How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis

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University of Nebraska - Lincoln

DigitalCommons@University of Nebraska - Lincoln

Agronomy & Horticulture -- Faculty Publications

Agronomy and Horticulture Department

2015

How good is good enough? Data requirements for reliable crop

yield simulations and yield-gap analysis

Patricio Grassini

University of Nebraska-Lincoln, [email protected]

Lenny G.J. van Bussel

Wageningen University, The Netherlands, [email protected]

Justin van Wart

University of Nebraska-Lincoln, [email protected]

Joost Wolf

Wageningen University, The Netherlands

Lieven Claessens

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Kenya

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Grassini, Patricio; van Bussel, Lenny G.J.; van Wart, Justin; Wolf, Joost; Claessens, Lieven; Yang, Haishun;

Boogaard, Hendrik; de Groot, Hugo; van Ittersum, Martin K.; and Cassman, Kenneth, "How good is good

enough? Data requirements for reliable crop yield simulations and yield-gap analysis" (2015). Agronomy &

Horticulture -- Faculty Publications. 765.

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Authors

Patricio Grassini, Lenny G.J. van Bussel, Justin van Wart, Joost Wolf, Lieven Claessens, Haishun Yang,

Hendrik Boogaard, Hugo de Groot, Martin K. van Ittersum, and Kenneth Cassman

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

https://digitalcommons.unl.edu/

agronomyfacpub/765

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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

How

good

is

good

enough?

Data

requirements

for

reliable

crop

yield

simulations

and

yield-gap

analysis

Patricio

Grassini

a,∗

_,

_Lenny

_G.J.

_van

_Bussel

b

_,

_Justin

_Van

_Wart

a

_,

_Joost

_Wolf

b

_,

Lieven

Claessens

c,d

_,

_Haishun

_Yang

a

_,

_Hendrik

_Boogaard

e

_,

_Hugo

_de

_Groot

e

_,

Martin

K. van

Ittersum

b

_,

_Kenneth

_G.

_Cassman

a

a_University_of_{Nebraska-Lincoln,}_PO_Box_830915,_Lincoln,_NE_68583-0915,_USA

b_Plant_Production_Systems_Group,_Wageningen_University,_PO_Box_430,₆₇₀₀_AK_Wageningen,_The_Netherlands c_{International}_Crops_Research_Institute_for_the_Semi-Arid_Tropics_(ICRISAT),_PO_Box_39063,₀₀₆₂₃_Nairobi,_Kenya d_Soil_Geography_and_Landscape_Group,_Wageningen_University,_PO_Box_47,₆₇₀₀_AA_Wageningen,_The_Netherlands e_Alterra,_Wageningen_University_and_Research_Centre,_PO_Box_47,₆₇₀₀_AA_Wageningen,_The_Netherlands

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received19December2014 Receivedinrevisedform7March2015 Accepted8March2015 Keywords: Cropsimulation Yieldgap Yieldpotential Weatherdata Croppingsystem

a

b

s

t

r

a

c

t

Numerousstudieshavebeenpublishedduringthepasttwodecadesthatusesimulationmodelstoassess cropyieldgaps(quantifiedasthedifferencebetweenpotentialandactualfarmyields),impactofclimate changeonfuturecropyields,andland-usechange.However,thereisawiderangeinqualityandspatial andtemporalscaleandresolutionofclimateandsoildataunderpinningthesestudies,aswellaswidely differingassumptionsaboutcropping-systemcontextandcropmodelcalibration.Herewepresentan explicitrationaleandmethodologyforselectingdatasourcesforsimulatingcropyieldsandestimating yieldgapsatspecificlocationsthatcanbeappliedacrosswidelydifferentlevelsofdataavailabilityand quality.Themethodconsistsofatieredapproachthatidentifiesthemostscientificallyrobust require-mentsfordataavailabilityandquality,aswellasother,lessrigorousoptionswhendataarenotavailable orareofpoorquality.Examplesaregivenusingthisapproachtoestimatemaizeyieldgapsinthestate ofNebraska(USA),andatanationalscaleforArgentinaandKenya.Theseexampleswereselectedto representcontrastingscenariosofdataavailabilityandqualityforthevariablesusedtoestimateyield gaps.Thegoaloftheproposedmethodsistoprovidetransparent,reproducible,andscientificallyrobust guidelinesforestimatingyieldgaps;guidelineswhicharealsorelevantforsimulatingtheimpactof cli-matechangeandland-usechangeatlocaltoglobalspatialscales.Likewise,theimprovedunderstanding ofdatarequirementsandalternativesforsimulatingcropyieldsandestimatingyieldgapsasdescribed herecanhelpidentifythemostcritical“datagaps”andfocusglobaleffortstofillthem.Arelatedpaper (VanBusseletal.,2015)examinesissuesofsiteselectiontominimizedatarequirementsandup-scaling fromlocation-specificestimatestoregionalandnationalspatialscales.

1. Introduction

Yieldpotential(Yp)isdeﬁnedastheyieldofanadaptedcrop cul-tivarasdeterminedbysolarradiation,temperature,carbondioxide, andgenetictraitsthatgovernlengthofgrowingperiod,light inter-ceptionby thecropcanopyand itsconversiontobiomass, and partitionofbiomasstotheharvestableorgans(Evans,1993;van IttersumandRabbinge,1997).Water-limitedyieldpotential(Yw)

∗Correspondingauthor.Tel.:+14024725554;fax:+14024727904.

E-mail addresses:[email protected] (P.Grassini), [email protected] (L.G.J.vanBussel).

isdeterminedbythesepreviousfactorsandalsobywatersupply amountanddistributionduringthecropgrowthperiodandfield andsoilpropertiesthataffectsoilwateravailabilitysuchasslope, plant-availablesoilwaterholdingcapacity,anddepthoftheroot zone(Lobelletal.,2009;vanIttersumandRabbinge,1997;Van Ittersum etal.,2013).Fora specificlocationand year,thecrop yieldgap(Yg)isdefinedasthedifferencebetweenYp(irrigated systems)orYw(rainfed)andaverageactualfarmyield(Ya).The magnitudeofYgprovidesabenchmarkofcurrentland productiv-ityinrelationtothebiophysicalyieldceiling,andanestimateofthe additionalcropproductionthatcouldpotentiallybeachieved,on existingcroplandarea,throughimprovedmanagementthat allevi-atesalllimitingfactorsotherthanweatherfactors.EstimatesofYp, http://dx.doi.org/10.1016/j.fcr.2015.03.004

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Yw,andYgalsoprovidethefoundationformoredetailedstudies toidentifyunderpinningcausesoftheobservedYg,andforex-ante evaluationofimpactfromadoptionofnewtechnologies,changing climate,andland-usechange.

Accuracy in Yg estimation depends onthe error associated with estimates of Yp (or Yw) and Ya1_. _Amongst _methods _to estimate Yp or Yw, crop simulation models provide the most robustapproachbecausetheyaccountfortheinteractiveeffects ofgenotype,weather,andmanagement(GxExM)onyieldsacross agro-ecological zonesand years (Van Ittersum et al., 2013).To minimizeerrorsinestimatingYpandYw,cropsimulationmodels requirehigh-qualityinput-dataonweather,soil,andcrop man-agement(Aggarwal,1995;Rivingtonetal.,2005;Bertetal.,2007). Thesemodelsneedalsotoberigorouslyevaluatedfortheirabilityto reproducemajorGxExMinteractions(Passioura,1996;Kersebaum etal.,2007;VanIttersumetal.,2013).Likewise,reliable simula-tionofYpandYwrequiresspeciﬁcationofthecroppingsystem andwaterregimeinwhichacropisgrownasdeterminedbycrop sequence,datesofsowingandphysiologicalmaturityforthemost widelyusedcultivars,andwhetherthecropisfullyirrigated, par-tiallyirrigated,orrainfed(Folberthetal.,2012;VanWartetal., 2013c).Finally,theerrorassociatedwiththeestimateofaverage annualYawillalsodeterminetheaccuracyoftheYgestimate.

Crop yield simulation is an important component of yield-gapanalysis,hence,theabove-mentionedsourcesofuncertainty relatedwithestimatesofYp(orYw)alsoaffectotherkindsof stud-iesthatrelyoncropyieldsimulationsandtherequireddatatherein. For example, studies on climate change, and land use change involvingcropsimulationmodelsappliedatglobalorregional spa-tialscalesareabundantinrecentliterature(e.g.,Challinoretal., 2014a;Rosenzweigetal.,2014).However,severalrecent publica-tionshaveidentiﬁedanumberofsubstantiveconcernsassociated withdatasourcesandmethodsusedinsuchstudies(VanIttersum etal.,2013;VanWartetal.,2013a).Theseconcernsinclude:(i)poor qualityofweatherandsoildata,(ii)unrealisticassumptionsabout thecropping-systemcontext,(iii)poorlycalibratedcropsimulation models,and(iv)lackoftransparencyaboutunderpinning assump-tionsandmethods.For example,Nelsonetal.(2010)used50-y monthlyaveragegridded(5resolution)weatherdataandcoarse assumptionsaboutthecroppingsystem(e.g.,asinglecropvariety wassimulatedfor theentireworld)toproducea global assess-mentofclimatechangeimpactoncropyieldsandland-usechange. AsimilarapproachwasfollowedbyBagleyetal.(2012)to simu-latechangesinwateravailabilityandpotentialcropyieldsinthe world’sbreadbaskets.Inbothstudies,noinformationwasprovided abouthowmodelswerecalibratedtosimulateyieldpotential. Sim-ilarly,Rosenzweig et al.(2014)used anensemble ofmodelsto simulatecropyieldsbasedongriddeddailyweatherdata,coarse assumptionsaboutcroppingsystems,andcropmodelparameters thatwereforcedtoreproducecurrentregionalornationalYa aver-ages.Anotherpitfallofthesethree studiesis failuretoaccount formultiple-cropsystems(i.e.,ﬁeldsplantedwithmorethanone cropinthesameyear,suchastherice-wheatsystemthatiswidely practicedinAsia)orcroppingsystemswhereirrigatedandrainfed systemsco-existwithinthesamegeographicarea.

