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University of Nebraska - Lincoln

University of Nebraska - Lincoln

DigitalCommons@University of Nebraska - Lincoln

DigitalCommons@University of Nebraska - Lincoln

Agronomy & Horticulture -- Faculty Publications

Agronomy and Horticulture Department

2015

Potential for crop production increase in Argentina through

Potential for crop production increase in Argentina through

closure of existing yield gaps

closure of existing yield gaps

Fernando Aramburu Merlos

Instituto Nacional de Tecnología Agropecuaria

Juan Pablo Monzon

Consejo Nacional de Investigaciones Científicas y Técnicas

Jorge L. Mercau

Universidad Nacional de San Luis/INTA/CONICET

Miguel Taboada

INTA, CIRN, Instituto de Suelos

Fernando H. Andrade

Instituto Nacional de Tecnología Agropecuaria

See next page for additional authors

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Merlos, Fernando Aramburu; Monzon, Juan Pablo; Mercau, Jorge L.; Taboada, Miguel; Andrade, Fernando

H.; Hall, Antonio J.; Jobbagy, Esteban; Cassman, Kenneth; and Grassini, Patricio, "Potential for crop

production increase in Argentina through closure of existing yield gaps" (2015). Agronomy & Horticulture

-- Faculty Publications. 974.

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Authors

Authors

Fernando Aramburu Merlos, Juan Pablo Monzon, Jorge L. Mercau, Miguel Taboada, Fernando H. Andrade,

Antonio J. Hall, Esteban Jobbagy, Kenneth Cassman, and Patricio Grassini

This article is available at DigitalCommons@University of Nebraska - Lincoln:

https://digitalcommons.unl.edu/

agronomyfacpub/974

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ContentslistsavailableatScienceDirect

Field

Crops

Research

j o ur na l h o me p a g e :w w w . e l s e v i e r . c o m / l o c a t e / f c r

Potential

for

crop

production

increase

in

Argentina

through

closure

of

existing

yield

gaps

Fernando

Aramburu

Merlos

a,∗

,

Juan

Pablo

Monzon

b

,

Jorge

L.

Mercau

c

,

Miguel

Taboada

d

,

Fernando

H.

Andrade

a,b

,

Antonio

J.

Hall

e

,

Esteban

Jobbagy

c

,

Kenneth

G.

Cassman

f

,

Patricio

Grassini

f

aInstitutoNacionaldeTecnologíaAgropecuaria(INTA),UnidadIntegradaBalcarce,Ruta226,Km73.5,CC276,Balcarce,BuenosAiresCP7620,Argentina bConsejoNacionaldeInvestigacionesCientíficasyTécnicas(CONICET),UnidadIntegradaBalcarce,Balcarce,BuenosAires,Argentina

cGrupodeEstudiosAmbientales,IMASL,UniversidadNacionaldeSanLuis/INTA/CONICET,SanLuis,Argentina dINTA,CIRN,InstitutodeSuelos,Hurlingham,BuenosAires,Argentina

eIFEVA,FacultaddeAgronomía-UniversidaddeBuenosAires/CONICET,BuenosAires,Argentina

fDepartmentofAgronomyandHorticulture,UniversityofNebraska-Lincoln,P.O.Box830915,Lincoln,NE68583-0915,USA

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received22July2015

Receivedinrevisedform1October2015 Accepted2October2015

Availableonline23October2015 Keywords: Soybean Wheat Maize Yieldgap ENSO

a

b

s

t

r

a

c

t

Favorableclimateandsoilsforrainfedcropproduction,togetherwitharelativelylowpopulationdensity, resultsin70–90%ofArgentinagrainproductionbeingexported.Noassessmenttodatehastriedto estimatethepotentialforextragrainproductionforsoybean,wheatandmaize,whichaccountfor78% oftotalharvestedarea,byyieldgapclosureonexistingcroplandareaanditsimpactataglobalscale. Theobjectivesofthispaperare(i)toestimatehowmuchadditionalgraincouldbeproducedwithout expandingcropareabyclosingyieldgapsinArgentina,(ii)toinvestigatehowthisproductionandyield gapsvariesacrossregionsandyears,and(iii)toanalyzehowtheseinter-annualvariationsarerelatedtoEl Ni ˜no—SouthernOscillationphenomenon(ENSO).Productionincreaseonexistingcropareawasassessed forsoybean,wheatandmaizebyquantifyingtheyieldgap(Yg),thatis,thedifferencebetween water-limitedyieldpotential(Yw)andactualyield(Ya).Abottom-upapproachwasfollowedtoestimateYwand Yg,inwhichtheseparameterswerefirstestimatedforspecificlocationsinmajorcropproducingareas andsubsequentlyup-scaledtocountrylevelbasedonspatialdistributionofcropareaandclimatezones. Locally-calibratedcropsimulationmodelswereusedtoestimateYwateachselectedlocationbasedon long-termweatherdataanddominantsoiltypesandmanagementpractices.Fortheanalyzedperiod, thenationallevelYgrepresented41%ofYwforbothwheatandmaizeand32%oftheYwforsoybean.If farmershadclosedYgfromtheselevelsto20%ofYw,Argentinacouldhaveincreasedsoybean,wheatand maizeproductionbyarespective7.4,5.2,and9.2Mt,withoutexpandingcroplandarea.Thisadditional productionwouldhaverepresentedanincreaseof9%,4%,and9%ofsoybean,wheat,andmaizeglobal exports.Thispotentialgrainsurpluswas,however,highlyvariablebecauseoftheENSOphenomenon: attainablesoybeanproductionwas12Mthigherinfavorable“ElNi ˜no”yearscomparedwithunfavorable “LaNi ˜na”years.Interestingly,Ygtendedtobehigherinwetyears,suggestingthatfarmersdonottake fulladvantageofyearswithfavorableconditionsforrainfedcropproduction.RegionalvariationinYg wasfoundinArgentinahighlightingtheusefulnessofthisworkasaframeworktotargetresearchand, ultimately,reducegapsinareaswherecurrentyieldsarewellbelowtheirpotential.

©2015TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

Cropproductionneedstoincrease60%by2050tocopewith increasingfooddemand(AlexandratosandBruinsma,2012).

Pro-∗ Correspondingauthor.

E-mailaddress:aramburumerlos.f@inta.gob.ar(F.AramburuMerlos).

ductionincreasecanbeachievedbyexpansionofcurrentcroparea, higheryieldperunitarea,orboth(Bruinsma,2009).Furthermore, yieldincreasesperunitareacanbeachievedthroughincreasesof yieldpotential(Yp)and/orthroughreductionsofyieldgaps(Yg) (Fischeretal.,2014).Ypisdefinedastheyieldofacultivarwhen growninanenvironmenttowhichitisadapted,withnutrientsand waternon-limitingandwithbioticstresseseffectivelycontrolled (Evans,1993;VanIttersumandRabbinge,1997;EvansandFischer,

http://dx.doi.org/10.1016/j.fcr.2015.10.001

0378-4290/©2015TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4. 0/).

