Input setting page of the HM2016 model with new features for estimating soil water at sowing and soil water balance as influenced by surface runoff and soil evaporation, which in turn are
Trang 1j o ur na l h o me pa 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
Haishun Yanga,∗, Patricio Grassinia, Kenneth G Cassmana, Robert M Aikenb,
Patrick I Coynec
a Department of Agronomy and Horticulture, University of Nebraska – Lincoln, P.O Box 830915, Lincoln, NE 68583-0915, USA
b Kansas State University Northwest Research-Extension Center, Colby, KS, USA
c Kansas State University Agricultural Research Center, Hays, KS, USA
a r t i c l e i n f o
Article history:
Received 5 August 2016
Received in revised form 26 January 2017
Accepted 27 January 2017
Keywords:
Maize
Crop model
Water-limited yield
Water deficit
Drought
Simulation
a b s t r a c t
ThispaperreportsrevisionsinformulationandnewfeaturesoftheHybrid-Maizemodel(releasedas HM2016),tobettersimulateyieldsinharshrainfedenvironments.Revisionsincludeupdated subrou-tinesforrootgrowthanddistributionwithinthesoilprofile,greatersensitivityofcanopyexpansionand senescencetowaterdeficits,anexpandedkernelsettingperiod,andsoilevaporationasinfluencedby surfacecoverwithcropresidues.Theupdatedmodelalsoincludesroutinesforsimulatingsurfacerunoff andestimatingsoilwatercontentatsowingbasedonsimulationofsoilwaterbalanceduringthe pre-cedingfallowperiod.Revisionsofmodelfunctionswerebasedonrecentadvancesinunderstandingand quantificationofmaizeresponsetoenvironmentalfactorsandmanagementpractices,aswellas char-acteristicsofnewmaizehybrids.Morerobustsimulationofmaizeyieldwasobtainedwiththeupdated modelunderrainfedconditions,especiallyinyearsandlocationswithseveredroughtoronsoilswith limitedwaterholdingcapacity.Capabilitytoquantifysoilwatercontentatsowingandtoperformbatch simulationsmakesHM2016moreusefulforpre-seasonyieldprojectionsinyearswithbelow-normal soilwaterrechargeandforin-seasonyieldforecastingacrossawiderangeofenvironments.Revisions
toroutinesgoverningrootdistributionandkernelsettingmakeHM2016amorepowerfultoolfor eval-uatinghybrid-specifictraitsandcropmanagementpracticesforabilitytomitigateyieldlossfromwater deficitsandforidentifyingmanagementoptionsforindividualproductionfields
©2017TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC
BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/)
1 Introduction
Cropsimulationmodelshavebeenwidelyusedinresearch,
edu-cation,extension,andtoinformpolicymaking(Boumanetal.,1996;
SinclairandSeligman,1996;Booteetal.,2010).Whileperformance
ofcropmodelsisgenerallymorerobustundernon-waterstress
conditionswithgoodmanagementofnutrientsandbioticstresses,
modelperformanceforcropsthatexperiencewaterdeficits(e.g.,in
harshrainfedsystemswithlowandhighlyvariablerainfallorsoils
withlimitedwaterholdingcapacity)hasbeenlesssatisfactory(Ko
etal.,2006;McMasteretal.,2011;Mastrorillietal.,2003).Poor
modelperformancehasbeenattributedtorelativelypoor
under-Abbreviations: LAI, leaf area index; WSI, water stress index; DS, development
stage; RGR, root growth rate (for depth); ET, evapotranspiration; ET0,
grass-referenced evapotranspiration.
∗ Corresponding author.
E-mail address: hyang2@unl.edu (H Yang).
standingandquantificationofseveralkeyphysiologicalprocesses thatgoverncropresponsestolimitedwatersupply(Sinclairand Seligman,1996;Rothetal.,2013)andphenotypicdifferencesin newcultivars,comparedtoolderones,that arenotyetusedin modeldevelopmentandcalibration(Booteetal.,1996).Formaize (ZeamaysL.)simulationmodels,severalprocessesrelatedtocrop growthandyieldformationunderwaterdeficitconditionshave beensuggestedforimprovingsomemodels,includingcroproot distributionandwateruptakefromsoil(Hammeretal.,2009),leaf expansionand senescence(BenNounaetal.,2000;Cakir,2004; Yangetal.,2009),andkernelsetting(Andradeetal.,1999,2002; Lizasoetal.,2007).Inaddition,theeffectsofcropresiduescovering thesoilsurfaceinconservationtillagesystemsonsoilevaporation andsurfacerunoffalsoneedtobeaccommodatedtoimprovemodel simulationofsoilwaterbalancethroughoutthegrowingseason(Bu
etal.,2013)
The Hybrid-Maize model (Yang et al., 2004, 2006; http://hybridmaize.unl.edu/) is a computer simulation model for maize under non-limiting (fully-irrigated) or water-limited
http://dx.doi.org/10.1016/j.fcr.2017.01.019
0378-4290/© 2017 The Author(s) Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/
Trang 2data Specifically, it allows users to: (a) assess yield potential
anditsvariabilityatagivenlocationbasedonhistoricalweather
records, (b) evaluate changes in yield potentialusing different
combinationsofsowingdate,hybridmaturityandplantdensity,
(c)identifyoptimaltimingandamountofirrigationapplications
