Assessinggeneticvariationingrowthanddevelopment
ofpotato
MuhammadSohailKhan
Thesiscommittee
Thesissupervisors
Prof.dr.ir.P.C.Struik
ProfessorofCropPhysiology,WageningenUniversity
Prof.dr.ir.F.A.vanEeuwijk
ProfessorofAppliedStatistics,WageningenUniversity
Thesisco‐supervisors
Dr.X.Yin
Assistantprofessor,CentreforCropSystemsAnalysis,WageningenUniversity
Dr.ir.H.J.vanEck
Assistantprofessor,LaboratoryofPlantBreeding,WageningenUniversity
Othermembers
Prof.dr.ir.M.K.vanIttersum,PlantProductionSystems,WageningenUniversity
Dr.J.‐P.Goffart,WalloonAgriculturalResearchCentre,Gembloux,Belgium
Prof.dr.ir.A.J.Haverkort,PlantResearchInternational(PRI),WageningenUR
Dr.ir.E.Heuvelink,HorticulturalSupplyChains,WageningenUniversity
Thisresearchwasconductedundertheauspicesof theC.T.deWitGraduateSchool
forProductionEcologyandResourceConservation.
Assessinggeneticvariationingrowthanddevelopment
ofpotato
MuhammadSohailKhan
Thesis
submittedinfulfilmentoftherequirementsforthedegreeofthedoctor
atWageningenUniversity
bytheauthorityoftheRectorMagnificus,
Prof.dr.M.J.Kropff,
inthepresenceofthe
ThesisCommitteeappointedbytheAcademicBoard
tobedefendedinpublic
on20September2012
at11a.m.intheAula.
MuhammadSohailKhan(2012)
Assessinggeneticvariationingrowthanddevelopmentofpotato
245pages
PhDThesis,WageningenUniversity,Wageningen,theNetherlands
Withreferences,withsummariesinEnglishandDutch
ISBN978–94–6173–359–7
Abstract
Khan,M.S., 2012.Assessinggeneticvariation ingrowthanddevelopmentofpotato.
PhDthesis,WageningenUniversity,Wageningen,theNetherlands,withsummariesin
EnglishandDutch,245pp.
Due to increasing food demand and changing diets potato (Solanum tuberosum L.) is
becoming a subsistence crop in many regions. However, the agronomy and whole crop
physiologyoftuberyieldproductionisextremelycomplex,duetogenotypeandenvironment
specific effects on crop physiological and morphological characteristics. Such intrinsic
complexities complicate the manipulation of yield determining traits and make their
prediction a challenging task. The genetic improvement of tuber yield can be understood
moremechanisticallyby investigatingand interpreting the relationshipsbetween themain
attributesofcropgrowth.
This thesisaimstodevelopanapproachtoquantify theyieldof individualgenotypes
and to estimate parameters which may reveal the effects of genetic and environmental
factorsontheimportantplantprocessescontrollingtuberyieldvariationamongalargesetof
F1 (SH83‐92‐488 RH89‐039‐16) genotypes of potato and a set of standard cultivars
coveringawiderangeofmaturitytypes.
Itfirstpresentsamodelapproachtoanalysethetimecourseofcanopycoverandtuber
bulkingduringtheentirecropcycleasafunctionofthermaltimeintermsoflargenumberof
physiological component traits and explain their inter‐relationships and impact on crop
maturity and tuber yield production across six contrasting field experiments. The results
indicatedthatthelengthofthecanopybuild‐upphase(DP1)wasconservativewithrespectto
genotype’smaturity type,but thedurationofmaximumcanopycover (DP2)and thedecline
phase(DP3)variedgreatly,withlatergenotypeshavinglongerDP2andDP3andthusahigher
areaunderwholegreencanopycurve(Asum).Valuesoftuberbulkingrate(cm)werehighest
for earlymaturing genotypes followed bymid‐late and then late genotypes. Latematuring
genotypeshadlongesteffectivedurationoftuberbulking(ED)followedbymid‐lateandearly
genotypes.Asaresulttuberyield(wmax)washigherinlategenotypesthaninearlygenotypes.
Theradiationuseefficiency(RUE)valueswerehighestforearlymaturinggenotypesfollowed
bymid‐lateandlategenotypeswhereasnitrogen(N)useefficiency(NUE)washighestinlate
maturinggenotypesfollowedbymid‐earlyandearlygenotypes.
High genetic variability and high heritability for most of these traits were found.
Resultsindicatedthatincreasedtuberyieldbyindirectselectionforoptimalcombinationof
important physiological traits can be achieved. While using these traits as a criterion for
selection, the causal physiological relationships and trade‐offs must be considered
simultaneously.
Our molecular dissection of traits determining the dynamics of canopy cover, tuber
bulking, and resource (radiation, nitrogen) use efficiencies identified several QTLs, the
mappingpositionofeach identifiedQTL, the interactionofQTLwithenvironment(QTLE)
and the magnitude of the QTL effect in explaining genetic variance in both SH and RH
parental genomes. The QTL results indicated that one particular chromosomal position at
18.2cMonpaternal(RH)linkagegroupVwastightlylinkedtothegenotype’searlinessand
controlling nearly all the traits and explaining thephenotypic variance byup to79%.This
suggestedthepleiotropicnatureoftheQTLformostofthetraitsdeterminingcropmaturity
andtuberyields.AnumberofQTLsfortraitswerenotdetectedwhentuberyieldpersewas
subjectedtoQTLanalysis.ThephenotypicvarianceexplainedbytheQTLsfortuberyieldper
sewasalsolowerthanforothertraits.
The physiological and quantitative knowledge gained was used to evaluate the
conventionalsystemofmaturitytypeandtoquantifyandre‐definetheconceptofmaturity
type on a physiological basis for a large set of genotypes. Four new physiological based
maturity criteria were developed based on four canopy cover and tuber bulking traits.
Physiologicalmaturity typecriteria tended todefinematurityclasses lessambiguouslyand
were easily and clearly interpretable compared to the conventionally used method of
definingmaturity.
Thecapabilityofanecophysiologicalmodel‘GECROS’wastestedtoanalysedifferences
in tuber yield of potato. Themodel yielded a reasonably good prediction of differences in
tuber yield across environments and across genotypes. Model analysis identified the
genotypickey‐parametersaffectingtuberyieldproductionandNmax(i.e.totalcropNuptake)
contributedmost to thedeterminationof tuberyield.Theresultsconcludedthatgenotypes
withhigherNmaxandlowertuberNcontentexhibitedhighertuberdrymatteryield.Further
analysis of the genotypic parameters should be performed in conjunction with molecular
markersinordertodeterminetheirgeneticcontrolandtoproceedtowardsQTL‐basedcrop
modellingapproach.
Thisthesisidentifiedthedominantcomponenttraitsmostlyinvolvedintheformation
of a tuber yield and gave insight into the possibilities of genetically and physiologically
manipulating the size or number of such traits. The information obtained should help in
marker‐assisted selection as well as in designing ideotypes for specific and/or diverse
environments.However, tomake significant contributions forbreeding, there is aneed for
furtherresearcheffortstoevaluatethecombinedphysiologicalandgeneticapproach.
Keywords:Potato(SolanumtuberosumL.),segregatingpopulation,canopycoverdynamics,
tuberbulkingdynamics,beta function, thermal time, componentsofvariance,genotype‐by‐
environment interaction (GE), heritability, QTL mapping, QTL‐by‐environment (QTLE)
interaction, complex traits, ecophysiological cropmodel, GECROS, cultivar choice,maturity
type,ideotypebreeding,tuberyield.
Dedicatedtomybelovedparents&
highlyesteemedsupervisors
Contents
Chapter1: Generalintroduction 1
Chapter2: Analysinggeneticvariationinpotato(SolanumtuberosumL.)
using standard cultivars and a segregating population. I.
Canopycoverdynamics
13
Chapter3: Analysisofgeneticvariationofpotato(SolanumtuberosumL.)
using standard cultivars and a segregating population. II.
Tuberbulkingandresourceuseefficiency
83
Chapter4: Model‐based evaluation of maturity type of potato using a
diverse set of standard cultivars and a segregating diploid
population
119
Chapter5: Anecophysiologicalmodelanalysisofyielddifferenceswithin
an F1 segregating population of potato (Solanum tuberosum
L.)grownunderdiverseenvironments
137
Chapter6: Generaldiscussion 159
References 191
Summary 219
Samenvatting(SummaryinDutch) 225
Acknowledgments 231
Curriculumvitae 241
PE&RCPhDEducationCertificate 243
Funding 245
Abbreviations Tb Basetemperature(˚C)
To Optimumtemperature(˚C)
Tc Ceilingtemperature(˚C)
tm1 Inflexionpointduringcanopybuild‐upphase(td)
t1,DP1 Period fromplant emergence tomaximumcanopy cover (moment and
durationsinceemergence,respectively)(td)
t2 Onsetofcanopycoversenescence(td)
te Timeofcompletesenescenceofcanopycoverorcropmaturity(td)
vmax Maximumlevelofcanopycover(%)
cm1 Maximumcanopygrowthrateduringbuild‐upphase(%td‐1)
c1 Averagecanopygrowthrateduringbuild‐upphase(%td‐1)
c3 Averagecanopysenescencerateduringdecline‐phase(%td‐1)
DP2 Totaldurationofmaximumcanopycoverphase(td)
DP3 Totaldurationofcanopysenescencephase(td)
A1 AreaundercanopycurveforDP1(td%)
A2 AreaundercanopycurveforDP2(td%)
A3 AreaundercanopycurveforDP3(td%)
Asum Areaunderwholegreencanopycurve(td%)
tB Onsetoflinearphaseoftuberbulking(td)
tE Endoflinearphaseoftuberbulking(td)
ED Totaldurationoftuberbulking(td)
cm Rateoftuberbulking(gtd‐1)
wmax Maximumtuberdrymatteratcropmaturity(gm‐2)
RUE Radiationuseefficiency(gDMMJ‐1)
NUE Nitrogenuseefficiency(gDMg‐1N)
mV Periodfromplantemergencetoonsetoftuberbulking(td)
mR Durationbetweenonsetoftuberbulkingandcropmaturity(td)
Hmax Maximumplantheight(m)
nSO MaximumtuberNconcentration(gNg‐1DM)
Nmax TotalcropNuptakeatcropmaturity(gNm‐2)
td Thermalday
cM centiMorgans
CHAPTER1
Generalintroduction IncreasingsustainableyieldAmajoragriculturalchallenge facing theworld today isprovidingsufficient foodto
meet thedemandsof a rapidly growingpopulation.The goal of anyplant breeding
programme is the development of new, improved cultivars or breeding lines for
particulartargetareasandtoincreasethesustainableyieldandqualityofcropplants
to meet projected increases in global food demand (FAO, 1996). This normally
involvesmanipulatingcomplextraitsassociatedwithplantgrowthanddevelopment,
usuallyinproductionenvironmentsthatarehighlyvariableandunpredictable.
Genetic improvementcanbeachievedbyselectingdirectly foraprimary trait
(suchasyield)inatargetenvironment(CeccarelliandGrando,1996).Thishasbeen
referredtoasempiricalor traditionalbreeding.Another‐ indirectway ‐ is toselect
forasecondarytrait(s)thatisputativelyrelatedwithahigheryieldpotentialand/or
totheimprovedbehaviorofthecropwhengrowninaparticularenvironment.Thisis
knownasanalyticalorphysiologicalbreeding.
Over thepastcentury,mostof theprogress inbreeding inmostcropsspecies
hasbeenderivedfromempiricalbreeding,whichhastakenyieldasthemaintraitfor
selection(Austinetal.,1989;Khush,1987;Russell,1993;Evans,1993;Slaferetal.,
1994).Forinstance,incereals,yieldincreaseshavebeenpossiblethroughthegradual
replacement of traditional tall cultivars by semi dwarf and fertiliser‐responsive
varietieswith superiorharvest indices.The term “GreenRevolution”was coined to
describethisprocess.
However, most agronomic traits are physiologically and genetically complex
(Larketal.,1995;Orfetal.,1999;DaniellandDhingra,2002;Stuberetal.,1999).For
instance, yield is the outcome ofmany underlyingmorphological and physiological
processes (Bezant et al., 1997). Furthermore, it is a quantitatively inherited trait
underpolygeniccontrol,andcharacterisedby lowheritabilityandahighgenotype‐
by‐environment(GE)interaction(AllardandBradshaw,1964;Jacksonetal.,1996;Tardieu, 2003). Such intrinsic complexities complicate the manipulation of
quantitativecroptraitsandmaketheirpredictionachallengingtask.Thereforenew
approache(s) which could complement traditional with analytical selection
methodologies to further improve crop yields and the overall efficiency of the
breedingprocessareofconsiderableinteresttoplantbreeders.
Chapter1
2
GenomicsbasedapproachesRecentadvancesinagriculturalgenomicsandmarker‐assistedselectionhavetotally
revolutionisedandenhancedtheplantbreedingprocess(SomervilleandSomerville,
1999;Yinetal.,1999a;Huangetal.,2002;Knight,2003).TheadventofDNA‐based
molecularmarkers(Botsteinetal.,1980;Patersonetal.,1988)andnewstateofthe
art statisticalmethods (Lander andBotstein, 1989; Jansen, 1993; Jansen and Stam,
1994) havemade it possible to localise polygenes controlling complex quantitative
traits and study the effect of individual genes on the trait, which was difficult in
classicalquantitativegenetics.
TheacronymQTLstandsforQuantitativeTraitLocus,withpluralQTLs,andhas
come to refer to a genomic region associated with a quantitative trait (Yin et al.,
1999a).Numerousstudieshavebeenreportedon identificationofQTLs forvarious
quantitativetraits inhumans,animals,andplants(Yinetal.,1999a).Theincreasing
knowledgeonQTLsforimportantagronomictraitscanprovidenewavenuesforthe
breederstospeeduptheprocessofincreasingcropyields(MorandiniandSalamini,
2003).
AlthoughQTLmappingtechnologieshaveallowedsignificantadvancesincrop
genetic improvement, there still exist challenges to increase the yield potential of
crops (Yin et al., 2003a). One major difficulty in the use of QTLs of traits is their
instability across environments (Reymond et al., 2003) due to complex QTL‐by‐
environment(QTLE)interactions.Forinstance,RibautandHoisington(1998)found13QTLsinastudyoffloweringdatesofmaize,butonlythreewerecommontothree
experimentswithdifferent levelsofwaterdeficit. Consequently,manyQTLs canbe
onlydetectedinanarrowrangeofenvironmentalconditions(Zhouetal.,2007)and
theclassicalgeneticmodelsbuiltfromQTLanalysishaveacorrectpredictiveability
onlyinalimitedrangeofconditions.
PopularQTL‐analysismethodswillhavedifficultieshandlingthesecomplexities.
Without increasing population sizes, limited reliability in QTL detection and
estimationcanbeachieved(KearseyandFarquhar,1998).Toovercomethesebottle
necks, support from other disciplines is required. There is an identified need to
separate factors influencing a given phenotypic trait and shifting from highly
integrated traits tomore gene‐related traits (Yin et al., 2002). Several studieshave
underlinedthepotentialinterestinbuildingsuchalink(Hammeretal.,2002,2006;
Tardieu, 2003; Yin et al., 2004). Functional genomics, systems biology, and crop
physiologycanjointlyimprovegeneticanalysisandbreedingefficiencies.
RoleofsystemsapproachesinmodelbasedbreedingThereisincreasingadvocacyfortheapplicationofcropphysiologicalknowledgeand
integrativemodellinginbreedingforcomplextraits(Camposetal.,2004;Edmeades
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3
et al., 2004; Yin et al., 2004; Hammer et al., 2005). Crop modelling can play a
significant part in system approaches by providing a powerful capability for
phenotypepredictionandyieldscenarioanalysis(Hammer,1998;Cooperetal.,2002;
Hammeretal.,2004).
Crop growth models are simplified mathematical representations of the
interacting biological and environmental components of the dynamic plant system.
Ecophysiological crop growth models have been developed extensively during the
last decades by integrating knowledge across many disciplines such as crop
ecophysiology,micrometeorology,soilscience,andcomputingtechnologies(Loomis
et al., 1979; Ritchie, 1991; Boote et al., 1996; Bouman et al., 1996;McCown et al.,
1996). Thesemodels have been conventionally used to predict the performance of
given cultivars under various environmental conditions, and are now increasingly
beingusedinbreedingprogrammes(Aggarwaletal.,1997).
One of themain applications of ecophysiological crop growthmodels is their
explanationof differences in yieldpotential of genotypeson thebasis of individual
physiologicalparameters(Kropffetal.,1995;HaverkortandKooman,1997).Thisis
possible because their parameters can be used to mimic genetic characteristics of
plants when crop growth models become more mechanistic and comprehensive
(Boote et al., 1996). Values of these parameters are specific to each genotype and
constantunderawiderangeofenvironmentalconditionsthereforeareoftenreferred
toasgeneticcoefficients(Hoogenboometal.,1997;Booteetal.,2001;Tardieu,2003;
Bannayan,2007), reflecting theawareness that thevariationof theseparameters is
undergeneticcontrol(Stam,1996).Theseparameterscouldbethereforeconsidered
asquantitativetraitsandareamenabletofurtheranalysis(Yinetal.,2004;Quilotet
al.,2005),e.g.forQTLmapping,andevaluatinganddesigningideotypes(Loomisetal.,
1979;Kropffetal.,1995;Booteetal.,1996;HaverkortandKooman,1997;Yinetal.,
2004;YinandStruik,2008).
Ecophysiological models can make it possible to explore the impact of new
genotypes and the contribution of individual physiological traits on yield by
simulatinggenotypesunderanydefinedenvironmentalscenariosaswellasarange
of management options (Stam, 1996; Bindraban, 1997; Hoogenboom et al., 1997;
Asseng and Turner, 2007). Such models could help in understanding of GEinteraction andmay speedup crop improvement for specific environments (Kropff
andGoudriaan,1994;Robertsetal.,1996;Booteetal.,2001;Slafer,2003;Yinetal.,
2004; Mayes et al., 2005) and to assist plant breeding to better quantify crop
genotype‐phenotype relationships (Yin et al., 2000a, 2004; Reymond et al., 2003;
Hammeretal.,2006).
Improvedphenotypicpredictionsviacropmodellingresultfromtheirabilityto
Chapter1
4
dealwiththecomplexinteractionsamongplantgrowthanddevelopmentprocesses,
environmental effects and genetic controls. Thispredictive capacity of thedynamic
model could be used for favourably weighting more important QTLs during their
selection in marker‐assisted breeding programmes. Crop physiology using crop
models therefore can also reinforce the QTL analysis of complex traits, thereby
improving breeding efficiency and enhancing genetic design (Yin et al., 1999a,b,
2000a).
WhenQTL information is incorporated into cropmodels, the ‘QTL‐basedcrop
models’ could provide valuable insights into which combinations of alleles favour
adaptation to specific environments, therefore should narrow down genotype–
phenotypegaps.Theycanpotentiallybeapowerfultooltoresolvethephenomenon
ofGE interactionand thereforeovercome the limitation thatQTLmapping cannotextendanexperimentalresultofoneenvironmenttoanotherenvironmentcondition
(Messinaetal.,2006).Basedonrecentstudies(e.g.Reymondetal.,2003;Yinetal.,
2005a; Gu et al., 2012), there is now increasing advocacy forQTL‐basedmodelling
approaches,i.e.linkingcropmodellingwithQTLmappingforcomplextraits.
If cropphysiologyandgeneticsarecombined judiciously,cropphysiologyand
modellingresearchcanreinforce thegeneticanalysisof complex traitsand thereby
canofferthepossibilitiesofmodel‐basedbreeding.Promotingthisnovelroleofcrop
physiology,however,requiresanewgenerationofcropmodels.Suchmodelsshould
have robust model architecture, numerical consistency, stability, and enhanced
heuristics. Most importantly, crop models should include genotype‐specific
parameters for incorporating the latest findings offered by the rapid advances in
functionalgenomics(Yinetal.,2004).Suchmodelswouldgreatlyenhanceourability
to translate short‐time scale gene‐level information to the crop performance in
continuouslychangingfieldenvironments.
GECROS:AnimprovedstateoftheartecophysiologicalmodelRecently, a state of the art ecophysiological crop growth model called GECROS
(Genotype‐by‐Environment interaction on CROp growth Simulator), based on the
above lines of thinking for improved model structure and input parameters, was
developedbyYinandVanLaar(2005).GECROSusesnewalgorithmstosummarise
current knowledge of individual physiological processes and their interactions and
feedbackmechanisms.Itattemptstomodeleachsub‐processataconsistentlevelof
detail, so that no process is overemphasised or requires toomany parameters and
similarlynoprocessistreatedinatrivialmanner,unlessunavoidablebecauseoflack
of understanding. GECROS also tries to maintain a balance between robust model
structure,highcomputationalefficiency,andaccuratemodeloutput.Themodelcan
Generalintroduction
5
be used for examining responses of biomass and dry matter production in arable
cropstobothenvironmentalandgenotypiccharacteristics.Inthisstudyweexplored
the ability of GECROS to analyse differences in tuber yield in a set of varieties
covering awide rangeofmaturity types and awell‐adapteddiploidF1 segregating
populationofpotato(SolanumtuberosumL.).
Potato:OriginandimportancePotato(SolanumtuberosumL.)isanannualcropfromthegenusSolanum(family
Solanaceae) (Khurana et al., 2003). The potato originates from the mountains of
SouthAmericawhereithasbeenanimportantfoodcropforalongtime(Rowe,1993).
Inthe16thcenturythepotatowasintroducedtoEuropeasacuriosity,firsttoSpain
and then intoEngland (Hawkes,1990; Spooner et al., 2005).Gradually it becamea
majorfoodcrop,especiallywhenvarietieswereselectedwhichwereadaptedtolong
dayconditions.Inthe18thand19thcenturiesitwasalreadyanimportantfoodcrop,
especiallyforthepoorinvariouscountriesinEurope.Fromhere,potatospreadtoall
overtheworldasamajorfoodcrop(Harris,1992).
The genus Solanum comprises of many species of which over 200 are tuber
bearingaccording to the latest taxonomic interpretationbyHawkes (1990).Within
this genus, the section Tuberarium (Correll, 1962), also known as section Petota
(D'Arcy,1972),includesthetuber‐bearingmembers,ofwhichthecultivatedpotatois
best known. Solanaceous plants can be found throughout the world, though most
speciesdwellintropicalregionsofCentralandSouthAmerica.
Potatoes can be grown wherever it is neither too hot (ideally average daily
temperatures below21 oC) nor too cold (above 5 oC), and there is adequatewater
from rain or irrigation (Govindakrishan andHaverkort, 2006). The growing season
canbeasshortas75daysinthelowlandsubtropics,where90‐120daysisthenorm,
andas longas180daysinthehighAndes.Inthelowlandtemperateregionswhere
planting isdone inspringandharvesting inautumn,cropdurationis typically120‐
150 days, and yields are potentially high. Average fresh‐weight yields vary
tremendously by country from 2 to 45 t ha‐1 with a global average of 17.4 t ha‐1
(FAOSTAT,2010).Likemanyotherimportantcrops,potatoisapolyploid.Cultivated
potatovarietiesaretetraploid(4n=48)andmanywildspeciesarediploid(2n=24)
butmayrangeuptohexaploid(6n=72).Besides,potatoisoutcrossinginnatureand
phenotypic and genotypic variation is very common in potato. Therefore potato is
heterozygous, a characteristic that contributes to its extreme genetic diversity and
hasprobablybeenakeyfactorinitssurvival.
Potatoisamongtheworld’smostimportantagronomiccropsandisthehighest
Chapter1
6
rankednon‐cereal.Theroleofpotatoisnowwellrecognisedinhumannutrition,food
security, and national economy ofmany countries. Due to increasing food demand
and changing diets the traditionally high consumption of potatoes in Europe and
North America has recently combinedwith potato becoming a subsistence crop in
manyotherregionsparticularlyinareaswherepovertyishighlyconcentrated(Scott,
2002;http://www.cipotato.org).Currentlythecropisgrownatasignificantscalein
about 130 countries, and covers yearlyworldwide about 18million ha (Struik and
Wiersema,1999;FAOSTAT,2010).
Potatohighyieldpotential andhighnutritionalqualityhasmade themoneof
themostimportantplantfoodsources.Itisawholesomefoodwithalltheextremely
important and necessary dietary constituents, which are needed for health and
growth(Pushkarnath,1978;Li,1985;Burton,1989)(Figure1.1).
Comparedtootherrootsandtubersandalsomanycereals,potatotubershavea
high ratio protein to carbohydrates with a high nutritional value of the protein
(Shekhawatetal.,1994).Potatocanbeusednotonlyasahumanfoodoranimalfeed,
but also for seed tuber production, and industrial use. Various food processing
industries use potato to produce crisps, French fries, flakes, and canned potato,
whereas the non‐food industry uses potatoes to produce starch, alcohol, etc.
(Feustel,1987;StruikandWiersema,1999;Khuranaetal.,2003).
Figure1.1.Nutrientcontentofpotatoesper100g,afterboiling inskinandpeelingbefore
consumption.Source:UnitedStatesDepartmentofAgriculture,NationalNutrientDatabase.
In short, potato as a major food staple is contributing to the United Nations
MillenniumDevelopmentgoalsofprovidingfoodsecurityanderadicatingpoverty.In
recognitionoftheseimportantroles,theUNnamed2008astheInternationalYearof
Generalintroduction
7
Potato.With the growing global potato production requiresmodernisation of crop
improvement techniques and efficient utilisation of resources towards high yield
production.
YielddeterminingcomponentsinpotatoYield production can be expressed as the integrated response of distinct plant
processes to resources suchas radiation,nitrogenorwater.This response involves
the following twomain steps: theproductionofphotoassimilates, and their further
transformation into an economic (usually harvestable) component. In potato tuber
dry matter yield can be quantified by the summation of the daily incoming
photosynthetically active radiation (PAR) times the daily fraction of that radiation
interceptedbythecroptimestheradiationuseefficiencytimesthedailyfractionof
thetotaldrymatterpartitionedtothetubers(Struiketal.,1990).Thisisillustrated
bythefollowingequation:
1.1
wherePARinc=daily incomingphotosyntheticallyactiveradiation, fPARint= fraction
ofthePARintercepted,RUE=radiationuseefficiency,andHI=theharvestindex.
The seasonal rate of dry matter accumulation in potato is a function of
interceptionandutilisationofincidentphotosyntheticactiveradiation.Differencesin
the rate of dry matter accumulation can be attributed to differences in light
interception caused by variability in maximum LAI (leaf area index), in leaf
senescence(e.g., ‘canopycover’ inpotato)duringtuberbulking,andincanopy‐level
efficiency of utilisation of intercepted radiation as a result of changes in the
functionalityof leafphotosynthesisduringtuberbulking(Pashiardis,1988;Spitters,
1988;VanDeldenetal.,2001).Severalauthors(AllenandScott,1980;Burstalland
Harris,1983;VanderZaagandDoornbos,1987;Spitters,1988;Koomanetal.,1996)
haveanalysedthedifferencesinyieldofpotatocropsbasedoninterceptedradiation,
theefficiencyoftheuseofthisradiationfordrymatterproduction,andtheharvest
index.
