Reprint of “Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia”

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ContentslistsavailableatSciVerseScienceDirect

Field

Crops

Research

j o ur na 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

Reprint

of

“Quantifying

yield

gaps

in

rainfed

cropping

systems:

A

case

study

of

wheat

in

Australia”

夽

Zvi

Hochman

a,∗

_,

_David

_Gobbett

b

_,

_Dean

_Holzworth

c

_,

_Tim

_McClelland

d

_,

_Harm

_van

_Rees

e

_,

Oswald

Marinoni

a

_,

_Javier

_Navarro

_Garcia

a

_,

_Heidi

_Horan

a

a_CSIRO_Ecosystem_{Sciences/Sustainable}_Agriculture_Flagship,_EcoSciences_Precinct,₄₁_Boggo_Road,_Dutton_Park,_QLD_4102,_Australia b_CSIRO_Ecosystem_{Sciences/Sustainable}_Agriculture_Flagship,_PMB_2,_Glen_Osmond,_SA_5064,_Australia

c_CSIRO_Ecosystem_{Sciences/Sustainable}_Agriculture_Flagship,_Toowoomba,_QLD_4350,_Australia d_BCG_PO_Box_85,_Birchip,_VIC_3483,_Australia

e_Cropfacts_P/L,₆₉_Rooney_Rd.,_RSD_Mandurang_South,_Victoria_3551,_Australia

a

r

t

i

c

l

e

i

n

f

o

Keywords: Foodsecurity Yieldgap Wheat Yieldmap Assessment Cropmodel Simulation

a

b

s

t

r

a

c

t

Tofeedagrowingworldpopulationinthecomingdecades,agriculturemuststrivetoreducethegap betweentheyieldsthatarecurrentlyachievedbyfarmers(Ya)andthosepotentiallyattainableinrainfed farmingsystems(Yw).Theﬁrststeptowardsreducingyieldgaps(Yg)istoobtainrealisticestimatesof theirmagnitudeandtheirspatialandtemporalvariability.Inthispaperwedescribeanewyieldgap assessmentframework.Theframeworkusesstatisticalyieldandcroppingareadata,remotelysensed data,croppingsystemsimulationandGISmappingtocalculatewheatyieldgapsatscalesfrom1.1kmcells toregional.Theframeworkincludesadhocon-groundtestingofthecalculatedyieldgaps.This frame-workwasappliedtowheatintheWimmeraregionofVictoria,Australia.EstimatedYgoverthewhole Wimmeraregionvariedannuallyfrom0.63to4.12Mgha−1withanaverageof2.00Mgha−1.Expressedas arelativeyield(Y%)therangewas26.3–77.9%withanaverageof52.7%.Similarlylargespatialvariability

wasdescribedinaWimmerayieldgapmap.Suchmapscanbeusedtoshowwhereeffortstobridgethe yieldgaparelikelytohavethebiggestimpacts.BridgingtheexploitableyieldgapintheWimmeraregion byincreasingaverageY%to80%wouldincreaseaverageannualwheatproductionfrom1.09Mtonnesto

1.65Mtonnes.ModelestimatesofYwandYgwerecomparedwithdatafromcropyieldcontests, exper-imentalvarietytrials,andon-farmwateruseandyields.Thesealternativeapproachesagreedwellwith themodellingresults,indicatingthattheproposedframeworkprovidedarobustandwidelyapplicable methodofdeterminingyieldgaps.Itssuccessfulimplementationrequiresthat:(1)Yaaswellasthearea andgeospatialdistributionofwheatcroppingarewelldeﬁned;(2)thereisacropmodelwithproven performanceinthelocalagro-ecologicalzone;(3)dailyweatherandsoildata(suchasPAWC)required bycropmodelsareavailablethroughoutthearea;and(4)localagronomicbestpracticeiswelldeﬁned.

1. Introduction

Exploitingthegapbetweenyieldscurrentlyachievedonfarms

andthosethatcanbeachievedbyusingthebestadaptedcrop

vari-etiesandbestcropandlandmanagementpracticesisakeypathway

toovercomingtheconsiderablechallengeoffeedingmorethan9

billionpeopleby2050.Accurateestimatesofcurrentexploitable

DOIoforiginalarticle:http://dx.doi.org/10.1016/j.fcr.2012.07.008.

夽 Thisarticleisareprintofapreviouslypublishedarticle.Forcitationpurposes, pleaseusetheoriginalpublicationdetails:FieldCropsResearch136(2012)85–96. ∗ Correspondingauthorat:CSIROEcosystemSciences/SustainableAgriculture Flagship,POBox2583,Brisbane,QLD4001,Australia.Tel.:+61738335733; fax:+61738335505.

E-mailaddress:[email protected](Z.Hochman).

gapsinkey cropssuchaswheatisthereforeessential toassess

futurefoodproductioncapacityandtohelpformulatepoliciesand

researchprioritiestoensurelocaland globalfoodsecurity(van

Ittersum and Rabbinge, 1997; Dobermann and Cassman, 2002; Fischeretal.,2009;vanIttersumetal.,2013).Afulldescription

oftherational,deﬁnitionsandassumptionsbehindyieldgap

anal-ysisandtheneedforayieldgapatlaswasprovidedbyvanIttersum

etal.(2013).

1.1. Yieldgapestimationmethodsfor‘datarich’environments

Datarichenvironmentsmaybecharacterisedasthosethathave

reliablecensusbasedstatisticaldataataregionalscaleonareas

wherespeciﬁccropsaregrownandontheannualproductionof

thesecropsatthesamescale.Suchdataenableustoestimateactual

Open access under CC BY-NC-ND license.

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farmyields(inMgha−1)atthislevelofaggregationandtoupscale

thesetowideragro-ecologicalzonesandtoanationalaveragefor

targetedcrops.Whereactualfarmyields(Ya)reﬂect‘farmers’

natu-ralresourceendowment,theiraccesstotechnology,andtheirskill

andexposuretorealmarketeconomics.’

Atthesmallestunitofdataaggregation,thereisagreatdealof

variationinactualyields(Ya)achievedduetodifferencesinsoils,

localvariationsinclimateandtofarmers’managementof

individ-ualﬁelds.Itisthereforeimportantthatsufﬁcientdataarecollected

fromarepresentativesampleofﬁelds.Consistentcollectionofsuch

dataoverperiodsof15ormoreyearscanfurtherprovideauseful

quantiﬁcationofthevolatilityofsuchyieldsinresponsetoclimate

variability.

Giventhatmuchoftheworld’scroppingareasarerainfed(70%of

wheat;Portmanetal.,2010),water-limitedyield(Yw)isan

impor-tantconcept.Ywcanbedeﬁnedastheyieldofanadaptedcrop

varietyorhybridwhengrownunderrainfedconditionswithout

growthlimitationsfromnutrients,pestsordiseases.Estimating

waterlimitedyield(Yw)atoneortwolocations,nomatterhow

accurately,cannotbeassumedtoberepresentativeofYwforthe

regionorstatisticalsubdivision.Thevariationinsoilsandclimate

insidethisareahasaninﬂuenceonYwsothatanymethodfor

esti-matingitsvaluemustaccountforthisvariation.Ifgeo-referenced

andlocationspeciﬁcestimatesofYwaremadeatsitesthat

ade-quatelyrepresentthespatialheterogeneityoftheareaofinterest

thenYwestimatescanbeexploredspatiallythroughinterpolation

andgeographicalinformationsystems(GIS)techniques.

