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Socio-ecologicalandnutritionalbenefitsoftraditionalmountainsheepgrazing

EnekoGarmendia,AitorAndonegi,ArantzaAldezabal,GonzaloGamboa,IkerEtxano,OihanaGarcia,LuisJavierR.Barron

Paper#19

Apirila–Abril–April24,25,262017

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Socio-ecologicalandnutritionalbenefitsoftraditionalmountainsheepgrazing

EnekoGarmendia,AitorAndonegi,ArantzaAldezabal,GonzaloGamboa,IkerEtxano,OihanaGarcia,LuisJavierR.Barron

Abstract

Rural landscapes in Europe are changing due to the globalization of the foodsystem,newpoliciesliketheCAP(CommonAgriculturePolicy)andevolvingrurallifestyles,amongothers.Thesechangesareremarkableinmountainareaswheretraditional activities that contribute to food sovereignty are threatened byindustrial production systems. In this context, it becomes critical to assess thesocio-ecologicalandnutritionalbenefitsoftraditionalfoodsystemslikeextensivesheepgrazinginordertoensuretheircontributiontoasustainablefoodsystem.Using as a case study Aralar (Basque Country), this article analyzes thecontribution of traditional mountain sheep grazing to rural development, theprovision of high quality food and biodiversity conservation. With this aimwedevelop a multi-criteria evaluation system that integrates socio-economic (e.g.employment, added value, profitability) and ecological (e.g. biodiversityconservation, soil quality) indicators. This evaluation system allows theassessment of future scenarios from multiple perspectives and contributes tobetterunderstandtheperformanceofthiscomplexsocio-ecologicalsystemunderglobalandlocalchangingconditions.

Keywords: Sustainability; multi-criteria evaluation; sustainable food system;livestockgrazing;mountainareas;ruraldevelopment;biodiversity;foodquality.

*[email protected]@ehu.eus

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1.Introduction

Inthetimeofglobalization,wherethelinkbetweenanimalproductionareasandfood producing areas is becoming weaker (Naylor et al., 2005), recent studieshighlight the capacity of ruminants to ensure a sustainable food system whichcould be beneficial, not only for human health, but also for the health of theplanet (Eisleretal., 2014). Likewisewithin the sustainabledevelopment agendaefforts to improve social, environmental, ecological conditions in agro-pastoralsystems, including initiatives contributing to animal healthcare, are increasingnotably.

Inthisregardawellmanagedextensive livestockgrazingsystemcanencompassan appropriate production system to ensure sustainable food productionsustainability. Inadditiontothebenefitsassociatedwiththeproductionof highquality food, these systems contribute to the conservation of biodiversity,enhancementofecosystemservicesassociatedwiththe grazing, thepromotionofthelocaleconomyandothersocio-culturalbenefits(Odriozola,2016).

In terms of environmental benefits, extensive livestock grazing reinforces thespatial heterogeneity of mountain pastures, improving the landscape andbotanical diversity (Adler et al., 2011). On the sameway, it ensures a strongerecological stability against climatic changes (Volaire et al., 2014), providing aneffectiveadaptationmechanism(Kreylingetal.,2012).Thechangesthathappenontheuppersideofthesoilalsobenefitthesubsoil,asgrazinghasadirectimpactonthedevelopmentoftheundergroundpartsoftheplants(Wardle,2005).Inthisregard,thegrazingmanagementisalsoimportantformicrobialcommunitiesandcontributes to processes that condition the production of mountain pasturesystems,suchasdecompositionandrecyclingofnutrientsandnitrogen fixation.Moreover,thosecommunitiesregulatenitrousoxide(N2O),carbondioxide(CO2)andmethane(CH4)emissionsatsoil level. Inthecaseofstomachmethane,theyarealsoable to regulate it,dependingon thebotanicalcompositionofpastures(HopkinsandDelPrado,2007).

Last but not least, in socio-economic terms, the relevance of extensive grazingsystems in the primary sector (Malagón, 2009) has been associated with theconservation of traditional heritage and the consolidation of local population interritoriesthatsufferdemographicdecline(Corbera,2006).

