a combined geophysical-pedological approach for precision ... · correct plan for precision...

Bollettino di Geofisica Teorica ed Applicata Vol. 54, n. 2, pp. 165-181; June 2013

DOI 10.4430/bgta0079

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A combined geophysical-pedological approach for precision viticulture in the Chianti hills

E. Martini1, C. CoMina2, S. Priori3 and E.a.C. CoStantini3

1 UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany2 Department of Earth Science, Università degli Studi di Torino, Italy3 CRA - ABP, Research Centre for Agrobiology and Pedology, Firenze, Italy

(Received: May 16, 2012; accepted: August 13, 2012)

ABSTRACT Theaimofthepresentworkwastotestanddevelopacombinationofbothgeophysicaland pedological survey techniques devoted to the definition of a correct plan for precision viticulture.Inparticular,theobjectiveofthestudywastoevaluatethepotentialityofacombineduseofthesetechniquestoidentifyareaswithuniformsoilpropertieswithin4 testvineyardsof the“BaroneRicasoli” farm, located in theChiantiwinedistrict(Tuscany,centralItaly)andtoevaluatetherelationshipsbetweensoilpropertiesandwine quality.Two different geophysical methods based on the measurement of theelectricalconductivitywereused:anelectro-magneticinductionmethodandelectricresistivity tomographies; these were combined with detailed pedological analysesand with the evaluation of remote sensing maps. All results were compared anddiscussed, and finally a cluster analysis based on the evidences of geophysical tests and on pedological data was performed. For each of the identified uniform areas, a separatewinemakingwassuccessivelymade,andthequalityofthewinesisdiscussedand correlated to the geophysical-pedological results. The study has shown thatthe approachused is suitable formapping andunderstanding the anomalies in soildistribution which partially reflects in the quality and effectiveness of wine production. Moreover,itwasdemonstratedthatthegeophysicaldataalonearenotabletoprovideanypedologicalinformationbecause,intheinvestigatedarea,electricalconductivityisaffectedbyvarioussoilpropertiesinacomplexmanner;however,thesemethodsareveryusefultointegrateandcomplementpedologicaldataintheaimsofprecisionviticulture.

Key words: EMIsensors,geophysics,soilscience,apparentelectricalconductivity,precisionviticulture.

1. Introduction

Precisionagricultureisagrowingdisciplinecombininggeospatialdatasets,state-of-the-artfarm equipment technology, GIS, and GPS receivers to support spatially variable field application offertilizer,soilamendments,pesticides,andtillageeffort(MorganandEss,1997).Thisapproachovercomes the paradigm of uniform field treatment by site-specific data acquisition to cope with within-field variability. The benefits of precision agriculture to farmers are maximized crop yields and reduced input costs, allowing a farm field to be divided into different management zones

© 2013 – OGS

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Boll. Geof. Teor. Appl., 54, 165-181 Martini et al.

for the overall purpose of optimizing economic benefits and environmental protection. Recent research on precision viticulture has focused on the use of management zones, which are defined as subfield regions within which the effects on the crop of seasonal differences in weather, soil, management,etc.areexpectedtobeuniform(Lark,1998).For thispurpose, it isoftenusefulto define classes from a set of multivariate spatial data. Cluster analysis procedures have been effectively used to divide a field into potential management zones (Stafford et al.,1998).

During the lastyears,someimportant farmshaveadoptedprecisionviticulture tooptimizevineyardperformanceaccording tograpeyield,winequality, and sustainabilityof thenaturalresources (Proffitt et al., 2006).The adoption of precision viticulture requires the knowledgeofsoilspatialvariabilityintermsofphysical,chemicalandhydrologicalproperties,whicharefunctionaltowinemanagement.Thefarmerneedsgeo-referencedmaps,displayingareaswithsimilarsoilcharacteristicsandqualitieslikesoiltextureanddrainage,whicharerelatedtosoilwateravailability.

