Statistical Modeling of Phenotypic Plasticity under Abiotic Stress in Triticum durum L. and Triticum

31 Pages·2017·2.88 MB·English

agronomy
Article
Statistical Modeling of Phenotypic Plasticity under
Abiotic Stress in Triticum durum L. and Triticum
aestivum L. Genotypes
AbdullahA.Jaradat
1 UnitedStatesDepartmentofAgriculture–AgriculturalResearchService(USDA-ARS),IowaAvenue,Morris,
MN56267,USA;[email protected];Tel.:[phone]
2 DepartmentofAgronomyandPlantGenetics,UniversityofMinnesota,803IowaAvenue,Morris,MN
56267,USA
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:19July2018;Accepted:2August2018;Published:4August2018
Abstract: Futurechallengestotheroleofdurumandbreadwheatinglobalfoodsecuritywillbe
shapedbytheirpotentialtoproducelargeryieldsandbetternutritionalquality,whileincreasingly
adaptingtomultiplebioticandabioticstressesintheviewofglobalclimatechange. Thereisadearth
of information on comparative assessment of phenotypic plasticity in both wheat species under
long-termmultipleabioticstresses. Phenotypicplasticitiesoftwodurumandbreadwheatgenotypes
wereassessedunderincreasingabioticandedaphicstressesforsixyears. Combinationsofnormal
andreducedlengthofgrowingseasonandpopulationdensity,withorwithoutrotation,generated
increasinglevelsofcompetitionforresourcesandimpactedphenotypicplasticityofseveralplant
andyieldattributes,includingproteinandmicronutrientscontents. Allthephenotypicplasticity
(PPs)estimates,exceptfortheC:NratioinbothgenotypesandgrainproteincontentinT.aestivum
genotype,wereimpactedbyabioticstressesduringthesecondstressphase(PSII)comparedwiththe
first(PSI);whereas,covariateeffectswerelimitedtoafewPPs(e.g.,biomass,populationdensity,
fertiletillers,grainyield,andgrainproteincontent). Discriminationbetweenfactorlevelsdecreased
from abiotic phases > growth stages > stress treatments and provided selection criteria of trait
combinations that can be positively resilient under abiotic stress (e.g., spike harvest and fertility
indicescombinedwithbiomassandgrainyieldinbothgenotypes). Validationandconfirmatory
factormodelsandmultiwayclusteranalysesrevealedmajordifferencesinphenotypicplasticities
betweenwheatgenotypesthatcanbeattributedtodifferencesinploidylevel,lengthofdomestication
history,orconstitutivedifferencesinresourcesallocation. Discriminantanalyseshelpedtoidentify
genotypic differences or similarities in the level of trait decoupling in relation to the strength of
theircorrelationandheritabilityestimates. Thisinformationisusefulintargetedimprovementof
traits directly contributing to micronutrient densities, yield components, and yield. New wheat
ideotype(s)canbedesignedforlargergrainyieldpotentialunderabioticstressbymanipulating
yieldcomponentsthataffectkernelsm−2(e.g.,numberoftillers,numberoffloretsperspikelet,and
eventuallyspikefertilityandharvestindices)withoutimpactingnutrientdensitiesandkernelweight,
thusraisingharvestindexbeyonditscurrentmaximum.
Keywords: abioticstress;micronutrients;latentvariables;plasticity;wheat;yieldcomponents
1. Introduction
Thedramatictransformationofwheatproductionduetounprecedentedproductivitygrowth
fromtheGreenRevolutionbenefitedbothproducersandconsumersofwheatthroughlowproduction
costsandlowfoodprices[1]. However,futurechallengestotheroleofwheatinglobalfoodsecurity
willbeshapedbyitspotentialtoproducelargeryieldsandbetternutritionalquality,whileincreasingly
Agronomy2018,8,139;doi:10.3390/agronomy8080139 www.mdpi.com/journal/agronomy
Agronomy2018,8,139 2of31
withstandingmultiplebioticandabioticstresses[2,3].Slowingproductivitygrowth,whichiscausedby
bioticandabioticstresses,isfurthercomplicatedbychangingconsumptionpatternsandthegrowing
demand for diverse and more nutritious wheat products in the developing world [1,4]. Average
annualgrowthratefortheUnitesStates(US)andCanadaduringthelast15yearswas1.65%witha
negativeareagrowthrateof−1.25%,negativeconsumptionof−1.06%,andamodestaverageyieldof
2.97Mgha−1[1].
Wheatgenotypeswithimprovedadaptationtoabioticstressesarerequiredtosatisfyincreasing
demandformorequantityandbetterqualityofwheatproducts[2],especiallyproteincontentand
micronutrientdensity[5]. Renewedinterestinphenotypicplasticity(PP)inagronomicresearchled
tofreshinsightsintothecomplexrelationshipsamongyieldcomponents[6,7];and,theirassociation
with phenological and architectural traits, expressed, respectively, by growing degree days, GDD;
and,fractaldimension,Do[8,9]. Toassessthebenefitorcostofplasticityinwheat,itisimperativeto
estimatetherelationshipbetweenatraitplasticityandgrainyieldperse,aswellasothertraitsthat
areassociatedwithgrainyield[10,11]. Grainyielddependslargelyonresourceallocationtodifferent
partsofthewheatplant[12,13]. Inturn,resourceallocationmayinfluencenearlyallmajorstructures
and functions of the wheat plant; is affected by a range of interacting physiological pathways; is
temporallyvariable;and,interactswithseveralenvironmentalfactors[14]. Remarkably,largediversity
ofphenotypicallyplasticstrategiesevolvedinresponsetochangesinresourceavailabilitycaused
eitherbyclimatechangeormanagementpractices[15].
