesforplanningand
Characteristicsthatdescribefamiliaritywiththelandscapeand
policypayattentiontotheheterogeneityofpreferencesbetween
sense of place of the beneficiary are examined by a number of
beneficiarygroups(Rogge,Nevens,&Gulinck,2007).Belowwedis-
empiricalstudies.Soini,Vaarala,andPouta(2012)analyzebenefici-
cussthemostimportantfindingsfromtheliteraturethatunderlie
aries’senseofplaceinrelationtotheirstatedlandscapepreferences
theformulationofhypothesisonwhichtheselectionofvariables
usingafactoranalysisinaFinnishcasestudy.Localbeneficiaries
forouranalysisisbased.
withtiestotheagriculturalsectorstatedarelativelylowpreference

for naturalness and high adaptability toward landscape change,
2.5.1. Socio-economicanddemographiccharacteristics
whilebeneficiarieslessfamiliarwiththelandscaperegardedthe
General socio-economic and demographic explanatory vari-
landscapemoreasastaticentityandweremorereluctanttoward
ables, such as income, gender, level of education and age, are
landscape changes (Soini et al., 2012). Some studies find signifi-
addressedinmostoftheempiricalstudies.SevenantandAntrop
cant relations of preferences with the familiarity of a landscape
(2010)findastatisticallysignificantrelationshipbetweenageand
by including the frequency of visits and recreational activities
preferences for Flemish agrarian landscapes. In their latent class
as a dummy variable in a regression model (Dachary-Bernard &
analysis,olderpeople(70–80years)haverelativelystrongprefer-
Rambonilaza, 2012; Sayadi, González-Roa, & Calatrava-Requena,
ences for stewardship and restricted urbanization. Van den Berg
2009). In a choice experiment study, Hasund et al. (2011) find a
andKoole(2006)findsimilarresults,indicatingarelativelystrong
significantpositiverelationbetweenfamiliaritywithalandscape
preferenceformanagedlandscapesofrespondentsolderthan50.
element (stonewalls, headlands, and ponds) and preferences for
Variousstudiesincludegenderintheiranalyses,butoftennosta-
thisparticularlandscapeelement.
tisticallysignificantresultsarefound(Howleyetal.,2012;Junge,

Lindemann-Matthies,Hunziker,&Schüpbach,2011;VandenBerg

&Koole,2006).Theeffectsofincomehavebeentestedespecially
in econo mic va luat ion stu die s; the t heore tical expect ation that 2.5.4. Residentiallocation
in comeandW TPestim atescorre late positivelyi softenconfir med Re sidential lo cation (e.g. visitors versus local residents, rural
(e.g. Ca mpb ell, 2 007). Hig hly educ ated bene fic iaries generally versusurbanre sidents, distan cetoare a)isth emos tdominant vari-
expr essastrong erprefe renceth anlowere ducatedbenefi ciariesfor ablead dresse dintheca sestudi es (e.g.B ar ros oeta l.,2012;So liva,
multifun c tionality ,ecological resto ration andwild ernessofagr ar- Bolli ger,&Hun zik er,2 010 ).Reside ntia llocation is oft enrel atedto
ianlandscapes(How leyetal., 2012;Linde man n-Matthies ,Ju nge,& the way p eople use landsca pes: in ma ny cases re crean ts do n ot
Ma tthies,2010 ;Vanden B erg &Koo le,2006). resi dein thelan dsca peofintere st, where asloc alresident so ften
do not us e th e landscap e for recre ation. Typ ologi es of the bene-

2.5.2. Environmentalattitude ficiaries are often based on residential location (Hunziker et al.,
Th e beneficiaries ’ environmental attitudes often strongly 2008; R amb onilaz a & Da cha ry-Bernard , 2007). R ambonilaz a a nd
affect p references for landscape attrib utes. A nu mber of studies Dacha ry-Bernard(2 00 7),forexample,fin dina Bretoncasest udy
addre ss the influe nce of membe rship of an e nvironm ent al non- that residents ha ve a str ong er prefere nce for h edgero w re stora-
governm ent alorganiza tio n(Jungeetal .,2 011 ;Liekensetal.,2 013; tion, whiletou rists pr eferthei ntegrationo ffa rmbuildin gsinthe
Sevenant&An trop,2010).Ju ngeet al .(20 11)fin dthato rg ani zation lands cape.
B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101 93

2.5.5. Othercharacteristics landcoverattributes,whereastheyareexpectedtobepositively

Anumberofotherbeneficiarycharacteristicsareaddressedby related to preference scores for the dominance forest/nature land

someofthecasestudies.Severalstudiescomparepreferencesof coverattributes.

