Farmland abandonment in Europe: Identification of drivers and indicators, and development of a composite indicator of risk

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ContentslistsavailableatScienceDirect

Land

Use

Policy

j ou rn a l h om ep a ge : w w w . e l s e v i e r . c o m / l o c a t e / l a n d u s e p o l

Farmland

abandonment

in

Europe:

Identiﬁcation

of

drivers

and

indicators,

and

development

of

a

composite

indicator

of

risk

Jean-Michel

Terres

a,∗

_,

_Luigi

_Nisini

_{Scacchiaﬁchi}

a

_,

_Annett

_Wania

a

_,

_Margarida

_Ambar

b

_,

Emeric

Anguiano

c

_,

_Allan

_Buckwell

d

_,

_Adele

_Coppola

e

_,

_Alexander

_Gocht

f

_,

Helena

Nordström

Källström

g

_,

_Philippe

_Pointereau

h

_,

_Dirk

_Strijker

i

_,

_Lukas

_Visek

c

_,

Liesbet

Vranken

j

_,

_Aija

_Zobena

k

a_European_Commission_Joint_Research_Centre,_Institute_for_Environment_and_{Sustainability,}_Ispra,_Italy b_EIP-AGRI_Service_Point,_Brussels,_Belgium

c_European_Commission_Directorate_General_for_Agriculture_and_Rural_Development,_Brussels,_Belgium d_Institute_for_European_{Environmental}_Policy_(IEEP),_London,_United_Kingdom

e_University_of_Naples_Federico_II,_Department_of_Agriculture,_Portici,_Italy f_Johann_Heinrich_von_{Thünen-Institute,}_{Braunschweig,}_Germany

g_Swedish_University_of_Agricultural_Sciences,_Department_of_Urban_and_Rural_Development,_Uppsala,_Sweden h_Solagro,_Toulouse,_France

i_University_of_Groningen,_Faculty_of_Spatial_Sciences,_Groningen,_The_Netherlands

j_Katholieke_Universiteit_Leuven,_Department_of_Earth_and_{Environmental}_Sciences,_Leuven,_Belgium k_University_of_Latvia,_Faculty_of_Social_Sciences,_Department_of_Sociology,_Riga,_Latvia

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received20February2014

Receivedinrevisedform27February2015 Accepted13June2015

Keywords:

Farmlandabandonment Europe

Commonagriculturalpolicy Compositeindicator Landmanagement

a

b

s

t

r

a

c

t

AccountingformorethanhalfoftheEuropeanUnion’s(EU)territory,agricultureensuresfood

produc-tion,managesimportantnaturalresourcesandsupportssocio-economicdevelopmentofruralareas.

Moreover,itisestimatedthat50%ofallplantandanimalspecies(includingsomeofthatarelistedinthe

EUHabitatDirective)dependonagriculturalpractices.Thecontinuationofappropriateagriculturalland

managementisessentialtoensuretheseprimaryfunctions.Avoidanceoffarmlandabandonmentis

there-foreanimportantrationalefortheEU’sCommonAgriculturalPolicywhichrequiresimprovedknowledge

ofthisphenomenonattheEuropeanlevel.Thisstudyassessestheriskoffarmlandabandonmentinthe

27EUMemberStates.ItsummarizestheworkperformedbyanexpertpanelofEuropeanscientistsand

nationalrepresentativeswhichaimedtoidentifythemaindriversoffarmlandabandonmentinEurope,

todeﬁneindicatorsforassessingitsriskofoccurrenceandtotestthevalueofEuropean-widedata

sourcestoachievetheseaims.Driverswereidentiﬁedundertworationales:lowfarmstabilityand

via-bility,andnegativeregionalcontext.Indicatorsweredeﬁnedusingrecentsocio-economicfarmdataand

geospatialdatasets.Someindicatorswerethencombinedtomakeacompositeriskindicator.Regions

withhigherriskoffarmlandabandonmentarelocatedinPortugal,Spain,Italy,Greece,Latvia,Estonia,

Finland,SwedenandIreland.ThispaperdemonstratesthechallengesofperformingaEuropean-wide

assessmentofaphenomenoninﬂuencedbydriverswhoseeffectsvaryatlocallevels.Otherproblems

encounteredaredataheterogeneityintermsofspatialresolutionandquality,aswellasaccessto

micro-data(localleveldata).HighspatialresolutionEuropeandatasetsmeasuringfarmlandabandonmentare

neededtovalidatethedeﬁnedindicatorsaswellastobenchmarkthemethodology.Furthermore,such

datacouldbeusedtoestablishaweightingsystemforthedrivers.

∗ Correspondingauthorat:EuropeanCommissionJointResearchCentre,Institute forEnvironmentandSustainability,ViaE.Fermi2749,21027Ispra,VA,Italy.

E-mailaddress:jean-michel.terres@jrc.ec.europa.eu(J.-M.Terres).

1. Background

Accountingfor almosthalfoftheEuropeanUnion’s(EU) ter-ritory,agricultureplaysanimportantroleintheconservationof theEU’s environmentalresource (EC, 2006).It interacts witha widerangeofvaluablehabitats,andthemaintenanceofa num-berofecosystemsthathaveemergedfromagriculturalcultivation http://dx.doi.org/10.1016/j.landusepol.2015.06.009

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depends on the continuation of appropriate land management practices(Benayasetal.,2007).Eventhougha cessationofland managementcanhaveapositiveinﬂuenceonbiodiversity (rewil-ding), theabandonment of agriculturalland may alsothreaten farmlandbiodiversity(Siramietal.,2008;Plieningeretal.,2014; Zakkaketal.,2014),inparticularfunctionaldiversity(Pecoetal., 2012)associatedwithanthropogeniclandscapesofhighnature val-ues.Theconceptofhighnaturevaluefarming(HNV)recognizesthat low-intensityfarmingsystemsarecrucialfortheconservationof biodiversityinsomesettings(Oppermannetal.,2012).

Besidesits influenceon biodiversity,land abandonment has arangeofconsequencesforecosystemfunctionsand the provi-sionofecosystemservices(Benayasetal.,2007).Thisinfluenceis oftencontext-specific,e.g.,wildfirefrequencyandintensity, nutri-entcycling,carbonsequestration,culturallandscapevalues,and waterbalance.

Moreover,foodsecuritybeingoneofthemajorchallengesfor thefuture,theEUhasajustiﬁedstrategicinterestinkeepingits agriculturalproductionpotential,inviewofshortandlongterm needssuchasfoods,feed,ﬁberandbiomassproduction(Kastner etal.,2012).Furthermore,agriculturalactivityunderpins impor-tantaspectsoftheruraleconomyinmanypartsoftheEU(Terluin etal.,2010).

Duringthepastdecades,Europeanfarminghaschanged(EC, 2006): specialization, intensification and technological devel-opments have made farming more competitive but have also increasednegativeimpactsontheenvironment.Atthesametime, theabandonment offarming is of concernbecauseof negative social, economic andenvironmental effects (EC, 2006; Moravec andZemeckis,2007).Environmentalandsocialproblemsrelatedto abandonmentinclude:(1)thereductionoflandscape heterogene-ityandpromotionofvegetationhomogenization(oftenassociated withincreasedfirerisk);(2)soilerosionanddesertification;(3)the reductionofwaterstocks;(4)biodiversitylossandreduced popu-lationofadaptedspecies;and(5)thelossofculturalandaesthetic value(Benayasetal.,2007).

Landabandonmentcanoccureverywhere,eveninareaswith goodyieldpotential,duringsatisfyinggeneraleconomicsituations (Strijker,2005)oroutsidemarginalareas(HatnaandBakker,2011). InwesternEurope,theproblemoflandabandonmenttendstobe minor,whilstinsouthernandcentralEuropeitismoreimportant (Moravec andZemeckis,2007).Incentralandeastern European countriesthechangeofpoliticalsystemfrom1990onwards,which triggeredlandprivatizationandthedismantlingofcollectivefarms, mayhaveledtolandabandonmentbecausetheconditionsfor prof-itableandcommercialfarminghadbecomedifficult(Vrankenetal., 2004).Thisphenomenoncanevolveratherquickly,especiallyinthe newEUMemberStates(MoravecandZemeckis,2007).Driversvary betweenregionsandcountries,andthispotentiallyinfluencesthe definitionorimportancegiventoit(Strijker,2005).

The abandonment of farmland has long been a contentious issuewithinEurope(e.g.,seeBaudry,1991;Brouweretal.,1997; Pointereauetal.,2008)inpartbecausethisphenomenonisdifficult todefineandmeasure(KeenleysideandTucker,2010;Moravecand Zemeckis,2007).Infact,thereisnosingledefinitionand, depend-ingonthediscipline,differentinterpretationsexist(Moravecand Zemeckis, 2007). Despite the acknowledged importance of the phenomenon(e.g.,EC,2006),thereisnoconsistentmeasurement acrosstheEUandasaconsequencethecurrentextentof abandon-mentisnotwellknown(Pointereauetal.,2008).Severalstudies havedemonstratedthelossofagriculturallandin(partsof)the EUthroughcomparisonoflandusedata(HatnaandBakker,2011; KeenleysideandTucker,2010;Mülleretal.,2009)orusingfarm statistics (Corbelle-Ricoet al., 2012; Pointereauet al., 2008).A numberofstudieshavealsoanalyzedtheissueunderarangeof futurescenarios(e.g.Elbersenetal.,2013;Helmingetal.,2011;

Nowickietal.,2007;Verburgetal.,2010)andtheseresults sug-gestthatfarmlandabandonmentinEuropewillcontinueoverthe next20–30years.Thehighest projectedlevelsof abandonment aresimulatedforscenarioswithstronglevelsofglobal competi-tioninagricultureandlowlevelsofCommonAgriculturalPolicy (CAP)supportforextensivefarming(Renwicketal.,2013).Some abandonmentisalsoprojectedunderscenarioswithreducedglobal competitiveness,highlevelsofsupportforagricultureandthe envi-ronmentaswellasstrongregulations.AsrelatedbyRenwicketal. (2013)thereisfurthermoreafearthattradereformswillreduce theeconomicviabilityoffarming inEuropeandleadtofurther abandonmentofthemoremarginalagriculturalareas.