Despiteall thesebenefits forsocietyandtheenvironment(Bernúesetal.,2011;GodberandWall,2014),nowadaysextensivelivestockgrazingisatriskinEuropeingeneralandinCantabrianMountainsinparticularandstronglyconditionedbytheintensificationofthesector(Rounsevelletal.,2006).Justtogiveanexample,inSpainbetween2000and2007thenumberofsheepfarmsfellby26%,whilethenumber of sheep fell only by 10,4%, showing the strong intensification of thesector(Bernúesetal.,2011).

The last Common Agriculture Policy (CAP) includes some subsidies for thelivestocksectorincludingextensivesheepfarming(EuropeanCommission,2013).However, the main challenges that threaten the sustainability of the extensivesheep grazing system in the CantabrianMountains and the Basque still persist.Scarce communication channels among the stakeholders and shepherds; lackofsocial recognition; conflicts with activities like biodiversity conservation ortourism, hard working conditions; infrastructure deficits and a regulatory

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frameworkthatdoesnottakeintoaccounttherealneedsofthesector,arejustsomeexamples(HAZI,2016).Thelackofeconomicviabilityandincreasingdeclineofruralareasarealsoreflectedintheabandonmentofnumerousfarms(Bernúesetal.,2011;Leizaola,1999).

IntheAtlanticpartoftheBasqueregion,sheepfarmingisalsowitnessingasimilartrend but to a lesser extent. In this territory, the extensive livestock grazing ofruminant animals is still active (mainly sheep production), due to the ruggedorographyoftheBasqueMountains,andhasastronglinkwithterritorialidentity(Batalla, 2015). An example of this is the local Idiazabal cheese production,awardedworldwide,whichisproducedwiththemilkofthelocalsheepbreed,theLatxasheep(Batalla,2015).59%ofthesecheeseisstillproducedbyshepherdsinfarms (EUSTAT, 2013) which still practice the traditional transhumance,connectingthefarmsusedinwinter,whichareinloweraltitudes,withthenearbymountainous areas grazed in an extensive way in summer (Mendizabal, 2009;Odriozola,2016).Inthiscontext,thepresentarticleanalysesthesocio-ecologicalandnutritionalbenefitsofsheepgrazingusingasacasestudytheCommunityofEnirio-Aralar(BasqueCountry).Moreprecisely,throughamulti-criteriaevaluationframeworkinthisstudyweassesstheperformanceofdifferentlivestocksystems(e.g dairy and cheese producers), at farm and landscape level based on socio-economic,ecologicalandnutritionalindicators.

2.Materialandmethods

2.1.Studyarea

ThestudyareaislocatedintheAralarNaturalPark(Gipuzkoa,BasqueCountry),aSpecial Area of Conservation (SAC) part of the EuropeanNatura 2000 network.With a surface of 11000 ha and an oceanic climate, it has a mean annualtemperature of 12,4ºC and a mean annual precipitation of 1400 mm. Thevegetation in the park comprises a mosaic of gorse-heather shrublands andgrasslandsthatsupports livestock,mainly18000dairysheepoftheLatxabreed.Theareatraditionallyusedbylivestock(dairysheep,beefcattleandhorses)fromMay to the end of October has a surface of 2077 ha, which encompasses the18,9%oftheparkarea.ThisareaisdominatedbynativegrasslandsincludedintheHabitat Directive, being the most relevant one the Jasiono-Danthonietumgrassland(code6230,subtypea),primarilycomprisingperennialgraminoidssuchas Festuca rubra, Agrostis capillaris L. and Luzula campestris (L.)DC andherbaceous dicotyledons such as Galium saxatile L., Trifolium repens L. andCerastiumfontanumBaumg.(Aldezabaletal.,2015;Mendizabal,2009;Odriozolaetal.,2014).UnlikemostoftheAtlanticmountainpastures,thelivestockloadofAralarhasremainedalmoststableoverthepastdecade(Aldezabaletal.,2014).