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1999).Hence,Yp isdeterminedbysolarradiation,temperature, carbon dioxideconcentration,and cropphysiological attributes governinglight interception, conversionintobiomass, and par-titioninto theharvestable organs. In rainfedcropping systems, water-limitedyield potential(Yw)isdetermined alsoby water supplyamountanddistribution,andsoilandlandscapeproperties influencingwateravailability,suchassoilavailablewaterholding capacityandterrainslope(Lobelletal.,2009;VanIttersumetal., 2013).Whenwatersupplyisnotsufficienttosatisfycropwater requirements,YgisestimatedasthedifferencebetweenYwand actualfarmyield(Ya)(VanIttersumetal.,2013).ThesizeofYg canbetakenasaproxyforthecurrentunexploitedgrain produc-tioncapacity(Cassmanetal.,2003;Lobelletal.,2009).Inturn,the gapbetweenYpandYw,hereaftercalled‘waterlimitationindex’ (WLI),providesameasureofthedegreetowhichcropsarelimited bywater.

Detaileddescriptionsofweather,soils,andcroppingsystemsof ArgentinacanbefoundinHalletal.(1992),Calvi ˜noandMonzon (2009) and Satorre (2011). Crop production area in Argentina occupiesca.32Mha.Majorcropsaresoybean,wheatandmaize, accountingfor78%oftotal croparea(FAOSTATand FAO,2015). Argentinahasafavorabletemperateclimateforrainfedcrop pro-duction,with total annual rainfall that ranges,across cropping regions,from600(south-west)to1400mm(north-east).Mostof Argentinecropareaisunder theinfluenceof ElNi ˜no-Southern Oscillationphenomenon(ENSO).The“ElNi ˜no”phaseisreflectedin anincreaseinspring/summerrainfallsandhighersummercrops yields,whiletheoppositeoccurswith“LaNi ˜na”events(Podestá etal.,1999;Iizumietal.,2014).Dominantsoilscorrespondtothe Mollisolsorder, withoutimpedancestocroprooting,exceptfor someregionswhereacalichelayerlimitsrootingdepth.

Argentine cropping systems have experienced important changesoverthelast20years.Cropyieldshaveincreasedrapidly (28,40and128kgha−1y−1forsoybean,wheatandmaize, respec-tively)drivenby a wideadoption of no-till systems,increasing amountsofcommercialfertilizers,anddevelopmentof herbicide-and insect-resistant crop varieties with high yield potential (Satorre,2011;Grassinietal.,2013;F.H.Andradeetal.,2015).At thesametime,expansionincroppingareahasoccurredmainly inareasthatwerepreviouslyusedforlivestockproductioninthe Pampasregionaswellasattheexpenseofnaturalforested ecosys-temsinthenorthernregion,which resultsingrowingconcerns aboutenvironmentalfootprint(Viglizzoetal.,2011a;Volanteetal., 2012;Lambinet al.,2013).Therefore,robustyield-gapanalyses canhelptodetermineareaswithgreatestpotentialforgrain pro-ductionincrease on existing cropland area, and its consequent impactatcountrylevel.Likewise,yield-gapassessmentalso pro-videsthefoundationforfuturestudiesoncropintensification,land usechange,climatechangeimpact,andassessmentofirrigation expansion.

Argentina is thethird soybean exporter country, first world exporter of soybean derivatives (cake, oil and biodiesel), and respectivesecondandsixthexporterofmaizeandwheat.1 Since itsinternalfooddemandisexpectedtoremainflatinthefuture, anyfutureincreaseincropproductioninArgentinawillresultin aparallelincreaseinexports(AlexandratosandBruinsma,2012). Whilemostyield-gapassessmentstodateareglobalstudieswith limitedlocalrelevance,aspointedbyVanIttersumetal.(2013), orarefocusedonlow-inputsubsistencesystemswithoutaccessto technology,markets,andextensionservices(Fermontetal.,2009; Waddingtonetal.,2010;TittonellandGiller,2013;Kassieetal., 2014),noattentionhasbeenpaidtomajornon-subsidizedexporter

1 Basedon2006–2011statisticsfromFAO(2015).ItIncludesflouraswheat

equiv-alents.

countrieslikeArgentina.Ontheotherhand,climatevariabilityhas aclearinfluenceoncropproduction,worldmarketsupplies,and commodityprices,asithappenedin2007(PiesseandThirtle,2009; Trostle,2010;Iizumietal.,2014).Hence,ananalysisofhowmuch extragrainamajornetexportercountrycanproduceonits exist-ingcropareaandhowYaandYgareaffectedbyclimatevariability isnovelandcrucialtoassessfuturegrainexport/importscenarios andisrelevanttoglobalfoodsecurity.

Inthepresentstudy,well-calibratedcropsimulationmodels, coupledwithhigh-qualityweather, soil, and cropmanagement data, wereused to assess Yg of soybean, wheat,and maize in Argentina,followingtheprotocolsoftheGlobalYieldGapAtlas project(Grassinietal.,2015;VanBusseletal.,2015,http://www. yieldgap.org/methods).Ygwereestimatedforspecificlocationsin majorproducingareasandresultswereup-scaledtoclimatezones andcountrylevels.Specificobjectivesofthisworkwere:(i)to quan-tifythepotentialforcropproductionincreaseinArgentinathrough closureofexistingYgoncurrentcroplandarea,(ii)toanalyzethe regionaland inter-annual variability of attainablecrop produc-tionandYg,and(iii) toevaluatetheattainablecropproduction asrelatedtotheENSOphenomenon.

2. Materialsandmethods

2.1. Datasourcesandselectionofweatherstations

Data onsoybean,wheatand maize cropharvested areaand averageYawereretrievedforeachdepartment(i.e.,thesmallest administrativeunitinArgentina,averagesizeca.4000km2)from

theArgentineAgriculturalMinistry(http://www.siia.gov.ar/).Only dataforthe2006–2012timeperiodwasusedinordertoaccount fortherecentexpansionincropareaduringthelasttwodecadesas reportedbyViglizzoetal.(2011a),andtoavoidthesteeptrendsin averageYaasrecommendedbyVanIttersumetal.(2013).Indeed, analysisofsequentialaverageYastartingfromthemostrecentyear andgraduallyincludingmoreyearsbackintimeindicatedthat7 yearswereappropriatedforrobustestimationsofaverageYaand itsvariation,withanadequatecontroloftechnologicalchanges (SupplementaryFig.1).Previousassessment ofcropproduction statisticsqualityinArgentinaindicatedreasonablygoodaccuracy (Sadrasetal.,2014).Onlyrainfedcropswereaccountedforinthe presentstudyasirrigatedareaaccountsfor<3%ofareasownwith thethreecrops(Siebertetal.,2013).