GiventheimportanceoftheCAPinEUpoliciesanditsvarious objectives,theriskoffarmlandabandonmentwasincludedinthe listof28agri-environmentalindicatorsthatareusedtoassessthe integrationofenvironmentalconcernsintotheCAP(EC,2006).As theindicatorisnotfullyoperationalyetandrequiresconceptual andmethodologicalimprovement,apanelofEuropeanexpertswas commissionedwiththemethodologicaldevelopmentofthe indi-cator.Thispapersummarizesthefirstresultsofthepanel’swork whichcoveredtheidentificationofprincipalfarmland abandon-mentdriversandthedefinitionofindicatorsformeasuringitsrisk ofoccurrencefortheEU-27.Furthertothisthematicobjective,this studyalsoaimedtoassessthesuitabilityofavailable European-widedatasetsincludingtheidentificationofgapsandlimitations. Theworkstartedwiththeidentificationofdriverswhichis summa-rizedinthefollowingSection2.Thiswasthebasisforthedefinition ofindicatorsinSection3andtheiranalysisinSection4.Conclusions andperspectivesforfutureworkontheindicatordevelopmentare providedinSection5.

2. Definitionanddrivingforcesoffarmlandabandonment Forthisstudy,farmlandabandonmentwasdefinedbytheexpert panelasacessationoflandmanagementwhichleadstoundesirable changesinbiodiversityandecosystemservices.Astheindicator willbeusedtoassesstheintegrationofenvironmentalconcerns intotheCAP,thedefinitionwassteeredtowardspotentialthreats totheenvironment,whichinthiscasearelinkedtolossof biodiver-sity(Siramietal.,2008;Plieningeretal.,2014;Zakkaketal.,2014; Pecoetal.,2012)and ecosystemservices(Benayasetal.,2007). Theindicatortobedevelopedaddressestheriskoffarmland aban-donment(probabilityofoccurrence),nottheconsequencesorthe extenttowhichfarmlandabandonmentactuallyhappens.

Thereasonsforfarmlandabandonmentaremultidimensional, andthereisnoclear-cutdivisionamongdriversasitratherdepends ontheresultoftheirco-occurrenceandinteractions(Betheand Bolsius, 1995; Coppola,2004).Driversare usuallygroupedinto eithernaturalconstraints,landdegradation,socio-economic fac-tors,demographicstructure,andtheinstitutionalframework(FAO, 2003)orecological,socio-economicreasonsandreasonsrelatedto unadaptedagriculturalsystems(Benayasetal.,2007).Inthisstudy drivingforcesareclassiﬁedintounsuitableenvironmental condi-tions,lowfarmstabilityandviability,andtheregionalcontext. 2.1. Unsuitableenvironmentalconditions

Amongstthebiophysicalfactorsoftenmentionedas unfavor-abletoagriculture,lowsoilproductivity,poorclimate,steepterrain andsigniﬁcantaltitudearethemainnaturalconstraintsto agricul-tureandmaythereforeincreasetheriskoffarmlandabandonment. Thereisalargeliteratureanalyzingthesedriversandtheir nega-tiveimpactsonagriculturalproduction,economicproﬁtsandland usemanagementopportunities.Mottetetal.(2006),Mülleretal. (2009)andSysetal.(1991)assessedelevationandslopeeffects

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onagricultureconditions.Farmingdifﬁcultiesduetounfavorable soil chemical and texture properties were documented by the FAO(1983,1988)andThomassonandJones(1989),whilelimited soildrainage and excessivesoil moisturewerefound responsi-ble forcropproduction reduction (Schulte et al.,2005)and for farmingrestrictions(Earl,1997;Fitzgeraldetal.,2008;Jonesand Thomasson,1993).AdverseclimateforagricultureinEuropecan eitheroccurduetolowtemperaturelimitingcropgrowth,short growingseason(Fischeretal.,2007),ortodryconditionsandwater stressaffectingcropphysiologicalprocesses(HassanandDregne, 1997;LeHouerou,2004).

Theabovebiophysicalcriteriaarecurrentlyunderevaluationin theEU-27forthedelimitationofareaswithnaturalconstraintsto agricultureaspartofthenewRuralDevelopmentregulationforthe programmingperiod2014–2020.Consequently,thesedriverswill notbedirectlyadressedinthisstudy.

2.2. Lowfarmstabilityandviability

Farmviabilitymainlydependsonthefarm’seconomicsituation. Farmlandistypicallyabandonedasaneconomicresourcewhenit ceasestogenerateasufﬁcientlyhighincome(MacDonaldetal., 2000).Althoughthisisnot asolecause,andalthoughitcanbe triggeredbyanumberoffactors,thereisastronglinkandfarm incomeplaysaprominentroleinfarmers’strategyregardingland use(Strijker,2005).

However,in the agriculturalsector,the rule of proﬁt maxi-mizationisnotalwaystrueandonecannotonlyrefertostrictly economicvariables toexplain farmlandabandonment(Coppola, 2004).Schmitt(1997),quotedbyStrijker(2005)notesthat farm-erswillonlyleaveagriculturewhentheirincomebecomesvery lowsincetherearemanyreasonsfornotleavingthesector.Some arethestrongpreferenceforbeingafarmerorthelackofgainful alternativesfortheirland,machines,buildings,andlabor.

Someagriculturalpolicyinstrumentslinkpublicsupporttoland managementcommitmentandareconsequentlyreducing aban-donment(e.g., farmerscommitted for atleast ﬁveyears in the agri-environmentalmeasuresetc.).ThiswasconﬁrmedbyRenwick etal.(2013)throughmodellingvariouspolicyscenarios.

Newinvestmentsonthefarmcouldbearelevantindicatorof farmdynamism,itsadaptationcapacityandforward-looking strat-egy.Thiscanbeaproxyforthewillingnesstocontinuefarmactivity, whilelowlevelofinvestmentsmightindicateafarmingactivityin decline(DeStefano,1985).

Farmers’ageisgenerallyacknowledgedintheliteratureasan importantdriverforlandabandonment(Kristensenetal.,2004; Mishraetal., 2010;Potter andLobley, 1992).Information pub-lishedbytheEC(2010)confirmsthestructuralholdingdifference betweenyoungandelderlyfarmers.Youngerfarmerstendtobe abovetheEUaveragewithrespecttoeconomicsize,utilized agri-culturalareaandlaborforce.Kristensenetal.(2004)highlighted therelationshipbetweenfarmer’sageandlandscapechanges.In particular,otherfactorsbeingconstant,farmlandextensification andabandonmentaremorelikelytooccurwhenthefarmerisold andclose toretirement.Thenumber offarmers nearing retire-mentagemayreflecttheexpectedtransitionofthelandandits structureinaperiodoftenyears(Baldocketal.,1996). Addition-ally,thisproxycanbecomemoremeaningfulifinformationabout successionisknown(percentageofsuccessorfarmers).Potterand Lobley(1992)identifiedadirecteffectoffarmmanagement.The investmentindicatorcould,inthisregard,beaproxyforsuccession probability.Moreover,iftheholdinghasaloweconomicviability andinappropriatestructure,thesuccessionpossibilitywillbelow. Trainingandinformationareimportanttobeabletoadaptto changingeconomiccircumstancesandtoguaranteetheintegration ofthedifferentfunctionsofagricultureatfarmlevel(Baldocketal.,

1996;Labarthe,2009).Theuseofafarmadvisorysystemisaproxy fortheprofessionalismofthefarm,andthewillingnesstoinvest intermsofhumancapitalandknowledgewithasufﬁcienttime horizon(Mishraetal.,2010).Insomeexamples(e.g.,Ireland), Mem-berStateshaveintroducedvoluntaryeducationalschemeswiththe statedobjectiveofprotectingagainstlandabandonment.

Finally,farmphysicalstructurecanalsobeahandicapforfarm viability.Smallandscatteredplotscanconstituteanobstacleto succession.Smallparcelslocatedfarfromthefarm,aremorelikely tobeabandonedthanthosethatarelargerormoreaccessible,due tohighertransportationandlaborcosts.Moreover,smallfarmsare morelikelytohavedifficultaccesstocreditandotherinstitutional servicesrequiredtoincreasetheircompetitiveness(Rahmanand Takeda,2007).Generally,largerholdingscanbenefitfromlower productioncostsandaremorecompetitiveforfarmpractices(use ofmachineryorabetterinputuseefficiency).Theyaremore fre-quentlyassociatedwithinnovationandusuallymorecompetitive andviableineconomicterms(e.g.,BojnecandLatruffe,2013;Lange etal.,2013).

2.3. Regionalcontext

Atregionalornationallevels,imbalancedeconomic develop-ment between sectors (agriculture, industry and services) may increasethetransferof laborforces. Riskoffarmland abandon-mentmayincreasewhenagriculturalincomeissubstantiallybelow thatoftherestoftheeconomy(regionalornationalincome).This tendencywouldbereinforcedwiththeincreaseofopportunities outsidetheagriculturalsector.