2.2Livestockfarms:definitionanddifferenttypologies

There are twomain types of livestock farms in the study area: production andreproductionfarmsandfarmsrelatedtopastures.Inthefirstcase,animalsarefedandmaintainedwiththeaimtoobtaineconomicgainortodirectthemtofamiliarconsumption. Inthesecondcase, livestock isgrazed inpastures inordertotakeadvantageofnaturalorplantedproductions.

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Focusing on the production and reproduction farms, there are 4 differenttypologies in the Community of Enirio-Aralar (for more information aboutdifferenttypologiesseeAppendix1):

- Cheese-maker shepherds.TheyhaveLatxa sheepand theirmain sourceof income comes from the sale of cheese, which is sold directly toconsumers, in localmarketsandor inthefarmitself.Someofthemalsosell a shareof their cheeseproduction through cooperatives.Under thistypology only a little additional income comes from milk, lamb and/orlivestockselling.

- Milkproducershepherds.TheyhaveLatxasheepandtheirmainsourceofincomecomesfrommilkproduction,whichissoldmainlytocooperatives.Othersourcesofincomeinclude:cheese,lambandlivestockselling.

- Cattlerangers.Therearejustafewprofessionalcattlerangers,whilemostgetcomplementaryincomefromthisactivity.Meatissoldtoslaughtersintheregion.

- Horse rangers. Only obtain small complementary income. They havemainlymaresandsomehorsesandthesmall incometheyobtaincomesfromlivestockselling.

2.3Definitionofscenarios

Livestock load (sheep, cattle andmare load) is themain feature that has beentaken into account in order to design thedifferent scenarios and also to assesscurrentandalternativedistributionsofthatload.Inaddition,followingMassam’s(1988) suggestion,we also consider theBusiness as Usual, the ideal andworsescenario as well as other hypothetical situations located between these twoextremes to define the scenarios. Following these criteria we designed thefollowingscenarios:

I. BAU(BussinessAsUsual):thisscenariorepresentsthecurrentsituationwith17260sheep,853beefcattleand742maresdistributedalongtheentire study area . These numbers have been taken from the 2016pasturecensus.

II. BYE-Grazing: the scenario without livestock, which simulates grazingabandonment.

III. MIX-Up:thisscenariosimulatesthegrazingabandonmentinthehigherremote altitudes of the Community. Estimated livestock quantities:11480dairysheep,817beefcattle,and293mares.

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IV. MIX-Down: This scenario simulates the grazing abandonment of thelower altitude pastures of the Community. Estimated livestockquantities:5369dairysheep,36beefcattleand385mares.

In the case of theMIX scenarios, cattle andmare spatial distribution has beentaken into account, using the data and the digital cartography obtained byMendizabal(2009),whostudiedthehabitatuseoftheseanimalsfollowingthem“insitu”. Basedonthatdata,beefcattleandmarespatialdistributionhasbeenobtained by a Kernel density map (see Appendix 2) done with public domainsoftware calledQuantum Gis 2.12. Once identified the higher density of theseanimalsintheupperandlowerareasoftheCommunity,twopolygonswerebuiltinordertodeterminethesurfaceofthoseareas.Finally,thespecifiednumberofbeef cattle andmares in each polygonwas determined.With regard to sheep,therewasnotaccurateinformationabouttheirhabitatuse,sothedatathathasbeen used is the number of sheep linked to shepherd cabins.With the aim tosimulatethespatialdistributionofdairysheep,thedigitalcartographyofpastureshasbeenused.

2.4Multi-CriteriaEvaluation(MCE)frameworks

The aim of the MCE framework is to simplify and structure complex decision-making problems in a systematic way (Proctor and Drechsler, 2006). Thisframeworkcanbehelpfulasadecisionaidtooltoincontextcharacterizedbyhighdegrees of complexity where confronted interest and values coexist (Munda,2004, 2008). TheoriginofMCE lie in the fieldsofmathematics andoperationalresearch, however in recent years it has been widely used in the context ofnatural resource management and other sustainability related issues, such asrenewableenergy (GamboaandMunda,2007),hydrological resources (Panequeetal.,2009),coastalmanagement(Garmendiaetal.,2010);mining(Walteretal.,2016)orprotectedareaplanning(Etxanoetal.,2015).

