Assessing and Governing Ecosystem Services Trade-Offs in Agrarian Landscapes

17 Pages·2016·2.76 MB·English

land
Article
Assessing and Governing Ecosystem Services
Trade-Offs in Agrarian Landscapes: The Case
of Biogas
ChristianAlbert1,2,*,JohannesHermes1,FelixNeuendorf1,ChristinavonHaaren1
andMichaelRode1
1 InstituteofEnvironmentalPlanning,LeibnizUniversitätHannover,HerrenhäuserStr.2,30419Hanover,
Germany;[email protected](J.H.);[email protected](F.N.);
[email protected](C.v.H.);[email protected](M.R.)
2 DepartmentofEnvironmentalPolitics,HelmholtzCentreforEnvironmentalResearch—UFZ,
Permoserstraße15,04318Leipzig,Germany
* Correspondence:[email protected];Tel.:[phone];Fax:[phone]
AcademicEditors:BenjaminBurkhard,StefanHotes,HubertWiggeringandPeterVerburg
Received:8July2015;Accepted:12January2016;Published:22January2016
Abstract: Thispaperdevelopsamethodtoexplorehowalternativescenariosoftheexpansionof
maizeproductionforbiogasgenerationaffectbiodiversityandecosystemservices(ES).Ourapproach
consistsoffoursteps: (i)definingscenariotargetsandimplementationofassumptions;(ii)simulating
cropdistributionsacrossthelandscape;(iii)assessingtheESimpacts;and(iv)quantifyingtheimpacts
foracomparativetrade-offanalysis.ThecasestudyistheregionofHannover,Germany.Onescenario
assumesanincreaseofmaizeproductioninalittleregulatedgovernancesystem;twoothersreflect
anincreaseofbiogasproductionwitheitherstrictorflexibleenvironmentalregulation. Weconsider
biodiversityandthreeES:biogasgeneration,foodproductionandthevisuallandscape. Ourresults
showthattheexpansionofmaizeproductionresultsinpredominantlynegativeimpactsforotherES.
However,positiveeffectscanalsobeidentified,i.e.,whentheintroductionofmaizeleadstohigher
localcropdiversityand,thus,amoreattractivevisuallandscape. Thescenariooflittleregulation
portraysmorenegativeimpactsthantheotherscenarios. Targetedspatialplanning,implementation
and appropriate governance for steering maize production into less sensitive areas is crucial for
minimizingtrade-offsandexploitingsynergiesbetweenbioenergyandotherES.
Keywords: landscape planning; ecosystem services; landscape services; landscape functions;
trade-offs;biogas
1. Introduction
Policy,businessandcivilsocietyactorsinmanyEUmemberstateshavepushedforanexpansion
ofbiogasproductionasarenewableenergysource. InGermany,forexample,theRenewableEnergy
SourcesAct(2004)hasprovidedsubstantialfinancialincentivesfortheestablishmentofbiogasplants
andforthecontributionofbiogastotheenergysystem.
Inresponsetothesecalls,manystates,countiesandcommunitieshavesetthemselvesambitious
targetsforrenewableenergygeneration,andforbiogasinparticular,tobeachievedapproximately
between2025and2050. Numerousbiogasplantshavebeeninstalled,causingasubstantialchange
in the crop cycles within their proximity in order to expand the production of biomass for biogas
generation. InsomeGermanmunicipalities,forinstance,increasedmaizecultivationforbiogasand
fodderproductionencompassesmorethan85%ofallarableland[1,2].
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Unfortunately,inmanyregions,theincreaseintheproductionofmaizeforenergyproductionhas
dramaticnegativeeffectsonbiodiversityandseveralecosystemservices(ES)[3,4]: Trade-offs,which
comefromanincreaseintheareaappropriatedforthegenerationofbiogas, includeprovisioning
services,suchasfodderandfood,duetoagriculturallandcompetition[5,6],regulatingservices,such
asdecreasedwaterquality,duetohigherinputsoffertilizersandalongerperiodofopensoilthan
inothercrops[7],andculturalservicesthroughincreasedmonoculturesand,thus,decreasesinthe
diversityofthevisuallandscapeintheproximityofbiogasplants[8,9].
