Use of agro-climatic zones to upscale simulated crop yield potential

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ContentslistsavailableatSciVerseScienceDirect

Field

Crops

Research

j o ur n a l ho 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

Use

of

agro-climatic

zones

to

upscale

simulated

crop

yield

potential

Justin

van

Wart

a,∗

_{, Lenny}

_G.J.

_van

_Bussel

b

_{, Joost}

_Wolf

b

_{, Rachel}

_Licker

c

_{, Patricio}

_Grassini

a

_,

Andrew

Nelson

d

_,

_Hendrik

_Boogaard

e

_,

_James

_Gerber

f

_,

_Nathaniel

_D.

_Mueller

f

_,

_Lieven

_Claessens

g

_,

Martin

K. van

Ittersum

b

_,

_Kenneth

_G.

_Cassman

a

a_Department_of_Agronomy_and_{Horticulture,}_University_of_{Nebraska-Lincoln,}_Lincoln,_NE_68583-0915,_USA b_Plant_Production_Systems_Group,_Wageningen_University,_P.O._Box_430,₆₇₀₀_AK,_Wageningen,_The_Netherlands c_Woodrow_Wilson_School_of_Public_and_{International}_Affairs,_Princeton_University,_Princeton,_NJ_08544,_USA d_{International}_Rice_Research_Institute_(IRRI),_Los_Ba˜_nos_4031,_Philippines

e_Alterra,_Wageningen_University_and_Research_Centre,_P.O._Box_47,₆₇₀₀_AA,_Wageningen,_The_Netherlands

f_Institute_on_the_Environment_(IonE),_University_of_Minnesota,₃₂₅_Learning_and_{Environmental}_Sciences,₁₉₅₄_Buford_Avenue,_Saint_Paul,_MN_55108,_USA g_{International}_Crops_Research_Institute_for_the_Semi-Arid_Tropics_(ICRISAT),_P.O._Box_39063,₀₀₆₂₃_Nairobi,_Kenya

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received23October2012 Receivedinrevisedform 27November2012 Accepted30November2012 Keywords: Agroecologicalzone Climatezone Yieldpotential Water-limitedyield Yieldgap Extrapolationdomain Globalfoodsecurity

a

b

s

t

r

a

c

t

Yieldgapanalysis,whichevaluatesmagnitudeandvariabilityofdifferencebetweencropyieldpotential (Yp)orwaterlimitedyieldpotential(Yw)andactualfarmyields,providesameasureofuntappedfood productioncapacity.Reliablelocation-specificestimatesofyieldgaps,eitherderivedfromresearchplots orsimulationmodels,areavailableonlyforalimitednumberoflocationsandcropsduetocostandtime requiredforfieldstudiesorforobtainingdataonlong-termweather,croprotationsandmanagement practices,andsoilproperties.Giventheseconstraints,wecompareglobalagro-climaticzonationschemes forsuitabilitytoup-scalelocation-specificestimatesofYpandYw,whicharethebasisforestimating yieldgapsatregional,national,andglobalscales.Sixglobalclimatezonationschemeswereevaluated forclimatichomogeneitywithindelineatedclimatezones(CZs)andcoverageofcroparea.Anefficient CZschemeshouldstrikeaneffectivebalancebetweenzonesizeandnumberofzonesrequiredtocovera largeportionofharvestedareaofmajorfoodcrops.ClimateheterogeneitywasverylargeinCZschemes withlessthan100zones.Oftheotherfourschemes,theGlobalYieldGapAtlasExtrapolationDomain (GYGA-ED)approach,basedonamatrixofthreecategoricalvariables(growingdegreedays,aridityindex, temperatureseasonality)todelineateCZsforharvestedareaofallmajorfoodcrops,achievedreasonable balancebetweennumberofCZstocover80%ofglobalcropareaandclimatehomogeneitywithinzones. WhileCZschemesderivedfromtwoclimate-relatedcategoricalvariablesrequireasimilarnumberof zonestocover80%ofcroparea,within-zoneheterogeneityissubstantiallygreaterthanfortheGYGA-ED formostweathervariablesthataresensitivedriversofcropproduction.SomeCZschemesare crop-specific,whichlimitsutilityforup-scalinglocation-specificevaluationofyieldgapsinregionswithcrop rotationsratherthansinglecropspecies.

1. Introduction

Growingdemandforfoodincomingdecadeswillrequire sub-stantialincreaseincropproduction(Godfrayetal.,2010).Given disadvantagesand limitations ofmassive expansion of existing cropland,suchaslossofbiodiversity andincreasingGHG emis-sions,itisofcriticalimportancetoknowwhereandhowbestto increasecropyieldonexistingcroplandarea(Foley etal.,2005; Tilmanetal.,2002).Yieldgap(Yg)analysis,anevaluationofthe differencebetweencropyieldpotentialandactualfarmers’yields

∗Correspondingauthor.

E-mailaddress:[email protected](J.vanWart).

(Lobell etal.,2009),providesaquantitativeestimateofpossible increaseinfoodproductioncapacityforagivenlocation,whichis acriticalcomponentofstrategicfoodsecurityplanningatregional, nationaland globalscales.For irrigated croppingsystems,yield potential(Yp)isdeﬁnedastheyieldofcropcultivarwhengrown withoutlimitationsfromwater,nutrients,pestsanddiseases;in rainfedcroppingsystem,water-limitedyieldpotential(Yw)isalso determinedbywatersupplyamountanddistributionduringthe croppingseason(vanIttersumetal.,2013).Atagivenlocation,Yg isthedifferencebetweenYporYwandactualyield.