landscapeprofessionalstopreferencesofarepresentativelocalor The share of agricultural land cover in the case study area is

regionalsample,inordertotesttheimpactofexpertknowledge expected to be negatively related to landscape preferences for

(Roggeetal.,2007;Tveit,2009).Othercasestudiestesttheeffects landscapesdominatedbyagriculturallandcover.Thishypothesis

of the relation of beneficiaries to farming (e.g. farming relatives, is based on an expected scarcity effect, i.e. in less favored areas

employedintheagriculturalsector)(Sayadietal.,2009),language where large scale intensification is a rare phenomenon, agricul-

group(Solivaetal.,2010),householdsize(Kallas,Gómez-Limón,& turallandcoverisoftenperceivedasapositiveattribute(Sayadi

Arriaza,2007;Sayadietal.,2009),andsocialclass(Howley,2011; et al., 2009; Willis & Garrod, 1993). An inverse scarcity effect is

Moranetal.,2007). expectedfortheshareofforestlandcover,whichwehypothesize

tobenegativelyrelatedtopreferencesforforest/naturelandcover

2.6. Meta-regressionanalysis:Preferencesforlandcover attributes.

compositionattributes Various case studies in our database explicitly address the

consequences of abandonment on stated preferences for land-

Two groups of explanatory variables are used in the meta- scapechanges(Arnberger&Eder,2011;Gomez-limon,1999).We

regressionanalyses:thefirstgroupisderivedfromthecasestudy hypothesizethatcasesthatexplicitlyaddressabandonmentyield

descriptionsdirectly,whilethesecondgroupofvariablesisderived relativelylowstatedpreferencescoresforlandscapesdominated

fromindependentspatialdatasetsusingthelocationsofthecase byforestornaturallandcovers.Abandonmentcaseswereidentified

studyareas,followingmethodsproposedbyVanAsselen,Verburg, as those studies where abandonment as a concept was explic-

Vermaat,andJanse(2013)andBranderetal.(2012).Thefirstgroup itly mentioned in the paper; acknowledging that this may still

includesbothvariablesthatrelatetothecharacteristicsofthebene- cover a range of different abandonment types and stages. We

ficiariesthathavestatedtheirpreferencesandtothemethodsused exploredtherelationwithotherpotentiallyimportantspatialfac-

inthecasestudy(seecodebook,S4oftheSI).Thesecondgroupof tors,suchasaccessibility,shareofprotectedareasandagricultural

explanatoryvariableswasextractedthroughspatialanalysisbased land use intensity (Ode et al., 2009; Swanwick & Hanley, 2007),

onthegeographiclocationsofthecasestudyareas(centroidpoint buteitherwefoundastrongmulticollinearitywithotherexplana-

ofthearea).Withrespecttolocal/landscapescalecases,thecase toryvariablesorwefoundthattherewasnoeffectonlandscape

studyareawasassumedtobelocatedwithina10kmradiusaround preferences.

the centroid and mean values for this area were extracted from

spatialdatasets.Withrespecttonationalscalecases,explanatory 2.6.2. Meta-regressions

variableswereobtainedfromnationaldatasets.Forthecomplete As a result of sample size constraints we can only perform a

listofdatasourcesfortheexplanatorycontextvariablesseeS5of meta-regression analysis for those cases that address land cover

theS.I. compositionattributes.Weapplyaweightedleastsquaresregres-

sion analysis in which cases are weighted by the inverse of root

2.6.1. Researchhypotheses samplesize,whichiscommonpracticeinmeta-analysesinenvi-

Theexplanatoryvariablesareincludedinthemeta-regression ronmentaleconomics(Nelson&Kennedy,2008).

analysesbasedonasetofaprioriexpectationsderivedfromliter- We estimate two different regression models. The first

ature,whicharetranslatedintoresearchhypotheses.Thefirstset meta-regressionisappliedtoallcasesthataddresslandcovercom-

ofhypotheses(1.aand1.binTable1)referstothefirstresearch positionattributes.Explanatoryvariablesinthismeta-regression

question: are there generic preferences for particular landscape are, among others, the second level attribute type dummy vari-

attributes?Basedonlandscapepreferencetheory(Appleton,1975; ables(e.g.dominanceagriculturallandcover,dominancemosaicland

Kaplan & Kaplan, 1989) and several case studies providing local coverordominanceforest/naturallandcover).Thisregressiontests

empiricalevidence(e.g.Howley,2011;Hunzikeretal.,2008;Ode hypotheses1.aand1b.Thesecondtypeofmeta-regressionmodel

et al., 2009; Vecchiato & Tempesta, 2013), we hypothesize that testshypotheses2.ato2.d.Thistypeofmeta-regressionisapplied

landscapes dominated by a mosaic of land covers are most pre- tothesubsetofdominanceagriculturallandcoverandthesubset

ferredacrosscasestudies.Buildingonlocalempiricalevidencewe ofdominanceforest/naturelandcoverattributes.