The spatial implications of increased maize production and the resulting negative effects on
biodiversityandESare,however,onlymarginallyconsideredinpolicyandthedecision-makingof
countiesandcommunities. Thedearthofinformationisakeychallengefordecision-makers, and
consequently,thepotentialadverseeffectsonESandeconomiclossesforsocietyarenotconsidered[10,11].
Decision-makingabouttheamountofbiogastobegeneratedinaparticularspatialarea,aswell
asthesitingofbiogasplants, aretopicalissuesinwhichtheimpactandtrade-offsofdecisionson
biodiversityandESareonlypartiallyconsidered. Landscapeplanning,asaforward-lookingaction
to preserve, enhance and restore landscapes (cf. [12]), is arguably well positioned to provide the
necessaryenvironmentalinformation,impactassessmentsandsolutionoptionsforovercomingthis
shortcomingandsupportingabetterinformeddecision-makingprocess(e.g.,[13]). However,despite
someinitialexplorations[3,10,14,15],landscapeplanningstilllacksnecessaryconceptsandmethods.
Somesophisticatedapproachesareavailableforinvestigatingtheimpactsofbiogasproductiononthe
farmtolandscapelevel[16–19]. Somespatially-explicitintegratedmodellingframeworksalsoexistfor
exploringopportunitycosts[20–22]. However,theseapproachesandtoolsarerelativelycomplex,too
resourceintensiveforpracticalapplicationand,thus,notbestsuitedtoassistinlandscapeplanning
anddecision-making.
The focus of this paper is on the development and testing of assessment methods to assist in
landscapeplanninganddecision-making. Thesetoolsaretestedusingsimulateddataonthecurrent
andpotentialfuturedistributionofcropsacrossthestudyareaoftheregionofHannover,Germany.
However,thesesimulationsarenotthecentralconcernofthepaper,butareratherusedtoillustrate
theapplicationofthetools
2. CaseStudyArea
ThecasestudyfortheanalysisistheregionofHannover(RegionHannover),locatedinthesouth
oftheGermanfederalstateofLowerSaxony(Figure1). TheurbancenteristhecityofHannover,
whichissurroundedbyruralareas. Morethanhalfoftheregion1sareaisagriculturalland(55%of
totalarea). Thedominantagriculturallanduseformiscropland(40%oftotalarea),andabout19%of
thetotalareaiscoveredwithforests. RegionHannoverrepresentsaninterestingcasestudydueto
itsvaryingbiophysicalconditionsandtheresultingdiversityofcultivatedcrops. Thesouthernpart
ischaracterizedbyveryfertileloesssoilsthatarealmostexclusivelycultivatedwithsugarbeetand
wheat. Inthenorthernandnorth-easternpart,however,sandyandlessfertilesoilsdominate,and
moremaize,barleyandryeiscultivated. Thepercentageofgrasslandisalsomuchhigherinthenorth.
InchoosingRegionHannoverasacasestudy,webenefitedfromgooddataavailability,including
statisticaldataoncultivatedcrops,spatialdataonlanduseandbiogasplantsandinformationabout
objectivesconcerningtheexpansionofrenewableenergies. Furthermore,wecouldbuilduponprior
contacttolocalpolicymakers,administrators,farmersandothercivilsocietystakeholders,whotook
partinworkshopsinconnectiontotheresearch.