A wide range of phenotypically plastic strategies evolved in response to changes in resource
availability forT. durum under Mediterraneanclimate [4,16] andfor T. aestivum under continental
climate[17,18].Underbothclimates,naturalselectionusuallyfavoredtheevolutionofaphenotypically
plastic resource allocation when the optimal resource allocation strategy changes due to resource
availability[12].Differencesbetweendurumandbreadwheat,whicharemainlyattributedtoevolution
under domestication, intensity of breeding and selection [16], and eco-geographical adaption (i.e.,
Mediterranean vs. Continental) [10,19], have been documented in a few studies [20]. However,
assessmentofbiomass,andpotentialgrainyieldanditscomponents(e.g.,tillers,kernelsm−2 and
1000-kernel weight) in response to abiotic stresses, such as water deficit [21] and micronutrient
deficiencies[22],areofincreasinginteresttowheatscientists. Grainyieldofwheatunderabioticstress
canbeexplainedbytraitsthatarefullyindependentofthespecies(e.g.,durumandbreadwheat)or
genotypicresponsetostressenvironment[19,20,23]; therefore,itisimportanttoproperlyseparate
plasticity of constitutive (e.g., kernel weight) from adaptive (e.g., kernels m−2), maladaptive (e.g.,
plantheight),orneutral(e.g.,daystomaturity)traitsinsearchforhighyieldingwheatunderabiotic
stress. High-yieldingwheatgenotypesareexpectedtoexhibitstrongPP,particularlyinnumberof
fertiletillersandspikesize. Alternatively,wheatgenotypes(andspecies,e.g.,einkorn,emmer,orspelt)
withahighsurvivalrateunderincreasingabioticstress,butwithlowPP,mightbeofvaluetofarmers
underfutureclimatechange[17,19,24].
Phenotypicplasticity,asanantipodetostability,isamechanismbywhichsingleplantsinacrop
adjusttheirexpressedtraitvaluesinarchitecturalandphysiologicalfunctions,therebyresponding
dynamicallytoabioticstressesthatarecausedbyresource(in)availability,mainlylight,water,and
nutrients[10,25–27]. TheclassicalconceptofPP[28]indicatesthatarangeofphenotypesisproduced
by a single genotype under varying environmental conditions. However, the ability of different
genotypes(e.g.,inalandrace)thatcanassumethesamephenotypeisanalternativeformofPP[29]. In
thiscase,apopulationmaymaintainapracticallevelofgeneticdiversitywhileexhibitingarelatively
similar level of phenotypic expression under varying field condition. Additionally, plasticity at a
populationlevelmaybeusedasastatisticalmeasureofaveragedifferencesamongenvironments
acrossgenotypes[19,30]. Nevertheless,whenexpressedatapopulationlevel,plasticityisastatistical
measureofhowtheacross-genotypesphenotypicmeanofatraitchangeswithabioticstress[30].
Phenotypicplasticityofwheatplantsinresponsetoabioticstressesisimportantforadaptingtoa
changingclimate;therefore,futurewheatgenotypesneedtobemoreflexibleandadaptabletoabiotic
Agronomy2018,8,139 3of31
stressesandexhibitlargerlevelsofplasticitytoenvironmentaldiversity[4,5,17]. Wheatplantsunder
drylandproductionsystemsexperiencecomplexmultidimensionalandspatiotemporalenvironments,
therefore,wheatscientistsarekeenlyinterestedinunderstandingthemechanismsofplantresponseto
singleandmultipleabioticstresses[20,24,31]. Resourcelimitationsduetoabioticstress,inadditionto
fieldspatialheterogeneity,impactwheatyield,duetointra-populationcompetition[32]. However,
commonoccurrenceofinteractions(i.e.,non-additivity)betweenplantresponsestoabioticstress(e.g.,
limitingwater,light,andnutrientsresources)[8],andthecomplexityandtrait-specificmechanismsof
suchresponsesmakeitdifficulttoextrapolatefromsingletomultipleabioticstressscenarios[9,33].
Additionally,recentempiricalstudiesexploringtheeffectsofgrowthstageonplasticitysuggestedthat
aplasticresponseatanearliergrowthstage(e.g.,nutrientstressatseedlingstage)mightadjust(in
magnitudeordirection)theabilityofaplantorpopulationtorespondtofutureabioticstressatalatter
growthstage[21,22,33].
Thewheatphenotypeisadynamicandplasticsystemofsuccessiveprocessesoforganinitiation,
differentiation, resource capture, growth, reproduction, recycling of internal carbon and nutrient
resources (i.e., C:N ratio as indicator of their coupling, yield), and senescence [4,7,18,34]. The
remarkable stability of this performance across diverse environments and population densities in
the absence of physiological stresses is a result of adaptive plasticity, or compensatory growth, of
sequentiallyinitiatedandinter-dependentyieldcomponents[6,7,35]. Recentreports[7,22]attributed
thestabilityofwheatyieldtogreaterplasticityofitscomponenttraits,especiallyharvestindex(HI),
which is approaching its physiological limits [35,36]. Since the early 1990s, almost no systematic
progressinHIwasachieved;therefore,futureyieldgainswilldependmoreonincreasedbiomass,
whichdemonstratedacloseassociationwithhighergrainyield[3,19,24].