1.2. Mappinglanduseandcropareas

Forrainfedcropstheyieldgap(Yg)isthedifferencebetween

YwandYa.ThefocusofYgestimatesinageographicalregionison

currentproductionareas(inthispaperregionisusedtodenotea

substate,catchment,orlocalgovernmentareascaleincontrastto

thegeo-politicalorgroupofnation-statesscale;inAustralia,each

ofthesixstateshasnumerouslocalgovernmententitiesdeﬁnedas

statisticalsubdivisionsforcollectionofcensusinformationandas

shiresorcatchmentregionsforlocalgovernancepurposes).

Calcu-lationofbothactualandpotentialyieldsmustbeconstrainedto,

andrepresentativeof,thespeciﬁcpartsoftheregioninwhichthe

cropofinterestiscurrentlybeingproduced.Methodsfor

estimat-ingthisareaarebasedonsatellitedatathatmatchthephenological

proﬁleofthecrop,combinedwithdataprovidedbycensusesof

agriculturalproducers.

1.3. Quantifyingactualyields

Yaismostcommonlyestimatedfromstatisticaldatagathered

atvariouslevelsof spatialdetail (Monfredaetal.,2008).

Satel-liteacquiredremotesensingdata,calibratedagainstobservedﬁeld

dataandaﬁxedharvestindex(Lobelletal.,2010)anddatafrom

monitoringofalargenumberoffarmers’ﬁeldsoveranumberof

seasons(Grassinietal.,2011;vanIttersumetal.,2013;Hochman

etal.,2009a)havealsobeenusedtoestimateYa.

Precisionagricultureandyieldmappingtechnologiescurrently

allowforﬁneresolution ofyield estimatesand theirvariability

withinaﬁeld.However,notenoughsuchdataarecurrently

avail-abletoprovidearobustrepresentationofactualyieldsoveralarger

areasuchas a localstatistical area(SLA) or region.More

com-monly,farmersmeasuretheircropyieldsonawholeﬁeldbasis

wheretheareaofrainfedwheatﬁeldsintheAustraliangrainzone

istypicallyintherangeof 50–300ha.Averageyieldscalculated

fromsuchdataaresuitableforsinglepointcomparisonsagainst

measuredorcalculatedpotentialyields.Datafromsuchﬁeldsare

collectedannuallybytheAustralianBureauof Agricultural and

ResourceEconomicsandSciences(ABARES)throughitsAustralian

AgriculturalandGrazingIndustriesSurvey(AAGIS)whilethemore

comprehensiveAgricultural Censusdataarecollectedeveryﬁve

yearsbytheAustralianBureauofStatistics(ABS,2009).

1.4. Quantifyingwaterlimitedyields

Yieldcontestresults(DuvickandCassman,1999),the95th

per-centileofregionalyield statistics(Lickeret al.,2010),breeders’

trials(Hallet al.,2012), and cropmodels(Grassini etal.,2011;

Hochmanetal.,2009a)haveallbeenusedtoestimateYw.

Simulationusingalocallyvalidatedcroppingsystemmodelcan

beusedtodetermineYwatanygeospatiallocationprovidedthat

aminimaldatasetisavailableincluding:(1).Dailyweatherdata

includingminimumandmaximumtemperatures,rainfall,

evapo-rationandsolarradiation;(2)Soilcharacterisationthatmatchesthe

localsoiltype,especiallywithrespecttoitswaterholding

capac-ity;(3)Estimatesofsoilwaterstatusatthetimeofsowing;(4)A

speciﬁedbestpracticethatcanbeconsistentlyappliedwithregard

totimeofsowing,sowingdensity,varietyanddatesandratesfor

applicationofnitrogenfertilizer.

1.5. Quantifyingyieldgaps

Inthispaperweproposeaframeworkforrealisticallyestimating

YgbyfocusingonacasestudyofrainfedwheatintheWimmera

region(asdeﬁnedbyABS)ofVictoria,Australia.Thisframework

usesvarioussourcesofdata(local statisticaldata,satellitedata,

spatiallydistributedbutsitespeciﬁchistoricalweatherdata,soil

characterisationdata,soilmaps)andcalculationmethods

(simu-lation,GISmapping)toderivethecomponentsoftheyieldgap.

Thesecomponents(Ya,Yw,cropped area,yield maps)are

inte-gratedtoachievespatiallyandtemporallyexplicitestimatesofYg.

TheframeworkincludesadhocgroundtestingofYgandits

com-ponentpartsbasedonﬁeldlevelmonitoringandfarmerrecords.

2. Methods

Theproposedframework(Fig.1)ismadeupofthreelayers,a

‘data’layer,a‘calculation’layeranda‘groundtesting’layer.The

roleofthedatalayeristoprovidedataforinputintotheothertwo

layers.Inthecalculationlayer,dataareusedtocalculatetheyield

gapanditsdistributionintimeandspace.Thegroundtestinglayer

usesdataandcalculationtoprovidealternative,on-farmor

exper-imentalestimatesofYaandYwwhere theycanbeascertained.

Because theyield gap is calculated by thedifference of values

derivedfromseparateestimationmethods,eachwithitsown

inde-pendentuncertainties,itisdifﬁculttoassignanuncertaintyvalue

aroundthecalculatedyieldgap.Topartiallyalleviatethis

prob-lem,welookforindependent,iflesssystematic,on-grounddata

thatcanbeusedtoconﬁrmorrefute thevaluesderived inthe

calculationlayer.Consideredcomparisonoftheoutputsfromthe

calculationlayerandthegroundtestinglayershouldbeusedto

questionthevalidityofthecalculatedoutputsandtopointtheway

toimprovementsinthecalculationprocessandifnecessarytoseek

improvementintheinputdata.

Thedatalayerisdividedaccordingtothefunctionforwhichthe

dataareused.Survey,censusandremotesensingdataare

primar-ilyusedinthecalculationlayertodetermineYa,theareacropped

andtheirspatialdistribution.Weatherand soildataareusedas

inputsintocroppingsystemsimulationtocalculateYwatmultiple

geo-referencedpointsintheregion.On-farmdataareusedinthe

ground-testinglayertoprovidealternative(on-ground)sourcesof

assessmentofYaandYw.

Inthecalculationlayertheareacropped,anaggregatedﬁgure

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Fig.1.AsystematicframeworkforYgassessment.

NDVItomaptheareacroppedtowintercerealsandthecropmapis

combinedwiththesurveyedYadataandwithNDVIdatatoproduce

aYayieldmap.Aparallelcalculationprocessisusedtocombine

thecropmapwiththeinterpolatedsimulatedYwestimatesto

pro-duceaYwyieldmap.ThecalculatedYaandYwvalues,atvarious

aggregationlevelsfrom1.1kmcellstoSLAtoRegional,arethen

comparedandthedifferenceis theyieldgapwhichcanalsobe

expressedatarelativeyieldgap.

Thegroundtestinglayerusesdatafromvarioussources

includ-ingon-farmexperiments,on-farmcropandsoilmonitoringdata,

water-useefﬁciencyfrontierdata,andcropcontestdata.Suchdata

areusedtocalculateYaandYwindependentlyoftheprocessesused

inthecalculationlayer.Thegroundtestingresultsarethen

consid-eredwithregardtotheirconsistencywiththosederivedfromthe

calculationlayer.

2.1. Thedatalayer

InthiscasestudythefocusisonwheatcropsintheWimmera

regioninthestateof Victoria,Australia. In2009,theWimmera

produced922,000Mgofwheatfrom435,000haor4.2%ofthe

Aus-tralianwheatharvestfrom3.1%oftheareasowntowheat(ABS,

2011).

