An indicator of pesticide leaching risk to groundwater

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An indicator of pesticide leaching risk to groundwater

Anna Lindahl, Christian Bockstaller

To cite this version:

Anna Lindahl, Christian Bockstaller. An indicator of pesticide leaching risk to groundwater. Ecolog-

ical Indicators, Elsevier, 2012, 23, pp.95-108. �10.1016/j.ecolind.2012.03.014�. �hal-01137065�

ContentslistsavailableatSciVerseScienceDirect

Ecological Indicators

j ou rn a l h o m e pa g e :w w w . e l s e v i e r . c o m / l o c a t e / e c o l i n d

An indicator of pesticide leaching risk to groundwater

Anna M.L. Lindahl

, Christian Bockstaller

aEquipeAgriculturedurable,INRA,BP20507,68021Colmar,France

bDepartmentofSoilandEnvironment,SwedishUniversityofAgriculturalSciences,P.O.Box7014,75007Uppsala,Sweden

cINRA,UMR1121Nancy-Université– INRA,IFR110,BP20507,68021Colmar,France

a r t i c l e i n f o

Articlehistory:

Received23December2011 Receivedinrevisedform6March2012 Accepted14March2012

Keywords:

Data-mining Fuzzyinferencesystem I-Phyindicator MACROmodel Pesticideleaching Preferentialflow

a b s t r a c t

Sincethe90sanincreasingnumberofassessmentmethodsusingoperationaltoolslikeindicatorshave beenproposedforenvironmentalissueslinkedtopesticides,amongthem,groundwatercontamination bypesticidetransfer.Toourknowledgenoneoftheseindicatorsaddresspreferentialflow,animpor- tantprocessdeterminingpesticideleaching.Theobjectiveofthisstudyistwofold:(i)todevelopanew groundwatersubindicatorforanexistingindicator,I-Phy(formerIpest),thatexplicitlytakepreferential flowintoaccount,and(ii)totestthepossibilityofdevelopinganindicatorbymeansofdata-mining methodsusingsimulationsofamechanisticmodel.Thegroundwatersubindicatordevelopedisinthe formofdecisiontreesbasedonfuzzyinferencesystems.Itwasderivedthroughneuro-adaptivelearning ondatasetsfromsimulationsrunningtheprocess-basedMACROmodel.Unlikethepreviousversion,the newindicatorconsiderspreferentialflow,climaticdifferencesanddifferencesinsoiltexturewithdepth.

Otherbenefitsarelessdependencyonexpertknowledgeandthepossibilitytointegrateabroadrange ofconditions.

©2012ElsevierLtd.Allrightsreserved.

1. Introduction

Theintensiveuseofpesticidesinmodernagriculturehavehad animpactonhumanhealth,livingorganisms,ecosystemfunction- ing,waterquality,etc.(Perrin,1997).Duringthelasttwodecades, thishasledtoagrowingconcernamongdifferentgroupsofstake- holders.Developmentofdifferentkindsofsolutions,suchasnew technologies, newresistant cultivars, redesign of plant protec- tionsystemsand croppingsystems, isontheagendatoreduce the dependency on chemicals (e.g. ENDURE, 2011). A general agreementisthatthereductionofpesticiderisksinconventional agriculturegoeshandinhandwiththedevelopmentofassessment toolsofpesticideriskandimpact(Bockstalleretal.,2009).Sincethe 90sanincreasingnumberofassessmentmethodsusingoperational toolslikeindicatorshavebeenproposedforenvironmentalissues linkedtopesticides.Theseindicatorsaremoreadvancedthanthe formerassessmentsbasedsolelyofpesticidesweightandvolume (Levitan,2000).Toourknowledge,noneoftheexistingpesticide environmentalriskindicatorsaddresspreferentialflowasamech- anisminvolvedingroundwatercontamination(seee.g.reviewsby ReusandLeendertse,2000;Maudetal.,2001;Bockstalleretal., 2009).Nevertheless,preferentialflowisimportantinarangeof

∗ Correspondingauthor.Presentaddress:DepartmentofSoilandEnvironment, SwedishUniversityofAgriculturalSciences,P.O.Box7014,75007Uppsala,Sweden.

Tel.:+4618671169;fax:+4618673156.

E-mailaddress:anna.lindahl@slu.se(A.M.L.Lindahl).

differentsoils(Flury,1996)andoccursinstructuredsoilsthrough macropores(BevenandGermann,1982),asfingerflowinwater repellentsandysoils(Ritsemaetal.,1993;DekkerandRitsema, 1996)orlayeredsoils(HillandParlange,1972),orasfunnelflow (Kung,1990).Simulationstudieshaveshownareducedinfluenceof pesticidepropertiesonleachinginthepresenceofmacroporeflow (LarssonandJarvis,2000).Infieldstudies,thesimultaneousarrival ofvariouspesticidesintiledrainsdespitetheirdifferentsorption characteristics(Kladivkoetal.,1991;Traub-Eberhardetal.,1994) hasbeenattributedtopreferentialflow.Insituationsofpreferen- tialflow,indicatorsbasedonasimpleindexconsideringpesticide properties onlyare unabletocorrectlyassess thefateofpesti- cideconsideredas‘non-leachable’.AnexampleistheGUS-index (Gustafson,1989),commonlyusedforestimatingtheleachability ofpesticidesthroughweighingtheeffects ofthepartition coef- ficienttosoilorganiccarbon(Koc)andthedegradationhalf-life (DT₅₀)ofthepesticide. Preferentialflow occurringshortly after applicationcancausehighlossesevenforfastdegradingpesticides.

Furthermore,experimental(Reichenbergeretal.,2002)andmod- elingevidence(McGrathetal.,2008)suggestthatrapidpreferential transportincreaseswithincreasingsorptioncapacity.Thismaybe duetothatweaklysorbingpesticideswillbetransportedawayfrom thesoilsurfacebymatrixflowfollowingsmallamountsofgentle rain.Atsuchweatherconditions,morestronglysorbingpesticides willbemoresusceptiblefortransportbypreferentialflowincase ofarainstormatalaterdatesincetheyareretainedinthesoil nearthesurfaceforalongerperiodoftime(McGrathetal.,2008).

Soilwatercontentatthetimeofapplicationandthesubsequent 1470-160X/$–seefrontmatter©2012ElsevierLtd.Allrightsreserved.

doi:10.1016/j.ecolind.2012.03.014

precipitationpatternarethusimportantfactorssincetheyregulate whetherpreferentialflowistriggeredornotandtowhatextent.

Approachesfordeterminingworst-casepesticidepropertiesfora specificsoil,withrespecttoleaching,thereforeneedtotakeinto accountnotonlypesticidespropertiesbutalsofactorsdetermining andregulatingpreferentialflow.

Deterministicsimulation modelsof pesticidefateand trans- portinthesoilthataccountformacroporeflowareavailable(e.g.