expect that generally landscapes dominated by forest or natural Inadditiontotheexplanatoryvariablesdescribedearlierinthis

landcoversreceivehigherrelativepreferencesthanthosedomi- section,wecontrolfortheeffectsofthenumberofattributesin

natedbyagriculturallandcover(García-Llorenteetal.,2012;Van thecasestudyandfordifferencesbetweeneconomicvaluationand

Berkel&Verburg,2014). non-economicvaluationstudies.Economicvaluationstudiesmight

The second set of hypotheses (2.a to 2.d in Table 1) refers to finddifferentpreferencescoresbecauseinthesestudiesrespon-

thesecondresearchquestion:whichvariablesexplainpreference dentsareforcedtomakeafinancialtradeoff,therebyplacingthe

heterogeneityforparticularlandscapeattributesacrosscasestud- landscape preferences in a fundamentally different context. We

ies?Basedontheliteraturereviewed,wehypothesizethatregional controlforthenumberofattributesbecausethenormalizedpref-

beneficiarieshaveastrongerpreferenceforlandscapesdominated erencescoreswereobtainedbasedontherelativepreferencefor

byagriculturallandcover,whereasnon-regionalbeneficiariespre- thatparticularattributewithinthecasestudy.Therefore,normal-

ferlandscapesdominatedbyforestsornaturallandcovers(Soini izedpreferencescoresmaybeaffectedbythenumberofattributes

etal.,2012;Solivaetal.,2010). inthecasestudy.

Preferencesforlandscapesthataredominatedbyforestsornat- To test the robustness of the results, all meta-regression

urallandcoversareexpectedtobehighinrichperi-urban/green models were estimated with both min–max normalized scores

belt areas, while they are expected to be low in sparsely popu- and normalized-rank preference scores. Variance inflation fac-

latedruralareaswithlowerincomelevelsandthateconomically tors(VIFs)weremonitoredtoavoidmulticollinearityamongthe

depend on agricultural production (Buijs, Pedroli, & Luginbühl, explanatoryvariables.Giventhesmallsamplesizeofourdataset,

2006; UK National Ecosystem Assessment, 2011; Zasada, 2011). explanatoryvariableswithaVIFvalueabove3wereexcludedfrom

Hence,wehypothesizethatGDPandpopulationdensityareneg- theanalysis.AllowingexplanatoryvariableswithhigherVIFswould

ativelyrelatedtopreferencescoresforthedominanceagricultural furtherinflatethevarianceofthecoefficients,whichcouldcause
94 B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101

Table1

Hypothesesderivedfromliteraturereviewwithregardtogenericpreferencesforlandscapeattributetypes(1.aand1.b)andvariablesthatpotentiallyexplainvariancesin

statedpreferencesforlandscapeattributes(2.a–2.d).

Hypothesisno. Description Reference

1.a Mosaiclandcoverattributesreceivehigherpreferencescoresthan Howley(2011),KaplanandKaplan(1989),Odeetal.(2009)

dominanceagriculturallandcoverordominanceforest/natureland

coverattributes

1.b Dominanceforestnaturelandcoverattributesreceivehigher García-Llorenteetal.(2012),VanBerkelandVerburg(2014)

preferencescoresthandominanceagriculturallandcoverattributes

2.a Regionalbeneficiariesstatehigherpreferencesfordominance E.g.Soinietal.(2012),Solivaetal.(2010)

agriculturallandcoverandlowerpreferencesfordominance

forest/naturelandcoverattributesthannon-regionalbeneficiaries

2.b GDPpercapitaandpopulationdensityarenegativelyrelatedto Buijsetal.(2006),UKNationalEcosystemAssessment(2011),Zasada(2011)

preferencescoresfordominanceagriculturallandcoverattributes

andpositivelyrelatedtopreferencescoresfordominance

forest/naturelandcoverattributes

2.c The%agriculturallandcoverinalandscapeisnegativelyrelatedto Sayadietal.(2009),WillisandGarrod(1993)

preferencescoresfordominanceagriculturallandcoverattributes;

the%forestlandcoverinalandscapeisnegativelyrelatedto

preferencescoresfordominanceforest/naturelandcoverattributes

2.d Inabandonmentstudies,dominanceforest/naturelandcover ArnbergerandEder(2011),Gomez-limon(1999)

attributesreceivelowerpreferencescoresanddominance

agriculturallandcoverattributesreceivehigherpreferencescores

that coefficients lose statistical significance (Wooldridge, 2009). the landscape of interest. A smaller number of cases focused on

In addition to the full models presented and discussed in this tourists(n=33),students(n=19)andfarmers(n=17).Onlyafew

paper—backwardstepwiseregressionwascarriedouttoassessthe casesaddressedexpertsorpoliticians(n=6),secondhomeowners

stabilityoftheeffectsoftheexplanatoryvariables.Resultsfromthe (n=2)andhunters(n=2).

twoapproachesaresimilar,unlessdiscussedotherwiseinSection Inmostofthecasesinthedatabase,preferencesforlandscape

3.3. attributesaremeasuredusingapreferencerating(n=143;Fig.4B).