Farmers are surveyed annually by the Australian Bureau of

Agricultural and Resource Economics and Sciences (ABARES)

through its Australian Agricultural and Grazing Industries

Sur-vey(AAGIS).Theannualdataforwheatareaggregated upfrom

individual farms to SLAs to 11 regions in three ago-climatic

zones. The 11 regions in the Australian wheat-sheep zone are

viewedby ABARES asthe smallestunit for which theirannual

survey is designed to produce reliable wheat crop estimates

(ABS, 2009). These dataare available through the Agsurf

web-site (http://abare.gov.au/ame/agsurf/agsurf.asp; last accessed 20

February2012).ThelessfrequentlysampledAgriculturalCensus

dataprovidereliablecropestimatesattheSLAlevel(thereare7

SLAsintheWimmeraregion).Atthetimeofwritingthelatest

agri-culturalcensusresultswerefromthe2005/2006censusandassuch

relatedtothe2005wheatcrop(ABS,2008).

Remotely sensed Normalised difference Vegetation Index

(NDVI) from the MODIS satellite captures the greenness of a

pixel.NDVI datafor2005wereobtainedfromtheMODIS

web-site(http://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php? MODNUMBER=13;lastaccessed20February2012).Theweather

datausedareasubsetofacomprehensivearchiveofAustralian

climatedata(Jeffreyetal.,2001)availablethroughtheSilo

web-site (http://www.longpaddock.qld.gov.au/silo/; last accessed 20

February2012).Amapoftheplantavailablewatercapacity(PAWC)

ofsoilsintheregionwasaccessedfromanationalsoilsdatabase

(Johnstonetal.,2003;McKenzieetal.,2005).Torepresenttherange

ofsoilsintheregionweusedsoilcharacterisationdataof5local

soilsfromanationalsoilsdatabaseofover700APSIMreference

soilsinthewheat-sheepzone (Dalglieshet al.,2009).Thesoils

wereselectedtorepresenttherangeofPAWCvaluesofsoilsin

theWimmera.

Asummaryofthevariousdatasourcesused,thetypeofdata

theycontain,thescaleofdataaggregationandthefrequencyof

updatesisprovidedinTable1.

On-farmdataforgroundtestingwerederivedfromanumberof

sources:

1.Crop contest yield data from the 2009 Longerenong

Crop-ping Challenge was accessed from the International Plant

Research Institute Australia and New Zealand website

(http://anz.ipni.net/articles/ANZ0010-EN; last accessed 20

February2012).

2.Grainyielddatafrom5NationalVarietyTrialsitesinthe

Wim-meraregionin2009wereaccessedthroughtheNVT website

(www.nvtonline.com.au;lastaccessed20February2012).

3.Aliteraturesearchandpersonalcommunicationwithan

experi-encedlocalresearcherwereusedtoﬁndrecordsofhighyielding

wheatcropsfrompublishedreseachstationexperiments.

4.ThirtysubscriberstoYieldProphet(www.yieldprophet.com.au;

lastaccessed20February2012;Hochmanetal.,2009b)provided

dataontheir2007cropyieldsaswellasonsoiltypes,

start-ingsoilwaterandnitratetodepth,cropmanagementvariables

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Table1

VaryingscaleandfrequencyofdatasourcesusedtodetermineYg.

No. Source Typeofdata Scale Frequency

1 ABARESSurvey Croparea Farmtoregion Annual

2 ABARESSurvey Cropproduction Farmtoregion Annual

3 ABSCensus Numberoffarms FarmtoSLA 5yearly

4 ABSCensus Croparea FarmtoSLA 5yearly

5 ABSCensus Cropproduction FarmtoSLA 5yearly

6 MODISNDVI Croparea 1.1kmcells Every16days

7 MODISNDVI Peakcropbiomass 1.1kmcells Every16days

8 YieldProphet® _Wheat_yields_(Ya) _Field _Annual

9 YieldProphet® _Starting_soil_water _Field _Annual

10 YieldProphet® _Actual_farm_management _Field _Daily

11 APSoil Soilcharacterisation Geo-referencedpoint Static

12 ASRIS SoilPAWCmap Sub-SLAdeﬁnition Static

13 Silopatchedpoint Rainfall Geo-referencedpoint Daily 14 Silopatchedpoint Temperature(maxandmin) Geo-referencedpoint Daily 15 Silopatchedpoint Evaporation Geo-referencedpoint Daily 16 Silopatchedpoint Radiation Geo-referencedpoint Daily

sowing,seedingrate,etc.Thesedataareassociatedwithanearby

weatherstationandon-farmrainfalldata.

5.OneYieldProphet subscribersuppliedadditionalwheatyield

datafor theyears 1990–2011fromanumber of ﬁeldsonhis

family’sfarm.

2.2. Thecalculationlayer

2.2.1. MappinglanduseandYa

Alandusemap,showingareasmostlikelytohaveproduced

wintercerealcropsinthe2005season,ataspatialresolutionof

1.1km,wasgeneratedfromABARE–BRS(2010)dataset.Thismap

isbasedonSPREADII(Stewartetal.,2001;Knappetal.,2006),

whichusesremotelysensedNDVIdataandcensusinformationto

spatiallydisaggregatelandusewithinareaconstraintsprovidedby

theagriculturalcensus.TheSPREADIIalgorithmusesaBayesian

MarkovChainMonteCarloapproachtoproduceprobability

sur-facestoapproximateamaximumlikelihoodestimateoflanduse

consistentwiththeareaconstraintsprovidedbythecensus.Amore

detailedexplanationoftheprocedurecanbefoundinBryanetal.

(2009).

Spatially more accurate crop estimates within an SLA can

be obtained by intersecting the reported yields with a map

(ABARE–BRS,2010)thatshowswhereindividualcommodities(in

thiscasewheat)weregrown.Thereportedyieldsofwheatarethen

exclusivelydistributed acrosspixelsthatrepresentwheatcrops

usingatwo-stepprocess.First,thereportedtotalproduction(tons)

ofwheatwithinanSLAisdividedbythetotalreportedarea(ha)

sowntowheat.Thisgivesanaveragevalueforeachwheatpixel

withinanSLA.Afurtheradjustmentoftheyieldvaluesassignedis

thenrequiredtoaccountforthespatialvariationofwheatyields.

WeusedthepeakNDVIvalue(selectedfromsamplestakenevery

16daysduringtheseason)foreachcellasaproxyofthatcell’s

yieldrelativetoothercellsintheSLA.Inasecondstepwetherefore

producedtwosurfaces;onethatrepresentsthepeakNDVIofeach

pixelandasecondlayerthatrepresentstheaverageofthepeak

NDVIvaluesofthefullsetofwheatpixelswithineachSLA.Finally,

theyieldforeachpixelwasadjustedbymultiplyingtheoriginally

assigned(SLAaverage)yieldvaluebytheratioofthepeakNDVIof

eachpixeltotheaveragepeakNDVIforwheatintheSLA(Marinoni

etal.,2012).Usingthisapproach,adetailedYamapwasproduced

fortheWimmeraregionfor2005,thelastyearforwhichSLAlevel

datawasavailable.

2.2.2. CalculatingandmappingYw

ToaccountfortheWimmeraregion’sspatialandtemporal

vari-abilityweused30yearsofweather datafrom56localweather

stationsinandsurroundingtheWimmera.Simulationswereset

upinAPSIM(Keatingetal.,2003)a croppingsystemssimulator

that hasbeenpreviouslyvalidated againstboth researchyields

(Asseng et al., 1998; Hochman etal., 2007; Wanget al., 2003)

andfarmeryields(Carberryetal.,2009;Hochmanetal.,2009a).