BothYpandYwaresite-speciﬁcbecausetheyaredetermined byweather,management,lengthofgrowingseason,andsoil prop-ertiesthataffectroot-zonewaterstoragecapacity(thelatterfor Yw only).Bothcanbeestimated fromresearchplots, in which

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J.vanWartetal./FieldCropsResearch143(2013)44–55 45

thecropisgrownwithoutlimitations,orbysimulationusingcrop models(Lobelletal.,2009).Inarecentcomparisonofthesetwo optionsacrossarangeofcroppingsystemsandenvironments,van Ittersumetal.(2013)concludedthatuseofcropsimulationwitha long-termweatherdatabaseprovidesamorerobustestimateofYp andYwthanresearchplotsbecausesimulationbetteraccountsfor theimpactofvariationintemperature,solarradiation,andrainfall overtime.Butuseofcropmodelsrequiresreliablelocation-specific dataonsowingdate,cultivarmaturity,plantpopulation,soilsand weatherandsuchdataarenotgenerallyavailableformostlocations (Ramirez-VillegasandChallinor,2012).Obtainingthesedataata largenumberoflocationsistime-consuming,costly,andoften sim-plynotfeasible.Therefore,anupscalingmethodisneededtoextend coverageofestimatesofYpandYwbasedonlocation-specific infor-mationtoanappropriateextrapolationdomainusingaprotocol thatminimizesthenumberoflocation-specificsimulations.Ideally, extrapolationdomainswouldbesmallenoughtominimize varia-tioninclimateandcropmanagementpracticeswithinthedomain, andlargeenoughtominimizedatacollectionrequirementsto esti-mateYgatregionalandnationalscales.Likewise,relevanceofa zonationschemeforsimulationofYpandYwisdeterminedbythe quality,resolution,extentandchoiceofvariablesusedtodelineate boundaries.

Previousstudieshavedistinguishedgeographicalspaceby cli-mateandsoilclassificationschemesasabasisforextrapolating and applying agricultural information and research to broader spatialscales(Wood andPardey, 1998;Padburyet al.,2002).A region can be divided into agro-climatic zones (CZs) based on homogeneity in weather variables that have greatest influence oncropgrowthandyield,whileagro-ecologicalzones(AEZs)are definedasgeographicregionshavingsimilarclimateandsoilsfor agriculture(FAO,1978).Such zonationschemeshavebeenused toidentifyyieldvariability andlimiting factorsforcropgrowth (Caldiz et al., 2002; Williams et al., 2008), to regionalize opti-malcropmanagementrecommendations(Seppelt,2000),compare yieldtrends(GallupandSachs,2000),todeterminesuitable loca-tionsfornewcropproductiontechnologies(Geertsetal.,2006; Arayaetal.,2010),andtoanalyzeimpactsofclimatechangeon agriculture(Fischeretal.,2005).Table1includesadescriptionof previouslypublishedzonationschemesusedtoevaluate extrapola-tiondomainsforagriculturaltechnologiesandinyieldgapanalysis. OurreviewfocusesonCZschemesandtheclimaticcomponents of AEZschemes withthe goalof identifyingan appropriate CZ schemefor upscalinglocation-specificestimates ofYporYwto regionalandnationallevels.Toourknowledge,nosuchreviewhas beenpreviouslypublishedwiththisgoalinmind.Specific objec-tivesofthisreviewareto:(1)evaluatezonationschemesbasedon thedegreeofvariabilityinweathervariableswithinzones,and(2) evaluatetheusefulnessandlimitationsofthesezonationschemes forupscalinglocation-specificestimatesofYpandYwtonational levels.

2. Agro-climaticandagro-ecologicalzonationschemes

Zonationschemesessentiallyfallintotwocategories:matrix andcluster.Inthissectiondifferencesbetweenmatrixand clus-termethodologiesareexplained,andsixglobalmatrixandcluster zonationschemesusefulforextrapolationofestimatesofYporYw aredescribed.

2.1. Matrixmethodologies

Perhapsthebestknownandearliestexampleofamatrix zona-tionschemeisdescribedbyKöppen(1900).Köppendevelopeda climateclassiﬁcationsystembasedonmultiplevariablesrelated

totemperatureandprecipitation,andusedhissystemtoidentify thetypeofvegetation,includingsomecrops,thatcouldgrowin eachzone.Ina matrixzonation,each variableusedtodelineate zonesisdividedintoclassesorclass-ranges.Classcutoffvaluesfor eachvariablecanbebasedonexpertopinionorfrequency distri-butionsofthevariable’srangeofvalues.Zonesareformedbythe matrix“cells”ofintersectingclasses.Forexample,amatrixzone cellmightbeageographicareainwhichmeanannualtemperature isbetween20and25◦Candmeanannualprecipitationisbetween 300and400mm.

Matrixzonationschemesareadvantageousinthattherangeof inputparametersforallzonesisknownandspecificallydefined by theresearchers. The size of thezones in a matrix zonation resultsfromthenumber ofinputvariablesusedandthedegree ofspecificityinclassesforeachvariable,i.e.moreclassvariables andmoresub-divisionswithineachvariableresultinalarger num-berofzoneswithsmallerarea.Thus,matrixmethodologyallows for highdegreeof controlover thenumbertheresulting zones asdetermined byintendeduseof thezonationscheme.Robust matrixschemesforuscalingYpandYwwouldusethemost sensi-tiveweathervariablesforsimulationofcropyieldsunderirrigated andrainfedconditions.