MACRO;Larsboetal.,2005;Köhneetal.,2009).However,exten- sivedatarequirementsmakethesemodelsdifficult,orimpractical, touseforsite-specificexposureassessments.Awaytoovercome thismajordrawbackcouldbetoderiveindicatorsfromcomplex dynamicmodelsusingmeta-modelingapproaches(Pi ˜nerosGarcet etal.,2006;Bockstalleretal.,2008a).Amajoradvantageofsuch model-based indicators is that the causesof an impactcan be deduced(i.e.,mainfactorsresponsibleforaneffectcanbeiden- tified)whensimulatingdifferentscenarios.Inthedevelopmentof suchmeta-modelsitshouldbeconsideredthatsimulationmod- elsofpesticidefateandtransportarenon-linearintheirresponse to changes in soil and pesticide parameters. Simulation meta- models using linear regression techniques are therefore likely tofail(Bouzaheretal.,1993).Approachesaddressingnon-linear relationsin meta-modeling are however available.Examples of meta-modelingtechniquesusedtoassesstheprocessesofpesti- cidefateandtransportinthesoilsareneuralnetworks(Stenemo etal.,2007)andlook-uptablesoftheMACROmodel(Holmanetal., 2004),andthefittingofsimulationstoasimplemathematicalfunc- tion(asdonebyBouzaheretal.(1993)fortheRUSTICmodeling system(Deanetal.,1989)).

I-Phy(formernameIpest)(vanderWerfand Zimmer,1998;

Bockstalleretal.,2008b)isawell-documentedpesticideindicator and a constituent ofthe INDIGO methodfor assessing sustain- abilityofagriculturalsystems(Bockstalleretal.,1997,2009).It has been compared to other European indicators (Reus et al., 2002), adapted andimplemented indifferentsituations aswell asin researchprojects (Bues et al.,2004; Arregui et al., 2010;

Chikowo et al., 2009) and more than 130 extensions projects (e.g. Novak et al., 2009). But, in common with many other indicators,itsgroundwatermoduleforassessingpesticideleach- ing risk neglects preferential flow and heavily relies on the GUS-index. Consequently, there is a risk that I-Phy underesti- mate thepesticide leaching risk for soilsprone to preferential flow.

Theobjective of this study istwofold: (i) todevelop a new groundwatersubindicatorofI-Phyaddressinginanexplicitway preferentialflow,and(ii)totesttheapproachstatedbyBockstaller etal.(2008a),i.e.,developinganindicatorbymeansofdata-mining methods usingsimulations of a mechanistic model. Toachieve theseobjectives,wedecidedtobasethenewindicatoronsimu- lationscarriedoutwiththeprocess-basedmacroporeflowmodel MACRO (Jarvis, 1994; Larsbo et al., 2005). To avoid creating a

‘blackbox’ofstatisticalempiricalfunctionslackinganyclearmean- ing,ascanbetheproductofmetamodeling(e.g.Pi ˜nerosGarcet etal., 2006;Stenemo et al.,2007), wechoseto keepthe I-Phy structurewhich isin theformof decisiontrees usinglinguistic rulesthatareeasytounderstandand freefromcomplexmath- ematicalfunctions. We alsodecided toapply fuzzysubsets for continuousinputvariablestoavoidknife-edgeeffectsattheclass boundaries(Prato,2005).Wetherebyretainedthedesignchoice oftheoriginalI-Phyindicatorasitalsoisafuzzyinferencesys- tem.Adata-miningmethodofneuro-adaptivelearningtypewas appliedtoallowfortheparameterizationofthemanymember- shipfunctionsandrulesoftheinferencesystemdeveloped.The resultingscoresofthenewindicatorwerecomparedtothat of theformerforsomecommonscenariosofpesticideapplicationin France.

2. Materialsandmethods

2.1. Indicatordesign

2.1.1. Overviewoftheformerindicator

I-Phy(vanderWerfandZimmer,1998;Bockstalleretal.,2008b) isanexpertsystemforcalculatingindicatorsreflectingthepoten- tialenvironmentalimpactoftheapplication ofa pesticidein a field crop. The model is basedon literature,experimentaldata andexpertknowledge,addressinguncertaintyusingafuzzylogic approach.I-Phycompriseseveralmodules(e.g.forestimatingthe riskofcontaminationofsurfacewatersortheair)offuzzyinference systemtype.Themodulesarestructuredintheformofdecision treesrepresentingtheextremesituationsofcombinationsoftwo fuzzysubsets;favorable andunfavorablestatesofthevariables.

A membershipfunction is associated witheach variable ofthe decisiontreesothatafuzzyclasscanbecalculatedforvariable values thatfallbetweentheextremes.The finaloutputof each moduleisanindicatorscorecalculatedusingtheSugeno’sinfer- encemethod(Sugeno,1985)alsoimplementedbyvanderWerf andZimmer(1998)aswellasbyFragoulisetal.(2009).Adetailed descriptionofI-Phy,withcalculationexamples,isgiveninvander WerfandZimmer(1998).InthelatestversionofI-Phy(Bockstaller etal.,2008b),theindicatorscoresrangeonascaleofenvironmental performance,from0(representingahigh-riskscenario)to10(rep- resentingano-riskscenario),withanacceptableriskdefinedat indicatorscore7.Thesamescaleisusedforallindicatorsincluded intheINDIGOassessmentmethod(Bockstalleretal.,1997,2009).

ThechoiceofthegeneralscaleofINDIGOwasdrivenbytheneedto provideuserswithascalethatiseasytounderstand.Toimprovethe farmers’perceptionoftheassessmentmethods,apositivescalewas chosenratherthanariskscale.Thereferencevalueof7allowstoset anoperationalandacceptable(betterthanaverage)targetwhich isnotthe“ideal”targetof“zeroimpact”,sometimesimpossibleto reachinshortterm.

2.1.2. Indicatordesignworkflow

The development of the groundwater indicator module (referredtoastheindicator)consistedofthefollowingfoursteps (seeFig.1):

Step1.Selectingrelevantinputvariables:

Theselectionof variables isanimportant step.Allvariables, whichhaveasignificantimpactonthepesticidedynamicsrele- vantforthespecificconditionsforwhichthedevelopedindicator istobeapplied,needtobeidentified.Potentialinputvariables wereselectedaccordingtoourexpertknowledgeofthepesticide leachingprocessandpesticideuse,soilandcroppingconditions withinagriculturalsystemsinFrance.Furthermore,theinputvari- ablevaluesshouldbereadilyavailableindatabases(e.g.pesticide properties)orknowntotheendusers(e.g.information onsoil properties,pesticideapplication,climate,etc.).Thiscriterionfacili- tatestheuseoftheindicator.Togiveinsightintohowpesticideloss relatestotheinputvariables,itisalsodesirablethattheindicator iseasytounderstandandinterpret.Avastnumberofinputvari- ableswouldproduceoverlycomplexdecisiontrees.Wetherefore strivetolimittheirnumber.Thevariablestakenintoconsider- ationwhendevelopingtheindicatoraresummarizedinTable1.