For 88 cases a choice modeling methodology with a payment

3. Results vehicle was applied. In 12 other cases a non-monetary latent
class ch oice model w as app lied to ident ify classes of bene ficiar-

3.1. Descriptionofthedatabase ies that explain stated preference heterogeneity. Other methods
rep resen tedint hesam pleare:mu lti-attributeco ntinge ntvalua-

Fig. 2 shows the geographic location of case study areas in tion(n=35),%respondentchoice(n=30),linearmodel/regression
Europe a nd the year of publicat ion of th e c ases. Easter n Euro pe (n=1 5) , and co rrelation/An ova(n =8 ).

isunderrepresentedinthedatabasebecausetherewerenocases

foundinpost-socialistcountriesthatmatchedthesearchcriteria.

ManyempiricalstudiesinthedatabaseoriginatefromGreatBritain 3.2. Descriptiveanalysisofpreferencesforlandscapeattributes
orSw itzerland, butalso Ir elan d,Norwa yandSpa inar ewell repre-

sented.Insomecountries,suchasIrelandandSwitzerland,many Fig. 5 provides an overview of the mean values and distri-
studies w ere aim ed at me asuri ng prefere nces for agrarian land- butions o f the nor ma lized land sca pe p refere nces sc ores of the
scapeso nana tional sca le,identifyi ng,forinstan ce, preferenc esfor casesfo rth ese condlevella ndscapeatt ributetypes, showin gb oth
Irishag rar ia nlandsc apesi ngeneral.F urth ermore, Fig.2shows an min–m ax no rmaliza tionv aluesand normaliz ed-ran ktransfo rma-
expo nentialg rowthofpu bl ishedcas esoverthela sttw o decad es. tionvalue s.Thepreferen cedirec tion tablesdisplayco untsofcases
Thedatabas econsist so f345case s. that arecat egor izedaspos itive,neut ralor negative .Thea ttr ibute
A ll cases in the d ata base were categorized in accordance to type s po int elements , in tensive a gricultu re, farm stew ard ship, and
the fir st lev el of t he typolo gy of landscape a ttr ibutes (Fig. 1). fieldm argin scontain lessthan 10cases.Fo rthes eattributet ypes
Fig. 3 sh ows th e num ber of cas es per attribu te type. Th e larg est prefe rence sc ores are sho wn p er individ ual score instead o f in a
cate go ry – 1 70 c ases – c on tains l and scape attr ibute s rel ated to boxplot.

land cover composition. Agricultural management practices were Thenormalizedmeanpreferencescoresarehighestformosaic
addr essed in 41 cases, whereas 66 cases addres sed prefer ences landco ver,historicb uildin gsandprese nceofl ives tock(Fig .5) ,which
for landsca pe ele ments. Cases th at f ocus o n landscap e attributes confi rms h ypothe sis 1.a. F or m ost attri bu te types , the no rmal-
des cribingbio physicalfe atures oflan dscap esa rerelativel ysparsein izedpref erencescore s–m in– maxn ormalizat ionan dno rmalized
thedataba se(n=13).T helarge st classofattr ibu tesinthefi rstlev el rank transforma tion– sh owsimila rresults.Thep refer encedirec-
of t he typolo gy , land cov er comp ositio n, consists o f 5 6 c ases that tion, however, indic a tes sli ghtly d ifferent resu lts and re quires
ad dres s preferen ces for do minance fore st land co ve r/na tural land a dif ferent inte rpretation as com pared to the nor maliz ed pref-
coverat tributes,i.e.c ase sthatrefer toland scape swherefores tand e rence sco res. While the no rmalized s co res represent re lative
natur allandcove rty pesa redo mina nt; 41casest hatad dress pre- prefere nces fo r a par ticu lar landscap e attrib ute within a case
ference sfor mosai cland cov erattribut es; anda cate goryofc ases study, the p refe re nce directio n describe s whether the lan ds cape
that add ress es pref erenc es for dominanc e agr ic ultural la nd cover attribu te i s interpret ed as pos itive, neu tral or n ega tive. With
attri butes(n=6 6). respect t o d ominance fo res t/natural l and cov er, four cases from
Fig. 4A di sp lays the count of the different beneficiary groups studies wi th less than three attribu tes o btained a po sitive pref-
that ar e a ddressed by the c ase stu dies in t he database . Most erence (i.e. these case s did not obtai n a trans fo rmed-ran k or
stud iesf ocusedond raw ing arep resentat ive nati onal(n=1 04)or min–m ax n ormali zed sc ore) . Th e same e ffect is observed for
regiona l (n=77) sa mple of la ndscape benefi ciaries. S ev en ty tw o preferenc e scores case s for fa rm st ewards hip an d g rey linear ele-
casesfoc use d on prefere nce sfromloca lpopulations livingw ithin ments.
B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101 95