Selection of data sources and quality control followed the Global Yield GapAtlasguidelines (Grassini et al., 2015; http:// yieldgap.org/methods). Dailymaximum and minimum temper-ature and precipitationdata werederived fromINTA (National InstituteforAgriculturalTechnology;http://siga2.inta.gov.ar/)and SMN(NationalWeatherService;http://www.smn.gov.ar/)weather stations.SMNandINTAweatherstationshavealargenumberof consecutivemissingvalues fordailysolarradiationdata.Hence, datafromNASA-POWER(http://power.larc.nasa.gov/)wereusedas sourceofdailyincidentsolarradiation.Recentevaluationsofthe NASA-POWERsolarradiationdataindicateverygoodagreement withmeasuredsolarradiationdatainareaswithflattopography (Whiteetal.,2011;VanWartetal.,2013a).Similarresultswere foundforcroppingregionsinArgentina (n=18,375daily obser-vations,SupplementaryFig.2).Completeweatherrecordsforthe 1983–2012periodwereobtainedbycombiningtemperatureand precipitationfromINTAandSMNweatherstationsandsolar radi-ationfromNASA-POWERdata.Thenumberofyearsusedforthe simulationswasappropriateforrobustestimationofaverageYw anditsvariability(Grassinietal.,2015).Noconsistenttrendin tem-peratureandprecipitationwasdetectedinArgentinawithinthe periodusedforthesimulations(Fernández-Longetal.,2013).

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Qual-itycontrolandfilling/correctionofweatherdataforthetargeted weatherstationswasperformedbasedoncorrelationsbetweenthe targetweatherstationandtwoadjacentweatherstations follow-ingHubbardetal.(2007).Thenumber ofcorrections/filleddata afterthequalitycontrolprocedurewasalwayslowerthan3%for allvariables.

FollowingVanBusseletal.(2015),weatherstationsusedfor thisstudy,hereaftercalledreferenceweatherstations(RWS),were selectedbasedoncrop-specificharvestedareawithinabufferzone areaof100kmradiuscenteredoneachRWSandclippedbythe cli-matezone(CZ)wheretheRWSwaslocated.EachCZcorrespondsto aparticularcombinationofgrowingdegreedays,aridityindex,and temperatureseasonality(VanWartetal.,2013c).RWSwere iter-ativelyselectedstartingwiththeonewiththegreatestharvested areacoverageuntilreachingca.50%ofcrop-harvestedareaand morethan70%coveragebytheCZwheretheRWSwerelocated.

DominantsoilserieswereidentifiedforeachRWSbufferbased ondataprovidedbytheSoilInstituteofINTA(http://geointa.inta. gov.ar/).Dominantsoilseries(twotothreeperRWS)wereselected basedon(i)province-levelsoilmaps(1:50,000and1:100,000),and (ii)producer’spreferenceforgrowingcertaincropsinbestsoils(cf. Section2.3).Functionalsoilpropertiesrequiredtoruncrop sim-ulationmodels(e.g.,fieldcapacityandpermanentwiltingpoint) werederived fromsoilseriesdescriptionsfollowingRitchieand Crum(1988),aftertherevisionsmadebyGijsmanetal.(2003). Maximumrootingdepthforwheat,maizeandsoybeanwassetat 1.8mexceptforthoselocationswhereacalichelayerrestrictsroot growth(Dardanellietal.,1997).Acompletelistofthesoilsused ateachRWS,andspecificsoilproperties,areavailableon Supple-mentaryTable1.

2.2. Cropsimulationmodelsusedforestimationsofyield potentialandwater-limitedyieldpotential

SimulationswereperformedusingCERES-Maize,CERES-Wheat andCROPGRO-SoybeanmodelsembeddedinDSSATv4.5(Jones etal.,2003;Hoogenboometal.,2010).Geneticcoefficientswere derivedfromMercauetal.(2007,2014),Monzonetal.(2007,2012), andunpublisheddatafromwell-managedexperiments.Thethree modelswereevaluatedontheirperformancetosimulateYpandYw bycomparisonofmodelsimulatedyieldsagainstmeasuredyields fromwell-managedrainfedandirrigated fieldexperimentsthat exploreawiderangeofsowingdates,sites,years,andwater avail-ability(Fig.1).Theagreementbetweenobservedandsimulated datawasassessedthroughtherootmeansquareerror,expressed aspercentageofobservedmean(V),anditscomponents(Kobayashi and Salam,2000).Measuredinaccuracy in simulated yieldwas fairlylowforthethreemodels(Fig.1).

2.3. Simulatedcroppingsystems

Dataoncropmanagementpractices(e.g.,sowingdate,cultivars andplantpopulationdensity)donotexistorarenotpublicly avail-ableforcroppingsystemsinArgentina.Hence,cropmanagement practicesforeachRWSwereretrievedfromlocalagronomists.One renownedagronomistwasidentifiedperRWSandaskedtoprovide allmanagementpracticesrequiredforsimulationofYpandYw. Requestedinformation included:dominantcropsequences,soil type,sowing dates,cultivarnameand maturity,andplant pop-ulationdensity(SupplementaryTable2).Inordertoaccountfor differencesininitialsoilwateratsowingamongyears,theentire cropsequencewassimulated,assuming50%ofplantavailablesoil waterinthefirstyearofthetimeseries.However,atfewlocations characterizedbyerraticsoilwaterrechargeduringfallow(Rafaela and Pilar), producers will sow wheatonly in those fields with ≥50%ofavailablesoilwater.Hence,wheatsimulationsatthese

0 2 4 6 0 2 4 6 V = 17 SB = 3 SDSD = 1 LCS = 96

a

Observed (Mg ha-1) Si m u la te d (M g ha -1 ) 0 2 4 6 8 10 0 2 4 6 8 10 V = 8 SB = 1 SDSD = 29 LCS = 70

b

Observed (Mg ha-1) Si m u la te d (M g h a -1 ) 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 V = 15 SB = 4 SDSD = 1 LCS = 95

c

Observed (Mg ha-1) Si m u la te d (M g h a -1 )

Fig.1.Comparisonofsimulatedandobservedcropyieldsdatafor(a)soybean,(b) wheat,and(c)maize.Thesolidredlinerepresentsy=x,andthedashedredlines rep-resentsy=x±20%.Therootmeansquareerrorexpressedaspercentageofobserved mean(V),anditscomponents:squaredbias(SB),squareddifferencebetween standarddeviations(SDSD),andlackofcorrelationweightedbythestandard devi-ation(LCS),expressedaspercentageofmeansquarederror,areshownininset. Geneticcoefficientsanddatapointsusedforthemodelevaluationwereobtained fromMercauetal.(2007,2014)andMonzonetal.(2007,2012)andunpublished well-managedexperiments.

locationsassumed50%ofavailablesoilwateratsowingforthose casesinwhichthisvaluewas<50%inordertoportrayfarmer’s choiceofgrowingwheatonlyinfieldswithareasonablelevelof storedsoilwater.Thesimulatedcropsequenceswere:(i)2-year soybean–maize,(ii)2-yearsoybean–soybean(i.e.,continuous soy-bean),and(iii)2-yearsoybean–wheat/soybeandoublecrop,except forPigüé,wherelowsummerrainfallsconstraincropsequences to: (i) 2-year soybean–soybean and (ii)2-year wheat–soybean. Separatesimulationswereperformedforpotential(Yp)and water-limitedconditions(Yw),assumingnolimitationstocropgrowth bynutrientsand pests.AtmosphericCO2 concentrationwasset

constantat380ppm. 2.4. Upscalingmethod

FollowingVanBusseletal.(2015),eachsimulatedcropsequence –soiltypecombinationwasweightedbytheirrelative contribu-tiontothecrop-specificharvestedareawithintheRWSbufferto