Inmostcases,useofpoorqualityorcoarse-scaleweather,soil, andcropping-systemdataforyield-gapanalysis,aswellasforother studiesonclimatechange,foodsecurity,andland-usechangethat relyoncropyieldsimulations,isduetothefactthathighquality dataatﬁnerspatialresolutiondonotexist,sopragmaticshort-cuts arerequiredtoachievethefullterrestrialcoverage.These short-cuts,however,arerarelyevaluatedfortheirabilitytoreproduce

1 _Accuracy_is_the_closeness_of_a_measurement_(or_simulation)_to_the_true_value.

Yp,YwandYgvaluesestimatedusinghigh-quality,measureddata. Withoutsuchvalidation,Yp,Yw,and Ygestimateswith coarse-scaledatasources canseriouslydistortresults, decreasingtheir usefulness toinform regional or national policiesand effective prioritizationofresearchanddevelopmentinvestmentsfor agri-culture(Rivingtonetal.,2004;VanWartetal.,2013a,c).Incontrast, onecanfindstudiesonyield-gapanalysisforspecificlocationswith datathatareonlyavailableforfewandspecificsite-years,whichare notrepresentativeoflargerspatialareasanddonotallow upscal-ingtoregionalorgloballevels(e.g.,Fermontetal.,2009;Grassini etal.,2011).Surprisingly,despitewideuseofcropsimulation mod-elsforyield-gapanalysis(263resultsintheWebofScienceby Nov15th,2014),therearenopublishedguidelinesaboutstandard sourcesandqualityofdatainputforweather,soil,actualyields, andcropping-systemcontext,orrequirementsfor calibrationof cropmodelsusedinsuchstudies.

In summary, a robust approach to simulate accurate crop yieldpotentialandestimateYgrequires:(i)inputdatathatmeet minimumqualitystandards attheappropriatespatialscale, (ii) agronomicrelevancewithregardtocropping-systemcontext,(iii) propercalibration ofcrop modelsused, and (iv)flexibility and transparencytoaccountfordifferentscenariosofdata availabil-ityandquality.Hereweaddressthecurrentlackofguidelineson dataandmethodsforyieldgapanalysis,bydevelopingasystematic approachforselectionofdatainputsbasedonthelessonslearned fromestablishingtheGlobalYieldGapAtlas(www.yieldgap.org). Thepaperfocussesonyield-gapanalysisatspecific‘point’ loca-tions,andtheirsurroundinginferencezone,basedonapplication ofcropsimulationmodelstoestimateYporYw(hereaftercalled ‘targetedareas’).Aninferencezoneisdefinedasanareawithsimilar climatesuchthatthereisrelativelylittlevariationincrop manage-mentpractices.Thispaperhasimplicationsnotonlyforyield-gap analysisbutalsoforotherstudiesrelatedwithclimatechange,food security,andland-usechangebecausethesestudiestypicallyrely oncropyieldsimulationsandtherequireddatatherein.Aseparate paperdescribesthemethodologyforsiteselection,spatial delimi-tationoftheinferencezonearoundalocation,andup-scalinglocal estimatesofYgtoregionalandnationalscales(VanBusseletal., 2015).

2. Datarequirementsforyield-gapanalysis 2.1. Overview

Yield-gap analyses at large spatial scale require enormous amountsofinputdata,becausesimulatedandactualcropyields arestronglydeterminedbythespatialandtemporalvariationin environmentalconditionsandcroppingsystemcontext.Basedon theconceptthatitisbettertouseprimarydataforcropgrowth simulationsthantouseaggregatedorinterpolatedaverageinput data(DeWitandVanKeulen,1987;RabbingeandvanIttersum, 1994;PenningDe Vriesetal.,1997), theGlobalYieldGapAtlas (www.yieldgap.org)utilizesa‘bottom-up’approachforyield-gap analysis. A limited number of locations are selected such that theseaccountforthegreatestproportionoftotalnational produc-tionofthecropbeingevaluated.Fortheselocations,‘point-based’ estimates of Yp, Yw, Ya,and Yg are derived, which are subse-quentlyup-scaledtoclimatezonesandnationalspatialscales(Van Wart et al.,2013b; Van Bussel etal., 2015).This site selection andup-scalingprocesshelpstolimitthenumberoflocationsfor which site-speciﬁcdataonweather, soils,andcropping system arerequired,whichinturnfacilitatesthefocusonqualityofthe underpinningdataandhelpsensurelocaltoglobalrelevanceof theanalysis.Principlesthatunderpinthedataselectionapproach

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Table1

Qualityandavailabilityofdatarequiredforyieldgapanalysisinthreestudyregions.

Datainput Region

Nebraska,USA Argentina Kenya

Weather

Source HPRCC,NWS INTA-SIGA,SMN KMS

Availabilityofrequiredvariables All All,exceptsolarradiation All,exceptsolarradiation

Availabledata-years >20yr >20yr 3–18yr

Spatialdistribution High Medium Low

Dataqualitya _High _Medium _Low

Publiclyaccessible Yes Yes No

Soils

Source USDA-NRCS INTA-GeoINTA,INTA-Soildivision ISRIC-WISE

Spatialresolution High Intermediate Coarse

Availabilityofrequiredvariables All All All,exceptrootabledepth

Cropmanagementb

Source USDA-RMA None None

Availabilityofrequiredvariables Onlysowingdate None None

Modelcalibration

Source ResearchfarmsHigh-yieldproducerﬁelds Researchfarms None

Actualyield

Source USDA-NASS MinistryofAgriculture-SIIA MinistryofAgriculture

Finestspatialresolutionlevelc _County_(≈2000_km2₎ _Department_(≈4500_km2₎ _District_(≈2500_km2₎

Availabledata-years Allyears Allyears Everyother2–3yr

Dataqualitya _High _Intermediate _Poor

Publiclyaccessible Yes Yes No

HPRCC: High Plains Regional Climate Center (http://www.hprcc.unl.edu/); NWS: National Weather Service (http://www.weather.gov/); INTA: Instituto Nacional de Tecnologia Agropecuaria (http://inta.gob.ar); SIGA: Sistemas de Informacion Clima y Agua (http://climayagua.inta.gob.ar/); SMN: Argentina National Mete-orological Service (http://www.smn.gov.ar/); KMS: Kenya Meteorological Service (http://www.meteo.go.ke); NRCS: National Resource Conservation Service (http://www.nrcs.usda.gov/wps/portal/nrcs/site/soils/home/);GeoINTA:(http://geointa.inta.gov.ar/web/);ISRIC-WISE:InternationalWorldSoilReferenceandInformation Center;Worldinventoryofsoilemissionpotentials(http://www.isric.org/projects/world-inventory-soil-emission-potentials-wise);USDA:UnitedStatesDepartmentof Agriculture(http://www.usda.gov/wps/portal/usda/usdahome);NASS:NationalAgriculturalStatisticsService(http://www.nass.usda.gov/);RMA:RiskManagementAgency (http://www.rma.usda.gov/);SIIA:SistemaIntegradodeInformacionAgropecuaria(http://www.siia.gob.ar/).

a_See_Sections_2.2.2_and_2.5.2_,_{respectively,}_on_weather_and_actual_yield_data_quality.

b_Includes_information_on_(or_to_estimate)_dominant_crop_sequences_and_their_relative_proportion,_sowing_date,_plant_density,_and_cultivar_maturity. c _Average_size_of_{administrative}_units_located_within_the_major_maize_production_areas_in_each_region.

implementedbytheGlobalYieldGapAtlas(www.yieldgap.org) include:

(i)preferenceforusingmeasuredinsteadofestimatedor inter-polateddata,

(ii)transparency,reproducibility,and consistencyindata selec-tion,

(iii)useoflocalexpertisetocorroboratedatainputs(andcollect themifnecessary),andtoensureagronomicrelevance,and (iv)strongpreferenceforpubliclyaccessibledata.

ThemethodologydevelopedbytheGlobalYieldGapAtlas con-sistsofatieredapproach,foreachdata-inputtype(i.e.,weather, croppingsystem,soil,Ya,andmodelcalibration),whichfirstdefines the‘ideal’databaseforyield-gapanalysisfollowedby“second-or third-choice”alternativesfor casesin which thepreferreddata sourcedoesnotexistorisnotavailable.Infact,fewcountriesor regionshavegoodqualitydataatthefinedegreeofspatial res-olutionrequiredforhighlyreliableyieldgapanalysis.Giventhis situation,weevaluaterainfedmaizeyieldgapsinNebraska(USA), Argentina,andKenyatoillustratehowtodealwithawiderangeof dataqualityandavailability(Table1,Fig.1).