Fig.2. anoverviewofthegeographiclocationyearofpublicationofthecasesinthismeta-analysis.(A)Showslandscape-andnationalscalecasestudyareasonthemapof

Europe.Onenationalorlandscapescaledotreferstoonecasestudyarea.Oftenmultiplestudiesandcasesoriginatefromonecasestudyarea.Inset(B)revealstheincrease

inthenumberofempiricallandscapepreferencestudiesduringtheperiod1990-presentand(C)showsthenumberofcasesandstudiespercountry.

180
160
170
140
120
100
80
60
66
40
20 41
0 13
agricultural land cover landscape biophysical
management composition elements features
1st Level
2nd Level
Agricultural management Land cover composition 7 Landscape elements

4
ipnrte e snesnivce e aogf rliivce uslttuorcek 9 3 6 lddaoonmmd iicnnoaavnneccree magoriscauicltural 56 66 ggrree ey nli nlienaera er leelmemenetnsts 6 19
farm stewardship land cover historic buildings
14 dominance fore st/nat ural point elements 27
9 land cover 10
other agricultural other land cover 41 other landscape element
mana gem ent at tr ibutes composi tion at tr ibutes at tr ibutes
Fig.3. Thenumberofcasesperlandscapeattribute-type.Thebarchartshowsthecountofthefirstleveloftheattributetypology.Fiftyfivecasesinthedatabasedidnotfit

eitheroftheattributetypesandwereleftoutoftheanalysis.Thepiechartsshowthecountofthesecondleveloftheattributetypologyforthelargesttypes.
96 B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101

A) B)
120 160
140
100
120
80 100
60 80
60
40
40
20 20
0 0
Repres. natioRnealpres. regiLooncalal population Tourists Students FaErxmpeerrsts/politi2cinad nshome owners Hunters Preference raCtihnoigce experimMeulntti-attri% bruetse pCoVndenLitsn ecahr oicmeodel/regressioLnatent claCsos rrCeElation/anova Other
Fig.4. (A)Countofdifferentbeneficiarygroupsthatwereaddressedand(B)countofdifferentmethodsthatwereappliedinthecases.

Agricultural management Land cover composition Landscape elements

1.00 1.00 1.00
.80 12 .80 29 29 .80 24 24
e 12
or .60 .60 51 .60
c
s
d
e 61 51
aliz .40 .40 61 .40
m
r
no .20 .20 .20
17 17
.00 .00 .00
reference direction ++-/- 312 1130 313 261 ++-/- 122529 21550 211348 ++-/- 1720 334 1458 402
pattribute type inteangrsiicvue lture preselinvcees tofo ck farm stewardship ominancle aangd riccouvlterural mloasnaid ccoverminancel faonrde sct/ovneartural greene llienmeearntsgrey linear elementshistoric buildingpsoint elements
d o
d
Fig.5. Normalizedpreferencesforlandscapeattributes.Thegreyboxplotsshowmin–maxnormalizedscores;blackboxplotsshowpreferencescoresobtainedthrough

normalized-ranktransformation.Whitebarsintheboxplotsindicatemeanpreferencescore.Thenumbersintheboxplotsrefertothenumberofcases.Forattributetypes

thatconsistoflessthan10cases,normalizedscoresofthecasesaredisplayedindividually.

Table2

Estimatedcoefficientsforthemeta-regressionsincludingallcasesthataddresspreferencesforlandcovercompositionattributes.Thedependentvariableislandscape

preferencescore,obtainedviamin–maxnormalizationandnormalized-ranktransformation.