Thegenetic improvementofyieldcanbeunderstoodmoremechanisticallyby
inspectingandinterpretingtherelationshipsbetweenthemainattributesofgrowth
andbypartitioningtheyieldcomponentsdescribedabove.Studiesonthemechanistic
basisbywhichbreedinghassuccessfullyincreasedyieldmayshedlightonpotential
futurealternatives.
HIRUEPARPARYield intinc f
Chapter1
8
NeedofthisstudyTheagronomyandphysiologyofyielddevelopment inpotato isextremelycomplex
due to genotype specific and environmental effects (i.e. GE) on crop physiologicaland morphological characteristics that are conditioned by the combination of
genotypeandenvironment,i.e.GE(Pashiardis,1988;AllenandScott,1992;JefferiesandMacKerron,1993;Tourneuxetal.,2003;Schittenhelmetal.,2006).Despite the
importanceofthisfact,fewstudiessoughttounderstandthephysiologicalprocesses
leadingtoyielddevelopmentandthefactorsaffectingtheseprocessesandtheseare
stillnotwellunderstood.Mostreportsareconcernedwithexperimentscarriedoutin
growthrooms,whichoftenuseveryshortstemsectionsasplantingmaterialandin
whichenvironmentalconditions,especiallylightintensity,differedgreatlyfromthose
normally prevailing during the period of initiation in the field. Insight into the
aboveground shoot development, particularlywith respect to canopy cover and its
relation with whole‐plant physiology are still underdeveloped (Almekinders and
Struik,1996).
Traditionally, crop growth simulation models have principally been used to
study and predict crop performance in response to environmental conditions and
managementpractices,whereasgenotypic impactsoncropperformance(especially
inthecontextofplantbreedingwherelargenumbersofgenotypesareinvolved)have
receivedlittleattention.Littleeffortshavefocusedonmodellingcanopydynamicsin
relationtogenotypewithenvironmentinteraction(GE),presumablyduetolackofsuitablemodellingapproachesanddatasets.Thereisaneedforamodelthatincludes
cropreactions to temperature,day lengthandsoilnitrogenavailabilityon theyield
dynamics of the crop (Allen and Scott, 1980;Kooman andRabbinge, 1996). Such a
modelmayalsoofferbreedersthescopetobreedforcultivarswhichmakethemost
effectiveuseofagivenenvironment.
MainobjectivesThe analysis conducted in this thesis is based primarily on data collected from six
contrasting field experiments carried out in Wageningen (52˚ N latitude), the
Netherlands, during 2002, 2004 and 2005. The plant material used in this study
consisted of 100 F1 diploid (2n= 2x= 24) potato genotypes derived from a cross
between two diploid heterozygous potato clones, SH83‐92‐488 RH89‐039‐16(RouppevanderVoortetal.,1997;VanOs,2006)referredtoasSHRH.Besides,fivestandard cultivars (i.e. Première, Bintje, Seresta, Astarte, and Karnico) were also
includedinourstudies.Thethesishasthefollowingobjectives:
To develop an approach to quantify and analyse the dynamics of important
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9
growthanddevelopmentrelatedprocesses(i.e.dynamicsofgreencanopycover
and tuber bulking), where these processes are dissected into biologically
meaningful and genetically relevant component traits. We want to study the
impactoftheseprocessesontuberyieldproductionunderdiverseenvironmental
conditions;
To analyse how these processes are influenced quantitatively by genotype (G),
environment(E),andGEinteraction; Todevelopaphysiologybasedunambiguousmethodofassessingmaturitytype
inpotato;
ToidentifyfavourableQTLallelesforyielddeterminingphysiologicalcomponent
traits of potato for specific environment(s) to complement marker assisted
breedingprogrammes;
Toexamine theabilityandpotentialof theecophysiological cropgrowthmodel
‘GECROS’ in explaining yield differences in potato to help design strategies for
potatoideotypebreedingforspecificgenotypesand/orenvironments;
Toidentifyimportantphysiologicalandgenotypictraitswhichcouldbeusefulas
aselectioncriterionforimprovingcropyieldinpotato.
OutlineofthethesisThis thesis is composed of six chapters including this introduction (Chapter 1).
Chapters2 to5 present themain results of the study.Chapter2 andChapter3
describeamodellingapproachtoanalyseandquantifythedynamicsofcanopycover
andtuberbulking,respectively,duringtheentirecropcycleasafunctionofthermal
timeanditsvariabilityamongpotatogenotypesandtobreakthetimecourseandits
variationdownintobiologicallymeaningfulandgeneticallyrelevantcomponenttraits.
Wedissectandassess theroleofgenotype(G),environment (E),andGEon tuberyieldproduction;discusstheheritabilityofthesetraitsandtheirgeneticbackground
(i.e.QTLs)onthepaternalgenomes.Usingthisapproach,theaimistoquantitatively
analyse the sourcesof variation intouseful components of canopy cover and tuber
bulkingdynamicsandget insights into themostvitalprocesses thatcanbeused to
explore the possibilities of genetically manipulating potato tuber yield. Chapter4
discusseshowtoquantifyandassessmaturitytypeunambiguouslyforalargesetof
genotypes. It presents an improved approach to re‐define the concept of maturity
type in terms of important physiological traits thus relating the maturity to crop
phenology and physiology. Chapter 5 examines the performance of the
ecophysiologicalcropgrowthmodel‘GECROS’toanalysedifferencesintuberyieldin
asetofvarietiescoveringawiderangeofmaturitytypesandadiploid(SHRH)F1
Chapter1
10
segregatingpopulationofpotato.Finally,Chapter6discussesthemainfindingsand
overallcontributionofthisthesis.Itconcludeswithnewdirectionsandopportunities
of linking cropphysiologywith genetic systems and indicatesways to enhance the
power ofmolecular breeding strategies in potato. Figure 1.2 illustrates the overall
frameworkofthisstudy,whileFigure1.3furtherelaboratesthatplanandprovidesa
schematicoutlineofthisthesis.
Figure1.2.Overallframeworkofthisstudy.
Generalintroduction
11
Figure1.3.Schematicthesisoutline.
CHAPTER2
Analysinggeneticvariationinpotato(SolanumtuberosumL.)usingstandardcultivarsandasegregatingpopulation.
I.Canopycoverdynamics*
M.S.Khan,P.C.Struik,P.E.L.vanderPutten,H.J.Jansen,H.J.vanEck,F.A.vanEeuwijk,X.Yin
AbstractWe present a model based on the beta function to analyse the genotype‐by‐environment (GE) interactions for canopy cover dynamics in potato (Solanumtuberosum L.). The model describes the dynamics during the build‐up phase,maximum cover phase, and decline phase of canopy development through fiveparameters defining timing and duration of three phases and maximum canopycover (vmax). These five parameters were estimated for 100 individuals of an F1population, their parents, and five standard cultivars, using data from six fieldexperiments, and used to estimate secondary traits, related to rate of increase ordecreaseofcanopycoverandtheareaunderthecanopycovercurve for thethreephases.Thelengthofthecanopybuild‐upphasewasconserved,butthedurationofmaximum canopy cover (DP2)and the decline phase (DP3)varied greatly, with latematuringgenotypeshavinglongerDP2andDP3andthusahigherareaunderwholegreen canopy cover curve (Asum). Genetic variance of the onset (t2) or end (te) ofcanopysenescenceandAsumcontributedgreatly to thephenotypicvariance.StrongpositivephenotypicandgeneticcorrelationswereobservedbetweenDP2andvmaxorte,indicatingthatgenotypeswithlongerDP2couldbeindirectlyobtainedbyselectinggenotypes with high vmax or te. High broad‐sense heritability estimates across sixenvironmentswere recorded for t2, te,DP2, andAsum. Several quantitative trait loci(QTLs)weredetected for ourmodel parameters andderived traits explaining thevariancebyupto74%;manyoftheseQTLswerelocatedonpaternallinkagegroupV. Some of the QTLs were mapped to similar positions in the majority ofenvironments, but only few QTLs were stable across environments.We concludethatourapproachyieldedestimatesforagronomicallyrelevantcropcharacteristicsfor defining future breeding strategies in potato and the information obtainedshouldhelpinmarker‐assistedselection.Keywords:Potato(SolanumtuberosumL.),canopycoverdynamics,beta function,thermaltime,componentsofvariance,genotype‐by‐environment(GE)interaction,genetic variability, heritability, maturity type, QTL mapping, QTL‐by‐environment(QTLE)interaction._____________________________________*SubmittedtoFieldCropsResearch.
Chapter2
14
1.Introduction
Tuber drymatter yield in potato (Solanumtuberosum L.) can be quantified by the
summationofthedailyincomingphotosyntheticallyactiveradiation(PAR)timesthe
daily fraction of that radiation intercepted by the crop times the radiation use
efficiency times the daily fraction of the total drymatter partitioned to the tubers
(Struiketal.,1990).Thispaperisonthefirstpartoftheequation:thefractionofthe
incomingradiationintercepted,asinmanypotatogrowingregionstheabilityofthe
crop to produce and maintain a green canopy able to absorb and use incoming
radiation during the available growing season for biomass production determines
tuberyields(MollandKlemke,1990).
Thecropcanopycoverateachtimeduringthegrowingseasonistheresultant
oftherateanddurationofcanopygrowthandtherateofcanopysenescence(orthe
timing of the haulm killing) (Struik et al., 1990; Struik, 2007). This canopy cover
directly correlates with the fraction of incoming PAR absorbed (Khurana and
McLaren,1982;MacKerronandWaister,1985a;VanderZaagandDoornbos,1987;
VanDeldenet al., 2001).Under optimal conditions andwith long growing seasons,
earlyestablishmentoffullcanopycoveranditspersistenceoveralongperiodleadto
a higher yield due to better interception of incoming radiation (Martin, 1995),
although the biomass needs to be partitioned to tubers to obtain economic yield.
Thereisaclose,positivecorrelationbetweencanopycoverandtuberyield(Vander
Zaag, 1982; Vander Zaag and Demagante, 1987; Fahem and Haverkort, 1988). The
proper quantification of canopy dynamics is therefore one of the most important
elementsofmodellingpotatoproduction(Hodges,1991).
The variation in tuber yield among potato genotypes can – under conditions
without abiotic or biotic stress −be analysed in termsof differences in cumulative
light absorption, the efficiency with which the absorbed light energy is used for
biomass production, and partitioning of dry matter to the desired plant organ
(Pashiardis,1988;Spitters,1988;VanDeldenetal.,2001).Mostpotatogenotypesare
indeterminate (Allen and Scott, 1992; Struik and Ewing, 1995, Almekinders and
Struik, 1996) and (the continuation of) canopy growth largely depends on the
appearance of lateral (basal or sympodial) branches, and on the appearance,
expansion, and senescenceof leaveson thosebranches (Allen andScott, 1992;Vos
andBiemond,1992;Vos,1995a;AlmekindersandStruik,1996;Fleisheretal.,2006).
Thecanopycoverinpotatocropsmaythereforecompriseofleavesfrommainstems,
secondarystems,andaxillarybranches(Allen,1978).Potatogenotypesdifferinthe
degree towhich theybranchand theextentof sympodialbranching is indicativeof
theirmaturitytypeandthedegreeofdeterminacyintheirgrowthhabit(Firmanetal.,
1995).Once tuberbulkinghasbecomerapid, thebranch formationcomes toahalt
Geneticvariationinpotatocanopycoverdynamics
15
and the leaf senescence is no longer compensated by new leaf formation. The
durationofcanopycovercanbeconsidereda trait reflecting thematurity typeofa
specificcultivarorgenotype(Chapter4).
Genotypicdifferencesinpotatocanopycoverarelarge(Spitters,1988;Jefferies
andMacKerron, 1993; Tourneuxet al.,2003). Spitters (1988) for example showed
that differences in yields among five contrasting varieties could be explained by
variation in canopy cover over time (resulting in differences in cumulative light
absorption) and in lightuse efficiencyandharvest index. Likemanyother complex
quantitative traits, canopy cover may be controlled by many interacting genes of
which each only has a small effect (Lark et al., 1995; Orf et al., 1999; Daniell and
Dhingra, 2002; Stuber et al., 2003). The principal approach for the analysis of
quantitativetraitsistheuseofapopulationwhichsegregatesforthetraitsofinterest
(Tanksleyetal.,1989).Forpotato,individualsofsuchasegregatingpopulationcanbe
produced by crosses made between commercial cultivars or with individual
genotypesdevelopedfrompopulationimprovementproceduresandtheyarereadily
propagatedbyasexualreproduction(Bradshaw,1994).Commercialcultivarsutilised
as parents are heterozygous and segregation of characterswill be found in the F1
generationfollowinghybridisation.
Canopy cover dynamics also shows a high genotype‐by‐environment (GE)interaction (Pashiardis,1988;AllenandScott,1992;Schittenhelmet al.,2006).The
mainenvironmental factors influencing thecanopydynamicsunder fieldconditions
aretemperature,photoperiod,lightintensity,watersupply,andnitrogen(N)supply.
For our study the effects of temperature and N are most relevant and these are
thereforesummarisedinthisintroduction.
Temperaturestrongly influencesstemelongationandbranching(Marinusand
Bodlaender,1975;AllenandScott,1980;Struiketal.,1989a,AlmekindersandStruik,
1994, 1996) and leaf appearance, expansion, and senescence (Kirk and Marshall,
1992;Vos,1995a;Firmanetal.,1995;StruikandEwing,1995;VanDeldenetal.,2001;
Fleisher and Timlin, 2006; Fleischer et al., 2006; Struik, 2007), with optimal and
maximum temperatures for these processes contributing to canopy development
being 23‐25 ˚C and about 32 ˚C, respectively (Struik, 2007). N supply affects the
number of lateral (basal and sympodial) branches, the number of leaves on those
branches, the individual leaf size and leaf senescence (Fernando, 1958;Humphries
andFrench,1963;VosandBiemond,1992;AlmekindersandStruik,1996).Nitrogen
helpstoattaincompletecanopycoverearlyintheseason,especiallyunderrelatively
resource‐poorconditions(HaverkortandRutavisire,1986;Vos,2009),toextendthe
periodoffullcanopycover;andtoslowdownsenescence(SantelizandEwing,1981),
thusleadingtoincreasedlightinterception(Martin,1995).
Chapter2
16
Thereisaclearneedtoinvestigatethecomponentsofgeneticvariation(e.g.in
canopy dynamics) and to determine the GE interaction for a diverse populationundercontrastingenvironmentalconditions(Tarnetal.,1992;Bradshaw,1994).The
genetic variation and the GE interaction for canopy cover need to be defined bypreciselyassessingtherateanddurationofthebuild‐upphase,themaximumcanopy
coverandthedurationoftheperiodduringwhichthismaximumismaintainedand
therateanddurationofthecanopysenescence.Thesevariablesneedtobeoptimised
in such a way that the crop can complete its growth cycle within the period of
favourableweather conditions thus realising a high yield and an adequate harvest
index. There is a growing awareness that inorder to better analyse complex traits
using increasingly available genomic information, integration of quantitative crop
physiologywithgeneticsisrequired(Tardieu,2003;Yinetal.,1999a;Yinetal.,2004;
Hammeretal.,2006;YinandStruik,2008;Chenuetal.,2009;Messinaetal.,2009;
Hammeretal.,2010;TardieuandTuberosa,2010;YinandStruik,2010).
In this paper we first describe a quantitative approach to analyse the time
courseofcanopycoverduringtheentirecropcycleasafunctionofthermaltimeand
itsvariabilityamongpotatogenotypesandtobreakthetimecourseanditsvariation
down into biologicallymeaningful and genetically relevant component traits. Using
this approach, we aim to quantitatively analyse the sources of variation in canopy
cover dynamics among full‐sibs of an outcrossing segregating population, their
parents and a set of standard cultivars of potato. The heritability of and genetic
variation among different component traits of canopy cover are estimated (Van
Eeuwijk et al., 2005). Finally,we performQTLmapping of our traits to investigate
theirgeneticbasisanddiscusstheco‐locationsofQTLsforthesetraitsandtheirQTL‐
by‐environment (QTLE) interaction.Withsuch information,dominantcomponentsofcanopycovercanbe identifiedandtheirphenotypicandgeneticcorrelationscan
be assessed, with the ultimate goal of designing an effective breeding strategy of
potato.
2.Materialsandmethods
2.1.F1segregatingpopulationofSHRHandstandardcultivarsTheplantmaterialusedinthisstudyconsistedof100F1diploid(2n=2x=24)potato
genotypes derived from a cross between two diploid heterozygous potato clones,
SH83‐92‐488RH89‐039‐16(RouppevanderVoortetal.,1997;VanOsetal.,2006).Parentmaterialwasalso includedinthestudy.ThefemaleparentalcloneSH82‐93‐
Geneticvariationinpotatocanopycoverdynamics
17
488 is referred to as “SH” and themale parental clone RH89‐039‐16 as “RH”. Like
manyotherfull‐sibpopulations,theSHRHpopulationsegregatesformaturitytype,which is strongly associatedwith thedurationof canopy cover (VanderWal et al.,
1978;VanOijen,1991). It shouldbenoted thatdiploidpotatoesperfectlyresemble
tetraploidcultivarswithrespecttomanydevelopmentalprocesses,includingcanopy
development, but the most noticeable difference is a somewhat smaller plant size
(Hutten,1994).
Wealso included five standard (tetraploid) cultivars inour studies:Première,
Bintje, Seresta, Astarte and Karnico. These cultivars were chosen because of their
differences inmaturity typewhengrown in theNetherlands and they ranged from
early (Première) to very late (Karnico). This selection of standard cultivars would
allowabenchmarkingofconsequencesofmaturitytypeonthetimecourseofcanopy
cover.
We aimed at using disease‐free, uniform‐sized seed tubers of similar
physiologicalqualityandthereforeproducedseedtubersunderthesameconditions
and stored them together at 4 °C for approximately 6 months until planting. The
graded seed tubers were disinfected chemically to control Rhizoctonia and pre‐
sproutedbeforeplanting.
2.2.Fieldexperimentsandmeasurements
Six field experiments were carried out in Wageningen, the Netherlands (52˚ N
latitude),during2002,2004,and2005,withtwoexperimentsineachyear,torecord
canopycoverdynamics for100F1genotypes,2parents,and4 (Exps5and6)or5
(Exps 1‐4) standard cultivars. Karnicowas not included in the two experiments in
2005. Experiments differed in environmental conditions because theywere carried
out in different years, on different soils, and under different N fertiliser regimes,
thereby creating six contrasting environments (Table 2.1). Each experiment was
conductedusingarandomisedcompleteblockdesignwithtwoblocks.Thegenotypes
wererandomisedwithineachblock,consistingof106or107plots.Eachplothadsix
rows of 16‐18 plants. The seed bed was prepared following standard cultivation
practices. Seed tuberswereplanted in rows spaced0.75mapartwith0.27‐0.30m
betweenplantswithinthesamerows.Nitrogenfertiliserwasbroadcastedatplanting
in amounts depending on experiment (Table 2.1). Soil phosphorus and potassium
levels were kept sufficiently high to sustain normal crop growth. Crops were
managedwelltoensurethattheywerefreeofpests,diseases,andweeds.Irrigation
wasappliedwhennecessarytoavoiddroughtstress.
Chapter2
18
Table2.1.Descriptionoftheexperimentalsitesandexperimentalmethodsappliedin
Wageningen,theNetherlands.
Experimentno. 1 2 3 4 5 6
Year 2002 2002 2004 2004 2005 2005
Soiltype Clay Sand Clay Sand Sand Sand
Plantingpattern
(mm)0.750.30
0.750.30
0.750.27
0.750.27
0.750.27
0.750.27
Plantingdate 25April 25April 28April 28April 4May 26April
Plantdensity
(plantsm‐2)4.4 4.4 4.9 4.9 4.9 4.9
Netplotsize
(m2)23.0 23.0 20.7 20.7 20.7 20.7
SoilNat
planting
(kgha‐1)
n.d.1 n.d.2 12.5 23.0 81.0 49.5
FertiliserN
(kgha‐1)143 175 70 200 125 50
TuberNuptake
(kgha‐1)†− − 112.3 172.0 153.6 156.1
Weatherconditionsduringgrowingseason
2002 2004 2005
Airtemperaturemax(oC) 20.9 20.6 20.7
Airtemperaturemin(oC) 11.2 10.4 9.9
Rainfall(mm) 364.1 367.1 369.1
Relativehumidity(%) 78.2 75.6 76.5
Solarradiation(MJm‐2d‐1) 15.3 15.8 15.81n.d.=nodata.Guestimate:40kgNha‐1.2n.d.=nodata.Guestimate:60kgNha‐1.†DataforExperiments1and2werenotavailableandreplacedby“−”.
Plant emergencedatewas observed on thedatewhen50%of the plants had
emerged from the soil surface. Afterwards, green canopy cover (%) was visually
assessedonweeklybasisforeachplotduringthewholecropcyclebyusingagridas
Geneticvariationinpotatocanopycoverdynamics
19
describedbyBurstall andHarris (1983).Thegridconsistedofanaluminium frame
with thedimensions0.75m0.90m,adjusted to thecommonplantingpattern forpotato(rowwidth0.75m,plantingdistancewithintherow0.30m).Theframewas
dividedinto100equalrectanglesof0.075m0.090m.Thegridwasplacedabovethe potato canopy at 1 m from the ground and only those rectangles more than
halffilled with green leaves were counted by observing vertically above to avoid
parallaxerror(CadersaandGovinden,1999).Thegridwasplacedhalfwayoneach
side of the potato row to sample three plant positions. Observations were always
made at the same position in the field. The number of observations made on the
canopycoverduringthegrowingseasonperindividualplotwas17‐21.
Attheendofthegrowingseason,tubersofeachplotwereharvestedanddried
inanovenat70˚Ctoconstantweight.Insamplesofthegrowingseasonsof2004and
2005, tuber nitrogen content was determined by micro‐Kjeldahl digestion and
distillation (AOAC, 1984) in a fully accredited commercial laboratory. Average N
uptake was calculated and was used to characterise some of the environments in
termsoftotalNavailableforcropgrowth.
2.3.Modelapproach
2.3.1.Amodelforphasicdevelopmentofcanopycoverdynamics
Canopycoverdynamicsinpotatoasquantifiedbythegridmethodtypicallyfollowsa
patternthatcanbesubdividedintothreedistinctphases(Fig.2.1),i.e.build‐upphase
(P1),maximumcoverphase(P2),anddeclinephase(P3).
Build‐upphase(P1)
This first phase covers the period from emergence to full canopy cover, and is
dominated by appearance of stems, lateral branches and leaves, and expansion of
thoseorgans(AllenandScott,1992;VosandBiemond,1992;Vos,1995a;Fleisheret
al.,2006).Toobtainflexibleandasymmetricalcurves,canopycoverduringP1canbe
written,accordingtoasigmoidpartofthebetafunctionfordeterminategrowth(Yin
etal.,2003a),as:
11m11
1max with1 m11
1
tttt
tttt
vv ttt
0 (2.1)
Chapter2
20
Can
opy
cove
r (%
)
Thermal days (td)
vmax
0 m1t 2t1t et
P1 P2 P3
Figure2.1.Temporalcourseofpotatocanopydevelopment(fullcurve)fromtime0
to tedescribed by our three‐phase model, eqns (2.1‐2.3), respectively. P1 (canopy
build‐upphase),P2(maximumcanopycoverphase),andP3(canopydeclinephase).
Thedashedcurveisthemathematicalextensionofeqn(2.1).
where tm1 is the inflexion pointand vmax is themaximum value of canopy cover v,
whichisreachedattimet1(Fig.2.1).Equation(2.1)canbeappliedtocanopycover
withinthetimespanof0≤t≤t1;otherwise,vhastobesetat0ift<0oratvmaxift>
t1.
Maximumcoverphase(P2)
Duringthisphasethecanopycoverretainsitsmaximumlevelvmax.Thecanopycover
duringP2issimplygivenby:
21max with tttvv (2.2)
Geneticvariationinpotatocanopycoverdynamics
21
wheret2reflectsthelasttimepointwhencanopycoverisstillatitsmaximumand/or
onsetofcanopysenescence(Fig.2.1).
Declinephase(P3)
Thecanopycover starts todeclineafter time t2, and reaches zeroat theendof the
cropcyclei.e.te.Usuallythisdeclinefollowsareversedsigmoidpattern,whichcould
be formulatedasvmax[1‐f(t)] (cf.Yinetal.,2009),where f(t) is the function,suchas
eqn (2.1), describing a normal sigmoid pattern with time. Therefore, for P3, an
equationcouldbeformulated,usinganinflexionpointofthedeclinephase.However,
inmanycasestheestimatesforparametersshowedlargestandarderrors,indicating
over‐fittingwhenamodelhaving this inflexion isapplied incombinationwitheqns
(2.1)and(2.2)todescribethedataoftheentiretimecourseofcanopydynamics.To
reducethechanceofover‐fitting,wedevelopedanequationforthecanopycoverin
Phase3excludingthissecondinflexion,yetyieldingsatisfactoryresults:
e21
21
2e
emax with 2e
1
tttt
ttttttt
vv ttt
(2.3)
wheretereflectsthetimepointwhencanopycoveriszero.Thisequationisbasedon
adeclinepartofthebetaequation(Yinetal.,1995)andassumesthattediffersfrom
theextendedendpointofeqn(2.1)by(t2‐t1)(Fig.2.1).Equation(2.3)canbeapplied
withinthetimespanoft2≤t≤te;otherwise,vhastobesetat0ift>te.
Combiningeqns(2.1,2.2,and2.3)yieldsamodelwithfiveparameters:tm1,t1,t2,
te,andvmax.Anyfurtherreductionofparametersresultedinsignificantlossoffit(data
notshown).Themodeldescribescanopydynamicsforagivengenotype‐environment
combination. The model contains three segments but is smooth because the first
derivatives of vwith respect to tare zero at the joining points t1 and t2. Values of
model parameters tm1, t1, t2, te,and vmaxwere estimated for each individual plot of
every experiment with the iterative non‐linear least‐square regression using the
Gaussmethod,asimplementedinthePROCNLINoftheSASsoftware(SASInstitute
Inc.,2004).
2.3.2.Calculatingsecondarytraits
Once themodel parameterswere estimated, several secondary traitswere derived
reflecting the duration of the phases, the rates of canopy cover dynamics, and the
overallpotentialtointerceptlightduringthedifferentphases.