2.2. Weatherdata:Thefoundationforreliablecropsimulation 2.2.1. Howmanyyearsofweatherdataareneeded?

Daily weather data of sufficient quantity and quality are requiredforrobustsimulationofYpandYwandtheirtemporal variability(quantifiedbythecoefficientofvariation[CV]).Akey questionishowmanyyearsofweatherdataareneededtoobtain arobustestimateofYp,Yw,andYginordertoaccountfor year-to-yearvariationinweather.Theanswerdependsonlocationand waterregime.Thisisillustratedbylookingattherangeofpossible

Yp and Yw estimates, simulated based ondifferent number of years ofweather data,for rainfedand irrigated maizeat North Platte(Nebraska,USA)andrainfedmaizeinRioCuartoandBarrow (favourable and harsh rainfed cropenvironments in Argentina, respectively)(Figs. 1and2,seedetailsonmodelsimulationsin AppendixA).Simulationswereperformedusingcropmodelsthat have been successfully validated on their ability to reproduce yieldsmeasuredunderoptimalmanagementconditionsineachof theregions(seeSection2.6.4).Whereasrainfallisrelativelylow and highlyvariable atboth North Platteand Barrow,thelatter hassoilsinwhich acalichelayerlimitstherootablesoildepth. Thesitescanbecategorizedaccordingtotheiraverageyieldand inter-annualvariationasfollows:irrigatedmaizeatNorthPlatte and rainfedmaize at Rio Cuarto (highestyield, lowestCV) and rainfedmaizeat NorthPlatteandBarrow(lowestyield,highest CV). In favourable environments, 10 years of weather dataare sufﬁcient to estimate an averageyield and CVthat are within

±10%oftheestimatesobtainedwiththeentire30-yeardatabase (e.g.,NorthPlattewithirrigationandrainfedatRioCuarto)(Fig.2). The number of required years increases to 15 to 20 years in lessfavourableenvironments(rainfedmaizeatNorthPlatteand Barrow). Hence,dependingupon water supply, 10 (irrigatedor favourablerainfedenvironments)to20yearsofdailyweatherdata (harshrainfedenvironments)areneededforreliableestimatesof Yp(irrigated)orYw(rainfed)andtheirvariability.Thesefindings areconsistentwithVanWartetal.(2013c),whoshowedthat6 to15 years of weather data arerequired for reliableestimates of Ywacrossan east-westtransectin theU.S. CornBelt where total rainfall, duringthe maize cropgrowing season,decreases from900mm(east)to400mm(west).Therefore,thenumberof availableyearsofobservedweatherdatashowninTable1seems sufficient for robust estimation of Yp and Yw in Nebraska and Argentina(>20yr)butisprobablyinsufficientformanylocations

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Fig.1.MapsofNebraska,USA(A),Argentina(B),andKenya(C).Notescaledifferencesamongpanels.Greenintensityindicatesmaizeharvestedareadensityretrievedfrom USDA-NASS(Nebraska,USA),MinistryofAgriculture-SIIA(Argentina),andglobalSPAMmaps(Kenya;Youetal.,2014).Dotsindicatelocationsofmeteorologicalstationswith ≥3yearsofdailyweatherdatasituatedwithinthemajormaizeproducingregionsineachcountry.Linesindicatetheboundariesofadministrativeunitsatwhichactualyield dataareavailable:county(Nebraska,USA),department(Argentina),anddistrict(Kenya).Meteorologicalstationsusedforspeciﬁcanalysesinthepresentarticlearecircled andtheirnamesareshown.MeteorologicalweathernetworksareHighPlainsRegionalClimateCenter(HPRCC);USNationalWeatherService(NWS);InstitutoNacionalde TecnologíaAgropecuaria,SistemasdeInformaciónClimayAgua(INTA-SIGA);ArgentinaNationalMeteorologicalService(SMN),KenyaMeteorologicalService(KMS).

inKenya(3to18yearsdependinguponlocation)whererainfall islowandhighlyvariable.Useofinsufﬁcientnumberofyearscan biasestimatesofYwduetoinclusionofextremeweatheryearsor short-termclimatecyclesintheweatherdatatimeseries. 2.2.2. Requiredweathervariablesforcropmodellinganddata quality

Daily incident solar radiation and temperature (maximum [Tmax] and minimum [Tmin]) are required for estimating Yp, whereas estimation of Yw alsorequires precipitation. Depend-ingonthemethodusedtoestimatereferenceevapotranspiration (ETO)inthesimulationmodel,vapourpressureandwindspeed mayalsobeneeded.Althoughmeasureddataarealways prefer-ableto propagated or derived weather data, daily datafor the othervariablesrequiredforcropmodellingbesidesTmax,Tmin,and precipitation(i.e.,solar radiation,vapour pressure) canbe esti-mated, in absenceof measureddata, witha reasonable degree ofaccuracyusingtemperaturedataorretrievedfromotherdata sources. An exception is wind speed, which cannot readily be

estimatedfrom othervariables, hence, a default world average value of 2ms−1 _is _typically _used _to _estimate _ET

O when mea-suredwind speed data arenot available(Allenet al.,1998).In contrast,solar radiation can beestimated using equations that rely on sunshine hours (e.g., Angstrom formula) or tempera-ture(e.g., Hargreaves formula) (Allen et al.,1998).Likewise, in regionswithrelativelylevel topographyand littleair pollution, griddedsolarradiationreportedbyThePredictionofWorldwide EnergyResource(POWER)datasetfromtheNationalAeronautics andSpaceAdministration(http://power.larc.nasa.gov/),hereafter calledNASA-POWER,canbeusedwithconﬁdenceforcrop simula-tion(Baietal.,2010;Whiteetal.,2011;VanWartetal.,2013a,c). Vapourpressureistypicallyderivedfromrelativehumidityordew pointtemperaturemeasurements.Inabsenceofmeasureddata, vapourpressurecanbeestimatedfromthemeasuredTmin assum-ingthatdewpointtemperatureisnearthedailyTmin(Allenetal., 1998).Inallcases,itisdesirabletolocallyvalidatetheseapproaches usinggoodqualityobserveddatafromarepresentativesubsetof yearsandlocationsintheregionofinterest.

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Fig.2. Averagesimulatedmaizeyieldpotentialanditstemporalvariability(estimatedbythecoefﬁcientofvariation[CV])asafunctionofthenumberofyearsofweather datausedinthesimulations.Simulationswereperformedforfavourable(bluesymbols)andunfavourableenvironments(yellowsymbols)formaizeproductioninNebraska (USA)andArgentina.Waterinputsfromirrigationandrainfalldecreaseinthisorder:irrigatedmaizeatNorthPlate>rainfedmaizeatRioCuarto>rainfedmaizeatNorth Platte≈rainfedmaizeatBarrow.SoilsweredeepatNorthPlatteandRioCuarto(≥1.5m)butshalloweratBarrow(0.8–1.2m).SimulationsbasedonHybrid-Maize(Nebraska) andCERES-Maize(Argentina)modelsusingobservedweatherdataanddominantsoilsandmanagementineachlocationandwaterregime(seeTableS1).Thedatapoints, foragivenny,representtheaverageyieldpotentialandCVvaluesascalculatedbasedon30subsetsofnyre-sampledfromthe30-yrweatherdatabase.(Forinterpretation ofthereferencestocolourinthisﬁgurelegend,thereaderisreferredtothewebversionofthisarticle.)

Besidesdataavailability,robustnessofsimulatedYpand Yw dependsonthequalityofmeasureddata.Weatherdataquality canbeevaluatedbyprevalenceofsuspiciousandmissingvalues. Qualitycontrolscreeningmethodshavebeendevelopedto iden-tifysuspiciousvaluesinweatherdatasets(e.g.,Allenetal.,1998; Hubbardetal.,2005).Asageneralguideline,wedefineayearof weatherdataassuitablefordirectuseincropmodels,when_≥80% ofalldataforTmax,Tmin,andprecipitationarerecordedand<20 con-secutivedaysaremissingorsuspiciousforTmaxandTmin,and<10 consecutivedaysforprecipitation.Forcountriesandregionswhere theweatherstationnetworkisrelativelydense(e.g.,inNebraska, onaverage,thereisoneHPRCCandNWSmeteorologicalstation per3180and860km2_,_{respectively),}_and_each_station_has long-termdailyweatherrecords,arobustapproachtoqualitycontrol withregardtoidentificationandreplacementofsuspicious val-uesandfillingofmissingdata,isbyevaluatingcorrelationsamong adjacentweatherstations(e.g.,Hubbardetal.,2005; Youetal., 2008).Unfortunately,inmanyregionsoftheworldweatherstation networkshavecoarserspatialand temporalcoverages.Inthese cases,identificationofsuspiciousvaluesismoreproblematic. Lin-earinterpolationcanalsobeemployed,toacertainextent,tofill-in missingorerroneousTmaxandTmindata,whilegridded precipita-tiondatafromNASA-POWERortheTropicalRainfallMeasuring Mission(TRMM,http://trmm.gsfc.nasa.gov/)canbeusedtofill-in missingdays(althoughTRMMdataareonlyavailableoverthe lati-tudeband50◦N–S).AnalternativeforfillingmissingTmaxandTmin istouserelationshipsbetweenobservedandgriddedweatherdata basedona limitednumberofdata-yearstoperforma location-specificcorrectionofthelatterandusethesetofillinvaluesfor missingdays(e.g.,Chaneyetal.,2014;VanWartetal.,2015).