forhighestyieldandirrigationwateruseefficiency,and(d)make
in-seasonyield forecastsbased onreal-time weather uptothe
current date and a probability distributionof final yield based
onhistoricalweather recordsfortheremainderof thegrowing
season.TheHybrid-Maizemodeldoesnotaccountforyieldlosses
duetosuboptimalnutrientmanagementorfromweeds,insects
andpests,diseases,lodging,andotherstresses
The Hybrid-Maize model combines the strength of two
maizesimulationapproachesrepresentedbyWageningenmodels,
includingWOFOST(VanDiepenetal.,1989)andINTERCOM(Kropff
and van Laar, 1993; Lindquist, 2001), and by the CERES-Maize
model(JonesandKiniry,1986;Kiniryetal.,1997).Theprevious
versionsoftheHybrid-Maizeweredevelopedin2004(Yangetal.,
2004)and2006(Yangetal.,2006).Sincethen,researchhasledto
improvedunderstandingandquantificationofcropgrowth
pro-cessesandresponsestowater deficit,andmaize breedershave
continuedtoimprovedroughttoleranceandothertraitsofmaize
hybrids.Theseadvanceshavenotyetbeenincorporatedintothe
Hybrid-Maizemodeltoimproveitsrobustnessandapplicability
acrossdiverseenvironmentalandmanagementconditions
EarlierversionsofHybrid-Maizehavebeenusedtoassessmaize
yieldpotentialandyieldgaps(VanWartetal.,2013;Farmahaetal.,
2016;van Ittersumet al., 2016), evaluatemanagement options
(Chenetal.,2011;Grassinietal.,2011a;Wittetal.,2006;Meng
etal.,2013),theimpactofclimatechange(Cassmanetal.,2010;
Chenet al.,2013; Lobellet al.,2009; Menget al.,2014), water
productivity(Grassini etal.,2009,2011b),yieldand production
forecasting (Sibleyet al., 2014; Morellet al., 2016), and
nutri-entmanagement(Mengetal.,2012;Setiyonoetal.,2011)across
diverse maize systems and mostly favorable production
envi-ronmentsworldwide.Feedbackaboutperformanceundersevere
waterdeficit,however,indicatedroomformodelimprovement
Likewise,evolutionofcomputeroperatingsystems,softwareand
hardwarecontinuetoprovideopportunitiestoimprove
function-alityofapplicationsoftwarelikeHybrid-Maize.Asdevelopersof
theoriginalHybrid-Maizemodel,wealsoreceivedfeedbackfrom
usersaboutopportunitiesforaddingnewmodelfeaturesand
appli-cations,allofwhichprovidedmotivationforrevisionofthemodel
Specificobjectivesofthispaperareto:(1)documentrevisionsto
theHybrid-MaizemodelasnowincludedinHM2016,ascompared
tothe2006version(HM2006),withregardtorootdistribution,
canopyexpansionandsenescenceinresponsetocropwaterdeficit,
kernelsetting,surfacerunoff,soilevaporationandcrop
transpira-tion,estimationofsoilwatercontentatsowingbasedonsimulation
ofwaterbalanceduringthefallowperiod,andanewbatchrun
func-tion,and(2)evaluatetheabilityoftherevisedmodeltoreproduce
awiderangeofmeasuredmaizeyieldsfromwell-managedfield
studiesunderrainfedandirrigatedconditions.Descriptionofthe
modelandadetaileduser’sguidedescribingallmodelfunctions
andunderpinningequationscanbefoundatwww.hybridmaize
unl.edu
2 Revisions of model functions
2.1 Rootgrowthandsoilprofiledistribution
InHM2006,rootlengthdistributionbysoildepthlargely
fol-lowed the CERES-Maize approach (Jones and Kiniry, 1986) In
essence,rootingdepthprogressesfollowinggrowingdegreedays
Fig 1.Schematic representation of root length distribution in HM2016 and HM2006.
(GDD) accumulation and reaches the user specified maximum depthatdevelopmentstage(DS)1.15.Therootinglength distri-butionisV-shapedwiththetipatthemaximumrootingdepth (Fig.1).However,somestudieshavereportedthatrootsofnew maizehybridscanreach150cmormoreinsoilswithoutconstraints
torootgrowth(Dardanellietal.,1997;DjamanandIrmak,2012; Tolketal.,2016),andtheeffectivelateralrootlengthdistributionis morecylindricalintheupperrootingzone(0–30cm)followedby
aconicalshapeatlowerdepths(Hammeretal.,2009)(Fig.1).This suggeststhat,giventhesamesoildepth,thesoilvolumefromwhich themaizerootsystemacquireswater(andnutrients)isgreaterthan simulatedinHM2006.IntherevisedroutineofHM2016,maximum rootingdepthstilloccursatDS1.15(typically5–7daysafter silk-ing),buttheincreaseofrootingdepth(Depthroot)fromemergence
toDS=1.15issimulatedasafunctionofgrowing-degreedays(GDD,
Tbase=10◦C)asfollows:
ifDepthroot<Depthmax,thenDepthroot=sumGDD10∗RGR else,Depthroot= Depthmax
in which Depthmax is theuser-specified maximum soil rooting depth,sumGDD10isthesumofgrowingdegreedaysfrom ger-minationtoaparticulardate,andRGRistherootgrowthrate(cm perGDD).RGRiscalculatedaspotentialhybridrootingdepth(one
ofthehybrid-specificparametersthatcanbemodifiedbytheuser anddifferentfromDepthmax)dividedbysumGDD10toDS1.15
Ingeneral,rootgrowthofmostcropsdecreasessubstantiallyor ceasesatonsetofrapiddrymatteraccumulationinreproductive structures(BorgandGrimes,1986).Althoughtherearefewdataon genotypicdifferencesinpotentialrootingdepthofmodernhybrids,