Chapter2
22
Asdefinedearlier,ourmodelsetstimezeroastheonsetofP1;sotheduration
ofP1 is numerically equal to t1, and theduration of the entire cycle is numerically
equaltote.ThedurationofP2(DP2)wascalculatedast2−t1.ThedurationofP3(DP3)
wascalculatedaste−t2.
Averagegrowthrate forP1(c1) isdefinedas:vmax/t1, andaveragesenescence
rate during P3 (c3) is vmax/Dp3. Maximum growth rate during P1 (cm1), which is
achieved at tm1, could be estimated according to the equation given by (Yin et al.,
2003a)as:
max1
m1
m111
m11m1
2 m11
m1
vtt
ttttt
c ttt
(2.4)
Theareaundereachsectionofthecurvewasestimatedbyintegratingeqns(2.1,
2.2,and2.3)overtimefortherespectivephases.Inanalogytothesolutiontothearea
forareversedsigmoidcurve(Yinetal.,2009),theintegralforP1canbeexpressedas:
232
m11
m111max1
ttttt
vA (2.5)
TheintegralforP2issimplyexpressedas:
12max2 ttvA (2.6)
TheintegralforP3ismorecomplicatedandcanbefoundas:
2‐22 1
1
12e12e
12e
2emax3
2e
1
tt
tttttt
tttttv
Att
t
(2.7)
ThesumofA1,A2,andA3results in theareaunderwholegreencanopycurve,Asum.
ThevalueofAsumreflectsthecapabilityofthecroptointerceptsolarradiationduring
thewholegrowingseason(cf.Vos1995b,2009).
2.4.Calculatingthermaltime
Ourexperimentswereconductedunder fieldconditions.To takeaccountofdiurnal
and seasonal variation in temperature under these conditions, the time axis in the
Geneticvariationinpotatocanopycoverdynamics
23
aboveequationsshouldbeinthermaltime,whichhaslongagobeenintroducedasa
scaleofreferenceforontogeny(Gallagher,1976,1979;MilfordandRiley,1980;Ong,
1983,Milfordetal.,1985;Bonhomme,2000).
WefollowedtheapproachofYinetal.(1995,2005),whodevelopedanequation
todescribethenon‐linearrelationshipbetweentemperature(T)andrateofgrowth
orofdevelopment(g(T))asafunctionofthreecardinaltemperatures, i.e.base(Tb),
optimum(To)andceilingtemperature(Tc):
t
oc
bo
bo
b
oc
c )(
c
TTTT
TTTT
TTTT
Tg
(2.8)
where ct is the temperature response curvature coefficient. When T≤ Tbor ≥Tc,
growthordevelopmentdoesnottakeplace.WhentemperatureisbetweenTbandTc,
g(T)hasabell‐shapedcurvewithmaximumvalue1atTo.
We used data sets of leaf appearance and leaf expansion rates from growth
chamber experiments of Fleisher et al. (2006) and Fleisher and Timlin (2006) to
estimatethevaluesofTb,To,Tc,andctofeqn(2.8).Theirdataonindividualleafarea
(cm2)andleafappearancerate(leavesplant‐1day‐1)ofpotatocultivarKennebecwas
usedtoestimateleafareaperplant(cm2plant‐1)atsix(14/10,17/12,20/15,23/18,
28/23,and34/29˚Cforday/night)temperatureswitha16/8hdiurnalcycleunder
ambient [CO2] of 370μmolmol‐1. From this data set the values ofTb,To,Tc, and ct
were determined using the iterative procedure of SAS‐NLIN, which follows a non‐
linear least squaresmethod forestimationofparameters (SAS Institute Inc.,2004).
Estimates (± standarderrors)were:Tb=5.5 (±5.2) ˚C,To=23.4 (±0.5) ˚C,Tc=34.6
(±1.3) ˚C,ct =1.6 (±0.5).Basedon thedataset,muchextrapolationwas involved in
estimatingTbwhich resulted in high standard error. Obviously,we have to assume
that the temperatureresponseswere thesame forallgenotypesand forallphases,
basedonassertionsthatwithinacropspecies,thegeneticdifferencesintemperature
sensitivity of phenology are relatively small (Ellis et al., 1990) and that cardinal
temperaturesinvariousdevelopmentalprocessesarequitecloseinpotato(e.g.Struik,
2007). A sensitivity analysis showed that uncertainties in cardinal‐temperature
valueshadlittleimpactonassessinggeneticdifferencesincanopydevelopment(data
notshown).
After the estimation of the temperature response parameters, eqn (2.8) was
used to convert days after emergence into thermal days after emergence (td).
Because the function g(T) is non‐linear and temperature fluctuates diurnally, g(T)
Chapter2
24
wasestimatedonanhourlybasisandhourlyg(T)valueswereaveragedtoobtainthe
daily value (Yin et al., 2005a). By definition, one thermal day is equivalent to one
actual day only if temperature at any moment of that day is at To (= 23.4 ˚C).
Obviously, every actual day was less than one unit thermal day. Required data of
hourlyairtemperatureswereobtainedfromaweatherstationinWageningenlocated
nearbytheexperimentalsites.
2.5.Statisticalandgeneticanalyses
All statistical analyses were performed in Genstat (Payne et al., 2009). A general
analysisofvarianceacrossenvironmentswasperformedtotestthesignificanceand
extent of differences between environments, all genotypes (including the F1
population, the parents, and standard cultivars) and GE interactions, where theeffectofblockwithinenvironmentwasincludedinthemodel.Meansofgenotype(G),
environment(E),andGEinteractiontermswerecomparedusingtheFisher’s leastsignificant difference (LSD) test. Further statistical analyseswere performed using
onlythe100genotypesoftheF1populationandthesearedescribedbelow.
2.5.1.Estimationofvariancecomponents
Weusedastatisticalmodel(VanEeuwijk,2003)toestimatethevariancecomponents
andtoassesswhatthecontributionwasofthegenotypicmaineffectsandtheGEtothetotalphenotypicvarianceforthemodelparameters(i.e.tm1,t1,t2,te,andvmax)and
secondarytraits(cm1,c1,c3,DP2,DP3,A1,A2,A3,andAsum):
ijkijijkjijk GEGEy (2.9)
where i= 1,…, 100; j= 1,…, 6; k= 1, 2; and yijk denotes the response variable of
genotype i, in environment j, blockk;µ is the grandmean;Ej is the environmental
maineffect;βjkstands forblockwithinenvironmenteffect;Gi is thegeneticeffectof
genotype i;GEij is the genotype‐by‐environment interaction effect for genotype i in
environment j,and ijk isaresidual term.Theunderlinedtermswereconsideredas
randomeffects,whichwereassumed tobenormallyand independentlydistributed
withzeromeanandapropervariance.
Therestrictedmaximumlikelihood(REML)procedurewasusedtoestimatethe
variancecomponents.Thesignificanceofthevariancecomponentswastestedusing
thelikelihoodratio(LR)test(Morrell,1998).Thistestdeterminesthecontributionof
asingle(random)factorbycomparingthefit(measuredasthedeviance,i.e.‐2times
Geneticvariationinpotatocanopycoverdynamics
25
the log likelihood ratio) for models with and without the factor. The phenotypic
variance,σ2Phwasestimatedfromthesevariancecomponentestimatesas(Bradshaw,
1994;FalconerandMackay,1996;LynchandWalsh,1998):
2ε
2GE
2G
2β
2E
2Ph (2.10)
whereσ2E=environmentalvariance,σ2B=blockvariance,σ2G=geneticvariance,σ2GE
=GEvariance,andσ2ε=experimentalerrorvariance.
2.5.2.Phenotypicandgeneticcoefficientofvariation
Scalingof the varianceby the traitmean, i.e. calculating the coefficient of variation
provides a more appropriate way to compare traits (Johnson et al., 1955; Houle,
1992). We therefore calculated the coefficient of variation (%) for each model
parameterandderivedtraitacrosssixexperiments,accordingto:
1002
CV (2.11)
where is thegrandmeanof thepopulation,and2
isavariancecomponent (i.e.
σ2phorσ2Gorσ2Eorσ2GE)fromeqn(2.9).
2.5.3.GGEbiplotanalysisStudyingquantitative traits is complicateddue toGE interactions (Uptmoor et al.,2008).GGEbiplotanalysisisaneffectivemethodtofullyexploremulti‐environment
trials for G and GE components of variation. It allows visual examination of therelationshipsamongthetestenvironments,genotypesandtheGE(Yanetal.,2000).For any particular environment, genotypes can be compared by projecting a
perpendicular fromthegenotypesymbols to theenvironmentvector, i.e.genotypes
thatare furtheralonginthepositivedirectionof theenvironmentvectorarebetter
performing and vice versa. The greater the distance from the origin to the
intersection of a genotype projection on an environment vector, the more this
genotype deviates from the average in this environment (Kroonenberg, 1997;
Chapman et al., 1997). GGE biplot analysis was performed to analyse the inter‐
relations among genotypes and environments. GGE biplots were constructed by
plotting the first principal component (PC1) scores of the genotypes and the
environments against their respective scores for the second principal component
Chapter2
26
(PC2).Theenvironment‐standardisedmethodofYan(2002)wasused.
2.5.4.Phenotypicandgeneticcorrelation
Product‐moment (Pearson) correlations were calculated, using genotypic means
acrossblockswithintrials,amongmodelparametersandsecondarytraits.
Thegeneticcorrelationswereestimatedusingthefollowingequation(Holland,
2006):
2G
2G
2G
G ji
ijijr
(2.12)
whereσ2Gijistheestimatedgeneticcovariancebetweentraits iand j;σ2Giandσ2Gjare
thegeneticvariancesoftraitsiandj,respectively.ThemultivariateREMLprocedure
was used to estimate the genetic variance and covariance estimates (Meyer, 1985;
Holland,2006).Thesignificanceofgeneticcorrelationswasdeterminedusingat‐test
after a z‐transformation of the correlation coefficients (Sokal and Rohlf, 1995;
Guttelingetal.,2007).
2.5.5.Heritability
We estimated the broad‐sense heritability (H2) (%) across all six environments by
using the estimatedvariance components of the linearmodel described asper eqn
(2.9),throughthefollowingequation(Bradshaw,1994;FalconerandMackay,1996):
100
t
2ε
e
2GE2
G
2G2
nn
H
(2.13)
wherene=6represents thenumberofenvironments,andnt=12 is theproductof
numberofblocksandenvironments.
The broad‐sense heritability (H2) was also estimated for each individual
experimentbyusingthefollowingformula(Bradshaw,1994):
100
b
2ε2
G
2G2
n
H
(2.14)
Geneticvariationinpotatocanopycoverdynamics
27
where σ2G = genetic variance; σ2ε = experimental error variance, and nb = 2
represents the number of blocks per individual environment. The variance
componentswereestimatedperindividualtrialbasis.
2.5.6.ExtensionofanAFLPmarkermapoftheSHRHpopulationTheAFLPprimer combinations used in this study have been previously applied to
createtheultra‐densegeneticmapof130SHRHgenotypesasdescribedinVanOsetal.(2006).Asour100F1lineswereonlypartlygenotypedforcreatingthatmap,
120 new SH RH genotypeswere fingerprintedwith AFLPTM (Vos et al., 1995) tomakeamapfor250individuals.GenomicDNAwasextractedfromfrozenleaftissue
accordingtoVanderBeeketal.(1992).AFLPmarkersweregeneratedaccordingto
standard protocols with radioactive labels, using four Eco‐Mse primer combinations
(Vosetal.,1995).TheAFLPprofilesof theparental cloneswerecomparedwithan
ultra‐densegeneticmapandAFLPproductsofequalelectrophoreticmobilitywhich
segregated inbothsetsof the lineswere identified.TheAFLP(only1:1segregating
markers)were firstmapped in theSHRHmappingpopulationusing JoinMap4.1(VanOoijen,2006)usingabinmappingapproach(VanOsetal.,2006).Themarker
dataweresplitintotwosetsonthebasisoftheirsegregationtype.Markersthatwere
heterozygous in thematernal parent (SH) and absent in the paternal parent (RH)
werescoredas<abaa>;“paternal”markersheterozygous inRHandabsentinSHwerescoredas<aaab>.
For map construction, recombination frequencies were converted into map
units (cM) by the use of the Kosambi function. Only markers with recombination
valueof<0.25wereconsideredasdescribedbyVanOsetal. (2006).Thematernal
andpaternal data setsweredivided into 12 and13 linkage groups, respectively. A
graphic representation of a map was made by the Map Chart software (Voorrips,
2002).
2.5.7.QTLdetection
Genstat version 14 (Payne et al., 2009) softwarewas used to identify QTLs for all
component traits. Eighty‐eight genotypes of our 100 F1 lines were covered in the
sample of the aforementioned 250 lines of SH RH population for the extendedmarkermap;dataofthese88lineswerethereforeusedfordetectionofQTLsforfive
model parameters and nine secondary traits. QTL analysis was performed
individuallyforallsixexperiments(environments).
QTL models were fitted for the two parents separately. Initially, the
Chapter2
28
conventionalSimple IntervalMapping (SIM)procedureasdescribedbyLanderand
Bostein(1989)andHackettetal.(2001)wasusedtoscanthegenomeforthemajor
QTLsperindividualenvironment.AQTLwasdeclaredtobesignificant(P<0.05)for
the threshold value (‐log10 (P) > 3.4). Secondly, amore sophisticatedQTLmapping
procedure, the Composite Interval Mapping (CIM) was performed to increase the
reliability of the QTL analysis (Zeng, 1994; Jansen, 1993; Jansen and Stam, 1994;
Jansen, 1995). In thismethod, backgroundmarkerswere selected to take over the
roleoftheputativeQTLsascofactorstoreducetheresidualvariance.Inouranalysis,
backgroundmarkersclosesttotheindicatedregionofputativeQTLswith–log10(P)
scoresexceedingthethresholdweregraduallyusedascofactors.Thisprocedurewas
repeateduntil no furtherQTLswere found.Thepercentageof the total phenotypic
variationexplainedbyQTLsidentifiedforeachtraitwasestimatedastheR2‐value.
3.Resultsanddiscussion
3.1.Modelperformanceindescribingcanopycoverdynamicsofgenotypes
Theuseofdatainthermaldaysresultedinamorestableparameterestimationthan
theuseofdata indays (datanot shown), as the confoundingeffectsofdiurnal and
seasonal temperature fluctuations during the experimental period could effectively
be removed using thermal days. The model for the canopy cover dynamics (i.e.
combinedeqns(2.1,2.2,and2.3))fittedwellforeachgenotypeofpotatosegregating
population,theirparentsandstandardcultivarsintheentiredataset,withR2values
rangingfrom0.94to1.00(n=17‐21;datanotshown).Figure2.2showsthecurve‐
fitting results for the SH and RH parents and for the standard cultivars that were
present in each experiment. In most cases, the standard error (SE) values for
observed points (i.e. average of two blocks) were much higher during the canopy
decline phase than during the first two phases, consistent with the fact that the
estimationof te involvedmuchextrapolationdue to lackofdatapoints in the third
phase of canopy growth. Overall, combined eqns (2.1‐2.3) could be very useful in
analysing the canopy cover dynamics of a diverse set of potato genotypes under
various environments and making inferences about the underlying process
controllingcanopygrowth.
3.2.Modelparametersandsecondarytraits
Usually,theestimatedvalueofeachmodelparameterdifferedverylittlebetweenthe
Geneticvariationinpotatocanopycoverdynamics
29
Figure2.2.Observed(obs)pointsandfitted(fit)curvesofstandardcultivarsandSH
andRHparentsforallsixexperiments(environments).Dataforcv.Karnicowerenot
availableinExps5and6(seethetext).
twoblocksandtherewasagoodpositivelinearcorrelationbetweenthetwoblocks
forallmodelparameters(R2valuesusuallyabove0.7;n=107).Table2.2givesthe
estimatedvaluesofthefivemodelparametersandtheninederivedsecondarytraits
for twoparents (SHandRH), five standardcultivars, and themeanvalueof theF1
0
20
40
60
80
100SH
0
20
40
60
80
100Première
0
20
40
60
80
100
0 20 40 60 80
Seresta
0
20
40
60
80
100Bintje
0
20
40
60
80
100
0 20 40 60 80
Astarte
0
20
40
60
80
100
RH
Thermal days (td)
Can
opy
cove
r (%
)
Chapter2
30
populationforallsixenvironments.
Environmenthadahighlysignificant(P<0.01)impactonallmodelparameters
and derived secondary traits, at least partly due to the purposeful variation in
availability ofN across trials (Table 2.1). Nitrogen has amain influence on canopy
development(PerumalandSahota,1986;Vos,1995a,b,2009),arisingfromeffectsof
Nonrateanddurationofappearanceofleavesandbranchesonthepotatoplant,and
on the active life span of individual leaves. Due to the lack of precise information
aboutamountofmineralNbecomingavailableduringthecourseofcanopygrowth,
weusedtheamountofNuptakebytubersasanindicatorofNavailability(Table2.1).
CropNuptakeisasite‐specificindicatorofNthatis“available”tothecrop(Sullivan
et al., 2008). Experiments with lower N uptake (especially Exp 3) also had lower
estimates for vmax, DP2, and canopy growth rates (cm1, c1, and c3). In potato, the
duration of P2 and whether or not full canopy cover is attained are very much
affectedbyNsupply.ThetotalgrowthperiodisprolongedforlargerrateofNsupply
becauseP2 extendswith betterN nutrition. Usually,within agronomically relevant
ranges,NhascomparativelylittleeffectonP1andaffectstherateofsenescenceonly
marginally (Vos, 2009). However, in ourmost extreme experiment (Exp 3)we did
observe such effects even to such an extent that the end of the crop cycle (i.e. te)
occurredlaterinthelowNenvironment(i.e.Exp3)incomparisontoenvironments
withhigherNavailability(hightuberNuptake)(Table2.2).
Themeanperformanceof standardcultivars,parentalgenotypes(SHandRH)
and the F1 population across the experimental sites (i.e. environments) varied
significantly (P<0.01) for all model parameters and secondary traits (Table 2.2).
Potatogenotypesvariedinthedurationoftheentirecropcyclewhichisreflectedin
variation in te. There were significant differences (P<0.05) among the standard
cultivars and parents SH and RH for t1, t2, and te, whereas tm1 did not differ
significantlyamonggenotypes(Table2.2).Latecultivarsgavehighervaluesfort2andte followedbymid‐lateandearlycultivars.Thereweresmalldifferencesamongthe
cultivars for vmax, but under certain environmental conditions late cultivars might
reach higher values than earlier ones, due to their comparatively later tuber set.
KoomanandRabbinge(1996)foundthat,comparedwithlatecultivars,earlypotato
cultivarsallocatea largerpartof theavailableassimilates to thetubersearly in the
growingseason,resultinginshortergrowingperiodsandalsoloweryields.Theinitial
phaseofcanopygrowth(i.e.t1orDP1)wasnotmuchaffectedbythematuritytypeof
thegenotypes,buttherewereverylargedifferencesamongstandardcultivarsforthe
durationofP2andP3(i.e.DP2andDP3,respectively).ThedurationofP2(DP2)tended
tobe longer for later cultivars.Estimatedvaluesof canopygrowth ratescm1andc1
variedsignificantly(P<0.05)amongthestandardcultivarsandtwoparents.However,
Geneticvariationinpotatocanopycoverdynamics
31
Table2.2. Estimatedmean values of fivemodel parameters (tm1,t1,t2,te, vmax)and
ninederived secondary traits (cm1, c1,c3,DP2,DP3,A1,A2,A3,Asum) as obtained from
across environment ANOVA for the two parents (SH and RH) and five standard
cultivars(listedinorderofincreasinglylongercropcycle).tdstandsforthermalday.
Dataforcv.KarnicowerenotavailableinExps5and6andarereplacedby“−”.
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 Mean¶
tm1(td)
SH 16.0 13.5 9.4 8.8 10.8 10.2 11.4a
RH 19.7 15.3 6.2 9.6 9.4 8.7 11.5a
Première 16.3 14.8 8.4 6.7 11.7 8.4 11.1a
Bintje 15.3 14.0 8.5 8.9 11.0 8.3 11.0a
Seresta 14.4 11.2 11.1 8.1 13.0 11.2 11.5a
Astarte 16.8 13.0 10.0 9.3 10.9 8.6 11.4a
Karnico 14.0 12.4 9.3 8.6 − − 11.1a
Mean† 16.1a 13.5b 9.0d 8.6e 11.1c 9.2d
F1mean‡ 18.0a 14.7b 9.7e 10.7d 11.6c 9.8e t1(td)
SH 30.1 25.0 21.5 19.9 24.1 17.4 23.0a
RH 27.6 26.1 23.6 16.5 14.7 14.4 20.5ab
Première 27.2 22.3 17.6 14.9 18.0 13.3 18.9b
Bintje 26.8 20.2 23.8 17.2 17.8 14.4 20.0ab
Seresta 26.3 19.1 22.6 15.9 20.1 17.7 20.3ab
Astarte 27.9 22.1 36.0 18.6 17.0 15.1 22.8a
Karnico 23.6 19.2 31.2 16.9 − − 22.7a
Mean† 27.1a 22.0c 25.2b 17.1e 18.6d 15.4f
F1mean‡ 31.0a 23.0d 25.8b 19.7e 23.8c 16.4f
Chapter2
32
Table2.2.(Continued)
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 Mean¶
t2(td)
SH 41.4 38.6 43.1 29.7 45.3 40.3 39.7cd
RH 36.1 35.9 29.0 31.1 47.6 28.3 34.7d
Première 42.2 28.6 31.6 28.2 40.6 38.9 35.0d
Bintje 40.5 36.5 42.2 45.9 52.5 41.1 43.1c
Seresta 52.1 47.0 56.3 62.4 69.8 55.7 57.2ab
Astarte 43.2 38.8 70.3 68.0 75.7 64.9 60.2a
Karnico 31.7 28.5 75.0 72.3 − − 51.9b
Mean† 41.0e 36.2f 49.6b 48.2c 55.3a 44.9d
F1mean‡ 41.8b 34.0e 37.9d 40.2c 49.0a 40.9bc te(td)
SH 57.8 51.8 63.7 57.8 58.2 54.4 57.3e
RH 51.3 51.1 52.9 53.8 57.1 47.4 52.3f
Première 50.8 45.7 54.0 41.3 57.3 51.3 50.1f
Bintje 67.7 60.6 76.9 65.3 65.5 57.2 65.5d
Seresta 69.7 71.7 85.2 73.3 78.3 72.1 75.0c
Astarte 80.6 80.2 79.4 78.3 84.5 78.0 80.2b
Karnico 82.9 82.5 87.5 90.4 − − 85.8a
Mean† 65.8c 63.4d 71.4a 65.7c 66.8b 60.1e
F1mean‡ 58.2c 53.4e 62.1b 58.1c 64.9a 57.2d vmax(%)
SH 100.0 100.0 64.7 100.0 98.0 100.0 93.8c
RH 93.6 64.2 57.8 100.0 100.0 100.0 85.9d
Première 100.0 100.0 68.9 99.8 100.0 100.0 94.8bc
Bintje 100.0 100.0 96.1 100.0 100.0 100.0 99.4ab
Seresta 100.0 99.9 88.2 100.0 100.0 100.0 98.0abc
Astarte 100.0 100.0 93.6 100.0 100.0 100.0 98.9ab
Karnico 100.0 100.0 100.0 100.0 − − 100.0a
Mean† 99.1b 94.9c 81.3d 100.0a 99.7ab 100.0a
F1mean‡ 95.3d 99.2ab 71.0e 98.8b 97.5c 99.9a
Geneticvariationinpotatocanopycoverdynamics
33
Table2.2.(Continued)
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 Mean¶
cm1(%d‐1)
SH 5.1 6.4 4.7 7.4 6.0 9.5 6.5b
RH 6.6 4.1 3.6 9.6 11.7 11.7 7.9a
Première 6.0 7.9 5.9 9.9 9.7 13.0 8.8a
Bintje 6.0 9.3 5.9 9.0 10.3 11.2 8.6a
Seresta 5.9 8.5 6.0 9.5 9.3 9.7 8.2a
Astarte 5.9 7.6 3.9 8.4 10.1 10.8 7.8ab
Karnico 7.0 9.0 4.8 8.9 − − 7.4ab
Mean† 6.1e 7.5d 5.0f 9.0c 9.5b 11.0a
F1mean‡ 5.1e 7.6c 4.3f 8.7b 7.0d 10.4a c1 (%td‐1)
SH 3.3 4.1 3.0 5.0 4.1 5.9 4.2b
RH 3.4 2.5 2.5 6.0 6.8 7.0 4.7b
Première 3.7 4.5 3.9 6.7 5.6 7.5 5.3a
Bintje 3.7 5.0 4.0 5.9 5.7 7.0 5.2ab
Seresta 3.8 5.3 3.9 6.3 5.1 5.7 5.0ab
Astarte 3.6 4.6 2.7 5.4 5.9 6.7 4.8ab
Karnico 4.3 5.2 3.2 5.9 − − 4.7b
Mean† 3.7e 4.4d 3.3f 5.9b 5.5c 6.6a
F1mean‡ 3.1d 4.4c 2.8e 5.2b 4.4c 6.2a c3 (%td‐1)
SH 6.1 7.6 3.2 3.9 8.1 8.2 6.2a
RH 6.2 4.3 2.5 4.6 11.1 5.2 5.7a
Première 11.6 5.9 3.1 7.4 6.7 8.1 7.1a
Bintje 3.8 5.0 2.8 5.4 8.6 6.3 5.3a
Seresta 5.7 4.1 7.3 11.2 11.9 7.2 7.9a
Astarte 2.7 2.5 11.0 9.7 12.7 8.5 7.9a
Karnico 2.0 1.9 8.1 8.0 − − 5.0a
Mean† 5.4c 4.5d 5.4c 7.2b 9.9a 7.2b
F1mean‡ 6.5b 6.1b 3.4c 6.4b 7.5a 7.1a
Chapter2
34
Table2.2.(Continued)
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 Mean¶
DP2(td)
SH 11.3 13.6 21.5 9.8 21.3 22.9 16.7c
RH 8.5 9.8 5.3 14.5 33.0 13.8 14.2c
Première 15.0 6.2 14.1 13.3 22.7 25.6 16.1c
Bintje 13.7 16.3 18.4 28.7 34.7 26.7 23.1b
Seresta 25.8 27.9 33.6 46.5 49.7 38.1 36.9a
Astarte 15.4 16.7 34.3 49.4 58.8 49.8 37.4a
Karnico 8.1 9.3 43.8 55.4 − − 29.1b
Mean† 14.0e 14.2e 24.4d 31.1b 36.7a 29.5c
F1mean‡ 10.7d 11.1cd 12.0c 20.5b 25.1a 24.5a
DP3(td)
SH 16.5 13.2 20.7 28.1 12.9 14.1 17.6bc
RH 15.1 15.3 23.9 22.7 9.5 19.2 17.6bc
Première 8.7 17.2 22.4 13.1 16.7 12.4 15.1c
Bintje 27.1 24.2 34.7 19.4 13.0 16.1 22.4b
Seresta 17.6 24.7 28.9 10.9 8.5 16.4 17.8bc
Astarte 37.4 41.5 9.1 10.3 8.8 13.1 20.0bc
Karnico 51.3 54.0 12.5 18.1 − − 34.0a
Mean† 24.8b 27.1a 21.7c 17.5d 11.6f 15.2e
F1mean‡ 16.5d 19.4b 24.3a 17.9c 16.0d 16.3d
A1(td%)
SH 1455.5 1192.3 723 1047.7 1239.1 785.4 1074a
RH 946.2 743.1 810 753.8 610 636 750b
Première 1201.1 900.3 612.3 777 738.7 561.2 798b
Bintje 1231.3 763.7 1286.5 844.4 756.8 658.7 924ab
Seresta 1247.5 862 1011.7 786.5 827.7 745.7 914ab
Astarte 1232.2 995.2 2023.2 919.2 708.6 691.6 1095a
Karnico 1045.7 795.8 1811.1 836.5 − − 1122a
Mean† 1194a 893b 1183a 852c 813d 680e
F1mean‡ 1331a 948d 1076c 904e 1159b 722f
Geneticvariationinpotatocanopycoverdynamics
35
Table2.2.(Continued)
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 Mean¶
A2(td%)
SH 1130 1361 1394 978 2091 2288 1540d
RH 797 610 326 1454 3296 1380 1311d
Première 1498 623 966 1323 2267 2558 1539d
Bintje 1373 1627 1752 2871 3472 2671 2294c
Seresta 2580 2789 3050 4648 4967 3806 3640a
Astarte 1537 1665 3171 4939 5875 4983 3695a
Karnico 807 926 4376 5543 − − 2913b
Mean† 1389e 1372e 2148d 3108b 3661a 2948c
F1mean‡ 1050c 1098c 906d 2065b 2472a 2442a A3(td%)
SH 1127.5 903.6 895.1 1826.9 859.9 941.9 1092bc
RH 971.4 679.9 910.6 1482 645.8 1257.8 991c
Première 605 1158.1 1010 878.1 1107.3 828.5 931c
Bintje 1807.5 1583.4 2185.9 1277.4 875.5 1065.5 1466b
Seresta 1196.8 1622 1545.7 732.8 588.5 1095.1 1130bc
Astarte 2445.7 2655.5 584.1 707.4 604.2 880 1313bc
Karnico 3257.6 3373.6 866.9 1188.4 − − 2172a
Mean† 1630b 1711a 1143c 1156c 780e 1011d
F1mean‡ 1074cd 1286a 1112bc 1169b 1039d 1073cd
Asum(td%)
SH 3713 3457 3012 3853 4190 4015 3707d
RH 2715 2033 2046 3690 4552 3273 3051e
Première 3304 2681 2589 2979 4113 3948 3269e
Bintje 4412 3974 5224 4993 5104 4396 4684c
Seresta 5024 5273 5607 6168 6383 5647 5684b
Astarte 5215 5316 5778 6566 7188 6555 6103a
Karnico 5110 5095 7054 7568 − − 6207a
Mean† 4213e 3976f 4473d 5116b 5255a 4639c
F1mean‡ 3455d 3332e 3094f 4138c 4685a 4246b
Means followed by different letters are significantly different according to Fisher’s
MultipleRangeTest(P<0.05).