Variable Min–maxnormalization Normalized-ranktransformation

Standardizedcoefficient pValue Standardizedcoefficient pValue

Mosaiclandcover 0.44*** 0.000 0.45*** 0.000
Domina ncef orest/ naturallandcover 0.21** 0.017 0.19** 0.031
Linearelem entsattribute instu dy 0.16* 0.090 0.26*** 0.005
Histori cbuilding sattribut ei nstud y −0.11 0.216 −0.10 0.217
Non-reg ionalbene ficiaries −0.02 0.902 −0.00 0.952
Populationde nsitycasestu dyarea 0.18 * 0.073 0.14 0.145
GDPperca pitacase stud yarea 0.22** 0.038 0.17 0.102
Econ omi cvalua tion study −0.21* 0.063 −0.11 0.307

Numberofattributesinstudy 0.09 0.301 0.03 0.671

Nationalscalecase 0.05 0.618 0.08 0.365

n 129 129
A djustedR2 0 .16 0.17

AIC 118 118

* Statisticallysignificantat10%.
** Statistically significant at 5%.
*** Statistically significant at 1%.
B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101 97

3.3. Meta-regression preferences for the dominance of agricultural land cover. These

groupsdonotdependonagriculturefortheirlivelihoodsandoften

Table 2 displays the output of two weighted least squares uselandscapeforrecreationalpurposesandratherprefermosaicor

meta-regressionmodelsthatexploretheinfluenceofexplanatory forested/naturallandscapes.GDPpercapitahasnosignificantinflu-

variablesonnormalizedpreferencescoresforcasesthataddress enceonpreferencesfordominanceagriculturallandcover(Table3),

land cover composition attributes (n=129). The objective of this partlyrejectinghypothesis2.b.

analysis was to test hypotheses 1.a and 1.b: to assess whether As predicted in hypothesis 2.b, the population density in the

there are generic preferences for particular landscape attributes casestudyareasisnegativelyrelatedtopreferencescoresfordom-

(e.g.mosaiclandcover,dominanceforest/naturallandcover),while inanceagriculturallandcover,althoughthecoefficientisstatistically

controllingforanumberofstudyandcontextcharacteristics.The insignificant.Thisindicatesthatpreferencesforthedominanceof
adjustedR2 va lu esof0.1 6a nd0.1 7ar erelativ elylow,which was agriculturall andc overincre ase whenpopul atio nde nsityinlan d-

expectedsincethemeta-dataincludeaheterogeneouscollection scapes decreases. Although not statistically significant at usual

ofcasesandprimarilyfocusesonhypotheses1.aand1.b. criticalsignificancelevels,pvaluesforpopulationdensityarerela-

InTable2,thedummyvariablemosaiclandcoverhasasignificant tivelycloseto10%significancelevelsinbothmodelspecifications.

positive influence on preference scores for both normalizations. Percentage agricultural land cover is negatively related, and

Thestandardizedcoefficientsindicatethatnormalizedpreference significantatp<0.05,todominanceagriculturallandcoverforthe

scoresare0.44or0.45higherforlandscapesdominatedbymosaic min–maxnormalization.Thisresultconfirmshypothesis2.c:pre-

land cover than for the reference category dominance agricul- ferencesforattributesthatdescribethedominanceofagricultural

tural land cover. To a lesser extent, dominance forest/natural land land cover increase when the percentage agricultural land cover

coverattributesalsoreceivehighernormalizedpreferencescores in the landscape decreases. Especially in case studies conducted

thanthereferencecategory(0.21or0.19).Theseresultsindicate inlessfavoredareas,dominanceagriculturallandcoverattributes

that,whencontrollingfortheotherexplanatoryvariablesinthis are highly preferred. Both the number of attributes in the case

regression,preferencescoresformosaiclandcoveranddominance studyandthedummyvariableabandonmentstudydemonstratea

forest/naturelandcoveraresimilartothefindingspresentedinFig.5. negative,statisticallyinsignificantrelationtopreferencesfordom-

The explanatory variables linear elements and historic build- inance agricultural land cover attributes. The economic valuation

ingsdescribewhetherthecasestudyalsoincludedattributesthat study dummy variable was left out the model because of multi-

describe(bothgreenandgray)linearelementsorhistoricbuild- collinearitywiththeotherexplanatoryvariables.

ings in the landscape. Significantly higher stated preferences for Table4displaysthecoefficientsofthemeta-regressionanaly-

landcovercompositionattributeswerefoundwhenthecasestudy sisthatexplainspreferenceheterogeneityamongstcases(n=32)

also addressed preferences for linear elements. The dummy vari- that focused on dominance forest/nature land cover. The adjusted
able representi ng the pres ence of h istoric bui ldin gs obtain ed a R2 v aluesof 0.3 3and0.28 oftheregres sions inTab le4 arehigh

negative coefficient, which indicated that land cover composi- comparedtotheotherregressions.Inaccordancewithhypothe-

tion attributes are likely to receive lower preferences when the sis2.a,non-regionalbeneficiariesoftenstatedahigherpreference

study also addressed preferences for historic buildings. However, scorefordominanceforest/naturelandcoverattributesthanregional