Managementsettingswerebasedonazerotill,stubbleretained

continuouswheatcroppingsystemanda‘YieldMaximising’

man-agementstrategythatwasshowntoachieveanaverageWUEclose

totheWUEboundary(andhencegrainyieldsclosetoYw)in334

ﬁeldsintheAustralianwheatzone(Hochmanetal.,2009a).This

managementstrategyspeciﬁesasowingdensityof150plantsper

m2_,_sowing_date_to_occur_as_soon_as_at_least₁₅_mm_of_rain

accumu-latesoveraconsecutive3-dayperiodafterthe24thofApril,and

50kgNha−1 fertilisertobeappliedbetweensowingand

anthe-siswheneversoilnitrateintherootzonefallsbelow50kgNha−1.

BecauseYitpiwasthemostcommonlyusedvarietyintheﬁelds

simulatedinthisstudy,indicatingthatitiswasthepreferred

vari-etyamongleadingfarmersintheregion,weuseditaspartofthe

‘YieldMaximising’strategy.

Intheabsenceofdataonsoilmoistureconditionsatthestart

oftheseason,wechose1981asthestartingyearandsetavailable

soilmoistureatthestartofthesimulationtoamodest10%.We

chose1981asthestartingyearbecauseitwasafavourable

sea-sonfollowedbyaseveredroughtseasonin1982.Weexpectedthis

combinationofseasonstocorrectanyerrorsinthestarting

condi-tionandwediscardedtheﬁrstﬁveyearsofYwoutputstoallow

thesimulationstoselfcorrectforanyresidualerrors.Theusable

simulationsprovidedafactorialcombinationof26years×56

sta-tions×5soilstoproduceamatrixwith7280Ywoutcomes.

WeusedtheGIStechniqueofInverseDistanceWeighting(IDW;

Shepard,1968)tointerpolatethesimulatedYwoutcomesforeach

of the56 stations and5 soilsto createa grid surface peryear

from1986to2011foreachsoil.The5soilspeciﬁcgridswerethen

mosaicedtomatchthemappedsoiltype(DigitalAtlasofAustralian

Soils),andfurtherclippedtotheextentoftheNDVIderivedwinter

cerealscroppingarea(describedinSection2.2above).For2005,Yw

valueswereaggregateduptoSLAandRegional(Wimmera)level

forcomparisonwithaveragefarmeryieldsfromthestatisticaldata.

2.2.3. YieldgapbasedonthedifferencebetweenYaandYw

Foreachyearofthestudy,theyieldgap(Yg)wascalculatedfrom

thedifferencebetweentheaverageyieldsobtainedbyaggregating

thespatially distributedYwyieldstotheregionalscaleandthe

averageregionalyieldasdeterminedintheABARESstatistical

sur-veys(Ya).Table2providesasummaryofthevariousdatasources

usedinestimatingeachcomponentofYg,indicatingthescaleof

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Table2

DatasourcesusedinestimationofcomponentsofYg.

Id. Estimate Functionof(no.inTable1orId.thisTable) Scale Frequency

A Landuse 4,6 1.1km 5yearly

B Areacropped 1,A Region Annual

C

Ya

1,2 Region Annual

D 3,4,5 SLAa ₅_yearly

E 3,4,5,6,7 1.1km 5yearly

F Yw–WUEBoundary 9,13 Field Once

G Yw–Simulated B,11,12,13,14,15,16 1.1km Annual

H Yg–WUEBoundary F,8 Field Once

I

Yg–Simulated

G,C Region Annual

J G,D SLA 5yearly

K G,E 1.1km 5yearly

a_Statistical_Local_Area.

Theyieldgapwasalsoexpressedasarelativeyield(Y%)

calcu-latedbyapplyingEq.(1):

Y%=

100×Ya

Yw (1)

For2005,Ygwasalsocalculatedfromthedifferencebetweenthe

averageyieldsobtainedbyaggregatingthespatiallydistributedYw

yieldsforeachSLAandtheaverageYavaluesfromthe2005/6ABS

censusforeachSLA.Bycomparingeachcellofthe2005Yamap

withthesamecellinthe2005YwmapweproducedaYganda

Y%maptoshowthespatialtrendsinyieldgapsintheWimmera

region.

Asafarmer’syieldsapproachYw,thelawofdiminishingreturns

wouldsuggestthatfurthergainswillbecomemoredifﬁcultand

lesseconomicallyattractivetoachieve.Consequently,averagefarm

yieldscanbeexpected topeakat 75–85%ofYw. We therefore

distinguishbetweentheabsoluteyieldgap(Yg)andthemore

prag-maticexploitableyieldgap.Forrainfedwheatwedeﬁneexploitable

yieldgapasYw×0.80−Ya.

2.3. Thegroundtestinglayer

2.3.1. CropcontestdataforestimatingYw

CropcontestshavefallenoutoffavourinAustraliaoverthepast

twodecadesorso.However,in2009theLongerenongCollegeheld

acontestcalledtheLongerenongCroppingChallenge,inwhich14

teamsmadeupoflocalagronomists,consultants,farmers,college

staffandstudentsattemptedtogrowthemostproﬁtablecropin

thesameﬁeld.1 _Treatments_were_all_applied_by_college_staff_but

variedinfertiliserapplied,variety,sowingdate,etc.Wereviewthe

resultsofthiscontestasanexampleofanon-groundestimateof

YwinasingleseasonandasinglelocationintheWimmeraregion.

2.3.2. Nationalvarietytrials(NVT)andexperimentstationdata

fordeterminingYw

Wheat variety testing is conducted annually at the Grain

ResearchandDevelopment’s(GRDC)NVTtrialnetworkofaround

100trialsitesthroughoutthewheatzone.Currentandrecently

releasedvarietiesarecomparedinreplicatedsmallplottrials

con-ductedeitheronfarmsoronresearchstations.In2009therewere

6suchsitesintheWimmeraregion.Weinvestigatedtheresultsof

thesetrialsasapossiblesourceofongrounddataforconﬁrming

predictedYw.

The Wimmera region hasa signiﬁcant agriculturalresearch

facility.TheGrainsInnovationParkHorshamwasestablishedinthe

1960s.WeinvestigatedthepublishedyielddatafromthisVictorian

1_While_the_contest_aimed_to_reward_the_most_proﬁtable_crop,_the_highest_yielding

cropinthecontestwasnotthemostproﬁtable.Herewefocusontheproduction data.

DepartmentofPrimaryIndustriescentretoascertainevidenceof

highyields.

2.3.3. Farmerdata(Ya)from30individualﬁeldsin2007

Thegrainyielddatafrom30individualcommercialﬁeldsinthe

WimmeraweresuppliedbysubscriberstoYieldProphet.Themean

Yavaluesfromtheseﬁeldsandtheirstandarddeviationswere

com-paredtotheregionalYavaluesintheregionforthe2007crop.

2.3.4. YwbasedontheWUEofYieldProphetﬁelds

DataforcalculatingWUEwereavailableforthe30YieldProphet

ﬁeldsofSection2.3.3.Weexploredtheyieldsthatcouldhavebeen

achievedontheseﬁeldsattheWUEboundaryascalculatedbyusing

SadrasandAngus(2006)WUEfunction:

Yw₌(water use₋60)_×22 (2)

where:wateruseisestimatedbyaddingplantavailablesoilwater

atsowingtoin-croprainfall.Inthisanalysisthepre-sowingplant

availablesoilwaterwasmeasuredgravimetricallyintheﬁeld.This

useofmeasuredsoilwaterdataisapointofdifferencefrom

previ-ousAustralianbroad-scaleWUEstudies(e.g.Stevensetal.,2011).