Twootherfactorsinﬂuencequalityofweather datafor agri-culturalassessments.Theﬁrstisthedegreetowhichthelocation ofaselectedweatherstationisrepresentativeofthesurrounding

agriculturallandonwhichthesimulatedcropisgrown.Solar radia-tion,Tmax,andTmincanbebiasedbytopography,waterbodies, sur-roundingvegetation,andurbanareas.Foragriculturalapplications, weatherdatashouldideallybemeasuredatmeteorological sta-tionssituatedinaruralsettingsurroundedbyagriculturalland(e.g., HPRCCandINTAweathernetworksinNebraskaandArgentina). Still,observedweatherdatafromstationslocatedincitiesor air-portsarepreferabletogriddedweatherdata(seeVanWartetal., 2013a).Second,cropmodellingtorepresentweather,soil,current cropmanagementandcroppingsystemsshoulduseweatherdata fromrecent decades(preferablylast 2-3 decades)becausedata frompreviousdecadesmaynotberepresentativeofcurrent cli-matewheretherehavebeensigniﬁcantchangesinweatherdueto climatechange(e.g.,Kassieetal.,2014;Rurinda,2014).

2.2.3. Selectionofweatherdatasources

Selectionofsourcesofweatherdataisbasedonthegoalofusing asmuchobservedweatherdataaspossiblewhilereachingthe min-imumnumberofyearsrequiredforrobustestimatesofYporYw andtheirvariability(Fig.2).Inmanypartsoftheworld,weather dataavailabilityandqualityarefarfromoptimalforsomeorall requiredweathervariables.Hence,ourprotocolfollowsatiered approach(Fig.3)inwhichthefocusshiftsfromtheidealscenario towardsacquisitionoftheminimallyrequiredweathervariables forthesimulation(i.e.,Tmax,Tmin,andprecipitation)asdata qual-ityand availabilitybecomelimiting.Tothis end,threelevelsof weatherdataavailabilityaredeﬁned:

-Level1:suitableweatherdataavailablefor>10years,preferably fromrecentdecadestoavoidmisleadingeffectsofclimatechange. Whilewerecognizethat10–15yearsofdatamaystillbe insufﬁ-cientforarobustestimateofYwanditsvariabilityinsemi-arid

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Fig.3. FlowchartforselectionofweatherdataforcropsimulationmodellingasusedintheGlobalYieldGapAtlas(www.yieldgap.org).

environments,thisisstillsuperiortouseofpropagatedweather dataorgriddedweatherdatabases(VanWartetal.,2013a,2015). -Level2:suitableweatherdataavailablefor≤10years.Inthese cases,thebestoption istousetheexistingweather dataand togeneratethemissingyearsofdatafollowingthe methodol-ogydescribedbyVanWartetal.(2015),toobtainaminimum of15–20yearsofweatherdata.Brieﬂy,thismethodconsistsof (a)correctinglong-term,dailyNASA-POWERTmaxandTmin val-uesonthebasisof,atleast,3yearsofobservedTmaxandTmindata and(b)retrievingprecipitationdatafromTRMMorNASA-POWER databases.

-Level3:suitableweather dataareavailablefor<3years ordo notexist atall.In this case theonlyoption is tousegridded orgeneratedweatherdatabases;however,resultingsimulations need to be flagged as less reliable than Yw or Yp estimates basedonobservedweatherdataand updated,whenobserved weatherdatabecomeavailableforthetargetedarea.Itis diffi-cult,however,torecommendthebestgriddedweatherdatabase to use, because, without site-specific correction, all of them appeartohavesubstantialbiaseswhencomparedagainst mea-suredweather data,and thebiases arenot consistentinsign and magnitude across locations (Mearns et al., 2001; Baron et al., 2005; van Bussel et al., 2011; Van Wart et al., 2013a, 2015).

2.2.4. Selectionofweatherdataforthethreecasestudyareas The three countries shown in Table 1 illustrate how the protocol can be appliedacross the spectrum of data availabil-ity.Nebraska approachesthe‘ideal’scenario,whereallrequired weathervariablesaremeasuredandavailablefromHPRCC mete-orologicalstationslocatedonagriculturalland,witha sufﬁcient numberoflocationsandyears,anddataaresubjectedtorobust measuresof quality control. Argentina deviated from theideal conditionbecause(i) solar radiationdata are not available,(ii) somemeteorologicalstationsarelocatedinairportsorcities(those belongingtotheSMNnetwork),anddataqualityisanissuefor

somelocationsortimeperiods.Solarradiationwasretrievedfrom theNASA-POWERdatabase,whichwasevaluatedagainstmeasured solarradiationforasubsetoflocation-years(totalof18,375daily observations),showingremarkablygood agreement(root mean square error: 3.5MJm−2_d−1_, _r2₌_0.84). _To _comply _with_quality standards,alldailyobservationsforeachvariablewerescreened bylookingatcorrelationsbetweentheselectedweather station andthetwoadjacentstationsfollowingthemethoddescribedby VanWartetal.(2013c).Incontrast,almostallmeteorological sta-tionsinKenyawerelocatedatairportsorincitiesanddidnothave suitabledataforasufﬁcientnumberofyears(<10years).Forthose targetedareaswhere≥3yearswereavailable(butlessthan10), thepropagationtechniquedeveloped byVanWartetal.(2015) wasappliedtoproducelong-termweatherdata(≥10years), keep-ingallobserveddatawithinthedatasetandonlyusingpropagated dataformissingtimeperiods.NASA-POWERwasusedassourceof solarradiationdataandalsotoestimatehumidityfromdewpoint temperature.Forthosetargetedareasthathave<3yearsofdata ornodataatall,NASA-POWERweatherdataforallvariableswere usedwithoutcorrection,butresultswereﬂaggedashighly suspi-ciousgiventheuncertaintyinweatherdataqualityforthesitein question.

2.3. Cropping-systemcontext

2.3.1. Whatisthecropping-systemcontext?

Specification of dominant water regimes (i.e., rainfed, fully-irrigated, or partially-irrigated), crop sequence(s), and their proportionoftotalharvestedcroparea,areessentialforaccurate estimationofYp,YwandYgatlocaltonationalscales.Explicit quan-titativeaccountingofthis croppingsystemcontextis especially importantwhererainfedand irrigatedcropsco-existwithinthe samegeographicareaandwheretheclimateallows2–3cropcycles peryearonthesamefield.Likewise,thesamecropcanbegrownin verydifferentcropsequencessothatYp(orYw)differsdepending onsequence.Eachwaterregimeandcroppingsystemisdefined

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byaveragesowingdate2_,_cultivar_maturity_(growing_degree_days or,whennotavailable,timedurationfromsowingtophysiological maturity),andplantdensity(numberofplantsperha).Storedsoil wateratsowingintherootzonealsoneedstobespeciﬁedfor rain-fedorpartially-irrigatedcroppingsystems(seeSection2.6.4).For eachwaterregime,separateYp(orYw)aresimulatedforeachcrop cycleand,ifthereismorethanonecycle,aweightedaverageis esti-matedbasedontherelativeproportionoftotalharvestedcroparea ofeachcycle.Asimilarapproachisfollowedwhenthesamecrop isgrownindifferentcropsequences.Thisaggregationisneeded becauseYadataaretypicallyreportedonaper-harvestedhectare basis,withoutdisaggregationbycropcycle(seeSection2.5.1). 2.3.2. Sourcesoferrorassociatedwithcroppingsystemdata

Inmanycroppingsystems,availabilityofmachineryandlabour constraintimelycropsowing,andplantdensityissometimes sub-optimalduetohighseedcostormanualsowing.Incasesinwhich thereisa clearindicationthatsowingdateorplantdensityare sub-optimal,itisusefultodistinguishbetweensimulationsbased onactualmanagementversusthoseusing‘optimal’management andprovideajustiﬁcationforthelatter.Inallcases,the‘optimal’ managementscenariomustbeconstrainedwithintheboundaries imposedbythecropsequenceundertheassumptionthat,in gen-eral,farmers areefﬁcientin allocationofland,labour,and time withinthelimitationsimposedbyexistingeconomicand biophys-icalenvironments(HerdtandMandac,1981;Hopper,1965;Sheriff, 2005).

Becausebreedingeffortsformostcropshaveimprovedyields and yield stability over thepast 30 years (Connor etal., 2011; Fischeretal.,2014), simulationsofYpandYw shouldbebased onrecentlyreleasedhigh-yieldingcropcultivars,growninpure stands,widelyusedbyfarmersintheregion.Ideally,itis desir-abletohavecultivarmaturity reportedingrowing-degreedays (GDD)from sowing to maturity, preferably alsothe GDDfrom sowing-to-flowering,and,forthosecultivarsinwhichdevelopment is also modulated by photoperiod and vernalisation require-ments,tohaveallthecultivar-specificparameters thataccount forthedevelopmentalresponsestothesetwo factors.In devel-opedcountries,this informationissometimesavailablethrough seedcataloguespublishedorprovidedonwebsitesbyseed com-panies orfrompublic-sector cultivartesting programs. Inmost developingcountries,however,theonlyindicatorofcultivar matu-rity isaveragecropcycle duration,that is,the numberof days ‘typically’requiredforacropataspecificlocationtoreach physio-logicalmaturity.Abackwardsprocedurecanbefollowedinthese casestoderivecultivarGDDbyrunninglong-termsimulationsand adjustingphenology-relatedcoefficientsuntilsimulations repro-ducethereportedaveragedateofphysiologicalmaturity.When this approach is used, estimated Yp or Yw can still be biased due to uncertainties in the simulated flowering date, or when cropcycle duration is based on thedate of harvestinstead of physiologicalmaturity(e.g.,Bagleyetal.,2012).Forexample,in large-scale,mechanizedcommercialfarming,harvesttakesplace whengrainmoisturecontentreachesalevelatwhich mechani-calharvestispossibleanddryingcostsareminimized.Hence,in somecases,harvestcantakeplaceupto4weeksafterthecrop hasreachedphysiological maturity. By contrast,in small scale, non-mechanized farming in tropical and semi-tropical regions, reported harvestdate is typically much closer to physiological maturityduetothevalueofcropresiduesforlivestockfeeding, riskofyieldlossesduetoinsects,diseases,birds,androdents,and opportunitiestoplantsubsequentcropsinthesameyear.Using

2_Average_sowing_date_is_deﬁned_as_the_approximate_calendar_date_at_which_50%

oftheﬁnalsownhectarageiscomplete.