2.2. Clustermethodologies

Clustermethodologies[alsoreferredtoasstatistical stratifica-tion(Hazeuetal.,2011)]reliesonmultivariatestatisticalanalyses to separatecells into a researcher-specified number of distinct zones. Clustering essentiallyinvolves assigning grid-cell values derived from mathematical or statistical modeling of categori-calvariables. Groupingor“clustering”grid-cellsbasedonthese derivedvaluesisaccomplishedusingavarietyoftechniquessuch as assigning a certain value or range of values as a class or cluster,minimizingthesumofthedifferencebetweengrid-cells within clusters, or more sophisticated Iterative Self-Organizing DataAnalysis(ISODATA).Inthelatter,thenumberofcluster cen-tersisspecified,randomlyplaced,andthenclustersaredivided ormergedbasedonstandarddeviationofgrid-cellsassigned to eachcluster(TouandGonzález,1974).Theprocesscontinuesuntil reassignment of grid cells nolonger improvescluster standard deviation.Duetothestatisticalnatureof“clustering,” subjectiv-ityisavoidedinselectionofclassrangesforeachvariable.Though class ranges may be more objective in clustering compared to matrixmethodology,sizeofzonesispartiallydependenton num-ber of zonesspecified by theresearcher, which may introduce subjectivity.Unlike matrixzonation,thenumberofzonesisnot determinedbythenumberofweathervariables thatdetermine thezonation.Therefore,arelativelylargenumberofvariablescan beconsideredwithoutnecessarilyreducingthesizeoftheresulting zones.

Oneofthebetterknownexamplesofaclusterzonationwas createdthroughclimate-basedmodelingofnaturalvegetationon grid-cells,whichwerethengroupedintoregionsbasedon domi-nantplanttypes(Prenticeetal.,1992).Clustermethodologiesalso havebeenusedtodeterminetheapplicabilityoffarmmanagement researchin differentregions (Seppelt, 2000), tostudy potential impacts ofclimatechangeonecosystemsand theenvironment (Metzgeretal.,2008),and toidentifypotentialnewproduction areasforbio-energycrops(EEA,2007).

2.3. Zonationschemesthatcanbeusedinestimationofyield

potential

2.3.1. TheGlobalAgro-EcologicalZonemodelingframework

TheGlobalAgro-EcologicalZonemodelingframework(GAEZ) was developed to spatially analyze agricultural systems and

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Table1

Previouslypublishedglobalzonationschemes(AEZ).

AEZscheme Numberofzones TypeofAEZ Variablesconsidered,methodology Reference

FAOa ₁₄ _Matrix _Mean_growing_period_temperature_and_length_of_growing_period, determinedbyannualprecipitation,potentialevapotranspirationand thetimerequiredtoevapotranspire100mmofwaterfromthesoil proﬁle

FAO(1978)

CGIAR-TACb ₉ _Matrix _Mean_annual_and_growing_period_temperature,_and_length_of_growing period(determinedthesameasintheFAOzonationscheme)

SivakumarandValentin(1997) Prentice 17 Cluster Soiltexturebasedwater-storagecapacity,monthlyprecipitation,

sunshinehours,potentialevapotranspiration,growingdegreedays, minimumtemperature,meantemperature.Thesevariableswereused inamodelwhichcalculatedmostlikelyvegetationtypeforthe environmentofthisgridcellandcellsweregroupedbasedon vegetationtype.

Prenticeetal.(1992)

Pappadakis 74 Matrix Precipitationandtemperatureareusedincalculationsofavarietyof seasonalstatistics.Rangesofvariablesforeachzonearebasedoncrop requirements.

Papadakis(1966)

Köppen-Geiger 31 Matrix Meanannualtemperature,minimumandmaximumtemperatureof warmestandcoolestmonths,accumulatedannualprecipitation, precipitationofdriestmonth,lowestandhighestmonthly precipitationforsummerandwinterhalfyears,andadryness thresholdbasedonseasonalityofprecipitation

Kotteketal.(2006)

Holdridge 100 Matrix Meanannualtemperature,meanannualprecipitation,elevation (evaporativedemandandfrostwerealsoconsideredindetermining climaterangesofzones).

Holdridge(1947)

GAEZ-LGPc ₁₆ _Matrix _Temperature,_{precipitation,}_potential_{evapotranspiration}_and_soil characteristicsareusedtocalculatelengthofgrowingseason.

Fischeretal.(2012) HCAEZd ₂₁ _Matrix _Mean_{temperatures,}_elevation,_and_GAEZ-LGP_are_used_to_deﬁne

thermalregimesandtemperatureseasonality.

Woodetal.(2010) SAGEe ₁₀₀ _Matrix _Growing_degree_days_(GDD;

_{Tmean–crop-speciﬁc}_base

temperature)andsoilmoistureindex(actualevapotranspiration dividedbypotentialevapotranspiration).

Lickeretal.(2010)

GLIf ₂₅ _Matrix _Harvested_area_of_target_crop,_{crop-speciﬁc}_GDD_and_soil_moisture index(actualevapotranspirationdividedbypotential

evapotranspiration).

Muelleretal.(2012)

GEnSg ₁₁₅ _Cluster ₄_variables_(GDD_with_base_temperature_of₀◦_C,_an_aridity_index, evapotranspirationseasonality,temperatureseasonality)usedin iso-clusteranalysisto“cluster”grid-cellsintozonesofsimilarity.

Metzgeretal.(inpress)

a_Food_and_Agricultural_{Organization.}

b _Consultative_Group_on_{International}_Agricultural_Research_{– Technical}_Advisory_Committee. c _Global_{Agro-Ecological}_Zone_Length_of_Growing_Period.

d _{HarvestChoice}_{Agro-ecological}_Zone.

e_Center_for_{Sustainability}_and_the_Global_Environment. f _Global_Land_Initiative.