In2012,biogasplantswerepredominantlyfoundininthenorthernpartofthestudyarea. The
installedcapacitywas19,729kW[23]. Maizecultivationtookplaceon11%ofthearableland[24],
whereasenergymaizeclaimedonly6%(owncalculation,basedon[23–25]). InLowerSaxony,the
shareofmaizecultivationwas23.8%ofagriculturalland,whilemaizecultivationforbiogastookplace
on9%. However,therearehugeregionaldifferences. Inregionswithahighdemandformaizeas
fodder,maizecultivationforbiogashasincreasedthealreadyhighlevelofcultivationto,attimes,
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morethan50%. Intheseregions,theshareofmaizecultivationforbiogaswasbetween19%and38%.
In other regions, maize cultivation took place on generally only 6%–13% of the arable area and is
predominantlyusedforbiogasproduction[26]. Thepotentialforanincreaseofbiogasproductionin
theregionofHannovercanthusbeconsideredashighcomparedtoothercountiesinLowerSaxony.
Figure1.LanduseinthecasestudyareaoftheregionofHannover.
3. Methods
3.1. ResearchDesign
Scenario-basedlandscapeplanning[27–32]ispotentiallyausefulapproachtoassesspossible
impacts from biogas production on nature and the landscape and to develop spatially-explicit
solutionstrategiesandimplementationoptions. Scenario-basedlandscapeplanningmakesreasonable
assumptionsaboutfuturedevelopmentswithandwithoutplanninginterventions,simulateslikelyland
usechangesandanalysespotentialeffectsonnatureandlandscape.Scenario-basedlandscapeplanning
therebyenablessociallearningandbetterinformeddecision-making(cf.[33]). Morespecificallyinour
case,itenablesbetterdecision-makingabouttheamountofbiogasthatcouldbegeneratedinagiven
regionandhowthisproductionwouldneedtobespatiallyallocatedinordertoavoidorminimize
unwantedeffectsonbiodiversityandES.
Weemployedaplanninganddecision-supportapproachthataimedatconnectingpolicyand
decision-making with science and planning through exchange and discussions among scientists,
decision-makersandstakeholders. Currentbiogasgovernanceregulationsandobjectivesforbiogas
expansion,asstatedbypolicy-anddecision-makers,wereintegratedintheformulationofscenario
assumptions. Furthermore,thescenariosassumeddifferentoptionsforbiogasgovernance. Wethen
simulated the scenarios1 effects on agricultural land use and cropping patterns and assessed the
potentialimpactsconcerningselectedbiodiversityandESindicators. Theresultsofthescenariostudy
werefedbacktopolicy-anddecision-makerstosupporttheireffortsinrefiningbiogasexpansion
objectives and creating sustainable pathways for its governance. Finally, conclusions concerning
implementationoptionsweredrawn.
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3.2. ScenarioAssumptions
Three scenarios were developed with a time horizon to 2050. The first scenario assumes a
market-baseddevelopmentwithoutenvironmentalconstraints(Table1). Thetargetvalueforthearea
ofmaizeproductionisanincreaseof280%oftheshareofthecurrentareadevotedtothisproduction
type. Numerically,thismeansanincreasefrom7102ha(19,729kW)–27,036ha(75,100kW).Thistarget
resultsfromaparticipatoryscenario-workshoponthetopicofexpansionofrenewableenergies. This
wasconductedwithadministratorsfromtheregionofHannoverinSeptember2012. Theincreasewas
separatelycalculatedandsimulatedforeachmunicipality,therebyrespectingthelocalcontextand
conditions,e.g.,locationcharacteristics,agrarianstructureandcultivationpreferencesoffarmers.
Table1.Scenarioassumptions.