Alargermagnitudeofassociationbetweenyieldplasticityandmaximumgrainyield,asopposed
toamuchsmalleronethatisassociatedwithminimumgrainyield,indicatesthatlargerplasticity
isfavoredunderoptimalconditions,withoutayieldpenaltyunderlow-yieldingconditions[11,37];
thus,negatingtheclassicalattitudetowardslargephenotypicdiversityasanegativetraitbecause
ofitsassociationwithlowgrainyieldunderlow-yieldingconditions[38]. Nevertheless,genotypes
with higher yield potential generally express their advantage under a wide range of suboptimal
environments [39]. Large grain yield plasticity is often associated with short plants, less lodging,
morekernelsm−2,lowergrainproteincontent(grainproteincontent),latematurity,andlargekernel
weight[7,37]. Associationsbetweenplasticityestimatesofthesetraitsaretypically,butnotalways,
positive. Inthecaseofdurumandbreadwheat,andregardlessofcleardifferencesbetweenthese
speciesinkernelsm−2,andkernelweight,theiryieldresponsestoabioticstressareindependentof
thelatterandareexclusivelymediatedbytheformeryieldcomponent[40].
Recentadvancesinphenotypingmadeitpossibletostreamlinequantitativemeasurementsand
qualitativedescriptionoflargenumbersofcropplantsandtheiryieldcomponents[29]. Thiswas
achieveddespitenegativerelationshipsbetweenphenotypicplasticityandheritabilityestimatesof
yieldcomponentsduetothecollectiveeffectsofstrongenvironmentalimpact,geneticandphysiological
controls, and evolutionary constraints [36,41], which may restrict their value in breeding [16] and
agronomicapplications[41]. Insuchcase,phenotypicplasticity,asameasureofhowawheatgenotype
mayrespondtotheenvironment,couldimpactimportantqualitytraits,suchasproteincontentand
micronutrientsdensity[11,42],especiallyofhigh-yieldingwheatgenotypes[43].
Objectivesofthisstudywereto(1)conductacomprehensiveassessmentofdifferencesbetweena
T.durumandaT.aestivumgenotypeatthereproductiveandfullmaturitystages;(2)estimatevariance
components, PP, and their heritability estimates for yield, yield components, and micronutrients;
(3)buildcalibrationandvalidationmodelsformicronutrientdensitiesunderabioticstresses; and,
(4) estimate regression and correlation coefficients in confirmatory factor analyses to integrate the
effectsatthestart(nostress)andend(extremestress)ofabioticstresssix-yearfieldexperimentfor
eachwheatgenotype.
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2. MaterialsandMethods
Amulti-year(2006–2011),multi-species(twoTriticumspp. andtwogenotypeseachofCarthemus
tinctoriusL.,CiceraerietinumL.,Glysinmax(L.)Merr.,andZeamaizeL.)fieldexperimentwasconducted
toevaluateintra-andinter-specificvariationunderabioticstress[43]. Thispartofthestudyreportson
thephenotypicplasticityofonegenotypeeachofTriticumaestivumandT.durumspeciesunderthree
levelsofabioticstressascomparedtono-stresscontrolduringsixyearsoffieldstudies(Table1).
Table1. Designofafactorialfieldexperimentevaluatingdurum(T.durum;cv. “Alsen”)andbread
(T.aestivum;cv.“Lebsock”)wheatgenotypesinarandomizedcompleteplotdesign,includingabiotic
stressphases,treatments,factors,meteorologicaldata,andexperimentalunitsforsixyears(2006–2011).
Factors AbioticStressTreatments
Stressphase(SP) SPI(2006–2008) SPII(2009–2011)
Normallengthofgrowingseason/Normal Normallengthofgrowingseason/25%Higher
populationdensity(NN,Control) populationdensitythannormal(NH)
25%Shortergrowingseasonthan 25%Shortergrowingseasonthannormal/25%
normal/Normalpopulationdensity(SN) Higherpopulationdensitythannormal(SH)
Croprotation Continuouswheat Wheatrotatedwithsoybean
Genotypes/cultivars T.aestivum,cv.“Alsen”andT.durum,cv.“Lebsock”
Mainplotdimensions(m) 6×12
Samplingsub-plots(m2) 0.5
SowingJuliandate 110±5
Samples/genotype/year 80–100
Variables Year
Weathervariables;100years
2006 2007 2008 2009 2010 2011
(mean±SD)
Rainfall,mm(589±87) 460 550 490 450 570 550
Growingdegreedays(1930±190)
◦C 1950 1940 1850 1895 1920 1900
Temperature(14.7±2.8)◦C 17.9±4.6 18.2±4.2 16.1±3.9 17.4±4.2 17.8±4.2 17.4±4.7
Photothermalquotient(1.84±0.5)
MJm−2day−1◦C−1>0.0 1.47±0.7 1.95±0.8 1.85±0.5 1.75±0.6 1.68±0.5 1.69±0.7
2.1. ExperimentalDesign
A factorial experiment in a randomized complete block design, with three replicates, was
establishedattheSwanLakeResearchFarm,nearMorris,MN,USA(45◦41’N,95◦48’W,andelevation
370m). Thelandareawaspreviouslyuniformlyplantedwithsoybeantominimizespatialvariability.