Chapter2
36
¶Genotype(twoparentsandfourorfivecultivars)meanacrosssixexperiments(i.e.
environments).
†Meanofeachindividualenvironmentacrossgenotypes(twoparentsandfourorfive
cultivars).
‡F1meanofeachindividualenvironmentacrossF1population(100genotypes).
tm1:LSDforgenotype=1.6;LSDforenvironment=0.3;LSDforGE=2.8t1:LSDforgenotype=3.0;LSDforenvironment=0.5;LSDforGE=5.1t2:LSDforgenotype=5.5;LSDforenvironment=0.9;LSDforGE=9.6te:LSDforgenotype=4.0;LSDforenvironment=0.7;LSDforGE=6.9vmax:LSDforgenotype=4.9;LSDforenvironment=0.8;LSDforGE=8.5cm1:LSDforgenotype=1.3;LSDforenvironment=0.2;LSDforGE=2.3c1:LSDforgenotype=0.5;LSDforenvironment=0.1;LSDforGE=0.9c3:LSDforgenotype=2.7;LSDforenvironment=0.5;LSDforGE=4.8DP2:LSDforgenotype=6.1;LSDforenvironment=1.0;LSDforGE=10.6DP3:LSDforgenotype=6.6;LSDforenvironment=1.1;LSDforGE=11.5A1:LSDforgenotype=224.1;LSDforenvironment=37.2;LSDforGE=388.1A2:LSDforgenotype=568.2;LSDforenvironment=94.3;LSDforGE=984.2A3:LSDforgenotype=392.7;LSDforenvironment=65.2;LSDforGE=680.2Asum:LSDforgenotype=386.4;LSDforenvironment=64.1;LSDforGE=669.2the differences were not large. These rates were comparatively higher for early
maturingcultivarsthanthosematuringlate.AveragesenescencerateduringP3(i.e.
c3)wasnotsensitivetothematuritytypeofthegenotypes.
The total biomass production and accumulation of potato cultivars are
dependent on the intercepted PAR (Spitters, 1988; Vos and Groenwold, 1989; Van
Delden,2001).Theamountoflightinterceptedisinproportiontotheareaunderthe
wholecanopycurve(Vos,1995b).MeanestimatesforAsumrangedfrom3051to6207
td%forfivestandardcultivarsandtwoparentalgenotypes.Asumvalueswerehigher
for late cultivars likeKarnicoandAstarte than for early cultivars suchasPremière
(Table2.2).TheareaundercanopycoverduringP2,i.e.A2,significantlycontributed
to the higher values of Asumin late cultivars (Table 2.2). Mean values of Asum also
showedhighlysignificant(P<0.05)variationwithintheF1populationacrossthesix
environments(Table2.2).
The variation (the median, minimum and maximum values) of these model
parameters and secondary derived secondary traits for the F1 population per
individualexperimentisillustratedinFigs.2.3and2.4,respectively.Therangesofthe
Geneticvariationinpotatocanopycoverdynamics
37
five model parameters were consistently wider in Exp 3 than in the other
experiments(Fig.2.3).Incaseofgrowthrates(i.e.cm1,c1,andc3)widerrangeswere
observed in Exps 4 and 5 (Fig. 2.4). Wide ranges of variation were observed for
durationof thethreephasesofcanopydevelopment i.e.t1inExp3, forDP2inExp4,
andforDP3inExp2.Inthecaseoftheareasunderthecurves,therangeswithinthe
populationwerelargestinExp3forA1,inExps4and5forA2,andinExp2forA3.For
Asum,therangeswerehighestinExp3andvalueswerebetween1030and6101td%
(Fig.2.4).Theseresultswereinlinewiththeresultsforthelengthofthreephasest1,
DP2, and DP3. Within the F1 population, some genotypes particularly SHRH34‐H6,
SHRH83‐L9, and SHRH‐136) recorded the highest averageAsum with values (6146,
5836, and5739 td%, respectively). This shows that genotypes like thesemayhave
higher potential to intercept the PAR and tuber yield production. Most of the
parameterswerenearlynormallydistributed(Figs.2.5and2.6)whichmightindicate
theirtransgressivesegregation.
3.3.Phenotypic,geneticandenvironmentalvariances
Table 2.3 presents estimated values of phenotypic, genetic, and environmental
variancesforalltheparametersandthederivedsecondarytraitsintheF1population.
The results revealed considerable phenotypic and genetic variances for the traits.
Almostallthecomponentsofvariationweresignificant(P<0.01)(Table2.3).
Themajorportionofthephenotypicvariancewasaccountedforbythegenetic
variance component for t2, te, and Asum (Table 2.3). The contribution of the
environmentalvariancetothephenotypicvariancewasrelativelylargefortm1,t1,vmax,
andthederivedsecondarytraitscm1,c1,c3,DP2,andA2.Thecontributionof theGEinteractionvariancecomponenttothephenotypicvariancewaslargefortraitsDP3,A1,
andA3(Table2.3).
Estimatesofphenotypic(CVPh)andgenetic(CVG),environmental(CVE),andGEinteraction (CVGE) coefficients of variation for model parameters and derived
secondarytraitsacrossthesixexperimentsarepresentedinTable2.4.Estimatesof
CVPhrangedfrom15.4%to69.4%.Theseestimatesweresmallestforteandvmaxand
highest forDP2 andA2.Traitswith higherCVPh exhibitmore total variance and are
useful as selection criteria in breeding provided the trait is also heritable. Our F1
population reflected high CVG for almost all traits except for tm1, t1, vmax, and A1.Relatively higherCVG and lowerCVE estimateswere obtained for t2, te,A3, andAsum
suggesting that these traits, compared with the other ones, are under a greater
influenceofgeneticcontrol.TheCVEwascomparativelyhigherthanCVGfortraitstm1,
Chapter2
38
0
10
20
30
40
50
60[td] t1
0
5
10
15
20
25
[td] tm1
0
20
40
60
80
100
[td] te
0
20
40
60
80
1 2 3 4 5 6
[td] t2
0
20
40
60
80
100
1 2 3 4 5 6
[%] vmax
Experiment no.
Figure2.3.BoxplotsofgeneticmeansofanF1populationforfivemodelparameters
inall sixexperiments.Theboxesspan the interquartilerangeof the traitvalues, so
thatthemiddle50%ofthedatalaywithinthebox,withahorizontal lineindicating
themedian.Whiskersextendbeyondtheendsoftheboxasfarastheminimumand
maximumvalues.
Geneticvariationinpotatocanopycoverdynamics
39
Figure 2.4. Box plots of genetic means of an F1 population for seven secondaryderived traits in all six experiments. The boxes span the interquartile range of thetraitvalues,sothatthemiddle50%ofthedatalaywithinthebox,withahorizontallineindicatingthemedian.Whiskersextendbeyondtheendsoftheboxasfarastheminimumandmaximumvalues.
0
2
4
6
8
10c1
[% td-1]
0
5
10
15
20
1 2 3 4 5 6
[% td-1] c3
0
1000
2000
3000
4000[td %] A1
0
2000
4000
6000
8000A2
[td %]
0
1000
2000
3000
4000[td %] A3
0
2000
4000
6000
8000
1 2 3 4 5 6
[td %] Asum
0
5
10
15
20
25cm1
[% td-1]
Experiment no.
Chapter2
40
Figure2.5. Distribution of five model parameters among F1 genotypes across six
experiments.Thevaluesoftwoparents‘SH’and‘RH’areindicatedbyfullarrowand
dashedarrow,respectively.
100
30
25
70 90
20
15
80
10
5
85 95 75 0
80 50 75 45 65
30
25
55
20
15
60 70
10
5
0
Num
ber
of F
1 ge
noty
pes
14 15
25
20
15
8 12
10
5
10 0
11 13 9
tm1
20 32 28
25
20
18 24
15
10
5
0 26 22 30
t1
35 65 55 30 45
25
20
15
50 40
10
5
0 60
t2
te vmax
Geneticvariationinpotatocanopycoverdynamics
41
Figure 2.6. Distribution of seven secondary derived traits among F1 genotypes
acrosssixexperiments.Thevaluesoftwoparents‘SH’and‘RH’areindicatedbyfull
arrowanddashedarrow,respectively.
0800 1100 900
18
1200
15
13
10
1000
8
5
3
1800 800 1400
25
20
1000
15
10
1200
5
1600 0
20
2000 5000 0
10
3000 6000
5
4000
30
25
15
15
0 4000
5
2000 3000 0
1000
30
25
20
10
10
5.5 3.0 4.5 0
3.5 5.0 4.0
25
20
15
5
8
5
3
4 8 6
20
18
0 7 9
5
15
13
10
Num
ber
of F
1 ge
noty
pes
cm1 A2
c1 A3
A1
5 10 4
30
8
25
20
3 6
15
10
5
0 7 9
c3 Asum
Chapter2
42
Table2.3. Variance components for five model parameters (tm1, t1, t2, te, vmax)and
nine derived secondary traits (cm1, c1, c3, DP2,DP3, A1, A2, A3, Asum) within the F1
populationacrossallsixexperiments.
Parameter2
ph 2
E 2
β 2
G 2
GE 2
ε
tm1(td) 14.4 10.7** 0.1** 0.5** 1.2** 1.9
t1(td) 47.8 25.1** 0.5** 1.7** 13.6** 6.9
t2(td) 98.0 22.8** 2.1** 36.2** 13.7** 23.2
te(td) 88.2 15.4** 0.9** 54.1** 6.3** 11.5
vmax(%) 207.6 124.9** 0.2NS 16.9** 45.7** 19.9
cm1(%td‐1) 8.4 4.9** 0.4** 0.6** 1.0** 1.4
c1(%td‐1) 2.2 1.5** 0.1** 0.2** 0.2** 0.2
c3(%td‐1) 9.6 1.9** 0.1** 0.3** 1.5** 5.8
DP2(td) 128.5 44.3** 2.2** 33.5** 19.9** 28.6
DP3(td) 59.9 8.4* 2.5** 5.1** 11.6** 32.3
A1(td%) 166690 42225** 3096** 0.01 81689** 39680
A2(td%) 1348961 527517** 13971** 376931** 184725** 245817
A3(td%) 197626 4618NS 4557** 30015** 44409** 114027
Asum(td%) 1206327 372964** 9647** 611003** 99925** 112788**Significantat1%,*Significantat5%,NSNon‐significant.1Thevarianceestimatewasnegativesoassumedzero.
σ2Ph = phenotypic variance, σ2ß = block variance, σ2G = genetic variance, σ2 E =
environmentalvariance,σ2GE=genotypeenvironmentalinteractionvariance,σ2ε=residualvariance.
t1,vmax,cm1,c1,c3,DP2,DP3,andA2,thusreflectenvironmentalsensitivityofthesetraits.
TheCVGE ranged from4.3% to28.0%among the traits.The results further showed
thatCVGE exceeded theCVG for traitstm1, t1, vmax, cm1, c3,DP3, andA3. These results
showthemajorcontributionofGEtotheCVPhofthesetraitsinourexperiments.Itisnormally difficult to select for traits that are sensitive to environmental factors.
However, our results indicated that overall considerable genetic variability existed
for most traits which could be exploited for improvement of light interception
efficiency.
Geneticvariationinpotatocanopycoverdynamics
43
Table 2.4. The phenotypic coefficient of variation (CVPh%), genetic coefficient of
variation(CVG%),environmentalcoefficientofvariation(CVE%),andGEinteractioncoefficientofvariation(CVGE%)forfivemodelparameters(tm1,t1,t2,te,vmax)andnine
derivedsecondarytraits(cm1,c1,c3,DP2,DP3,A1,A2,A3,Asum)withintheF1population
acrossallsixexperiments.
Parameter Mean¶ CVPh CVG CVE CVGE
tm1(td) 12.4 30.5 5.7 26.3 8.8
t1(td) 23.3 29.7 5.6 21.5 15.8
t2(td) 40.7 24.3 14.8 11.7 9.1
te(td) 59.0 15.9 12.5 6.7 4.3
vmax(%) 93.6 15.4 4.4 11.9 7.2
cm1(%td‐1) 7.2 40.3 10.8 30.9 14.2
c1(%td‐1) 4.3 33.9 9.6 28.7 9.4
c3(%td‐1) 6.2 50.4 9.3 22.3 19.7
DP2(td) 17.4 65.1 33.3 38.3 25.6
DP3(td) 18.4 42.1 12.3 15.8 18.5
A1(td%) 1021 40.0 0.0 20.1 28.0
A2(td%) 1674 69.4 36.7 43.4 25.7
A3(td%) 1129 39.4 15.3 6.0 18.7
Asum(td%) 3801 28.9 20.6 16.1 8.3
¶GrandmeanoftheF1segregatingpopulationacrossallsixexperiments.
3.4.GGEbiplotanalysisHere we discuss only the results for trait t1, which had both a strong G and GE
component.TheGGEbiplotsrevealedthatthe1standthe2ndprincipalcomponents
accounted for 85.84% of the GE variation (Fig. 2.7). The environment vectorscovered a wide range of Euclidean space, indicating the existence of strong GEinteractionsamongthesixenvironmentsevaluated.
Theresultswerefurtheranalysedforuniquenessofenvironments.Asshownby
Fig.2.7,fourenvironments(Exps1,2,4,and6)weregroupedtogether.Thissuggests
thattheseenvironmentswerehighlycorrelatedandrelativelysimilarinthemanner
Chapter2
44
Figure 2.7. GGE biplot chart showing relationships among six experiments
(environments) for model parameter t1 in an F1 segregating population. AEC
indicates average environment coordinate (Yan, 2001; Yan andKang, 2003).A line
that passes through the biplot origin and average environment indicates themean
performanceofgenotypes.
theydiscriminateamonggenotypes.Foranyparticularenvironment,genotypescan
be compared by projecting a perpendicular from the genotype symbols to the
environmentvector, i.e.genotypesthatarefurtheralonginthepositivedirectionof
the environment vector are better performing and vice versa. The greater the
distance from the origin to the intersection of a genotype projection on an
environment vector, the more this genotype deviates from the average in this
environment (Kroonenberg, 1997; Chapman et al., 1997). The results showed that
environments (Exps 2 and 6) fell close to the origin. This could mean that these
Geneticvariationinpotatocanopycoverdynamics
45
environments may have little variability across genotypes (Kroonenberg, 1997).
However,markerofenvironment(Exp3)wasstandingfurthestapartfromtheorigin
which may suggest that this environment caused maximum variability across the
genotypes. These results are in line with our earlier described results that Exp 3
causedthemaximumvariabilityforsometraits.Thisenvironmentmightthereforebe
themaincontributortotheoverallGEonaccountofitslowestNavailabilitythantherestoftheenvironments.Itwouldnotbeexpectedthatthesamegenotypewouldbe
the most effective in most of the environments and therefore a focus on why
particulargenotypesperformexceptionallywellineitherloworhighinputsituations
couldenableselectionstrategiestobedevelopedforimprovedvarieties.Thisanalysis
therefore allowed a partial understanding of the environmental causes of the
observedGEinteraction.
3.5.Phenotypiccorrelationsofmodelparametersandthesecondarytraits
Table2.5illustratesthephenotypiccorrelationcoefficientsamongallthetraitswithin
theF1population.Theresultsshowedvariationintherelationshipacrossindividual
experiments.Forthecaseofthefivemodelparameters,therewereweakcorrelations
(r≤0.50)betweentm1andt1,andbetweent1andt2inmostexperiments,butnotin
Exps2and3,wheremoderatepositivecorrelationswerefound.Therewerestronger
positivecorrelations(r≥0.80)betweent2andteinExps3,4,5,and6.Exps1and2,
however,showedmoderate(r=0.63)tolow(r=0.44)t2‐tecorrelations,respectively
(Table 2.5). The results further showed little phenotypic correlation between the
thermaldaysrequiredforthedifferentphasesandthemaximumsoilcover:tm1‐vmax,
t1‐vmax, t2‐vmax, and te‐vmax. Only Exp 3 showedpositive strongcorrelationswith r =
0.51,0.79,0.69,0.69,respectively(Table2.5).
Therewerestrongnegativephenotypiccorrelationsbetweengrowthrates(cm1andc1)andt1andbetweenc3andDP3,whichsuggestsa trade‐offbetweenrateand
durationofthecanopybuild‐upandsenescence,respectively(Table2.5).Theresults
further showed strong negative correlations between t1 and DP2 in several
experiments. The correlations between DP2 and DP3were mostly weak and non‐
significant, only Exps 3 and 4 showed significant (P<0.05) and strong (r = ‐0.59)
negative correlations (Table 2.5). These results suggest that genotypes with slow
canopy build‐up had a relatively short period of maximum canopy cover (DP2).
Furthermore,t1andDP2alsodependeduponvmax,witht1generallybeinglongwithalowvmaxbutwithDP2generallybeingshortwhenvmaxwasbelow100%,whereasDP2
could be (much) longerwhen the canopy reached 100% cover. The results further
revealed strong positive correlations between DP2 and te, whereas in half of the
Chapter2
46
experimentsDP3andtewerepositivelycorrelated.ThissuggeststhatbothDP2andDP3
contribute positively to higher values of te. Later cultivars tended to senescemore
slowly (i.e. tended to have a longerDP3) than earlier cultivars. The results showed
weak correlations between A1, A2, and A3 (Table 2.5). The correlation coefficient
ranged from−0.03 to0.44.These results are in linewith those for t1,DP2, andDP3.There was a strong, positive correlation between A2and Asum in all experiments,
underliningtheimportantcontributionofvariationinA2tovariationinAsum.
Inshort,ourresultssuggestalargecomplexityofthegeneticandphysiological
inter‐relationsbetweenthevariousmodelparametersandderivedsecondarytraits.
3.6.Geneticcorrelationsofmodelparametersandthesecondarytraits
Table 2.5 illustrates the genetic correlation coefficients between the model
parametersandsecondarytraitswithintheF1population.Theresultsshowedweak
(r≤0.50)geneticcorrelationsbetweentm1andt1inExps4and5,andbetweent1and
t2inallexperimentsexceptExp2.Theotherexperimentsshowedpositiveandstrong
(r>0.50)tm1‐t1andt1‐t2geneticcorrelations.Positivegeneticcorrelationsbetweent2
andtewerestronginallexperiments(Table2.5).Therewaslittlegeneticcorrelation
between the thermal days required for the different phases and themaximum soil
cover: tm1‐vmax, t1‐vmax, t2‐vmax,and te‐vmax showedpoorcorrelations.However, these
correlations were stronger and positive in Exp 3 with r = 0.59, 0.87, 0.75, 0.75,
respectively (Table 2.5). There were strong negative genetic correlations between
growthrates(cm1andc1)andt1andbetweenc3andDP3.
The results further revealed weak genetic correlations between t1 andDP2in
Exps2,3,and6,whereasExps1,4,and5showedstrongnegativecorrelations(Table
2.5). The genetic correlations between DP2 and DP3were mostly weak and non‐
significant,onlyExp5showedsignificantanegativegeneticcorrelationwithr=‐0.79.
There were strong positive genetic correlations between DP2 and vmax in all the
experimentsexceptinExps3and6.ThetraitDP2alsoshowedstrongpositivegenetic
correlationswithteinallexperiments.Abouthalfofexperimentsalsoshowedstrong
positivegeneticcorrelationsbetweenDP3andte.Theseresultsindicatethatgenotypes
withlongerDP2andDP3couldbeindirectlyobtainedbyselectinggenotypeswithhigh
vmaxandte.
TherewereweakgeneticcorrelationsbetweenA1andA2,andbetweenA2andA3
in most experiments. However, there were strong positive genetic correlations
between A2 and Asum in all experiments and between A3 and Asum in half of the
experiments(Table2.5).Theseresultsare in linewiththose for t1,DP2,andDP3and
highlighttheimportanceofbothA2andA3tobeusedasanindirectselectionmeasure
Geneticvariationinpotatocanopycoverdynamics
47
**Significantat1%,*Significantat5%,N
S Non‐significant.
Table2.5.Phenotypic(low
ertriangle)andgenetic(uppertriangle)correlationcoefficientsam
ongallpairwisecomparisons
offivemodelparam
eters(tm1,t 1,t2,t e,v
max)andninederivedsecondarytraits(c m
1,c 1,c3,DP2,D
P3,A
1,A 2,A
3,A s
um)withintheF1
populationperindividualexperiment.Theunitforallmodelparam
etersistherm
alday(td)exceptforv
max(%
)andthe
derivedtraits A
1,A2,A3,Asum(td%)andc m
1,c 1,c3(%
td‐1).
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.1
t m1
0.56**
‐0.13N
S ‐0.12NS
‐0.11N
S ‐0.27**
‐0.40**
‐0.03NS
‐0.38**
‐0.05NS
0.21*
‐0.36**
‐0.10NS
‐0.30**
t 1
0.43**
‐0.44**
‐0.41**
‐0.60**
‐0.91**
‐0.92**
‐0.10NS
‐0.82**
‐0.20*
0.71**
‐0.82**
‐0.39**
‐0.70**
t 2
‐0.02NS
‐0.30**
0.78**
0.43**
0.53**
0.50**
0.07
NS
0.88**
0.21*
‐0.23*
0.87**
0.30**
0.82**
t e
‐0.07NS
‐0.38**
0.62**
0.37**
0.51**
0.48**
‐0.52**
0.72**
0.78**
‐0.27**
0.72**
0.80**
0.88**
v max
‐0.09NS
‐0.48**
0.35**
0.32**
0.85**
0.87**
0.26**
0.60**
0.15
NS
0.04
NS
0.62**
0.48**
0.73**
c m1
‐0.07NS
‐0.85**
0.42**
0.47**
0.75**
0.98**
0.15
NS
0.83**
0.27**
‐0.50**
0.84**
0.52**
0.82**
c 1
‐0.32**
‐0.88**
0.40**
0.44**
0.83**
0.94**
0.17
NS
0.81**
0.25*
‐0.41**
0.82**
0.51**
0.82**
c 3
0.02
NS
‐0.02NS
0.30**
‐0.47**
0.23*
0.10
NS
0.12
NS
0.10
NS
‐0.88**
0.12
NS
0.10
NS
‐0.69**
‐0.17NS
DP2
‐0.24*
‐0.75**
0.86**
0.64**
0.51**
0.75**
0.75**
0.22*
0.24*
‐0.52**
1.00**
0.40**
0.90**
DP3
‐0.08NS
‐0.21*
‐0.12NS
0.70**
0.09
NS
0.20
NS
0.20
*‐0.86**
0.03
NS
‐0.20NS
0.25**
0.95**
0.55**
A1
‐0.05NS
0.70**
‐0.14NS
‐0.24*
0.13
NS
‐0.53**
‐0.37**
0.13
NS
‐0.48**
‐0.18NS
‐0.52**
‐0.15NS
‐0.27**
A2
‐0.24*
‐0.75**
0.85**
0.65**
0.55**
0.77**
0.78**
0.21*
1.00**
0.04
NS
‐0.46**
0.42**
0.91**
A3
‐0.10NS
‐0.33**
‐0.01NS
0.74**
0.38**
0.41**
0.43**
‐0.74**
0.17
NS
0.95**
‐0.11NS
0.20*
0.72**
Asum
‐0.28**
‐0.62**
0.72**
0.85**
0.70**
0.73**
0.79**
‐0.11NS
0.84**
0.43**
‐0.17NS
0.86**
0.61**
Chapter2
48
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.2
t m1
0.82**
0.08
NS
‐0.10NS
‐0.45**
‐0.40**
‐0.76**
0.23*
‐0.21*
‐0.18NS
0.47**
‐0.21*
‐0.18NS
‐0.21*
t 1
0.69**
‐0.10NS
‐0.43**
‐0.78**
‐0.86**
‐0.99**
0.42**
‐0.42**
‐0.46**
0.89**
‐0.43**
‐0.46**
‐0.51**
t 2
0.05
NS
‐0.11NS
0.58**
0.48**
0.16
NS
0.13
NS
0.06
NS
0.94**
0.04
NS
‐0.14NS
0.94**
0.05
NS
0.72**
t e
‐0.07NS
‐0.31**
0.45**
0.46**
0.48**
0.45**
‐0.75**
0.67**
0.84**
‐0.53**
0.66**
0.85**
0.97**
v max
‐0.16NS
‐0.34**
0.21*
0.25*
0.76**
0.85**
‐0.19NS
0.69**
0.25**
‐0.75**
0.71**
0.27**
0.60**
c m1
‐0.25**
‐0.84**
0.15
NS
0.35**
0.41**
0.89**
‐0.39**
0.43**
0.49**
‐1.00**
0.44**
0.49**
0.52**
c 1
‐0.66**
‐0.97**
0.13
NS
0.33**
0.52**
0.87**
‐0.40**
0.45**
0.46**
‐0.89**
0.46**
0.47**
0.53**
c 3
0.17
NS
0.22*
0.26**
‐0.64**
‐0.05NS
‐0.18NS
‐0.20*
‐0.08NS
‐0.96**
0.47**
‐0.08NS
‐0.97**
‐0.62**
DP2
‐0.19NS
‐0.45**
0.94**
0.52**
0.30**
0.43**
0.45**
0.16
NS
0.19
NS
‐0.43**
1.00**
0.20*
0.82**
DP3
‐0.12NS
‐0.27**
‐0.20NS
0.78**
0.13
NS
0.28**
0.27**
‐0.89**
‐0.09NS
‐0.55**
0.19
NS
1.00**
0.71**
A1
0.26**
0.86**
‐0.14NS
‐0.33**
‐0.15NS
‐0.93**
‐0.80**
0.17
NS
‐0.43**
‐0.26**
‐0.44**
‐0.56**
‐0.55**
A2
‐0.20NS
‐0.45**
0.94**
0.52**
0.31**
0.43**
0.46**
0.16
NS
1.00**
‐0.09NS
‐0.43**
0.20*
0.82**
A3
‐0.12NS
‐0.27**
‐0.19NS
0.79**
0.18
NS
0.29**
0.29**
‐0.89**
‐0.08NS
1.00**
‐0.25*
‐0.07NS
0.72**
Asum
0.82**
0.08
NS
‐0.10NS
‐0.45**
‐0.40**
‐0.76**
0.23*
‐0.21*
‐0.18NS
0.47**
‐0.21*
‐0.18NS
‐0.21*
Table2.5.(Continued)
**Significantat1%,*Significantat5%,N
S Non‐significant.