weexpectmostcommercialhybridscanextractwaterfrom1.5m depthwhichisthedefaultsettingforthehybrid-specificpotential rootingdepthinHM2016.Wedonotrecommendthatusersmodify thisdefaultvalueunlesstheyhavestrongevidencethatthehybrid theysimulatehasadeeperorshallowerpotentialrootingdepth
Incontrast,Depthmaxrepresentsthedepthofsoilwithout phys-icalorchemicalrestrictionstorootgrowth.Usersshouldreduce thedefaultvalueforsimulationsonsoilswithrestrictionstoroot growthatashallowerdepthduetohardpans,bedrock,caliche, sandlens,soiltoxicity,salinity,oracidity.Forexample,ifthereisa hardpanat75cmdepththatrootsdonotpenetrate,thenDepthmax shouldbesetat75cm
Trang 3distributionisassumedtoabeVshapedasdescribedasJonesand
Kiniry(1986):
WUweightabsolute=exp[−VDC∗Depthlayer/Depthroot]
WUweightrelative=WUweightabsolute/
WUweightabsolute whereWUweightabsoluteandWUweightrelativearetheabsoluteand
relativewateruptakeweightofthelayer,respectively,Depthlayer
isthedepthofthelayer(toitslowerend),Depthrootisthecurrent
rootingdepth,andVDCistheverticaldistributioncoefficientthat
determinestheshapeoftheexponentialfunction.Thegreaterthe
VDC,thegreatertheWUweightforupperlayers.Thedefaultvalue
ofVDCissetat3
Asrootsgrowdeeper(>30cm),rootsin theuppersoillayer
willlikely cross into spaceoccupied byneighboring roots, and
asaresult,theeffectiveextractionzoneforindividualplantswill
becomeacylindricalontopandaVshapeunderneath,similarto
thesemicircularrootprofilesevaluatedbyHammeretal.(2009)
FieldobservationsbyDwyeretal.(1988)alsosupportthis
proposi-tion.Itisassumedthatthissituationoccurswhenrootsaredeeper
than30cmandtherelativewateruptakeweightofthetopthree
layersbecomesequal:
WUweight1
absolute=WUweight2
absolute=WUweight3
absolute
WUweightrelative=WUweightabsolute/
WUweightabsolute
Inwhichsuperscript1,2and3denotelayers1,2and3withadepth
of10cmforeachlayer
2.2 Impactofwaterdeficitoncanopyexpansionandsenescence
InHybrid-Maize,dailycropwaterstressindex(WSI)isdefined
asWSI=(1–Tranpactual/Transpmax),whereTranpactualistheactual
dailycroptranspirationrate,andTranspmaxisthemaximum
tran-spirationifthecropiswellwatered.InHM2006,cropwaterdeficit
affectscanopyexpansionbyreducingphotosynthesisand,hence,
netassimilationtosustainleafareaexpansion.Keatingetal.(2003)
suggestagreaterreductionincanopyexpansionrateduetothe
direct(i.e.,otherthanmediatedthroughcarbonavailability)impact
of water deficit on leaf area expansionthan currently used in
HM2006.FollowingKeatingetal.(2003),dailyleafareaexpansion
(PLAG)decreaseslinearlyuntilWSI=0.5whenleafareaexpansion
ceases
Canopyexpansionstopsatsilkingandsenescencebegins
there-after,althoughsomeleafareasenescencecanoccurearlierdueto
ageingandlightcompetition(JonesandKiniry,1986;Lizasoetal.,
2003).Inadditiontotheleafsenescencecausedbyleafaging,
shad-ing,andheatstress,whichwerealreadyaccountedforbyHM2006
(Yang etal.,2006),HM2016includesanadditional routinethat
acceleratescanopysenescenceduetowaterdeficit: atWSI of1
(i.e.,fullstress),LAIwilldecreasebyafixedfractionofcurrentLAI
(default=3%perday,butthisvaluecanbemodifiedbyusers)and
alinearinterpolationisusedtoestimatethefractionofsenesced
leafareaforWSIrangingfrom1to0(Saseendranetal.,2008)
2.3 Kernelsetting
Kernelsettingdeterminesthesizeofthesinkformaizegrain
fill-ing(Kiniryetal.,1992;Yangetal.,2004).Howmuchofthis‘sink’is
realizedwilldependondailynetphotosynthesisduringgrain
fill-ing,contributionofcarbohydratereservestokernelswhendailydry
matterproductionandcarbonremobilizationfromstemdoesnot
meetthedemandforgrainfilling,anddurationofthegrainfilling
period.HM2006usedthetotaldrymatterproducedfromsilking
tothestartofeffectivegrainfillingforestimationofkernelsetting (Yangetal.,2004).Morerecentstudiessuggestawiderwindowof timeforkernelsettingdetermination(Oteguietal.,1995;Andrade
etal.,1999;OteguiandAndrade,2000).Therefore,inHM2016,a curvilinearfunctionwasusedtosetthenumberofviablegrainsper plant(GPP)basedontheaverageplantgrowthrate(PSKER)during
acriticalkernelsettingwindowof340GDD8(basetemperatureof
8◦C)centeredonsilkingdate:
PSKER = sumP/(1+GRRG)∗1000/IDURP∗3.4/5
GPP= G2−676/(PSKER/1000)
inwhichsumP(gCH2Oplant−1)isthecumulativenetdry mat-teradjusted for maintenancerespirationof grain(GRRG;0.49g
CH2Og−1),IDURPisthedurationindaysofthe340ofGDD8period, PSKER is the averagedaily dry matter accumulation per plant (mgd−1)duringthisperiod,G2isthepotentialnumberofgrains perplant,andthevalueof676isthemaximumkernelnumberper plantaveragedforcommonhybridsinNorthAmerica(Jonesand Kiniry1986;Yangetal.,2016).ThethresholdvalueofPSKERfor grainsettingis1000mgd−1plant−1,asfoundbyTollenaaretal (1992)andAndradeetal.(1999,2002),whichisslightlygreater thanthethresholdforgrainsettingusedinHM2006(Yangetal.,