Brieflyspeaking,whenusingMCEframeworks,firstlytherelevantdimensionandindicators need to be identified ideally with the help of all the relevant socialactorsand thenan impactmatrix isbuilt toevaluate the scenariosaccording tothese criteria and indicators. In this case, socioeconomic, ecological andnutritional indicators havebeen considered. Further details of themethodologyusedinthisstudycanbefoundinMunda(2004and2008).

2.4.1Socioeconomicindicators

The socioeconomic indicators have been evaluated at farm and landscape levelaccordingtothedatacollectedfortwelvefarms(forfivecheese-makerfarms,twomilkproducersandsixbeefcattlefarms)andduringthreeyears(2013,2014and2015) by Lurgintza, a livestock farmers’ association. Horse rangers are notprofessional and in this case we have not been able to collect reliable dataassociatedwith their activity. Therefore,due to the lackofdataand their smallcontribution insocio-economictermsthecontributionofthesefarmers insocio-economictermshasbeenomittedfromtheanalysis.

Amoredetaileddescriptionofeachsocio-economicindicatorisprovidedbellow:

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− Net margin per family Agrarian Work Unit (AWU) (at farm level): thedifferencebetweenincomesandcostsofthefarmperfamilyAWU.

− Totalnetmargin (at landscape level): thenetmargintaking intoaccountthetotallivestockloadoftheCommunity.

− AddedvalueperLivestockUnit (LSU) (at farm level): theadditionofnetmargin,includingsalariesandvalue-addedaftertax,perLSU.

− Total added value (at landscape level): the added value taking intoaccountthetotallivestockoftheCommunity.

− Employment (at farm level): thenumberof employeesworking full timeduringayear.

− Total employment (at landscape level): employment taking into accountthetotallivestockloadoftheCommunity.

− Economicproductivityofemployment(atfarmlevel):thedivisionofgrossmarginandemployment(productionperemployee).

− Dependencyofsubsidies(atfarmlevel):thedivisionofsubsidiesandnetmargin.

− Total dependency of subsidies (at landscape level): dependency ofsubsidiestakingintoaccountthetotallivestockloadoftheCommunity.

2.4.2Ecologicalindicators

Taking into account the scale of these indicators, to obtain the score of thisindicatorsweconsiderthelandscapelevel.DatawasobtainedthoughtaliteraturereviewofpeerreviewarticlesandotherrelevantscientificsourceslikePhD.thesisand technical documents elaborated for the corresponding authorities. BuildinguponthisinformationthescoresforeachindicatorhasbeencalculatedusingQGISandthesurfaceofdensegrassland.

These are the ecological indicators that have been considered (for moreinformationaboutthecalculusofeachindicatorseeAppendix3):

− Plantspeciesdiversityindensegrassland(S):numberofplantspecies indensegrassland(Odriozola,2016).

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− Nitrogen content in dense grassland (pasture forage quality): Nitrogentonesindensegrassland(Odriozolaetal.,2014).

− Soilmicrobialmetabolicquotient(qCO2):SoilmicrobialCO2emissionspermicrobialbiomass(Aldezabaletal.,2015).

2.4.3Impactmatrixesandspiderdiagrams

Onceindicatorshavebeenselected,thenextstepistobuildanimpactmatrix.Theaimofanimpactmatrixistoevaluateeachscenariowiththeselectedindicators.These indicatorscanbequantitativeorqualitativeandtheycanbemeasured indifferent units (€, tones, jobs etc.), both important features to deal withsustainability related issues and incommensurability of values (Martinez-Alieretal.,1998).