Alternativeemploymentopportunitiesinothersectors,aswell asalowproportionoffull-timefarmsarefactorswhichincrease theprobabilityofabandonment(Rickebuschetal.,2007). How-ever,therecouldalsobeareaswithawell-establishedandstable systemwherefarmersareemployed part-timeinothersectors. Forinstance,intheiranalysisofmarginalizationinDenmark,Bethe andBolsius(1995)stressedthatpart-timeemploymentwasoften afactorofstability.

Severalstudieshaveanalyzedtherelationshipbetween farm-landabandonmentandproximitytosettlementsandroads(e.g., Corbelle-Rico and Crecente-Maseda, 2014; Corbelle-Rico et al., 2012; Gellrich and Zimmermann, 2007; Lange et al., 2013). In general,proximitytoboth,andtherelatedaccessibilityof oppor-tunities,activitiesorassetsexistingcloseby,reducestheriskof farmlandabandonment.Indeed,distancetoagricultural infrastruc-ture(retailers,inputs suppliers,slaughterhouse,etc.),aswellas tosocialinfrastructuresuchasschoolsandhospitals,are identi-ﬁedasstrongdrivers(Mottetetal.,2006;NordströmKällström, 2008).Proximitytourbancentresalsomakesitpossibletocombine farm-workwithasecondjob(Terluinetal.,2010).

Landtenurestatuscouldinﬂuenceinvestmentandfarmholder dynamismifthelong-termperspectiveforlandasafactorof pro-ductionisunclear.Alargeproportionoftenant-farmedagricultural areascansometimesindicateatendencyforinstabilityifproperty rightsareinsecureorlandtenancylawsareweak(Swinnenetal., 2006).IntheIRENAoperation(IndicatorReportingonthe Integra-tionofEnvironmentalConcernsintoAgriculturePolicy)theprice ofthelandwasalsoconsideredasagoodsupplementary indica-torofmarginalization,asitexpressesthedemandforland.Alow demandforlandusuallytranslatesintolowtransactionprices (sell-ingorrenting)andaweaklandmarketisagoodproxyforahigher riskoflandabandonment(CiaianandSwinnen,2009).However, thisshouldbeconsideredinaregional/nationalcontextas prop-ertylawsandlocalusagevarybetweenMemberStates(Deininger, 2003).

ThetransitionperiodofthenewMemberStateswas accompa-niedbymajorchangesinagriculturalstructureinmostofthese

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countries,sometimesinvolvingthebreak-upoflargecollectiveor statefarmsandtheprivatizationofland.TheFAO(2003)stressed thatcentralEuropeancountriesinthetransitionphaseface difficul-tiesregardinglandownership(registration),insufficientlydefined property rights, and thelack of operationalland salesmarkets (Swinnenetal.,2006).Thispreventsthereconstitutionofviable farmingunitsthroughlandconsolidation.Becauseoftheabsence ofanytypeoflandmarketduringthisphase,parcelsremained frag-mentedandledtoamassiveco-ownershipsituation.Thisoccurred forexampleinBulgaria,creatingimperfectpropertyrightsonland, inefficientland allocationamong farmstructures,and farmland abandonment(Vrankenetal.,2011).

Finally,previoustrendsinfarmlandabandonmentmight inﬂu-ence its future evolution, assuming that regions facing the phenomenonaremorefragileandthereforepronetofurtherland abandonment(Pointereauetal.,2008).

3. Dataandmethods 3.1. Datasets

Asindicatedintheprevioussection,thisstudyfocusesonthe driversoffarmstability andviabilityfromtheregionalcontext, notthoserelatedtoenvironmentalconditions.Therefore,mainly informationonthestructureofagriculturalholdingswasrequired. Pre-conditionsfor thesedatawere(i)availability fortheentire territoryoftheEuropeanUnionand (ii)comparability between MemberStates.WeusedthetwoEuropeandatasetsfarm accoun-tancynetwork(FADN)andfarmstatisticalsurvey(FSS)whichfulﬁll bothconditions.ThecollectionofFSSand FADNdatais harmo-nizedthroughouttheMemberStatesbutwithdifferences.FADN focusesoncollectingeconomicinformation.FSScollectseconomic information,butalsocollectsinformationonthedemographicand environmentalcharacteristicsoffarms.FADNdataare collected inannualsurveysfromasampleofagriculturalholdingsabovea minimumsizeinordertoinclude themostrelevantpartofthe agriculturalactivity(commercialfarms).Instead,FSSdataare col-lectedforalmostallfarms(oralmosttheentireutilizedagricultural area)duringagriculturalcensusesthat areperformedeveryten yearswithintermediatesamplesurveyseverytwoorthreeyears. ComparedtoFSS,FADNdataexcludesmallfarmsandasa conse-quencecoveronlyapercentageofallfarmsintheEU(39%in2007). TheFADNsampleofholdingsisrepresentativeforthesocalled FADNregionswhichcorrespondinmostofthecasestothe admin-istrativeNUTS2regions (NUTS,nomenclatureof territorialunits forstatistics).FSSdataarecollectedatindividualholdinglevelbut areavailableonlyataggregatedadministrativeunitlevel.FSSdata providethebasisforextrapolatingFADNdata.

Aninitialqualitycheck ofthe annuallyavailableFADNdata revealedambiguitiesinthe2009data.Asaconsequence,datafrom theperiod2006–2008wereselectedforthisstudyandtheaverage ofthoseyearswascalculated.ForindicatorsrequiringFSSdata,one indicatorwasonlyavailablefromthe2000census;otherindicators wereavailableintheFSSintermediatesurveys.

Table1showsforbothdatasetsthevariablesthatwereusedfor calculatingtheindicators(formoreinformationseethedatabase weblinksattheendofthepaper).

Otherstatisticaldatasetsusedarethenationalaccounts pro-videdbyDGEurostat(forgrossnationalproduct)andpopulation numbersfromtheinfra-regionalinformationsystem(SIRE).

Furthermore,Corinelandcoverfrom2006(CLC)wasusedto locateagriculturalareas.MissingdataforGreeceinCLC2006were complemented withtherespectiveCLC 2000data.Urban areas werealsoidentiﬁedfromCLCandcompletedbytheurban mor-phologicalzones(UMZ)andcitystatisticsfromtheurbanaudit.

Table1

VariablesfromtheFADNandFSSdatasetsusedtobuildtheindicatorsondriversof farmlandabandonment(UAAutilisedagriculturalarea).

Database(provider) Data/variableused(unit) Farmaccountancydata

network(FADN)

• Rentpaid,includingrentforbuildings,quotas, etc.(Euro)

(DGAgricultureand RuralDevelopment)

• RentedUAA(ha)

• Farmnetvalueaddedperannualworkingunit FNVA/AWU(Euro)

• Totalinvestmentbeforedeductionofsubsidies (Euro)

• TotalUAA(ha)

• Weightofthefarminthesample (non-dimensional)

Farmstructuresurvey (FSS)

• Holder’sbeinganaturalperson:age65years andover(numberofpersons)

(DGEurostat) • Agriculturaltrainingoffarmer(%offarmers withpracticalexperienceonly,basictraining,or fullagriculturaltraining)

• PercentageoffarmswithaUAAunder50%ofthe NUTS3holding’saverageUAAandbyfarmtype (%)(specialdatarequest)

• Areaofcertiﬁedorganicfarming(ha),totalUAA (ha)

FeaturesfromtheEuroRegionalMap(ERM)wereusedfortheroad network.

Referenceunitsfordataprocessingweretheadministrative(or statistical)unitsasdeﬁnedintheEuroBoundaryMap(EBM)version 2.2from2006.TheseunitsaretheNUTSwithNUTS0representing countries,NUTS2regionsandLAU2communes(local administra-tiveunits,formerlyNUTS5)(seereferenceattheend).

3.2. Deﬁnitionofindicatorsforindividualdrivers

Indicatorswerebuiltforeachdriverusingtheavailabledatasets. Table2showsthedriversandthedeﬁnedindicators.Italsoshows theireffectonfarmlandabandonment.

Eachindicatorwasassessedintermsofdataavailabilityand quality,anditsrelevanceforthedriver.Theevaluationfollowsthe approach adoptedfortheevaluationoftheactualusefulnessof individualIRENAagri-environmentalindicators(EEA,2005). Eval-uationcriteria,theirdescriptionandscoringschemeareshownin Table3.

Data were processedfor all EU-27 countriesat NUTS2level which represents the basic regions for the application of EU regionalpolicies,andwhichisthe‘ofﬁcial’reportinglevelfor agri-environmentalindicators.ForGermanyandtheUnitedKingdom datawereprocessedatNUTS1level,asinthesecountriesNUTS2 unitsaremuch smallerthan intheothercountries.For Cyprus, Estonia,Lithuania,Luxemburg,Latvia,MaltaandSloveniaNUTS2 areidenticaltoNUTS0(thereisnosubdivisionofcountriesinto smallerterritorialunits).

Formappingtheindicatorresults,valueswereclassiﬁedin quin-tiles(20%ofdataineachoftheﬁveclasses).

3.2.1. Weaklandmarket(D1)

Asindicatorsofweaklandmarket,aregionalweighted aver-ageoftherentalpriceforagriculturallandandtheshareofrented UAA(utilizedagriculturalarea)onthetotalUAAwerecalculated.