The major soil series identified within the experimental site was Barnes loam (fine-loamy, mixed,
superactivefrigidCalcicHaplotoll)[44]. Averageannualprecipitationinthisregionwas645±53mm
andtheaveragetemperaturesduringthewheatgrowingseason(June–August)rangedfrom15.3◦Cin
Aprilto26.7◦CinAugust[45].
Fullfactorialcombinationsoftwowheatgenotypes,oneeachofT.aestivum,(cv. “Alsen”)and
T.durum,(cv. “Lebsock”);normal(1800±54GDD)andshort(1350±48GDD)lengthofthegrowing
season in growing degree days for the period 2006–2011; and, two population densities (normal:
450 live seed m−2 and high: 560 live seed m−2). The same factorial treatment combinations were
maintainedoneachexperimentalplotforthefirstthreeyears(2006–2008)toenforceadditionaledaphic
stress on the crop (SP I), then wheat was rotated with soybean in a two-year rotation during the
nextthreeyears(2009–2011)toreleasetheedaphicstress(SPII),whilemaintainingthesamelevels
ofotherabioticstressesasinSPI(Table1). Theshortergrowingseasondeprivedthewheatplants
from fully utilizing inputs for full development, and the larger population density created more
competitionforinputsandnaturalresources. Landmanagementthatistypicalofwheathusbandry
(tillage,plantingdepth,weedcontrol)intheUpperMidwestwasfollowedduringthedurationofthe
experiment. Fertilitymanagement(N,P,Krate,method,andtimeofapplication)wasbasedonannual
soilanalysis. Samplingsub-plotswereselectedeveryyeartorepresenttheaveragestandineachof
wheatgenotype-populationdensity-lengthofthegrowingseason-growthstagefactorialcombinations.
Landdimensionsofeachexperimentalplotaccommodatingafactorialcombinationwere6.0×12.0m,
with0.3mbetweenrows.
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2.2. Covariates
Threecovariateswereestimatedormeasuredandusedtoadjustforspatiotemporalvariation
intheexperimentalareaduringthedurationofthesix-yearfieldexperiment;theseswere,rowsand
columnsofexperimentalplots,photothermalquotient(PTQ;MJm−2day−1◦C−1)priortoanthesis[35],
andanintegratedverticalandhorizontalcontinuousmeasurementsofsoilelectricalconductivity(ECa;
dSm−1)usingEM-38afteremergenceandafterharvest[43].
Dailyphotothermalquotient(PTQ,theratioofmeandailyincidentshort-waveradiationtomean
dailytemperaturemorethan4.5◦C)wascalculated,accordingtothefollowingformulae[46].
PTQ=Solarradiation/(T−4.5)(IfT>10;whereT=dailymeantemperature,◦C),andPTQ=Solar
radiation×[(T−4.5)/5.5]/5.5(If4.5<T<10);otherwise,PTQ=0,ifT<4.5.
Thegrowingdegreedays(GDD)werecalculated[6]whileusingmaximum(Tmax)andminimum
(Tmin)dailytemperaturesandbasetemperature(Tb=4.5◦C)as[{(Tmax+Tmin)/2}−Tb].
2.3. SamplingProtocol
Wheatplantsweremonitoredduringthegrowingseasontodeterminesamplingtimeofbiomass.
AbovegroundbiomasswassampledatGS69(i.e.,about15daysaftercompletionofanthesis)and
atfullmaturityfrompermanentgeo-referencedsamplingsites(0.15m2 from50cmlengthofthree
centraladjacentrows)ineachreplicate,abioticstresstreatment,andyear[47,48]. Numberofplant
samplespergenotypeperyearrangedfrom80to100.
2.4. SampleProcessing
Eachsamplingsitewasassessedforpopulationdensity(plantsm−2),numberoffertiletillersper
unitarea,andnumberandweightofspikes. Ateachsamplingstage,plantsampleswereseparated
intostems,leaves,andspikes,thendriedinaforced-airovenat60◦Cfor48handweighed. Grain
weightandkernelsm−2 atfullmaturitywereestimatedonthreesub-samplesfromeachsampling
siteandaveragedforstatisticalanalysis. Fivespikeswererandomlyselectedfromeachsampling
siteandgrowthstagewasusedtoestimatespikeharvestindex(kernelweight/spikeweight)and
spikefertilityindex(kernels100-spikelets−1). Therelationshipofgrainyield(anditsPP)withkernel
weightisminimal(R2=0.07)whencomparedtothatofkernelsm−2(R2=0.85),therefore,anddueto
constitutivedifferencesinkernelweightbetweenT.durumandT.aestivum(>10mgkernel−1),number
ofkernelsm−2wasusedinthecurrentstudy.Carbonandnitrogenweredeterminedonkernelsamples
asapercentofdryweightusingLECOFP-428analyzer(LECO,St. Joseph,MI,USA),andthenthe
C:Nratiowascalculated. PercentnitrogenvalueswereusedtoestimatecrudeproteincontentasN%
×6.25,whilemicronutrients(Cu,Fe,Mn,andZn)determinationwascarriedoutusingsimultaneous
ICP-OES(inductivelycoupledplasma-opticalemissionspectroscopy)instrument[42].