Traditionalsoilsurveysandsoilanalysisareusuallytime-consumingandexpensive,especiallyforhighresolutionmaps.Inthelasttwodecades,afterthematurationofGPSandGIStechniques,manytechnologicaltoolswerehoweverdevelopedtoimprovethespatialandtemporalinformationaboutsoilnutrientandwatercontent.Developmentinsoilspatialinformationtechnologysuchasproximalsensinghasbeencontinuouslyimprovedandappliedinmanystudies(CorwinandLesch,2005).Inthiscontext,geophysicalmethodsofferavaluablemeantoobtainsubsidiarydatain an efficient way, and have been widely applied in soil sciences for a considerable period of time (Samouëlianet al.,2005).Inparticular,geoelectricalsoilmappinghasbecomewidelyacceptedinprecisionfarming.Atpresentitisthemostsuccessfulgeophysicalmethodprovidingthespatialdistributionofrelevantagronomicinformationthatenablestodeterminemanagementzonesforprecisionfarming(Lücket al.,2009).Methodsbasedontheelectricalpropertiesdemonstratedparticularly promising because important soil physical properties are strongly correlated toelectrical conductivity and can be potentially quantified indirectly from this parameter. For this reasonarapid,non-invasiveandrelativelycheapmappingofthesoilapparentelectricconductivity(ECa),e.g.,carriedoutbyElectro-MagneticInduction(EMI)sensors,representsaveryusefultoolfor identifyingsoilmapunitsandsoilproperties inrespect toclaycontent(Morariet al.,2009),soildepth(Saeyet al.,2009),watercontent(Davies,2004;Cousinet al.,2009;Lücket al.,2009;Tromp-vanMeerveldandMcDonnell,2009)andwatersalinity(Doolittleet al.,2001).Thesametechnologywasusedtoupgradeexistingsoilmapsandtosupportthehydropedologicalmodelofacertainarea(Indoranteet al.,2008;Costantiniet al.,2009;Tromp-vanMeerveldandMcDonnell,2009).

AlthoughthevariationsinEMIresponseareusuallydrivenbythedominantsoilproperty,theECacanbeaffectedinacomplexmannerbyanumberofdifferentsoilpropertiesat thesametime;therefore,alimitedsoilsamplingandanalysisprogramistypicallyrequiredtodeterminewhich soil properties have the greatest influence on the ECa spatial variability. Soil properties informationbasedonECameasurementisneededforplanningmanagementpractices;ontopofthat,sincethespatialpatternofcropyieldcommonlyexhibitsastrongcorrelationwiththespatialECapattern,itfollowsthatECamapsdrawnbyresistivityandelectromagneticinductionsurveyscan be a valuable precision agriculture tool, providing insight on how to best divide a field into zonesbasedonsoilpropertydifferences(Allredet al.,2006).Furthermore,themappedhorizontalECa patterns for a farm field often tend to remain consistent over time, which implies that the ECa

Combined Geophysical-Pedological approach for precision viticulture Boll. Geof. Teor. Appl., 54, 165-181

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patternismainlygovernedbylateralvariationsinsoilproperties(Lundet al.,1999;Allredet al.,2005,2006)andnotbytemporaryvariationsinsoilwatercontent.Toimprovethecharacterisationof single soil profiles, the EMI method can be coupled with electrical resistivity tomographies (ERT)toincreasetheverticalresolutionofsubsurfaceelectricalimages(Rizzoet al.,2004),andbetterunderstandtheoverlyinggeologyonwhichtheshallowestsoilhasbeendeveloped.

Thegoalofthisworkwas,therefore,totestanddevelopacombinationofsurveytechniquesbasedongeophysical,pedological and remote sensingmethods, allowing theapplicationof acorrect plan for precision viticulture. In particular, the objective of the study was to identify,withinthetestvineyards,smallerareaswithuniformsoilproperties.Secondaryobjectiveofthestudywas to testgeophysicalmethods to identify thedrainagepathsand,more ingeneral, toprovethesuitabilityofdifferenttechniquesfordetailedsoilmapping.