2.3.5. Afarmer’s16yearYghistory

Inadditiontoapplyingasubjectiveplausibilitytest,basedon

expert knowledgeof theregion,totheYw maps wesought to

groundtestthemapsataspeciﬁclocationbycomparingthe

inter-polatedYwoutcomesagainstanelitefarmer’swheatyieldrecords.

Foreachyearfrom1996to2011wecomparedYafromthefarmer’s

bestyieldingwheatﬁelds,inahighyieldpotentialareabetween

Horshamand Stawell,againsttheinterpolatedYwfor thesame

locationandyears.

3. Results

3.1. Calculationlayerresults

3.1.1. Estimatingandmappingfarmers’yields(Ya)

The Wimmera study region, itsSLAs and the towns in and

arounditareshownwiththelocationoftheWimmeraregion

out-linedonaninsertedmapoftheAustraliancontinent(Fig.2a).The

areacroppedtowintercereals,thelocationofweatherstationsand

amapoftheestimatedsoilPAWCvaluesintheWimmeraregion

areindicatedinFig.2bandc.AgSurfdataforthe20yearsfrom

1990to2009(Table3)showthatboththeareasownperfarmunit

(average=120ha)andtheaverageyieldperhectare(2.21Mgha−1)

variedconsiderably fromyeartoyear withrespectivestandard

deviations(Sd)of38haand0.84Mgha−1.Theseresultsillustrate

theimpactofclimatevariabilityonactualyields.Theimpactof

thedroughtperiodfrom2002to2008masksanyyieldincreases

fromtechnologyimprovementsthatmighthaveotherwisebeen

(6)

Fig.2.MapsoftheWimmeraregionwith(a)StatisticalLocalAreas(SLAs)andtowns,(b)locationofcerealcroppingareasandweatherstations,(c)soilplantavailablewater characteristics,and(d)spatialdistributionoffarmyieldsinthe2005season.

Amoredetailedestimateofthespatialdistributionofwheat

grainyieldsfor2005,whenthelatestavailableAgriculturalCensus

dataprovidedcropestimatesattheSLAlevel,isprovidedinTable4.

MeanSLAyieldsrangedbetween2.10Mgha−1inYarriambiack–

Northand3.02Mgha−1inN.Grampians–StArnaud.Further

disag-gregatingthe2005yieldvaluesbyusingtheremotelysensedNDVI

dataresultedinadetailedspatialmapofYaintheWimmeraregion

(Fig.2d).

Table3

AveragewheatproductionperfarmintheWimmeraregion.

Year Wheatareasown(ha) Wheatproduced(Mg) Yield(Mgha−1)

1990 100 203 2.03 1991 71 177 2.49 1992 85 255 3.00 1993 103 313 3.04 1994 96 126 1.31 1995 109 315 2.89 1996 71 215 3.03 1997 97 167 1.72 1998 80 159 1.99 1999 107 285 2.66 2000 118 364 3.08 2001 116 366 3.16 2002 133 50 0.38 2003 156 433 2.78 2004 126 227 1.80 2005 223 582 2.61 2006 142 83 0.58 2007 172 267 1.55 2008 145 213 1.47 2009 153 406 2.65 Mean 120 260 2.21 Sd 38 128 0.84

Source:ABARESAgsurfwebsite.

Table4

AveragewheatproductionperStatisticalLocalAreaintheWimmeraregionin2005. SLA Area(ha) Wheatproduced

(Mg) Yield (Mgha−1₎ Hindmarsh 75,149 168,967 2.25 Horsham 40,255 117,733 2.92 N.Grampians– StArnaud 18,650 45,043 2.42 N.Grampians–Stawell 11,487 34,693 3.02 WestWimmera 42,646 124,684 2.92 Yarriambiack– North 157,005 329,526 2.10 Yarriambiack–South 149,032 379,041 2.54 Wimmeraregion 494,257 1,199,703 2.43 Source:ABS(2008).

3.1.2. Estimatingandmappingwaterlimitedyieldpotential(Yw)

Statisticalanalysisoftheresultsofsimulationof56 weather

stationsby5soiltypesover26years(Table5)showedsigniﬁcant

effects(p<0.001)oflocation(station),season(year)andsoiltype

(PAWC).Therearealsosigniﬁcantinteractions(p<0.001)between

locationandseason,betweenlocationandsoiltypeandbetween

Table5

Analysisofvarianceofresultsofsimulatedgrainyield;responsetolocation(weather station),season(Year)andsoilPAWC.

Df SumSq MeanSq Fvalue Pr(>F) PAWC 4 1550.7 387.67 321.13 <0.001 Station 55 8422.6 153.14 126.85 <0.001 Year 25 12706.7 508.27 421.03 <0.001 Residuals 7325 8842.8 1.21 Year× Station 1375 6584.9 4.79 7.487 <0.001 Residuals 5954 3808.5 0.64 PAWC×Station 220 224.9 1.02 0.3417 NS Residuals 7130 21324.6 2.99 PAWC×Year 100 782.8 7.83 3.4575 <0.001 Residuals 7280 16482.5 2.26

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Fig.3.AnnualvariationandspatialdistributionofYwintheWimmera,1996–2010. Valuesweresimulatedusing56weatherstationsandcorrespondingPAWCmap valuesandspatiallydistributedonto“wintercereal”cellsofthe2005landusemap (BlackdiamondsymbolsrepresentlocationofNVTsitesin2009map).

soiltypeandseason.ThisanalysisdemonstratesthatYwissensitive

tothesethreevariablesinacomplexwaythatrequiresadequate

representationofthesefactorsintheregion.Amaprepresenting

simulatedpotentialyields,takingintoaccountlocation,soilPAWC

andseasons(Fig.3)illustratesthespatialandtemporalvariationin

YwintheWimmerafortheyears1996–2010.

3.1.3. EstimatingandmappingofYgandY%

NextwecalculatedannualYwforthewholeregionby

aggregat-ingallindividualcellvaluestoproduceaspatiallyweightedYwand

comparedittotheregion’sstatisticalYaforeachyearfrom1996to

2009tocalculateYgandY%(Table6).Annuallyestimatedyieldgaps

rangedfrom0.66Mgha−1in2006to4.12Mgha−1in1992withan

averageYgof2.00Mgha−1 (Std=0.98).Y%rangedfrom26.3%in

2002to77.9%in2009withanaverageY%of52.7%(Std=12.7%).On

averageanexploitableyieldgapwasobservedeveryyearforthe

regionasawhole.

GiventhatYadataforthe2005seasonwereavailableattheﬁner

SLAresolutionwewereabletoestimateYgandY%attheSLAlevel

(Table7).YgwaslargestforN.Grampians–Stawell(4.41Mgha−1)

andleastforYarriambiack–North(0.60Mgha−1).Similarly,Y%was

highest(77.7%)atYarriambiack–Northandlowest(35.5%)atN.