g_Global_{Environmental}_{Stratiﬁcation.}

evaluate the impacts of agricultural policies at a global scale (Fischer,2009).DelineationofAEZswithinGAEZaredetermined by monthly weather data with a resolution of 10 (roughly 20km×20kmattheequator,or400km2_)._The_weather_data_were

obtainedfromtheClimateResearchUnit(Newetal.,2002)andthe GlobalPrecipitationClimatologyCentre(Rudolfetal.,2005). Cate-goricalvariablesused,orderived,fromthesedatatodefineanAEZ include:(a)accumulatedtemperaturesumsformeandaily temper-atureaboveabasetemperature[growingdegreedays(GDD)],(b) annualtemperatureprofiles,basedonmeanannualtemperature andwithin-yeartemperaturetrends,(c)delineationofcontinuous, discontinuous,sporadicandnopermafrostzones,(d)quantification ofsoilwaterbalanceandactualevapotranspirationforareference crop,(e)lengthofgrowingperiod(LGP),definedasthesumofdays whenmeandailytemperatureexceeds5◦Candevapotranspiration forthereferencecropexceedshalfof potential evapotranspira-tion,(f)multiplecroppingclassification,whichindicateswhether annualsingle,doubleortriplecroppingispossibleinagivenzone, basedontheLGP and assuminga growth durationper cropof 120days(Fischeret al., 2012).This GAEZframework hasbeen adaptedtoassessthepotentialproduction ofallmajorbio-fuel crops(FischerandSchrattenholzer,2001),toanalyzethe poten-tialimpactofacceleratedbiofuelproductiononfoodsecurityto 2050,andtoevaluatetheresultingsocial,environmentaland eco-nomicimpacts(Fischeretal.,2009).Additionalassessmentshave

usedaGAEZframeworktoevaluatescenariosoffuturelanduse andproductionofmajorcropsataglobalscale(Fischeretal.,2002, 2006).OfthevariousAEZschemesusedintheGAEZframework,we selectedtheonebasedonLGPinwhichLGPisderivedfrom temper-ature,precipitation,andsoilwaterholdingcapacityascategorical variables.TheGAEZ-LGPwasselectedbecauseitutilizesthemost agronomicallyrelevantcategoricalvariablesandhasthesmallest, andpresumablymostclimaticallyhomogenouszones,withinthe GAEZ-familyofAEZschemes(Figs.1a–5a).

2.3.2. CenterforSustainabilityandGlobalEnvironmentzonation

scheme

TheCenterforSustainabilityandtheGlobalEnvironment(SAGE) zonationschemewasgeneratedusingglobal,griddeddatafortwo variables knownto beimportant driversfor crop development andcropgrowth(Lickeretal.,2010):growingdegree-days(GDD) andacropsoilmoistureindex,thelattercalculatedastheratio ofactualtopotentialevapotranspirationfollowingtheapproach ofPrentice etal. (1992)and Ramankutty etal. (2002). Calcula-tionsutilized a 33-y monthlyaveraged weather databasefrom theClimateResearchUnit(Newetal.,2002)witha10 resolu-tion.Soiltexturedata usedtoestimatethesoilmoistureindex weretaken from theInternationalSoil Referenceand Informa-tionCenter witha 5 resolution (Batjes, 2006).By downscaling theweatherdatafroma10 toa5resolution,calculationswere

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Fig.1.ZonationofAfricafor(a)GlobalAgro-EcologicalZoneforlengthofgrowingseason(GAEZ-LGP),(b)CenterforSustainabilityandtheGlobalEnvironment(SAGE) zonationscheme(crop-specific,derivedusingGDDwithbasetemperatureof8◦Casusedformaize),(c)HarvestChoiceAgroecologicalZone(HCAEZ,d)GlobalLandscapes Initiative(crop-specific,derivedusingGDDwithbasetemperatureof8◦Casusedformaize),(e)GlobalEnvironmentalStratification(GEnS),(f)GlobalYieldGapAtlas ExtrapolationDomain(GYGA-ED).

carried outona 5 gridbasis (approximately 10km×10km, or 100km2_at_the_equator)._The_global_ranges_of_the_two_categorical

variableswereeachdividedintotenclasses,whichwerethenused todevelopamatrixof100uniquecombinationsofgrowing degree-dayandsoilmoistureconditions.Separatezonationschemeswere developed for each of 18 cropspecies using crop-speciﬁcbase temperaturesforcalculationofgrowingdegree-days(e.g.,8◦Cfor maize,5◦Cforrice).Thezonationschemeformaizeisshownin

Figs.1b–5b.

Thiszonationschemewasdevelopedtodeterminewithin-zone maximumyieldachievedforaspecificcropwithineachofthe100 zones.Ifthezonal-maximumyieldwaslargerthanobservedyields foraparticularregionwithinthezonetheauthorsconsideredthisa Ygandidentifiedtheregionashavinganopportunityforincreasing yields(Lickeretal.,2010).TheSAGEzonationwasalsoemployed byJohnstonetal.(2011)toexamineopportunitiestoexpandglobal biofuelproductionthroughagriculturalintensificationinregions withsimilargrowingconditions.

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Fig.2. ZonationofAsiafor(a)GlobalAgro-EcologicalZoneforlengthofgrowingseason(GAEZ-LGP),(b)CenterforSustainabilityandtheGlobalEnvironment(SAGE)zonation scheme(crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(c)_{HarvestChoice}_{Agroecological}_Zone_(HCAEZ,_d)_Global_Landscapes_Initiative (crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(e)_Global_{Environmental}_{Stratification}_(GEnS),_(f)_Global_Yield_Gap_Atlas_{Extrapolation} Domain(GYGA-ED).

2.3.3. ModiﬁcationsofGAEZandSAGEzonationschemes

AspectsofboththeSAGEandGAEZhavebeenutilizedor mod-iﬁedtodevelopimprovedAEZschemesforyieldgapanalysis.The HarvestChoice1 _AEZ _scheme_(HCAEZ), _developed _for_analysis _in

sub-SaharanAfrica,isanexample(Woodetal.,2000,2010).Itis

1 _{HarvestChoice}_is_a_large_{collaborative}_effort_to_provide_knowledge_products

aimedatguidinginvestmentstoimprovewell-farethroughmoreproﬁtable agri-cultureinSub-SaharanAfricaledbyscientistsfromtheUniversityofMinnesotaand

amatrixwith21zonesbasedonGAEZ-LGPandthermalregime classesfor thetropics, sub-tropics,temperate, andborealzones distinguishedbyhighlandandlowlandregions.Essentially,HCAEZ isacombination,orintersection,ofseveraldistinctand indepen-dentzonationschemesusedintheGAEZframework.Althoughit usesdataofmorerecentorgin,theHCAEZresemblesanearlier

theInternationalFoodPolicyResearchInstitute(IFPRI).Severalzonationschemes havebeenusedatHarvestChoice,basedonthesameunderlyingmethodology.