2.5. EstimationofPhenotypicPlasticity
The most direct method of estimating PP was used in the current study and it was based on
thenormalizedvariationofthephenotype(i.e.,byexpressingatraitvalueunderstressasaratioof
itsvalueunderno-stresstreatmentanditcanrangefrom0.0%to100%)[49–51]. However, thePP
estimationmethodthatwasusedinthecurrentresearchreturnsresultsthatareequivalenttothose
obtainedbyacommonmethodtoquantifyPP,whichinvolvesmodelsofphenotypevs. environment,
wheretheparameterestimatesofthefunctionsaretakenasameasureofplasticity[38].
2.6. DataManagement
Adatabasecomprisedofmeasuredandestimatedvariablesonplot-,plant-,andkernel-basisand
classifiedbyyear,stressphase,growthstage,andabioticstressfactors,wascompiledfor2006–2011,and
wasinspectedforoutliersaswellasforunivariateandmultivariatenormality. Nutrientconcentrations
were natural-log transformed; while, other traits were transformed following individual Box-Cox
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transformations to optimize normality when necessary, then back-transformed for reporting [52].
Percent change in PP (±SD) was estimated annually for each trait in each wheat genotype as the
normalizedabsolutedifferencebetweenitsvalueunderthecontrolanditsvalueundereachofthe
remainingabioticstresstreatments(e.g.,|NN−SH|;i.e.,thenormalizedvariationofthephenotype).
√
Phenotypic(PCV)andgenotypic(GCV)coefficientsofvariationwereestimatedas( σ2p/sample
√
mean)×100;and,( σ2g/samplemean)×100;respectively;then,thenarrow-senseheritabilitywas
estimatedforeachtraitash2=σ2g/[σ2g+(σ2ge/e)+(σ2r/re)],whereσ2pisthephenotypicvariance;
σ2gisthevarianceofthegenotype,σ2geisthevarianceofthegenotype×environmentinteraction,
σ2ristheresidual,eisthenumberofenvironments,andristhenumberofreplicates[13].
2.7. StatisticalAnalysesandModeling
Univariateandmultivariatestatisticalanalysesproceduresappropriateforgenerating,analyzing,
and modeling primary or secondary statistics were employed in assessing the effects of single or
multipleinteractingfactorsonthePPofagronomictraitsandmicronutrientsduringsixyearsoffield
testing. Frequencydistribution,mean±SDforPPofagronomictraitsaveragedoversix-yearswere
plottedforeachwheatgenotypeandstressphase;thevarianceaccountedforbydifferencesbetween
stressphaseswasestimatedandreported,asadjustedR2calculatedas1−[(1−R2)×n−1]/[n−k−
1];wherenisthenumberofdatapointsandkisthenumberofindependentregressors(i.e.,thenumber
ofvariablesinthemodel,excludingtheconstant)[53]. Box-plots(mean±95%confidenceintervals)
andmeanseparationwereperformedonphenotypicplasticitiesofbiomass,populationdensity,fertile
tillers,grainyield,spikefertilityindex,spikeharvestindex,kernelsm−2,grainproteincontent,and
C:N ratio, as well as micronutrients for wheat genotype-stress phase-growth stage-abiotic stress
factorialcombinations;meanseparationwasperformedusingTukey’sHSD(p<0.05). Amicronutrient
indexwascalculatedaspercentofthelargestcumulativemicronutrientsdensity.
Aspartofthestatisticalanalyses,measuredorestimateddatawereadjustedforcovariateeffects,
and a generalized linear model (GLM) was used to estimate the effects of main factors and their
two-wayandthree-wayinteractionsonplasticityestimatesofagronomictraits,C:Nratio,micronutrient
densities,andtheirindex. TheprobabilityofobtaininglargerF-valueforeachtestwasusedasan
indicatorofsignificantdifferencesbetweenfactorlevels;inwhichcase,pair-wisesignificantpercent
differences(PSPD)wereobtainedfromamatrixofmeanseparationprobabilitiesof[(n×n−1)/2],
wherenisthenumberofmeans,usingTukey’sHSD[53]. SummarystatisticsoftheGLMincluded
plasticity mean response (and its RMSE) to each factor and adjusted R2. Hierarchical multi-way
and multi-variate cluster analyses of standardized data, using Unweighted Pair Group Method
withArithmeticMean(UPGMA)linkageandWardvariancemethods[53],aswellasdiscriminant
analysis [54], were used to quantify trait relationships and the level of discrimination between
sub-factors (i.e., percent correct classification of stress phases, growth stages, and levels of abiotic
stress)foreachwheatgenotype.