Differenttypesofcroppingarealsoconsideredbutareexcluded fromTable1sincethecropsarefixedtothevariableseasonof application(i.e.,springapplicationonmaizeandwinterorautumn applicationonwinterwheat).Allinall,theMACROmodelwas parameterizedforfivediscreteandfivecontinuousvariables.The assessmentofthesensitivityofseveralMACROinputvariablesin apre-studyof1944simulationrunsenabledustorejectsomeof them.

Fig.1. Theworkprocessflowchart.Actionsinsquareswithgraydottedbordersandoutputsinsquareswithblacksolidborders.

Step2.Choosingthestructureoftheindicator,settingupthesim- ulationscenariosandrunningthemechanisticmodel:

Tolimitthecomplexityoftheindicator,adecisionwasmadeto treatsomeinputvariablesasdiscrete(e.g.seasonofapplication andclimaticzone).Asaconsequence,thestructureoftheindica- torconsistofanumberoftreesthattheuserhastochoosefrom dependingonthescenarioreflectedbythecombinationofthese variables(e.g.springapplicationinsouthernFrance).TheMACRO modelwasparameterizedusingthelistofpotentialinputvari- ables(establishedinstep1)usingpedotransferfunctionswhen necessary,reasonableworst-caseassumptionsanddefaultvalues.

Foreachcontinuousvariable,weselectedvaluesthatweapriori consideredtocorrespondtofavorableandunfavorableconditions inregardsofpesticideloss(seeTable1).Anumberofintermediate valueswerealsoselectedwithintheseintervals.Abroadrangeof differentcombinationsofcontinuousinputvariablevaluescould therebybeexecutedforalldiscretescenariospossible.Thedetails ofthissteparegiveninSection2.3below.

Step3.Assessingthepesticideleachingrisk:

Table1

Theselectedpotentialinputvariablesandtheirranges.^a

Variable Type Variablevalues

Depthofsoilprofile(cm) Continuous 40–100

foc(%) Continuous 1–3

Stoniness^b(%) Continuous 0–10

DT50(days) Continuous 5–60

Koc(cm³g⁻¹) Continuous 27–600

Topsoiltexture Discrete Coarse,medium,fine

Subsoiltexture Discrete Coarse,medium,fine

Tillage^c Discrete Primarytillage,notillage

Seasonofapplication Discrete Winter,spring,autumn

Climaticzone^d Discrete 1,2,11

afoc=topsoilorganiccarboncontent,DT50=degradationhalf-life,Koc=partition coefficienttosoilorganiccarbon.

bAppliestocoarsetexturedsoils.

c Appliestotopsoilsofmediumandfinetexture.

d FOOTPRINTclimaticzones(seeCentofantietal.,2008).

Itisnecessarytodefinewhattheindicatorscorescorrespondto inregardsofpesticideleachingrisktogroundwater.Wemadetwo choicesregardingthemeaningoftheindicatorscores:

(i)Wesettheacceptablelevelofpesticidelossto0.1gha⁻¹,cor- respondingtotheindicatorscoreof7(Table2).Thislevelof pesticidelossmaygenerateaconcentrationof0.1␮gL⁻¹(cor- respondingtotheEUdrinkingwaterlimit(EuropeanUnion, 1991))foradrainageof100mm.Thisisprobablyaconserva- tiveassumptionifweassumethatinmostsituations,drainage variesbetween50and650mm(ChoisnelandNoilhan,1995).

(ii)Wesettherelationshipbetweenthepesticidelossand the valueoftheindicatorscoresothattwoindicatorscoreunits correspondtoalog₁₀changeinpesticideloss.Thisgivesan indicatorcoveringabroadrangeofpossiblepesticideloads scoring0forpesticidelossesof550gha⁻¹ormoreto10for lossesof0.0055gha⁻¹orless.Thisapproachisjustifiedbythe uncertaintyofmodeloutputs.Thepointhereisnottodetect minordifferencesin pesticidelossbut rathertoassess the orderoftheloss(Lewisetal.,1999).

Step4.Derivingthefuzzyinferencesystem:

Theinput/outputdatasetsof4752MACROsimulationswere usedtoestimatemembershipfunctionsrepresentativeofthefea- turesofthedata.However,theoutputoftheMACROmodelhadto betranslatedtoindicatorscoresfirst,producinginput/outputindi- catordatalearningset.AfuzzyinferencesystemofSugeno-type

Table2

Relationshipbetweenpesticidelossandindicatorscores.

Pesticidelosses(gha⁻¹y⁻¹) Associatedscore

Lessthan0.0055 10

From0.0055to0.01 From10to9

From0.01to0.1 From9to7

From0.1to1 From7to5

From1to10 From5to3

From10to100 From3to1

From100to550 From1to0

Morethan550 0

wascreatedthroughapplyingtheMATLABFuzzyLogicToolbox (MathWorks,2002)algorithmsforneuro-adaptivelearningtothe dataset.A Sugeno inferencesystemis well suited for model- ingnon-linearsystems(Sugeno,1985;BabuˇskaandVerbruggen, 2003). During the learning process, theparameters associated withthemembershipfunctionsandassociatedrulesareadjusted accordingtoachosenerrorcriterion.Twomembershipfunctions arederivedforeachinputvariable,onefordeterminingthedegree towhichtheinputbelongstotheunfavorablefuzzysetandthe otherfordeterminingthedegreeofmembershiptothefavorable fuzzyset.Thepossiblecombinationsoffavorableandunfavorable valuesoftheindicatorinputvariableseachmakeuparule.The outputofeachruleismultipliedbyaweight(w)thatreflectsits strengthwithinthefuzzyinferencesystemforaspecificcombina- tionofvariablevalues.Theoutput,ofthefuzzyinferencesystem, I_base, is the weighted average(the only option for the neuro- adaptivelearningalgorithmoftheMATLABFuzzyLogicToolbox) ofallruleoutputs(z_itozN):

I_base=

i=1w_i·z_i

i=1w_i

, for 0≥I_base≤10. (1)

Auniquefuzzyinferencesystemwasdevelopedfrom44simu- lationsforeverycombinationofdiscretevariablesinfluencingthe pesticideleaching.However,somecombinationscouldbepruned awaysincesomedatasetswerevalidatedbyfuzzyinferencesys- temsbasedondifferentdatasets(inregardsofdiscretevariables).

Thenumberoffuzzyinferencesystemscouldtherebybereduced.

Aspruningcriterion,arootmeansquareerror(rmse)≤0.5indica- torscoreswasjudgedasasufficientmatchbetweenadatasetand aninferencesystem.

2.2. Simulationmodel

MACRO(Jarvis,1994;Larsboetal.,2005)isaone-dimensional dual permeability model simulating a full water balance and the fate and transport of pesticides at the column scale. The pore system is divided into two domains, the micropore and themacroporedomain,whichhavetheirownsoluteconcentra- tion, pressure head, water content and hydraulic conductivity.