Table2

Cropping-systemcontextinthethreecasesofstudypresentedinthisstudy. Region Croppingsystemfeature

Waterregime Cropintensity (maizecropsyr−1₎

Nebraska(USA) Irrigated&rainfed One

Argentina Rainfed One

Kenya Rainfed One(eastKenya)or

two(westKenya)

maturitieslongerthanthoseusedbyproducerstypicallyleadsto unrealisticallyhighYpin irrigatedsystemsor Ywin favourable rainfedenvironmentswhileYwcanbeunrealisticallylowand vari-ableatlocationswithsevereterminalwaterdeﬁcit.

2.3.3. Croppingsystemdatausedforthethreecasestudies

Differencesincroppingsystemsareillustratedforthethreecase studies(Table2).InNebraska,irrigatedandrainfedmaizeco-exist (with60and40%oftotalharvestedarea,respectively)anda sep-aratesetofmanagementpractices,inparticularplantpopulation density,isrequiredforeachwaterregime.Incontrast,maizearea underirrigationinArgentinaandKenyaisnegligible(<3%oftotal maize harvested area).Whereasonly one annualmaize cropis growninNebraskaandArgentina,typicallyina2-yrotationwith soybean,twomaizecropsaregrowninthesameﬁeldeachyear atmanylocationsinwestKenyawhereabi-modalannualrainfall patternoccurs.Hence,separatespeciﬁcationofmanagement prac-ticesforeachmaizecropcyclewasneededfortheselocationsin KenyaforanaccuratesimulationofYporYw.ResultingYpandYw needstobeaveraged,weightedbytheirrelativearea,asexplained inSection2.3.1.

Inallthreecasestudies,therequiredcropping-system infor-mationwasnotreadilyavailable,exceptforsowingdatedatain Nebraska. Data onsowing date progress are annuallycollected formajorU.S.crops,onacountybasis,bytheRiskManagement Agency(http://www.rma.usda.gov/).Whilethisinformationis col-lectedforinsurancepurposes,italsoprovidesanobjectivewayto definetherangeofsowingdatesatanadequatespatialresolution. Incontrast,dataondominantcultivarandplantdensity,foreach waterregime,arenotpubliclyavailableandsimulationsrelyon expertopinionfromlocalagronomistsandinformationprovided byseeddealersandseedcompanies.Oncethedominantcultivaris determined,theGDDfromemergencetofloweringandfrom flow-eringtophysiologicalmaturitycanberetrievedfromprivateseed companycataloguesandinformationavailableontheirwebsites. InArgentina,accurateinformationondominantcultivar,sowing dates,andrecommendedplantpopulationdensitieswereobtained from local agronomists working in each of the targeted areas. GDDofdominantcultivarswasavailablethroughseedcompanies andconfirmedwithdetailedphenologicalobservationsinresearch stationexperiments(Monzonetal.,2012).Allmanagementdata inKenyawerecollectedfromlocalcollaboratorsbut,incontrastto Nebraska(USA)andArgentina,widerangeswerereported(e.g.,a 2-monthwindowforsowingdate),reflectingimportantvariation inmanagementpracticesacrossyearsandfarmsduetovariation intimingofrainfallatthebeginningoftherainyseason.

2.4. Soildata

2.4.1. Selectionofdominantsoiltypes

Thepresentpaperdoesnotattempttoprovideareviewofthe availabledatasourcesordifferentapproachestoobtainsoilinput datarequiredbyeachcropmodel.Readersarereferredtopapers thatconsiderdifferentapproachesforobtainingadequatesoildata forcropyieldsimulations(e.g.,Ritchieetal.,1990;Gijsmanetal.,

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2007;Batjes, 2012;Romeroet al.,2012).Instead,ouraimisto developscientificallyjustifiableandefficientprotocolsforselecting themostwidelyusedsoilsforproductionofagivencropata spe-cificlocation,andthenspecifyingthesoilpropertiesforthosesoils thatarerequiredforcropmodelling(hereaftercalled‘functional’ soilproperties).Soilmappingunitsandsoilserieswereusedas thebasisforderivingrequiredsoilproperties.Asoilmapunitisa collectionofareasgroupedaccordingtolandscapeposition, pro-filecharacteristics,relationshipsbetweenthesetwo,suitabilityfor varioususes,andneedforparticulartypesofmanagementsuchas soilerosioncontrolpractices.Eachsoilmapunitmaybecomposed ofoneormoresoilseries.Itisimportanttodefinethedominantsoil seriesthataremostwidelyusedforproductionofthetargetedcrop intheareaofinterest(VanBusseletal.,2015).Toavoidbiasesdue toinclusionofsoilunitsnotrelevantforcropproduction,soilswith negligiblecroparea(i.e.,<10%coverageofcropharvestedareain theareaofinterest)orthosewheresustainablelong-termannual cropproductionisnotlikelysuchasshallowsoils(rootabledepth <0.5m),sandysoils(PASW<7cmcm−3_or_sand_content_>75%),_and soilserieswithverysteepterrain(slope>10%)areexcluded. More-over,allelsebeingequal,farmershaveapreferenceforgrowing certaincropsonthebestsoils,asitisthecaseofmaizeinArgentina andtheUSA.

2.4.2. Requiredsoilvariablesforcropmodelling

While soil input data required by different crop simulation modelstosimulateYwmaydiffertosomeextent,allsuch mod-elsrequirerootablesoildepthandvolumetricplant-availablesoil water holding capacity (PASW; in cm3_cm−3_). _Hence, _soil pro-ﬁledatashouldincludethese‘functional’soilproperties(e.g.,soil waterretentionlimits)or,atleast,datafromwhichthesecanbe derived(e.g.,soiltextureclass).Othersoilandterrainattributes suchasslopeanddrainageclassarealsoneededtodeterminethe amountofsurfacerunoff.Anaccuratesimulationofsurfacerunoff requiresa levelof modelprecisionanddata detailthat current dataavailabilitydoesnotallowinmostcountries,hence, semi-empiricalapproachesforrunoffestimationareacceptable(e.g.,Soil ConservationSystem(SCS),1972;CampbellandDiaz,1988).

Besidessoilwaterholdingcapacity,rootablesoildepthisthe mostimportantsoilpropertyinfluencingYwanditsyear-to-year variability(e.g.,SadrasandCalvino,2001).Therootablesoildepthis definedasthesoildepththatcanbeeffectivelyexploredbythecrop rootsystemtoabsorbwaterandnutrientswithoutsevere physi-calorchemicalconstraintstorootgrowthorfunctionality.Root growthrestrictionsincludebedrock,calichelayer,abrupttextural change,alkalinity,sodicity,acidity,etc.(USDA-NRCSNationalSoil SurveyHandbook).Eveninabsenceoftheseconstraints,thereisa limittotherootablesoildepthdefinedbycropgenotypeandlength ofthecropseason.Formostgraincropspeciesinrainfedsystems,a valueof≈1.5mcanbeassumedforsoilswithoutphysicalor chem-icallimitations(e.g.,Dardanellietal.,1997).Althoughdataneeded todefinetherootablesoildepthcanberetrievedfromsoilseries descriptions,inmanycasessoildataarelimitedtothetopsoiland itisnotclearifthesamplingdepthcanbetakenasaproxyforthe rootablesoildepth.Inabsenceofthisinformation,determinationof rootabledepthmustrelyonlocalexpertsthough,basedonourown experienceintheGlobalYieldGapAtlas,knowledgeaboutsubsoil propertiesisgenerallypoorinmanycountriesandshouldbeused withcaution.

TheothermandatoryvariableforsimulatingYwisplant avail-ablesoil water(PASW) asdetermined byupper andlower soil limitsforwaterretention(i.e.,ﬁeldcapacityandpermanentwilting point,respectively,whichcorrespondroughlytoasuctionof−33 and₋1500kPa).Actualmeasurementsofsoilwaterretention lim-itsarerarelyavailable,hence,thesearetypicallyestimatedusing pedo-transferfunctions(PTF) based onsoil texture.Many PTFs

areavailabletoderivesoilmoisturelimitsasdiscussedbyTietje andTapkenhinrichs(1993),Rawlsetal.(1991),andGijsmanetal. (2002).Animportant,thoughoftenoverlookedconsiderationwhen usingaPTFisthattherangeofsoiltextureandclaymineralogyof thetargetedareasshouldbewithintherangeofvalidityofthePTF. Inparticular,PTFsdevelopedfortemperatesoils(e.g.,Saxtonand Rawls,2006)shouldnotbeusedfor estimatingwaterretention limitsinstronglyweatheredtropicalsoils(Tomasellaetal.,2000; HodnettandTomasella,2002).

Thepotentialdegreeoferrorduetoincorrectspeciﬁcationof PASWandrootabledepthisillustratedfortwolocationsinKenya, whichrepresentfavourable(second-seasoncropatKisii)andharsh (single-seasoncropatThika)rainfedcropenvironments,andfor NorthPlatte,USA(Figs.1and4).MaizeYwwassimulatedusing (i)genericsoilwaterretentionlimitsreportedforeach textural classbyDriessenandKonijn(1992)fortemperatesoilsversus val-uesestimatedfrom a PTFdeveloped for tropicalsoils (Hodnett andTomasella,2002)and(ii)rootabledepthof1mversus1.5m (Fig.4).AverageYwanditsCVvarygreatlyamongcombinationsof PTF×soilrootabledepth.Forexample,averageYwrangedfrom 8.7 to10.8Mgha−1 _at _Kisii,_with_CV _ranging_from _24% _to_42%. TheserangeswererelativelymuchwideratNorthPlatte,where Ywrangedfrom3.4to6.3Mgha−1_,_with_CV_ranging_from₁₈_to 91%.