Phenotypicplasticityestimatesforeachfactorintheexperimentwheresubjectedtoin-depth
statistical analyses to explore the relationships and dependencies within and among independent
(biomass, population density, fertile tillers, grain yield, spike harvest index, spike fertility index,
kernels m−2, grain protein content, and C:N ratio) and dependent (Cu, Fe, Mn, and Zn) variables
atthebivariateandmultivariate(i.e.,matrix)levels. Dataforindependentvariablesweresubjected
to Factor analysis to quantify trait relationships within independent factors; Partial Least Squares
Regression(PLSR)wasusedtomodeltherelationshipbetweendependentandindependentvariables;
a validation step (using 30% of data) was included to verify the utility of the developed PLSR
model [53]. The resulting model coefficients for centered and scaled data were used to construct
acorrelationmatrixbetweentheindependentvariablesforeachsub-factor,whichwerethensubjected
toaManteltesttoquantifyandtestthesignificanceofrelationshipsatthematrixlevel[55,56].Similarly,
correlationmatricesweredevelopedforeachsub-factorwhileusingindependentvariables,andthen
were subjected to a Mantel test, as described before. Bivariate functional relationships between
Agronomy2018,8,139 7of31
micronutrients within sub-factors were estimated using Major Reduced Axis (RMA), and then a
Jackknifed R2 value was estimated for these relationships [57]. A multivariate, non-dimensional
distance(MahalanobisD2)wasestimatedbetweensub-factorswithineachfactorintheexperiment
andthentestedforsignificance[54]. Threelatentvariableswereproposedtoperformconfirmatory
factoranalysisasafinalstepinmultivariateanalysesofC:N,Cu,Fe,Mn,andZntoexplainthelatent
variable“Nutrients”. Asecondvariableset(fertiletillers,kernelsm−2,spikefertilityindex,andspike
harvestindex)wasusedtoexplainthelatentvariable“YieldComponents”. Athirdsetofvariables
Agronomy 2018, 8, x FOR PEER REVIEW 7 of 30
(biomass, grain yield, and population density) was used to explain the latent variable “Yield.” A
confirmatothrey efxapcetroimremnto adnedl t(hCenF Ates)tewda fsord seigvneifliocapnecde [f5o4r]. eTahcrhee olafteTn.ta veasrtiiavbulems wanerde pTr.odpuorseudm tou pseirnfogrmqu antitative
datasummcoanrfiizrmedatoforyr ftahcetocr oannatlryoslis( NasN a faintaSl PsteIp) ainn dmualttitvhaeriaetne danoaflytshese oefx Cp:eNr,i mCue, nFte,u Msinn, gansdu mZnm toa rizeddata
explain the latent variable “Nutrients”. A second variable set (fertile tillers, kernels m−2, spike fertility
forthedoubleabioticstressofshortgrowingseasonandhighpopulationdensity(SHatSPII)[58].
index, and spike harvest index) was used to explain the latent variable “Yield Components”. A third
set of variables (biomass, grain yield, and population density) was used to explain the latent variable
3. Results
“Yield.” A confirmatory factor model (CFA) was developed for each of T. aestivum and T. durum using
quantitative data summarized for the control (NN at SP I) and at the end of the experiment using
3.1. Inter-saunmdmInartirzaesdp decaitafi cfoVr athreia dtoiounblien abPihoteinc osttryepssic oPf slhaostrit cgirtoywing season and high population density
(SH at SP II) [58].
Frequency distribution, range, and mean ±SD of PP estimates of agronomic traits and
micronutr3i.e Rnetssulitns dexassessedontwowheatgenotypesrepresentingT.durumandT.aestivumand
then subjected to two stress phases are presented in Figure 1. Included is variance estimates (R2)
3.1. Inter- and Intraspecific Variation in Phenotypic Plasticity
accountedforbydifferencesbetweenabioticstressphases. MostPPswerenormallydistributedwhen
Frequency distribution, range, and mean ±SD of PP estimates of agronomic traits and
stressphaseswerenotconsidered. AfewPPsinT.durumwerenegatively(e.g.,populationdensity)or
micronutrients index assessed on two wheat genotypes representing T. durum and T. aestivum and
positivelyskewed(e.g.,grainyield,andnutrientindex). SlightlylargernumberofmeanPPswere
then subjected to two stress phases are presented in Figure 1. Included is variance estimates (R2)
≤0.75 thaancctohuonsteed wfori tbhy dmiffeearenncPesP bset>we0e.n7 5ab.ioBtioc tshtreossf pthhaesews. Mheosatt PgPse wnoertey npoermsadlliys dpilsatryibeudtead wwhiedne range of
PPs(0.40–s0tr.e9s0s )phfoasresb wioemre ansost ,copnospiduerleadti. oAn fedwe PnPssi tiyn, Ta. ndudrumm iwcreoren nuetgraiteivnetlsy. (eT.gh.,e poepsutilmatiaonte dsenfosirtyp) roductive
or positively skewed (e.g., grain yield, and nutrient index). Slightly larger number of mean PPs were
tillersandgrainyieldhadnarrowranges(0.75–0.95),andastillnarrowerrangewasfoundforspike
≤0.75 than those with mean PPs > 0.75. Both of the wheat genotypes displayed a wide range of PPs
fertilityindexinbothgenotypes(0.80–0.92). Largedifferencesbetweenwheatgenotypeswerefound
(0.40–0.90) for biomass, population density, and micronutrients. The estimates for productive tillers
forkernelsanmd g−r2aiann ydieltdh headsp niakrreowha rravngeesst (i0n.7d5e–0x..95), and a still narrower range was found for spike fertility
Frequinednecxy ind biostthr ibgeuntoitoynpess f(o0r.80m–0o.9s2t).P LPasrgde idfiffeferreedncbese tbwetweeeenn bwohtehats gtreensostyppehs awseerse. foEusntdim foart es during
PS I exceekderendelst hme−2i rancdo tuhne stepirkpea hratrsvedstu irnidnegx. PS II; however, with a few exceptions (e.g., kernels m−2
Frequency distributions for most PPs differed between both stress phases. Estimates during PS
in T. durum). Minimum PPs for PS II (e.g., biomass) or substantially (e.g., population density and
I exceeded their counterparts during PS II; however, with a few exceptions (e.g., kernels m−2 in T.