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Fig.2.MapsofArgentinashowing(a)selectedclimatezones(designatedbyRomannumeralswithindelineatedclimatezones),referenceweatherstations(closedtriangles), andbufferzones(hatchedareas);and(b)soybean,(c)wheat,and(d)maizeaverageharvestareadensityperdepartment(HAD,%oftotaldepartmentarea)forthe2006–2012 timeperiod.

retrieveaveragesYwandYp.Forsoybean,separateaverageswere calculatedforsinglesoybean(i.e.,afull-seasonsoybeancrop)and soybeanasthesecondcropofa doublecroppingsequence(i.e., soybeansownimmediatelyafterharvestofawintercerealcrop). AnnualYawascalculatedforeachRWSbasedontheYareportedfor thedepartmentslocatedwithintheRWSbufferandrelative con-tributionofeachdepartmenttototalcrop-specificharvestedarea withintheRWSbuffer.Finally,Yp,Yw,andYawereupscaledto CZandcountrylevels,basedontherelativecontributionofeach RWStototalcrop-specificharvestedarea.Forallspatialscales(i.e., RWS,CZ,andcountry),Ygwascalculatedasthedifferencebetween YwandYa,andalsoexpressedaspercentageofYw.Thedegreeto whichcropsarelimitedbywater,i.e.,theWLI,wascalculatedas thedifferencebetweenYpandYwandexpressedaspercentageof Yp.

2.5. NationalestimationofattainablecropproductionandENSO phenomenon

Attainableyieldwasestimatedtobe80%ofwater-limitedyield becausefarmers’yieldstendtoplateauwhentheyreach75–85%of YporYw(Cassmanetal.,2010;VanIttersumetal.,2013;Sadras etal.,2015).Attainablecropproduction(ACP)ofArgentinawas calculatedasfollows:

ACP=(Yw×0.8)×Area

whereareaisthecrop-specificharvestedareaofthelast(2011/12) croppingseasonanalyzed.

0 2 4 6 8 10 0 2 4 6 8 10

Maize

Soybean

Wheat

Ya

GYGA

(Mg

ha

-1

)

Ya

MA

(M

g

h

a

-1

)

Fig.3.Nationalaverageactualyields(Ya)reportedbytheArgentineAgricultural Ministry(MA,Mgha−1)asafunctionofnationalYaestimatedthroughtheupscaling

methodoftheGlobalYieldGapAtlasProtocolfollowedinthepresentstudy(GYGA, Mgha−1)foreachofthe2005/06to2011/12cropseasons.Thesolidlinerepresents

y=x.

In order to assess influence of the ENSO phenomenon on ArgentineYa,YwandACP,croppingseasonswerecategorizedin ENSOphases:Neutral,ElNi ˜no(typicallywetyears),andLaNi ˜na (typicallydry years),basedontheOceanicNi ˜noIndex (ONI)of theClimatePredictionCenterofNOAA’sNationalWeatherService (2015).YwandACPdifferencesbetweenENSOphaseswere eval-uated using non-parametric tests (Kruskal–Wallis and Levene’s tests),whiletheeffectsonYawereassessedbyanalyzingthe resid-ualsobtainedfromtheregressionanalysisbetweenYaandyear

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Fig.4.Water-limitedyieldpotential(Yw,Mgha−1)forCZlevel(coloredareas)andreferenceweatherstationlevel(piecharts,withsizeproportionaltoYwlevel)for(a)

soybean,(b)wheatand(c)maize.Actualyields(darkcolor)andyieldgaps(lightcolor)areshown,bothrelativetotheYw(innumbers),ineachpiechart.Bordersofthe CZswhereElNi ˜no—SouthernOscillationphenomenonhadasignificanteffectonYwarehighlightedinlightblue(a)andyellow(c)(Kruskal–Wallistest,P<0.05).(For interpretationofthereferencestocolourinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)

0 5 10 15 0 20 40 60 80 R2=0.55 R2=0.81 R2=0.68 Yw (Mg ha-1) Yw CV (% ) 0 20 40 60 80 0 20 40 60 80 Maize Wheat Soybean R2=0.90 R2=0.81 R2=0.75 WLI (%) Yw CV (% )

a

b

Fig.5.Coefficientofvariation(CV,in%)forwaterlimitedyieldpotentials(Yw),calculatedforreferenceweatherstations,asafunctionof(a)water-limitedyieldpotential (Yw),and(b)waterlimitationindex(WLI,i.e.,differencebetweenyieldpotentialandwater-limitedyield,expressedaspercentageofyieldpotential),forsoybean,wheat, andmaize.Significantnegative(a)andpositive(b)correlationswerefoundforthethreecrops(P<0.05).

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(bothabsoluteresidualsandrelativetotheYaestimatedforeach year).

3. Results

3.1. Selectedweatherstationsandcropareacoverage

Harvestedsoybeanand maizeareaaveraged17.2and3Mha duringthe2006–2012period,respectively.Spatialdistributionof soybeanandmaizeareawasremarkablysimilar,withhighestcrop areadensityinthecentralPampas(Fig.2).Incontrast,wheat pro-ductionarea(4.5Mha)wasconcentratedinthesouthernPampas.A relativelysmallnumberofRWSbuffers(16forsoybeanandwheat, and15formaize)wassufficienttocover53,50and48%ofnational soybean,wheatandmaizeharvestedarea,respectively. Further-more,theeightCZwheretheselectedRWSwerelocatedaccounted for81,70and78%oftotalnationalcropareaforsoybean,wheat,and maize,respectively(Fig.2).Fiveoftheseclimatezonesarelocated inthePampas(CZI–IVandVII),twointheChacoregion(CZVIand VII),andoneintheEspinal(CZVIII)(Halletal.,1992;Viglizzoetal., 2011b).

3.2. VariationinactualyieldsacrossArgentina

NationalaverageYacalculatedbytheupscalingmethodwas2.7, 3.0,and6.8Mgha−1 forsoybean,wheatandmaize, respectively (Table1).ThesevalueswereinagreementwiththenationalYa reportedbytheArgentineAgriculturalMinistryforthethreecrops (t-test,P>0.45),indicatingtherobustnessofthemethodusedto upscaleresultsfromRWSbufferstolargergeographicareas(Fig.3). TherewasalargevariationinYaacrossRWSbuffersinArgentinaas aresultofthelargespatialvariationinclimate,soilsandcropping systems(SupplementaryTable3–5).Forinstance,maizeYaranged from3.2to8.9Mgha−1acrossRWS(SupplementaryTable5).The highestmaizeandsoybeanYawereobservedinthecentralPampas, whilethehighestwheatYawasobservedinthesoutheastPampas. 3.3. Spatialandtemporalvariationinwater-limitedyield

potential

NationalYwwas3.9,5.2,and11.6Mgha−1forsoybean,wheat andmaize,respectively(Table1).WheatYwwashighestinthe southeastanddecreasedtowardsthenorthwestfrom6.9Mgha−1 in CZ IIto 2.1Mgha−1 in CZ V (Fig. 4b). Maize Yw was more stable across regions, ranging between 10.0 and 13.2Mgha−1, exceptinCZI(i.e.,southwestPampas)whereitbarelyexceeded 8.1Mgha−1(Fig.4c).ThehighestsoybeanYwwasfoundintheCZVI (5.2Mgha−1),whichcorrespondstothesub-humidChacoregion. However,YwinCZVImighthavebeenoverestimatedsincethe RWSwaslocatedinthewesternedgeofitscroparea,where pre-cipitationishigher.CZVII,IVandIII,incentralandwestPampas, alsopresentedhighsoybeanYw,ofca.4.0Mgha−1(Fig.4a). Low-estsoybeanYwwasfoundinthesouthwestPampas(2.2Mgha−1), whichisconsistentwiththeresultsformaize.Secondcrop soy-beanYwwasconsistentlylowerthansinglesoybeancropYw,with higherdifferencesinthesouth(upto30%)thaninthenorthern climatezones (SupplementaryTable3).Likewise,soybean dou-blecropshowedhigheryear-to-yearvariationinYwthansingle soybeancrop(SupplementaryTable3).