2.4.3. Soildataretrievalforthethreecasestudies

Soildatasourcesfor thethreecase studiesinclude:detailed nationalsoilmapsandprofiledatabasesinNebraskaandArgentina andtheISRIC-WISE(Batjes,2012)globalsoildatabaseforKenya (Table1).ForNebraskaandArgentina,relevantsoiltypesforcrop productioninthetargetedareascanbeeasilyidentifiedand infor-mationtodeterminetherootablesoildepthandPASWisavailable. ForKenya,theISRIC-WISEglobalsoildatabasewasselectedbecause thisdatabaseprovidestherequiredinformationonsoilproperties forcropmodelling.SourcesofuncertaintywhenusingISRIC-WISE (andotherglobalsoildatabases)include:(i)difficultytodetermine whichsoilunitsarerelevantforcropproduction,(ii)littleavailable dataonrootablesoildepth,and(iii)uncertaintyaboutselection ofanappropriate,well-calibratedPTFfortropicalsoils.Relevant soilunitsforcropproductioninthetargetedareasofKenyawere selectedfollowing therules for soiltype selection described in Section2.4.1,togetherwithinformationoncropharvested area distributionfromSPAM(Youetal.,2009,2014).PTFsderivedfor tropicalsoilsbyHodnettandTomasella(2002)wereusedto esti-matesoilwaterretentionlimitsbasedonthereportedsoiltexture. Duetolackofdataonrootablesoildepth,astandard1-mdepth wasusedforallYwsimulationsbasedonobservationsofNyeand Greenland(1960)aboutsavannahsoilsinSub-SaharanAfrica. 2.5. Actualyield:Oftenabottleneckforestimatingyieldgaps

Actualyieldisdeﬁnedastheaverageannualyieldobtainedby farmersinageographicareaforagivencropwithagivenwater regime.TherearefourkeyaspectsrelatedtoYadata:(i)levelof disaggregationbycropandwaterregime,(ii)numberofavailable data-years,(iii)spatialresolution,and(iv)dataquality.Another important,thoughoftenoverlookedaspect,isthedrymatter con-centrationatwhichYavaluesarereportedsothattheYaandYw (orYp)datausedforcalculationofYgareatequivalentmoisture content.Forexample,themostwidelyuseddatabaseforretrieving nationalYaaverages(FAOSTAT,FAO,2014;http://faostat.fao.org/) doesnotexplicitlydeﬁnethemoisturecontentatwhichcropyields are reported. In contrast, grain yieldsreported by government agenciesintheUSAandArgentinaareprovidedatstandard mois-turecontent(e.g.,ca.15%formaizegrain).

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Fig.4. Boxplotsofsimulatedmaizewater-limitedyieldpotentialatKisiiandThika(Kenya)andNorthPlatte,Nebraska(USA)usingHybrid-Maizemodelbasedon1mand 1.5mrootabledepthandsoilwaterlimitsretrievedfromDriessenandKonijn(1992)fortemperatesoilsversusvaluesestimatedusingpedo-transferfunctions(PTF)for tropicalsoils(HodnettandTomasella,2002).Lowerandupperboundariesforeachboxarethe25thand75thpercentiles.Thesolidanddashedlinesinsideeachboxindicate themedianandmean,respectively.Whiskers(errorbars)aboveandbelowtheboxindicatethe90thand10thpercentiles.Dotsaboveandbelowthewhiskersindicatethe 95thand5thpercentiles.Themeansoveryearsandtheinter-annualcoefﬁcientofvariation(in%)arealsopresentedabovethebars.Simulationswereperformedusinglocal weatherandmanagementdata.AtKisii,simulationswereperformedonlyforsecond-seasonmaizesownonSept9.ClayandsiltloamsoilswereusedforthesitesinKenya andNebraska,respectively.

2.5.1. Levelofdisaggregationandnumberofavailabledatayears Actualyieldsneedtobedisaggregatedbywaterregime wher-everirrigated andrainfedcropsystemscoexistwithinthesame geographicarea.Likewise,inmultiplecroppingsystemswhere2or morecyclesofthesamecroparegrownonthesameﬁeldeachyear orthesamecropcanbegrowninverydifferentcropsequencesso thatYwdiffersdependingonsequence,itispreferredtohave sep-arateYaestimatesforeachcropcycleandsequence,whichallows estimatingseparateYgvalues.Withfewexceptions,however,Ya dataarereportedonanaggregatedharvested-areabasis,without disaggregatingYabycropcycleorsequence.Hence,meanYgis esti-matedasthedifferencebetweenthelong-termweightedaverages ofYp(orYw)andYa,bothexpressedonaper-harvestedhectare basis(seeSection2.3.1).

ThenumberofyearsofYadatatocalculateaverageYashould bedeterminedonacase-by-casebasis,followingtheprincipleof includingasmanyrecentyearsofYadataaspossible,toaccountfor weathervariabilitybutnotclimatechange,whileavoidingthebias duetoatechnologicaltime-trend(VanIttersumetal.,2013). Like-wise,theyearsofYadatashouldbewithintherangeofyearsfor whichYw(orYp)wassimulated.Asageneralguidelinefor data-richcountriesthatshowasteepyieldtrend(ortrendbreak),we recommendusingtheYareportedforthe5mostrecentyearsfor thecalculationofaverageyield;ifthereisnotrend,theYareported forthemostrecent10yearscanbeused.However,thisapproach cannotbefollowedindata-poorcountrieswherelong-termyield statisticsarenotavailable.Forthesecases,werecommenda mini-mumof5recentyearsofYadata(3–4yearsareacceptableifmore yearsare notavailable), recognizingthatthis maynotbe sufﬁ-cienttoaccountforyear-to-yearvariabilityinYaduetoweather, especiallyinharshrainfedenvironments.

2.5.2. Actualyieldsource,spatialresolution,anddataquality Ideally, Ya should be based on yield statistics available for sub-nationaladministrativeunitssuchasmunicipalities,counties, departments, sub-districts, districts, or provinces. Ultimately, the location and extent of the administrative unit should be

(reasonably)congruentwiththelocationandspatialextentofthe targetedareaforyield gapanalysis.Iftwo ormore administra-tiveunits(orpartsofthem)arelocatedwithinthetargetedarea, a weightedaverageyield canbeestimatedbasedontheir rela-tivearea-basis coverage. Ya can alsobe estimatedfrom values reportedforlargeradministrativeunitssuchasregions,provinces, andstatesbutresultingYaestimatesneedtobeﬂagged(and even-tuallyreplacedbymorespatiallygranularestimates)becauseyield reportedatacoarselevelofspatialresolutionmaynotbe repre-sentativeoftheYaofthetargetedarea,whenthelatterissmaller thantheareareportingYa.

Inmanycases,Yadatacanbeaccesseddirectlythroughnational statistics bureaus websites (Table 1), FAO/IFPRI/SAGRE agro-maps (FAO/IFPRI/SAGRE, 2006; http://kids.fao.org/agromaps/), CountrySTAT (http://www.countrystat.org/), Eurostat (http:// epp.eurostat.ec.europa.eu/portal/page/portal/agriculture/data/ database),orretrievedbyagronomistsfromtheirlocalstatistical bureausorinstitutions.Aviablealternative,whennational statis-ticsatanappropriatelevelofspatialresolutiondonotexistorare unreliable,istoestimateYafromexistingdatacollectedthrough farmsurveysandbylocalagronomistsadministeredbynational agricultural research institutions, universities, CGIAR centers, World Bank (LSMS), private sector, or other on-going projects such as TAPRA survey panel (http://www.tegemeo.org/index. php/component/k2/item/258-tapra-ii-household-panel-survey-coverage). Spatial coverage of the survey should be consistent withthetargetedareaandincludeﬁveyears ofdatatoaccount forweathervariability(again,3–4yearsareacceptableifnomore yearsareavailable).Anothersourceofyielddataisfromon-farm experimentsthat includea treatmentthatfollows local‘farmer practices’overseveralyears(e.g.,Tittonelletal.,2008;Fermont etal.,2009;Wairegietal.,2010)orproducerself-reporteddata (e.g.,Grassini et al.,2014).These sources ofdata canbe useful to determineYa as long as the farmswhere the studies were conductedare representativeofthepopulationof farmswithin theareaofstudy.Ifnoyielddataareavailableatanysub-national levelorthroughsurveyorﬁeldtrialdata,Yacanbebasedonlocal

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knowledge(localagronomists,agriculturalinputorseeddealers, orothersengagedinbusinessesthatdealdirectlywithproducers). TheaimwouldbetoestimateaverageYainthemostrecentpast 3-yearperiod(preferablylonger)withthegoalofreplacingthese estimateswithofﬁcialstatisticswhenthesebecomeavailable.