2. Materials and methods

Thestudiedareaisdescribedinthefollowingparagraphstogetherwithadiscussionof thegeophysicalandpedologicalsurveys.ThecentralpartoftheworkconsistedinmonitoringtheexperimentalvineyardsusinganEMIinstrumentabletomeasure,atthesametime,theECaofsoil at two depths. Moreover, to investigate some spots of particular interest, identified on the basisofthehorizontalECadistribution,ERTwerealsoperformed.Thesegeophysicalsurveyswerecombinedwithpedologicalanalysesonseveralsoilsamples.

At a final stage a cluster analysis to evaluate the correlation between wine quality and soil features detected by pedological and geophysical methods was attempted. On the sub-areasselectedby theanalysis,separatewinemakingwasconductedand theresultingqualityof thewinesanalyzed.

2.1. Study site and soil mapThefourstudiedvineyards(17.6hainsize,intotal)werelocatedinsidethe“BaroneRicasoli”

farm,oneofthemostimportantwineryofthe“ChiantiClassico”winedistrictarea(centralItaly).Thisterritoryisplacedinthecentral-westernpartofTuscany,betweenthecitiesofFirenze,SienaandArezzo(Fig.1).

Thevineyardsareplacedonvariablemorphologicalconditionswithmoderateinclination,from5to15%,showingvariableexposition.Geologicalandpedologicalcharacteristicsofthevineyardareveryheterogeneousandaremadeevenmorecomplexbythevineyardgroundpreparation,landleveling,deepploughing,settingofdrainagesystems,etc..AccordingtoSoilSurveyDivisionStaff(1998),soilmoistureregimeisustic(soilmoistureislimited,butispresentwhenconditionsare suitable forplantgrowth)and soil temperature regime ismesic (withamoderateorwell-balancedsupplyofmoisture,asnormalinMediterraneanregions).

In2009adetailedsoilsurvey(mapscale1:15,000)wascarriedoutonall thevineyardsofthe farm(Costantiniet al.,2009),with the traditionalmethod,baseduponphotointerpretationand field survey (Soil Survey Division Staff, 1993). Five of the Soil Typological Units (STUs) describedinthefarm,arepresentinthestudyarea(Fig.2):

1) TOR,Torricellasoil:TypicHaplusteptclayey-skeletal,mixed,mesic,superactiveaccordingto Soil Taxonomy; developed on Tertiary carbonate flysch. Torricella soil is clay-loamy

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Fig.1-Geographicallocationofthestudiedareawithevidenceofthe“ChiantiClassico”winedistrictarea.

Fig. 2 - Pedological map of the studied area (modified from Costantini et al.,2009);inredthelimitsoftheexperimentalvineyards.


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and strongly gravelly, about 1.5 m deep, but it results poorly developed and with poorhorizontation.

2) NEB,Nebbianosoil:TypicPaleustalfsloamy,mixed,mesic,superactiveorCutanicLuvisol(Hypereutric, Profondic, Clayic); developed on Pleistocene fluvial deposits, Nebbiano soil shows more developed pedogenesis. It is very deep (> 1.5 m), loamy, with common fine andmediumgravels,wellstructured.

3) LEC1, Leccio 1 soil: Typic Haplustalf fine-loamy over clayey, active or Hypereutri Cutanic Luvisol(Profondic);developedonPliocenemarinesand.Leccio1soilshowsadvancedpedogenesisandisverydeep(>1.5m),sandy-clay-loam,averagestructuredandgravelly.

4) LEC2,Leccio2 soil:TypicHaplustepts sandyor sandy-skeletal, activeorEutriBrunicArenosol;representavariantoftheLeccio1soilandislessdevelopedandhighlyeroded(thickness<1.5m).Thetextureissandy-loamandpresentsanexcessivedrainage.

5) MIN, Miniera soil:Typic Endoaquepts clayey, mixed mesic, superactive or EndogleyicStagnosol(Eutric,Clayic);developedonPliocenemarineclayswithsomesandy-gravellylenses. Miniera soil results poorly structured or massive, especially in depth (thickness<1.5m),andveryclayey(clay:40-50%).