2004)
2.4 Surfacerunoff Surfacerunoffoccurswhenrainfallintensityexceedsthewater infiltrationrate.Fieldslope,soildrainageclass,andtheamountof cropresiduesonthesoilsurfacelargelydeterminetheamountof surfacerunoff(Littleboyetal.,1992;Niehoffetal.,2002).Anew rou-tinewasaddedtoHM2016thatestimateswaterlossfromrunoffto betteraccountforwaterinfiltrationtosoil,especiallyinfieldswith steepterrain,soilswithslowinfiltrationratesand/orlittleresidue cover.TheroutinefollowsthesimplifiedapproachofSoltaniand Sinclair(2012):
ifrain+irrigation<= 0.2×S,thenRunoff = 0
else,Runoff =(rain+irrigation−0.2∗S)2/(rain +irrigation+0.8∗S)
inwhichS(incm)istheretentionparameterandisestimatedas:
S=25.4∗(100/CNadj−1)
CNadj= CN−min(soilCoverFrac∗0.25,0.2)
inwhichCNisthecurvenumberforaparticularcombinationof slopeanddrainageclassbasedonRitchie(1998)andCNadjisthe adjustedCNafteraccountingfor thefractionof soilcoveredby cropresidues(soilCoverFrac),andministhefunctionthattakes theminimumoftwovaluesintheparentheses.Thissubroutineis mostrelevantforfieldswithrelativelyuniformslopethatarenot influencedbyrun-onfromneighboringfields.Picturesfor differ-entsoilcoverconditions,andassociatedsoilcoverfractions,canbe accessedthroughabuttonintheHybrid-Maizemodeluser inter-facetoaiddeterminationoftheresiduecoverconditionatthetime
ofsowingorinitiationofthesimulationduringthefallowperiod beforesowing.Seasonalreductionofsoilsurfacecoveragedueto residuedecompositionisnottakenintoaccountbythemodel
Trang 42.5 Croptranspirationandsoilevaporation
Dailygrass-referencedevapotranspiration(ET0)isoneofthe
dailyweatherinputsrequiredtorunHybrid-Maize(Allenetal.,
1998).IfET0isnotavailableintheweatherdata,themodelcontains
autilityprogram,calledWeatherAid,tocalculateET0fromsolar
radiation,maximumandminimumtemperature,airhumidity,and
windspeedusingtheFAOPenman-Monteithmethod(Allenetal.,
1998).HM2006usedthemethodofDriessenand Konijn(1992)
forestimationofactualcropET.Therevisedversionadoptedthe
methodofAllenetal.(1998)fortheestimationofcropET,which
islargelybasedonRitchie(1972).ET0fromtheweatherinputdata
mustfirstbeadjustedtoreflectETofawell-wateredmaizefield
withLAI>4,whichtypicallyhasgreaterETthanawell-watered
shortgrassduetodifferencesincanopyheightandaerodynamic
roughness(Connoretal.,2011).BasedonAllenetal.(1998),the
adjustmentis:
ifLAI<= 3.5,thenadjCoef = 1
elseadjCoef =1+(LAI−3.5)∗(1.2−1)/(4.5–3.5)
ifadjCoef>1.2,thenadjCoef = 1.2
adjET0= ET0∗adjCoef
inwhich adjET0istheETOadjusted formaize canopy,andthe
denominator(4.5–3.5)is therangeofLAI whentheadjustment
startsatLAIof3.5totheendofthetransitionwhentheadjustment
becomesthelargestat1.2atLAIof4.5.Maximumtranspiration
(Transpmax)isthenestimatedtoaccountforcanopysize(i.e.,LAI):
Transpmax= adjET0∗(1−exp(−LAI∗k))
inwhichkisthelightextinctioncoefficient(default=0.55)
Poten-tialevaporation(Evappot)isestimatedas:
Evappot=adjET0−Transpmax
Maximumevaporation(Evapmax)isestimatedbyaccountingfor
theeffectofsoilcoveragebycropresidues(Rosenbergetal.,1983):
Evapmax= Evappot∗exp(−soilCoverFrac)
inwhichsoilCoverFracisthefractionofsoilsurfacethatiscovered
bycropresidues
Actualsoilevaporation(Evapact)isestimatedusingthe2-stage
methodinAllenetal.(1998).Soilevaporationisassumedtooccur
inthetop10cmsoildepth,andactualevaporationratewillbeat
maximum(Evapmax)whensoiliswet(i.e.,Stage-1):
Evapact= Evapmax
Actual evaporation rate will begin decreasing, and does so
continuously,when soilwater contentdropsbelow athreshold
(Stage-2).Thisthreshold(stage2EvapWater)isestimatedfollowing
Allenetal.(1998):
EvapWatermax= (soilFCtheta−0.5∗soilPWPtheta)∗10
step2EvapWater= step2threshold∗EvapWatermax
waterForEvap=(soilTheta−0.5∗soilPWPtheta)∗10
Evapact= Evapmax∗waterForEvap/step2EvapWater
inwhichEvapWatermaxisthemaximumamountofwaterthatcan
evaporate,soilTheta,soilFCthetaandsoilPWPthetaarethetopsoil
volumetricwatercontents,topsoilfieldcapacity,andtopsoil per-manentwiltingpoint(allthreeinfraction),respectively,10isthe depth(incm)oftheevaporatingsoildepth,andstage2thresholdis
afractionofEvapWatermaxtypicallyrangingfrom0.5to0.7andis setat0.7asdefaultinHM2016
2.6 Estimationofsoilwaterrechargeduringthefallowperiod Soilwatercontentatsowingisthestartingpointfortrackingsoil waterbalancethroughoutthegrowingseason.InHM2016,users canchoosetostartthewaterbalancesimulationupto11months beforesowingandletthemodelsimulatethesoilwaterbalance andsoilwatercontentduringthefallowperioduptosowingdate (Fig.2).Themodelsimulatesthewaterinputfromprecipitationand lossesfromrunoff,soilevaporation,anddeeppercolationduring thefallowperiodtoestimatesoilwatercontentatsowinginthe seasontobesimulated.Thismethodismorereliablein environ-mentswhereprecipitationduringthefallowperiodmainlyoccurs