Determiningthedegreeofuncertaintyrelatedtotheaccuracy ofYadata isanimportant componentofyield gapassessment. Whereasitisnotfeasibletosurveymostfarmswithinaregionor yearinacost-effectiveway,comparisonofYausingseveral inde-pendentdata sources,forthesameregion-year,canbeusedto assesstheYadatauncertainty.Thiscomparisondoesnotdetermine whichdatasourceismoreaccurate,butasubstantialdifference inestimatesofYaamongdatasourcesprovidesinsightaboutthe uncertaintyinYaandYg.Unfortunately,thereareonlyveryfew examplesofveriﬁcationofYaestimatesusingtrulyindependent datasets(Sadrasetal.,2014andreferencescitedtherein).These previousstudieshaveshownthatestimatesofYafromdifferent datasourcesaresimilarormarkedlydifferent,dependinguponthe crop/countryinquestion,withthemagnitudeoftheYamismatch alsovaryingacrossyearsandregionswithinthesamecountry.In otherpublishedstudiesaimingatassessingqualityofgriddedYa data,thecomparisonisnotvalidbecausethedatabasescompared werederivedfromthesameunderpinningYadata,resultingina misleadingassessmentaboutthequalityofYa(e.g.,Iizumietal., 2013).

2.5.3. Actualyieldsourcesandquality-controlforthethreestudy cases

Availability and quality of Ya markedly differed among the threecasestudies(Table1,Figs.1and5).InNebraska,long-term (>30 years) annual Ya data were available through USDA-NASS (www.nass.usda.gov/) for each water regime and county (roughly 2000km2 _or _a _circle _with _radius _of _ca. ₂₅_km). Com-parison of Ya data reported by USDA-NASS against Ya data independentlycollectedthroughtheNebraskaNaturalResources Districts(http://www.nrdnet.org/)indicatedanoveralldifference of0.6Mgha−1_,_which_represented_only_6%_of_average_yield calcu-latedusingbothdatasources,so,thereisconﬁdenceinthereported Yadata. Data availability was similarin Argentina though at a coarserspatialaggregation(roughly4500km2_,_i.e.,_a_circle_around_a locationwithradiusofca.38km)andaveragemismatchbetween independentYadatasourcesrepresented14%oftheyieldmean (thoughrelativelylargedifferences>15%werefoundfor 33%of region-years).Finally,thoughthespatialresolutionoftheYadata inKenya wasacceptable,only a limitednumber ofyears of Ya datawereavailable(Table1)andtimeperiodswerenotconsistent acrosslocations.Alsonotablewasalargediscrepancybetweentwo sources(45%ofYamean),thoughdiscrepancywassmallinabsolute valuesduetoverylowaveragefarmyieldlevels(Fig.5).

2.6. Modelcalibrationandlong-termsimulationsofyield potential

2.6.1. Selectionofcropsimulationmodel

Desirableattributesofcropsimulationmodelswere summa-rizedbyVanIttersumetal.(2013)andarenotaddressedinthis paper.Like VanIttersumetal.(2013),we argueagainstusinga singlegenericmodelgloballybecauseitismoreimportantthatthe modelusedhasbeencalibratedandevaluatedfortheconditions tobesimulated. Thus, modelsmay differ forthe same cropin differentregionsorcountries,andfordifferentcrops,aslongas themodelsusedhavebeencalibratedunderthoseconditionsof thetargetedareas.Preferably,thesamemodelshouldbeusedfor thesamecroptosimulateYwandYpatlocationsthatarethen aggregatedtogiveestimatesatlargerspatialscales(VanBussel etal.,2015).Wealsoarguethatitispreferabletouseone(orfew)

Fig.5. Comparisonbetweentwoindependentsourcesofactualgrainyieldfor maizeinNebraska,USA(bluecircles),Argentina(yellowtriangles),andKenya (redsquares).DatafromNebraskaincludebothrainfedandirrigatedcrops(open andsolidcircles,respectively).YieldsourcesIandIIare,inthisorder,Natural ResourcesDistricts(www.nrdnet.org/)versusNationalAgriculturalStatisticsService oftheUnitedStatedDepartmentofAgriculture(www.nass.usda.gov/)inNebraska (USA),BolsadeCerealesdeBuenosAires(www.bolcereales.com.ar/pas)versus Mini-steriodeAgricultura,GanaderiayPesca(www.minagri.gob.ar/site/index.php)in Argentina,andTegemeoInstitute(www.tegemeo.org/)versusMinistryof Agricul-ture(www.kilimo.go.ke)inKenya.Dataarefrom12countiesandatleast6cropping seasonspercounty(Nebraska,USA),13regionsand9–10croppingseasonsper region(Argentina),and47districtsandoneseason(2011)forKenya.Average mis-matchbetweendatasourcesisshown(absolutevalueandaspercentageofthe meanofthetwoactualyielddatasources)foreachregion.DatafromNebraskaand ArgentinahavebeenadaptedfromSadrasetal.(2014).(Forinterpretationofthe referencestocolourinthisﬁgurelegend,thereaderisreferredtothewebversion ofthisarticle.)

well-calibratedsimulation models toestimateYp and Yw than usingensemblesof numerous,in manycases poorly-calibrated, modelsasproposedbyothers(e.g.,Assengetal.,2013;Rosenzweig etal.,2014;Challinoretal.,2014b).Infact,carefulexamination of this approach (i.e., ensembles) in recent publications shows thatitcanperformverypoorlyatspeciﬁclocations(e.g.,Martre etal.,2014).Indeed,astrongjustiﬁcationforusinganensembleof models,eachdevelopedfordifferentpurposesandfewvalidated fortheenvironmentalconditionsinquestion,hasyettobe artic-ulated.Likewise,mostcrop-modellingpapersdonotreportdata aboutmodelcalibrationwithinthetargetedagro-ecologicalzones understudy,andhowthemodelsperformintermsofreproducing YwandYpmeasuredinwell-managedexperiments.

2.6.2. Dataformodelcalibration

Different crop cultivars are planted across locations, hence, it is necessary to calibrate crop models to account for differ-encesincropphenologyandgrowth-relatedfactors(Jonesetal., 2003).ArobustcalibrationrequiresestimatesofYporYwfrom high-yieldingfieldexperimentsinwhichcropsaregrown with-out nutrient limitations or yield loss from biotic adversities (e.g., insects, disease, weeds), and where all required weather, soil, and management data are available to run the field-year specific simulations(see Appendix Bin Supplemental informa-tion).Varietytrials(ifofproperplotsizeandwithnear-optimal management)areagoodsourceofyieldandphenologydataas well.Ifsuchexperimentsarenotavailableforaspecificcountryor

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regionwithinacountry,analternativeistousecropgrowthdata fromexperimentsinwhichcropsaregrownwithoptimal manage-mentforanalogousregionsintermsofclimateandsoils.Ultimately, thegoalshouldbetoevaluatetheabilityofthemodeltoreproduce majorG×E×Minteractionsacrossa relevantrange ofpotential yields.

Ifrobustcalibrationisnot possibledue tolackof field stud-iesinwhichcropsweregrownwithnear-optimalmanagement, the methodology proposed by Van Wart et al. (2013c) can be used to calibrate the simulated crop phenology. Briefly, the modelcoefficientsrelatedtophenologycanbeadjusteduntilthe simulatedphysiologicalmaturitymatchesthetypicaldateof physi-ologicalmaturityreportedwithinthetargetedarea(seeSection2.3) whilegrowth-relatedcoefficientscanbebasedongenericmodel parameters reportedin the literatureor derived from previous modellingstudies(e.g.,vanHeemst,1988)oradjustedwithinlimits asdetailedinAppendixB.

2.6.3. Simulationoflong-termyieldpotentialanditsvariability Simpliﬁcation of the cropping-system features by averaging weather,soilorcropping-systemdata,typicallyresultsinbiased resultsandasubstantialreductioninagronomicrelevanceofYg estimates(DeWitandVanKeulen,1987).Therefore,thebasicunit foracropsimulationisgivenbyacombinationofcropcycle(within acroppingsystem)×soiltype×waterregime×year.These “sim-ulationunits”canthenbeaggregatedtohigherspatialscalesand longertimeperiodsbyweightingforharvested cropareaunder eachunitaspreviouslydescribedinSections2.3and2.4.

OnceYpandYwaresimulatedforagivensimulationunit, esti-matedvalues canbescreenedforinconsistencies orerrors.The followingquality-controlmeasurescanbeappliedtoscreen simu-latedyields:

(i)yearswithYporYw≤YA,

(ii)Yw_≈YpandYwhasasmallCVs(<5%)inwater-limited envi-ronments,

(iii) YporYworharvestindexestimatesfarbeyondreportedrecord yields,

(iv)yearswithYporYw≈0Mgha−1_,_and

(v)simulatedyieldsforparticularlocations/yearsthatlook ‘suspi-ciously’lowerorhigherthanintherestofthesites/years. OtherapproachestoderiveYporYw,suchasboundary func-tionsrelatingcropyieldtowateravailability,canalsobeusedto checksuspiciousvalues(e.g.,FrenchandSchultz,1984).Ifanyofthe abovecasesaredetected,underpinningweather,soil,management, andmodelparametersshouldbere-checkedforthesuspicious val-uesaswellasthevalueofYaitself.