Inthispapertwoimpact-matrixeshavebeenbuilt:(i)onerelatedtoprivatefarms(with socioeconomic indicators) and (ii) the other one related to the scenariossimulatedforlandscapelevel(integralevaluation).

Inthiscaseforthegraphicalrepresentationoftheimpactmatrixspiderdiagramshave been used. Each indicator has a different scale, so in order to normalizethesedifferencesthevaluesofeachindicatorhavebeenrescaledtoascalethatgoesfrom0to10.Thebestvalue ineach indicatorgot10points,andtheotherscoreswere calculated basing on that relationship. In the case of plant speciesdiversityindicator,thebestvalueobtained10points,theworst0and5theonesthatwerebetweentheprevioustwo.

Inspiderdiagramsanincreaseintheareasindicatesapositiveinfluence,buttherearetwoindicatorsinwhichtogetahighervalueisconsiderednegative.Thatisthecaseofdependencyof subsidiesandmicrobialCO2quotient. Inorder tocorrectthat,inthecaseofdependencyofsubsidies,ithasbeendoneasubtractionofthepossible biggest value (1) and the value obtained in each case. RegardingmicrobialqCO2,thesubtractionhasbeendonebetweenthehighervalueobtainedandthevalueobtainineachcase.

3.Resultsanddiscussion

3.1Farmlevelevaluationandcomparison

Thefollowingimpactmatrix(Table1)summarizesthescoresobtainedbyeachofthefarmtypologies(CS:Cheese-makershepherds;MS:Milkproducershepherds;BCR:Beefcattlerangers)accordingtotheselectedsetofindicators.

Dimension Indicator Unit Preferreddirection

CS MS BCR

Table 1. Impact matrix at farm level.

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As itcanbeseen inthespiderdiagrambelow(Figure1), thecheese-makersaretheonesgettingthebestscoresinalltheselectedindicators,mainlyduetotheirprofitabilityandhighercontributiontoemployment.

Inthecaseofnetmargin,themostrelevantfactorscontributingtothisindicatorareprofitsandtotalcosts.Focusingonthecosts,cheese-makershepherdsaretheones that have higher costs compared to the other typologies (cheese-makers74.706,42€;milkproducers43.865,03€andbeefcattlerangers68.070,64€),butin terms of profit, the cheese-makers earnmore than double compared to theothers (cheese-makers 101.383,37€; milk producers 47.209,51€ and beef cattle

Socioeconomic

Net marginper familyAWU

€/AWU Max(↑) 28.390 22.650 12.900

Addedvalue perLSU

€/LSU Max(↑) 765 705 600

Employment

AWU Max(↑) 2,083 1,417 1,694

Employment economicproductivity

€/AWU

Max(↑)

27.940

26.300

16.990

Dependency ofsubsidies

% Min(↓) 0,502 0,772 1,9

Figure1.Representationofsocio-economicindicatorsatfarmlevel.

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rangers 44.573,59 €), without counting subsidies. Consequently, cheese-makersgetabettervaluethantherestoffarms.

Intermsofemploymentthecheese-makersalsoobtainthebestscores (cheese-maker shepherds 2,083 AWU; milk producer shepherds 1,417 AWU and beefcattle rangers 1,694 AWU) reaching also the highest productivity in terms ofincomeperpersonemployed.

With regard tosubsidiesdependency,cheese-makersare theonesagaingettingthe best score. In contrast, beef cattle rangers are the ones that have highestdependency,withmore than 44.000 € per farm in average. In the case ofmilkproducer shepherds, the amount ofmoney they receive from subsidies is quitesimilar to cheese-maker shepherds’ on average (cheese-makers receive25.845,01€andmilkproducers28.745,62€). In relative term, ifwecompare thesubsidies with the net margin of each typology cheese-makers show the bestperformance (0,502) followedbymilkproducers (0,772) andbeef cattle rangers(1,9).