FormostRWS,lowYwwasassociatedwithhighinter-annual variabilityinYwandviceversa(Fig.5a).Variationinwatersupply (soilwatercontentatsowingplusin-seasonprecipitation)across RWSexplainedthepreviousrelationship,asindicatedbythe pos-itivecorrelationbetweenthecoefficientofvariation(CV)forYw andtheWLI(Fig.5b).TheWLImayalsoreflectsdifferencesin pro-ducer’spreferencetogrowcertaincropsinbestsoils.Forexample,

-50 0 50 100 150 200 250 0 20 40 60 80

Soybean

Wheat

Maize

R

2

=

0.55

R

2

= 0.57

R

2

=

0.33

Yield

gain

rate

(kg

ha

-1

y

-1

)

Yg

(%

)

Fig.6.Yieldgaps(Yg,2006–2012average)ateachreferenceweatherstationasa functionofyieldgainrate(kgha−1y−1)from1992to2012forsoybean,wheatand maize.Significantnegativecorrelationswerefoundforthethreecrops(P<0.05). SoybeanvaluesforCZIshowedadifferentpatternofYgbecauseofaseverewater limitation,andareindicatedas*.

theWLIofsoybeanandmaizeinCZIweredifferent(61versus49%, respectively),whichmayberelatedtoproducer’schoicetogrow maizeinthebestsoils(SupplementaryTable1).

3.4. Spatialandtemporalvariationinyieldgapsforsoybean, wheatandmaizeinArgentina

AverageYginArgentinawas1.3,2.1,and4.8Mgha−1for soy-bean,wheatandmaize,respectively (Table1).Yg, expressedas percentageofYw,wasremarkablysmallerforsoybean(32%)than for wheat and maize (41%), and this difference wasconsistent acrossRWS(Fig.4).Ygofthethreecropsvariedwidelywithinthe country,rangingfrom22to69%oftheYwacrossCZ.Despitesuch variability,therewasnoconsistentcorrelationbetweenYgandYw, YaoryieldCVs(P>0.45).Ingeneral,largestgapswerefoundinareas thathadbeenrecentlyconvertedintoannualcropproductionwhile smallestgapswerefoundinthoseareaswithlongagricultural his-tory.ThehighestYg(45to69%ofYw)werefoundinclimatezones VandVI,whicharelocatedintheChacoregion(Fig.4).Western cli-matezones(i.e.,VIIandVIII)alsoexhibitedlargegaps,rangingfrom 40to60%oftheYw.SmallYgwerefoundincentralPampas(i.e., cli-matezonesIIIandIV),reachingca.25%(forsoybean)andbetween 30and40%(formaizeandwheat)oftheYw.ThesouthernCZ(i.e.,I andII)hadintermediateYgformaizeandwheat(ca.40%ofYw),but withasharplongitudinalgradient,withdecreasingYwand increas-ingvariabilityfromeasttowest,duetoaparalleldecreaseinrainfall togetherwithanincreasingfrequencyofsoilswhereacalichelayer limitstherootingdepth(Monzonetal.,2012).Interestingly, soy-beancropsinCZIhadthelowestYw,withthehighestinter-annual variability,butthelowestyieldgap(SupplementaryTable3).There wasasignificantnegativerelationship(P<0.05)betweenthesize oftheYgandyieldgainratesobservedduringthelast20years ana-lyzed(1992–2012),suggestingthattechnologicalimprovementin croppracticeshavenothomogenouslyreachedand/orimpacted theentireArgentinegrainproductionarea(Fig.6).

Interestingly,magnitudeofYgatRWS,CZ,andnationalscales dependeduponyear(Fig.7a).Forthethreecrops,Yaapproached Ywindryyears(i.e.,inyearswithahighWLI),whileYgwas signif-icantlyhigherinwetyears(Fig.7b).Thecontrastingpatterninwet versusdryyears,whichwasconsistentatallspatiallevels,wasin agreementwiththefindingthatthelowestsoybeanYgoccurredin themostwater-limitedregion(i.e.,CZI,Fig.4).

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Table1

Actualyields(Ya),water-limitedyieldpotentials(Yw),yieldgaps(Yg),andattainablecropproduction(ACP)forsoybean,wheatandmaizeinArgentinabasedon2011/12 croparea.Actualyieldsare7-y(2005/06–2011/12)averages.SeeSection2.4fordetailsoncalculationofACP.

Ya(Mgha−1)a Yw(Mgha−1)a Yg(Mgha−1)b Croparea(Mha) ACP(Mt)a

Soybean 2.65(14%) 3.91(18%) 1.26(32%) 17.6 55(18%) Wheat 3.02(23%) 5.16(21%) 2.14(41%) 4.5 19(21%) Maize 6.79(18%) 11.60(14%) 4.81(41%) 3.7 34(14%)

aNumberbetweenbracketsshowsthecoefficientofvariation(in%). bNumberbetweenbracketsshowsYgasapercentageofYw.

2006 2008 2010 2012 0 10 20 30 40 50 60

a

Year Yg (% ) 0 20 40 60 80 0 10 20 30 40 50 60 Maize Wheat Soybean

b

WLI (%) Yg (% )

Fig.7. Argentineyieldgaps(Yg)foreachcroppingseason(2006–2012),expressedaspercentageofwater-limitedyields,forsoybean,wheatandmaize,asafunctionof:(a) harvestyearand(b)waterlimitationindex(WLI,i.e.,differencebetweenyieldpotentialandwater-limitedyieldforeachcroppingseason,expressedaspercentageofyield potential).AsignificantnegativecorrelationwasfoundbetweenYgandWLIforthethreecrops(P<0.05),withnosignificantdifferencesinthelinearregressionparameters amongcrops(P>0.46). 1985 1990 1995 2000 2005 2010 2015 1 2 3 4

Year

Ya

(

M

g

h

a

-1

)

-30 -20 -10 0 10 20 30 1985 1990 1995 2000 2005 2010 2015 2 4 6 8 10 ElNiño Neutral LaNiña

Yea

r

Y

a (

M

g ha

-1

)

-30 -20 -10 0 10 20 30

a

b

Fig.8.Trendsinactualyield(Ya)from1985to2015inArgentinaasrelatedto ElNi ˜no—SouthernOscillationphenomenon(ENSO)forsoybean(a)andmaize(b). TheinsetsshowtherelativeYaresiduals(%)obtainedfromtheregressionanalysis betweenYaandyear.Formaize,therewasasignificantdifferencebetweenthe slopesoftherelativeresidualsofElNi ˜noandLaNi ˜naphasesovertime(P<0.05).