kernelsmd−u2ruimn).T M.dinuimruumm) PwPse froer PslSi gIIh (etl.gy., lbairogmearsst)h oar nsutbhsetainrticaolluy n(et.egr., ppaorptusladtiuonr idnegnsPitSy aIn.dT hkeersneelds ifferences
contributemd−2t oint hTe. sdmuruamll)R w2e(r≤e 0sl.i2g0h)tlfyo rlabrgieorm thaasns athnedir pcoopunutlearptiaortns ddeunrisnigt yPiSn Ib. oTthhesgee ndioffteyrepnecse.s Also,they
contributecdonttoribmueteddi utom the(0 s.m20all> RR2 (2≤0≤.200) .f5o0r )bifoomragsrsa ainndy pioepldul,astipoink deefnesrittyi liint ybointhd geexn,oatynpdesm. Ailcsroo, nthueytr ientindex
contributed to medium (0.20 > R2 ≤ 0.50) for grain yield, spike fertility index, and micronutrient index
(micronutrientsindexinbothgenotypes);and,tolargevarianceestimates(>0.50)forgrainprotein
(micronutrients index in both genotypes); and, to large variance estimates (>0.50) for grain protein
contentincbonottehntg ine nbootthy gpeenso,tyapneds, faondr kfoerr kneernlselms m−−22 iinn TT. d.udruumru. m.

Figure1.Frequencydistribution(y-axis),range,andmean±SDofphenotypicplasticities(x-axis)of
Figure 1. Frequency distribution (y-axis), range, and mean ± SD of phenotypic plasticities (x-axis) of
agronomiacgtrroaniotmsiac ntrdaitms aincdr omnicurotrniuetnritesntisn inddeexx eessttimimataetde odn otwnot gwenootgyepneso rteyppreessenrteinpgr Te.s deunrutimn ganTd .Td. urumand
T.aestivumaeastnivdumth aennd tshuebn jseucbtjeedctetdo ttow twoos sttrreessss pphhasaesse, esa,ceha ocfh thorfeet hyeraeres.y ears.
Agronomy2018,8,139 8of31
3.2. AssessmentofAgronomicandMicronutrientsPPs
TeststatisticsthatwerederivedfromresultsofaGLManalysis,levelofsignificance,andpercent
significantpair-wisedifferencesbetweensub-factors;phenotypicandgenotypiccoefficientsofvariation
andnarrow-senseheritabilityofPPsofagronomictraitsandmicronutrientsofT.aestivumandT.durum
wheatgenotypesattributedtofixedsinglefactorsandtheirinteractionsinasix-yearfieldstudyunder
abiotic stress are presented in Table 2. The photo-thermal quotient (PTQ) was the most effective
covariate, accounting for significant variances (R2, 0.34–0.72) in PPs of agronomic traits, with the
exceptionofspikefertilityindex,andkernelsm−2; theintegratedsoilconductivitymeasure(ECa)
explainedsignificantportionsofvarianceinfertiletillers(0.21)andgrainyield(0.35);while,thespatial
variation,mediatedbycolumnsandrowsofexperimentalplotsascovariates,accountedforsmall
(0.25)varianceingrainyield;however,noneofthecovariatesaccountedforsignificantportionsof
variancesinmicronutrientsindexorC:Nratio.
Agronomy2018,8,139 9of31
Table2. Teststatisticsderivedfromresultsofageneralizedlinearmodelanalysis, levelofsignificanceandpercentsignificantpair-wisecomparisons(PSPD);
phenotypic,genotypicvariancecomponentsandnarrowsenseheritabilityestimatesofplasticityestimatesofagronomictraitsandmicronutrientsofT.aestivumand
T.durumwheatgenotypesattributedtofixedsinglefactorsandtheirinteractionsina6-yearfieldstudyunderthreeabioticstresslevelsascomparedtonon-stress
treatment(*,**,significantatthe5%and1%levelofprobability,respectively,usingTukey’sHSD).