In the micropore domain, water flow is described using the Richard’sequation(Richards,1931).Waterflowinthemacropore domainisdescribedbyamodifiedkinematicwaveequation,which containstwoparameters,macroporeconductivityandanexponen- tialreflectingmacropore connectivityand tortuosity(Germann, 1985).The water retentionfunction isdescribed by a modified formofthevanGenuchtenfunction(vanGenuchten,1980).The advection–dispersion equation (van Genuchten and Wierenga, 1976)isusedtodescribesolutetransport inthemicropores.An advectiveflow of solute, neglecting dispersion,is used for the macropores. Pesticide sorption is described using a Freundlich isotherm,whiledegradationisdescribedusingfirstorderkinetics, withtheratecoefficientgivenasafunctionofsoiltemperatureand moisturecontent(BoestenandvanderLinden,1991).Approximate first-orderexpressionsareusedtocalculatewatertransferfrom themacroporestothemicroporesandsoluteexchangebetween thetwodomains.Thistransferiscontrolledbythestrengthofthe macroporeflow, describedbytheeffectivediffusionpathlength whichisan‘effective’parameterreflectingsoilstructuraldevel- opment.

2.3. Simulationscenariosandparameterization

TheMACROmodelwasparameterizedfromsoilorganiccarbon content,soiltexture,pesticideproperties,cropproperties,tillage operationsand climaticproperties.Adetaileddescriptionofthe

Table3

TheeightclimaticfactorsdefiningtheclimaticzoningofEuropewithintheFOOT- PRINTproject,andtheirmeanvaluesforgridcellswithineachclimaticzone.

Climaticfactor Climaticzone

1 2 6 11

MeanApriltoJune temperature(^◦C)

13.4 11.5 5.9 13.0

MeanSeptembertoNovember temperature(^◦C)

11.7 9.8 4.8 13.0

MeanOctobertoMarch precipitation(mm)

485 368 765 606

Meanannualprecipitation (mm)

936 733 1695 942

Numberofdays(ApriltoJune) wheretotalprecipitation

>2mm

30.5 32.5 36.5 27.4

Numberofdays(ApriltoJune) wheretotalprecipitation

>20mm

2.6 1.5 3.3 1.7

Numberofdays(ApriltoJune) wheretotalprecipitation

>50mm

0.1 0.1 0.1 0

Numberofdays(Septemberto November)wheretotal precipitation>20mm

3.3 2.1 3.2 3.1

DatafromBlenkinsopetal.(2008).

parameterizationofsoilphysicalandhydraulicpropertiesisgiven inAppendixA.Thesimulationperiodwas26years,inwhichthe firstsixyearsareusedasawarmingupperiodinordertominimize theinfluenceoftheinitialconditions,andthelast20yearsareused asoutput.Pesticideamountlosttogroundwaterwascalculatedand the80thpercentileyearlyamountlost(i.e.,thefourthlargestofthe 20simulatedyearlyamountsofpesticidelost)wasidentifiedasthe targetoutputtobasetheindicatorupon.Thesimulationswerecar- riedoutintwosteps.Thesimulationsofthefirststepwereused asanaidinthedeterminationoftheindicatordesign.Thesesimu- lationswerealsousedforderivingtheparametersoftheindicator incombinationwithcomplementarysimulationscarriedoutina secondsimulationstep.

2.3.1. Climaticscenarios

A climatic zoningof Europe based on eight climaticfactors (Table 3), foundtomostlyaffect pesticidelossbyleaching and drainage,havepreviouslybeenperformed(Blenkinsopetal.,2008) withintheFOOTPRINTproject(FOOTPRINT,2011).Accordingto thisclassification,Europeisdividedintoatotalof16climaticzones ofwhich four(zone1,2,6and 11,following thenumberingof Centofantietal.(2008))arefoundwithinFrance(Fig.2).Themean valuesoftheeightclimaticfactorsofthesefourzonesarepresented inTable3.The‘NorthMediterraneanclimate’(zone1)canbesum- marizedaswarmandwithmoderateprecipitation.The‘Temperate maritimeclimate’(zone2)alsohaveamoderateprecipitationbut consistoffewerextremes.The‘Alpineclimate’(zone6)arecool andwetandcharacterizedbyrelativelymanyextremeevents.The

‘Modifiedtemperatemaritimeclimate’(zone11)iswarmandwet butwithratherrelativelyfewwetdaysinthespring.Zone6was excludedfromtheindicatorasapotentialclimaticscenariosince thecultivatedareainthispartofFrance(theAlps)isnegligible.

Modifiedclimaticseriesof26years(1975–2000)obtainedfrom weather stations that are representative of the climatic zones (Blenkinsopetal.,2008)foundinFrancewereusedasdrivingdata intheMACROmodel.Themodificationconsistedofdisaggregating dailyprecipitationtotalsintohourlyprecipitationusingascaling cascademodel(Olsson,1998)andsettingtheprecipitationto0mm onthedayofpesticideapplicationsincesprayingonadayofheavy rainfallisnotinaccordancewithgoodmanagementpractices.

Fig.2. ThefourclimaticzonesofFrance.Zone1:‘NorthMediterraneanclimate’,zone 2:‘Temperatemaritimeclimate’,zone6:‘Alpineclimate’,andzone11:‘Modified temperatemaritimeclimate’.

2.3.2. Cropandpesticideapplicationscenarios

Twodifferentcropscenarios wereconsidered,winterwheat sown in autumn and maize sown in spring. These crops were selectedsincetheyarethemaincropsregardingsownsurfacearea inFrance(Agreste,2010).Threedifferentseasonsofpesticideappli- cationwereconsidered,autumnapplicationonwinterwheaton the20thofSeptember,winterapplicationonwinterwheatonthe 1stofMarchandspringapplicationonmaizeonthe30thofApril.

Thecombinationsofclimaticzonesandseasonsofapplicationgive atotalofnineuniqueweatherregimes.

Themaximumrootdepthofthecropswaslimitedtothedepthof thesoilprofileexceptforsubsoilsofcoarsetexture(sandcontent

>65%andclaycontent<18%,accordingtoCEC(1985))forwhich therootpenetrationwasconsideredtobelimitedto45cm.The otherMACROcropparameters usedfollowsthoseoftheFOOT- PRINTproject(Jarvisetal.,2007)andMACROdefaultvalues.

Eventhoughthespringapplicationsoccurpost-emergence,all ofthepesticidewassimulatedassprayeddirectlyonthesoil,i.e., interceptionofcanopywasnotaccountedforintheMACROsimula- tions.Instead,thepesticidedosereachingthesoilsurface(referred toastheeffectivedose),Dose_eff,isgivenby:

Dose_eff=Dose_appl·F_int, (2)

whereDose_applisthepesticidedoseappliedonthefieldandF_int isafactorcorrespondingtotheproportionofthepesticidethat actuallyreachthesoilsurface(deductingfore.g.winddriftand cropinterception).