3.5. ENSOphenomenoneffectonArgentineactualandattainable cropproduction

Inrelativeterms,theeffectsoftheENSOphenomenonon soy-bean Ya was constant over time (Fig.8a), while there was an

increasinglyhigherdifferenceinmaizeYabetweenENSOphases overtime,bothinabsoluteandrelativeterms(Fig.8b).WheatYa wasnotaffectedbytheENSOphenomenon.

Yieldgapclosuretoalevelof20%ofYwwouldleadArgentina to a production of 55, 19, and 34Mt of soybean, wheat, and maize,respectively,withoutexpansionincroplandarea(Table1). However,nationalYw,andhenceACPvariedsignificantlyamong yearsbecauseofclimatevariability,withCVrangingfrom14to 21%.Inter-annualvariationinsummercropsACPwerepartially explainedbytheinfluenceoftheENSOphenomenon.During“La Ni ˜na” years, Argentine maize ACP was significantly lower and morevariablethanduring“ElNi ˜no”and Neutralyears(P<0.05,

Fig.9).Likewise,soybeanACPwashigherin“ElNi ˜no”yearsand lowerin“LaNi ˜na”years(P<0.05),withnosignificantvariationin theinter-annualvariabilitywithineachphase(Fig.9).TheENSO phenomenonhadastrongeffectonsummercropsYwandcrop productioninalimitedbuthighlyproductiveregionofArgentina (i.e.,CZIIIandIVforsoybean,andIIandIIIformaize,Fig.4).Onthe otherhand,theENSOphenomenondidnothaveaclearinfluence inwheatACP(P=0.72).

4. Discussion

Argentina isone ofthemajorgrainexportercountriessince early20thcentury.Assumingastandardnutritionalunitof500kg grainequivalentpercapitaperyear(Connoretal.,2011),Argentina producesenoughgraintofeedca.200millionpeople,thatis,five times its current population. In addition, Argentina couldhave potentiallyproducedanextra7.4Mtofsoybean,5.2Mtofwheat and9.2Mtofmaizeonexistingcroplandarea,byclosingnational averageYgcalculatedforthe2006–2012period(32to41%ofYw dependinguponcrop)toanattainablelevelof20%ofYw.Ifthe extracropproductionamountachievedthroughyieldgapclosure hadbeenexported,whichwasverylikelygiventhelowinternal demand,itwouldhaverepresentedanincreaseinsoybean,wheat

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El Niño Neutral La Niña 20 25 30 35 40 45

Maize

a

a

b*

ENSO phase

20 30 40 50 60 70 80

Soybean

a

ab

b

5 10 15 20 25 30

Whe

at

A

a

in

ab

le

Cr

o

p

Pr

od

u

c

o

n (

M

t)

Fig.9.Attainablesoybean,wheatandmaizeproductionofArgentinaasaffectedby “ElNi ˜no”—SouthernOscillationphenomenon(ENSO)basedon2011/12croparea. Differentletters,withinthesamepanel,indicatesignificantdifferencesamongENSO phasesatP<0.05(Kruskal–Wallistest).Distancebetweenhorizontaldashedlines represents10%ofglobalexportsforeachcrop(2006–2011average,FAOSTAT2015). AttainablemaizeproductioninLaNi ˜nayearspresentedasignificanthighervariance (Levene’stest,P<0.05).SeeSection2.4fordetailsoncalculationofattainablecrop production.

andmaizeglobalexportsofarespective9%,4%and9%.2Inturn,this increaseinglobalexportswouldhavebeensufficienttocoverthe foodrequirementsof44millionpeople.However,Argentine pro-ductionanditscontributiontoglobalgrainmarketsgreatlyvaries duetoclimatevariationasrelatedtoENSOphenomenon(Podestá etal.,1999;Iizumietal.,2014).Furthermore,thereportedeffects oftheENSOphasesonArgentinemaizeproductiontendedtobe greaterduringthelastcroppingseasons(Fig.8),despiteincrements oflate-sownmaize,whichhaslowerYpthanearlysowings,but withsignificantreductions inYw CV(Maddonni,2012; Mercau etal.,2014).Thispatternmightreflectthatattainableyieldsare evenmoresensibletotheENSOphenomenonthanYa,and,asYa approachesYw,theformerwillbecomemorevariable,ifcrop man-agementpracticesdonotchange.Forexample,in“LaNi ˜na”years thereisahighprobabilityofwidespreaddroughtsthatmayreduce Argentinemaize production capacityby morethan30%, witha parallel10% impacton globalmaize exports. Likewise,average attainablesoybeanproductionin“LaNi ˜na”yearsis12Mtlower

2 Globalexportswereestimatedfrom2006to2011averages(FAO,2015).

thanin “ElNi ˜no” years,which representsa reduction ofglobal exportsofsoybeanby15%(Fig.9).

In a global context, size of Yg of major Argentine cereal cropsismoderate.WheatandmaizeYginArgentinarepresented 41% of their respective Yw, which were similar to those esti-matedforsunflowerbyHalletal.(2013),butconsiderablyhigher thanthegaps reportedforsomemajorhigh-technology cereal-producingregions,e.g.,wheatinGermanyandmaizeinNebraska, USA,which had gaps of ∼20% (Grassini et al.,2011; Van Wart etal.,2013b).Attheotherextreme,YginArgentinaweremuch smallerthanthosereportedforsmallholderproductionsystems in Sub-Saharan Africa(Tittonell and Giller, 2013; Kassie et al., 2014). Considering an ‘S-shaped curve’ production function in responsetoinputs(DeWit,1992),Africansmallholderagriculture arelocatedatthelow-input/low-responsezone,andthehigh tech-nologycereal-producingregionsareatthehigh-input/plateauzone (Tittonell, 2013).Argentinecroppingsystemsarebetweenthese twoextremes,withinthe‘high-responsezone’,butwithhigh vari-abilityamongregionsandfarmers.Thiscouldpartiallyexplainthe highrateofcropyieldincreasethatArgentinahadduringthelast twentyyears.Infact,Argentinaisoneofthefewcountries exhibit-ingratesofyieldincreasethataresufficienttodoublecurrentcrop productionby2050,thoughthiswillonlybeachievedifcurrent ratesofyieldgainaresustainedoverthenext35years(Rayetal., 2013).EvenwithnochangesincurrentYw,ifArgentinaisable tosustainitscurrentyieldgainrates,theaveragenationalYawill reach80%ofYwby2025,2026and2038forsoybean,maizeand wheat,respectively.Moreover,thereisevidencethatYwandland productivitycanbefurtherincreasedinArgentina.Forexample, farmersareadoptingconceptsonzonemanagement,climate fore-casts(asrelatedtoENSO),andin-seasonmeasurement(likesoil wateratsowing)tofinetunecropmanagement(Bertetal.,2006; Monzonetal.,2007;Peraltaetal.,2013), whileland productiv-itycanbeincreasedbyintensifyingcropsequencesinthePampas (Monzonetal.,2014;J.F.Andradeetal.,2015).