Grampians–Stawell.ThespatialvariabilitythatwefoundforYa

andYwwasechoedinthespatialvariabilityofYgandY%.By

com-biningthedatafromFigs.2(d)and3(2005map),YgandY%can

Table6

AnnualyieldgapestimatesbasedcomparisonofspatiallyaggregatedsimulatedYw andstatisticallydeterminedYa(SourceABARESAgsurfwebsite)fortheWimmera regionofVictoria. YEAR Yw(Mgha−1₎ _Ya_(Mg_ha−1₎ _Yg_(Mg_ha−1₎ _Y_%_(%_of_=Yw) 1990 3.73 2.03 1.70 54.4 1991 5.06 2.49 2.57 49.2 1992 7.12 3.00 4.12 42.1 1993 6.51 3.04 3.47 46.7 1994 2.95 1.31 1.64 44.4 1995 5.30 2.89 2.41 54.5 1996 6.17 3.03 3.14 49.1 1997 4.95 1.72 3.23 34.7 1998 4.25 1.99 2.26 46.9 1999 4.75 2.66 2.09 56.0 2000 4.21 3.08 1.13 73.1 2001 4.45 3.16 1.29 70.9 2002 1.45 0.38 1.07 26.3 2003 5.32 2.78 2.54 52.2 2004 3.54 1.80 1.74 50.8 2005 4.65 2.61 2.04 56.1 2006 1.24 0.58 0.66 46.8 2007 3.04 1.55 1.49 50.9 2008 2.10 1.47 0.63 69.9 2009 3.40 2.65 0.75 77.9 Mean 4.21 2.21 2.00 52.7 Std 1.57 0.84 0.98 12.7

becalculatedforeach1.1kmcellfortheyear2005(Figs.4and5,

respectively)toshowingreaterdetailwherethelargestgapsare

likelytoexist.

3.2. Groundtestinglayerresults

3.2.1. CropcontestsasabasisfordeterminingYw

TheamountofgrowingseasonrainfallinLongerenongforthe

2009‘LongerenongChallenge’wasinthetop 20percentof

his-toricalrecordswithyieldpotentialbeinglimitedbyunusuallyhot

conditionsthatprevailedduringgrainﬁlling.ThecalculatedYwat

thislocationin2009was5.23Mgha−1.The14yieldoutcomesin

thiscompetitionrangedfrom0.95to4.93Mgha−1withameanof

3.67mgha−1.Thewinningyieldof4.93Mgha−1(followedclosely

bya4.82Mgha−1yield)wasclosetoYwandthusconﬁrmsYwfor

thisgridcellin2009.

3.2.2. NationalVarietyTrials(NVT)andexperimentstationdata

fordeterminingYw

Fig. 6 shows the distribution of yields at six NVT sites in

2009.Ifonecouldalwayspickthebestvariety,theaverageyield

acrosstheﬁvesiteswouldbe2.67Mgha−1.Ifbycontrast,variety

choiceiscompletelyrandom,thenyieldsacrossthesiteswould

be2.14Mgha−1.Ifthetop30%ofyieldsrepresentmanagerswith

goodknowledgeofvarietychoicefortheirﬁeldthenyieldsforsuch

farmerswouldbe2.58Mgha−1.Giventhisrangeofoutcomes,it

seemsthattheNVTtrialresultsfor2009wereclosetotheregional

Table7

YieldgapestimatesbasedoncomparisonofspatiallyaggregatedsimulatedYwand statisticallydeterminedYa(SourceABS,2008)forthesevenStatisticalLocalAreas oftheWimmeraregionofVictoriain2005.

SLA Yw (Mgha−1₎ Ya (Mgha−1₎ Yg (Mgha−1₎ Y% (%of=Yw) Horsham 5.82 2.25 3.57 38.7 N.Grampians–StArnaud 5.76 2.92 2.84 50.7 N.Grampians–Stawell 6.83 2.42 4.41 35.5 WestWimmera 6.23 3.02 3.21 48.5 Hindmarsh 3.88 2.92 0.96 75.2 Yarriambiack– North 2.70 2.10 0.60 77.7 Yarriambiack–South 4.74 2.54 2.20 53.6 Wimmeraregion 4.65 2.43 2.22 52.3

(8)

Fig.4.SpatialdistributionofYgintheWimmerainthe2005season.Eachcellvalue wasderivedbysubtractingYafromYwforeach“wintercereal”cell.

Yaaverage(2.65Mgha−1)andwellbelowYw.NVTsiteyields(top

30%)showedahighcorrelation(R2₌_0.86)_with_Yw_of_cells_within

twokilometresofthesites.However,whiletheyyieldedaboveYw

atlowyieldingsites,atsiteswithyieldsabove3Mgha−1Ywyields

wereconsiderablyhigher(datanotshown).Theseobservationsare

consistentwiththemanagementregimes(e.g.midseasonsowing

dates,nofungicides,andsub-optimalfertilisers)thatwere

imple-mentedatthesetrialsinpastyears.Anotherissuemightbethatthe

otherfourNVTsitesover-representedloweryieldingpartsofthe

Wimmeraregion(asindicatedon2009mapinFig.3).

AsearchoftheliteraturefromtheGrainsInnovationPark

Hor-sham(backedupbypersonalcommunicationwithGaryO’Leary)

revealedfewexamplesofyieldsabove5Mgha−1.Thehighestyield

Fig.5.SpatialdistributionofY%intheWimmerainthe2005season.Eachcellvalue

wasderivedbyexpressingYaasapercentofYwforeach“wintercereal”cell.

Fig.6.GrainYielddistributionatsixNationalVarietyTrials(NVT)sitesinthe Wim-merain2009.Eachlinerepresentsasiteandeachpointinalinerepresentsawheat variety.

foundwas5.72Mgha−1 atzerowater contentor6.5Mgha−1 at

12% water content, recorded in 1981 (Cantero-Martinez et al.,

1995).Whilethese resultsmayappear tosuggestcaution with

regards to the simulated Yw values in this study, evidence

fromcomparable researchstationsin theAustraliangrainzone

suggest that on averageWUE is about 15–16kggrainha−1mm

(CornishandMurray,1989;Siddiqueetal.,1990)comparedwith

theWUEboundaryvalueof22kggrainha−1mm,suggestingthat

researchstation yieldsmay beabout 30% below water limited

yields.IndirectsupportforhigherYwcomesfromirrigatedwheat

experimentselsewherein South EasternAustralia (Stapper and

Fischer,1990;Steineret al.,1985).Given thatin someseasons

parts of theWimmera arenot limited by available water such

experimentsprovide evidence insupport ofyields inexcess of

8Mgha−1 for themost favourablecombinations of season and

location.

3.2.3. YieldgapbasedontheWUEofYieldProphetFields

Forthe30ﬁeldsmonitoredforWUEin2007,Table8showedthat

availablewateraveragedat234mm(Sd=52)andgrainyields(Ya)

averaged1.98Mgha−1 (Sd=0.77).TheaverageYw,basedonthe

WUEboundaryformulawas3.5Mgha−1(Sd=1.44).Theaverage

gapbetweenYaandYwyieldswas1.51Mgha−1(Sd=0.79).

Theaverageresultproducedbythison-groundyieldgap

assess-mentwasclosetothecalculatedYgvaluederivedforthewhole

regionin2007fromthecalculationlayermethods(1.49Mgha−1).

AssuchtheWUEfrontiermethodofestimatingYgprovidedstrong

on-groundsupportforthesimulationbasedcalculationmethod,at

leastinaparticularyear.