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Table2

Driversoffarmlandabandonment,indicatorsandeffects.Indicatorsarecalculatedatregionallevel(NUTS2).Negativeeffect:ifthevalueoftheindicatorislow,theriskof farmlandabandonmentishigh;positive:iftheindicatorvalueishigh,theriskishighaswell.

Driver Indicatorname Indicatorcalculation Effect

D1Weaklandmarket Weightedaverageoftherental

price

Rentalprice= farms(rentpaid×farmweight) farms(UAArented×farmweight)

Negative

ShareoftherentedUAA ShareofrentedUAA=

farms(UAArented×farmweight) farms(UAAtotal×farmweight)

Negative

D2Lowfarmincome Averagefarmincome Averagefarmincome

=

farms(farmnetvalueadded×farmweight) farms(farmweight)

/GDP Negative D3Lowfarm dynamism/adaptationcapacity

Averageleveloffarminvestment Farminvestment= farms(investment×farmweight)

farms(UAA×farmweight)

Negative

D4Ageingfarmerpopulation Shareofoldfarmers Proportionofoldfarmers=

numberfarmers65years numberoffarmerstotal

Positive D5Lowfarmerqualiﬁcation Shareoffarmerswithpractical

experienceonly

Shareoffarmerswithpracticalexperience= numberfarmerspracticalexperienceonly

numberfarmerstotaloftrainedfarmers

Negative D6Previoustrendoffarmland

abandonment

Lossofagriculturalland Lossofagricultural land=UAAt2−UAAt1

wheret1andt2aretwopointsin time

Positive

D7Remotenessandlow populationdensity

Shareofremoteagriculturalarea ShareofremoteUAA=UAAremoteareas UAAtotal whereremoteareareas(LAU2)are atmorethan60minfromanurban centreandwithlessthan50 inhabitants/km2

Positive

D8Smallfarmsize Shareofsmallfarms Shareofsmallfarmsfarmtype=

farmsUAA<50%farmtyperegionalaverage farmsfarmtype

Positive

D9Farmenrolmentinspeciﬁc schemes

Shareoffarmsunderorganic farmingscheme Shareoforganicfarming= UAAcertiﬁedorganicfarming UAAtotal Negative Table3

Criteriausedtoevaluateindicators(evaluationschemabasedonEEA,2005).Dataareambiguousincaseofinterpretationproblemsoriftheparameterdeﬁnitionchanged duringtheobservationperiod.

Criteria Description Scoring

Availabilityofdata SpatialresolutionisNUTS2orbetter(atbestmicro-data) 2=NUTS2ormoredetailedlevel 1=resolutionlowerthanNUTS2 0=absenceofdata

Qualityofdata Mainlydeﬁnedbythepresenceofmissingandambiguousdata 2=highquality(nomissingorambiguousdata) 1=mediumquality

0=lowquality(missingandambiguousdata) Relevanceoftheindicator Pertinenceoftheindicatorinmeasuringthedriver 2=appropriatelymeasuringthedriver

1=measuresbutwithexceptions 0=doesnotmeasurethedriver

Therationalebehindthisisthathighrentalpricesaregenerally linkedtoahighdemandforagriculturallandandhencealowrisk ofabandonment.

3.2.2. Lowfarmincome(D2)

Theweightedaveragefarmincome normalized bythegross domesticproduct(GDPpercapita)wasdeﬁnedasanindicatorfor thelowfarmincomedriver.Incomewasestimatedfromthe‘farm netvalueadded’(FNVA)variablein FADN,whichmeasuresthe amountsavailableforremunerationofﬁxedfactorsofproduction (labor,landandcapital),expressedperannualworkingunit.Thus, itrepresentstheeconomicperformanceofthefarmfromwhich wages,rentsandinterestsstillneedtobepaidandownlaborand capitalneedtoberemunerated.FNVAiscomparedtothenational GDPpercapitaatmarketprices thatisaproxyforthenational income.ItisassumedthatwhendifferencesbetweenFNVAand nationalGDParelarge,theagriculturalactivitymaynotbe eco-nomicallysustainableanymore.Asaconsequence,peoplemight leavethefarmingsectorforpossibleopportunitiesinother sec-tors.BychoosingthenationalGDPratherthantheEuropean,itwas assumedthatfarmersmayquitagriculturetomovepreferablyto adifferentsectorintheirowncountryratherthanemigratingto anothercountry.

3.2.3. Lowfarmdynamism/adaptationcapacity(D3)

Asanindicatorforthelowfarmdynamismdriver,theFADN variable‘totalinvestmentsbeforedeductionofsubsidies’wasused. Itcoversinvestmentsforagriculturalland,building,rights,forest, machineryandcirculatingcapital.Giventhestructuraldifferences that occur between different farming systems, the amount of investmentperfarmwasnormalizedbythephysicalsizeofthe farm(UAA),assmallfarmswilloftenhavelowerinvestments(in absoluteterms)thanlargefarms.Thisindicatorassumesthatnew investmentsareasignalofamedium/longtermstrategyandcanbe aproxyofthewillingnesstocontinuefarmingactivityandadjust tonewavailabletechniques.

3.2.4. Ageingfarmerpopulation(D4)

Asanindicatorforanageingfarmerpopulation,theshareof farm holdersabove 65 years old,which is theaverageage for retirementinEurope,wascalculatedassumingthatfarmland aban-donmentismorelikelytooccurwhenthefarmerisoldandcloseto retirement.Itfurthermoreassumesthatevenifthefarmer contin-uesafterthisage,activitywillprobablydecline.Informationrelated tosuccessorswasnotusedasthisinformationwasnotavailablein theFSSdatabase.

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Fig.1. Maindriversoffarmlandabandonmentandinter-linkages.

3.2.5. Lowfarmerqualiﬁcation(D5)

Fortheevaluationofthefarmer’squaliﬁcationtheshareof farm-erswith‘practicalexperienceonly’onthetotalnumberoffarmers thatparticipatedintrainingwasused.Theassumptionhereisthat trainingandtheuseofadvisoryservicesareaproxyforthe profes-sionalismofthefarm,andwillingnesstoinvestintermsofhuman capitalandknowledgewithasufﬁcienttimehorizon,whichwill preventfarmlandabandonment.

3.2.6. Previoustrendoffarmlandabandonment(D6)

Awaytoassessthetrendoffarmlandabandonmentisto com-putethelossofagriculturalland(UAA)betweentwopointsintime estimatedfromatimeseries,assumingthatthelandwasnot trans-formedintoforestsorartiﬁcialareas(Pointereauetal.,2008).This non-utilizedagriculturallandisnolongerfarmedforeconomic, socialorotherreasons,andisnotincludedinthecroprotation system.Thecalculationneedstobedoneatlocallevel(LAU2)in ordertoavoidcompensationeffectsthatoccurwhencalculations aremadeinlargeradministrativeunits,especiallywhen urbaniza-tionorafforestationratesareimportant.Thereforethecalculation ofthisdriverrequiresusingUAAdatafromFSSatLAU2levelfrom atleasttwocensuses.Duetoconﬁdentialityrestrictions,FSSdata atLAU2levelwerenotaccessibleandthustheindicatorwasnot calculatedFig.1.

3.2.7. Remotenessandlowpopulationdensity(D7)

Thisdriverwasevaluatedthroughtheshareofagriculturalarea inremoteandlowpopulationdensityareas.Thegeneral assump-tionhereisthattheriskoffarmlandabandonmentishigherinareas withlowpopulationdensityandwhichareremotefromtheclosest urbancentre(accesstoservices).Forthecalculation,amethodology similartotheOECDregionaltypologymethodology(OECD,2011) wasappliedwhichinvolvesacombinationofgeographicdatasets. Fig.2depictstheworkﬂowusedtocalculatethisdriver.

Themethodrequirestheelaborationoftwodatalayers:(i)low populationdensityareasand(ii)remoteareas(LAU2).Thosetwo layersarethenusedtoquantifyatNUTS2leveltheagriculturalarea inscarcelypopulatedandremoteareas.Scarcelypopulatedareas areidentiﬁedfrompopulationstatisticsandweredeﬁnedasareas withapopulationdensitybelow50inhabitants/km2_._This_threshold

issetmuchlowerthanthethresholdusedbytheOECD(2011)for

classifyingruralareastotargetruralcommuneswithverylow pop-ulationdensity.Theremotenessofanareawasidentifiedthrough thedistancebetweenthecentroidofeachLAU2unit(origin)andthe closesturbancentre(destination).FollowingthemethodinDijkstra andPoelman(2008)urbancentresaredefinedbyapopulationof morethan50,000inhabitants.Thedistancewasidentifiedthrough theroadtraveltimebetweenoriginanddestination,and calcu-latedthroughroadspecificspeedlimits.Impedanceoftravelspeed incities(congestion)andfromrelief(slope)isincludedinthe cal-culationthroughtwoindicesdevelopedbyDijkstraandPoelman (2008).Finally,remoteLAU2unitswereidentifiedasthosewhere thetraveltimetotheclosesturbancentreismorethan60min.

Asalaststep,theshareofagriculturalarea(fromCorineland cover)inscarcelypopulatedandremoteareaswascalculatedat regionallevel(relativetotheagriculturalareainNUTS2).