3.2Landscapelevelevaluationandthecomparisonofscenarios

Inthissection,landscapelevelvaluesarepresentedforallthedesignedscenarios(BAU,BYE-Grazing,MIX-UpandMIX-Down),whichhavebeensummarizedusingan impactmatrix (Table2).BG:BYE-Grazing;*Score forupperpastures;**Scoreforlowerpastures.

Dimension Indicator Unit BAU BG MIX-Up MIX-Down

Ecological

Plantspeciesdiversity

Numberofspecies 32,33 25 25*/32,33**

32,33/25

Nitrogencontent

Tones 106,04 86,69 99,06 98,46

MicrobialqCO2

𝑚𝑔𝐶𝑂&ℎ-)

𝑚𝑔𝑚𝑖𝑘𝑟𝑜𝐶𝑘𝑔-)𝑙𝑢𝑟1,38·107

1,78·107

1,53·107 1,54·107

Socioeconomic

Total netmargin

€ 1.994.096

0 1.261.753,64

551.662,167

Totalemployment

AWU 90,28 0 65,84 22,77

Totaladdedvalue

€ 2.281.301

0 1.615.702,78

619.277,96

Totaldependency of

- 0,660 0 0,778 0,441

Table 2. Impact matrix at landscape level.

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subsidies

BAUisthescenariothatgetsthebestscoresinmostoftheindicatorsselectedfortheevaluation,bothinthesocioeconomicandecologicalindicators(seeFigure2andTable2).

Withintheecologicaldimensions,highlightsthefallofplantspeciesdiversitythatwouldtakeplaceifgrazingactivitywoulddisappear(7,33unitsfromBAUscenariotoBYE-Grazing scenario). In the caseofbothMIX scenarios, taking intoaccountthat grazing abandonment has been simulated in different areas, each of themshows different values. These results clearly show the negative influence ofgrazing abandonment in the floristic composition of the pasture. In that sense,MilchunasandLauenroth (1993) showed that innon-grazing conditions, relativeproportion of graminoid species increases and that dicot species decrease,especiallythosespeciesofFabaceaefamily.Similarresultshavebeenobservedinotherstudiestoo(Aldezabaletal.,2015;Odriozola,2016).

That indicator has a direct relationshipwith nitrogen content indicator. In fact,forbshaveahighernitrogencontentcomparedtograminoids(Semmartinetal.,2004), so if therelativeproportionof forbsdecreases,a fallofnitrogencontentcanbe expected. The results obtained in this study confirm that hypothesis. Allnon-grazingsimulatingscenariossufferadecreaseofnitrogencontentcomparedtoBAUscenario,withthehighestfallinBYE-Grazingscenario.

RegardingmicrobialqCO2,allthenon-grazingsimulatingscenarioshadanincreaseintheirvaluescomparedtoBAUscenario:BYE-Grazingincreaseda29,10%;MIX-Down a 11,41% and MIX-Up a 10,50%. According to Aldezabal et al. (2015),grazing abandonment induced shifts in floristic composition, soil insulation anddecrease of soil compaction at 0–10 cm depth are the main reason for theincreaseofthisindicatorvalues.Duetothosefactors,microbialenzymaticactivityandbiomasssufferareductionandCO2soilemissionsincrease.

Intermsofsocioeconomicindicators,allofthemdirectlyrelatedtolivestockload,BAU is the scenario that gets thebest results according tomost indicators. Theonlyexceptionisthedependencytosubsidies.Inthiscase,MIX-Downscenarioistheonegettingthe“best”score,becauseofthereductionofsubsidiesinducedbythe fall of sheep and beef cattle load. The values of the rest of indicators fallremarkably (over 70%) in comparison to the BAU scenario: netmargin 72,34%;added value 72,85%and employment 74,78%.With regard toMIX-Up scenario,the values of all the indicators fall, but not so remarkably (surrounding 30%).RegardingtheBYE-Grazingscenario,thescoreofallsocioeconomicindicatorsis0duetotheabandonmentoftheactivity.Takingintoaccounttheseresults, itcanbeconcludedthatthetotalabandonmentofgrazingactivity,apartfromecologicalandenvironmentaldamages,wouldsupposethelossofover90employmentsand2 million € in terms of added value. These results illustrate the importance ofgrazingactivityinthecaseofsomeruralareas.