An average Y% of 57.7% (Sd=16.1%) derived from the WUE

frontiermethodindicatesthatanexploitableyieldgapexistsfor

Wimmerafarmerswho subscribetoYieldProphet eventhough

thesefarmersareregardedaselitefarmers.Theyieldresultsalso

illustratetheconsiderablespatialvariabilityinYaandYwamong

YieldProphetﬁeldsintheregion.Theextenttowhichthesefarms

canbeconsideredtoberepresentativeoftheregionisuncleargiven

thatYaforthesefarmsin2007averagedat1.98Mgha−1compared

withtheregionalstatisticalYaaverageof1.55Mgha−1forthesame

year.Thedifferencebetweentheseﬁeldsandtheregionalaverage

inY%(57.7%comparedwith50.9%)suggeststhatthesefarmersare

moreproﬁcientwhilethesimilarityinabsoluteYgvaluessuggests

thattheyarealsofortunatetobelocatedinbetterthanaverage

(9)

Fig.7.ComparisonofYabasedonrecordsofasinglefarmer’sﬁeldandYwbasedoncropsimulationatthematchinggeo-referencedcelllocationover15years(1996–2011).

3.2.4. Onefarmer’sYghistory

Ya of this farm’s best yielding ﬁelds 1996–2011 (mean

Ya=3.4Mgha−1) were well below the interpolated Yw (mean

Yw=6.3Mgha−1)forthesameyears(Fig.7).Theresultingmean

Ygvalueonthisfarmwas2.4Mgha−1andthemeanY%was54%.

Thelargestyieldgapsonthisfarmoccurredin1997and1998.In

1997theyieldwasseverelyreducedbyahailstormandthe

insur-ancecompany’slossadjusterassesseddamagesat3.5–4Mgha−1

forvariousﬁelds.In1998aseverestemfrostcausedwidespread

damagestocropsoveralargeareaintheWimmeraincludingthis

farm.In2001and2006YawasclosetoYwandin2010an

exper-imentoncanopymanagement(controllingpestsanddiseasesand

Table8

Wateruseefﬁciencybasedyieldgapestimatesof30YieldProphetﬁeldsinthe Wimmeraregionin2007. Field number Available watera_(mm) Ya (Mgha−1₎ Yw (Mgha−1₎ WUEYg (Mgha−1₎ Y%(%) 1 274 2.33 4.37 2.04 53.3 2 273 2.70 4.37 1.67 61.8 3 273 2.40 4.37 1.97 54.9 4 273 2.70 4.37 1.67 61.8 5 309 2.49 5.14 2.65 48.4 6 312 3.70 5.21 1.51 71.0 7 226 2.20 3.33 1.13 66.1 8 281 3.20 4.54 1.34 70.5 9 207 1.48 2.91 1.43 50.9 10 210 1.48 2.96 1.48 50.0 11 269 3.20 4.27 1.07 74.9 12 183 1.20 2.38 1.18 50.4 13 286 2.30 4.63 2.33 49.7 14 215 2.30 3.09 0.79 74.4 15 168 0.90 2.04 1.14 44.1 16 162 1.10 1.91 0.81 57.6 17 191 1.20 2.54 1.34 47.2 18 253 0.90 3.92 3.02 23.0 19 262 2.00 4.11 2.11 48.7 20 184 1.70 2.40 0.70 70.8 21 227 1.70 3.34 1.64 50.9 22 220 1.95 3.20 1.25 60.9 23 248 2.60 3.80 1.20 68.4 24 199 0.65 2.72 2.07 23.9 25 164 1.85 1.96 0.11 94.4 26 162 1.38 1.90 0.52 72.6 27 183 2.10 2.38 0.28 88.2 28 330 1.93 5.61 3.68 34.4 29 162 1.00 1.92 0.92 52.1 30 310 2.90 5.16 2.26 56.2 Mean 234 1.98 3.50 1.51 57.7 Sd 52 0.77 1.14 0.79 16.1

a_Available_water_includes_available_water_at_sowing_plus_in-crop_rainfall.

experimenting withtimingand amountsof Nfertiliser)yielded

7.7Mgha−1(NickPoole,unpublisheddata)providingfurther

sup-portforthesimulatedYwvalues.Afterexcluding1997and1998

datafromtheanalysis,theaverageYgvaluewas2.4Mgha−1and

Y%was63%.Theseresultssuggestthatwhilethisfarmis

achiev-ingaboveaverageyields,notwithstandingitsfavourablelocation,

thereisstillanexploitableyieldgap,especiallyinmorefavourable

seasons.Simulationofyieldsbasedonthefarmer’sinputs

includ-ing nitrogen fertiliser, sowing dates, seeding rates and variety

choice resulted in a mean yield of 3.3Mgha−1 compared with

theobservedmeanof3.4Mgha−1.Thesimulatedyieldsexceeded

observedyieldsonlyin1997and1998(datanotshown).Given

thatthefarmeralwaysappliedhighseedingratesandtimely

sow-ingdates,itislikelythatthemainfactorcontributingtothegap

between Ya and Yw on this farm wasthe amountof nitrogen

applied,especiallyinyearswithYwgreaterthan4Mgha−1.

ThecomparisoninFig.7illustratesthatthehighYwvalues

cal-culatedinthisstudyarerealistic.HoweveritalsoshowsthatAPSIM

(incommonwithothermodelsofitskind)doesnotaccountforrare

andextremeeventssuchasseverefrostsandhailstormsandmay

consequentiallybeoverlyoptimisticaboutYwinsomeseasonsand

somelocations(2outof16seasonsatthislocation).Theriskthat

sucheventsposecertainlyaccountsformanyfarmers’riskaverse

tendencies.

Whileacknowledgingthatcautionisrequiredwhen

interpre-tingtheevaluationofdataforoneof7991geospatialgridcells

ona map,thevalueofsuchgroundtestingisbothindicativeof

thedifﬁcultyof‘validating’yieldgapsandillustrativeofthe

desir-abilityoftriangulationofmethodologiestoprovideacompelling

quantitativecaseregardingthelikelysizeoftheyieldgap.

4. Discussion

4.1. Reﬂectingonmethods

The focus of yield gapestimation is toestablish thesize of

thegapthat iscausedbysuboptimalmanagementofpestsand

diseases,nutrientsupply,timeofsowing,cropdensity,and

vari-etychoice.Thiscasestudyhasconsistentlyillustratedthat,ata

regionalscale,Ya,YwandYparesubjecttolargespatial(Figs.2–6

andTables 4,5,7and 8)and temporal(Figs.3,4,5 and7,and

Tables3,5,6and8)variabilityofamagnitudethat,ifnotproperly

accountedfor,couldleadtolargeerrorsinestimatingtheyieldgap

duetomanagement.Theimplicationofthisisthat,attheregional

level,Ygmustbedeterminedatasufﬁcientnumberoflocations

(10)

andclimaterelated)andoveranumberofyearsthatadequately

representclimaticseasonalvariability.Theproposedframeworkof

Fig.1providesamethodthatiswellequippedtorepresentthis

variability.

Despitethiscasestudyhavingtheluxuryofaccesstoarelatively

richsetofdata,wemustremainmindfulthatthedatawerenot

collectedforthepurposeofdeterminingyieldgapstheirsuitability

forthispurposeisuneven.Table1illustratesthedifferentscales

andfrequencyofdataobservationsthatwereusedinthisstudy.In

estimatingthevariouscomponentsoftheyieldgap(Table2)there

isaneedtointegratedataofdifferentscaleandfrequency.Theneed

tousemixedsourcesinthiswayreducestheaccuracywithwhich

theyieldgapcanbedetermined.

AllocatingYavaluestothe1.1kmlandusecellsisacaseinpoint.