thecoefficientisnotstatisticallysignificant,sothisrelationshipis beneficiaries. However, although the p-values are relatively low,

uncertain. thecoefficientsarenotstatisticallysignificant.Anegativerelation

Non-regional beneficiaries have not expressed significantly between GDP per capita in the case study area and preference

different preferences for land cover composition attributes than for dominance forest/nature land cover attributes is found statis-

regionalbeneficiaries.Thetwocasestudycontextvariables,popu- tically significant for the min–max normalization, which rejects

lationdensityandGDPpercapita,arebothpositivelyrelatedtothe hypothesis2.bonthisexplanatoryvariable.Incontrast,astrong

dependentvariable.Forthesevariables,thecoefficientsweresta- significant(p<0.05)positiverelationbetweenpopulationdensity

tisticallysignificantwithrespecttothepreferencescoresobtained inthecasestudyareaandthedependentvariableisfound.This

throughmin–maxnormalization.Thus,ingenerallandcovercom- indicatesthatpreferencesforforest/naturelandcoverattributesare

positionattributesreceiveahigherpreferencescoreincasestudy higherindenselypopulatedareas,confirminghypothesis2.b.

areaswitharelativelyhighpopulationdensityandGDPpercapita. In studies that focus on abandonment of agricultural areas,

The dummy variable economic valuation study has a negative dominanceforest/naturelandcoverattributesreceivesalowernor-

coefficient, which indicates that cases that have applied eco- malizedpreferencescorethanstudiesthatdonotexplicitlyaddress

nomic valuation received lower preference scores for land cover the consequences of abandonment. This result was found to be

composition attributes. This means that land cover composition statisticallysignificant(p<0.10)underbothnormalizationproce-

attributes receive lower preferences when a financial tradeoff is dures.Nostatisticallysignificanteffectswerefoundfortheother

involved.Theexplanatoryvariablesnumberofattributesinthestudy explanatoryvariablesinthemeta-regression.

and national scale case have small and statistically insignificant

coefficients,providingevidencefornostructuralvariationbetween

theseimportantdifferencesbetweencasestudies. 4. Discussionandconclusions

Tables3and4presentresultsfromthemeta-regressionmod-

els that aim to explain variation between expressed preferences Inthisstudywehavereviewedandappliedameta-analysisto

for dominance of agricultural land cover in landscapes and domi- studystatedpreferencesforagrarianlandscapesacrossEurope.The

nanceforforest/naturelandcover,usingbeneficiaryandcontextual methodologybuildsonbothmeta-analysesinthefieldofenviron-

explanatoryvariables.Table3givestheestimatedcoefficientsofa mentaleconomics(e.g.Brander,Florax,&Vermaat,2006;Brander

meta-regressionanalysiswithasubset(n=40)ofcasesthatfocus &Koetse,2011;Nelson&Kennedy,2008)andmeta-studiesinenvi-
ondominanceag ricultural land co ver.Th ea d just ed R2va lueo f0.15 ro nmenta lchan geresea r ch(e.g.Ge ist&La mb in,2002;Rude l,2 008;

and0.04oftheregressionsisrelativelylow.Thecoefficientsindi- VanAsselenetal.,2013).Inordertocompareresultsofstatedpref-

cate a strong beneficiary-effect; non-regional beneficiaries have erencestudiesfrommultipledisciplinesinaquantitativemanner,

stated significantly lower preferences for dominance agricultural weanalyzednormalized,within-study,preferencesforparticular

landcoverthanregionalbeneficiaries.Thisresultconfirmshypoth- landscapeattributes.Weassumedthattheserelativepreferences

esis2.a:beneficiariesfromoutsidethelandscapeoftenstatelower for landscape attributes best reflect the relative importance and
98 B.T.vanZantenetal./LandscapeandUrbanPlanning132(2014)89–101

Table3

Estimatedcoefficientsformeta-regressionmodelswithallcasesthataddressdominanceagriculturallandcoverattributes.Thedependentvariableislandscapepreference

score,obtainedviamin–maxnormalizationandnormalized-ranktransformation.