SoybeanYgisconsiderablylowerthanYgofmaizeandwheatin Argentina(32%versus41%ofYw).Thisdifferencecanbeexplained by: (i)higher vegetativeand reproductiveplasticityof soybean relative tomaize(Andrade,1995);(ii)Argentinesoybeancrops obtainedca.60%oftheirNfrombiologicalNfixation(Collinoetal., 2015),(iii) therequirementofP toreach90% ofthemaximum yieldforsoybeanisconsiderablylowerthanforwheatandmaize (Hanway and Olson,1980).Cropsare typically nutrient-limited in Argentina, as the rates of fertilizers applied have increased butarestilllow relativetocropnutrientrequirements(Calvi ˜no andMonzon,2009;LavadoandTaboada,2009),resultingin neg-ativenutrientbalances(Liuetal.,2010;MacDonaldetal.,2011; Lassalettaetal.,2014).Consideringthat wheat,maizeand sun-flowerYgwereremarkablysimilar,and10%higherthansoybean Yg,itislikelythatthesedifferencescanbepartlyrelatedtonitrogen deficiencies.

Argentinaisnotonlyaninterestingcaseofstudyforitsgreat potentialforcropproductionand grainexports, butalsoforits greatcroppingsystemvariabilityamongregionswhichresulted inawiderangeofYw,Yg(Fig.4)andyear-to-yearvariation(Fig.7). Thisvariability hadnotbeenproperlyquantifiedinpreviousYg assessments,mainlybecausethesewereglobalstudiesthat did not accountfor spatial variation onsoil and cropmanagement withinthecountry, or madenoattempt touseyearly weather data,orwerebasedoncoarseweather,soil,andmanagementdata (Neumannetal.,2010;Lickeretal.,2010;Foleyetal.,2011;Mueller etal.,2012).Forexample,Neumannetal.(2010)roughlyagreed withournationalestimatesofwheatandmaizeYg,butsuchwork wasnot sensitiveenoughtodetectregionalvariations,whereas

Lickeretal.(2010)andFoleyetal.(2011)grosslyunderestimated ArgentinemaizeandsoybeanYw.IthasbeensuggestedthatYgare

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higherwhentheriskassociatedtocropproductionisgreater,i.e., highcoefficientofvariationforyield(Fischeretal.,2009). Inter-estingly,despitethehighvariationinYw,YaandyieldCVsfound inArgentina,therewasnocorrelationbetweenanyofthese vari-ablesandtheYg.Othervariablesarelikelytoexplainbetterthe spatialvariationonYg,forexample,crophistory(i.e.,thenumber ofyearsthatagivenregionhasbeenundercommercial-scale agri-culture)andtechnologylevelappliedbyfarmers(Fig.6).Indeed,we candistinguishcontrastingscenariosformajoragriculturalregions ofArgentina.IntheChacoregion(i.e.,CZVandVI),theYgwas largestprobablybecauseoftherecentagriculturehistoryandsmall yieldgainratesobservedduringthelasttwentyyears(1992–2012). Futureeffortsonresearchshouldbemadetounderstandthe socio-economicfactorsthatexplainlowyieldgainsinthisregion.Atthe centralPampas(i.e.,CZIIIandIV),farmer’syieldshavesignificantly increasedduringthelast20yearsandYgtendstobelowerthanin therestofthecountry.Sincefarmer’syieldwillreachthe attain-ableyieldinthemedium-term,futureon-farmyieldincreasein thisregionmightrelyonincreasesinYwofindividualcropsor increasingcropintensity,orboth.

Thepresent study clearly shows thatYg varied significantly fromyeartoyear(Fig.7).ThetemporalvariationinYg,whichis anaspectthathasnotbeenanalyzedinpreviousyieldgap anal-yses,canbringsomelightonyieldgapcauses(Halletal.,2013; Laborteetal.,2012;VanReesetal.,2014;VanWartetal.,2013b). BothYwandYafollowedthesametrendacrossyears;however, Ywwasmoresensitivetowetyears,relativetoYa,resultingin higherYginthemorefavorablewetyears(Fig.7b).Inwetyears, othernon-waterrelatedfactorsbecamelimiting,suchasnutrient supplyorincidenceofinsect,pestsandpathogens,resultingina largegapbetweenYwandYa.Incontrast,indryyears,waterwas themostlimitingfactorforcropproduction,andYgwasrelatively smaller.Likewise,acombinationoflowsummerrainfallandlow soilwaterholdingcapacitywerethemajorlimitingfactorsfor soy-beanyieldsinCZI,hence,itwasnotsurprisingthatsoybeanYgwas thelowestinthisregion(Fig.4).ThecontrastingbehaviorofYgin favorableversusnon-favorableyearsmightberelatedtofarmer’s riskaversionbehavioranditsimpactonthelevelofappliedinputs andtechnology. Specifically,sincethelevel ofappliedinputs is likelytobedeterminedbasedontheyieldreachedwithnormalor moderatelyadverseweatherconditions,currentmanagementmay haveanunintendedopportunitycostinfavorableyearswithhigh Yw.AvailabilityofENSO-relatedclimateforecastsandother early-seasonindicators(suchassoilwatercontentatsowing)canhelpto reducetheuncertaintiesassociatedwithcropproduction, allow-ingfarmerstotakeadvantageofthefavorableyearsandreducethe economiclosesinadverseyears(Bertetal.,2006).

5. Conclusions

Yieldgap assessmentperformed in thisstudy indicatesthat Argentina hadthepotentialtosubstantially increase grain pro-ductionofsoybean,wheatandmaize,byarespective7.4,5.2and 9.2Mt,withoutexpandingcroplandarea.Thispotentialgrain sur-pluswouldhaveagreatimpactongrainglobalexports,butwith significantvariationsacrossyearsbecauseoftheinter-annual cli-matevariabilityrelatedtotheENSOphenomenon.Magnitudeof yieldgapinArgentinadependeduponyear,withlargestYginwet yearsandsmallestYgindryyears.Substantialvariationinyield gapswasfoundacrosscropproducingregions,whichhighlights theusefulnessofthespatialframeworkappliedinthisstudyto tar-getresearchand,ultimately,reducegapsinareaswherecurrent yieldiswellbelowitspotential.

Acknowledgments

We are gratefultolocal agronomistsin Argentina who pro-videddataonmanagementpractices:AgustínGiorno(Asociación Argentina de Consorcios Regionales de Experimentación Agrí-cola,AACREA),AlbertoQuiroga(INTA),EduardoMartínezQuiroga (AACREA),FernandoRoss(INTA),JuanMartínCapelle(AACREA),Lía OlmedoPico(INTA),MartínSanchez(AACREA),OctavioCaviglia (INTA),andPabloCalvi ˜no(AACREA).WealsothankHugoGrossi Gallegos(UniversityofLujan)forprovidingmeasuredsolar radia-tiondata.ThisworkispartofathesisbyFernandoAramburuMerlos inpartialfulfillmentfortheM.Sc.degree(UniversidadNacionalde MardelPlata).ThisprojectwassupportedbytheDaughertyWater forFoodInstituteatUniversityofNebraska-Lincoln.