The soils TOR, LEC2 and MIN are classified as Inceptisols (suffix –ept), i.e. early-stage soils, not well developed, where the physical-chemical weathering of the parent material isdominant. The soils NEB and LEC1 are Alfisols (suffix –alf), i.e. are more developed and, after theweathering,hydrologicalprocessesdrivestoamovement(lessivage)ofclaymineralstowarddeeperhorizons.

2.2. Electromagnetic induction sensor and measurement procedureThe EMI instrument used for surface mapping of electric conductivity was the EM38-MK2

(GeonicsLtd.,Ontario,Canada).Theinstrumentconsistsofatransmittingcoilandtworeceivercoils,spacedrespectively0.5and1.0mfromthetransmitter.ItmeasurestheECaattwodepthsat the same time for every measure location, using an operating frequency of 14.5 kHz.Thephysicalprinciplesbehindthesensorfunctioningcanbesynthetizedasfollows:analternatingcurrent flowing in a transmitting coil generates a primary magnetic field (Hp); the field creates an eddy current within the soil, which induces a secondary magnetic field (Hs),whichissensed,togetherwithHp, by the receiver coil. The secondary magnetic field and the depth of investigation arestronglyrelatedtointercoilspacing(s),operatingfrequency(f)andsoilconductivity(σ).TheratiobetweenHs andHp, providing toworkunder the low inductionnumber approximation,islinearlyproportionaltothesoilECa(McNeill,1980).Thecumulativesoildepthresponseoftheinstrumentisanon-linearfunctionanditishigherfortheverticaldipolemode(VDP,coilsperpendiculartothesoil)respecttothehorizontaldipolemode(HDPcoilsparalleltothesoil).The measured data for the two dipole configuration have different sensibility response, which results,fortheVDP,inamaximumsensibilitycorrespondingto0.25and0.50m(respectivelyforthetwointercoilspacing)andamaximumsurveydepthcorrespondingto0.75and1.50m(respectivelyforthetwointercoilspacing).

Themainadvantageof theEMI instruments is that the inductionprincipledoesnot requireadirectcontactwiththeground.ConsequentlyasurveycarriedoutusingEMIsensorscanbefasterthananequivalentsurveycarriedoutwithotherinstruments.Thesurveycanbeperformedbyasingleoperator,whileaGPSreceiver,connectedtotheinstrument,allowscollectinggeoreferedECadata.

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The EMI survey for the experimental area was carried out in VDP configuration, with a grid of 3x10 m (Fig. 3). For any vineyard, each survey was performed approximately at the same day timeandalltheareawascoveredwithintwoweeks;atthebeginningofeachsurveythesensorwascalibratedinaselectedsitetominimizeerrors.Indeedacarefulmanualcalibrationisneededbeforethemeasurementprocedureand,sometimes,interferenceoftheironwiresofthevineyardrows with the magnetic field is possible. Geostatistical approaches are then necessary for data post-processing. This was performed by Ordinary Kriging with 1 m resolution. The final result oftheEMIsurveyisthereforearegulargridofdatapointsincludingECafortwodepths.ThesehorizontalECamaps,togetherwiththepedologicalmapwereusedasbaselinedatainthenextstepsofthework.

Fig. 3 - Surveys performed on the studied area: EMI measurements points, location of the soil samplingsitesandERT.


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2.3. Soil sampling and laboratory analysisAftertheEMIdataspatialization,soilsampleswerecollectedtoestimatethemostrelevant

soilpropertiesrelated to thesoilECa.Onthebasisof theECamaps,42georeferedsamplingsiteswereselectedand,foreachlocation,twosoilsampleswerecollectedattwodifferentdepths(respectively at 0-40 cm and 50-75 cm). Totally 72 hand auguring have been analysed (Fig. 3). Laboratory analyses were performed according to the Official Italian Methods (Mi.P.A.F., 2000) in order to determine: gravimetric water content (θg), soil reaction (pH), soil electrical conductivity andtexture.