3.2.8. Smallfarmsize(D8)

Theindicatorforthesmallfarmsizedrivercalculatesforeach farmtypetheshareofsmallfarms.Afarmisconsideredassmall whenitsphysicalsize(haUAA)isbelowhalftheregionalaverage (NUTS3).Distinctionbetweenfarmtypesisnecessaryas special-ization can influence structure (e.g., physical size). They range fromspecialistfieldcrops,horticulture,permanentcrops, special-istgrazing livestockandgranivoretomixed cropping,livestock and crops-livestock.The indicatorisbased onthepositive rela-tionshipbetweenfarmsizeandfarmviabilityineconomicterms (competitiveness).Ingeneral,largerfarmscanbenefitfromlower productioncosts,aremoresuitableformostofthecompetitive agri-culturalpractices(useofmachineryorabetterefficiencyinthe useofinputs),theyaremorefrequentlyrelatedtoinnovationand usuallymorecompetitiveandviableineconomicterms.Moreover, fragmentationhasagenerallynegativeeffectonfarmoutputand, putsimply,themorefragmentedafarmis,themoresignificantthe negativeimpactonit’seconomicresults.

3.2.9. Farmenrolmentinspeciﬁcschemes(D9)

Welookatenrolmentinspeciﬁcagriculturalschemesandin particularinagri-environmentmeasures(AEM)asaproxyforthis driver.AEMsaredesignedtoencouragefarmerstoundertake envi-ronmentalcommitments.Farmerscommitforatleastﬁveyears tomanagetheirland accordingly,thereforekeepingthelandin

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Fig.2.Workﬂowforthecalculationoftheshareofagriculturalareainremoteandlowpopulationdensityareas.

production.Theenrolmentinorganicfarmingsystemsistheonly measureforwhichdataissystematicallycollectedbyFSS,i.e.,itis theonlymeasureforwhicharegionalbreakdownatNUTS2level isavailable.Theindicatorfor thisdrivercomputestheshare of agriculturalareaundertheorganicfarmingscheme.

3.3. Compositeindicator

Apossiblewaytogatherinformationfromallrelevantdrivers intoaﬁnalindicatorofriskoffarmlandabandonmentistointegrate theindicatorsintoacompositeindicator.Thiswasdonethrough anempiricalframeworkfollowingamethodologyproposedbythe OECD(2008).

Normalization was appliedprior to dataaggregation as the datasetsusedtobuildtheindicatorsareindifferentmeasurement unitsandhavedifferentrangesofvariation.Anormalization pro-cesswasappliedtohavearangeofvaluesbetweenzeroandone bysubtractingfromeachobservationtheglobalminimumandby dividingtheresultbythedatarange ofthewholedataset.The min–maxnormalizationwas performedat two geographic lev-els:(1)atEU-27leveland(2)atthelevelofindividualMember States.Theﬁrstlevelisanattempttoelaborateariskcomposite indicatorcoveringallMemberStatestogether.Thesecondattempt buildsariskcomposite indicatorforeach country.Inthis latter case,theassumptionismadethatonecannotcompare,inabsolute value,economicresultsfromMemberStateshavingheterogeneous economicandstructuraldevelopmentsintheiragriculturalsector. Otherwise,manyregionscouldbeidentiﬁedasbeingatriskinthe newMemberStatesandveryfewforwesternEuropeanregions. Dependingonthecorrelationbetweentheindicatorandrisk of farmlandabandonment,eachvalueXN

qc oftheqindicatorsfora

NUTS(N)regionofthecountrycistransformedinto: Eq.(1)IN qc=

X_qc−minN

_X_qcN

max

XN qc

−min

XN qc

ifthecorrelationispositive or Eq.(2)IN qc=1−

XN_qc−min

_XN qc

maxXN_qc−minXN qc

ifthecorrelationis negative-whereXN

qcare(i)theminimumandmaximumvalueofXqcNforEU-27

countriestogether;(ii)theminimumandmaximumvalueofXN qc

acrossallNUTSregionsincountryc,respectively.

Inthethirdandlaststep,thecompositeindicator(CI)was estab-lishedbysummingthenormalizedindicators(linearaggregation) andassigningequalweightstoallindicators:

CIN_C=

Q

q=1

WqINqc

with the sum of weights of each indicator qwq=1 and

0≤wq≤1,foreachindicatorq=1,....,Q.CINC hasarange between

zero(minimumrisk)andone(maximumrisk)[0,1]. 4. Resultsanddiscussion

In this section,the indicatorsof thefarmland abandonment driversarediscussed,and,inparticular,theirspatialvariationas wellastheavailabilityandqualityofthedatausedfortheir calcu-lation,aswellastheirrelevance.Table4summarizestheresultsof theevaluation.

4.1. Individualdrivers 4.1.1. Weaklandmarket(D1)

ThemainresultforthisdriverispresentedinFig.3(left).Eastern Europeancountries,theBalticStatesandregionsinSwedenhave verylowrentalprices.Toalowerextent,reducedamountsofpaid rentalsooccurinsomeregionsofSpain,France,Italy,theUnited Kingdom,BulgariaandinRomania.Theindicatorexcludesfarms wheretherentpaidortherentedUAAareequaltozero.Theﬁrst caseoccurswhenthelandisavailableforfree,e.g.,afamily mem-berisownerofthelandandlendsitforfree.Positiverentsforzero rentedUAAcanbeexplainedbyrentingbuildingsormilkquotas. Theseobservationsaffectthequalityofthedata.Excludingthese casesavoidsbiasingtheindicatorandensuresthatonlythereal marketofrentedlandisconsidered.InFig.3(right)theshareof rentedUAAwasmappedtoshowregionswherethismight inﬂu-encetheresults.Particularly,inregionswhererentalpricesarelow andwheretheshareofrentedlandishigh,theriskoffarmland

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Table4

Resultsoftheindicatorevaluation:individualscoresforthethreecriteriaandﬁnalscore(sum).

Driver Availability Quality Relevance Sumofscores

D1Weaklandmarket 2 1 1 4

D2Lowfarmincome 2 2 2 6

D3Lowfarmdynamism/adaptationcapacity 2 0 1 3

D4Ageingfarmerpopulation 2 1 1 4

D5Lowfarmerqualiﬁcation 2 1 0 3

D6Previoustrendoffarmlandabandonment 0 - - 0

D7Remotenessandlowpopulationdensity 2 2 2 6

D8Smallfarmsize 2 0 0 2

D9Farmenrolmentinspeciﬁcschemes 2 2 0 4

Fig.3. Rent(euro/ha)paidbyholding(left)andshareofrentedlandonthetotalagriculturalarea(right)(bothinquintiles;source:FADNdata2006–2008).Nodataforthe regionofAland(Finland)andthecitiesofVienna,Brussels,Bremen,BerlinandLondon.

abandonmentwillbehighbecauseintheseregionsthereiseither alowdemandforagriculturallandorregulationsand/or institu-tionsarehamperingthelandmarket(e.g.,bysettingamaximum rentalprice).Therelevanceoftheindicatorisaffectedbythebias inthedataforregionswherethelandtenuremodelismainly own-ership(inItaly,forinstance),otherconditionsbeingconstant.In thecaseoflandownershiptheriskofabandonmentisaccordingly lowerthanwhenlandisrented.Basedontheseobservations,the gooddataavailability(NUTS2)andtheirmediumquality,amedium overallscorewasassignedtothisdriver.

4.1.2. Lowfarmincome(D2)

Fig.4showsregionswithlowincomeinIreland,Portugal, south-ernFrance,centraland southernItaly,Slovenia,mountainareas inwesternAustria,centralandsouthernGreece,Cyprus,western Bulgaria,eastern Romania, central Slovakia, central/easternand southernPoland,andsomeareasinnorthernSwedenandeastern Finland.Forsomecountriesthedatavariabilityishigh(coefﬁcient ofvariation>2.0)suchasforBulgaria,Cyprus,Hungary,Ireland, Romania, Slovenia and the UnitedKingdom. The lowest values (<0.9)aremeasuredforAustria,BelgiumandGermany.

DifferentsituationsintheMemberStatesmayhavedifferent underpinningexplanations.Theeconomicandstructuralfarming contextisstill veryheterogeneousbetweenMemberStates and

applyingauniqueEuropeanthresholdvaluefortheincomemakes littlesense.AgriculturalincomeisstilldisparatethroughoutEurope where,e.g.,alowvalueinTheNetherlandscouldbehighin Bul-garia. There is indeed a ratio of 1–15amongst EU-27 Member Statesbetweenthesmallerandlargeraveragenationalagricultural incomes.

Theindicatorisbasedonfarmincomeonly,while usingthe totalhouseholdincome(tourism,externalincomefrompart-time work,externalincomeofthepartner,etc.)maychangethepicture. Forexample,thesituationinthewesternAustrianmountainswith lowagriculturalincomemaybeovercomebyexternaladditional incomefromfarmtourismandasaconsequencereducestherisk. However,thisinformationisnotavailableintheFADNdatabase. FSSprovidesqualitativeinformationonotheractivitiesbutnotas ameasurethatcouldbelinkedtothequantitativeincomeofthe FADNdata.Ingeneral,theconceptoftotalincomeofagricultural householdsisrelevantbutappropriatedataarenotavailable(Hill, 2008)andtheEU’sefforttocollectthisdatawasabandonedin2002. Moreover,thefarmincomeprovidesinformationonthereal perfor-manceoftheagriculturalactivitiesofthefarm,whilethehousehold incomecouldincluderevenuefromothersectors(e.g.,partnernot workinginagriculture).

Farmincomewasfurtheranalyzedwiththedistributionof farm-types(asshareofaffectedfarms)forNUTS2withthelowestvalue

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Fig.4.Ratioagriculturalincome/nationalGDP(quintiles)(sources:FADNdataand nationalaccounts2006–2008).NodatafortheregionofAland(Finland)andthe citiesofVienna,Brussels,Bremen,BerlinandLondon.