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Fig.3.ZonationofEuropefor(a)GlobalAgro-EcologicalZoneforlengthofgrowingseason(GAEZ-LGP),(b)CenterforSustainabilityandtheGlobalEnvironment(SAGE) zonationscheme(crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(c)_{HarvestChoice}_{Agroecological}_Zone_(HCAEZ,_d)_Global_Landscapes Initiative(crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(e)_Global_{Environmental}_{Stratification}_(GEnS),_(f)_Global_Yield_Gap_Atlas ExtrapolationDomain(GYGA-ED).

AEZschemedevelopedbytheTechnicalAdvisoryCommittee(TAC) oftheConsultativeGrouponInternationalAgriculturalResearch (CGIAR)(TAC/CGIAR,1992;SivakumarandValentin,1997).

TheSAGEzonationschemewasmodifiedbytheGlobal Land-scapesInitiative(GLI)groupattheUniversityofMinnesota,keeping theclassificationbasedoncrop-specificGDDbutreplacingthecrop soilmoistureindexbyannualtotalprecipitation.Another modifica-tionwasthatonlyterrestrialsurfacecoveredbyharvestedareafora specificcropwasconsideredbasedongeospatialcropdistribution

mapsofMonfredaetal.(2008).Climatezonesweredevelopedfor eachcropbydividingGDDandprecipitationeachintotenclasses, theintersectionofwhichformedamatrixof100individualCZs. Insteadofusingequal rangesfor theclasses,zoneswere deter-minedusinganalgorithmsuchthat1%oftheglobalharvestedarea ofthatspeciﬁccropwasineachzone,amethodologyknownas the‘equal-areaapproach’(Figs.1d–5d).ThisrevisionoftheSAGE zonationschemeformedthebasis oftheyieldgapestimatesin

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Fig.4.ZonationofNorthAmericafor(a)GlobalAgro-EcologicalZoneforlengthofgrowingseason(GAEZ-LGP),(b)CenterforSustainabilityandtheGlobalEnvironment (SAGE)zonationscheme(crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(c)_{HarvestChoice}_{Agroecological}_Zone_(HCAEZ,_d)_Global LandscapesInitiative(crop-specific,derivedusingGDDwithbasetemperatureof8◦_C_as_used_for_maize),_(e)_Global_{Environmental}_{Stratification}_(GEnS),_(f)_Global_Yield_Gap AtlasExtrapolationDomain(GYGA-ED).

2.3.4. TheGlobalEnvironmentalStratiﬁcationmethodology

(GEnS)

TheGlobalEnvironmentalStratiﬁcation(GEnS)byMetzgeretal. (inpress)istheﬁrstclustermethodologyaimingatestablishing aglobal,climate-explicit zonation system.GEnS wasdeveloped withintheGrouponEarthObservationsBiodiversityObservation Network(GEOBON,Scholesetal.,2008)andwillbeavailableto assistfurtherresearchonglobalecosystems.Thisclusterzonation

usesmonthlygriddedclimatedatafromtheWorldClimdatabase (Hijmans et al., 2005) and annual aridity and potential evapo-transpirationseasonalityderivedfromtheCGIARConsortiumfor SpatialInformation(CGIAR-CSI,Trabuccoetal.,2008;Zomeretal., 2008), with 30 resolution (approximately 1km2 _at _the

equa-tor).GEnSwasconstructedinthreestages.Intheﬁrststage,42 categorical variables werescreened toremove thosethat were auto-correlated.Amongthevariableswithhighauto-correlation,

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Fig.5.ZonationofSouthAmericafor(a)GlobalAgro-EcologicalZoneforlengthofgrowingseason(GAEZ-LGP),(b)CenterforSustainabilityandtheGlobalEnvironment (SAGE)zonationscheme(crop-specific,derivedusingGDDwithbasetemperatureof8◦Casusedformaize),(c)HarvestChoiceAgroecologicalZone(HCAEZ,d)Global LandscapesInitiative(crop-specific,derivedusingGDDwithbasetemperatureof8◦Casusedformaize),(e)GlobalEnvironmentalStratification(GEnS),(f)GlobalYieldGap AtlasExtrapolationDomain(GYGA-ED).

researchersselectedthemostsensitiveparametersandeliminated theothers topreventover-weighting thezonation byco-linear variables. In thesecond step, statisticalclustering analysiswas performedonremainingvariables:annualcumulativeGDDusing basetemperature=0◦C,temperatureandpotential evapotranspi-ration seasonalities(month tomonthvariation), and anannual aridityindex (calculatedastheratio ofmean annualtotal pre-cipitationtomeanannualtotalpotentialevapotranspiration).The statisticalclusteringwascarriedoutusing principlecomponent analysisanditerativeself-organizingdataanalyses,resultingin125 zones(Figs.1e–5e).A climaticstratiﬁcationofEurope(Metzger etal.,2005)hasbeenusedin modelingeffortstoquantifycrop production potential and yield gaps in Europe (Hazeu et al., 2009).

2.3.5. TheGlobalYieldGapAtlasExtrapolationDomain

(GYGA-ED)

The goal of the Global Yield Gap Atlas (GYGA) project (www.yieldgap.org)is toestimatetheyield gapformajor food cropsin allcrop-producingcountriesbasedonlocallyobserved data. Unlike past efforts to estimate Yg that rely on gridded weatherdataasdescribedabove,GYGAseekstousea “bottom-up” approach withlocation-speciﬁc observed weather data.To extrapolateresultsfromlocation-speciﬁcobserveddata,theGYGA approach utilizes a hybrid zonation scheme, called the GYGA ExtrapolationDomain (GYGA-ED),which combines components of otherzonationschemesas reviewedin thispaper.The chal-lenge of usinga bottom-up approach is thetime, expense and access to acquire observed weather data as well as associated

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location-speciﬁcinformationaboutcroprotations,soilproperties andfarmmanagement,whicharerequiredforrobustestimatesof YpandYw(vanIttersumetal.,2013).Therefore,theGYGAapproach strivesforazonationschemethatbalancesneedtominimizethe numberoflocation-speciﬁcsitesrequiringweather,soils,andcrop managementdatawiththegoalofminimizingclimatic heterogene-itywithintheCZs.