2.4. Electrical resistivity tomographiesResistivitybasedmethodsmeasuretheelectricalresistivity,whichcanbetransformedtoits

inverse,theelectricalconductivity(EC),forabulkvolumeofsoildirectlybeneaththesurface.Resistivity methods basically gather data on the subsurface electric field produced by the artificial applicationofelectriccurrent into theground. Inconventionalmethods, anelectriccurrent issupplied between two metal electrode stakes, partially inserted at the ground surface, whilevoltageisconcurrentlymeasuredbetweenaseparatepairofmetalelectrodestakesalsoinsertedat the surface. The current, voltage, electrode spacing, and electrode configuration are then used tocalculateabulksoilelectricalresistivity(orconductivity)value(Allredet al.,2008).

ERT were adopted in the study area to interpret vertical variations in EC along transectsselectedonthebasisofthesurfaceECadistribution.TheinstrumentusedwasSyscalJunior(IRISInstruments,France)with72electrodes.Totally,8ERTwerecollectedusingWenner-Schlumbergerelectrode arrays. Since the main interest of the surveys is the upper portion of soil profile a reduced spacingbetweentheelectrodeswasused(1m),fora total lengthof71mforeachtransect, toobtainanaccuratenearsurfaceresolution.Insomevineyardsdatafromdifferenttransectswereassembled, to obtain longer electrical sections (Fig. 3), with a roll along procedure and the overlap ofsomeelectrodes.ThesoftwareRES2Dinv(GeotomoSoftware)wasusedfortheinversion.

2.5. Selection of uniform sub-areas of wine makingIn the final step of the work, a correlation between wine quality and soil features detected by

pedologicalandgeophysicalmethodswasdone.Homogeneoussub-areaswithinthevineyardswere identified and separate wine-making were performed. For this purpose, a k-means cluster analysis was adopted using the software Statistica (StatSoft) and represented using ArcGIS(ESRI).Inputdatawere:

- ECavaluesobtainedbytheEMIsurvey;- ECa values obtained by a previous ARP survey (Automatic Resistivity Profile, Geocarta,

France)inthesummer2009;- AWC(AvailableWaterContent)frompedologicalmap;- NDVI(NormalizedDifferenceVegetationIndex)fromsatelliteimages(fortwoofthestudied

vineyards).Theclusteranalysiswassetuptoobtainsevensub-areas,adoptingSangioveseasreference

grape-variety.Withineachofthezonesthegrapeswerecarriedtothefarmwinery,wheretheywere separately vinified using stainless steel tanks with a capacity of 95 hectolitres. The wines wereanalyzedtomeasurealcoholcontent(%vol),pH,totalacidity(gl-1),dryextract(gl-1),colorintensity(nm),totalpolyphenols(mgl-1),andtotalanthocyanins(mgl-1).Protocolsforthe

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“ChiantiClassico”winerequestaminimumageingof2years,toallowthiswinesshouldhavesuitable oenological characteristic. Relatively high values of alcohol (13-14%), antochyanins and polyphenolsindicateprimeconditionsforwineageing,thereforetheyaregoodindicesofwinequalityandageingsuitability.

4. Results and discussion

ThedetailedECamapsofthefourexperimentalvineyardsobtainedfromtheEMIsurveyareshowninFigs.4and5forthetwointercoilspacingused;bothmaps,particularlytheshallowerone, are consistent with the pedological map shown in Fig. 2 and reflect soil variability in the area. ConsequentlywecanstatethattheEMIsurveycouldexpresswellthepedologicalvariabilityoftheareaandisabletohighlightECapatternswithinthevineyards.AcomparisonwiththeresultsofthepreviousARPinvestigationshowsverysimilarpatternstotheEMIsurveydemonstratingalso the consistence of electrical methods in defining soil variability. Moreover, since ECa maps areanuniformlydistributedsamplingoftheareaaqualitativecomparisonbetweenECamapsand pedological map (as shown in Fig. 6) can suggests some changes in the STUs boundaries. However, detailed GPS-driven observations are needed to confirm the reliability of these changes. Tofurtherconstraintheseobservations,asitwillbeshownbelow,theERTcanhelptounderstandthegeologicalandpedologicalassessmentindepth.