2.6.4. Modelcalibrationandlong-termsimulationsforthethree casestudies

Thethree examplespresentedin thepaperportraywellthe rangeofconditionsindataavailabilityformodelcalibrationand evaluation. Simulations of maize Yp and Yw in Nebraska and KenyawereperformedusingtheHybrid-Maizemodel(Yangetal., 2004).Modelcalibrationwasperformedusinghigh-qualitydata fromexperimentsandhigh-yieldproducerfieldsintheU.S.Corn Beltwherecropshadbeengrownundernear-optimalconditions (Yang etal.,2004).Model performanceatreproducingyieldsin well-managed crops has been exhaustively evaluated across a widerangeofenvironmentsintheU.S.CornBelt,withmeasured yieldsrangingfrom0.5to18Mgha−1_along_a_wide_range_of_water supplies (Yang et al., 2004; Grassini et al., 2009). In contrast, lack of high-quality experimental data in Kenya didnot allow anindependentevaluationoftheHybrid-Maizemodelandonly phenology-relatedcoefficientsweremodifiedtobetterrepresent

the cropcycle duration reportedby localcollaborators for the targeted areas. In Argentina, CERES-Maize (Jones and Kiniry, 1986),embeddedinDSSATv4.5(Jonesetal.,2003),wasusedto estimatemaizeYpandYw.Modelcalibrationwasperformedwith detailedmeasurementsfromanumberofwell-managedrainfed andirrigatedmaizeexperiments(Monzonetal.,2007,2012).

Availablesoilwatercontentatsowingwithintherootablesoil depthcanhavealargeimpactonYw,especiallyinharshrainfed environments.Ideally,cropsimulationmodelscanbeusedto simu-latethesoilwaterbalanceduringtheentirecroprotation,including thenon-growingseasonandthisapproachwasfollowedfor sim-ulatingthemaize-soybeanrotationinArgentina.However,itwas notpossibletofollowthisapproachinNebraska(USA)andKenya becausetheHybrid-Maizemodeldoesnotsimulatecroprotations. Forthesecases,thesoilwaterbalancewasinitializedoveraperiod oftime beforethesowingdate,beginning aroundphysiological maturityofthepreviouscropintherotation,assumingatypical lowinitialsoilwatercontentatendofthegrowingseasonofthe previouscropof50%ofavailablesoilwater(orasestimatedby expertopinion).

3. Discussion 3.1. Keyprinciples

Robustprotocolstosupportcropmodellingandyield-gap anal-ysisataspeciﬁclocationarepresentedbasedonthelessonslearned fromestablishingtheGlobalYieldGapAtlas(www.yieldgap.org). Thesemethodsweredevelopedtobeﬂexibleenoughtoaccount for a wide range of data availability and quality, while ensur-ingminimumstandardsofdataquality,agronomicrelevance,and transparencyinselectionand documentationofdatasourcesas summarizedinTable3.Applicationofthemethodologywas illus-tratedformaizeproductioninthreecountriesrepresentingawide range of data availability and quality. While the methodology doesnotovercomechallengesduetolackofdata,eitherbecause the required data do not exist or are not publicly available, it providesthemostappropriatealternativesconsistentwitha trans-parentframeworkandrationalethatcanbeusedforallcountries and crops. There are two guiding principles at thecore of the methodology.First,that thesimulationunittoestimateYpand Ywhasrelevantagronomiccontext(combininglocation×water regime×cropcycle×soiltype)and canbeaggregated tolarger spatialscalesthroughan upscalingprotocol basedonweighted cropareawithineachsimulationunit(VanBusseletal.,2015). Sec-ond,thatallunderpinningdatashouldrelyasmuchaspossibleon observeddata,andthesedatashouldbepubliclyavailabletothe extentpossible.Fordatathatareofpoorqualityorcurrentlydo notexistorareunavailable(e.g.,weatherdatainmanycountries inSub-SaharanAfrica),theglobalagriculturalresearchcommunity shouldstrivetoachieveopenpublicaccesstotheseweatherdata becauseoftheimportanceofestimatingyieldgapsandfood pro-ductioncapacitytosupportstrategicevaluationoflocaltoglobal foodsecurityscenarios(e.g.,GlobalOpenDataforAgricultureand Nutritioninitiative;www.godan.info).

3.2. Globaldatabasesandtheirlackoflocalprecision

Giventheproliferationofglobaldatabasesonweather,soil,crop systemsandactualyielddatathatproviderequireddataforcrop modellingatglobalscale,wecautionthatthese‘new’databases are,inmostcases,recycledexistingdataofhighlyvaryingquality andspatialresolution.Forexample,manyrecentdatabasesreport dataonYaatahighdegreeofspatialresolutioningriddedglobal databases(Monfredaetal.,2008;Rayetal.,2012;Iizumietal.,2013;

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Table3

Summaryofdatasourceselectiondependingupondataavailability. Data

availabilityand quality

Weather Croppingsystem Soil Dataformodel

calibration

Actualyield

High Measureddatawithgood quality,>10years

Nationaldatabases Nationalmapslinked withhighresolutionsoil proﬁledatabaseswith functionalsoilproperties

High-qualitysite-year experiments

Mostrecentannual valuesreportedataﬁne spatiallevel

Intermediate Propagated(i.e.,few yearsofmeasureddata usedtocreatelong-term weather)

Expertopinion Globalsoildatabases Defaultparameters retrievedfromthe literatureforsimilar regions

Annualyieldsreportedat coarserspatiallevelsor fromcensus,trials,etc. Low Bestavailablesourceof

griddeddata Globalcrop calendars Localexperts information Defaultparameters retrievedfromthe literatureforother regions

Yieldretrievedfromlocal expertsornational-level average

Youetal.,2014).Yetthisfineresolutionisachievedbyusingdata reportedatmuchcoarserspatialscalesandthuscangiveafalse senseofconfidenceaboutdataquality.Thisisespeciallytruefor manydevelopingcountrieswherereportingofactualyieldsisnot welldevelopedandweatherdataandsoildataareofpoorquality. Moreover,methodsusedtocreatethesedatabasesaretortuous,not verytransparent,andhaveundergonelittleindependentvalidation becauseofthetimeandeffortrequired.Likewise,dataoncropping systemsandagronomicpracticesatafinespatialscalearescarce. Andwhilerecentglobaldatabasescanhelptoidentifythedominant cropsequenceandmanagement(FAOCropCalendar,2010;Sacks etal.,2010;Wahaetal.,2012;HarvestChoice,2013),ingeneral, theyaretoospatiallycoarseforsimulatingYp,Yw,andYgatspecific locationsorinsmallgeographicregions.Hence,themost press-ingbottleneckforlocallyrelevant cropmodellingandyield-gap analysisisnotcomputingpowerorsophisticationofgeo-statistical methodsrunningmanythousandsofsimulationsandmappingthe results,butrathertheavailabilityofhigh-quality,relevant agro-nomicdataonweather,soil,croppingsystems,actualyields,and experimentaldata for model calibration. Indeed, theimproved understandingofdatarequirementsandalternativesforyieldgap analysisatlocaltoglobalscalesasdescribedherecanhelpidentify themostcritical“datagaps”andfocusglobaleffortstofillthem. Ourpaper providesa firststep inthis directionbyestablishing minimumrequirementsandqualitystandardsforeachdatatype (weather,cropsystem,soil,Ya,andmodelcalibration)butfurther researchshouldbedirectedtoquantitativelydeterminetherelative importanceofeachdatatype,relativetotheothers,foraccurateYg determination.

3.3. Publicavailabilityofweatherdata

An uncomfortable truth about weather data is that records takenbygovernmentmeteorologicalagenciesareoftennotmade publiclyavailable,ortheyareonlyavailableforaprice.Table4 sum-marizestheweatherdatasourcesandconﬁdentialityincountries whereyield-gapanalysiswasperformedoris beingundertaken bytheGlobalYieldGapAtlas(www.yieldgap.org).Ofalllocations whereyieldgapassessmentswereperformed(n=365),a respec-tive65%,20%,and15%reliedonobserved,propagated,andgridded weatherdata.Weatherdatacouldnotbemadepubliclyavailable for68%ofthelocationsforwhich observeddatawereavailable (n=237). In suchsituations, a viable alternative is to use syn-theticweatherdatacreatedforanadequatetime intervalusing thepropagationtechnique describedbyVan Wartetal.(2015). Thisoptionhastheadvantageofprovidingweatherdatathatare similar,thoughnotidentical,totheobservedweatherdata,while preservingdataconﬁdentiality.

3.4. Minimumstandardstoguideimprovement

Whereastheprotocoldescribedheresetsminimumstandards fordataselectionandqualityforyieldgapanalysis,thecurrent guidelinescanbefurtherimprovedasmoreandbetterweather, soil,andcroppingsystemdatabecomeavailable.Forexample, sow-ing dateis relativelystable across years in temperate climates ofNebraskaandArgentina,buthighlyvariableinKenyawherea tropicalorsub-tropicalclimategivesamuchwidersowing win-dow(whichcanbeaswideastwomonths)duetolargeyear-to-year

Table4

WeatherdatausedintheGlobalYieldGapAtlas(GYGA),andpublicavailabilityofmeasuredweatherdata.

Country Numberof

simulatedsites

Proportion(%)ofsiteswitheachtypeofweatherdata Proportion(%)ofsites forwhichmeasured weatherdatacanbe madepubliclyavailable Measured Propagated Gridded

Argentina 16 100 0 0 100 Australia 22 100 0 0 100 Brazil 39 100 0 0 0 Sub-SaharanAfricaa ₁₈₃ ₃₀ ₃₉ ₃₁ ₀c Bangladesh 11 100 0 0 100 Europeb ₉₄ ₁₀₀ ₀ ₀ ₂₉ Overall 365 65 20 15 32

a_Includes_Burkina_Faso,_Ghana,_Mali,_Nigeria,_Niger,_Ethiopia,_Kenya,_Uganda,_Tanzania,_and_Zambia. b _Includes_Denmark,_Germany,_Poland,_Spain,_and_The_Netherlands.

c _Some_of_these_datasets_are_available_for_purchase_from_the_national_{meteorological}_organization_and_were_made_available_to_the_Global_Yield_Gap_Atlas,_but_they_cannot