GYGA-EDisconstructedfromthreecategoricalvariablesalso usedbytheGEnS:(1)GDDwithbasetemperatureof0◦Cand(2) temperatureseasonality(quantiﬁedasthestandarddeviationof monthlyaveragetemperatures),and(3)anaridityindex(annual totalprecipitationdividedbyannualtotalpotential evapotranspi-ration).Gridcellsizefortheunderpinningweatherdatawasthe sameasforGLIbasedontheSAGEframework(5grid,orroughly 100km2 _at _the _equator). _Both_GDD _and _temperature

seasona-litywerecalculatedusingclimatedatafromWorldClim(Hijmans etal.,2005);thearidityindexvaluesweretakenfromCGIAR-CSI (Trabuccoetal.,2008;Zomeretal.,2008).FollowingMuelleretal. (2012),onlyterrestrialsurfacecoveredbyatleastoneofthemajor foodcrops(maize,rice,wheat,sorghum,millet,barley,soybean, cassava,potato,yam,sweetpotato,bananaandplantain, ground-nut,commonbeanandotherpulses,sugarbeets,sugarcane)was consideredinthiszonationscheme.Toavoidinclusionofareaswith negligiblecropproduction,onlygridcellswithsumoftheharvested areaofmajorfoodcrops>0.5%ofthegridcellareawereaccounted for,based onHarvestChoiceSPAM cropdistributionmaps(You etal.,2006,2009),whichupdategeospatialcropdistributiondata ofMonfredaetal.(2008).TheresultingrangeinvaluesforGDDand aridityindexweredividedinto10intervals,eachwith10%ofgrid cellswithharvestedareaofthemajorfoodcrops,andcombined inagridmatrixwith3rangesoftemperatureseasonalitytogivea totalof300AEZclasses.Ofthese,only265occurinregionswhere majorfoodcropsaregrown.

3. Comparisonoftheagro-climaticandagro-ecological zonationschemes

Zonationschemesvarywidelyindeﬁningthesizeand bound-ariesofregionswithsimilarclimate(Figs.1–5).Forexample,each oftheschemesrecognizesthesigniﬁcanceoftheSaharadesert,but theydifferbyasmuchas2◦or3◦ (roughly250–350km)in loca-tionofthesouthernborderinsomeareas.Differencesamongthe zonationschemesareconsideredinthefollowingsections accord-ingtorelevanceforassessingperformanceofcropsandcropping systemswithinazone,andinthedegreeofhomogeneityofthe underpinningweathervariables.

3.1. Keyvariablesusedwithinthezonationschemes

Allglobalzonationschemesanalyzedinthepresentstudyare associatedwithtemperatureandwateravailabilitybutthey dif-ferinselection of specificweather variables todelineatezones (Table1).Forexample,toaccountforthermalconditions,GDDis calculatedwithintheSAGEandGLIschemesusingcrop-specificbase temperaturesresultinginadifferentsetofCZsforeachcropwhile GEnSandGYGA-EDuseasingle,non-crop-specificbase tempera-ture(0◦C)tocalculateGDD,whichgivesasinglesetofCZsforall crops.Creatingadifferentzonationschemeforeachcrop,however, limitsopportunitiestoanalyzeYgforcroprotationsandmuchof theworld’scroplandproducesmorethanonemajorfoodcrop.For example,crop-specificschemesmakeitdifficulttoreconcile per-formanceofcropswithinaspecificcroppingsystem(e.g.doubleor triplericeorrice-wheatcroppingsystemsinAsia).Inadditionto GDD,GEnSandGYGA-EDincludeameasureoftemperature varia-tionduringtheyearbasedontemperatureseasonality.

Differentindexeshavebeenusedtoquantifythedegreeofwater limitation.WatersupplyintheGLIzonationiscalculatedastotal annualrainfall.However,thisapproachdoesnotaccountforthe degreeofwaterlimitationtocropgrowth,whichvaries depend-ingonthebalancebetweencropwaterdemand,hereaftercalled potentialevapotranspiration,andwatersupply.Incontrast, GAEZ-LGP,HCAEZ,andSAGEtrytoaccountforbothwatersupplyand demandusingactualandpotentialevapotranspiration.Speciﬁcally, thenumberofdaysinwhichactualevapotranspirationisgreater than50%ofpotentialevapotranspirationareusedbyGAEZ-LGP andHCAEZtodeterminewhencropgrowthispossibleduetolack ofwaterstress.SAGEconsiderstheratioofactual evapotranspi-ration topotential evapotranspirationas a soil moistureindex. Estimationofactualevapotranspirationisderived fromdataon soiltexture,bulkdensity,anddepthofrootzone(whichdeﬁnes plant-available water-holding capacity),temperature, precipita-tion,andleafarea.Thesoilcomponentsofthisestimatearederived fromspatiallyexplicitglobaldatabasesandrequireanumberof assumptionsinordertocalculatehydraulicconductivity.Finally, GEnSand GYGA-ED consider anaridityindex calculated asthe ratioofannualtotalprecipitationtoannualtotalpotential evapo-transpiration.WhilenotassophisticatedastheGAEZ-LGPorSAGE schemes,thisaridityindexisderiveddirectlyfromvariablesinthe weatherdatabaseanddoesnotrequiresoildataandtheassociated uncertaintiesofassumptionsusedtoestimatesoilwaterholding capacity.