2012 2050:Market 2050:RestrictiveProtection 2050:ConditionalProtection
280%increaseof Max.15%maize Max.15%maize
Assumptions biogasarea Exclusionofprotectedsites Exclusionofprotectedsites
(bymunicipality) Exclusionofsensitiveareas
Capacity(kW) 19,729 75,100 36,689 44,669
Demandedarea(ha) 7102 27,036 13,222 16,084
The second scenario is based on nature conservation requirements and contains different
restrictionsforbiogasproduction; meaningthatthereisno(further)cultivationallowedinnature
reserves, drinking water protection areas, flood plains, erosion-prone sites and areas of faunistic
importance. Datalayersfortheserestrictedareaswerederivedfromthelandscapeplanframework
fortheregionofHannover[34]. Furthermore,themaizeproductionareawascappedat15%ofthe
arable land outside of the excluded areas. Municipalities were regarded separately. However, in
municipalitiesthatalreadyhadmorethan15%oftheirarablelandcultivatedwithmaizeforbiogas,
theamountwasneitherreduced,norcoulditbefurtherincreased. Exploitingthe15%,thisscenario
resultsinamaizecultivationforbiogasareaof13,222haand36,689-kWinstalledplantcapacity.
Thethirdscenarioisalessrestrictivevariantofthesecondone. Itstillallowscultivationofmaize
forbiogasonerosion-pronesitesandinareasoffaunisticimportance. However,itisallowedonlyon
theconditionthatthestandardsofgoodagriculturalpractice(minimumrequirementsforagricultural
management;seeregulation(EC)No. 1750/1999Article28)arestrictlycompliedwithandthatfurther
measurestopreventenvironmentaldeteriorationaretakenifnecessary. Undertheseassumptions,the
areaofmaizecultivationforbiogascanbeincreasedupto16,084haandtheinstalledcapacityofthe
plantsto44,669kW.
As an increase of maize cultivation is implemented at the cost of the area available for other
crops,decisionsneedtobemadeaboutwhichcropswouldbemostlikelyto“loseout”incompetition
withmaize. Weassumedthatcropswiththelowestcontributionmarginswouldbegivenupfirst,
makingwayformaizecultivationforbiogas. Relativecontributionmarginswerederivedfromexpert
knowledge and, in particular, from the long-term experience of one of the co-authors (M.R.). We
furtherassumedthattheplantingareaofeachcropwouldonlybereduceduntilaminimumof10%of
theoriginalareapermunicipalitywasreached.
3.3. SimulatingtheSpatialDistributionofCrops
Akeychallengeforthestudywasthelackofaccesstospatialdataoncropdistributions. Data
onthisissuewereavailableinthereportingsystemoftheEUagriculturalpolicies’directpayments,
butdataarenotusuallyavailableforpublicorresearchuse. Inthemeantime,wedecidedtoadoptan
alternativeapproachthatmakesbestuseoftheavailablepublicdata. Alongthisline,wedrewupon
aggregatedaccountinginformationoncropcultivationpermunicipality,whichisavailablefromthe
statisticalofficeofthefederalstateofLowerSaxony. Inordertoprogressundertheseconditionsof
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uncertainty,wedecidedtosimulatethespatialcropdistributionsforthestatusquoandthedifferent
scenarios. While one could argue that such a simulation rarely reflects the exact distribution, we
consideritusefulasafirstattempttounderstandtheeffectsofcropdistribution. Severalsimulation
modelrunsandevaluationswouldbeneededtoinvestigatetheuncertaintyinherentinthesimulations
weperformed. However,thiswasnotpossiblewithinthescopeofthisproject.
Thesimulationofspatialcropdistributionswasconductedinthreesteps(seeFigure2). First,
theareaofarablelandwasidentifiedusingalandusemap. Then,theexistingbiogasplantswere
identifiedandtheirassociatedareasinwhichenhancedpressureofmaizecultivationforbiogastook
place. Weassumedhigherpressuresexistedforthecultivationofmaizecultivationforbiogaswithin
theproximityofplants,becauselongertransportationdistancesareusuallylessprofitable[35]. We
thereforedefined“areasofenhancedpressure”asthoseareaswithincreasedlikelihoodformaize
cultivationaroundtheplants. Locatingplantsandareasofenhancedpressurewasdonewiththehelp
ofthewebservice(energymap.info),whichprovidesinformationonplantlocationsandproduction
capacities. Weassumedthatplantswouldneed0.36haofarablelandper1kWcapacity(i.e.,180ha
for a 500-kW plant) to meet their demand for maize [25]. In 2012, approximately 80% of the area
usedforbiogascropswascultivatedwithmaize[24]. Thesizeoftheareaswithenhancedpressures
also depends on the capacities of a plant. We assumed a radii of 1.5 km for plants with less than
255kW,3kmforplantswith256–400kwand5kmforplantswithmorethan400kWcapacity(own
elaborationsbasedon[35,36]). TheresultingbasicspatialinformationisprovidedinFigure3.