2.3.3. Pesticidescenarios

The leaching of sevenhypothetical pesticides withdifferent DT50 and Koc was simulated for an effective dose of 1kgha⁻¹ (Table4).Thepesticidesvariedwithinaspanrangingfromhigh risk leachers (as they were both mobile (Koc=27cm³g⁻¹) and moderatelypersistentinthesoil(DT50=60days))tolowriskleach- ers(beingslightlymobile(Koc=600cm³g⁻¹)andnon-persistent (DT₅₀=5days)).Threeof thepesticideswereusedasanaidfor determiningtheindicatordesign(Table4a),theotherfourwere simulatedascomplementarypesticidesforderivingthemember- shipfunctionsoftheindicator(Table4b).

Pesticidedegradation usuallydecreaseswithdepth(Boesten andvanderLinden,1991).Tocapturethischaracteristic,thedegra- dation ratecoefficients of the second and third horizonswere calculatedbymultiplyingthecorrespondingparametervaluefor

Table4

Propertiesof(a)thepesticidessimulatedfordeterminingtheindicatordesign,(b) thecomplementarypesticidessimulated.^a

a)

Pesticidevariant DT50(days) Koc-value(cm³g⁻¹)

A 5 300

B 30 130

C 30 600

b)

Pesticidevariant DT50(days) Koc-value(cm³g⁻¹)

D 5 27

E 5 600

F 60 27

G 60 600

aAbbreviationsasinTable1.

Table5

Texturalcompositionofsoilsselectedtorepresentsoilsofdifferenttexturetype.

Texturetype Textureclass Claycontent(%) Siltcontent(%) Sand content(%)

Fine Siltyclay 45 50 5

Medium Loam 25 40 35

Coarse Loamysand 5 15 80

thefirsthorizonwiththefactor0.5and0.3,respectively.Thesefac- tors,basedonaliteraturereview,areusedinleachingassessments forpesticideregistrationintheEU(FOCUS,2000).

Thesorptionwasmodeledasinstantaneouslinearandthesorp- tioncoefficientwasobtainedby multiplyingK_oc (cm³g⁻¹)with theorganiccarboncontent.Therationaleforadoptingasimulation setupassuminglinearsorptionanddegradationisthatvariations inpesticidedosecaneasilybeaccountedforbyscalingtheleaching predictedatadoseof1kgha⁻¹.Giventherelationshipsbetween indicatorscoresandpesticidelossinTable2andthelinearrelation betweenpesticidelossandapplicationrate,indicatorscores(I_eff) foranyDose_effiscalculatedas:

For10^y≤Dose_eff<10^(y+1)kgha⁻¹,whereyisaninteger, ify≥0(i.e.,Dose_eff≥1kgha⁻¹)

I_eff=MAX

I_base−2·

y+Dose_eff·10^−y−1 9

;0

(3a) ify≤−1(i.e.,Dose_eff<1kgha⁻¹)

I_eff=MIN

I_base−2·

y+Dose_eff·10^−y−1 9

;10

(3b) whereI_base,istheoutputofthefuzzyinferencesystem(i.e.,the indicatorscoreobtainedforaneffectivepesticidedoseof1kgha⁻¹) calculatedaccordingtoindicatorequation(1)giveninSection2.1.2 above.

2.3.4. Soilscenarios

Threedifferenttexturalcompositionsofclay,siltandsandcon- tents(Table5)wereselectedtorepresentsoiltypesoffine,medium andcoarsetexture(fordefinition,seeTable6).Thesechoiceswere

Table6

Classificationofsoilsofdifferenttexturetype.

Texturetype Texture(USDA)

Fine Clay,clayloam,siltyclayandsiltyclayloam Medium Sandyclay,sandyclayloam,loam,sandyloam,siltand

siltloam

Coarse Loamysand,sand

Fig.3.Modelstructureoftheindicatorinferencesystems.

basedonapre-studyof MACROsimulationsofonerepresenta- tivesoilfromeachofthe12USDAtexturalclasses(USDA,2011) (resultsnotshownhere).Forfinetexturedsoils,thesiltyclaygave themostpesticideleachingandwasthereforechosentorepresent thesetypesofsoils.Amongthemediumtexturedsoils,theloamand thesandyclaywerefoundtobethemostsensitivetothedegree ofsusceptibilitytopreferentialflowandaretherebyalsopoten- tiallysensitivetotillageoperations(seeAppendixA).Ofthesetwo soilstheloamwasselectedtorepresentsoilsofmediumtexture.

Eventhoughthesandysoilgavethehighestpesticideleaching,the loamysandwaschosentorepresentcoarsetexturedsoilssinceit isuncommonthatsandysoilsarecultivatedforarablecrops.

In the first simulation step, the MACRO model was param- eterizedfor soil profiles of either 40cm or 100cm depth. The soilprofiles weredividedinto two orthree horizons(0–30cm, 30–60cmand60–100cm).Thesecondandthirdhorizons(jointly referredtoasthesubsoil)ofeachsoilprofilewerealwaysassigned the same textural composition while the textural composition ofthefirsthorizon(referredtoasthetopsoil)coulddifferfrom that of the subsoil. Allin all,nine textural combinations were simulated. For the topsoils, an organic carbon content of 1%

and3% weresimulated,whereas theorganiccarboncontent in thesubsoils wasfixedto 0.5%.These values were alsoused in the second simulation step, supplemented with soil scenarios having topsoil organic carbon contents of 2% and soil depths of70cm.

Itiscommonthatsandysoilscontainaratherhighpercentageof stonesandconsequentlycontainlessactivesoilmaterialfordegra- dationandadsorption.Asaneffect,suchsoilshaveapotentialto leachmorethanacomparablesoilwithoutstones.Inapre-study, theleachingofthepesticidesdefinedinTable4awerethereforealso simulatedforsoilprofilesofcoarsetexturedtopsoilwithastone contentof10%,overlayingeithercoarsetexturedsubsoilcontaining thesamestonecontent,ormediumorfinetexturedsubsoilwithout stones.

2.4. Comparisonofthenewandtheoldindicator

Acomparisonofthenewandoldgroundwaterindicatormodule wasconducted.Thecomparisonwascarriedoutfortwodifferent

soils,ashallow,finetexturedsoilandadeep,mediumtextured soil,locatedinclimatezone1.Theorganiccarboncontentis2%

forbothsoils.Thecomparisoncomprisesthetwocrops,maizeand winterwheat.Twomaizeherbicideswereappliedinspringand onewheatherbicidewascomparedforautumnorwinterapplica- tion.Additionally,glyphosatewascomparedonmaizeinspring and onwinter wheatinautumn. Dataonpesticidecharacteris- ticswerederivedfromthedatabaseusedfortheI-Phyindicator (non-published).Thisdatabaseisbasedonacompilationbetween theFrenchdatabasefor registrationAGRITOX(2011),andother sources (e.g.Tomlin, 2009). Twodifferent doses wereused for eachpesticide.Thesedosesareinlinewiththoserecommended bythemanufacturersandconsistentwithlevelsimplementedby farmers.