asrainfallratherthansnowfallandwheresoilwaterdoesnotfreeze duringwintertime,whichcanlimitinfiltrationofmeltingsnowat thesoilsurface
2.7 Otherrevisions ThenewversionoftheHybrid-Maizehasabatchrunfunction thatusesanExcelspreadsheettemplatetoallowinputsettingsfor
asinglesimulationonindividualrowsinthespreadsheet Instruc-tionsareprovidedonhowtoprovidetheinputdata.Usingthis batchfunction,userscanruninonebatchasmanysimulations
asoneExcelspreadsheetcanhold(i.e.,morethanonemillionin Excel2007) andsimulation resultsare savedtothesameExcel file.Using functionssuchascopyand pastecanmake the pro-cessofsettingupalargenumberofsimulationsverytimeefficient TheentireHM2016softwarepackage,includingitsutilityprogram WeatherAid,hasbeenupgradedtobefullycompatiblewiththe currentwindowsoperationsystem,includingWindows7–8 HM2016alsoincludesanewfunctiontoaccountforcropkilling
byfrostwhendailyminimumtemperaturereaches−2◦Corbelow. Thisfunction,however,doesnotinitiateuntil30daysafter emer-genceasthemaizeplantismoretoleranttofrostdamageduring theearlyvegetativegrowth(Carter,1995;NielsenandChristmas,
2005)
3 Datasets used for validation
TwodatasetswereusedtovalidateHM2016andfor compar-isontothepreviousversionofHybrid-Maize(HM2006).Dataset
1 consisted of 47 year-site combinationsin which maize yield wasmeasuredinfieldstudiesthatexplicitlystrivedforoptimal managementtoavoidyieldlossfromnutrientdeficiencies,weeds, insectsanddisease,andweather,cropmanagement,andsoildata wereavailable(Table1).Thisdatasetincludedirrigatedand rain-fedmaizefieldslocatedinfourmajorUSmaizeproducingstates, includingIllinois(IL),Kansas(KS),Iowa(IA)andNebraska(NE).An additionalthreesite-yeardatasetmeasuredseasonaldynamicsof LAI,totalabovegroundbiomass,aswellasfinalgrainyield.They wereusedtoevaluatethemodel’scapacityfordynamicsimulation
ofLAIandabovegroundbiomass
Inirrigated fields,waterwasappliedtoavoid waterdeficits Weatherdatafor eachsite wascollectedfroma nearbystation
intheHighPlainsRegionalClimateCenter(http://www.hprcc.unl edu/)ortheIllinoisClimatenetwork(http://www.isws.illinois.edu/
).Requiredsoilpropertiesforeachrainfedfield,includingsoil tex-tureandrootingdepth,weremeasuredin-situorobtainedfromthe USDA-NRCS database via http://casoilresource.lawr.ucdavis.edu/ gmap/.Simulationswerebasedoncropmanagementinformation
Trang 5Fig 2. Input setting page of the HM2016 model with new features for estimating soil water at sowing and soil water balance as influenced by surface runoff and soil evaporation, which in turn are as influenced by crop residue cover, slope, and drainage.
Table 1
Dataset 1 used for evaluation of HM2006 and HM2016 under rainfed and irrigated conditions.
Irrigated
Brunswick, NE 2003 110 d 1 17.4 8.6 Wortmann et al (2009)
Clay Center, NE 2002, 2005, 2006 112–114 3 14.5–16.2 6.9–7.3 Wortmann et al (2009) , Yang
and Irmak (unpublished data)
Lincoln, NE 1999, 2000, 2001, 2002, 2003 113–114 11 14.8–17.9 7.0–11.0 Yang et al (2004) , Yang et al.
(2006) , Wortmann et al (2009)
Mead, NE 2002, 2007 114–116 3 13–15.5 6.8–7.2 Wortmann et al (2009) , Irmak
et al (2012)
North Platte, NE 2005, 2006 112 2 13.3–14.2 7.1 Yang and Irmak (unpublished
data)
Scandia, KS 2003 115 d 2 14–15.7 6.9–10.4 Dobermann and Walters
(2004)
Rainfed
(2004)
Clay Center, NE 2005, 2006, 2012 110–114 4 3.9–11.7 5.6–7.1 Yang and Irmak (unpublished
data), Irmak (unpublished data)
Mead, NE 2001, 2003, 2005, 2009, 2011 111–113 5 7.7–12.0 5.0–6.1 Yang et al (2006) , Suyker and
Verma (2009) , Suyker (unpublished data) Mead, NE 2009, 2011, 2013 111 3 9.4–11.2 5.0–6.0 Arkebauer (2014, unpublished) North Platte, NE 1992, 1993, 1994,1995, 2005, 2006 108–115 6 0.6–13 7.1–7.9 Payero et al., 2006 ; Yang and
Irmak (unpublished data)
a Locations and corresponding USA state (IL: Illinois; IA: Iowa; KS: Kansas; NE: Nebraska).
b Relative maturity.
c Measured yields at standard water content of 15.5%.
d
Trang 6Table 2
Summary of Dataset 2 of the 33 field-years rainfed maize during 2001–2009 at Colby,
KS (39.38 N, −101.17 W) Hybrid maturity in total GDD to black layer (base
temper-ature = 10◦C) was 1414 for years 2001–2002, 1483 for years 2003–2006, and 1406
for years 2004–2009.
Soil available water in 1.5 m
depth at sowing, mm
Total rainfall from sowing to
silking, mm
Total rainfall from silking to
maturity, mm
Total rainfall from sowing to
maturity, mm
Total water supply 2 , mm 264 390 571 80
Grain yield dry matter, Mg ha −1 0 2.24 8.31 2.5
Total aboveground biomass,
Mg ha −1
0.46 5.72 12.42 3.2
Harvest index 0 0.26 0.72 0.22
1 standard deviation.
2 Sum of soil available water in 1.5 m depth at sowing and the total in-season
rainfall from sowing to maturity.