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Figure 2. Representation using a spider diagram of the results of thesocioeconomicandecologicalindicatorsatlandscapelevel.

4.Nutritionalbenefits

In term of nutritional benefits, extensive grazing systems provide safe and highquality food (e.g.goatandsheepcheese)which isproducedtaking intoaccountanimalwelfare(deRenobalesetal.,2012).

The composition of mountain pasture plants are related with the lipidcompositionofmilkandcheese(DeNoni&Batelli,2008;Falcheroetal.,2010).Inthis regard, the scientific evidence shows that fresh pasture intake lowers thesaturated FA content of milk fat and increases that of some unsaturated FAs(Abilleiraetal. 2009,Revello-Chionetal. 2010). Themostof thoseunsaturatedFAs are associated with potential antiatherogenic, antiobesity andanticarcinogenic properties that are beneficial for human health (Pintus et al.2013). Further details on the nutritional benefits associated with extensivemountaingrazingcanbefoundintherecentstudydevelopedbyValdivielsoetal.(2016)inthestudyareaofthisarticle

For all this factors it is not surprising that the milk and cheese obtained fromextensive grazing systems provide traditional foods that are perceived bymostconsumersandproducersthemselvesashigh-qualityfoods.

5.Conclusions

Cheese-makers obtain the best scores in all the indicators selected for theevaluationat farm leveland show thebest socio-economicperformanceamongall thetypologiesconsidered.Hence,withoutanydoubt itcanbesaythatthesefarmers are the most sustainable ones in economic terms at least in theCommunityofEnirio-Aralar.However,theyshowahighdependencyofsubsidies,even though they are the least dependent farm type of the Community. Thatfactor makes them vulnerable to the decisions, interest and commitments of

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publicinstitutions(prioritychanges,sectarianism,politicalinterestsetc.).Inordertoavoidthatsituation,itwouldbedesirabletopromoteandsupportalternativemeasuresandinitiativesthatcanservetostrengthentheautonomyoffarms.Forexample,inordertodiversifythesourcesofincomeandreducethedependencyto public subsidies it might be worth to explore the possibility to combine thetraditional farming activities with new education or leisure activities (e.g.ecotourism)thatareimplementedinotherregionsofEurope;toprioritizeshort-chain sale channels; to make an efficient use of communal lands in order toreducefeedcosts,ortodiversifytheproductssoldbyfarmers.Urgentmeasuresare also needed to shepherd cabins tomake their work easier and ensure theproductionofhighqualityfood,toimprovetheaccessibilitytothecabinswithoutcompromisingtheenvironmentalvaluesofthearea,andtoupdatetheregulationtothecurrentsituation.Thecontractofadditionalpeopletoassistshepherds inthe grazing season (e.g. to control the livestock) can be also beneficial both insocio-economicandenvironmentaltermsandshouldbeconsideredbythepublicadministration.All thesemeasureswould contribute to improving thequalityoflife of shepherds andmaking theirworkmore respectable,what ultimatelywilldetermine the sustainability of the activity and the continuity of futuregenerations.

As the landscape levelanalysis shows thesocio-economicandecologicalbenefitassociatedwith the current grazing systems aremultiple. However, the currentsocio-economicsituationthreatensthesustainabilityoftheactivity.Inthisregardthe study shows the cost associated with the abandonment of the extensivegrazing systems (simulated with the MIX-Down and BYE-Grazing scenarios). Inadditiontosocio-economicandnutritionallossestheabandonmentoftraditionalmountaingrazingactivitieswouldderive to reductions inplant speciesdiversity,decreasingpastureforagequalityandriseofsoilgreenhouseemission.

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NazioartekoHizketaldiaELIKADURARENETORKIZUNAETANEKAZARITZARENERRONKAKXXI.MENDERAKO:

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