Fig.4.Inputmembershipfunctionplotforsoilprofiledepth(cm)foranautumn applied,mediumtexturedsoilprofile,locatedinclimatezone1.

Fig.5. Ruleoutputparameters,z,ofthefuzzyinferencesystemforautumnapplied,untilledmediumtexturedsoilprofile,locatedinclimatezone1.Lcorrespondtoa 1^◦membershipofthelefthandmembershipfunctionand0^◦membershipoftherighthandmembershipfunctionandRcorrespondtoa1^◦membershipofthelefthand membershipfunctionand0^◦membershipoftherighthandmembershipfunction.AbbreviationsasinTable1.

3. Results

3.1. Decisiontreevariableselection

Accordingtothepre-study,pesticideleachingwasonlyslightly affectedby stonecontent incoarsetextured topsoil and tillage practiceonfinetexturedtopsoil.Forstoniness,thedivergencein pesticideleachingriskbetweensoilswithnostonecontentand soilswithastonecontentof10%exceeded0.5indicatorscoreunits for14outof324pairsofcomparisoncases(3pesticidevariants, 9weatherregimes,3subsoiltextures,2soildepthsand2topsoil organiccarboncontents).Theaverageandmaximumdivergence were0.1 and 1.2 indicator scoreunits, respectively. Thecorre- spondingvaluesforthedifferenttillagesystemsonfinetextured soilwerenineoutof324pairsofcomparisoncases(3pesticide variants,9weatherregimes,3subsoiltextures,2soildepthsand2 topsoilorganiccarboncontents)divergingmorethan0.5indicator scoreunits,withanaverageandmaximumdivergenceof0.1and1.4 indicatorscoreunits,respectively.Thesedifferenceswerejudged nottobesignificantenoughtojustifytheirinclusionintheindicator design.Stoninessincoarsetexturedsoilsandtheploughedoption forfinetexturedsoilswerethereforeexcludedfromtheindicator designatanearlystageofthedevelopingprocess.Thenumberof MACROsimulationsneededwastherebyreduced.Combiningthe ninetexturalscenarios(i.e.,allthetexturecombinationsoftopsoil andsubsoilpossible)withthetwooptionsoftillageforsoilprofiles havingamediumtexturedtopsoilandthenineweatherregimes consideredgiveatotalof108discretescenarios.

3.2. Fuzzyinferencesystems

Allthepredefinedtypesofmembershipfunctionsandweight functionsavailableintheMATLABFuzzyLogicToolboxwerecon- sidered.Foreachdiscretescenario,thedatalearningsetcomprised 44 uniquecombinationsof inputvariables and theirassociated indicatorscores.Thelearningprocesswascarriedoutuntilnofur- therimprovementwasachievedoruntilamaximumof100,000 adjustmentshadbeencarriedout.Asufficientfitbetweendatasets andinferencesystemswasdefinedasamaximumrootmeansquare errorof0.5indicatorscores.Allfuzzyinferencesystemsderivedful- filledthisrequirementwithregardtotheirassociateddatalearning

sets.Thestructureoftheinferencesystemsderivedispresentedin Fig.3.

Thesmallesterrors wereachievedforGaussian membership functions(e.g.,seeFig.4)withbackpropagationalgorithmincom- binationwithaleastsquaretypeofmodelasoptimizationmethod.

Theperformance(assessedbyrmse)offuzzyinferencessystems withweights calculated astheproduct (prod)ortheminimum ofvariablesdegreeofmembershiptotherelevantfuzzysetwere compared.Theprodweightingfunctionperformedbestandwas thereforeselectedtobetheindicatormethodofweighting.

TheGaussianmembershipfunctiondependsona variable,x, (e.g.soildepthasinFig.4)andtwoparameters,cand,asgiven by

f_mb(x)=e^{(−(gmb(x)−cmb)2/(2·mb2 ))} (4) wheremb=Lforthelefthandmembershipfunctionandmb=Rfor therighthandmembershipfunctionand

g_L(x)=max(c_L,x) (5)

and

gR(x)=max(cR,x). (6)

Ruleswerederivedforthe16possiblecombinationsoffavorable andunfavorablemembershipforthefourcontinuousvariablescon- sidered.Duetoconstraintsintheavailablenumberofsimulations (44)foreachdiscretescenario,constantoutputmembershipfunc- tionswerechosenforeachruleinfavorofthemoredatademanding linearoption.Eachofthefuzzyinferencesystemsconstructedcom- prise32parameters.Theindicatordevelopedisorganizedinsuch awaythateachfuzzyinferencesystemisrepresentedbyatable containing16membershipfunctionparametersandatreeofwhich eachpathmakesuponerule,givingatotalof16ruleoutputparam- eters(e.g.,seeTable7andFig.5).Fromthesedata,indicatorscores (foranydosage)canbecalculatedusingthesixindicatorequations givenabove.

3.3. Indicatorfinalizationthroughpruning

TheMACROoutputdatasetswerecomparedtoinferencesys- temsforwhichthesubsoildivergedfromthatofthedataset.Forsoil profileswithfinetexturedtopsoilthesimulatedleachingresultwas

Table7

Themembershipfunctionparametersetofthefuzzyinferencesystemforautumnapplied,mediumtexturedsoilprofile,locatedinclimatezone1.^a

Variablenumber Variablename cL L cR R

1 Topsoilorganiccarboncontent(%) 0.809 0.684 2.87 0.929

2 Depthofsoilprofile(cm) 36.2 21.0 97.6 27.1

3 Koc(cm³g⁻¹) 27.1 243 600 243

4 DT50(days) 1.89 20.3 57.7 25.2

aAbbreviationsasinTable1.

sensitivetosubsoiltexture(Fig.6)especiallyforautumnapplica- tion.Forclimatezone2,thedatasetsforsoilprofileswithmedium texturedtopsoilandsubsoilfittedthefuzzyinferencesystemsof thecorrespondingdatasethavingacoarsesubsoil,evenincase ofadivergingtillageoptions(rmse≤0.3indicatorscores)(e.g.,see Fig.7).

EachMACROoutputdatasets ofsoil profileswithploughed mediumtexturedtopsoilwascomparedtothefuzzyinferencesys- temsderived forthecorrespondinguntilledscenarios. Theroot meansquarederivationsdidnotexceedourpruningcriterionof 0.5indicatorscores.Thevariableconcerningtillagewastherefore completelyexcludedfromtheindicator.

Forsoilprofileswithcoarsetexturedtopsoil,indicatorscoresfor mediumtexturedsubsoilfittedthefuzzyinferencesystemdevel- oped (rmse≤0.5 indicator scores) fromthe data setfor coarse textured subsoil for all climateand application scenarios (data notshown).Asufficientlygoodmatchwasalsoachievedforfine texturedsubsoilsexceptforwinterapplicationinclimatezone1 (rmse=0.7indicatorscores)andspringapplicationinclimatezone 11(rmse=0.6indicatorscores)(datanotshown).Thesizeofthe indicatorcouldthereforebedecreasedto62uniquefuzzyinfer- encesystemstochoosefromdependingontheprevailingsituation regardingsoiltexture,climaticzoneandseasonofapplication.