fromthesourcesinTable1orourbestestimatewheninformation
wasmissing.Amongthe47cases,17wererainfedwhile30were
irrigated.Thisassessmentsubstantiallyexpandstheevaluationof
HM2006performedbyGrassinietal.(2009)
Dataset2consistedof33rotation-yearcombinationsofrainfed
maizeduring2001–2009inColby,KS(Table2).Exceptfor2001,
eachyearhadfourrotationtreatments,eachofthesetreatments
hadadifferentpreviouscrop.Hence,eachtreatmenthaddifferent
soilwatercontentatsowingtime.Cropmanagementfollowedbest
managementrecommendationsforrainfedmaizeinthatregion
Exceptforcropsequence,cropmanagementineachyearwas
con-sistentforthefourtreatmentsrelativetochoiceofhybrid,sowing
date,plantpopulation,nutrientinputs,andcropprotection.The
fieldhaduniformsilt-loamsoiltexturewithoutrestrictionstoroot
growthto1.5mdepth.Thetop30cmsoildepthhadafield
capac-ity(FC)of36%volumetricwatercontent(%v/v)andapermanent
wiltingpoint(PWP)of14%v/v,whilethesubsoilhadFCandPWP
of33%v/vand13%v/v,respectively.Soilwatercontentatsowing
wasmeasuredat30cmincrementsusinganeutronprobe.Daily
weatherdatawereobtainedfromanon-siteweatherstation.Crop
phenologywasbasedonsemi-weeklyobservations,andmaximum
LAIrepresentedLAIaroundsilking.Cropfailure(i.e.,zeroyieldin
2002and2003)andbelownormalrainfallinsomeyears(e.g.,only
199mmin2006)highlightstheseverewaterlimitationsomeof
thesecropsexperienced.Hence,thisdatabaseprovidesarigorous
testoftherevisedmodelinveryharshenvironmentsforrainfed
maizeproduction
4 Sensitivity analysis
Sensitivityanalysiswasperformedtoevaluatehowsimulation
resultschangeasaresultofthefivemajorrevisionsinHM2016
(asdescribedabove)comparedtoHM2006.Weusedgrainyield
andstoverbiomassasthetargetvariablesforthesensitivity
analy-sis.Becauseallfiveofthemajorrevisionswouldhavethegreatest
impactunderdroughtconditions,weusedthe33casesofDataset2
forthesensitivityanalysis,whichincludeanumberofobservations
underseverewaterlimitation.Eachofthefivemajorrevisionswas
incorporatedintoHM2006withoutchangingtherestofthe
rou-tines,andtherevisedprogramwassubsequentlyrunforthe33
casesofDataset2.Thesimulatedgrainyieldsandstoverbiomass
werecomparedwiththecorrespondingresultsofHM2006andthe
magnitudeofthechangesrelative tosimulationswithHM2006
wereusedtoassesshowthemajorrevisionshavecontributedto improvesimulationresults
5 Statistical analysis
Thecomparisonofsimulatedandmeasuredvaluesforyieldand othersimulatedvariableswerebasedontherootmeansquareerror (RMSE)andmeanerror(ME),whichwerecomputedasfollows:
RMSE=
(S−M)2 n
ME=n1
(S−M)
InwhichSisthesimulatedresult,Mthemeasuredresult,nthe numberoftotalpairsofsimulatedandsimulateddata.Inaddition, linearregressionanalysiswasperformedtoassessbiasesinthe relationshipbetweensimulatedandobservedyields
6 Results
6.1 Grainyieldandtotalabovegroundbiomass For thetotal of50 site-yearobservations underrainfed con-ditions,withmoderatetoseverewaterlimitationinmostcases, HM2016simulatedyieldsignificantlybetterthanHM2006(Fig.3) Thecoefficientofdetermination(r2)betweensimulatedand mea-suredyieldsincreasedfrom0.89forHM2006to0.94forHM2016 Relative to measuredyields, RMSE decreased from 2.5Mgha−1 (HM2006) to 1.2Mgha−1 (HM2016), while ME dropped from 1.9Mgha−1 (HM2006)to0.5Mgha−1 (HM2016).Moreover, the slope of the regression equation between simulated and mea-suredyieldsimprovedfrom0.79forHM2006to0.96forHM2016, while the intercept dropped from 2.92Mgha−1 (HM2006) to 0.71Mgha−1 (HM2016) For cases where severe water deficits causeddramaticyieldlosses,e.g.mostcasesatColby,KSandmany
atNorthPlatteNE,simulatedyieldsbyHM2016wereinmuch bet-teragreementwithmeasuredvaluesthanwithHM2006.Forthe
33casesofDataset2for whichmeasurementsofharvestindex (HI)wereavailable,simulatedHIfromHM2016agreedbetterthan HM2006withmeasuredvaluesasindicatedbylargereductionsin RSMEandME(Fig.4).Forirrigatedfields,however,therewasno sig-nificantdifferencebetweenthetwoversionsofthemodel(HM2006 simulationresultsnotshown)andsimulatedyieldsagreedwell withobservedvalues(PanelCofFig.3).Tosummarize,HM2016was considerablymorerobustatreproducingmeasuredyields,across
adiverserangeofenvironmentsandcropmanagementpractices withrespecttosowingdates,hybridmaturity,andplant popula-tionswithoutdetectablebiasacrossarangeofyieldsfromcomplete cropfailureto18Mgha−1
6.2 Dynamicsofbiomassproductionandcanopysenescence SeasonaldynamicsoftotalabovegroundbiomassandLAIwere measured only at Mead, NE in 2011 and 2013 In 2011, both HM2016andHM2006gavesimilarresultsintermsofseasonal pat-ternofabovegroundbiomassproductionandLAI.Afullsoilprofile
intermsofsoilwatercontentatsowing,totalrainfallof556mm duringthegrowingseason,andgoodrainfalldistributionfrom sow-ingtomaturity(insetinFig.5)indicatedthat2011wasarelatively wetyearatthislocationandthereforethecropdidnotsufferwater deficit.In2013,however,HM2016simulatedfasterleafarea senes-cenceduringthelastphaseofgrainfillingduetolackofrainfall andwaterdeficitsduringthisperiod(insetinFig.5).Further evi-denceofwaterdeficitin2013comesfromanirrigatedsimulation
Trang 7Fig 3. Simulated versus measured maize yields for rainfed fields (a) using HM2006 (left panel) and (b) using HM2016 (central panel), and (c) for both rainfed and irrigated crops using HM2016 (right panel) for Datasets 1 (in Table 1 ) and 2 (in Table 2 Root mean square error (RMSE), mean error (ME), and linear regression analysis parameters are indicated Diagonal dashed line indicates y = x Solid black lines show the fitted linear regression model Encircled data point was excluded from the calculation of RMSE,
ME and linear regression analysis because observed yield was affected by frost damage during grain filling Justification for excluding the site with frost damage from all regressions comes from the fact that frost damage can be very site-specific because it is often associated with small differences in elevation within a relatively flat landscape due to pooling of cold air Therefore, frost damage may be larger or smaller in a given field compared to simulated damage from the closest weather station.