Geneticvariationinpotatocanopycoverdynamics
49
Table2.5.(Continued)
**Significantat1%,*Significantat5%,N
S Non‐significant.
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.3
t m1
0.64**
0.69**
0.62**
0.59**
‐0.55**
‐0.32**
0.76**
0.35**
‐0.33**
0.57**
0.57**
0.26**
0.65**
t 1
0.40**
0.85**
0.77**
0.87**
‐0.65**
‐0.39**
0.94**
0.15
NS
‐0.42**
0.99**
0.45**
0.56**
0.88**
t 2
0.51**
0.70**
‐0.94**
0.75**
‐0.67**
‐0.39**
0.92**
0.67**
‐0.47**
0.82**
0.83**
0.29**
0.91**
t e
0.49**
0.69**
0.80**
0.75**
‐0.38**
‐0.23*
0.75**
0.66**
‐0.13NS
0.77**
0.73**
0.50**
0.91**
v max
0.51**
0.79**
0.69**
0.69**
‐0.12NS
0.13
NS
0.87**
0.16
NS
‐0.23*
0.92**
0.52**
0.72**
0.94**
c m1
‐0.02NS
‐0.53**
‐0.22*
‐0.22*
0.04
NS
1.00**
‐0.45**
‐0.33**
0.81**
‐0.49**
‐0.23*
0.22*
‐0.29**
c 1
0.02
NS
‐0.44**
‐0.16NS
‐0.16NS
0.17
NS
0.97**
‐0.16NS
‐0.19NS
0.46**
‐0.24**
‐0.04NS
0.30**
‐0.06NS
c 3
0.42**
0.60**
0.83**
0.48**
0.65**
‐0.09NS
‐0.02NS
0.41**
‐0.79**
0.95**
0.77**
0.28**
0.92**
DP2
0.31**
‐0.03NS
0.69**
0.42**
0.17
NS
0.22*
0.23*
0.56**
‐0.21*
0.12
NS
0.99**
‐0.22*
0.46**
DP3
‐0.21*
‐0.24*
‐0.60**
0.00
NS
‐0.22*
0.08
NS
0.05
NS
‐0.74**
‐0.59**
‐0.35**
‐0.54**
0.50**
‐0.31**
A1
0.37**
0.98**
0.71**
0.71**
0.87**
‐0.37**
‐0.27**
0.65**
0.02
NS
‐0.24*
0.45**
0.63**
0.90**
A2
0.45**
0.22
*0.83**
0.56**
0.47**
0.19
NS
0.24*
0.74**
0.93**
‐0.63**
0.29**
0.01
NS
0.73**
A3
0.18
NS
0.37**
‐0.05NS
0.44**
0.54**
0.07
NS
0.16
NS
‐0.21*
‐0.44**
0.67**
0.43**
‐0.26**
0.59**
Asum
0.53**
0.78**
0.87**
0.87**
0.93**
‐0.05NS
0.06
NS
0.72**
0.43**
‐0.29**
0.85**
0.68**
0.40**
Chapter2
50
**Significantat1%,*Significantat5%,N
S Non‐significant.
Table2.5.(Continued)
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.4
t m1
‐0.29**
0.57**
0.43**
0.25*
0.36**
0.24*
0.40**
0.55**
‐0.25**
‐0.39**
0.54**
‐0.25**
0.47**
t 1
‐0.39**
‐0.51**
‐0.54**
‐0.62**
‐0.86**
‐0.96**
0.11
NS
‐0.70**
‐0.19*
0.97**
‐0.71**
‐0.28**
‐0.64**
t 2
0.28**
‐0.31**
0.92**
0.50**
0.81**
0.65**
0.17
NS
0.97**
0.04
NS
‐0.62**
0.97**
0.10
NS
0.96**
t e
0.27**
‐0.37**
0.82**
0.32**
0.68**
0.60**
‐0.23*
0.91**
0.46**
‐0.64**
0.91**
0.52**
0.98**
v max
0.20*
‐0.34**
0.29**
0.21*
0.52**
0.71**
0.38**
0.59**
‐0.30**
‐0.54**
0.61**
‐0.15NS
0.51**
c m1
0.36**
‐0.81**
0.51**
0.50**
0.31**
0.89**
0.23*
0.90**
‐0.08NS
‐0.97**
0.90**
‐0.01NS
0.79**
c 1
0.31**
‐0.94**
0.43**
0.44**
0.50**
0.90**
0.08
NS
0.82**
0.04
NS
‐0.94**
0.82**
0.13
NS
0.72**
c 3
0.02
NS
0.03
NS
0.34**
‐0.21*
0.18
NS
0.15
NS
0.10
NS
0.11
NS
‐0.95**
0.08
NS
0.12
NS
‐0.95**
‐0.07NS
DP2
0.36**
‐0.59**
0.95**
0.82**
0.36**
0.70**
0.67**
0.28**
0.09
NS
‐0.79**
1.00**
0.17
NS
0.98**
DP3
0.01
NS
‐0.14NS
‐0.21*
0.39**
‐0.12NS
0.04
NS
0.05
NS
‐0.90**
‐0.13NS
‐0.18NS
0.07
NS
0.99**
0.29**
A1
‐0.56**
0.96**
‐0.35**
‐0.41**
‐0.22*
‐0.86**
‐0.90**
0.02
NS
‐0.61**
‐0.14NS
‐0.79**
‐0.25**
‐0.73**
A2
0.36**
‐0.59**
0.95**
0.82**
0.39**
0.70**
0.68**
0.29**
1.00**
‐0.14NS
‐0.61**
0.15
NS
0.97**
A3
0.03
NS
‐0.16NS
‐0.18NS
0.41**
0.03
NS
0.05
NS
0.10
NS
‐0.89**
‐0.10NS
0.99**
‐0.14NS
‐0.10NS
0.36**
Asum
0.26**
‐0.45**
0.91**
0.96**
0.40**
0.57**
0.56**
0.00
NS
0.92**
0.17
NS
‐0.46**
0.92**
0.22*
Geneticvariationinpotatocanopycoverdynamics
51
**Significantat1%,*Significantat5%,N
S Non‐significant.
Table2.5.(Continued)
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.5
t m1
0.12
NS
0.31**
0.40**
0.42**
‐0.18NS
‐0.16NS
0.18
NS
0.20*
0.05
NS
0.10
NS
0.21*
0.00
NS
0.32**
t 1
0.11
NS
‐0.05NS
0.05
NS
‐0.65**
‐0.99**
‐1.00**
‐0.56**
‐0.53**
0.30**
0.99
NS
‐0.52*
0.00
NS
‐0.26**
t 2
0.25*
‐0.04NS
0.97**
0.74**
0.11
NS
0.13
NS
0.79**
0.88**
‐0.75**
0.02
NS
0.88**
0.00
NS
0.99**
t e
0.28**
0.00
NS
0.83**
0.48**
‐0.06NS
‐0.03NS
0.72**
0.80**
‐0.60**
0.08
NS
0.80**
0.00
NS
0.93**
v max
0.30**
‐0.36**
0.47**
0.29**
0.51**
0.61**
1.00**
0.94**
‐1.00**
‐0.54**
0.93**
0.00
NS
0.77**
c m1
‐0.13NS
‐0.92**
0.07
NS
‐0.04NS
0.40**
0.99**
0.87**
0.57**
‐0.53**
‐1.00**
0.56**
0.00
NS
0.25**
c 1
‐0.13NS
‐0.96**
0.10
NS
‐0.01NS
0.49**
0.98**
0.83**
0.59**
‐0.52**
‐0.99**
0.59**
0.00
NS
0.29**
c 3
0.13
NS
‐0.09NS
0.54**
0.08
NS
0.37**
0.17
NS
0.18
NS
0.95**
‐0.85**
‐0.37**
0.97**
0.00
NS
0.98**
DP2
0.16
NS
‐0.52**
0.87**
0.71**
0.57**
0.51**
0.55**
0.50**
‐0.79**
‐0.46**
1.00**
0.00
NS
0.97**
DP3
‐0.06NS
0.07
NS
‐0.65**
‐0.11NS
‐0.44**
‐0.17NS
‐0.18NS
‐0.86**
‐0.59**
0.13
NS
‐0.80**
0.00
NS
‐0.85**
A1
0.07
NS
0.97**
0.02
NS
0.03
NS
‐0.18NS
‐0.91**
‐0.91**
‐0.03NS
‐0.46**
0.00
NS
‐0.46**
0.00
NS
‐0.19NS
A2
0.16
NS
‐0.52**
0.87**
0.71**
0.59**
0.51**
0.56**
0.50**
1.00**
‐0.58**
‐0.45**
0.00
NS
0.97
**
A3
0.00
NS
0.03
NS
‐0.58**
‐0.06NS
‐0.20*
‐0.12NS
‐0.10NS
‐0.86**
‐0.51**
0.96**
0.01
NS
‐0.50**
0.00
NS
Asum
0.23*
‐0.26**
0.92**
0.89**
0.62**
0.25*
0.31**
0.34**
0.91**
‐0.42**
‐0.17NS
0.92**
‐0.31**
Chapter2
52
**Significantat1%,*Significantat5%,N
S Non‐significant.
Table2.5.(Continued)
t m
1
t 1
t 2
t e
v max
c m1
c 1
c 3
DP2
DP3
A1
A2
A3
Asum
Exp.6
t m1
0.71**
0.29**
0.29**
0.00
NS
‐0.41**
‐0.72**
‐0.10NS
0.13
NS
0.11
NS
0.36**
0.13
NS
0.13
NS
0.20*
t 1
0.55
**
‐0.03NS
0.24*
0.00
NS
‐0.95**
‐1.00**
‐0.58**
‐0.24**
0.66**
0.91**
‐0.25**
0.67**
0.07
NS
t 2
0.24*
0.02
NS
0.92**
0.00
NS
0.16
NS
0.02
NS
‐0.13NS
0.98**
0.21*
‐0.18*
0.98**
0.21*
0.97**
t e
0.18
NS
0.16
NS
0.80**
0.00
NS
‐0.14NS
‐0.23*
‐0.53**
0.84**
0.58**
0.16
NS
0.84**
0.57**
0.98**
v max
‐0.18NS
‐0.25**
0.19
NS
0.15
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
0.00
NS
c m1
‐0.20*
‐0.88**
0.11
NS
‐0.06NS
0.26**
0.93**
0.59**
0.35**
‐0.68**
‐1.00**
0.36**
‐0.69**
0.02
NS
c 1
‐0.57**
‐0.99**
‐0.02NS
‐0.15NS
0.30**
0.89**
0.55**
0.23*
‐0.63**
‐0.91**
0.23*
‐0.64**
‐0.07NS
c 3
0.05
NS
‐0.22*
0.14
NS
‐0.42**
0.04
NS
0.24*
0.20*
0.00
NS
‐1.00**
‐0.70**
0.00
NS
‐1.00**
‐0.38**
DP2
0.11
NS
‐0.20*
0.98**
0.75**
0.24*
0.30**
0.19*
0.19
NS
0.06
NS
‐0.38**
1.00**
0.06
NS
0.92**
DP3
‐0.04NS
0.23*
‐0.13NS
0.49**
‐0.03NS
‐0.26**
‐0.22*
‐0.91**
‐0.18NS
0.80**
0.06
NS
1.00**
0.43**
A1
0.15
NS
0.91**
‐0.09NS
0.10
NS
‐0.17NS
‐0.96**
‐0.88**
‐0.28**
‐0.28**
0.29**
‐0.38**
0.80**
‐0.01NS
A2
0.11
NS
‐0.20*
0.98**
0.75**
0.25**
0.30**
0.20*
0.18
NS
1.00**
‐0.18NS
‐0.28**
0.05
NS
0.92**
A3
‐0.03NS
0.25**
‐0.13NS
0.49**
‐0.01NS
‐0.27**
‐0.23*
‐0.91**
‐0.18NS
1.00**
0.30**
‐0.18NS
0.43**
Asum
0.12
NS
0.03
NS
0.91**
0.97**
0.22*
0.05
NS
‐0.03NS
‐0.24*
0.88**
0.28**
‐0.01NS
0.88**
0.29**
Geneticvariationinpotatocanopycoverdynamics
53
forplantswithhighAsum.
3.7.Estimatesofbroad‐senseheritability
Theestimatesofbroad‐senseheritability (H2)across sixenvironments (eqn (2.13))
varied greatly with traits under investigation (Table 2.6). The heritability values
rangedfrom31.1%to96.4%.Highestimates(H2>70%)wererecordedfort2,te,c1,
DP2,A2, andAsum.Moderateestimates (50%<H2<70%)wererecorded for tm1,vmax,
DP3, andA3 (Table2.6).On theotherhand t1andc3hadaweakheritability (37.4%
and31.1%,respectively).However,highlysignificantgeneticvariationinmodeltrait
among the F1 population may offset the relatively low heritability estimates, thus
makingthemresponsivetoselection.AlsoaccordingtoJonesetal.(1986),heritability
estimatesaslowas40%couldbeconsideredfavourableprovidedthattheselection
techniques have enough precision. Table 2.6 also presents the estimates of broad‐
Table2.6. Broad‐sense heritabilityH2 (%) estimates across six experiments (eqn
2.13)andperindividualexperiment(eqn2.14)forfivemodelparameters(tm1,t1,t2,
te,vmax)andninederivedsecondarytraits(cm1,c1,c3,DP2,DP3,A1,A2,A3,Asum)within
theF1populationperindividualexperiment.
Parameter H2† Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6
tm1(td) 58.3 59.2 52.5 68.7 40.8 74.3 69.2
t1(td) 37.4 79.9 55.7 89.2 69.5 77.6 75.6
t2(td) 89.6 70.6 64.6 86.4 85.6 79.0 79.8
te(td) 96.4 89.2 92.1 92.8 92.0 85.8 89.4
vmax(%) 64.6 88.2 22.9 92.6 56.5 46.1 0.0
cm1 (%td‐1) 67.6 82.5 57.8 30.4 66.5 76.4 68.6
c1 (%td‐1) 79.4 91.2 59.3 46.8 72.3 81.4 78.6
c3(%td‐1) 31.1 59.7 55.4 55.5 49.0 9.3 32.9
DP2(td) 85.5 82.6 64.4 54.5 88.6 80.8 81.2
DP3(td) 52.4 60.6 73.3 30.0 51.3 23.3 39.6
A1(td%) 0.0 57.6 41.3 93.2 57.6 70.1 65.5
A2(td%) 88.0 84.8 65.0 72.4 89.3 82.3 81.3
A3(td%) 64.0 69.2 73.0 69.1 48.7 0.0 41.0
Asum(td%) 95.9 92.3 90.6 94.9 94.0 88.1 92.6†Broad‐senseheritabilityacrosssixenvironments.
Chapter2
54
sense heritability (H2) per individual experiment (eqn (2.14)), which, in principle,
should be higher than the estimates across environments, because for individual
environmentsthegeneticandGEinteractioneffectsarelessconfounded. The high heritability estimates showed that most of the traits were little
influencedbytheenvironmentandgeneticdifferencesareexpectedtoremainstable
under varied environmental conditions. Furthermore, high estimates of heritability
indicated that these traits could be used readily in breeding for light interception
efficiencyinpotato.Atraitcanrespondtoselectiononlywhenithasheritablegenetic
variation(FalconerandMackay,1996).
3.8.GeneticmappingAmongatotalof566markers,325segregatedduetopolymorphisminthematernal
(SH)parent<abaa>,while241segregatedfromthepaternal(RH)parent<aaab>.Outof566markers,atotalof407markersweremappedandthetotaldatasetwas
split into maternal (SH) and paternal (RH) data sets. Lack of sufficient bridging
markerspreventedmakinganintegratedmap.
Thematernaldatasetcouldbesplitinto12linkagegroupsatarecombination
frequencythresholdof0.25.Twelveparentspecificlinkagegroupswereobtainedfor
both SH and RH (Figs. 2.8‐2.9). However, linkage group I was divided into two
subgroups(denotedasIAandIB,respectively)inthepaternal(RH)mapduetoalack
of a sufficient number of in‐between markers. Ninety‐five of the 325 AFLP SH
markerscouldnotbeassignedtotheSHlinkagegroups.IncaseofRHmarkers,64out
of241markerscouldnotbeassignedtotheRHlinkagegroups.ThenumberofAFLP
markers finally retained in thematernal andpaternalmapswas therefore230 and
177,respectively,coveringthegenomesizeof1902.9cM.
The length of the linkage groups in SHparentalmap ranged from35.7 cM to
129.3 cMwithamediandistanceof2.0 cMbetween the loci.TheRHparentalmap
rangedfrom28.0to101.1cMandthemediandistancebetween lociwas2.5cM. In
bothparentalmaps,thelargestgapbetweenlociwasonlinkagegroupXof10.5cM
and14.1cMinSHandRH,respectively.
Our linkagemapwasgenerallyconsistentwith theultra‐densemapdescribed
byVanOsetal.(2006).However,theSHlinkagegroupsVIIIandXwere37%andRH
linkagegroupVIIwas29%shorterthanintheultra‐densemapofVanOsetal.(2006).
Thisdiscrepancymay simplebedue todifferences in the sizeof SHRHmappingpopulations(cf.MaterialsandMethods).
Geneticvariationinpotatocanopycoverdynamics
55
EA
AC
MC
CA
_296
.5__
1_2
0.0
EA
CT
MC
TC
_240
.1__
1_2
2.0
PA
G/M
AG
C_1
26.5
__1_
23.
3P
AC
/MA
CT
_232
.4__
1_2
6.6
PA
C/M
AC
T_1
46.6
__1_
47.
8E
AG
TM
CA
G_1
98__
1_5
10.8
PC
A/M
AC
T_2
80.5
__1_
2024
.3(D
P2_
Exp
-4/A
2_E
xp-4
)---
----
EA
GT
MC
AG
_458
__1_
3033
.1E
AC
TM
CT
C_3
59.5
__1_
3135
.0(c
m1_
Exp
-4/c
1_E
xp-4
)---
----
EA
AC
MC
AG
_187
.9__
1_32
36.3
EA
TG
MC
AG
_147
.0__
1_32
37.1
PA
G/M
AC
T_1
29.5
__1_
3237
.5P
AC
/MA
GG
_107
.0__
1_32
37.9
EA
CA
MC
AG
_301
.9__
1_32
38.3
(vm
ax_E
xp-3
)---
----
EA
CA
MC
AG
_381
.9__
1_32
38.7
EA
GT
MC
AG
_110
__1_
3239
.1E
AG
AM
CT
C_4
03__
1_32
40.1
(t2_
Exp
-4,5
/DP
3_E
xp-5
/Asu
m_E
xp-4
)---
----
EA
AC
MC
CT
_217
.8__
1_32
41.6
EA
CA
MC
GT
_169
.6__
1_32
42.8
(A2_
Exp
-2)-
----
--P
AC
/MA
GC
_62.
6__1
_32
44.0
EA
GT
MC
AC
_119
__1_
3245
.2E
AA
CM
CC
T_3
31.6
__1_
3245
.8E
AC
TM
CT
G_1
15.1
__1_
3246
.4E
AA
CM
CA
G_2
04.5
__1_
3248
.1E
AA
CM
CA
G_8
1.5_
_1_3
249
.3E
AA
CM
CA
G_3
05.0
__1_
3250
.1E
AA
CM
CC
A_2
49.0
__1_
3251
.7E
AA
CM
CC
A_1
82.8
__1_
3252
.5E
AG
AM
CA
G_1
94.9
__1_
3254
.1P
CT
/MA
GG
_85.
9__1
_32
55.9
(A1_
Exp
-4)-
----
--P
CA
/MA
AC
_211
.6__
1_32
57.8
EA
TG
MC
AG
_510
.0__
1_32
59.4
PA
C/M
AA
C_2
88.4
__1_
3259
.8P
CT
/MA
GG
_106
.3__
1_32
60.2
EA
CA
MC
CT
_131
.7__
1_32
60.6
(DP
2_E
xp-4
/A2_
Exp
-4)-
----
--E
AG
AM
CA
G_2
28.3
__1_
3664
.0(t
1_E
xp-4
/vm
ax_E
xp-3
/cm
1_E
xp-4
/Asu
m_E
xp-3
)---
----
PA
G/M
AC
C_3
22.2
__1_
4269
.0(t
2_E
xp-4
)---
----
PA
T/M
AA
C_2
07.7
__1_
5280
.7(t
m1_
Exp
-5/A
sum
_Exp
-4)-
----
--E
AA
CM
CT
G_1
93.9
__1_
5281
.5P
AC
/MA
CT
_231
.3__
1_54
84.3
PA
C/M
AA
C_1
33.6
__1_
7094
.3E
AG
AM
CT
C_1
35__
1_75
100.
0P
AC
/MA
TG
_201
.9__
1_77
102.
1P
AC
/MA
GC
_270
.3__
1_82
107.
1P
AT
/MA
GG
_178
.6__
1_82
108.
8(D
P2_
Exp
-4/A
2_E
xp-4
)---
----
PA
T/M
AA
C_2
59.4
__1_
8411
0.0
PC
T/M
AG
G_2
36.9
__1_
8611
1.6
PA
C/M
AG
G_1
39.0
__1_
8611
4.5
PC
T/M
AG
G_1
51.2
__1_
9011
8.7
(t2_
Exp
-4)-
----
--P
AC
/MA
CT
_132
.6__
1_92
122.
0P
AG
/MA
CT
_190
.5__
1_95
124.
0P
AC
/MA
GG
_221
.8__
1_95
125.
6E
AC
TM
CT
G_3
19.0
__1_
9512
7.3
PC
A/M
AC
T_1
83.2
__1_
9512
9.3
SH
I
EA
CT
MC
TG
_13
3.9
__2
_40
.0
EA
GT
MC
AC
_31
7__
2_4
11.
5E
AA
CM
CC
T_1
77.
9__
2_4
11.
9E
AA
CM
CA
G_5
84.
6__
2_4
12.
4P
CT
/MA
GT
_50
3.9
__2
_21
3.5
PC
T/M
AG
G_1
39.
2__
2_2
15.
5
EA
AC
MC
AG
_23
6.3
__2
_12
24.
2
PA
G/M
AC
T_
133
.4_
_2_
203
2.0
PA
G/M
AG
T_
377
.9_
_2_
203
2.4
(A1_
Exp
-1)-
----
--E
AA
CM
CC
T_1
38.3
__2_
253
7.5
EA
AC
MC
AG
_22
6.4
__2
_43
53.
9
EA
CT
MC
AC
_21
1.2
__2
_52
60.
8
EA
GA
MC
AG
_14
2.7
__2
_56
64.
5
EA
CT
MC
TC
_18
4.1
__2
_88
95.
5
EA
GT
MC
AC
_397
__2
_95
108
.0
SH
II
PA
C/M
AG
T_1
13.6
__3_
140
.0
EA
GT
MC
AC
_212
__3_
12
2.5
EA
CT
MC
AG
_210
.1__
3_2
22.
9
EA
TG
MC
TC
_290
.5__
3_21
38.
8
PA
C/M
AG
G_1
58.8
__3_
425
7.5
EA
CT
MC
AC
_172
.0__
3_42
58.
3P
AT
/MA
GT
_397
.4__
3_4
25
9.5
PA
C/M
AG
T_2
61.8
__3_
637
5.2
EA
AC
MC
CA
_153
.6__
3_71
81.
8
PA
C/M
AG
C_3
07.7
__3_
758
6.4
SH
III
Figure2.8.LocationsofAFLPmarkersonmaternal(SH)map
(linkagegroupsI‐III).Thenum
beronrightsideisthegenetic
distanceincentiMorgans(cM
),leftsideismarkerdesignations.The
markerinboldshow
sthepositionofQTLassociatedwithtraits,
acrossenvironments.