Figure2.Approachforspatialsimulationofcropdistribution.
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Figure 3. Locations of biogas plants [23] and areas of enhanced pressure from maize cultivation
forbiogas.
Data on the cultivation area for different crops are provided by the agricultural statistics [1].
Asdatawereavailableforthemunicipallevel,cropdistributioncouldbeperformedseparatelyfor
everymunicipalityinthestudyarea. Similarly,thedifferencesinthecharacteristicsofagricultural
productionbetweenthenorthandthesouthofthestudyarea,asdescribedabove,couldbebetter
incorporated. Thesimulationmethodconsistedofseveralsteps,performedmanuallywithLibreOffice
CalcinattributetablesfromESRIArcGIS:
(1) Uniformly-distributedrandomnumberswhereassignedtoeachagriculturalparcel
(2) Withineachbiogasplantareaofenhancedpressure:
(a) Parcelswereselectedwithreferencetotherandomnumbers, whilesumminguptheir
areauntilthetotalsummedupareamatchedthedemandofareaformaizecultivationfor
theplant.
(b) Maizecultivationwasassignedtotheselectedparcels.
(3) Withineachmunicipality:
(a) The remaining maize cultivation area was calculated according to the agricultural
statisticsavailable.
(b) Ifthedistributedmaizecultivationareaexceededthestatisticalvalueofthatmunicipality
andifareasofenhancedbiogaspressurecrossedmunicipalboundaries,thenparcelswere
removedfrommaizecultivation,inreverseorderwithintheonemunicipality,andthe
respectiveareaofdemandwasrepartitionedtotheneighboringmunicipality(s).
(c) Theremainingmaizecultivationareawasdistributedbyselectingparcelsinorderoftheir
randomnumbersandsumminguptheirareauntilitmatchedthestatisticalvalue. Then,
maizecultivationwasassignedtotheselectedparcels.
(d) Theothercropsweredistributedontheremainingparcelsusingthesamemethodasfor
maize,butonlywithinthemunicipalborders.
Thegeneratedcropdistributionmapwasusedasthestatusquoforsimulatingchangesinthe
scenarios. Weusedthesameprocedureasdescribedabovetosimulateanincreaseofmaizeareaand
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a decrease of other crops. However, we did not consider new plant locations in the simulation of
thescenarios. Thedemandsformaizecultivationareas, asdescribedinthescenarioassumptions,
wereprojectedwithinmunicipalborders. Furthermore,therestrictedareaswereintroducedintothe
simulationofthetwoprotectionscenarios.
Theapproachwehavetakenhererepresentsonestrategyforconsideringpossiblelandusechange
arisingthroughscenarioprojections. Therearealsomorecomplexmodelsavailablethatcouldbe
employed. Forexample,sophisticatedapproachestomodellingofpotentialfuturecropdistribution
patterns, such as the land use and land cover change model CLUE-S [37,38], could result in other
simulatedscenariooutcomesthattakeintoaccountmoresitespecificcharacteristicsandhaveahigher
likelihoodofrepresentingfuturecroppatterns. TheapplicationofCLUE-Sislimitedbytheavailability
ofrelevantspatialdata, whichwerenotavailableforthestudyarea. Furthermore, thedecisionof
whichapproachtotakewillultimatelybebasedontheexpertise,dataandresourcesoftheorganization
engagedintheexercise. Asthefocusofthecurrentpaperisonexploringmethodologicalissues,we
haveemployedasetofsimplistic,butrealisticassumptionstoprojectpossiblelandusefutures. The
paperillustratesthatevenwithoutdetaileddataavailable,itispossibletouseasimplifiedframework
toexaminelandusechange.