Comparisonsofsimulatedpesticideleachingriskfordifferent climaticzonesshowedthatzone2and11differthemost(aver- agermseof1.3indicatorscores),especiallyforautumnapplication (averagermseof1.8indicatorscores).Thelargestresemblanceis foundfor climatezone1and 11(average rmseof0.9indicator scores),especially forspring appliedpesticide(average rmseof 0.6indicatorscores).Similarcomparisonfordifferentapplication

Fig.6.Rootmeansquareerrorfordatalearningsetsofsoilshavingafinetopsoil andsubsoilwhenappliedforfuzzyinferencesystemsbasedondatalearningsetsof soilshavingafinetopsoilandacoarsesubsoilfordifferentapplicationseasons.

seasonsshowedthatmostresemblanceinleachingriskwasfound forspringandwinterapplication(averagermseof 0.6indicator scores).Thelargestdifferencewasfoundforautumnandspring application(averagermseof1.7indicatorscores),especiallyforcli- matezone11(averagermseof2.1indicatorscores).Toretainagood indicatorstructure(i.e.,avoidingnumerousspecialcases),nofuzzy inferencesystemwasexcludedfromtheindicatorduetomatches foundbetweendifferentseasonsofapplicationorclimates.

3.4. Variablesensitivity

Theclimaticstatisticsaremorefavorableforzone2thanfor theother climatic zones(Table 3).This characteristic wasalso expressedbytheindicator,forwhichclimatezone2generallypro- ducethelowestpesticideleachingrisk(forexampleontheeffect ofclimateonindicatorscores,seeTable8a).

Accordingtotheresults,autumnapplicationgenerallypresents ahigherriskofpesticideleachingthanwinterorspringapplica- tion(forexampleontheeffectofseasonofapplicationonindicator scores,see Table8b).This is presumablydue to themore fre- quenteventsofhighdailyprecipitationamountduringtheautumn (Table3).

Theindicatordevelopedissensitivetopreferentialflow.Itcap- turesthecharacteristicsofpreferentialflowasbeingstrongestin finetexturedsoilsandmosteffectiveintopsoils(forexampleonthe effectofsoiltextureonindicatorscores,seeTable8c).Therefore, thereisariskforlargelossesforsoilspronetopreferentialflow underclimaticconditionsthattriggersuchflowsevenforpesti- cideshavingaverylowGUS-index(e.g.0.85forapesticidewitha DT50of5daysandaKocof600cm³g⁻¹).Afastflowthroughthesoil matrix,suchasinthecaseofcoarsetexturedsoils,canalsocausea

Fig.7. IndicatorscorecalculatedforMACROoutputs(blackdiamonds)forautumn applicationinclimatezone2onaploughedmediumtexturedsoilprofileandthe resultforthefuzzyinferencesystem(graysquares)forauntilledsoilprofilewith amediumtexturedtopsoilandacoarsetexturedsubsoilforthesameclimateand seasonofapplication.Thermseofthevaluesofthedatasetandtheresultingvalues ofthefuzzyinferencesystemis0.2indicatorscores.

Table8

Theresultingindicatorscoresforfourdifferentpesticidesandaneffectivedoseof1kgha⁻¹(a)appliedinspringonfourdifferentfinetexturedsoilprofileslocatedinthe threedifferentclimaticzonesofFrance,(b)appliedinthreedifferentseasonsonfourdifferentfinetexturedsoilprofileslocatedinclimatezone11and(c)intheautumnon 112soilprofilesofdifferenttexture,organiccarboncontentandsoildepthlocatedinclimatezone1.^a

a)

Continuousinputvariables Climaticzone

DT50(days) Koc-value(cm³g⁻¹) Soildepth(cm) Organiccarboncontent(%) 1 2 11

60 27 40 1 1 1 1

3 1 1 1

100 1 2 2 1

3 2 3 2

600 40 1 2 2 2

3 3 3 2

100 1 2 3 2

3 3 5 3

5 27 40 1 4 6 4

3 5 9 7

100 1 5 9 6

3 6 10 10

600 40 1 6 10 9

3 10 10 10

100 1 10 10 10

3 10 10 10

b)

Continuousinputvariables Season

DT50(days) Koc-value(cm³g⁻¹) Soildepth(cm) Organiccarboncontent(%) Autumn Winter Spring

60 27 40 1 0 1 1

3 0 1 1

100 1 1 1 1

3 1 1 2

600 40 1 1 2 2

3 1 3 2

100 1 1 2 2

3 1 3 3

5 27 40 1 2 1 4

3 2 2 7

100 1 2 1 6

3 3 3 10

600 40 1 2 6 9

3 4 10 10

100 1 3 9 10

3 5 10 10

c)

Continuousinputvariables Topsoiltexturesubsoiltexture

DT50(days) Koc-value(cm³g⁻¹) Soildepth(cm) Organiccarboncontent(%) C^bAll M^cC MM MF^d FC FM FF

60 27 40 1 0 0 0 0 0 0 0

3 0 0 1 1 0 0 1

100 1 0 0 1 1 1 1 1

3 1 1 1 1 1 1 1

600 40 1 5 3 2 2 2 1 1

3 8 6 5 4 2 2 1

100 1 9 7 5 2 5 3 1

3 10 10 8 6 7 4 2

5 27 40 1 2 2 2 2 2 2 1

3 4 3 3 3 2 2 2

100 1 4 5 5 3 5 4 2

3 6 6 6 4 5 4 3

600 40 1 10 7 5 4 5 3 3

3 10 10 10 9 7 5 5

100 1 10 10 10 6 10 7 4

3 10 10 10 10 10 10 7

Shadedscoresarejudgedtoindicateunacceptablepesticideleachingrisks.

aAbbreviationsasinTable1.

bCoarse.

c Medium.

d Fine.

highriskforpesticideleaching,especiallyformobilepesticidesin shallowsoils.

Regardingthecontinuousvariables,theindicatorismoresen- sitivetopesticidecharacteristicsthansoilproperties(forexample, seeTable8a–c).

3.5. Comparisonofthenewandtheoldindicator

A comparison of the new groundwater indicator and the former one shows that the newindicator is more sensitiveto soilconditions(Table9).DuetothelowGUS-indexofglyphosate (GUS-index=1.5),theoldindicatorclassifiestheriskforhighcon- centrationsofthispesticideinthegroundwaterasverylow.On thecontrary,byincludingtheprocessesofpreferentialflow,the newindicatorclassifiestheriskofglyphosatelossestogroundwa- terashightoveryhighforunfavorablesoilconditions.Thisresult issupportedbypreviousstudiesperformedatfieldandlysimeter scales(Vereecken,2005;Kjaeretal.,2005).Additionally,sincethe newindicatortakestheinfluenceofweatherpatternsintoaccount (affectedbythevariables climaticzoneand,asinthis compari- son,theapplicationseason),thedifferenceincontaminationrisk becomeslargerbetweenseasonsincomparisontotheriskaccord- ingtotheoldindicator.Fortheoldindicator,thedifferencesin riskbetweenapplicationseasons(seeindicatorscoresforisopro- turonfor winterandautumnapplicationin Table9)aresimply duetochangesintheeffectivedosecausedbydifferenceincrop development.