Table5

PresenceoffarmtypesinlowincomeNUTS2(NUTS2withratio‘incomeAWU/GDP percap’<0.59).

Farmtype Nb(%) Area(%)

Specialistﬁeldcrops 12 6.00

Specialisthorticulture 3 2.64

Specialistpermanentcrops 23 35.52

Specialistgrazinglivestock 20 31.68

Specialistgranivore 2 2.30

Mixedcropping 14 3.41

Mixedlivestock 11 3.72

Mixedcrops-livestock 15 14.74

(takingonly the ﬁrst quintile,where the indicator<0.59). This thresholdisindeedclosetotheEUrelativepovertyindexwhichis 0.6(i.e.,peoplefallingbelow60%ofmedianhouseholdincomeare saidtobeat-risk-ofpoverty).Table5showsthat23%ofthe hold-ingsare‘specialistpermanentcrops’typefollowedby‘specialist grazinglivestock’type(20%).Farmsoftheﬁrsttypealsoholdthe biggestshareinUAA.Mostofthesefarmsarelocatedinsouthern Europe(Spain,Greece,Italy,Portugal,andCyprus),inregionswhere olivegrovesaremostfrequent.Theothermostfrequenttypeof farm(‘specialistgrazinglivestock’)ismostlypresentinIrelandand incentralEurope(Austria,Slovakia,andSlovenia).Theseresults bringforwardthepotentialriskoffarmland abandonmentfrom farm-typesusinglargesharesofland(livestockgrazingsystem), ofteninanextensivemanner.Thisisalsothecaseforextensively managedolivegrovesinMediterraneancountries.

Dataavailabilityandquality,andtherelevanceoftheindicator arehigh,leadingtoanoverallhighscoreforthedriver.

4.1.3. Lowfarmdynamism/adaptationcapacity(D3)

TheamountofinvestmentperholdingisshowninFig.5.Regions withlowerinvestmentratiosarefoundin Spain,in centraland southernItaly, in mostof Greece,in Romania, inseveralCzech regionsandinwesternPoland.Inthiscontext,itisnoticeablethat

Fig.5.Averagelevelofinvestmentperholding(normalizedbyphysicalsize) (quin-tiles,source:FADN2006–2008).NodatafortheregionofAland(Finland)andthe citiesofVienna,Brussels,Bremen,BerlinandLondon.

thereareregionsinSpain,Italy,Greece,CyprusandRomaniawhere morethan60%oftheholdingshavemadezeroinvestmentduring 2006–2008.

Theeffectofnormalizingtheinvestmentperholdingbyits eco-nomicsizewastested,providing theamountofinvestmentper euroofeconomicsize(notshownhere).Forsomecountriessuch astheBalticstates,thisratioisveryhighforwhichtherearetwo possiblereasons:(i)somefarmsmayusecommonland,therefore appearingsmallerinphysicalandeconomicsizethanwhatthey areinreality;(ii)somerelativelysmallfarmsmaydevelop con-tractworkforotherfarmers,thenbuyingmoreequipmentthan theywouldneedfortheirownfarm.Despitethesepossible rea-sons,datasuggestthatinvestmentsintheBalticstateswerehigh duringtheobservationperiod.AcomparisonwithEurostatdataon ‘grossfixedcapitalformation’showsthatLatviaandLithuania(no dataforEstonia)haveindeedmadeimportantinvestmentsinthe agriculturalsectorinthelasttenyears.However,whennormalized byeconomicsize,theindicatorshowsahighvariabilityatregional level(e.g.,thecoefficientofvariationis15forCalabria,Italy,and 11forWesternGreece),leadingtoalowerconfidenceinthe repre-sentativenessoftheweightedregionalaverage.Consequently,only theratioofinvestmentdividedbythefarmphysicalsizewaskept. Quality issues related to ambiguous data were investigated further.Indeed,reportingdifficultiesintheFADNdatabasewere confirmedfortheinvestmentparameterforsome(Mediterranean) countries(DGAgricultureandRuralDevelopmentpersonal com-munication).Afterconsultation,MemberStatesatstakeexplained thatthefinancingofinvestmentcomesfromfamilyloans.Many farmersconsiderthoseasprivateanddonotreportthemintheir farmaccounts.Consequentlydebtsandinvestmentsaremissing forthesefarms.Moreover,someItalianexpertssuggested defini-tionissuesforthisFADNparameterduringtheobservationperiod intheircountry,acknowledgingpossibledatadeficiencies.Despite gooddataavailabilitythequalityofthedataislow,asisthe

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rel-Fig.6. Shareoffarmholdersagedmorethan65years(quintiles,source:FSS2007 data,nodataforthecityofBrussels).

evanceoftheindicator,resultingin alow overallscorefor this driver.

4.1.4. Ageingfarmerpopulation(D4)

The share of farm holders older than 65 years is very high (>39.8%)inPortugal,mostofItaly,southernGreece,Bulgaria, Roma-niaandLithuania(Fig.6).ItisalsohighinEngland,Wales,Northern Ireland,theremainingregionsofItalyandGreece,Estonia,Slovakia, SloveniaandSpain.

Thecalculationofthisindicatorraisedconcernsforbiasesdue todatainterpretationas,forsomecountries,resultsmightbe inﬂu-encedbyspeciﬁcinstitutionalsituationssuchaspensionscheme, taxsystem,farmtransmissionscheme,etc.ratherthanthereal sit-uationofactualfarmholders.OneexampleistheUnitedKingdom wherefarmowners(older)andfarmmanagers(younger)areoften notthesameperson.Thisambiguityreducesthedataquality,and despitethegooddataavailability,onlyamediumscoreisassigned toitsrelevance.Theoverallscoreforthisdriverismedium.

4.1.5. Lowfarmerqualiﬁcation(D5)

Mediterranean(Portugal,Spain,mostofItaly,Greece,Cyprus) andsoutheasternEuropeancountries(Slovakia,Hungary, Roma-niaandBulgaria)havemorethan80%offarmerswith‘practical experienceonly’,meaningthatveryfewofthem havefollowed agriculturaleducationcourses.To a lowerextent(60–80%),the sametraininglevelalsodominatesintheUnitedKingdom,Ireland andintheBalticandScandinaviancountries.

A high score was assigned to the data availability for this indicator(NUTS2 data).However, therather weakdeﬁnitionof qualiﬁcationlevelsintheFSSquestionnairenegativelyaffectsthe dataquality(farmers’response)forthisdriver.Asaconsequence ofthelowquality,alowscorewasassignedfortherelevanceofthe driver.Overall,thescoreforthisdriverislow.

4.1.6. Remotenessandlowpopulationdensity(D7)

Thecombinationoflowpopulationdensityandremoteness cri-teria(Fig.7)identiﬁesremoteandscarcelypopulatedareasinSpain andPortugal,southernFrance,Italy,Greece,mountainousregions ofRomaniaandAustria.Additionalareasarelocatedinnorthern Poland,intheBalticandNordicStates,inWalesandScotlandand inIreland.Regionswithahighershareofagriculturallandinremote andscarcelypopulatedareasoccurinPortugal,Spain,south-west FranceandCorsica,Tuscany,MoliseandSardiniainItaly,mostof Greece,theBalticStates,ScotlandandWales,andIreland.Inthese regions,morethan19%oftheagriculturallandisinlowpopulated areasandremotefrommainurbancentres.

Itmustbeunderlinedthattheremightbedifferencesbetween ofﬁcialagriculturalstatisticsandtheCLCdatathatwasusedto esti-matetheagriculturalareasaffectedbythisdriver.However,CLC istheonlyEuropean-wide agriculturallandinformation system accessibleforthecalculationofthisvariableatLAU2level.

TheavailabilityandqualityofdataatLAU2levelforallMember Statesisverygood,exceptforBulgariawherenodetailsfortheroad typeswereavailable.Ahighlevelofrelevanceisassignedtothis indicator.Asaconsequence,theoverallscoreforthedriverishigh. 4.1.7. Smallfarmsize(D8)

Computingthesmallfarmsizeindicatorrequiresfirsta consid-erationoffarm-types(as,e.g.,horticulturefarmswillde-factobe smallerthangrazinglivestockfarms),andthiscanhighly compli-catethecalculationofthisindicator.Consequently,theanalysisof farmsizetargetsonlythetwofarm-types‘specialistgrazing live-stock’ and‘specialist permanent crops’since theywerealready identifiedunderthedriver‘lowincome’asfarmtypeswithahigh riskoffarmlandabandonment.However,twoaspectsraise con-ceptualissuesinthedefinitionofthisdriver,and,consequently, suggestalowrelevanceofthisindicator.First,someareasmight beidentifiedbecausethefarmtypeinquestionisrare(compared tootherfarmtypespresentinthesurroundings)inaspecificarea andwithasizebelowtheaverageregionalfarmtypesize.Indeed, themapfor‘grazinglivestock’identifiesforexamplesome inten-sivecerealgrowingareasinFranceandintheUnitedKingdom, onlybecausetherearefew(small)grazinglivestockfarmsinthese regions.However,theseareasarenotatriskoflandabandonment. Second,forcountriessuchasSlovakia,HungaryandRomania,the distributionoffarmsizemayshowco-existenceoffewverylarge farmsandmanysmallfarmsresultingina largeshareof farms belowtheregionalaverage.

Whilethedataavailabilityisrankedhigh,dataqualityand rel-evanceoftheindicatorarelow.Accordingly,theoverallscorefor thisdriverislow.