3.4. AssessingPotentialImpactsofScenariosonVariousEcosystemServices
Biodiversityandtwoecosystemservices,namelyfoodproductionandlandscapeaesthetics,were
selectedtoassessthepotentialimpactsofeachscenario. Biodiversityandthetwoecosystemservices
wereselected,aschangesintheirsupplywereexpectedtooccurinresponsetochangesintheamount
ofbiogasproduced. Foreachaspectconsidered,wefirstreviewedindicatorsproposedintheliterature
(e.g.,[39–41])andthenselectedtheonesforwhichsuitabledataandmethodswereavailable. The
impactevaluationofbiodiversityreferredtodifferentiatedhabitatvaluescoresbasedonBierhalsand
vonDrachenfels[42]andvonDrachenfels[43]. Theywereamendedandrefinedfortheapplication
ofdifferentcroptypes[44]. Thisadaptedmethodisbasedontheevaluationofseveralbiodiversity
indices of different crop types and farm management intensities, for instance the species richness
ofthefieldflora. Asimplifiedapproachwasusedthatreliesonexpert-basedclassificationofcrop
typesaccordingtotheirenvironmentalriskforspeciesandhabitats[45]. Croptypeswithahigher
risk(maize,sugarbeet,winterwheat,rape,potato)wererankedloweronascalebetween1and2
(habitatvalueforarableland)[42],whilstcroptypeswithalowerrisk(oat,barley,springwheat)were
rankedhigher. Toevaluatethebiodiversityfunctionsofarablelandwithdifferentcroptypes,wechose
arangeof5classes,fromverylowtoveryhigh,andclassifiedthedifferentcroptypes(Table2). To
thisend,wesplitupeachofthecategories,“highrisk”and“lowrisk”definedbyUrbanetal.[45],
intotwoclassesrespectively,soastogetamoredifferentiatedresultthatstillfollowstheapproach
proposedbyBredemeieretal.[44].
Table2.Ecosystemservicevaluesforbiodiversityandlandscapeaesthetics.
LandscapeAesthetics*Shannon
BiodiversityRefinedHabitatValueScoresFollowing
Index(CropTypeDiversity)[57]
Bredemeieretal.[44]andUrbanetal.[45]
RasterCells Municipalities
Verylowvalue Habitatvaluescoreofď1.4(forexample:wheat) H=0–0.45 H=1.2–1.4
Lowvalue Habitatvaluescoreof>1.4–1.5(maize,sugarbeet) H>0.45–0.90 H>1.4–1.6
Mediumvalue Habitatvaluescoreof>1.5–1.6(barley) H>0.90–1.35 H>1.6–1.8
Highvalue Habitatvaluescoreof>1.6–1.7 H>1.35–1.80 H>1.8–2.0
Veryhighvalue Habitatvaluescoreof>1.7(rye) H>1.80–2.26 H>2.0–2.2
*Habitatvalues(H)betweenreferencelevels(i.e.,rastercellsandmunicipalities)arenotcomparablewitheach
otherduetothescaledependenciesoftheShannonindex.
Theimpactevaluationoffoodproductionconcernedboththeamountofareausedforproduction,
aswellastheleveloftheoreticalregionalself-sufficiencyforfoodconsumption. Theindicatorvalue
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wascalculatedasthepercentagetowhichtheannualfooddemandoftheregion1spopulation[46]
wasmetbytheannualfoodproductionfromagriculturewithinthesameregion. Thecomparison
wasbasedontheamountofenergydemanded/generatedperyearingigajoules. Thefooddemand
modellingassumedhomogenousnutritionpatternsof2400kJperdayformalesand1900kJperdayfor
females[47]. Foodsupplymodelingwasbasedonmeanyieldspersoilfertilityclassofdifferentcrop
typesintheHannoverregion[24]andanaverageenergyvalueforeachcrop[48–51]. Meatproduction
hasbeenindirectlytakenintoaccountbyusingamodifier(10%ofenergyinplantbiomass)[52]that
resemblestheenergyconversionoffoddercropsthathavebeencultivatedineachmunicipality[24].