Fig 4. Harvest index based on measured and simulated results using HM2006 and HM2016 Dataset 2, which includes 33 rainfed maize crops grown during 2001–2009 at Colby, KS (see Table 2
scenariofortotalbiomassandLAI(thedashedlinesinthelower
partofFig.5 whichshowedthattotalbiomasswouldcontinueto
increaseandtheLAIwoulddecreasemoreslowlywithoutwater
deficitduringthelaterphaseofgrainfilling
6.3 Kernelsettinganddurationofgrainfilling
Simulatedmaizekernelsettingwasevaluatedforthe33cases
atColby,KS,whichisaharshenvironmentforrainfedmaizecrop
asindicatedbysparsein-seasonrainfallamounts(Table2).When
waterdeficitoccurredduringthekernelsettingwindow
(approxi-matelythreeweeksbracketingsilking),HM2016predictedalarger
impactonkernelsettingthanHM2006(Fig.6).Fortheeightcases
withcropfailure(i.e.,zeroyield),HM2016simulatedzerokernel
settingrate(as%ofmaximumnumberofkernelsof675perplant)
andonly9%and 10%kernelsetting forothertwocases.In
con-trast,HM2006simulatedamuchhigherrateofkernelsetting,and
especiallyinyearswithcompletecropfailure.Acrossthe33cases,
HM2016simulatedanaveragekernelsettingrateof42%whilethe
valuefromHM2006was57%,whichdocumentstheimprovement
ofHM2016intermsofquantifyingtheimpactofdroughtstress
aroundsilkingonmaizekernelsetting
ThedifferencebetweenHM2016andHM2006in responsive-nessofcanopysenescencetowaterdeficitwasalsoshowninthe simulateddrought-inducedterminationofcropgrowthandthus thedurationofthegrainfillingphaseforthe33casesatColby,KS (Fig.7).Onaverage,HM2016simulated4daysshortergrain fill-ingdurationthanHM2006witharangefrom0to15days,withthe magnitudedependinglargelyonthein-seasonrainfallineachyear, especiallyduringreproductivegrowthphases
6.4 Sensitivityanalysis Amongthefivemajorrevisions(i.e.,revisions1–5),rootlength distributionandkernelsettinghadthegreatesteffectsina water-limitedenvironment,butinoppositedirections:therevisedroot length distribution resulted in higher grain yield, whereas the revisedkernelsettingledtolowergrainyield(Fig.8,leftpanel)
Onaverage,theabsoluteeffectfromkernelsetting(59%)ongrain yieldis greater thanthat of rootlengthdistribution(43%), and
asaresult,theneteffectisexpectedtoresultinloweryields.In comparison,theotherrevisions,includingcanopyexpansionand senescence,runoffandcropET,didnothavesignificantimpacts
onsimulatedgrainyield,withanaverageeffectrangingfrom−1%
Trang 8Fig 5.Simulated and measured seasonal dynamics of total aboveground biomass (left panels) and LAI (right panels) using HM2016 and HM2006 for rainfed maize grown in
2011 and 2013 at Mead, NE The inserts in the right panels show daily rainfall amounts from sowing to physiological maturity Rainfall in 2011 growing season was sufficient
to meet plant water requirements throughout the growing season whereas there was a dry period during part of the grain filling in 2013 Because 2013 was a dry year, simulation of fully irrigated maize crop (the dashed line) was added to show the difference in total biomass and LAI between rainfed and fully irrigated crops The field was
on annual rotation with soybean, with maize sown in odd-numbered years.
Fig 6.Simulated maize kernel setting rate, expressed as% of maximum kernel number (i.e., 675 per plant), by HM2006 and HM2016 for Dataset 2, which includes 33 rainfed maize crops grown during 2001–2009 at Colby, KS (in Table 2 Average kernel setting rate was 57% and 42% for HM2006 and HM2016, respectively.
to−13%.Despitetherelativelysmalleffectsfromtheseother
revi-sionswithinthelimitsofobservationsprovidedbyDataset2,we
canenvisionsituationsinwhichthesefactorsmayhavegreater
influenceonfinalyields.Forexample,thefieldsinwhichthe
exper-imentswereconductedforDataset2hadrelativelylittleslopeand
thustherunoff revisionwouldnot beexpectedtohavea large
influence
Theeffectsofmodelrevisionsonstoverbiomassweresmaller
thantheimpactonyield,ranging,onaverage, from8%to−9%
Anexceptionwaskernelsetting,whichhasalarge(58%)positive
effectonstoverbiomasswhenlimitedwatersupplyreducessink
size(Fig.8,rightpanel).Thistrendoccursbecauseof(1)
accumu-lationofphotosynthateinstem(Christensenetal.,1981)and(2)
lessuseofcarbohydratereserveinstemforgrainfilling(Humeand
Campbell,1972).However,themagnitudeoftheincreaseinstover
biomassunderreducedkernelsettingislikelyover-estimatedas photosyntheticrateisalsolikelytoreducewhenthesinksizeis reduced(Christensenetal.,1981)butthemodellacksthefeedback mechanism
7 Discussion
Modelingcropgrowthand yieldunderlimitedwater supply hasbeenasignificantscientificchallengeduetoalargenumberof interactingsoilandplantfactorsthatdeterminethefinalimpacton grainandbiomassyields.Tomeettheincreasingdemandformore robustsimulationsinwater-limitedenvironments,weattempted
torevisetheHybrid-Maizemodeltoimproveitscapacitytoaccount fortheimpactofwaterdeficitsonmaizedevelopment,growth, andyield.Tothat end,subroutineswererevisedwithregard to
Trang 9Fig 7.Difference in the duration (HM2006 minus HM 2016, days) of grain filling as
simulated by HM2006 and HM2016 for Dataset 2 (in Table 2 ) of the 33 rainfed maize
crops grown during 2001–2009 at Colby, KS The difference in simulated duration
was, on average, 4.2 days shorter with HM2016 than by HM2006.