Chapter2
56
EA
CT
MC
TC
_179
.3__
4_1
0.0
EA
CT
MC
TG
_207
.2__
4_13
10.7
EA
GA
MC
AG
_118
.4__
4_21
16.6
EA
GA
MC
AG
_178
.6__
4_21
17.0
(DP
2_E
xp2)
----
---E
AC
TM
CT
G_7
4.3_
_4_2
824
.8P
AC
/MA
CT
_198
.4__
4_28
25.6
EA
CT
MC
AC
_179
.3__
4_30
26.8
EA
GA
MC
AG
_137
.4__
4_30
28.0
EA
GT
MC
AG
_126
__4_
3229
.7E
AG
TM
CA
C_8
9__4
_31
30.5
EA
GA
MC
TC
_110
__4_
3131
.3
PA
C/M
AG
C_7
7.2_
_4_5
049
.4E
AA
CM
CT
G_3
06.1
__4_
5252
.3P
AG
/MA
GC
_432
.4__
4_52
52.7
EA
TG
MC
AG
_231
.0__
4_53
53.9
EA
TG
MC
AG
_187
.2__
4_63
61.2
PC
A/M
AG
G_9
3.4_
_4_7
374
.0
SH
IV
(c3_
Exp
-6/D
P3_
Exp
-6/A
3_E
xp-6
)---
----
SH
05B
012_
mat
uri
ty_l
ocu
s0
.0S
H05
B01
4_m
atu
rity_
locu
s0
.8S
H05
B01
5_m
atu
rity_
locu
s1
.2
EA
CT
MC
TG
_30
5__
5_28
13.
0E
AG
TM
CA
C_7
9__
5_28
14.
2
PA
G/M
AC
T_1
18.
5__
5_34
19.
7
PA
C/M
AG
T_2
81.
7__
5_38
22.
6
(vm
ax_E
xp-1
,4)-
----
--P
AC
/MA
AC
_190
.2__
5_44
26.
8E
AA
CM
CA
C_4
56.
2__
5_4
42
9.2
EA
AC
MC
AC
_25
0.1
__5_
44
32.
4E
AA
CM
CA
C_3
77.
6__
5_4
43
3.3
EA
CT
MC
TG
_36
5.9
__5_
44
35.
4E
AC
TM
CT
G_8
6.8
__5_
443
6.6
PA
C/M
AT
G_4
17.
8__
5_44
37.
8E
AC
TM
CT
C_1
92.
3__5
_43
39.
0P
AT
/MA
GG
_20
0.4
__5_
46
40.
6
EA
CT
MC
TG
_10
3.9
__5_
49
46.
6
PC
T/M
AG
G_3
94.
9__
5_5
35
7.1
PA
T/M
AG
G_2
06.
6__
5_5
76
1.4
PA
C/M
AG
C_3
58.
8__
5_7
07
1.6
(Asu
m_E
xp-3
)---
----
PA
C/M
AT
A_2
01.4
__5_
777
7.2
SH
V
PA
G/M
AG
C_3
41.7
__6
_60.
0
PA
G/M
AC
T_1
51.8
__6
_36.
9E
AG
AM
CA
G_2
93.9
__6
_38.
1P
AG
/MA
CT
_266
.8_
_6_1
9.9
PA
G/M
AC
T_1
45.2
__6
_512
.9E
AA
CM
CT
G_2
54.9
__6
_514
.3P
CT
/MA
GG
_114
.9_
_6_6
15.5
PA
C/M
AG
G_
81.2
__6
_615
.9P
AT
/MA
GG
_96
.5_
_6_8
18.8
EA
GT
MC
AG
_121
__6
_919
.6E
AT
GM
CT
C_1
91.
1__
6_11
21.2
EA
CA
MC
AC
_18
4.6_
_6_
1626
.2
PA
C/M
AT
A_3
30.
4__
6_21
35.5
PA
C/M
AC
T_
166.
3__
6_31
42.1
PA
C/M
AT
G_1
29.
6__
6_32
43.5
EA
AC
MC
TG
_15
4.7_
_6_
3344
.3E
AC
AM
CT
G_1
45.
9__
6_33
44.7
EA
AC
MC
AG
_14
1.8
__6_
3951
.1
(tm
1_E
xp-1
/Asu
m_E
xp-2
)---
----
EA
CA
MC
CT
_138
.9__
6_48
61.8
(t2_
Exp
-2/D
P2_
Exp
-2)-
----
--P
AT
/MA
AC
_155
.4__
6_56
73.5
PC
A/M
AA
C_2
73.
6__
6_63
82.0
PC
T/M
AG
G_
363.
2__
6_68
87.2
EA
GA
MC
TC
_21
2__
6_68
88.0
EA
CT
MC
AG
_29
5.3_
_6_
6892
.2
SH
VI
Figure2.8.(continued)LocationsofAFLPmarkersonmaternal(SH)map(linkagegroupsIV‐VI).Thenum
beronrightsideisthe
geneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinboldshow
sthepositionofQTLassociated
withtraits,acrossenvironm
ents.
Geneticvariationinpotatocanopycoverdynamics
57
EA
AC
MC
AC
_236
.0__
7_1
0.0
PC
T/M
AG
T_8
0.0
__7_
151
4.9
EA
GA
MC
TC
_461
__7_
252
2.9
PA
C/M
AG
T_
221.
5__
7_48
38.
0P
AG
/MA
GT
_18
5.5
__7_
493
9.7
PC
T/M
AG
G_
265.
3__
7_5
74
6.5
EA
CA
MC
GT
_98.
3__
7_63
52.
9
PA
C/M
AC
T_7
5.3
__7_
696
0.7
PA
C/M
AA
C_
297.
6__
7_70
62.
8E
AA
CM
CA
G_9
3.3
__7_
70
64.
0E
AG
AM
CT
C_3
13__
7_72
65.
2E
AG
TM
CA
C_
583.
1__
7_7
36
6.8
EA
CA
MC
TG
_66.
5__
7_71
67.
9E
AA
CM
CC
T_
136.
4__
7_71
68.
4E
AC
TM
CA
C_
440.
3__
7_71
69.
6E
AA
CM
CA
G_
390.
4__
7_72
73.
4P
CT
/MA
GG
_32
6.3
__7_
72
74.
6
EA
CA
MC
AC
_29
3.0
__7_
83
85.
8
EA
GA
MC
TC
_129
__7_
909
5.0
SH
VII
EA
AC
MC
TG
_11
7.2
__8_
10
.0
EA
CT
MC
TG
_23
8.4
__8_
66
.9
EA
CT
MC
AC
_474
.2__
8_13
14.
6P
AT
/MA
GG
_14
4.1_
_8_1
61
7.5
EA
CA
MC
AC
_87.
4__8
_16
18.
3E
AG
TM
CA
G_
90_
_8_1
72
0.4
EA
CT
MC
TC
_29
4.6_
_8_2
12
5.1
(DP
3_E
xp-6
/A3_
Exp
-6)-
----
--P
AT
/MA
AC
_283
.7__
8_29
33.
2E
AC
TM
CT
C_
126.
7__8
_31
35.
7
SH
VIII
PA
C/M
AT
G_9
4.2_
_9_3
0.0
PA
G/M
AA
G_3
37.6
__9_
1513
.5
EA
CT
MC
AG
_100
.9__
9_18
17.5
EA
AC
MC
CA
_208
.3__
9_19
20.4
PA
G/M
AC
C_2
28.0
__9_
2125
.0E
AC
AM
CC
T_1
27.0
__9_
2126
.6E
AG
TM
CA
C_14
1__
9_20
27.4
EA
AC
MC
CT
_431
.7__
9_21
27.8
EA
CA
MC
AG
_185
.8__
9_21
28.4
EA
CT
MC
AC
_430
.7__
9_21
29.5
EA
TG
MC
TC
_109
.7__
9_32
43.7
PA
G/M
AA
G_3
77.5
__9_
4954
.9P
AT
/MA
GG
_367
.0__
9_50
56.9
EA
CT
MC
TC
_160
.3__
9_74
80.5
(c3_
Exp
-2)-
----
--P
CA
/MA
GG
_294
.2__
9_74
80.9
EA
AC
MC
AC
_269
.9__
9_78
88.3
SH
IX
Figure2.8.(continued)LocationsofAFLPmarkersonmaternal(SH)map(linkagegroupsVII‐IX).Thenum
ber
onrightsideisthegeneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinbold
show
sthepositionofQTLassociatedwithtraits,acrossenvironm
ents.
Chapter2
58
E
AG
AM
CA
G_2
12.
3__1
1_1
0.0
EA
CA
MC
GT
_66.
3__1
1_6
5.5
PA
G/M
AG
T_1
47.
6__1
1_6
6.3
EA
GA
MC
TC
_28
9__1
1_6
7.1
(t1_
Exp
-6/c
1_E
xp-6
)---
----
EA
CA
MC
AC
_372
__11
_79
.2
(tm
1_E
xp5)
----
---P
AC
/MA
TA
_197
.8__
11_2
02
1.8
EA
CT
MC
TG
_138
.6__
11_4
53
6.7
PA
T/M
AG
G_1
32.7
__11
_45
37.
5
EA
TG
MC
AG
_84
.8__
11_4
84
2.2
PA
G/M
AC
T_1
94.4
__11
_58
57.
7
PA
C/M
AT
G_2
48.9
__11
_65
63.
0E
AC
AM
CA
G_1
52.6
__11
_66
64.
2P
AC
/MA
TA
_345
.5__
11_6
66
6.3
SH
XI
EA
CT
MC
AG
_195
.4__
12_4
90.
0
EA
CA
MC
AC
_187
.1__
12_5
616
.9E
AC
AM
CC
T_2
89.5
__12
_56
17.5
EA
CA
MC
AG
_85.
8__1
2_56
18.6
PA
C/M
AT
G_1
81.1
__12
_58
21.1
PA
T/M
AA
C_1
68.3
__12
_59
21.5
EA
TG
MC
AG
_236
.0__
12_6
021
.9E
AC
TM
CT
G_7
2.9_
_12_
6123
.1E
AA
CM
CC
A_1
65.1
__12
_63
26.4
PA
G/M
AC
T_2
09.8
__12
_72
36.7
SH
XII
PA
C/M
AG
T_1
29.5
__10
_1
0.0
PC
T/M
AG
G_1
27.
5__
10_
16
14.
6
PA
T/M
AG
G_2
38.
6__
10_
32
43.
4
EA
AC
MC
TG
_28
6.4
__1
0_4
85
7.7
PA
C/M
AT
A_2
55.
6__
10_
58
66.
2
EA
CA
MC
AC
_40
5.9
__1
0_6
67
1.4
PA
T/M
AG
G_3
79.
3__
10_
67
73.
5E
AC
TM
CA
G_1
49.
9__
10_
68
75.
1
PC
A/M
AG
G_3
75.
2__
10_
68
87.
5
SH
X
Figure2.8.(continued)LocationsofAFLPmarkersonmaternal(SH)map(linkagegroupsX‐XII).Thenum
ber
onrightsideisthegeneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinbold
show
sthepositionofQTLassociatedwithtraits,acrossenvironm
ents.
Geneticvariationinpotatocanopycoverdynamics
59
PC
A/M
AA
C_
153.
6__
1_1
0.0
PA
G/M
AG
T_
306.
4__
1_7
6.0
PA
C/M
AC
T_3
89.4
__1_
86
.8E
AG
TM
CA
C_
410
__1_
87
.6E
AA
CM
CC
A_1
27.
1__1
_91
0.0
PA
T/M
AA
C_2
79.8
__1
_10
12.
5E
AT
GM
CT
C_4
62.4
__1
_13
17.
5E
AT
GM
CT
C_1
92.9
__1
_13
18.
9E
AA
CM
CA
G_2
61.4
__1
_13
22.
0E
AA
CM
CA
G_1
39.9
__1
_13
23.
8E
AA
CM
CA
G_1
43.6
__1
_13
25.
1E
AA
CM
CA
G_1
96.4
__1
_13
26.
0E
AA
CM
CC
A_1
89.0
__1
_13
26.
9E
AC
AM
CA
C_2
26.7
__1_
132
8.5
EA
CT
MC
TG
_201
.4_
_1_1
32
9.0
PA
G/M
AG
T_3
09.9
__1
_13
29.
8E
AT
GM
CT
C_
96.7
__1
_13
31.
8E
AC
AM
CA
G_2
21.0
__1_
133
3.0
PA
T/M
AG
G_4
90.1
__1
_13
33.
4P
AC
/MA
GT
_70
.6_
_1_1
33
6.3
EA
CA
MC
TG
_150
__1
_13
37.
5E
AG
TM
CA
C_4
99.6
__1
_13
38.
7E
AC
TM
CT
C_3
77.7
__1
_13
39.
5E
AA
CM
CC
T_1
87.7
__1
_20
46.
4E
AC
AM
CT
G_1
52.6
__1_
204
8.0
(Asu
m_E
xp-6
)---
----
PA
T/M
AG
T_1
43.9
__1_
355
8.3
RH
IA
EA
CA
MC
AG
_58.
5__1
_60
0.0
EA
AC
MC
CT
_11
6.9_
_1_63
26.
1(t
2_E
xp-6
/DP
2_E
xp-6
/A2_
Exp
-6)-
----
--E
AC
AM
CC
T_1
44.
5__1
_63
27.
8
EA
CA
MC
GT
_296
.2__
1_71
33.
9
PA
T/M
AG
G_18
6.7_
_1_
8143.
4P
AT
/MA
GG
_19
4.5_
_1_
8144.
7E
AC
AM
CA
C_1
71.4
__1_
8447.
6
EA
AC
MC
TG
_237
.1__
1_95
55.
9
PC
A/M
AC
T_1
81.3
__1_
9860.
9P
AC
/MA
TA
_101
.3__
1_99
63.
0
RH
IB
EA
AC
MC
AC
_356
.7_
_2_1
0.0
PC
T/M
AG
G_2
12.4
__2
_1
4.2
EA
AC
MC
CT
_314
.9_
_2_1
4.6
EA
AC
MC
AC
_167
.7_
_2_6
10.9
EA
AC
MC
CA
_310
.7__
2_9
14.3
PA
G/M
AC
T_
132.
4__
2_20
23.0
(t2_
Exp
-5)-
----
--P
AG
/MA
CT
_230
.5__
2_21
23.4
EA
CT
MC
AC
_21
7.2_
_2_2
728
.1P
AT
/MA
AC
_57
0.4
__2_
3232
.2P
AC
/MA
CT
_21
1.1
__2_
33
33.5
PA
G/M
AG
C_
254.
4__
2_35
35.6
PA
G/M
AC
T_
146.
4__
2_37
38.2
PA
G/M
AG
C_
238.
8__
2_36
39.4
PC
T/M
AG
T_
225.
5__
2_5
149
.3E
AG
TM
CA
C_2
49__
2_5
150
.9E
AC
AM
CA
C_
266.
0__
2_51
52.1
(Asu
m_E
xp-5
)---
----
EA
CT
MC
TC
_444
.3__
2_72
73.8
PA
C/M
AT
A_9
6.8
__2_
7375
.5(A
sum
_Exp
-3)-
----
--P
AC
/MA
GG
_527
.2__
2_75
79.1
EA
GA
MC
TC
_130
__2_
80
84.7
RH
II
Figure2.9.LocationsofAFLPmarkersonpaternal(RH)map(linkagegroupsI‐II).Thenum
beronrightsideisthegenetic
distanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinboldshow
sthepositionofQTLassociatedwith
traits,acrossenvironm
ents.
Chapter2
60
E
AG
TM
CA
G_
129_
_4_
280.
0(t
1-E
xp-4
)---
----
EA
CA
MC
AC
_156
.4__
4_33
5.5
EA
AC
MC
TG
_23
3.9_
_4_
356.
8E
AG
AM
CT
C_
223_
_4_3
68.
4(t
e_E
xp-3
)---
----
EA
GA
MC
AG
_216
.2__
4_35
9.6
(tm
1_E
xp-2
)---
----
EA
CA
MC
TG
_69.
5__4
_35
11.2
(t1_
Exp
-2)-
----
--E
AC
AM
CT
G_1
38.9
__4_
3512
.8
EA
AC
MC
AC
_42
0.3_
_4_
4223
.5
EA
GA
MC
AG
_42
1.2_
_4_
6037
.0
(te_
Exp
-2)-
----
--E
AA
CM
CC
A_9
2.1_
_4_7
246
.9
RH
IV
EA
AC
MC
AG
_14
9.7_
_5_4
0.0
(c3-
Exp
-6/D
P3_
Exp
-6/A
3_E
xp-6
)---
----
PA
C/M
AG
T_1
90.2
__5_
41.
6E
AG
TM
CA
C_
290_
_5_4
2.8
(A1-
Exp
-1)-
----
--E
AC
AM
CC
T_1
09.4
__5_
44.
5
(tm
1_E
xp-3
/t1_
Exp
-1,3
)---
----
EA
GA
MC
TC
_470
__5_
1718
.2(t
2_E
xp-1
,3,4
,5,6
/te_
Exp
-1,2
,3,4
,5,6
/vm
ax_E
xp-3
)---
----
18.3
(cm
1_E
xp-1
,4/c
1_E
xp-1
,2,4
/c3_
Exp
-2,3
/DP
2_E
xp-1
,2,4
,5,6
/DP
3_E
xp-1
,2)-
----
--18
.4A
1_E
xp-3
/A2_
Exp
-1,4
,5,6
/A3_
Exp
-1,2
,3/A
sum
_Exp
-1,2
,3,4
,5,6
)---
----
18.5
EA
CA
MC
GT
_250
.1__
5_37
35.7
(A2_
Exp
-3)-
----
--E
AA
CM
CC
T_2
01.2
__5_
4243
.9P
CT
/MA
GT
_159
.2__
5_43
46.4
EA
TG
MC
AG
_304
.2__
5_46
48.9
EA
CT
MC
TC
_27
2.3_
_5_4
649
.3E
AC
TM
CT
C_3
44.
6__5
_46
50.5
EA
AC
MC
CA
_410
.3__
5_46
51.3
EA
AC
MC
AG
_135
.2__
5_46
52.5
EA
AC
MC
AG
_231
.8__
5_46
52.6
EA
CA
MC
AG
_102
.4__
5_46
53.3
EA
CA
MC
TG
_92.
9__5
_46
54.1
(c1_
Exp
-4)-
----
--P
AC
/MA
TA
_99.
4__5
_55
62.6
RH
V
PA
T/M
AA
C_2
98.3
__3_
30
.0P
AG
/MA
GT
_228
.5__
3_3
1.7
EA
GA
MC
TC
_13
2__
3_9
9.2
EA
CT
MC
AC
_13
3.1_
_3_1
61
5.8
PC
A/M
AA
C_2
14.
0__3
_35
35.
1
EA
GA
MC
AG
_41
0.1_
_3_3
73
9.5
EA
GA
MC
TC
_245
__3
_52
55.
1
PA
C/M
AG
T_2
67.
0__3
_72
74.
2
PC
A/M
AC
T_2
70.
0__3
_79
85.
5P
AT
/MA
GG
_15
7.1_
_3_8
08
7.2
RH
III
Figure2.9.(continued)LocationsofAFLPmarkersonpaternal(RH)map(linkagegroupsIII‐V).Thenum
beronrightsideis
thegeneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinboldshow
sthepositionofQTL
associatedwithtraits,acrossenvironm
ents.
Geneticvariationinpotatocanopycoverdynamics
61
EA
AC
MC
AG
_12
5.4b
__6_
10.
0
PA
T/M
AG
T_2
05.1
__6_
32
5.1
PA
G/M
AC
T_1
55.9
__6_
32
6.4
EA
AC
MC
CT
_37
7.0_
_6_1
53
3.2
EA
CA
MC
AG
_37
9.9_
_6_1
73
4.4
EA
CA
MC
TG
_269
.7__
6_18
35.
2E
AC
AM
CA
G_
193.
6__6
_17
35.
8E
AA
CM
CC
A_
231.
3__6
_16
37.
7E
AG
AM
CT
C_
539.
6__6
_21
40.
9E
AG
AM
CT
C_
466_
_6_2
14
1.8
EA
GA
MC
TC
_49
6.3_
_6_2
14
3.4
EA
GA
MC
TC
_52
6.0_
_6_2
14
3.8
EA
AC
MC
TG
_50
1.8_
_6_2
24
8.0
EA
CA
MC
AC
_390
.6__
6_23
48.
8E
AC
TM
CT
G_1
67.3
__6_
285
3.4
(te_
Exp
-3)-
----
--P
AT
/MA
AC
_272
.6__
6_28
53.
8(t
2_E
xp-3
)---
----
EA
CT
MC
AG
_39
3.7_
_6_3
45
9.7
PA
C/M
AT
G_1
09.4
__6_
436
6.0
EA
GT
MC
AG
_31
9__6
_45
66.
9E
AA
CM
CT
G_
152.
6__6
_46
69.
4E
AC
AM
CA
C_8
9.5_
_6_4
97
2.7
EA
GA
MC
AG
_50
7.5_
_6_4
97
4.1
EA
AC
MC
CT
_14
4.2_
_6_5
07
6.0
PA
T/M
AG
T_
251.
9__6
_52
78.
8
PA
C/M
AG
G_2
26.3
__6_
6910
1.1
RH
VI
PA
G/M
AG
T_1
80.1
__7_
440.
0P
AT
/MA
GT
_216
.8__
7_43
0.8
EA
GA
MC
AG
_555
.8__
7_66
19.5
EA
CT
MC
TG
_272
.0__
7_67
19.9
EA
CT
MC
TC
_337
.5__
7_68
20.3
(cm
1_E
xp-4
/c1_
Exp
-4)-
----
--P
AC
/MA
AC
_164
.8__
7_68
21.1
EA
CA
MC
AG
_90
.1__
7_73
28.0
RH
VII
PA
C/M
AT
A_2
66.2
__8_
50.
0
EA
AC
MC
AG
_107
.2__
8_2
210
.8E
AA
CM
CC
A_2
50.2
__8_
22
11.6
EA
AC
MC
CA
_437
.7__
8_2
213
.2E
AA
CM
CC
A_1
41.0
__8_
26
15.3
PC
T/M
AG
G_1
28.9
__8_
24
17.4
EA
CT
MC
AG
_274
.9__
8_3
628
.0
EA
GA
MC
TC
_19
8__8
_47
42.3
EA
CT
MC
TG
_140
.2__
8_8
273
.6P
AT
/MA
GG
_149
.8__
8_8
374
.5P
AG
/MA
GC
_298
.8__
8_8
376
.1
EA
AC
MC
AG
_290
a__8
_82
90.0
RH
VIII
EA
GT
MC
AG
_70_
_9_
10.
0E
AG
TM
CA
C_1
28__
9_1
2.1
EA
CA
MC
TG
_157
.0__
9_2
020
.5
PC
T/M
AG
G_1
15.9
__9
_33
38.4
PA
G/M
AA
G_1
14.5
__9
_55
60.1
EA
AC
MC
CT
_287
.5__
9_6
066
.8
PC
T/M
AG
G_4
86.3
__9
_67
76.6
PA
G/M
AG
C_
66.5
__9
_78
84.7
RH
IX
Figure2.9.(continued)LocationsofAFLPmarkersonpaternal(RH)map(linkagegroupsVI‐IX).Thenum
beronrightsideisthe
geneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.Themarkerinboldshow
sthepositionofQTLassociated
withtraits,acrossenvironm
ents.
Chapter2
62
P
AT
/MA
AC
_583
.1__
12_1
0.0
EA
GA
MC
TC
_372
__12
_54.
2
EA
CA
MC
AC
_243
.3__
12_2
123
.5
PC
T/M
AG
G_9
6.5_
_12_
2931
.7
PA
C/M
AC
T_2
29.7
__12
_43
43.8
PA
G/M
AC
C_2
29.9
__12
_46
47.2
EA
GA
MC
AG
_289
.0__
12_4
648
.4E
AG
TM
CA
G_2
44__
12_4
950
.5E
AG
TM
CA
C_1
11__
12_4
951
.3E
AA
CM
CA
G_2
86.3
__12
_49
51.7
EA
CT
MC
AG
_251
.5__
12_4
952
.1
EA
GA
MC
TC
_210
__12
_49
60.8
RH
XII
EA
AC
MC
AG
_409
.1__
10_1
0.0
PA
C/M
AG
G_2
71.
1__
10_1
110
.3
EA
AC
MC
CA
_21
6.8_
_10_
3431
.4
EA
TG
MC
AG
_14
0.3_
_10
_64
59.7
EA
AC
MC
TG
_11
2.7_
_10_
6460
.9
EA
CA
MC
AC
_51
3.7_
_10_
7975
.0
RH
X
EA
AC
MC
AC
_106
.6__
11_4
0.0
EA
AC
MC
AG
_291
.7__
11_
55.
3
EA
CT
MC
TG
_58
3.0_
_11_
22
20.8
PA
C/M
AG
C_
108.
2__1
1_27
28.7
EA
CA
MC
AC
_16
1.6_
_11_
3537
.9
EA
CT
MC
AG
_20
3.4_
_11_
4346
.4
PA
C/M
AG
C_
334.
1__1
1_61
57.3
EA
AC
MC
AG
_15
6.4_
_11_
6159
.0
EA
GT
MC
AC
_367
__1
1_8
278
.2P
AC
/MA
TG
_38
1.9_
_11_
84
81.5
PA
C/M
AT
A_
343.
3__1
1_84
82.4
EA
AC
MC
AC
_91.
1__1
1_84
84.0
RH
XI
Figure2.9.(continued)LocationsofAFLPmarkersonpaternal(RH)map(linkagegroupsX‐XII).The
numberonrightsideisthegeneticdistanceincentiMorgans(cM
),leftsideismarkerdesignations.
Themarkerinboldshow
sthepositionofQTLassociatedwithtraits,acrossenvironm
ents.
Geneticvariationinpotatocanopycoverdynamics
63
3.9.QTLdetectionQTL analysis for the fivemodel parameters and nine derived traitswas conducted
separately for the six environments (i.e. experiments). In total 41 QTLs were
identifiedonbothSHandRHparentalgenomesacrossallsixenvironments(Figs.2.8‐
2.9).IntheSHgenome,23QTLswereassociatedwitheightlinkagegroups(I,II,IV,V,
VI, VIII, IX, and XI). Eighteen QTLs were shared by the RH genome on six linkage
groups (I, II, IV, V, VI, and VII). The numbers of QTLs detected for each parental
chromosomearelistedin(Table2.7).
Table2.8summarisesthelistofQTLsdetected,theirparentalchromosomesand
mappositions and their characteristics (i.e. additive effects and variance explained
(R2) for each of the trait investigated for individual environment). All the QTLs
detected were significant at (P<0.05) with –log10(P) values ranging from 3.47 to
52.33.ThetotalfractionofphenotypicvarianceexplainedbyeffectsofeachQTLwere
moderate(rangingfrom<0.1%to74%),butahighpercentageofphenotypicvariance
wasaccountedforwhenconsideringtheglobalR2(rangingfrom28%to82%)(Table
2.8).