The energy values for corn silage, silage grass and pressed sugar beet pulp were calculated using
thenetenergylactationandthemetabolicenergy[51]. Wecalculatedtheleveloftheoreticalregional
self-sufficiencyforfoodconsumptionforeachmunicipality,aswellasfortheentireregion.
Impactsonlandscapeaestheticswereevaluatedaschangesinthecroptypediversity[53–55].
ThevalueofthisdiversityindicatorwascalculatedusingtheShannonindex[56],foreachcellofa
2.5-km2rastergridandforeachmunicipalityintheregion(Table2). Theresultsofthecalculationhave
beencategorizedinto5classes,rangingfromverylowtoveryhighforbothapproaches,usingequal
intervals. Astheactualareaofarablelandwithineachrastercelldiffered,wedecidednottosimply
counttherastercellstoidentifysharesofeachclass. Instead,wecalculatedtheareaofarableland
foreachcellthathadapositiveevaluation(highandveryhigh). Theresultswerethensummarized
andcomparedtothesameresultsforeveryotherclass. Forillustrationpurposes,thescenarioimpacts
werecomparedagainstthesituationin2012and2009,respectively. Datafortheareaattributedforthe
cultivationofdifferentcropsforthesetwoyearscouldbetakenfromtheactualproductionstatistics[1],
andthespatialallocationofcropstospecificfieldswasdonerandomlyasdescribedabove.
4. Results
4.1. SpatialCropDistributioninDifferentScenarios
The2012,thesimulatedspatialdistributionofcropsreflectsthedifferencesinthenaturalsoil
fertilitythroughtheassociateddifferencesinpreferencesforparticularcroptypes(seeFigure4). Inthe
northernpartoftheregionofHannover,whichboastslessproductivesoilsthanthesouthernpart,
thereistraditionallyamuchstrongeremphasisonthecultivationofmaizeandotherfoddercropsfor
livestockthaninthesouth. Furthermore,theareautilizedforfoddermaizeequalstheareaofmaize
demandedbythehighnumberofbiogasplantssituatedinthispartoftheregionin2012. Thisleadsto
substantialsharesofmaizecultivationinthenorth. Nexttomaize,ryecultivation,grasslands,barley,
rapeseedandpotatoesarepredominantlyproducedinthenorthernpart. Thesouthernpartofthe
regionofHannoverhasmorefertilesoilsandoffersseveralprofitablealternativestomaizeproduction.
Inthispartoftheregion,thecultivationofsugarbeetandwheatwaspredominantandonlyafew
biogasplantsdemandedthecultivationofmaize.
Inthemarketscenario,thesimulationincludesa280%increaseofeachmunicipality1sshareofthe
areadevotedtomaizecultivation. Thisresultsinanaccentuationoftheunevendistributionofbiogas
productionacrosstheregionofHannover(seeFigure4). Insomenortherncountiesoftheregion,the
shareofarablelandforbiogascropcultivationgrewto73%,whilesomemunicipalitiesinthesouth
stillexhibitmaizesharesoflessthan10%.
Theresultofthecropdistributionsimulationusingtherestrictiveprotectionscenarioshowsa
considerablymoreevendistributionoflandsharesformaizecultivationacrossthecommunitiesthan
themarketscenario. Inthenorthernpartoftheregion, littleadditionalincreaseinareasharesfor
maizecultivationforbiogastookplace. Thiswasbecausethemaximumcapof15%ofarableland
formaizecultivationforbiogaspermunicipalitywasalreadyreachedorovershot. Furthermore,the
scenario1sexclusionofprotectedareasandregionssensitivetoerosionorotherimpactsonfaunafrom
maizecultivationforbiogasonlyresultedinmarginalchangesinthenorthofthestudyarea. The
southernpart,however,showsaconsiderableincreaseofareasharesformaizeforbiogasproduction.