4.1.8. Farmenrolmentinspeciﬁcschemes(D9)

Formostoftheregions,theshareoforganicfarmingisrather low(below10%oftheUAA).OnlyincountriessuchasItaly,Austria andtheCzechRepublic,arethereregionsthathaveasharehigher than10%.Forthisreasonitisdifﬁculttoraiserobustconclusions ontheentireagriculturalarea.Giventhislimitationtherelevance ofthisdriverislowandaccordinglyitwasnotfurtherused. 4.2. Compositeindicator

Thefinalevaluationofeachdriverwithrespecttoavailability, qualityofdata,andinparticulartotherelevanceoftheindicator wasdecisivefortheselection ofdriverstobeusedtobuildthe compositeindicator.Asaresultofthisfinalevaluation(Table4), theindicatorswereclassifiedinthreeclasses:

1.TheindicatorsforD1(weaklandmarket),D2(lowfarmincome), D7(remotenessandlowpopulationdensity)areconsidered

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rel-Fig.7. TraveltimetomajorurbancentrescombinedwithpopulationdensityatLAU2level(left)andshareofagriculturalareainremoteandlowpopulationdensityLAU2 regions(right,quintiles;datasources:agriculturalandurbanareasfromCLC2006,UMZ,UA;populationfromSIRE;roadnetworkfromERM).

evant,datafortheircalculationareavailableandareofgood quality.

2.TheindicatorsforD3(lowfarmdynamism/adaptationcapacity), D4(ageingfarmerpopulation)areconsideredaslessrelevant. Dataforcalculatingtheseindicatorsareavailablebutareoflower qualitythanthepreviousgroup.

3.ForthedefinitionofindicatorsforD5(lowfarmerqualification), D6(previoustrendoffarmlandabandonment),D8(smallfarm size),D9(farmenrolmentinspecificschemes)therewereno dataavailable,orthequalitywaslow,ortheindicatorwasnot consideredrelevantforthedriver.

Onlythe indicators from the ﬁrst two classes were accord-inglyincludedinthecalculationofthecompositeindicator.They werecalculatedforeachNUTS2ineachcountryforthetwo geo-graphicnormalizationlevels.Theindicatorswereincludedinthe compositeindicatorwithequalweights.Thepossibilitytoassign individualweightswasrejectedafteraprincipalcomponents anal-ysis(PCA)revealedweakcorrelationamongtheindicators(through theKaiser–Meyer–Olkintest).

Figs.8and9displayarankingofNUTS2regionsfromlower (yel-low)tohigher(darkbrown)riskoffarmlandabandonment.Note thatnoresultsareprovidedforBulgariabecauseofmissingdata fortheindicatoronagriculturalareainremoteandlowpopulation densityareas.Regionswithhigherriskoffarmlandabandonment are foundin Portugal, Spain (south, Asturias,Pais Vasco),Italy (Centreand SouthItaly),partof Peloponnese/Macedoniaregion (Greece),Slovenia,Romania,allthreeBalticStates,Finland, north-ernSweden,andinConnachtandDonegalinIreland.

ItmaybesurprisingtoseeTuscanyasaregionwithahigher riskoffarmlandabandonmentinItaly.Datascreeningforthethree

economicdrivers(D1,D2andD3)showsthatvaluesforTuscany arelowerthanforsouthernItalianregions.Additionally,the val-uesfortheindicatorsonthefarmers’ageandremotenessofUAA arerelativelyhigh,identifyingconsequentlyTuscanyasbeingat higherriskoffarmlandabandonment.Theunexpectedlowvalue fortheD2indicatormightbeduetothepresenceofothersources ofincomeoutsideagriculture(e.g.,diversiﬁcationactivitiessuchas farmtourism)whicharecurrentlynotincludedinthefarmincome butwhichshouldbeconsideredinfuturemodiﬁcationsofthedata collectionschemes.

Furtheranalysiswascarriedouttoidentifyfarmtypeswithin NUTS2regionsﬂaggedwithahigherrisk(riskindex>0.72).The mostfrequent farm-types are‘specialist grazing livestock’ with around30%,followedby‘specialistﬁeldcrops’(23%)and‘specialist permanentcrops’(20%).Theseresultsshowthattheriskof farm-landabandonmentispotentiallyhigherforfarm-typesusinglarge sharesof land(livestock grazing system),oftenin anextensive manner.Thisisalsothecaseforextensivelymanagedolivegrovesin Mediterraneancountries.Thelikelyimpactsoffarmland abandon-mentfromfarmswithpermanentgrazinglivestockandpermanent cropsmaybenegativeformaintainingthelandscapeandfor bio-diversitythat dependonextensivelymanagedagriculturalland (typicallyoccurringinsemi-naturalgrasslandand/orhighnature valuefarmland)(PlieningerandBieling,2013).Infact,land aban-donmentaffectsextensivelymanagedfarmlandmuchmorethan intensively managedfarmland (Cocca etal., 2012).Abandoning this land maybe negativefor maintaining theactual biodiver-sitybecausevegetationsuccessionleadstospecies-poorandmore homogeneousvegetation types. Other environmentaleffects of abandonmentincludethelossofsmallscalemosaicsoflanduseand theircharacteristicspecies(Siramietal.,2008).Furthermore,itmay

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Fig.8.CompositeindicatoroftheriskoffarmlandabandonmentforEU-27 (nor-malizationatEU-27 level) calculatedfrom driversD1weak land market,D2 low farmincome, D7 remoteness and low population density, D3low farm dynamism/adaptationcapacityandD4ageingfarmerpopulation.Regionsare clas-sifiedinquintileswiththefirstfourquintilessummarisedinthefirstclass(<0.71, 80%oftheobservations)andthelastquintilesubdividedintwoclasses(80–90%risk indexbetween0.72and0.80,and90–100%index>0.81).Nodatafortheregionof Aland(Finland),allregionsinBulgariaandthecitiesofVienna,Brussels,Bremen, BerlinandLondon.

reducegeneticdiversityofbothwildspeciesandoflocalbreedsof livestockorvarietiesofcrops(whichareoftenwelladaptedtothe semi-naturalhabitats),andincreaseﬁreriskinareaswheregrazing areasactasﬁrebreaks(Pecoetal.,2012).

Fig.9showsexamplesofthenormalizationatMemberStates levelforSpain,FranceandPoland.NUTS2regionswereclassified usingfiveclasses,startingfromacompositeindicatorvalueof0.5 (medium)uptoavalueof1(veryhighrisk).Theresultsforthese threecountriesshowindeedthatwhenonlythenationaleconomic conditionsareconsideredintheriskindicatorcalculation,the dif-ference betweentheregions ismore pronounced.While in the EU-27calculationnoregioninFrancewasidentifiedasbeingathigh risk,thecalculationatnationallevelshowsasouth–northgradient withallsouthernregionsathigherrisk.InSpainthecenter-west withExtremadura,Castilla-LaManchaandMadrid,Asturiasinthe northaswellastheBalearicIslandsareathigherrisk(>0.7).In Poland,contrarytotheEU-27calculationstworegionsinthenorth (Warmia–Marusia)andsouth(Podkarpackie)areathighrisk.

4.3. Dataissues

4.3.1. Validationoftheobservedrisk

Giventherathertheoreticalprocessof buildingthe compos-iteindex,itiscriticaltoverifytheresultsandtoconfrontthem withotherindependentestimates.ThelackofEuropeandatasets measuringactualfarmlandabandonmentpreventsvalidatingthe resultsandbenchmarkingtheproposedmethodology.Such mea-surements,ifavailable,couldalsobeusedtoestablishtheweighting systemforthedriversincludedinthecompositeindicator.

Nevertheless,severalalternativesforcross-checkingtheresults canbementioned. First, theuseof agriculturaleconomic mod-elswhichmayprovideinformationatregionallevel(NUTS2)on agriculturalactivitynotperformingwell(ineconomicterms), bear-inginmind,though,thatsomeoftheassumptionsusedinthese models(e.g.,thelandsupplyfunction)arethemselvesnoteasily validated.However,theeconomyisnottheonlydriverthatneeds

Fig.9. CompositeindicatoroftheriskoffarmlandabandonmentforSpain,FranceandPolandcalculatedfromdriversD1weaklandmarket,D2lowfarmincome,D7 remotenessandlowpopulationdensity,D3lowfarmdynamism/adaptationcapacityandD4ageingfarmerpopulation(normalizationatMemberStateslevel).

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tobetakenintoaccountinthecaseoffarmlandabandonment.A literaturereviewhasalsoidentiﬁedbio-physical,sociologicaland behaviouralreasonswhicharenotyetintegratedintheavailable agricultural-economicmodelsinEurope.Second,dataontrends ofUAAatLAU2levelfortwosuccessivecensusescouldbeused (2000 and2010, for example).Thiswould require(i) accessto thesedatasetsatLAU2level,and(ii)auxiliarydataontherateof urbanizationandafforestationatthesamelocallevels.