AppendixA. Supplementarydata

Supplementarydataassociatedwiththisarticlecanbefound,in theonlineversion,athttp://dx.doi.org/10.1016/j.fcr.2015.10.001.

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1

Supplementary Table 1. Soil type, relative contribution to crop harvested area within

reference weather station (RWS) buffer, and main soil characteristics. Note that

different soil weights are given for maize, compared with wheat and soybean, since

maize is typically grown in the best soils.

RWS

Soil types

Soil Weight (%)

Depth

(m)

Topsoil texture

Subsoil texture

Slope

(%)

Maize

Wheat & soybean

Azul

Typic Argiudol

Typic Argiudol

Typic Natrudol

50

50

-

40

40

20

1.8

1.3

0.5

Loam

Clay loam

Clay loam

Clay Loam

Clay

Clay

2

2

0

Balcarce

Typic Argiudol

Typic Argiudol

Petrocalcic Paleudol

50

50

-

35

35

30

1.8

1.3

0.8

Loam

Clay loam

Clay loam

Clay loam

Clay

Clay

2

2

2

Barrow

Typic Argiudol

Typic Argiudol

Typic Argiudol

Petrocalcic Paleudol

Petrocalcic Paleudol

50

-

50

-

-

-

50

-

30

20

1.8

1.5

1.3

0.8

0.6

Sandy clay loam

Loam

Loam

Clay loam

Sandy clay loam

Clay

Clay loam

Clay loam

Clay

Clay

0

0

1

1

1

Famaillá

Typic Haplustol

Entic Hapludol

Aquic Ustiflivent

50

35

15

50

35

15

1.8

1.8

1.8

Loam

Sandy loam

Silt loam

Sandy Loam

Sandy Loam

Sandy Loam

0

0

0

General Pico

Entic Hapludol

Entic Haplustol

Typic Ustipsament

50

25

25

50

25

25

1.8

1.8

1.8

Sandy loam

Sandy loam

Loamy sand

Sandy loam

Sandy loam

Loamy Sand

0

0

0

Gualeguaychú

Aquic Argiudol

Argiudolic Peludert

Argi-cromic Peludert

50

30

20

50

30

20

1.8

1.8

1.8

Silty clay loam

Silty clay loam

Silty clay

Silty clay

Silty clay

Silty clay

0

3

5

Laboulaye

Udortentic Haplustol

Udic Haplustol

80

20

80

20

1.8

1.8

Sandy loam

Loam

Sandy loam

Sandy loam

0

0

Las Breñas

Udic Argiustol

Ustic Ustocrept

Typic Durostol

40

60

-

30

40

30

1.8

1.8

0.6

Loam

Loam

Loam

Loam

Clay loam

Loam

0

0

0

Marcos Juárez

Typic Argiudol

Aquic Argiudol

Udic Haplustol

40

30

30

40

30

30

1.8

1.8

1.8

Silt loam

Silt loam

Silt loam

Silty clay loam

Silt loam

Silty clay loam

0

0

0

Paraná

Aquic Argiudol

Aquic Argiudol

Typic Argiudol

50

25

25

50

25

25

1.8

1.2

1.8

Silty clay loam

Silty clay loam

Silt loam

Silty clay

Silty clay

Silty clay loam

3

3

0

Pehuajó

Entic Hapludol

Entic Hapludol

Thapto-argic Hapludol

45

40

15

45

40

15

1.8

1.8

1.8

Sandy loam

Sandy loam

Loam

Sandy loam

Sandy loam

Sandy clay loam

0

0

0

Pergamino

Typic Argiudol

Vertic Argiudol

Typic Argiudol

35

40

25

35

40

25

1.8

1.8

1.8

Loam

Silty clay loam

Silty loam

Clay loam

Clay

Silty clay loam

0

0

0

Pigüé

Typic Argiustol

Petrocalcic Paleudol

Typic Haplustol

-

-

-

35

35

30

0.6

1.0

1.8

Sandy clay loam

Loam

Loam

Clay

Clay loam

Loam

0

0

0

Pilar

Entic Haplustol

Typic Haplustol

75

25

75

25

1.8

1.8

Silt loam

Silt loam

Silt loam

Silt loam

0

0

Rafaela

Typic Argiudol

Aquic Argiudol

Typic Argialbol

35

50

15

35

50

15

1.8

1.8

1.8

Silt loam

Silty clay loam

Silty clay loam

Silty clay loam

Silty clay loam

Silty clay loam

0

0

0

Río Cuarto

Entic Haplustol

Typic Haplustol

Typic Ustorhent

45

15

40

45

15

40

1.8

1.8

1.8

Sandy loam

Silt loam

Loam

Loamy sand

Silt loam

Loam

0

0

0

(14)

2

Supplementary Table 2. Soybean (single and double crop), wheat and maize management practices, as retrieved from local agronomists, applied to estimate

water-limited and potential yields at each reference weather station (RWS).

Soybean

Maize

Wheat

RWS

Maturity group

Plant density

(plant m

-2

)

Sowing date

Hybrid

Maturity

a

Plant

density

(plant m

-2

)

Sowing

date

Cultivar

maturity

Plant

density

(plant m

-2

)

Sowing

date

Single Double Single Double Single

Double

Azul

IV

III

30

35

5-Nov

28-Dec 117

7

20-Oct Inter-short 290

1-Jul

Balcarce

III

III

30

35

5-Nov

1-Jan

117

7

20-Oct Inter-short 290

1-Jul

Barrow

IV

III

25

30

25-Nov

1-Jan

117

7

20-Oct Inter-long

220

1-Jul

Famaillá

VIII

VIII

26

26

25-Dec

25-Dec 134

5.5

20-Dec Inter-short 180

1-Jun

General Pico

IV

IV

25

30

5-Nov

10-Dec 124

6

20-Sep Inter-long

270

20-Jun

Gualeguaychú VI

VI

30

40

1-Nov

5-Dec

124

7.5

1-Sep

Inter-short 300

15-Jun

Laboulaye

IV

IV

28

36

25-Oct

5-Dec

124

7

5-Oct

Inter-long

220

1-Jun

Las Breñas

VIII

VIII

22

30

10-Dec

1-Jan

134

4.5

1-Jan

Inter-long

180

15-May

Marcos Juárez IV

IV

28

30

25-Oct

5-Dec

124

7.2

25-Sep Inter-long

250

1-Jun

Parana

VI

VI

30

40

15-Nov

5-Dec

124

7.5

25-Oct Inter-short 350

20-Jun

Pehuajó

IV

IV

28

40

1-Nov

15-Dec 124

6.8

1-Oct

Inter-long

320

5-Jun

Pergamino

III

IV

32

35

1-Nov

10-Dec 124

7

25-Sep Inter-long

240

1-Jun

Pigüé

III

-

20

-

20-Nov

-

-

-

-

Inter-long

200

15-Jun

Pilar

VI

VI

28

33

25-Nov

28-Nov 124

7

10-Dec Inter-long

200

10-May

Rafaela

VI

IV

30

40

15-Nov

20-Dec 124

7.5

25-Oct Inter-short 350

20-Jun

Río Cuarto

IV

IV

28

36

25-Oct

5-Dec

124

6.5

1-Dec

Inter-long

220

1-Jun

References

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