soilwaterbalance,soilprofilerootdistributionandwateruptake,
andtheimpactsofwaterdeficitoncanopyexpansion,senescence,
drymatterproduction,andkernelsetting.ComparedtoHM2006
(theearlierversion),HM2016(therevisedversion)produced
sim-ulationresultsin muchbetter agreementwithmeasuredyield,
abovegroundbiomass,andharvestindex,acrossawiderangeof
environmentswithyieldsrangingfrom0to18Mgha−1.Amongthe
majorrevisions,therevisionofrootdistributionledtoagreaterroot
lengthdensityintheentiresoilprofile,resultinginhighergrainand
stoveryieldsinwater-limitedenvironments.However,thiseffect
waspartlyoffsetbyreducedkernelsettingthatwassimulatedwith
HM2016duetotheimpactofdroughtstressoncropgrowthrate
aroundsilkingstage.Whiletheotherrevisionshadrelativelysmall
effectsongrainandyield,together,onaverage,theycontributedto
agrainyieldreductionof1–13%
Thesemodelimprovementsareconsistentwithrecentadvances
in understanding the improved performance of newer maize
hybrids in terms of stress resistance, including drought,
com-paredtoolderhybrids,especially asrelated tothecompetition
for resourcesimposed by increasing inter-plant competition at
higherplantdensities(TollenaarandLee,2002;Camposetal.,2004,
2006).Indeed,producerplantpopulationshaveincreasedsteadily
inrecentdecades(Pioneer,2014;Grassinietal.,2014),leavinga
smallerlateraltopsoilvolumeforwaterextractionbyindividual
plants.Thismayleadtoa changeinrootdistributionfroma V
shapethroughouttheprofiletoacombinedcylindricalshapein
top-soilandaV-shapebelow,thusincreasingthesoilvolumeforplant
wateracquisition,whichmayimpartadditionaldroughttolerance
assuggestedbyHammeretal.(2009)
Inadditiontoimprovedsensitivityofkernelsettingunderwater deficit,HM2016bettercaptures impactoflimitedwater supply duringgrainfilling through acceleratedleaf senescenceand, as
aresult,earlierterminationofgrainfillingwhenthereis persis-tentwaterdeficit,asdocumentedbySainiandWestgate(2000) These improvements are make the revisedmodel considerably moreresponsiveinsimulatingmaizeyieldsinharshrainfed envi-ronmentslikethosefoundinthewesternUSCornBeltwherecrop waterdeficitislikelytooccuraroundpollinationandduringthe grainfillinginamajorityofyears
Theinclusionofroutinesforwaterrunoffcanpotentiallymake therevisedmodelmoreaccurateatsimulatingsoilwaterstatusin fieldswithsteepterrainandenvironmentswithhighfrequencyof intenserainfallevents.Theaddedeffectsofcropresiduecoveron waterrunoffandsoilevaporationmakeHM2016simulationsofsoil waterbalancemoreresponsivetotillagemethodandconservation practices
DespitetheimprovedpredictivepowerofHM2016,relativeto HM2006,thereremainotherimpactsofseverewaterdeficitthat arenotyeteffectivelyaccountedforHM2016.Thesefactorsinclude reduction of effectiveLAI due toleaf rolling(Asim and Rabiye,
2007),theincreaseincanopytemperaturethatoccursunderwater limitedconditions(Gonzalez-Dugoetal.,2005), andheatstress duringpollination(Cicchinoetal.,2013;Gabaldon-Lealetal.,2016) Eachoftheseeffects maylastonlyafewhoursduringmid-day heatandaredifficulttoquantifyinmodelswithadailytimestep temperature.Additionalfielddataarealsoneededtodevelopand calibrateusefulsubroutinesforquantifyingtheseeffectsonyield, andamodelthatrunsonanhourlytimestepmaybeneededas well
8 Conclusion
Moderate to severe water deficit is common in many rain-fedmaizeproductionenvironmentsworldwide.Yieldlossesfrom waterdeficitresultfromanumber ofinteractingprocessesthat involvesoilwaterbalance,cropgrowth,canopysenescence,and kernelsetting.Mostoftheprocessesaredifficulttomathematically describeandsimulateincropmodels.WerevisedtheHybrid-Maize model in an attemptto improve maize yield simulation under rainfedconditionswithwaterdeficit.Therevisionsandnew
rou-Fig 8.Sensitivity analysis of each of the five major revisions in HM2016 (including root and distribution, canopy expansion and senescence, kernel setting, runoff, and crop ET) on simulated grain yield (left panel) and stover biomass (right panel) for the 33 cases of Dataset 2 The relative change was calculated as (HM2016–HM2006)/HM2006
*100% Positive and negative values indicate respective higher or lower values relative to those obtained using HM2006 The bottom and top of each box represent the first and third quartiles, respectively, while the horizontal line band inside each box is the median, the x sign is the mean The upper and lower end of the bars indicate the
Trang 10them.Likewise,weimprovedsimulationofsoilevaporation,
sur-facerunoff,andestimationofsoilwatercontentatsowingbased
onthesoilwaterbalanceduringthefallowperioduptosowing
time.Together,thesechangesmaketherevisedHybrid-Maizemore
robustatsimulatingyieldsovera wider rangeofenvironments
andfieldconditions.Consequently,theseimprovementsmakethe
model moreusefulin researchoncroptraits and management
optionstomitigateyieldlossesfromwaterdeficits,andfor
sup-portingin-seasoncropmanagementdecisionsandyieldforecasts
Documentationofthesechangesasprovidedinthispaper,andin
greaterdetailintheusers’guide(Yangetal.,2016),willfacilitate
futuremodeldevelopmentbyothers,andtheHM2016hasbeen
releasedtothepublicandisavailableathttp://hybridmaize.unl
edu/
Acknowledgements
wethankDrSuatIrmak(DepartmentofBiologicalSystem
Engi-neering,UniversityofNebraska–Lincoln)andDr.AndrewSuyker
(SchoolofNaturalResources,UniversityofNebraska–Lincoln)for
providingsomeofthedatausedinthestudy.WealsothankJenny
ReesandKeith Glewen,Extension EducatoroftheUniversityof
NebraskafortheirfeedbackonapplicationsoftheHybrid-Maize
model
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