Athirdalternativearisesfromstudiesonthedeterminantsof farmlandabandonment availableatregional orlocal level(e.g., Langeetal.,2013;Mülleretal.,2009).However,usingthese stud-iesforvalidatingtheriskidentifiedhereinisnotstraightforward becauseofdifferencesinthedatasourcesandspatialresolutions.In moststudieslandusechangeisusedtoquantifyfarmland abandon-ment(thatmaydiffertothedefinitionadoptedherewhichrefersto acessationofmanagementwithnegativeconsequencesfor ecosys-temservicesandbiodiversity).Tosomeextentthesestudiescover thedeterminantsanalyzedinthisstudy(e.g.,populationdensity, farmfragmentation, distancetoroads, farm diversification) but inregressionmodelsdriversfromtheenvironmental (biophysi-cal)context(elevationandsoils)dominateoversocio-economic drivers.Asdeterminantsoffarmlandabandonmentareidentified foronesingleregion(NUTS2orNUTS3),theresultingmodeland itspredictorsaretunedtothisregionandintheEuropean con-textthepowerofpredictorsmightbedifferent(Corbelle-Ricoand Crecente-Maseda,2014).

4.3.2. Datalimitations

Thescientificliteraturewidelyagreesthatfarmland abandon-mentisalocal-specificphenomenon,requiringavailabilityoflocal datatoestimateitsrisk.Corbelle-Ricoetal.(2012)suggested work-ingatleastatmunicipalitylevel(LAU2).Inrecentlypublishedwork theyanalysedtherelationshipbetweenthedriversdefinedhere andchangesinagriculturalareaforoneSpanishregion(Galicia) withdataatLAU2level.Theirresultsconfirmthattherisk indica-tordefinedhereismeaningful,buttheyalsorevealeddifferences intheimportanceofthedriversthattheypartiallyattributetothe differencesinthescaleofapplication(oneregionvs.EU-27).

ForanassessmentatEuropeanlevel,thelackofaccessibilityto localdataisclearlyanissue,especiallywhentheunderlying vari-abilityoftheaggregatedmicro-data(FSS)isnotknown.Moreover, forbothFADNandFSSdatasets,theresolutionofinputdata avail-ableinEuropeandatabasesvariesbetweenMemberStates,ranging fromNUTS3inthebestcasetoNUTS0intheworst.This hetero-geneityofinputdataisasourceofdifﬁcultyandinaccuracyinthe calculationandaggregationprocessesaimingtobuildcomparable andhomogeneouspan-Europeandriversoffarmlandabandonment atNUTS2level.

NUTS2regionsoften holddiverseagro-economicconditions, which need not inﬂuence farmland abandonment uniformly in thewholearea.Analyzingdataatcoarselevel(NUTS2)may over-lookthe actual risk of land abandonment occurringin smaller sub-regions.Thismighthappenwhenthevariabilityof individ-ualdriversishighwithanaveragevaluemaskingverydifferent situations.Consequently,onepossibleimprovementoftheresults wouldrequiretheavailabilityofstatisticalandsocio-economicdata atmuchﬁnerspatialresolution(i.e.,LAU2).

Infuturework,thefollowingaspectsshouldbeconsidered: • Forthedriveronweaklandmarket,theFADNparameteronthe

paidrentdoesnotreferonlytolandbutincludesallrents (build-ings,quotas,etc).Itthereforegoesbeyondtherentalpricefor solelytheland.However,itwastheonlyavailablevariablefor theobservationperiod.Inthefutureitwouldbepreferabletouse informationinwhichonlythelandisconsidered.Thisvariableis collectedfrom2009onwards.Asecondpossibleimprovementof

thisdriverwouldbetheuseoflandpriceinsellingtransactions asthiswouldgivearelevantbasetoassessthelandmarket.In regionswithlowrentalpricesbuthighsalespricesandalow shareofrentedland,theriskoffarmlandabandonmentmight alsobelow.DataonthelandmarketatEuropeanlevelwerenot availableandEurostathasjuststartedtocollectdataforthisitem. Therefore,onlyrentpaidisconsideredandisassumedtobea goodproxyfortheriskoflandabandonmentiftheshareofrented landishigh.

• Forthelowfarmincomedriveritwouldbemoreappropriateto compareafarmincomeperannualworkingunittothenational GDPperactivepersonrather thantothenationalincomeper capita.Furthermore,informationonthetotalagricultural house-holdincome wouldbemore relevantbutisunfortunatelynot available.

• Itwouldbebeneﬁcialtoincludetheavailabilityofasuccessor inthedriveronageingfarmerpopulationbecause,ratherthan theageing(overageing)itself, it maybethelackofa succes-sorthatmoreoftenleadstofarmlandabandonment(Schnicke, 2010).However,informationonthesuccessorisnotavailablein thedatasetsusedinthisstudy.

• Concerningthesmallfarmsizedriver,ithastobehighlighted thatthethresholdontheminimumsizeoffarmsthatare sam-pledinFADNdatamayleadtoacertainunder-representationof thesmallestfarms,whichinthecaseoffarmlandabandonment maybeanimportantissue.Inaddition,FADNdataarestatistically representativeonlyatNUTS0,1and2levelsandthusarerather coarse.However,therearenootherdatasetsavailablewithsuch relevantvariablesattheEUlevel.

5. Conclusions

ThecausesoffarmlandabandonmentinEuropearemanifold, dependingontheareaandtheperiodunderconsideration.Itisa complexprocesswhichcanhaveawiderangeofdrivers,varying betweenMemberStatesandsometimeswithinasinglecountry. Indeed,theagriculturalsituationdiffersfromregiontoregion,asa consequenceofnaturalconditions,historicdevelopmentsandthe economic,socialanddemographiccontext.

Thisstudyaimedtoidentifytheprincipaldriversoffarmland abandonmentinEuropeandtotestthesuitabilityof European-widedatasourcesforthedefinitionofindicatorsformeasuringthe riskofoccurrence.Thefocuswasondriversoffarmstabilityand via-bility,aswellasondriversfromtheregionalcontext.Ninedrivers wereidentified,ofwhichsixareassociatedtofarmstabilityand viability,andthreewithregionalcontext.Driversforfarmstability andviabilityarelowfarmincome,lowfarmdynamism/adaptation capacity,ageingfarmerpopulation,lowfarmerqualification,small farmsizeandenrolmentinspecificagriculturalschemes.Drivers fromtheregionalcontextareweaklandmarket,previousfarmland abandonment,andremotenessandlowpopulationdensity. Indica-torswerebuiltforeachdriverusingEuropeanagriculturalstatistics fromFADNandFSS.Eachindicatorwasmappedatregionallevel (NUTS2)forallEuropeanUnionMemberStatesandthenassessed intermsofdataavailabilityandquality,andrelevancetothedriver. Theassessmentusessomeofthecriteriaandthescoringscheme developedduringtheIRENAoperation(EEA,2005).Forsome indi-catorsitwasnotpossibletoobtainappropriatedatasets.Forothers thequalityoftheavailabledatawasnotsufficientortheavailable datawerenotrepresentativeenoughfortheindicator.Thiswasthe caseforlowfarmerqualification,smallfarmsize,enrolmentin spe-cificschemes,andpreviousfarmlandabandonment.Theotherfive driverswereusedtobuildacompositeindicator‘riskoffarmland abandonment’.Accordingly,thecompositeindicatorwasdefined fromlowfarmincome,lowfarmdynamism/adaptationcapacity,

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ageingfarmerpopulation,weaklandmarket,andremotenessand lowpopulationdensity.ItwasbuiltfollowingOECD recommen-dationsusingthenormalizedvaluesoftheindividualindicators, eitheratEU-27levelinanattempttoelaborateacompositerisk indicatorcovering EU-27ina homogeneousmanner,orat indi-vidualMemberStatelevel.AtEU-27levelregionswithahigher riskoffarmlandabandonmentwereidentifiedinPortugal,Spain, Italy,Greece,Romania, Slovenia,thethree Balticstates, Finland, SwedenandIreland.ThecompositeindicatoratMemberStatelevel illustratesthattherearelargevariationsintheriskoffarmland abandonmentamongregionswithinacountry.Themostfrequent farmtypesatriskidentifiedintheseregionsare‘specialist per-manent grazing livestock’, ‘specialist field crops’ and ‘specialist permanentcrops’.Allthreeuselargesharesofland,mostlyinan extensivemanner.Lackofmanagement insuchareasmaylead tonegativeconsequencesforlandscapeandbiodiversity mainte-nance.

The successof building comparable and homogeneous pan-EuropeanindicatorsfordriversoffarmlandabandonmentatNUTS2 levelwasclearlyinfluencedbytheavailabilityandqualityofthe data.Despitetheirgoodqualityandhighvalue,thetwoEuropean datasetsused in thisstudy limitthe analysisofa local-specific phenomenonsuchasfarmlandabandonment.Thelackof accessi-bilitytolocaldataandtheheterogeneityofinputdata(intermsof resolutionandinformationcollected)arethemainreasons.These shouldbeconsideredinfuturemodificationstothedatacollection schemesandinthedistributionofdata.

Thestudycontributestoassessingtheriskoffarmland aban-donmentinEurope,butvalidationthroughmeasurementsofits occurrence is still needed. This requires European or national datasetsmeasuringactualfarmlandabandonmentwhichare cur-rently not available. Given the high priority of avoidance of farmland abandonment in the European agricultural policy,an improvedknowledgeof thephenomenon includingits quantiﬁ-cation at the Europeanlevel is necessary.Once data onactual farmlandabandonmentare available,driversfromthedifferent contextsandinterlinkagesbetweendriverscanbeanalysed.This would providethe basis onwhich toevaluatepolicymeasures aimedatreducingfarmlandabandonmentinEurope.

Acknowledgements

Theworkwascarriedoutintheframeoftheadministrative arrangementwiththeEuropeanCommissionDirectorateGeneral forAgricultureandRuralDevelopment#AGRI-2011-0295.In addi-tion,wearegratefulforhelpfulcommentsonthemanuscriptmade byoneanonymousreviewer.

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