Variable Min–maxnormalization Normalized-ranktransformation

Standardizedcoefficient pValue Standardizedcoefficient pValue

Non-regionalbeneficiaries −0.33* 0.064 −0.40** 0.041

GDPpercapitacasestudyarea 0.16 0.355 0.11 0.543
Popu lati onden sityc asest udya rea −0.27 0.134 −0.25 0.209
Percentage agricult ural landco ver(CORINE) −0.43** 0.024 −0.20 0.313
Numberof attributesin stud y −0.08 0.655 −0.21 0.274
Abandon m entstudy −0.16 0.411 −0.05 0.799

n 40 40
A djustedR2 0 .15 0.04

AIC 24 25

* Statisticallysignificantat10
** Statistically significant at 5%.

value of a particular type of landscape attribute in a particular Withrespecttothecontextvariables,themeta-regressionanal-

landscapecontext.Thetwonormalizationmethodsthatwereused ysis has revealed that in relatively densely populated agrarian

inthemeta-regressionanalysisshowedsimilarresults.Although landscapes – often referred to as green-belt or peri-urban areas

p-valuesvary,thedirectionofthecoefficientsoftheexplanatory –beneficiariesexpressedhigherpreferencesforthoselandscape

variablesofthenormalized-ranktransformationandthemin–max characteristics associated with forest and natural land cover. In

normalizationaresimilarintheregressionanalyses. contrast,althoughnotstatisticallysignificant,populationdensity

In spite of large variation within landscape attribute types provednegativelyrelatedtopreferencesforagriculturallandcover.

and between the approaches used in the individual case stud- Atthesametime,thepreferencesforagriculturallandcoverare

ies,wefoundcross-casegenericpreferencesforseverallandscape higherinareaswitharelativelylowpercentageofagriculturalland

attributes. The analysis of relative preferences (Fig. 5) indicates cover. A plausible interpretation of this result is that in remote,

highmeanpreferencescoresforattributesthatdescribehistoric marginalagriculturalareas,agriculturallandcoverishighlyappre-

buildingsinthelandscape,mosaiclandcoverandthepresenceof ciated,motivatedbybothregionaleconomicviabilityandaesthetic

livestockinthelandscape.Withregardtothelandcovercomposi- landscapequalityconsiderationsofbeneficiaries(e.g.Sayadietal.,

tionattributes,thesefindingswereconfirmedinameta-regression 2009).

analysispresentedinTable2.Mosaiclandscapesand,toalesser An important question is whether the case studies included

extent,landscapeswithadominanceofforestornature,obtained in our analysis are representative for agrarian landscapes in the

higher preference scores than landscapes dominated by agricul- EuropeanUnion.Fig.2showsaconsiderablenumberofcasestudy

ture. areas in Western Europe, Scandinavia, the Iberian Peninsula and

Especially the normalized preference scores for landscapes Switzerland, while Central and Eastern European agrarian land-

dominated by agricultural land cover and landscapes dominated scapesareunderrepresented.Fortheseregionsonlyasmallnumber

by forest/natural land cover are heterogeneous across cases. In ofstudiesthatmeetourcriteriaareavailable,whichislikelydueto

order to explain observed preference heterogeneity, many case thefactthattheseareasarenotoftensubjecttostudyandbecause

studieshaveexploredrelationsbetweenbeneficiarycharacteristics ofdifferencesinresearchfundingandinterestsbetweenregions.

and landscape preferences. One of the most common explana- Fig.6comparesthedistributionofdifferenttypesofagricultural

tions for preference heterogeneity is the residential location of landscapesacrossthecasestudyareastothedistributionoftheEU-

the beneficiary. Often local residents state high preferences for 27asawholebasedonalandscapetypologyof(VanderZanden

attributesassociatedwithagriculturallandcover,whereasvisitors etal.,inreview).Thislandscapetypologyaccountsforanumber

statehigherpreferencesforattributesassociatedwithforestand of important characteristics of agricultural landscapes: manage-

naturallandcover(e.g.Hunzikeretal.,2008;Solivaetal.,2010). ment intensity (Temme & Verburg, 2011); linear elements (Van

These findings were confirmed in our meta-regression analyses derZanden,Verburg,&Mücher,2013)andfieldsize.Large-scale

presentedinTables2and3. extensive arable landscapes and medium-scale intensive arable

Table4

Estimatedcoefficientsformeta-regressionmodelswithallcasesthataddressdominanceforest/naturelandcoverattributes.Thedependentvariableislandscapepreference

score,obtainedviamin–maxnormalizationandnormalized-ranktransformation.

Variable Min–maxnormalization Normalized-ranktransformation

Standardizedcoefficient pValue Standardizedcoefficient pValue

Non-regionalbeneficiaries 0.20 0.252 0.27 0.132
GDPpercapit acasestudya rea −0.25 * 0.095 −0.24 0.105
Popu lati onden sityc asest udya rea 0.39** 0.019 0.36 ** 0.036
Percentage forestla ndc over(C ORIN E) 0.01 0.966 −0.01 0.941
Economicv aluatio nstu dy −0.26 0.248 −0.23 0.318

Numberofattributesinstudy 0.28 0.133 0.22 0.231
Abandon m entstudy −0.37 * 0.080 −0.43 * 0.050

n 32 32
A djustedR2 0 .33 0.28

AIC 34 30

* Statisticallysignificantat10
** Statistically significant at 5%.