TheresultsoflaboratoryanalysiswerecomparedwiththeEMIsurveydatainordertoestablishaconnectionbetweenthemeasuredECaandsoilproperties.Todothis,foreverysoilsample,therespectiveECavaluewasextractedfromtheEMIsurveyresults.FortheexperimentalvineyardsitwaspossibletoidentifyacoherenttrendbetweenECavaluesandlaboratorydata(electricalconductivity of soil, clay content and water content). However the Pearson’s coefficient was not significantly high, as reported in Table 1. These results agree with what is widely reported

r2 MINIERA vineyard

clay θg EClab ECEM38

clay /

θg 0,65 /

EClab 0,12 0,09 /

ECEM38 0,27 0,19 < 0,01 /

r2 LECCIONE vineyard


clay /

θg 0,89 /

EClab 0,20 0,14 /

ECEM38 0,43 0,34 0,10 /

r2 TORRICELLA basso vineyard


clay /

θg / /

EClab / 0.06 /

ECEM38 0,12 0,11 0,06 /

r2 TORRICELLA alto vineyard


clay /

θg 0,17 /

EClab < 0,01 < 0,01 /

ECEM38 0,25 0,02 0,08 /

Table 1 - Pearson correlation coefficient (r2)betweenclaycontent,gravimetricwatercontent,estimatedECandmea-suredEC.


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Fig.4-ECamapsasaresultoftheEMIsurveyfor0.5mintercoilspacing.

inliterature:ECavaluesareaffectedbyvarioussoilpropertiesinacomplexmanner,andit isdifficult to discriminate the weight that each soil parameter has on the final apparent measured ECa.Moreover, itmustbeobserved that thevolume investigatedby theEMI surveyand theoneoflocalizedsamplingarequitedifferentsothataveragingprocessescouldberelevantinthecorrelation. Note that clay content was determined only on the fine soil fraction (µ < 2 mm), i.e. after removing the skeleton (µ > 2 mm), in the aim of normalizing the texture: recent studies (e.g., Prioriet al.,2010c)haveshownthatclaycontentmeasuredinthiswayismoreconsistentwiththeECameasureofproximalsensing.Otherwise,theclaycontentwouldbeunderestimatedforskeleton-richsamples,andoverestimatedforskeleton-poorsamples.Thegrainsizedistributioncurves, on the fine soil fraction, for the five STUs of this study are reported in Fig. 7: it is possible to observe the different fine fraction content of each soil which affect both the geophysical and the pedological characterization, having an influence on wine production, as it will be underlined later.

High resolution ERT sections were obtained in different STUs, focusing on the upper portion of the soil profile. Fig. 8 shows the results of the three most interesting sections. Results are reportedintermofelectricalconductivitydataforaconsistentcomparisonwithECamapsandatdifferentscalestofocusonthepeculiaraspectsofeachsection.Inallsituationsthereisaverygoodcoherenceinthedeterminationofthedifferentsoilboundaries.

TheMINsection(Fig.8a)clearlyevidencesthegeologicalcontactbetweensandyandclayeyformations.ItisparticulardetailedthetransitionfromLEC2sandy-loamsoil,ontopoftheslope,and the MIN clayey soil on the bottom of the slope. Moreover, it is interesting to observe aconductiveanomalyintheupperpartoftheslope(attheprogressiveof50m),thatcorrespondstoahumidzoneobservedinthesoilsurfaceandunderlinedalsobytheECamap,representinga preferentially drainage direction. We can suppose that this feature may produce instabilityphenomenaalongtheslopeconsequentlytowetseasonandaffectgrapeyieldandmustquality.

The TOR B2 section (Fig. 8b) clearly shows the geological contact between differentunits,leadingtorelativedifferencesinsoilECa.Indeed,intherightsideofthesection,ahigh

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Fig.5-ECamapsasaresultoftheEMIsurveyfor1mintercoilspacing.