Oneofthemostinfluentialdifferencesamongzonationschemes iswhethertheydefinezonesovertotalterrestrialareaoronlythe fractionofthatareainwhichcropsaregrown.Forexample,GEnS, GAEZ-LGP,HCAEZand SAGEallconsidertotal terrestrialareain constructingtheirzonationschemes.Incontrast,GLIconsidersonly harvestedareaofindividualmajorfoodcropspeciestogive sep-aratezonationschemesforeach cropwhileGYGA-ED considers oneschemebasedonharvestedareaofallmajorfoodcrops.Asa resulttheareaoverwhichzonesaredefinedistherefore signifi-cantlyreducedforthoseAEZschemesthatonlyconsiderharvested croparea(Figs.1–5).

3.2. Climaticvariabilitywithinthezones

Climatehomogeneityforagivenzonationschemewas evalu-atedbycalculatingfrequencydistributionsoftherangeofgrid-cell values found within each zone for mean annual temperature, cumulative annual water deficit (precipitationless evapotrans-piration),temperatureseasonality,and precipitationseasonality (monthtomonthcoefficientofvariationinprecipitation)basedon WorldClimdataat5resolution(Hijmansetal.,2005).Inadditionto calculatingrangesofthesevariablesforeachzoneinagiven zona-tionscheme,rangesofmeanannualtemperatureandcumulative annualwaterdeficitwerecalculatedonlyforthosecellsinwhich wheatis grownbased onspatialcropdistributionofPortmann etal.(2010),inordertominimizebiasforthosezonationschemes thatarenotcrop-specific.ThegeospatialdistributionofPortmann etal.(2010)waschosenforuseinthisanalysisovertheSPAMor

Monfredaetal.(2008)databecausethesetwodatasetswereused inthederivationofoneormoreofthezonationschemesexamined. However,itshouldbenotedthatclimatedatausedforthisanalysis arethesameasthoseusedintheGEnS,GYGA-ED,andHCAEZ.

3.2.1. Temperaturevariability

ZonesizewaslargestinGAEZ-LGPandHCAEZ(Table2).Large zoneareawithschemesthatconsidercompleteterrestrial cover-ageresultsinawiderangeofwithin-zonetemperatureasindicated by thecumulative frequency distributionof mean annual tem-perature (Fig. 6a). For example, 50% of the GAEZ and HCAEZ zoneshavearangeofmeanannualtemperature>29◦Cand24◦C,

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Table2

AEZschemecoverageofglobal,ChinaandUSArainfedwheatandmaizebasedondatafromPortmannetal.(2010).Valuesinparenthesisindicate(±SD)ofthemean.

AEZscheme Numberofzones Averagezonearea(Mkm2₎ _Rainfed_maize_area_per

zone(Mha)

Numberofzonestocover80%of rainfedmaizeharvestedarea

Global China USA

GAEZ-LGPa ₁₆ _20.2_(18.2) _7.5_(7.2) ₇ ₆ ₄ HCAEZb ₂₁ _15.3_(28.0) _5.8_(8.2) ₆ ₃ ₂ SAGEc ₁₀₀ _2.7_(4.7) _1.2_(2.1) ₂₈ ₁₁ ₅ GLId ₁₀₀ _2.9_(2.0) _1.2_(0.7) ₆₆ ₃₇ ₂₅ GEnSe ₁₂₅ _2.6_(2.5) _1.0_(1.7) ₃₀ ₁₃ ₅ GYGA-EDf ₂₆₅ _0.3_(0.3) _0.4_(0.7) ₄₉ ₂₁ ₉

a_Global_{Agro-Ecological}_Zone_Length_of_Growing_Period. b_{HarvestChoice}_{Agro-ecological}_Zone.

c _Center_for_{Sustainability}_and_the_Global_Environment. d _Global_Land_Initiative.

e_Global_{Environmental}_{Stratiﬁcation.} f _Global_Yield_Gap_Atlas_{Extrapolation}_Domain.

respectively. In contrast, zonation schemes with smaller zone sizehaveconsiderablyless within-zonetemperaturevariability. For example,therangeof meanannualtemperaturefor 50%of theGLIandGEnSzonesis>4◦C.Whenonlycroppedterrestrial areaisevaluated(whetherforaspeciﬁccropormultiplecrops), within-zonetemperaturevariabilitydecreasessubstantially.The clusteringmethodologyofMetzgeretal.(inpress)alsoresulted in zones with small ranges in temperature variability despite consideringtotalterrestrialareawithinzones.Apparentlythelarge numberofcategorical variablesconsideredintheGEnS cluster-ingschemeresultsinrelativelyhomogeneoustemperatureregime despitecompleteterrestrialcoverage.Whenonlywheatharvested areaisconsideredinallzonationschemes,thefrequency distribu-tionnarrowssubstantially(Fig.6b).

3.2.2. Wateravailability

Similartotemperaturevariabilitywithinzones,schemeswith thelargestzonearea(GAEZ-LGPandHCAEZ)havegreatestrange ofcumulativewaterdeﬁcit(Fig.6c).Likewise,crop-areazonation schemes,suchas GYGA-EDand GLIhave greatesthomogeneity withinzones.Consideringonlyharvestedwheatareawithin zona-tionschemesthathavecompleteterrestrialcoveragedecreasesthe within-zonerangeofwaterdeﬁcitofthezonalschemessomewhat, buttherangeisstillrelativelylarge(Fig.6d).