DetailedinvestigationoftheQTLsidentifiedforfivemodelparametersshowed
thatfiveQTLsweredetectedfortm1scatteredacrosslinkagegroups(SHI,SHVI,SH
XI,RHIV,andRHV)inalltheexperimentsexceptExps4and6,wherenoQTLwas
found(Table2.8).ThefractionofphenotypicvarianceexplainedbyindividualQTLs
ranged from 12% to 16%. The QTL (116_5_17) detected in Exp 3 shared the
maximum negative additive effect (‐4.1 td) and explained the maximum (16%)
phenotypic variance. However, two QTLs detected in Exp 5 (i.e. 66_1_52 and
Table2.7.DistributionofQTLsdetectedonparentalgenomes.
SH RH
Linkagegroup No.ofQTLs Linkagegroup No.ofQTLs
I 12 IA 1
II 1 IB 1
IV 1 II 3
V 3 IV 5
VI 2 V 5
VIII 1 VI 2
IX 1 VII 1
XI 2
Total 23 18
Chapter2
64
Table2.8.Maincharacteristicsofquantitativetraitloci(QTL)identifiedforfivemodelparam
eters(tm1,t 1,t2,t e,v
max)
andninederivedsecondarytraits(cm1,c 1,c3,DP2,D
P3,A1,A2,A3,Asum)withinthe‘SHRH’populationperindividual
experiment(i.e.environm
ent.).DatagivenintablearefromtheCIMm
appingm
ethod.QTLsm
arkedasboldare
detectedonlybytheCIMmethod,otherwisebyboththeCIMandSIMmethods.Exp.,experiment;position,positionof
maximum
–log 1
0(P);a,additiveeffectofthepresenceofparentalalleleatamarker;R
2 ,theindividualcontributionof
oneQTLtothevariationinatrait;globalR2 ,thefractionofthetotalvariationexplainedbyQTLsofthesametrait
withinsingleenvironment;td,thermalday.Sym
bol‘−’meansnoQTLwasdetected.
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
t m1(td)
1199_6_48
SHVI
EACAMCCT_138.9__6_48
61.8
3.47
2.902
0.13
299_4_35
RHIV
EACAMCTG_69.5__4_35
11.2
3.67
1.975
0.14
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
3.95
‐4.113
0.16
4−
−−
−−
−−
566_1_52
SHI
EAACM
CTG_193.9__1_52
81.5
4.61
2.427
0.16
0.29
293_11_20
SHXI
PAC/MATA_197.8__11_20
21.8
3.584
‐2.095
0.12
6−
−−
−−
−−
t 1(td)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
6.23
7.404
0.23
298_4_35
RHIV
EACAMCTG_138.9__4_35
12.8
3.48
3.167
0.13
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
18.02
‐26.586
0.60
463_1_42
SHI
PAG/M
ACC_322.2__1_42
69
4.738
‐5.754
0.17
0.28
96_4_33
RHIV
EACAMCAC_156.4__4_33
5.5
3.423
4.691
0.11
5−
−−
−−
−−
6291_11_7
SHXI
EACAMCAC_372__11_7
9.2
3.371
‐2.591
0.14
Geneticvariationinpotatocanopycoverdynamics
65
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
t 2(td)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
6.66
‐9.584
0.24
2200_6_56
SHVI
PAT/M
AAC_155.4__6_56
73.5
3.8
8.055
0.15
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
19.52
‐31.694
0.49
0.56
154_6_34RHVI
EACTMCAG_393.7__6_34
59.7
3.53
10.453
0.04
4116_5_17
RHV
EAGAMCTC_470__5_17
18.2
17.24
‐26.021
0.55
0.67
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
1.445
6.521
0.10
67_1_52
SHI
PAT/M
AAC_207.7__1_52
80.7
0.248
1.915
0.01
81_1_92
SHI
PAC/MACT_132.6__1_92
122
1.555
5.433
0.01
5116_5_17
RHV
EAGAMCTC_470__5_17
18.2
16.008
‐27.999
0.52
0.64
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
3.096
9.011
0.14
64_2_21
RHII
PAG/M
ACT_230.5__2_21
23.4
1.913
‐6.698
0.01
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
10.018
‐18.544
0.27
0.43
47_1_63
RHIB
EACAMCCT_144.5__1_63
27.8
5.463
11.577
0.08
Table2.8.(Continued)
Chapter2
66
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
t e(td)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
19.5
‐19.351
0.51
2116_5_17
RHV
EAGAMCTC_470__5_17
18.2
24.25
‐27.232
0.55
0.62
108_4_72RHIV
EAACM
CCA_92.1__4_72
46.9
3.93
8.408
0.02
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
26.91
‐29.497
0.58
0.68
101_4_35RHIV
EAGAMCAG_216.2__4_35
9.6
3.95
7.58
0.03
153_6_28RHVI
PAT/M
AAC_272.6__6_28
53.8
3.62
7.112
0.02
4116_5_17
RHV
EAGAMCTC_470__5_17
18.2
20.612
‐32.356
0.65
5116_5_17
RHV
EAGAMCTC_470__5_17
18.2
19.527
‐24.133
0.63
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
52.33
‐49.912
0.74
v max(%)
1170_5_44
SHV
PAC/MAAC_190.2__5_44
26.8
5.66
22.66
0.21
2−
−−
−−
−−
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
26.28
‐54.862
0.56
0.65
21_1_32
SHI
EACAMCAG_381.9__1_32
38.7
3.8
4.243
0.02
63_1_42
SHI
PAG/M
ACC_322.2__1_42
69
4.78
16.533
0.12
4
170_5_44
SHV
PAC/MAAC_190.2__5_44
26.8
4.291
8.836
0.18
5
−−
−−
−−
−
6
−−
−−
−−
−
Table2.8.(Continued)
Geneticvariationinpotatocanopycoverdynamics
67
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
c m1(%td
‐1)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
7.8
‐2.413
0.28
2
−−
−−
−−
−
3
−−
−−
−−
−
4
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
5.21
‐3.987
0.16
0.41
18_1_32
SHI
EAACM
CAG_187.9__1_32
36.3
4.23
1.272
0.06
63_1_42
SHI
PAG/M
ACC_322.2__1_42
69
4.27
2.381
0.12
173_7_68RHVII
PAC/MAAC_164.8__7_68
21.1
3.55
2.997
0.08
5
−−
−−
−−
−
6
−−
−−
−−
−
c 1(%td
‐1)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
7.34
‐1.165
0.26
2
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
3.63
‐0.693
0.14
3
−−
−−
−−
−
4
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
3.88
‐1.055
0.13
0.44
18_1_32
SHI
EAACM
CAG_187.9__1_32
36.3
5.82
1.236
0.18
131_5_55RHV
PAC/MATA_99.4__5_55
62.6
3.49
‐0.387
0.03
173_7_68
RHVII
PAC/MAAC_164.8__7_68
21.1
3.80
1.081
0.11
5
−−
−−
−−
−
6291_11_7
SHXI
EACAMCAC_372__11_7
9.2
3.477
0.899
0.14
Table2.8.(Continued)
Chapter2
68
ParameterExp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
c 3(%td
‐1)
1−
−−
−−
−−
2
116_5_17RHV
EAGAMCTC_470__5_17
18.2
8.76
4.649
0.29
0.39
265_9_74SHIX
PCA/M
AGG_294.2__9_74
80.9
3.64
2.619
0.13
3
116_5_17RHV
EAGAMCTC_470__5_17
18.2
6.905
‐5.338
0.28
4
−−
−−
−−
−
5
−−
−−
−−
−
6
1_5_12
SHV
SH05B012_maturity_locus
0
3.28
3.595
0.07
0.28
115_5_4
RHV
PAC/MAGT_190.2__5_4
1.6
5.641
4.523
0.18
DP2(td)
1116_5_17RHV
EAGAMCTC_470__5_17
18.2
11.48
‐16.987
0.37
2
133_4_28SHIV
EACTMCTG_74.3__4_28
24.8
4.09
6.655
0.09
0.35
200_6_56SHVI
PAT/M
AAC_155.4__6_56
73.5
4.30
6.996
0.13
116_5_17RHV
EAGAMCTC_470__5_17
18.2
4.08
‐10.208
0.14
3
−−
−−
−−
−
4
116_5_17RHV
EAGAMCTC_470__5_17
18.2
26.28
‐30.089
0.56
0.70
14_1_30
SHI
EAGTMCAG_458__1_30
33.1
6.82
2.83
0.03
62_1_36
SHI
EAGAMCAG_228.3__1_36
64
8.39
9.433
0.15
76_1_84
SHI
PAT/M
AAC_259.4__1_84
110
4.15
4.61
0.02
5
116_5_17RHV
EAGAMCTC_470__5_17
18.2
11.983
‐31.322
0.45
6
116_5_17RHV
EAGAMCTC_470__5_17
18.2
8.355
‐17.625
0.22
0.38
47_1_63
RHIB
EACAMCCT_144.5__1_63
27.8
4.686
11.283
0.04
Table2.8.(Continued)
Geneticvariationinpotatocanopycoverdynamics
69
ParameterExp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
DP3(td)
1116_5_17RHV
EAGAMCTC_470__5_17
18.2
5.26
‐9.768
0.20
2
116_5_17RHV
EAGAMCTC_470__5_17
18.2
14.39
‐19.569
0.42
3
−−
−−
−−
−
4
−−
−−
−−
−
5
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
4.101
‐8.657
0.17
6
1_5_12
SHV
SH05B012_maturity_locus
0
4.573
‐8.988
0.08
0.46
242_8_29SHVIII
PAT/M
AAC_283.7__8_29
33.2
4.134
‐6.785
0.08
115_5_4
RHV
PAC/MAGT_190.2__5_4
1.6
9.326
‐12.803
0.24
A1(td%)
1102_2_25SHII
EAACM
CCT_138.3__2_25
37.5
3.48
275.871
0.13
0.33
114_5_4
RHV
EACAMCCT_109.4__5_4
4.5
6.23
412.691
0.22
2
−−
−−
−−
−
3116_5_17RHV
EAGAMCTC_470__5_17
18.2
20.042
‐2246.416
0.64
4
58_1_32
SHI
PCA/M
AAC_211.6__1_32
57.8
4.77
‐458.085
0.18
5
−−
−−
−−
−
6
−−
−−
−−
−
Table2.8.(Continued)
Chapter2
70
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
A 2(td%)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
12.35
‐1764.776
0.39
2
53_1_32
SHI
PAC/MAGC_62.6__1_32
44
4.5
987.678
0.17
3
118_5_42
RHV
EAACM
CCT_201.2__5_42
43.9
4.398
‐1215.599
0.18
4
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
25.9
‐3039.945
0.55
0.70
62_1_36
SHI
EAGAMCAG_228.3__1_36
64
8.48
973.111
0.15
76_1_84
SHI
PAT/M
AAC_259.4__1_84
110
4.17
469.087
0.02
14_1_30
SHI
EAGTMCAG_458__1_30
33.1
6.86
281.737
0.03
5116_5_17
RHV
EAGAMCTC_470__5_17
18.2
16.32
‐3198.355
0.45
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
8.35
‐1768.77
0.24
0.37
47_1_63
RHIB
EACAMCCT_144.5__1_63
27.8
4.634
1124.769
0.07
A 3(td%)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
7.32
‐771.481
0.26
2
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
14.76
‐1251.073
0.43
3
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
4.473
‐787.222
0.18
4
−−
−−
−−
−
5
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
3.68
‐436.12
0.14
6
1_5_12
SHV
SH05B012_maturity_locus
0
4.37
‐559.686
0.08
0.46
242_8_29SHVIII
PAT/M
AAC_283.7__8_29
33.2
3.85
‐422.482
0.08
115_5_4
RHV
PAC/MAGT_190.2__5_4
1.6
6.72
‐804.803
0.24
Table2.8.(Continued)
Geneticvariationinpotatocanopycoverdynamics
71
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
A sum(td%)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
19.65
‐2359.408
0.50
0.57
43_1_35
RHIA
PAT/M
AGT_143.9__1_35
58.3
3.6
786.981
<0.1
2
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
21.09
‐2188.154
0.52
0.59
199_6_48SHVI
EACAMCCT_138.9__6_48
61.8
3.91
719.654
0.03
3
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
28.68
‐4112.081
0.69
0.82
63_1_42
SHI
PAG/M
ACC_322.2__1_42
69
3.953
878.855
0.07
179_5_77SHV
PAC/MATA_201.4__5_77
77.2
4.883
974.202
<0.1
81_2_75
RHII
PAC/MAGG_527.2__2_75
79.1
4.481
‐938.074
<0.1
4116_5_17
RHV
EAGAMCTC_470__5_17
18.2
40.06
‐2996.801
0.67
0.73
66_1_52
SHI
EAACM
CTG_193.9__1_52
81.5
4.11
419.656
0.06
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
4.51
552.856
0.08
5116_5_17
RHV
EAGAMCTC_470__5_17
18.2
29.21
‐2991.844
0.59
0.65
79_2_72
RHII
EACTMCTC_444.3__2_72
73.8
3.7
‐815.825
<0.1
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
18.77
‐2185.245
0.49
Table2.8.(Continued)
Chapter2
72
293_11_20) together explained 29%of the phenotypic variancewith their additive
effectsrangingfrom(2.4tdto‐2.1td).
SixQTLswereassociatedwitht1onlinkagegroups(SHI,SHXI,RHIV,andRHV)
acrossenvironmentswiththeiradditiveeffectsrangingfrom(‐2.9tdto‐26.6td)with
phenotypicvarianceexplainedbyindividualQTLrangingfrom(11%to60%)(Table
2.8).MajorityofenvironmentsshowedoneQTLexceptExp4,wheretwoQTLswere
foundwith28%combinedexplainedphenotypicvariance.NoQTLcouldbefoundin
Exp5.AmongtheQTLsdetected, theQTL(116_5_17)on linkagegroupRHVwasa
majoronewithmaximumadditiveeffectof(‐26.6td).ThisQTLwasdetectedinboth
Exps1and3explaining23%and60%ofphenotypicvariance,respectively.
AtotalofeightdifferentQTLsweredetectedfort2acrosstheenvironmentswith
theiradditiveeffectsrangingfrom(1.9td to‐31.7td)(Table2.8).TheseQTLswere
associatedwith five parental linkage groups (SH I, SH VI, RH IB, RH II, and RH V)
explaining the phenotypic variance ranging from (1% to 55%). Two ormoreQTLs
were detected for the majority of environments. The QTL (116_5_17) on linkage
group RH V was a major one detected consistently throughout the environments
apartfromExp2.ThisQTLhadanegativeadditiveeffectthroughouttheexperiments
withmaximumeffectinExp3.ThemaximumphenotypicvariancebythisQTLwasin
Exp4(i.e.55%).
FourQTLswereassociatedwithteacrosstheenvironmentsexplainingthetotal
phenotypicvarianceranging from2%to74%(Table2.8).TheseQTLsweremainly
located on three paternal linkage groups (RH IV, RH V, RH VI) with their additive
effectsrangingfrom7.1tdto‐49.9td.ThemajorityofenvironmentsshowedoneQTL
except Exps 2 and 3, where two and three QTLs were found with 62% and 68%
combinedexplainedphenotypicvariance,respectively.AmongtheQTLsdetected,the
QTL(116_5_17)on linkagegroupRHVwasamajorone inall thesixenvironments
withmaximumadditiveeffectof(‐49.9td)inExp6.OnlythreeadditionalminorQTLs,
one inExp2(108_4_72)andtwoinExp3(101_4_35and153_6_28)weredetected,
withtheiradditiveeffectsrangingfrom7.1tdto8.4tdwithonly2‐3%ofassociated
phenotypicvariance.
FourindividualQTLswerefoundforvmaxlocatedonthethreeparentallinkage
groups (SH I, SHV, andRHV) (Table2.8). Their associated additive effects ranged
from 4.2% to ‐54.9% and the phenotypic variance ranged from 2% to 56%. QTLs
weredetected inonlyhalfof theenvironments (i.e.Exps1,3, and4).Among these
environments,atotalofthreeQTLsweredetectedinExp3withtheir65%combined
phenotypic explained variance. The QTL (116_5_17) on linkage group RHV was a
major one in this environment withmaximum additive effect of (‐54.9%). A QTL
(170_5_44)associatedwithlinkagegroupSHVwascommonlydetectedinbothExps
Geneticvariationinpotatocanopycoverdynamics
73
1and4with amaximumof 22.7%additive effect and21%associatedphenotypic
varianceinExp1.
Forthetraitsrelatedwithcanopygrowthrate,fourQTLsweredetectedforcm1,
fiveQTLsforc1,andfourQTLsforc3(Table2.8).TheseQTLswerescatteredonsix
parental linkage groups (SH I, SH V, SH IX, SH XI, RH V, RH VII). The phenotypic
varianceexplainedbyQTLs ranged from(3% to29%). In caseofcm1,oneand four
QTLs were found in Exps 1 and 4, respectively. No QTLs were found in other
environments.ThefourQTLsfoundinExp4jointlyexplained41%ofthephenotypic
variance. However, among these QTLs, (116_5_17) on linkage group RH V was a
major QTL with the maximum additive effect of ‐3.9 % td‐1. This QTL was also
commoninbothenvironments(Exps1and4)explaining28%and16%phenotypic
varianceforcm1.Incaseofc1,oneQTLwasdetectedinExps1,2,and6,whereasfour
QTLscouldbefoundinExp4.NoQTLswerefoundinExps3and5.ThethreeQTLs
found in Exp 4 jointly explained 44% of the phenotypic variance. The major QTL
(116_5_17)onlinkagegroupRHVwascommonlyfoundinExps1,2,and4withthe
additive effect ranging from ‐0.7 to ‐1.17 % td‐1 and 13% to 26% explained
phenotypicvariance.IntotalfourQTLsweredetectedforc3,twoeachinExps2and6,
oneinExp3,whereasnoQTLswerefoundintheotherenvironments.Thecombined
phenotypicvarianceinExps2and6was39%and28%,respectively.ThemajorQTL
(116_5_17) locatedon linkagegroupRHVwascommonlydetected inExps2and3
with positive (4.6% td‐1) and negative (‐5.3% td‐1) additive effectswith 29%and
28% explained phenotypic variance, respectively (Table 2.8). This switch from
positivetonegativeeffectisveryinterestingphenomenonandmightberelatedwith
theuniquenessofthesetwoenvironmentsasExp3wasclearlylowerinNavailability
thanExp2(Table2.1).Growthratecanbeseenastheintegrationofawiderangeof
processes and thus may depend on many factors. As we already mentioned in
previous sections, the canopy senescence ratewas slowed down to such an extent
thattheendofcropcycle(te)occurredverylateinthesaidenvironment(Table2.2).
For the traits related with total duration of canopy maximum and decline
phases(i.e.DP2andDP3),sixandfiveQTLsforeachweredetected,respectivelyacross
theenvironments(Table2.8).TheseQTLswerescatteredonsevenparental linkage
groups(SHI,SHIV,SHV,SHVI,SHVIII,RHIB,andRHV).Thephenotypicvariance
explainedbyQTLsrangedfrom2%to56%.ForDP2,oneQTLwasdetectedinExps2
and5,whilethree,fourandtwoQTLsweredetectedinExps2,4,and6,respectively.
NoQTLcouldbedetected inExp3.The fourQTLs found inExp4 jointlyexplained
70% of the phenotypic variance. Among the total QTLs detected for this trait,
116_5_17onlinkagegroupRHVwasamajorQTLdetectedinnearlyallenvironments
withitsadditiveeffectsrangingfrom‐10.2tdto‐31.3tdandexplaining14%to56%
Chapter2
74
phenotypicvarianceforDP2.IncaseofDP3,oneQTLwasdetectedinExps1,2,and5,
whereasthreeQTLswerefoundinExp6.NoQTLswerefoundinExps3and4.The
three QTLs found in Exp 6 jointly explained 46% of the phenotypic variance. The
majorQTLwas116_5_17onlinkagegroupRHVcommonlyfoundinExps1and2with
the additive effect ranging from ‐9.8 td to ‐19.6 td and 20% to 42% explained
phenotypicvariance,respectively.
Forthetraitsrelatedwiththeareaunderthecanopygrowthcurve(i.e.A1,A2,A3,
andAsum),QTLsdetectedwerescatteredonnineparentallinkagegroups(SHI,SHII,
SH V, SH VI, SH VIII, RH IA, RH IB, RH II, and RH V) (Table 2.8). The phenotypic
variance explained by QTLs ranged from <0.1% to 69%. A total four QTLs were
detectedforA1,i.e.twoinExp1andoneeachinExps3and4,whereasnoQTLswere
foundintheotherenvironments.ThephenotypicvarianceexplainedbyQTLsranged
from13%to64%.ThecombinedphenotypicvariancefortwoQTLsinExp1was33%.
AmongtheQTLsdetected116_5_17locatedonlinkagegroupRHVwasconsidereda
major QTL with its associated additive effect of ‐2246.4 td % and with 64%
phenotypic variance in Exp 3. Seven QTLs were found for A2 throughout
environments with their explained variance ranging from 2% to 55%. Among the
environments, oneQTLwas detected in Exps 1, 2, 3, and 5,whereas four and two
QTLs were estimated in Exps 4 and 6, respectively. The combined phenotypic
variance accounted byQTLs in Exps 4 and 6was 70% and 37%, respectively. The
majorQTL(116_5_17)on linkagegroupRHVwascommonly found inExps1,4,5,
and6withtheadditiveeffectrangingfrom‐1215.6td%to‐3198.4td%and18%to
55% explained phenotypic variance. In total five QTLs were detected for A3 with
phenotypicvariancerangingfrom8%to43%.OneQTLwasfoundeachinExps1,2,3
and 5; three in Exp 6,whereas noQTLswere found in Exp 4. The combinedQTLs
explainedphenotypicvariance inExp6was46%.QTL116_5_17 locatedon linkage
groupRHVwasamajoroneandcommonlydetectedinExps1,2,and3withadditive
effects ranging from ‐771.5 td% to ‐1251.1 td% with 18% to 43% range of
phenotypic explained variance. In case ofAsum,we estimated themaximumof nine
QTLsacrosstheenvironments.ThephenotypicvarianceexplainedbyindividualQTLs
ranged from<1% to69%.ThenumberofQTLs found in thevarious environments
was:oneQTLinExp6,twoinExps1,2,and5,threeinExp4,andfourQTLsinExp3
were found (Table 2.8). The combined phenotypic variance explained by the QTLs
rangedfrom57%to82%.TheQTL116_5_17onlinkagegroupRHVwasdetectedin
all the environments explaining the range of 49% to 69% phenotypic variance for
Asum.ThisQTLwasassociatedwithamajoradditiveeffectof‐4112.1td%inExp3.
In summary, QTLs with major effects were associated with paternal (RH)
linkagegroups,especiallyRHV(Table2.9),whereintotaloffiveQTLsweredetected
Geneticvariationinpotatocanopycoverdynamics
75
(Table2.7).QTLsonthis linkagegrouphadnegativeadditiveeffects, indicatingthat
RHallelesonthislinkagegroupshareanantagonisticeffectonthephysiologicaltraits
relatedwithcanopycover.OneparticularQTL(116_5_17)onthislinkagegroupwas
detected fornearlyall traitswithamajoradditiveeffectandexplainedmostof the
totalphenotypicvariance.Largenumberof additionalQTLswithminoreffectswas
mostlyassociatedwithmaternal(SH)linkagegroups(Tables2.8and2.9).Ourresults
areinlinewiththosebyVandenBergetal.(1996),whoalsoreportedthatmostof
thelocihadsmalleffects,butaQTLwithmaineffectwasfoundonchromosomeV.We
alsoobserved theco‐localisationofQTLs formany traits.For instanceclusteringof
manyQTLswere foundonposition18.2 cMonpaternal (RH) linkagegroup (Table
2.10). Here most of the traits (e.g. t2, DP2, A2, Asum) were tightly linked with QTL
(116_5_17) inmostoftheenvironments.ThiscouldmeanthatthisQTLisplayinga
pleiotropicroleindeterminingthesetraits.Thephenomenonofpleiotropycanhave
important implications for our understanding of the nature of genetic correlations
between different traits in certain regions of a genome and also for practical
applications in breeding because one of the major goals in breeding is to break
unfavourablelinkage(JiangandZeng,1995).Highgeneticcorrelationsbetweenthese
traitsconfirmtheserelations(Table2.5).
Table2.9.ListofparentallinkagegroupswithmajorandadditionalminorQTLs.
Parameter SHlinkagegroup RHlinkagegroup
MinorQTLs MajorQTLs MinorQTLs
tm1 I,VI,XI V IV
t1 I,XI V IVt2 I,VI V IB,II,VIte − V IV,VIvmax I,V V −cm1 I V VIIc1 I,XI V VIIc3 V,IX V −DP2 I,IV,VI V IBDP3 I,V,VIII V −A1 I,II V −A2 I V IBA3 I,V,VIII V −Asum I,V,VI V IA,II
Chapter2
76
QTL
Linkage
group
Markername
Position
(cM)
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
Exp.6
14_1_30
SHI
EAGTMCAG_458__1_30
33.1
DP2,A2
18_1_32
SHI
EAACM
CAG_187.9__1_32
36.3
c m
1,c1
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
t 2,A s
um
t 2,D
P3
62_1_36
SHI
EAGAMCAG_228.3__1_36
63.9
DP2,A
2
63_1_42
SHI
PAG/M
ACC_322.2__1_42
68.9
v max,
Asum
t 1,c
m1
66_1_52
SHI
EAACM
CTG_193.9__1_52
81.5
Asum
t m1
76_1_84
SHI
PAT/M
AAC_259.4__1_84
109.9
DP2,A
2
1_5_12
SHIV
SH05B012_maturity_locus
0
c 3,D
P3,
A3
199_6_48
SHVI
EACAMCCT_138.9__6_48
61.8
t m1
Asum
200_6_56
SHVI
PAT/M
AAC_155.4__6_56
73.5
t 1,D
P2
242_8_29
SHVIII
PAT/M
AAC_283.7__8_29
33.2
DP3,A
3291_11_7
SHXI
EACAMCAC_372__11_7
9.2
t 1,c
147_1_63
RHIB
EACAMCCT_144.5__1_63
27.8
t 2,D
P2,
A2
115_5_4
RHV
PAC/MAGT_190.2__5_4
1.6
c 3,D
P3,
A3
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
t 1,t2,t e,
c m1,c 1,
DP2,D
P3,
A2,A3,
Asum
t e,c
1,c 3,
DP2,D
P3,
A3,Asum
t m1,t 1,
t 2,te,
v max,c
3,A1,A3,
Asum
t 2,t e,
c m1,c 1,
DP2,A2,
Asum
t 2,te,
DP2,A
2,
Asum
t 2,te,
DP2,A
2,Asum
173_7_68
RHVII
PAC/MAAC_164.8__7_68
21.1
c m
1,c 3
Table2.10.Listofco‐localisedQTLsi.e.QTLsweresameformorethanonetrait.