Landuseisallocatedprobabilisticallytoeachcell.NDVIvalueswere

usedtoallocateayieldvaluetothewholeofeachcellwitha

des-ignatedlanduseof“wintercereals”.Thisprocessdoesnotaccount

forthefactthatsomecellscovermorethanoneﬁeldandmay

con-tainamixofcroporlandusetypesorthatpartofthecellmay

beinfallow.Consequently,someerrorinestimatingyieldsof

indi-vidualcellsisinevitable.However,theextentofthisproblemis

reducedinsituationssuchastheAustraliancroppingzonesince

wheatcropstendtodominatethelandscape.Furthermore,since

thecalculationmethodincorporatestheYavalueoverthewhole

SLA,theerrorsinindividualcellsmustaverageoutwithineachSLA.

Higherresolutionlandusemappingwouldreducethiserror.

ThereareanumberofsourcesoferrorindeterminingYa.These

includetheerrorintheyieldandlandareadatacollectedbythe

ABSandABARES,theallocationoflandusetocells,theassumption

ofuniformlandusewithincells,andtheuseofNDVIasanindicator

ofrelativegrainyield.EstimatinguncertaintyinYwisalsosubject

tosourcesoferrorincludinginthequalityofweatherandsoildata,

modelerrorsandmodelparametererrors.Deﬁninguncertaintyin

estimatingyieldgapsislikely tobea majorchallengerequiring

furtherresearch.

ThemismatchindataforYaandYwmeansthatYgcanbest

bedeterminedbyestimatingeach separatelyatmany sitesand

overmanyyearstodetermineifarobustestimateofthedifference

betweenthetwovaluesemerges.InAustralia,onfarmexperiments,

cropcontestsandnationalvarietytestingarenotnumerousenough

toprovideareliableestimateofeitherYaorYwataregionalscale

intheirownright.Howevertheycanprovidevaluable datafor

groundtestingYgandassuchtheymakeavaluablecontributionto

theoverallframework.Considerationshouldbegivento

establish-ingstrategicallylocatedreferencesitestoprovideamorereliable

groundtestingdatasetforsimulatedYwvalues.Agreed

manage-mentandmeasurementprotocolsappliedtodesignatedfarmers’

ﬁeldswouldensureauniformbenchmarkYaisavailablefor

com-parisonwithsimulatedyieldsattheselocations.

4.2. ReﬂectingontheyieldgapintheWimmeraregion

Thefullspatialandtemporalsimulationanalysisofthewhole

Wimmera region over 26 years (Table 6) resulted in annually

estimatedyieldgapsof0.66–4.12Mgha−1withanaverageYgof

2.00Mgha−1.Ongroundtestingofthisestimatethroughayield

contest,WUEboundaryanalysisof30farmsandafarmer’slong

termwheatgrainyieldrecordproducedresultsthatwere

consis-tentwiththisrangeofvalues.Weproposethatthereisacompelling

casefortheframeworkofFig.1andtheresultantassessmentofthe

magnitudeoftheyieldgapintheWimmeraregion.

Giventhata relativeyieldof80% istheexploitableyield for

rainfedcropsinavariableclimate,thattheareaoftheWimmera

croppedtowheatin2005was494,257haandthattheaverageYa

between1990and2009was2.21Mgha−1,wecancalculatethat

exploitingtheyieldgapbyincreasingrelativeyieldsfrom53%to

80%willincreasewheatproducedintheWimmeraregionfroman

averageof1,092,308tonnesto1,648,767tonnesperannum.

Themoredetailed spatialanalysisoftheyieldgapindicated

whichSLAswithintheregionarealreadyclosetoachievingthe

exploitableyield(Yarriambiack–NorthandHindmarsh)andwhich

SLAs(e.g.N.Grampians–StawellandHorsham)havethe

great-estpotentialforyieldimprovements.Theresultsindicatethatthe

yieldgapiswiderwhereYwishigherandnarrowerwhereYwis

lower.Thispatternislikelytoreﬂectaneedforfarmersinthemore

marginalareastoinvestthenecessaryinputstoensurethatthey

don’tmisstheopportunitytomaximiseproductioninagood

sea-son,whilefarmersinthehigherYwareasareproﬁtableinmost

yearsevenatlowerthanoptimalinputlevelsandaretherefore

moreriskaverseduetotheirconcernwithdownsideriskincaseof

extremeeventssuchasfrostofhaildamagetotheircrops.

Investi-gationofthedifferenceinmanagementpractices(timeofsowing,

fertilitylevels,weedanddiseasecontrol,andothers)betweenﬁelds

inthecontrastingSLAsislikelytorevealwhichmanagementfactors

shouldbemosteffectivelytargetedtoclosetheyieldgap.

ThemoredetailedmapsofFigs.4and5showedthatimportant

differencesoccurwithinsomeSLAs.Informationprovidedatsuch

arelativelyﬁnescaleislikelytobehighlyvaluedbyagronomic

advisersworkingdirectlywithfarmersasitcanprovidea

bench-markagainstwhichfarmerscangaugetheperformanceoftheir

ﬁeldsonanannualbasis.

5. Conclusions

Theyieldgapassessment frameworkproposedin thispaper

anddemonstratedintheWimmeracasestudyshouldbeapplicable

toasigniﬁcantproportionoftheworld’srainfedcropproduction

regions.Therangeofitssuitabilityislimitedtothemoredeveloped

countrieswherequalityinputdataareavailableataspatial

reso-lutionthatcancapturelocalspatialvariability.Locallymeasured

longtermclimatedata,soilcharacterisationdataandmapsanda

reliablerecordofstatisticalproductiondataarerequired.

Speciﬁ-callytheframeworkrequiresthatanumberofconditionscanbe

satisﬁed:(1)Yaaswellastheareaandgeospatialdistributionof

wheatcroppingmustbewelldeﬁned;(2)thereisgoodcoverage

throughouttheareaofdailyweatherdataandofsoilproperties

data(suchasPAWC)requiredbycropmodels;(3)localagronomic

bestpracticeiswelldeﬁned;and(4)thereisacropmodelwith

provenperformanceinthelocalagro-ecologicalzone.

Alternativemethodsmust bedevelopedfor countrieswhere

suchdataarestillscarce.Anotherlimitationofthismethodisthat

itassumesannualwinter(orsummer)cropswithacropping

inten-sityofonecropperyear.Inregionswherebothsummerandwinter

cropsaregrownandwherelongfallowsareroutinelypracticed,this

methodwouldneedtobemodiﬁed.

We anticipate that future improvements in the accuracy of

yield gap assessment, and of the level of uncertainty around

these estimates, would require improvements in input data

quality, improved cropping systems models, improvement in

remote sensing technologyand thesetting upof instrumented

geo-referencedvalidation sitesfor a monitoringand evaluation

programrequiredtoinformacontinuousimprovementcyclefor

yield gap assessment. More comprehensive survey data, more

weatherstationsmeasuringmoreweatherparameterssuchasdaily

solarradiation,bettersoilcharacterisationdataandsoil

charac-terisationmaps,improvementsinremotesensingtechnologyand

itsinterpretationwouldeachprovidemoreaccurateinputsinto

estimatesofYaandYw.Parallelimprovementsincropgrowth

mod-elsandtheirembodiedcropandsoilsciencewouldimprovetheir

predictivepower.Establishingstrategicallyplacedgroundtesting

(11)

forvalidationofYavaluespredictedfromremotelysenseddata

isnecessaryformonitoring,evaluationandimprovementoftheYg

assessmentframework.

IntheWimmeracasestudyweestimatedthatfarmersinthis

regioncan increase theaverageannual wheat produced inthe

regionfrom1.09Mtonnesto1.65Mtonnes.Thisscaleofincrease

ingrainproduction,inaregionthatrepresentsrainfedcropping

inavariableclimate,supportsclaimsthatbridgingtheexploitable

yieldgapisanimportantpathwaytofutureglobalfoodsecurity.

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