4. Discussion

Themain improvement ofthe newindicatordeveloped is a reduceddependencyontheexpertknowledgeof thedesigners.

TheimplementationoftheMACROmodelenabledustoexplore abroaderrangeof situationsofpesticideleachingthanwhat is availableintheliteratureonexperimentaldataabouttheeffect of different factors. Furthermore, this method allowed for an inclusionofnon-linearprocessesof whichsomeareinterlinked andtherebyimpossibletopredictsufficientlyenoughbyexperts.

Anotherimprovementoftheindicatoristhat,incomparisontothe oldversionthenewindicatortakesmorevariablesintoaccount andconsidersenvironmentalparametersetsonclimateandsoil structure.

Wateristhedominanttransportmediumofpesticidesinsoil.

Consequently,precipitationpatternfollowingapplicationisavery importantfactorgoverningpesticidelosses.Alsoofimportanceis thesoilwatercontentatthetimeofapplication,especiallyifthesoil ispronetopreferentialflow(Jarvis,2007).Pesticideleachingisthus governedbybothpreviousandcurrentclimateconditions.Their interactionsarestillpoorlyunderstoodandtheeffectsofprecipi- tationintensityanddurationonpesticideleachingdependalsoon soilpropertiesandpesticidecharacteristics(McGrathetal.,2008;

Nolanetal.,2008).MACROsimulationstudieshavebeenperformed toidentifykeyclimaticfactorsgoverningthetransportofpesti- cideleaching(Lewanetal.,2009;Blenkinsopetal.,2008;Nolan etal.,2008).Manyoftherelevantfactorsareassociatedwithrain- falleventsinafarfuture.Suchfactorscannotbepredictedandare thereforeunfittingasindicatorvariablesunlesssupplementedwith sitespecificfactorsofrisk(e.g.reflectingtheriskofexceedingcer- tainrainfallamountsduringthewintermonths).Additionally,to includeallthesignificantclimaticfactorsforspecificsoil-pesticide combinationsas separate indicatorparameters would result in averycomplex decisiontree.Instead,precipitationpattern and soilwatercontentatthetimeofapplicationareimplicitlytaken intoaccountbytheindicator.Thiswasachievedthroughbasing theindicatoronthe80thpercentileyearlyamountpesticidelost

calculatedfromMACROsimulationsof20yearsofweatherdatafor eachFrenchclimaticzone(basedontheeightsignificantclimatic factorsidentifiedbyBlenkinsopetal.(2008))andapplicationsea- sonconsidered(i.e.,spring,autumnandwinter),Consequently,the indicatortaketheoverallriskofbothpreviousandcurrentclimate conditionsintoaccountforeachcombinationofclimaticzoneand applicationseason.

Thepreviousversionoftheindicatordidnotaccountfordif- ferences in texture with depth. For the new indicator, subsoil texturecansignificantly affecttheresulting score.For example thepesticideleachingfromsoilswithfinetexturedtopsoilisvery sensitivetosubsoiltextureregardlesstheclimaticzoneorappli- cation season. Soil data are thus more explicitly introduced in thisnewversion.Theformerversionincludesavariablereflect- ing the overall soil leaching potential instead. This variable is assessedeitherinaseparatedecisiontree(Bockstaller,2004)or by expertise in thefield (e.g.according to themethod of Réal (2004)).In acomparison,theassessmentbymeansofthedeci- siontreehasbeenfoundtobeweaklyrelatedtofieldassessment ofleaching risk(Novaketal., 2009).Theadvantageof allowing for integrationof field assessmentsis not availablein thenew version.

Preferentialflowistakenintoaccountbytheindicatorthrough itslinktotheMACROmodel.Sincethepreviousversionignoresthis process,itconsidersfinetexturedsoilstopresentasmallerriskof pesticideleachingthancoarsetexturedsoilsandcategorizespesti- cidesoflowGUS-indexasnon-leachers.Onthecontrary,thenew indicatorshowsthatfinetexturedsoilsmaypresentahigherrisk forpesticideslossestothegroundwatercomparedtocoarsetex- turedsoils.Additionally,duetotheinfluenceofpreferentialflow, thenewindicatordoesnotrelyontheGUS-indexforestimating thepesticideleachabilitybuttakesboththepesticidecharacteris- ticsDT₅₀andKocintoconsiderationseparately.Intheoldversion, pesticidedosewasnot consideredbythegroundwatermodule itself,butwasdealtwithinaseparatemodule.Thedoseisnow moreexplicitlyintegrated,inquantitativeinteractionwithinter- ception,takingintoaccounttheassumptionoflinearsorptionand degradation.

Neithertheoldnorthenewversionallowsforanymanipula- tionoftillagebytheenduser.Forthenewindicator,anoptionof tillage/notillagewastestedasapotentialinputvariable.But,asa consequenceofthetransformationofpesticidelossintoindicator score(alog₁₀changeinpesticidelosscorrespondtoachangeof twoindicatorscoreunits)tillageimplementsdidnotsignificantly affecttheresultingoutput.Thetillage/notillageoptioncouldthus beprunedaway.Fieldstudies(Allettoetal.,2010)haveshownthat suppressionofploughingcanincreaseleachingbyafactorof60%in average(N=21,atotalof11differentpesticidesreviewedinnine studies).Hence,onecanexpectapositiveeffectoftillageonpes- ticidelosstogroundwaterbutitisnotasignificantfactorwhen assessingtheriskofpesticideleachinginaworst-casescenario.

Thenewindicatorisapplicablefor62scenarioscomprisinga broadrangeofconditions.Foreachofthesescenarios,thestructure ofthedecisiontreeisthesame(seeFig.5)buttheshapeoftheir membershipfunctions(determinedbytheirmembershipfunction parameterset)andtheirsetofruleoutputparametersareunique.

Thestructureofthenewindicatoristherebymorecomplexthan theformerindicatorwhichconsistsofasinglesetofmembership functionparametersandruleoutputparameters.Bygroupingthe scenariosintonineschemesaccordingtoclimaticzoneandappli- cationseason,theindicatorbecomeseasiertohandlewhenlooking upwhichparametersettochooseforaparticularsituationofpesti- cideapplication.Itisalsopossibletoreducethenumberofscenarios bysomeadditionalpruningofclimaticzonesincombinationwith applicationseasons.Thiswouldhowevernotfacilitatetheuseofthe indicatorsincethesetwovariablesshouldalwaysbeknowntothe