Covariates PlasticityofAgronomicTraits PlasticityofC:NandMicronutrients&TheirIndex,MNI
BM PD FT GY SFI SHI KNO GPC C:N Cu Fe Mn Zn MNI
Varianceexplainedbycovariate(R2)
Photothermalquotient(R2) 0.69** 0.56** 0.72** 0.62** 0.25 0.34* 0.09 0.45* 0.05 0.11 0.09 0.07 0.05 0.06
IntegratedECa(HandV) 0.15 0.09 0.21* 0.35* 0.08 0.08 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0
GIS(Columns×Rows) 0.14 0.0 0.07 0.25* 0.15 0.11 0.0 0.05 0.0 0.06 0.08 0.0 0.10 0.05
Fixedfactors Teststatistics(Probabilityof>Fduetofixedfactor)
Genotypes 0.47 0.83 0.26 0.001 0.001 0.001 0.001 0.01 0.30 0.05 0.009 0.001 0.013 0.02
PSPD - - - 100 100 100 100 100 - 100 100 100 100 100
Stressphases 0.13 0.20 0.05 0.04 0.05 0.05 0.04 0.05 0.98 0.005 0.14 0.68 0.03 0.34
PSPD - - 100 100 100 100 100 100 - 100 - - 100 -
Growthstages 0.32 0.05 0.04 0.01 0.08 0.12 0.001 0.01 0.12 0.05 0.04 0.02 0.03 0.001
PSPD 100 100 100 - - 100 100 100 100 100 100 100
Abioticstresses 0.71 0.02 0.05 0.28 0.77 0.51 0.05 0.05 0.003 0.05 0.22 0.30 0.16 0.79
PSPD - 100 33 - - - 30 25 50 17 - - - -
Genotypes×Stressphases 0.81 0.26 0.05 0.05 0.05 0.05 0.04 0.05 0.05 0.04 0.35 0.01 0.88 0.05
PSPD - - 33 67 67 67 67 25 10 67 - 30 - 30
Genotypes×growthstages 0.82 0.05 0.05 0.05 0.05 0.05 0.001 0.01 0.05 0.001 0.05 0.001 0.021 0.001
PSPD - 16 33 50 50 50 75 25 17 83 82 82 83 50
Genotypes×Abioticstresses 0.001 0.05 0.05 0.001 0.001 0.001 0.001 0.01 0.001 0.05 0.001 0.001 0.001 0.001
PSPD 100 7 25 70 64 70 72 50 35 10 50 82 56 72
Growthstage×abioticstresses 0.05 0.03 0.59 0.07 0.11 0.09 0.02 0.08 0.05 0.91 0.05 0.03 0.21 0.21
PSPD 18 18 - - - 50 - 20 - 35 63 - -
Growthstage×abioticstress×Genotypes 0.23 0.26 0.05 0.32 0.25 0.19 0.05 0.12 0.05 0.046 0.05 0.011 0.033 0.001
PSPD - - 10 - - - 10 10 35 28 60 25 79
Stressphase×Abioticstress×Genotypes 0.15 0.35 0.05 0.02 0.04 0.02 0.03 0.22 0.37 0.03 0.05 0.05 0.02 0.008
PSPD - - 8 62 53 50 62 - - 52 17 30 30 67
Stressphase×Growthstage×Genotypes 0.55 0.89 0.05 0.19 0.34 0.62 0.50 0.31 0.25 0.011 0.05 0.047 0.05 0.029
PSPD - - 25 - - - - - - 79 30 43 43 50
SummarystatisticsofGeneralizedLinearModelFit
MeanofResponse(PPs) 0.68 0.64 0.86 0.88 0.86 0.59 0.82 0.75 0.78 0.67 0.79 0.66 0.75 0.72
RMSE 0.045 0.046 0.022 0.015 0.006 0.022 0.013 0.022 0.035 0.035 0.037 0.044 0.047 0.054
AdjustedR2 0.75 0.82 0.80 0.95 0.95 0.93 0.98 0.79 0.80 0.92 0.76 0.87 0.82 0.69
Percentchangeinphenotypicplasticityestimatesduetoabioticstress(mean±95%confidenceintervals;differencebetweenSHin2011andNNin2006)
T.aestivum −36.5(12.8) −22.3(7.7) −27.3(5.6) −32.0(9.5) −25.5(5.0) −12.8(3.8) −15.3(2.8) 3.8(1.9) 4.2(2.0) 5.4(0.9) 6.2(1.8) 8.1(2.8) 5.9(1.4) 6.6(1.5)
T.durum −40.2(9.2) −32.8(6.5) −29.6(7.2) −38.3(9.0) −37.2(7.8) −15.2(1.6) −13.8(3.1) 4.6(1.8) 3.4(1.5) 5.0(1.1) 6.2(2.0) 7.5(2.2) 5.5(1.4) 6.2(1.7)
PhenotypicCoefficientsofVariation(PCV,%)(2006–2011)
T.aestivum 22.9 28.8 29.9 28.6 8.9 6.3 5.2 16.8 3.4 15.2 17.9 10.5 8.3 12.5
Agronomy2018,8,139 10of31
Table2.Cont.
Covariates PlasticityofAgronomicTraits PlasticityofC:NandMicronutrients&TheirIndex,MNI
T.durum 18.6 28.0 27.5 29.5 9.5 6.5 5.0 15.3 3.0 18.6 19.0 12.5 9.5 13.5
GenotypicCoefficientsofVariation(GCV,%)(2006–2011)
T.aestivum 23.3 24.6 25.6 25.4 7.2 5.6 4.9 15.0 3.0 13.8 14.9 8.3 7.9 10.8
T.durum 24.6 23.3 25.3 25.0 8.3 5.9 4.9 14.2 3.0 17.5 12.3 9.5 8.8 11.2
Narrow-senseheritability(h2)(%)
T.aestivum 67.5 64.3 62.3 59.8 75.3 88.9 80.5 56.9 89.3 38.9 34.6 48.9 52.8 43.2
T.durum 69.2 65.3 65.5 56.8 77.6 87.0 87.2 58.2 88.6 40.2 35.8 46.3 50.7 42.5