3.2.3. Temperatureandprecipitationseasonality

TheGYGA-ED, which considers three rangesof temperature seasonality as categorical variables, and the GEnS scheme, for whichtemperatureseasonalityisanexplicitinputparameter,have smallestrangeintemperatureseasonalitywithinzones.Whilethe HCAEZ,whichalsoaccountsfortemperatureseasonality,hasless heterogeneityforthisvariablethanzonationschemesthatdonot explicitlyconsiderit,itslargezonesizeresultsinagreaterrange thanforGYGA-ED.TheGAEZ-LGPhasthelargestwithin-zonerange oftemperatureseasonalitybecauseitsdelineationisbasedmoreon wateravailabilityandmanyofitszoneshaverelativelylargenorth tosouthextension,capturingawiderangeoftemperatureregimes. Rangeofprecipitationseasonalitywasalsosmallestinthe GYGA-EDschemeeventhoughthisparameterisnotexplicitlyconsidered initsderivation.

3.3. Balancingnumberofzonesandwithin-zoneclimatic

heterogeneity

Anappropriatezonationschemeforextrapolatingpoint-based estimatesofyieldpotentialwhilelimitingrequirementsfordata collectionisonewhichoptimizesthetrade-offbetweenachieving climatichomogeneitywithinzonesandminimizingthenumberof

zonesnecessarytocapturelargeportionsofharvestedareaoftarget crop.Whilezonationschemeswithfewzonesandlargezonearea, suchasGAEZ-LGPandHCAEZ,require<10zonestocover80%of globalrainfedmaizeharvestedarea(Table2),theyhavelarge vari-abilityinweathervariablesthatinfluencecropgrowthandyield (Fig.6).Amongschemeswithatleast100zonesandsmallerzone size,thoseschemesthatusetheclusteringmethodology(GEnS) orathree-parametermatrix(GYGA-ED)appeartohavethebest balancebetweennumberofzonesfor80%coverageofharvested area(Table2)andhomogeneityinweathervariableswithinzones (Fig.6).Whilethecrop-specificGLIzonationschemehasrelatively homogeneousweatherwithinitszones,itrequiresthelargest num-berofzonestoachieve80%coverageofrainfedmaizearea,andit requiresaseparatezonationschemeforeachcropspecies.In con-trast,theSAGEschemerequiresthesmallestnumberofzonesfor 80%coverageofrainfedmaizeareabuthashighdegreeof variabil-ityinweathervariableswithinitszonesdespiteuseofcrop-specific basetemperaturesusedtoderiveGDD.

4. Discussion

TheGAEZ-LGPand HCAEZschemesaresimplytoocoarsefor usein estimatingand extrapolating yieldgap analysesbecause climateheterogeneitywithinzonesistoolarge. BothSAGEand GLIschemesarecrop-specificanduseatwo-parameterzonation matrix.Ofthetwo,theGLIapproachgivesmuchgreater homo-geneity of weather variables within zones, but it requires the largestnumberofzonestocovercroparea.Bothschemesrequire separatezonation schemes foreach cropwhich would make it cumbersometoestimateYp,Yw,andyieldgapsinregionswhere morethan onecropwasgrowninrotation. BothGYGA-ED and GEnSapproachesarenotcropspecificandachieverelativelylow within-zoneheterogeneityinkeyweathervariables.WhereasGEnS requiresfewerzonestoachieve80%coverageofrainfedmaizearea andhasslightlylessheterogeneityinmeantemperature, GYGA-EDhassubstantiallylesswithin-zoneheterogeneityincumulative waterdeficitandinseasonalityoftemperatureandprecipitation. Bothmethodsappear tobewell-suitedfor up-scalingyieldgap analysis.

Severalconclusionsfollowfromthisevaluation.Climatezones usedasextrapolationdomainsforyieldgapanalysisofcurrent pro-ductionshouldfocusonareaswherecropsaregrowntominimize within-zoneweather variability.While theclustermethodology alsoappearsefﬁcientatlimitingthenumberofzonesrequiredto covercropareaandminimizingwithin-zoneheterogeneity,they are less intuitivethanmatrix zonation schemes becauseof the sophisticatedmathematicsandlargenumberofweathervariables considered.However,formatrix-basedzonationschemesithasnot

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Fig.6. Frequencydistributionofwithin-zonerangeofmeanannualtemperature,annualwaterdeficit(precipitationlessevapotranspiration),temperatureseasonalityand monthtomonthcoefficientofvariationinprecipitationbasedonWorldClimdataat5_resolution_(Hijmans_et_al.,₂₀₀₅₎_for₆_climate_zonation_schemes._All_terrestrial_area coveredbythezonesareconsidered(panelsa,c,e,f);meanannualtemperaturesandannualwaterdeficitwasalsocalculatedconsideringonlywherezonesoverlapwheat harvestedarea(bandd).Thelatterevaluationeliminatesbiasofgenericzonationschemesthatevaluateallterrestrialarea(GAEZ-LGP,GEnS,SAGE,HCAEZ)andallmajor crops(GYGA-ED).

beentestedhowtobestdeterminetherange-boundaries,whether byequaldistributions(Lickeretal.,2010),frequencydistributions (GYGA-ED),oranothersetofcriteriasuchasquantityofharvested areawithinzones(GLI).Beneﬁcialfutureworkwouldbevalidation and comparison of zonation schemes using weather data from differentweatherstationswithinazoneorbyperformingand com-paringyieldgapanalysisforseveralsiteswithinazone.

Allzonationschemesarelimitedbychoiceandqualityofthe underpinningdatausedtoderivethem.Thisincludesavailability anddistributionofhigh-quality,locationspeciﬁcweatherstation

data.UsinganyzonationschemetoestimateYp,Ywandyieldgaps atlargerscalesalsorequiresdataonsoilsandmanagement varia-tionwithinzones(vanIttersumetal.,2013),andqualityofthose datawillalsoaffecttheaccuracyanduncertaintyinsuchlargescale estimates(vanWartetal.,2013).

Acknowledgments

TheauthorswouldliketothanktheBillandMalindaGates Foun-dationwhosesupportoftheGlobalYieldGapAtlasprojectmade

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thisresearchpossible.Secondly,wethankDr.MarcJ.Metzgerfrom theUniversityofEdinburghforprovidinguswiththedataonGEnS.

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