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Thisresultsinagenerallymoreevendistributionacrosstheregion. Anoverallshareof14%ofthe
region1sarablelandiscultivatedwithmaize.
Thescenarioofconditionalprotectiondiffersonlyslightlyfromtherestrictiveprotectionscenario.
Thestrongerincreaseofmaizeforbiogasproductioninthesouthandsoutheastisduetolessarea
being excluded from increases in maize for biogas production on the basis of protection status or
ecological sensitivity. Altogether, 16% of the regions arable land is cultivated with maize. In this
scenario,thetotalamountisabithigherthantheallowed15%,becausemaizecultivationwasnot
reducedinmunicipalitiesthatalreadyhadmorethan15%.
Figure4.Resultsofthespatialsimulationofcropdistributioninthestatusquoandthescenarios.
4.2. ScenarioImpactsonBiodiversityandEcosystemServices
Theimpactevaluationforbiodiversityin2012showsthat19.2%ofthearablelandisofmedium
to very high value in terms of cropland habitats (see Figure 5). Conducting an impact evaluation
forbiodiversityinthemarketscenarioleadstoaconsiderabledecreaseofthisshareofareatoonly
10.1%. Intherestrictiveprotectionandconditionalprotectionscenarios,theareaofarablelandwitha
veryhighvalueisalmostthesame. However,theshareofareasofmediumtohighvaluedecrease
respectivelyby3%and4%. Thiscanbeexplainedbyachangeincultivationofalargepercentageof
areas,frombarleyandryetomaize,whichhasaslightlyworsecroplandhabitatvalue.
The results of the impact evaluation for food production shows that in 2012, the region of
Hannoversuppliedabout101%ofitstheoreticaldemandforfood. Inthemarketscenario,thelevelof
self-sufficiencydecreasedto89%. Therestrictiveprotectionandtheconditionalprotectionscenarios
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ledtoadecreaseoftheself-sufficiencylevelto96%and94%,respectively. Theregionwouldtherefore
loseitscurrentpotentialofbeingtheoreticallyself-sufficientintheprovisionofagriculturalproduce.
Theevaluationoflandscapeaestheticsofarablelandintermsofcroptypediversityshowsthat
in2012, about72%ofthearablelandwasevaluatedasofhighorveryhighvalue. Thesimulated
changesincropdistributionsinthemarketscenariohavedivergenteffectsoncroptypediversity. In
total,theshareofarablelandwithinrastercellsevaluatedasofhighorveryhighvaluedecreases
considerablyto64.2%. Thelandscapeaestheticvalues,inthemunicipalitiesinthewesternpartofthe
regionofHannover,generallydecreasebyonevaluationlevel. InthenortheastregionofHannover,no
changesincroptypediversityoccur,asthealreadyhighshareofmaizeproductionwaskeptmoreor
lessconstant.
Therestrictiveprotectionandconditionalprotectionscenariosleadtoonlymarginalincreasesof
theshareofarablelandwithinrasterfields,whichwereevaluatedasahighorveryhighvalue.Aslight
increaseofthecroptypediversitycanbedeterminedintwomunicipalitiesinbothprotectionscenarios.
Figure5.Resultsofimpactmodelling.
Comparingthevariousimpactsofthethreescenariosonbiodiversityandthetwoecosystem
services,itispossibletoillustratetrade-offsandsynergies(Figure6). Theincreaseofmaizeproduction
haspredominantlynegativeeffectsonbiodiversity,foodproductionandlandscapeaesthetics. This
especiallyapplieswhentheshareofmaizeissignificantlyincreased,asshowninthemarketscenario.
Despitethesegenerallynegativeeffects,anincreaseofmaizecultivationforbiogascanalsohavea