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Fig. 6 - Comparison between STU boundaries and ECamap:a)detailedviewoftheTorricellaBassovineyard;b)LeccioneandMinieravineyards;thelocationofelectrictomographiesisalsoshown.

Fig. 7 - Grain size distribution curves on the fine soil fraction for the five STUs of this study.

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conductivityclayeysoil isobserveddelineatingazoneof the subsectionwhichevidences thepresenceofasedimentationbasinslopingtowardSE.Thelowconductivityanomaliesintheleftside of the section are typical of the flysch formation. The high consistence with the ECa map in evidencing the boundary between TOR and NEB soils confirms that the boundaries in the soil maparesomehowincorrect,andneedchangesasevidencedinFig.6.

The TOR A section (Fig. 8c) is located on a uniform and resistive soil (Torricella STU) so that this result is not particularly interesting from a pedological point of view. However, twohighlyrestiveanomaliesareevidentat theprogressivedistancesof20and50mrespectively,correspondingtotwodrydrainagepipes.Thisobservationwashelpfulforthefarmholdersinordertoverifytheirlocationandeffectiveness.

Finally,inFig.9,theNormalizedDifferenceVegetationIndex(NDVI)mapextrapolatedfroma multi-spectral image of the Kompsat-2 satellite (resolution of 4 m), is reported. The map refers totheTorricellavineyardsandwastakenduringthesamematurationseasoninwhichtheothergeophysicaltestswereperformed(August2010).Itisinterestingtonotethatneverthelessthesevineyardsarecharacterizedbyaquiteuniformsoildistributionsimilarpatternscanbeobservedboth in the NDVI map and in the ECa map. Once again this is a confirmation of the potentiality oftheEMIsurveyforprecisionagriculturescopes.

Fig.8-InvertedERTsections,pleasenotethedifferentscales:a)Minieravineyard(MIN);b)TorricellaBassovineyard(TORB2)andc)TorricellaAltovineyard(TORA).


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5. Wine-making areas and wine quality

Theclusteranalysiscombininggeophysical,pedologicalandremotesensingdatawassetuptoobtainsevensub-areas,adoptingSangioveseasreferencegrape-variety.ItwasunderlinedthattheEMI survey maps have the strongest influence in clustering. The sub-areas geometries resulting from the cluster analysis were however too complex. For this reason, to make the proposedmethodologyeasilymanageableinpracticalterms,theboundariesofthesub-areasweremanuallymodified (e.g., to make them parallel to the directions of the rows). The resulting map is shown inFig.10.

Thesevenresultingsub-areas,correspondingtoacombinationofpedologicalandgeophysicalfeatures,are:

• sub-area1:Pliocenesands(MinieraandLeccionevineyards);• sub-area2:PlioceneclaysandLeccio1soils(MinieraandLeccionevineyards);• sub-area 3: Tertiary flysch and Leccio 1 soils (Miniera and Leccione vineyards);• sub-area4:Plioceneclays(Torricellavineyard);• sub-area 5: Tertiary flysch and Nebbiano soils (Torricella vineyard);• sub-area 6: Resistive Tertiary flysch (Torricella vineyard);• sub-area 7: Conductive Tertiary flysch (Torricella vineyard).

Fig.9-NDVImapofTorricellavineyards.

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It is worthy note that sub-areas 6 and 7, both belonging on the same STU, were separated only bydifferentelectricalproperties.Grapesfromsub-areaswereharvestedseparatelyandeveryoneproducedawine,thenanalyzedbythe“BaroneRicasoli”farm.Winemadefromanysub-areaofwine-makingshownallexcellentquality.Indeed,theChiantiClassicodistrictisstronglysuitedtotheSangiovesegrapesproduction.Moreover,itmustbeunderlinedthatthemanagementofgrapesduring the ripening and the harvesting influenced the results in terms of quantity and quality: only goodqualitygrapeswereharvested,leavingontheplantsthegrapesinterestedbyBotrytisCinerea(afungusthataffectsthegrapesandwasparticularlywidespreadinthatyear).Duetothat,insub-areas2and4(developedonclayeysoilsandlocatedatthebottomoftheslopes,thereforemorehumid)theproductionwasreducedofabout50%inquantityandresultinlowestqualitywithreferencetotheotherwines.Sub-area2inparticularistheonewherethiseffectwaspredominant;indeed, theresultsofgeophysical testsconducted in thiszoneof thevineyardsunderlined thepresence of highly conductive clayey soils that could have the greatest water retention (Figs.4, 5 and 8a).This water retention was the main cause for the increased presence of BotrytisCinerea on these areas. As result of this practice, grapes quality was “artificially” improved, and thedifferencesbetweengrapesfromdifferentsub-areaswerefurthermoreminimized.Although,

