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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
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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.
https://digitalcommons.unl.edu/agronomyfacpub/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
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
faInstitutoNacionaldeTecnologíaAgropecuaria(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/).
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).
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 = 70b
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 = 95c
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
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
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).
(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
-1y
-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).
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 Soybeanb
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 LaNiñaYea
r
Y
a (
M
g ha
-1)
-30 -20 -10 0 10 20 30a
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
El Niño Neutral La Niña 20 25 30 35 40 45
Maize
a
a
b*
ENSO phase
20 30 40 50 60 70 80Soybean
a
ab
b
5 10 15 20 25 30Whe
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
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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