Fig. 10 - Wine-making areas map, manually modified.


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Fig. 11 -Oenological parameters comparison: thewines1-7havebeenproducedby the seven sub-areas ofwine-makingofFig.9.

differencesinsomeoenologicalparameters(colorintensity,dryextract,alcohol,anthocyanins)andtastepeculiaritieswerefoundbetweenthesub-areas(Fig.11).Whatisparticularlyinterestingarethedifferencesobservedbetweensub-areas6and7which,asmentioned,weredifferentiatedonlyonthebasisofelectricconductivity.

6. Conclusions

Anintegratedmethodologyisproposed,allowingminimizingerrorsandobtainingECamapswithgoodresolutionandabletoshowthesoilhorizontalvariabilityinelectricalproperties.ECamapsresultedverysimilartoothermapsobtainedbyEMIandgeoelectricalsurveysinpreviousstudies in the same vineyards. This outcome confirmed that i) soil ECa is substantially constant in time,dependingon the lateralvariations insoilproperties (Lundet al.,1999;Allredet al.,2006)andii)wellcorrelatedECavaluescanbemeasuredbyEMIandgeoelectricalinstruments(Sudduthet al., 2003; Priori et al.,2010a,2010b),producingsimilarmaps(Doolittleet al.,2002;Prioriet al.,2010a,2010b).

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ERT allowed to obtain further information, otherwise non achievable by the EMI survey,althoughEMIsurveyismoresuitable thanERTtoexpresssoilvariability. Indetail,electricalsectionscanbeveryuseful tounderstandgeologicalasset, todetectandmonitorundergrounddrainagesystemsandtorecognizeparticularstructuresthatcaninducegeotechnicalproblems.

Geophysical-pedological combined survey techniques can help in understanding the STU boundaries.Ontheotherhand,thepedologicalsurveycannotbefullyreplacedbynon-invasivemethods, which can only represent a support for agricultural aims. Moreover, measured ECawaspoorlycorrelatedtosoilfunctionalproperties,inasmuchasasimpleEMIsurveycouldnotprovideasoilpropertiesestimate,althoughthegeophysicalstudyallowedamorecompleteandfastassessmentofsoilspatialvariability.

Fortheviticulturalapplication,inallphasesoftheworkanobjectivecriteriahasbeenadoptedtomakeanystepreproducible.Inconclusion,theproposedmethodologyissuitabletoidentifyareaswithdifferentproductionpotential.

Acknowledgements. Authorsaregrateful to the farm“BaroneRicasoli s.p.a.”,Gaiole inChianti,Siena(Italy)fortheeconomicsupportoftheresearchandforgrantingtheaccesstoitsvineyards.SpecialthankstotheCRA-ABPforthesupportduringthework.AuthorsareindebtedwithPolitecnicodiTorinoforthepermission of using the electric tomography instrumentation. This work has been presented during the “30° Convegno Nazionale GNGTS”, Trieste (Italy), in the session “Geofisica Applicata – Metodi Integrati”, on November16,2011.

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Corresponding author: Edoardo Martini Department of Monitoring and Exploration Technologies Helmholtz Centre for Environmental Research – UFZ Permoserstraße 15, 04318 Leipzig, Germany Phone: +49 3412351038; fax +49 34123451038; e-mail: [email protected]

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