Geneticvariationinpotatocanopycoverdynamics
77
This may indicate the difficulties of manipulating correlated traits
simultaneously. However, QTLs with similar behaviours could also be interesting
targetsforbreedingprogrammesastheyaremorelikelytobestableundervarious
environments.Moreover,itiswellknownthatlinkagegroupVharbourstheQTLfor
plantmaturityandvigourinpotato(Collinsetal.,1999;Oberhagemannetal.,1999;
Viskeretal.,2003;Bradshawetal.,2008).Ourresultsalsoconfirmthisfactasmostof
ourcanopygrowthanddevelopmenttraits(particularlyAsum)couldbeveryusefulin
definingthematuritytypeinpotato(seeChapter4).Ourresultsgiveaclearpicture
thatmaturityinpotatoismainlyexpressedfromthepaternal(RH)side.Besides,we
also found largenumberof independentQTLs(i.e. theydidnotcoincidewithother
traits)(Table2.11).TheseQTLshowever,withminoreffectscouldbeofgreatvalue
tobreedersforfurtherzoomingin.
AlthoughmanyoftheQTLsidentifiedweremappedtoasimilarpositioninmost
experiments,mostofthemwereexpressedinoneenvironmentbutnot intheother
ones(i.e.QTLE).Thiswasmostlyevidentfortraits(tm1,t1,vmax,cm1,c1,c3,DP3,A1,andA3)(Table2.12).ForallthesetraitstheGEcomponentofphenotypicvariancewasgreaterthantheGvariancecomponent(Table2.3).QTLscontrollingsuchtraitsoften
show low stability (Veldboom and Lee, 1996; Reymond et al., 2004). There were
markeddifferencesamongtheenvironmentsduetoNavailability(Table2.1).Besides,
the population was segregating for maturity. Variation in maturity type would be
expectedtocausevariationinanumberoftheparameterspresented(andfinalyield)
because of its relationship with the crop duration (e.g. te) and its effect on the
alignment between crop development processes and environment. These complex
interactions might have caused the lower repeatability of many QTLs over the
environmentsduetoQTLE.Severalresearchershaveidentifiedlocithat interactedwith the environment in different plant species e.g., photoperiod plasticity in
Arabidopsis(Ungereretal.,2003),growthandyieldinrice(Hittalmanietal.,2003),
floweringphenologyinbarley(Yinetal.,2005b)andyieldinbarley(Yinetal.,1999b;
Teulatetal.,2001;Voltasetal.,2001).
Only few QTLs were stable across the environments and therefore did not
show much of the QTLE (Table 2.13). For instance, the QTL 116_5_17 on RH VshowedupinalltheexperimentsforAsum(Fig.2.10). SuchQTLscouldbeusefulfor
markerassistedbreeding.
Chapter2
78
QTL
Linkage
group
Markername
Position
(cM)
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
Exp.6
21_1_32
SHI
EACAMCAG_381.9__1_32
38.7
v max
53_1_32
SHI
PAC/MAGC_62.6__1_32
44.0
A2
58_1_32
SHI
PCA/M
AAC_211.6__1_32
57.8
A1
67_1_52
SHI
PAT/M
AAC_207.7__1_52
80.7
t 2
81_1_92
SHI
PAC/MACT_132.6__1_92
121.9
t 2
102_2_25
SHII
EAACM
CCT_138.3__2_25
37.5
A1
133_4_28
SHIV
EACTMCTG_74.3__4_28
24.8
DP2
265_9_74
SHIX
PCA/M
AGG_294.2__9_74
80.9
c 3
293_11_20
SHXI
PAC/MATA_197.8__11_20
21.8
t m1
43_1_35
RHIA
PAT/M
AGT_143.9__1_35
58.3
Asum
64_2_21
RHII
PAG/M
ACT_230.5__2_21
23.5
t 2
79_2_72
RHII
EACTMCTC_444.3__2_72
73.8
Asum
96_4_33
RHIV
EACAMCAC_156.4__4_33
5.5
t 1
101_4_35
RHIV
EAGAMCAG_216.2__4_35
9.6
t e
98_4_35
RHIV
EACAMCTG_138.9__4_35
12.8
t 1
108_4_72
RHIV
EAACM
CCA_92.1__4_72
46.9
t e
114_5_4
RHV
EACAMCCT_109.4__5_4
4.5
A1
118_5_42
RHV
EAACM
CCT_201.2__5_42
43.9
A2
153_6_28
RHVI
PAT/M
AAC_272.6__6_28
53.8
t e
154_6_34
RHVI
EACTMCAG_393.7__6_34
59.7
t 2
Table2.11.ListofindependentQTLs(i.e.QTLsdetectedonlyonceforaparticulartraitinanyexperiment(environm
ent)).
Geneticvariationinpotatocanopycoverdynamics
79
Table 2.12. Number of QTLs detected across six experiments (environments).
Symbol‘−’meansnoQTLwasdetected.
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6
tm1 1 1 1† − 2 −
t1 1 1 1† 2 − 1
t2 1 1 2† 4 3 2
te 1 2 3 1 1 1†
vmax 1 − 3† 1 − −
cm1 1 − − 4† − −
c1 1 1 − 4† − 1
c3 − 2 1† − − 2
DP2 1 3 − 4 1† 2
DP3 1 1† − − 1 3
A1 2 1 1† 1 − −
A2 1 1 1 4 1† 2
A3 1 1† 1 − 1 3
Asum 2 2 4† 3 2 1
TotalQTLs 15 17 18 28 12 18
†QTLswithmaximumadditiveeffects.
Chapter2
80
QTL
GroupMarkername
Position
(cM)
Exp.1Exp.2Exp.3Exp.4Exp.5Exp.6
39_1_32
SHI
EAACM
CCT_217.8__1_32
41.6
×
×
170_5_44SHV
PAC/MAAC_190.2__5_44
26.8
×
×
116_5_17RHV
EAGAMCTC_470__5_17
18.2
××
××
××
Table2.13.ListofstableQTLsacrosssixexperiments(i.e.SimilarQTLsdetectedinmajorityofenvironments).
Geneticvariationinpotatocanopycoverdynamics
81
Figure2.10.QTLEadditive effects for total areaunder canopy curve (Asum, td%)acrosspaternal(SHandRH)genomesforsixexperiments.Lettersaboveorbelowthe
barsindicatedifferentlinkagegroups.
4.Conclusions
In this study, we presented a quantitative model to describe canopy dynamics of
potato.Combinedwithgeneticanalysisweaimedtofurtherourknowledgeregarding
both the genetics and physiology of this trait of potato. The model successfully
describedthequantitativedifferencesincanopydynamicsofdiversegenotypesina
segregating F1 population of potato under varied environments and gave
physiological insightusingagronomicallymeaningfultraitsthatcharacterisecanopy
formation in potato. These traits are directly related to the ability of the adapted
genotypes to interceptphotosynthetically active radiation (PAR)and thus to create
high tuber yields, as we will show in Chapter 3. In this way, ourmodel approach
yieldedestimates foragronomically relevantcropcharacteristics thatareuseful for
definingfuturebreedingstrategiesinpotato.
The scope of crop improvement by breeding is determined by the amount of
heritable or genetic variation relative to that of non‐heritable or environmental
variation,andbythenatureandmagnitudeofGEinteractionwhichmaycomplicateselectionand testing inbreedingprogrammesandresult inreducedoverallgenetic
gain. Formost traits quantified in themodel, considerably high genetic variability
along with high heritability were recorded among the F1 population under study.
Thereareopportunities, therefore, toexploit thegeneticvariabilityavailable in the
-5000
-4000
-3000
-2000
-1000
0
1000
2000
1 2 3 4 5 6
Add
itive
eff
ects
Experiment no.
SH RH
V
V
II
IA
VV
II
V V
VI V II I
Chapter2
82
F1populationandtoselect forhighlyheritable traits inorder to improveradiation
interceptionefficiency.
In our second analysis, estimated physiological traits were subject to QTL
analysis.TheAFLPmarkerswere thereforegenerated foranextendedmarkermap
(Fig.2.9),towhichQTLweremappedforthemodelparametersandderivedtraits.In
total 42 QTLs were identified on both SH and RH parental genomes across all six
environments. QTLswithmajor effectswere associatedmainlywith paternal (RH)
linkagegroupV.OneparticularQTL(116_5_17)on this linkagegroupwasdetected
for nearly all traits with a major additive effect and explained most of the total
phenotypicvariance.SomeoftheQTLsweremappedtosimilarpositionsinmajority
oftheenvironments.OnlyfewQTLswerestableacrosstheenvironmentsandmostof
them showed QTLE. The stable QTLs across environments could be useful formarkerassistedbreeding.
ManyresearchersidentifiedQTLsfordifferenttraitsliketuberdormancy(Van
den Berg et al., 1996), tuber yield and starch content (Schäfer‐Pregl et al., 1998),
tubershape(VanEcketal.,1994),tuberfleshcolour(Bonierbaleetal.,1988),tuber
skincolour(Gebhardtetal.,1991),yield,agronomic,andqualitytraits(Bradshawet
al.,2008)inpotatoundernormalconditions.However,knowledgeaboutgeneticsand
physiologyoftraitsrelatedtocanopydynamicsandlightinterceptionisstill limited
andhardlyanyQTLshavebeenidentifiedforthesetraitsinpotato.
Ourquantitativeapproachincombinationwithmarkersofthewidelyavailable
and easy‐to‐use AFLP marker system identified QTLs that could be useful in the
developmentofmarkerassistedbreedingstrategiesinpotato.
Wehighlighted thepotentialofusingamodel toassist thegenetic analysisof
quantitative crop trait like canopy cover. Potato models intended for use inyield
predictions should first focus on improving genotype‐specific estimates of canopy
light interception under varied environmental conditions. Research investigations,
such as identifying potato canopy characteristics important for breeding trials,can
benefit by using our approach to provide an additional level of detail in canopy
development.
CHAPTER3
Analysisofgeneticvariationofpotato(SolanumtuberosumL.)usingstandardcultivarsandasegregatingpopulation.
II.Tuberbulkingandresourceuseefficiency*
M.S.Khan,X.Yin,P.E.L.vanderPutten,H.J.Jansen,H.J.vanEck,F.A.vanEeuwijk,P.C.Struik
AbstractQuantitative differences in the dynamics of tuber bulking of 100 genotypes in asegregatingF1population, theirparents (SH,RH)and five contrasting cultivarsofpotato(SolanumtuberosumL.)undervariousenvironmentswereanalysedusingapiece‐wise expolinear function. Tuber bulking was characterised by threeparameters: cm, ED and wmax, where cm and ED were growth rate and effectiveduration, respectively, of the linearphaseof tuberbulking, andwmaxwas the finaltuber dry weight.We also analysed radiation‐ and nitrogen‐use efficiencies (RUEandNUE,respectively),andtheirrelationshipswiththemodelparameters.Valuesofcmwere highest for earlymaturing genotypes followed bymid‐late and then lategenotypes.Latematuringgenotypeshadlongestperiodoftuberbulkingfollowedbymid‐lateandearlygenotypes.Asaresultwmaxwashigherinlategenotypesthaninearly genotypes. The RUE values were highest for early maturing genotypesfollowedbymid‐lateandlategenotypeswhereasNUEwashighestinlatematuringgenotypes followed by mid‐early and early genotypes. Genetic variability andheritabilitywerehighformosttraitsandphenotypicandgeneticcorrelationswerehigh(r>0.50)forthesetraitsaswell.PathcoefficientanalysisshowedthatRUE,cm,and a previously quantified parameter for total canopy cover Asum, had a majorinfluenceonwmax. Sixteen QTLs were detected for our model parameters and derived traitsexplainingthephenotypicvariancebyupto66%.QTLscontrollingmostofthetraitswereonpaternal(RH)linkagegroupV.OneparticularQTL(116_5_17)onpaternallinkage group V at position 18.2 cM was detected for all the traits with a majoradditiveeffectandexplainedmostofthetotalphenotypicvariance.AdditionalQTLsmostlyassociatedwithRHlinkagegroupsweredetectedfortraits(cm,tE,andED),whereasbothSHandRHlinkagegroupswereassociatedwithtraitsNUEandwmaxforminorQTLs.AnumberofQTLsfortraitswerenotdetectedwhentuberyieldpersewassubjectedtoQTLanalysis.ThephenotypicvarianceexplainedbytheQTLsfortuberyieldpersewasalsolowerthanforothertraits.Thegeneticparametersfoundin this study indicate that there are opportunities for improving tuberdrymatteryieldbyselectionforoptimalcombinationofimportantphysiologicaltraitsRUE,cm,andAsum.Keywords: Potato (Solanum tuberosum L.), tuber bulking dynamics, piece‐wiseexpolinear function, components of variance, genotype‐by‐environment (GE)interaction,geneticvariability,heritability,pathcoefficientanalysis,maturity type,QTLmapping,QTL‐by‐environment(QTLE)interaction.
___________________________________*SubmittedtoFieldCropsResearch.
Chapter3
84
1.Introduction
Tuberformationinpotato(SolanumtuberosumL.)consistsofacomplexanddynamic
sequenceofseveralindependentlyregulatedevents(EwingandStruik,1992;Jackson,
1999), including induction, initiation,set,bulking,andmaturation(Vreugdenhiland
Struik, 1989). These events are only possible when environment‐dependent steps
occurinanorchestratedway,includingthearrestofstolongrowth(Vreugdenhiland
Struik,1989),initiationofradialgrowth(CatchpoleandHillman,1969;Mingo‐Castel
etal.,1976;Struiketal.,1988;VreugdenhilandStruik,1989;EwingandStruik,1992)
andresourcestorage(Park,1990;Müller‐Röberetal.,1992).Thedifferentstepsand
events can occur independently of each other and are regulated by specific genes
(Struik et al., 1999; Kloosterman et al., 2005). The resulting, economically relevant
process of tuber bulking is, therefore, regulated by a large set of interacting genes
(Bachemetal.,2000).
The onset of tuber bulking greatly impacts subsequent growth, development,
andphysiologyoftheentirepotatocrop(Ewing,1990,EwingandStruik,1992;Van
Dam et al., 1996; Walworth and Carling, 2002), because the developing tubers
become thedominantsinkofbothcarbonandnitrogenassimilates (Oparka,1985).
Theonsetoftuberbulkingleadstoamoreorlessabruptpreferentialpartitioningof
assimilates to the tubers, thereby causing a reduction in the growth rate and
ultimatelyacompletehaltingofgrowthoffoliageandroots(MoorbyandMilthorpe,
1975; Ewing and Struik, 1992). However, the abruptness depends on thematurity
typeandotheraspectsofthegenotype‐specificphysiology(Struik,2007).Earlyonset
of tuber bulking may result in small plants with limited canopy cover and
consequently lowfinal tuberyields,whilst lateonsetof tuberbulking leadsto large
plantswithhighfinaltuberyields(BremnerandRadley,1966;Struik,2007).
Information on the different processes involved and factors affecting plant
developmentandtuberformationinpotatoareabundant(IvinsandBremner1965;
Ewing and Struik, 1992; Almekinders and Struik 1996; Kolbe and Stephan‐
Beckmann,1997;O’Brienetal.,1998;Jackson,1999;Struiketal.1999;Claassensand
Vreugdenhil,2000,andreferencestherein).However,moststudieshave focusedon
oneorveryfewofthesedevelopmentalprocesses,orononeoraverylimitednumber
ofgenotypes(cultivars).Moreover,thephysiologicalandgeneticbasesofvariability
amongsuchtraitshavenotbeenthoroughlyinvestigated,althoughsomeeffortshave
been made to study the temporal dynamics of important potato developmental
processesunderdiverseenvironmentalconditionsonasetofcontrastinggenotypes.
For instance,Spitters (1988)analysed thegenotypicdifferences in tuberbulkingof
potatoonalargesetofcommercialvarieties.Celis‐Gamboaetal.(2003)usedahighly
segregating diploid population of potato to study the temporal relationships
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
85
underlyingthedynamicsoftuberformationandotherdevelopmentalprocesses.
Difficultiesinmanipulatingyieldarerelatedtoitsgeneticcomplexity:polygenic
nature, interactions between genes (epistasis), and environment‐dependent
expressionofgenes(RibautandHoisington,1998).Manyenvironmentalandphysical
factors, such as temperature, day length, light intensity, water availability, and
nitrogen (N), have been demonstrated to influence potato tuberisation (Ewing and
Struik,1992; Jackson,1999).Forexample, temperatureexertsamajor influenceon
tuberisationanddrymatterpartitioningtotubers(Ewing,1981,1985),withcoolair
temperatures favouring induction to tuberise (Bodlaender, 1963; Gregory, 1965;
Epstein, 1966; Ewing, 1981; Manrique et al., 1984; Struik and Kerckhoffs, 1991),
whereas an increase in air temperaturemay reduce tuber drymatter content and
yield(Struiketal.,1989b).Nitrogenalsoplaysamajorroleintuberisation(Werner,
1934; Gregory, 1965). Nitrogen helps to attain complete canopy cover early in the
season, especially under relatively resource‐poor conditions (Haverkort and
Rutavisire, 1986; Vos, 2009) and to extend the period of full canopy cover thus
leading to increased light interception and tuber yield (Martin, 1995). Radiation is
important for dry matter accumulation (Monteith, 1977; Goudriaan and Monteith,
1990) and affects the early processes in tuber formation (Ewing and Struik, 1992)
andtherateoftuberbulking(BurstallandHarris,1983)anditsduration.Therefore,
resource(radiation,N)useefficiencymayhaveastrongbearingontuberbulkingand
finaltuberyields.
In our companion paper (Chapter 2), we have quantitatively analysed potato
canopy cover dynamics, using a set of varieties covering awide range ofmaturity
typesandawell‐adapteddiploidF1segregatingpopulation.Here,usingthesameset
ofplantmaterials,weaimtoanalysethedynamicsoftuberbulkinganditsvariability
by breaking them down into biologically meaningful and genetically relevant
component traits.We also analyse radiation use efficiency (RUE) and nitrogen use
efficiency (NUE) and study their relationships with the tuber bulking traits. We
quantify thegeneticparameters(variancecomponentsandheritability),phenotypic
andgeneticcorrelations,andpathcoefficientsofthesetraits.Finally,weperformQTL
mappingofthetraitsanddiscusstheirgeneticbasis.Thecombinedinformationfrom
Chapters2and3shouldgiveinsightsintothemostvitalprocessesthatcanbeusedto
explorethepossibilitiesofgeneticallymanipulatingpotatotuberyield.
Chapter3
86
2.Materialsandmethods
2.1.F1segregatingpopulationofSHRHandstandardcultivarsTheplantmaterialusedinthisstudyconsistedof100F1diploid(2n=2x=24)potato
genotypes derived from a cross between two diploid heterozygous potato clones,
SH83‐92‐488RH89‐039‐16(RouppevanderVoortetal.,1997;VanOsetal.,2006),orsimplythe‘theSHRHpopulation’.Thepopulationsegregatesformaturitytype.
BesidestheindividualF1genotypesandtheirtwoparents,wealsoincludedfive
standard cultivars in our studies: Première, Bintje, Seresta, Astarte, and Karnico.
These cultivars were chosen because of their differences in maturity type when
grownintheNetherlands,rangingfromearly(Première)toverylate(Karnico).This
selection of standard cultivars would allow a benchmarking of consequences of
maturitytypeonthetemporaldynamicsoftuberbulking.Furtherinformationabout
plantmaterialsisgiveninChapter2.
2.2.Fieldexperimentsandmeasurements
Six field experiments were carried out in Wageningen (52˚ N latitude), the
Netherlands,during2002,2004,and2005,withtwoexperimentsineachyear,using
the aforementioned plant materials. Details on the methodology and the
environmentalconditionshavebeendescribedinChapter2.Karnicowasnotpresent
inthetwoexperimentsin2005.
Tuberdrymatterwasmeasuredat threeharvestsduring thegrowingperiod.
Thefirstandsecondharvestswereplannedinsuchawaytomeasurethetuberdry
matterinthelinearbulkingphase,whilethelastharvestwasperformedatmaturity.
Tubersofeachplotwereharvestedanddriedinanovenat70˚Ctoconstantweight.
Forsamplesofthegrowingseasonsof2004and2005,nitrogen(N)concentrationin
tuberssampledattheendofthegrowingseasonwasdeterminedbymicro‐Kjeldahl
digestionanddistillation(AOAC,1984).TotalamountofN in tuberswascalculated
fromtheNconcentrationandtuberdryweight.
2.3.Amodelfortuberbulkingdynamics
Likethelifecycleofanyplantoritsorgan,potatotubergrowthasafunctionoftime
followsasigmoidpattern,includinganearlyacceleratingphase,alinearphaseanda
ripeningphase(Fig.3.1).WeusedtheexpolinearfunctionofGoudriaanandMonteith
(1990)todescribethetubergrowthduringtheexponentialphase:
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
87
Bm
m with1ln Bm tterc
w ttr (3.1)
wherewis tubermass,tis time,tBis themomentatwhichthe linearphaseof tuber
bulkingeffectivelybegins,rmistherelativegrowthrateinthe‘exponentialphase’,and
cmisthegrowthrateinthe‘linearphase’.
Eqn (3.1) can, inprinciple, beused todescribe the tuber growthof the linear
phase as well. However, eqn (3.1) tends to under‐estimate the true growth rate
becauseofitscurvilinearnature.Toobtainanobjectiveestimationofthegrowthrate
ofthelinearphase,weusedthefollowinglinearmodeltoquantifythesecondphase:
EBBmB with tttttcww (3.2)
wheretEis theend timeof the linearphase,wBis the tuberweightat time tB. Ifwe
knowrmandcm,thenwBcanbeestimatedfromeqn(3.1)as0.693cm/rm.
To represent a deflection in growth towards the third phase,Goudriaan and
Monteith(1990)suggesteda truncatedcurve that terminatesgrowthat the time tE,
when the maximum weight (wmax) is achieved. This is a brutal method, because
growthstopsgraduallyrather thanabruptly.However,given the limitednumberof
measurements in the time series (see above), we adopted the truncated curve
approach,with:
Emax with ttww (3.3)
wheretEiscalculatedastB+(wmax−wB)/cm.
Combiningeqns(3.1,3.2,and3.3)yieldsamodelwithfourparameters:rm,cm,
tB,andwmax,whilethetwootherparameterswBandtEarecalculatedas0.693cm/rm
andtB+(wmax−wB)/cm,respectively.Obviouslyanover‐fittingwouldbeobtainedifall
the fourparametersweretobedirectly fitted fromour limiteddatapoints foreach
genotype. IngramandMcCloud (1984) andVanDamet al. (1996) reported that rm
wasconservativeacrosspotatocultivarsatagiventemperature.Theirrmvalue0.34
d‐1 at the optimum temperature was used here for all genotypes. We also fixed
parameterwmaxas theaverageof twoblocksof the finalmeasuredweight.The two
remainingparameters (i.e.cm, tB) canbe estimated, butwith the valueof tB having
large standard error, probably due to insufficient data points for the early season.
Using these estimated values, we found that the initial weight (w0) at time zero,
calculatedusingeqn(3.1),didnotvarymuchacrossgenotypes.We,therefore,used
the value for w0 = 0.13 (g m‐2), the averaged w0 across all six
Chapter3
88
Figure 3.1. Tuber bulking dynamics in potato represented by the piece‐wiseexpolineargrowthfunction.
experiments and all genotypes, to further reduce the number of parameters to be
estimated.Whenw0isfixed,parametertBcanbecalculatedfromeqn(3.1)as:
1ln
1 m
m0
mB
crw
er
t (3.4)
This is inaccordancewithGoudriaan (1994),whosuggested that itmaybeamore
naturalsequencetoexpresstBasafunctionofinitialweight(w0)atemergence.
Eqn(3.4)andtheformulaeforcalculatingwBandtE,werecombinedwitheqns
(3.1‐3.3), for curve fitting. The fitting was performed for each genotype of every
experiment with the iterative non‐linear least‐square regression using the Gauss
method, as implemented in the PROCNLIN of the SAS software (SAS Institute Inc.,
2004). Obviously, our procedure estimated only cm, which together with wmax as
primary model parameters, characterises genotypic and environmental effects on
tubergrowth.ParameterstB,tE,andwBwerecalculatedfromtheequationsdescribed
earlier. The effective duration of tuber bulking (ED), an additional useful trait,was
calculatedastE−tB.
As in our first analysis for canopy cover (Chapter 2), all time variables and
Thermal days (td)
maxw
Bt Et
mc
mr
Tub
er d
ry m
atte
r (g
m-2
)
Bw
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
89
durationwereexpressedasthermaldays(td)toaccountfortheinfluenceofdailyand
seasonaltemperaturefluctuationsontubergrowth.Themethodforconversionofthe
actual days into tdwas givenbyYin et al. (2005) and its application to our potato
genotypes has been described in Chapter 2.Note that the td is equal to or smaller
thanthenumberofchronologicaldays.
2.4.CalculationofRadiationUseEfficiency(RUE)
Theradiationuseefficiency(RUE;gDMMJ‐1PARint)wasestimatedforeachindividual
plotbydividingthetotaltuberdrymatteratmaturity(gDMm−2)bythecumulative
interceptedphotosyntheticallyactiveradiation(MJPARintm−2)fortheentiregrowth
period.Dataofincidentglobalsolarradiationwereobtainedfromaweatherstation
in Wageningen located nearby the experimental sites. Daily incident PAR was
calculated as half of the global solar radiation (Spitters, 1988). To calculate
cumulativePARint,ourextensivedataonthepercentagegreencanopycover(Chapter
2)wereconvertedtoPAR‐interceptionpercentage,usinga linearrelationshipgiven
byBurstall andHarris (1983)asPARint(%)=0.956canopycover (%)–4.95.Dailyvalues of PARint were summed and the obtained cumulative PARint was used to
calculateseasonalaverageRUEonthetuber‐dryweightbasis.
2.5.CalculationofNitrogenUseEfficiency(NUE)
Aswe did notmeasure dryweight andN content in the organs other than tubers,
nitrogen use efficiency (NUE; g DM g‐1 N) was expressed on the tuber drymatter
basis, aswasRUE,bydividing the total tuberdrymatter (gDMm−2)by total tuber
nitrogen uptake (g N m−2). This way of presenting NUE means that NUE is
mathematicallyequivalenttotheinverseoftuberNconcentration(gNg‐1DM).
2.6.Statisticalandgeneticanalysis
All statistical analyses were performed in Genstat (Payne et al., 2009). Combined
analysisofvarianceacrossexperiments(i.e.environments)wasperformedtotestthe
significanceandextentofdifferencesamongenvironmentsandgenotypes(including
the F1 population, the parents, and standard cultivars). Means of genotype and
environmenttermswerecomparedusingtheFisher’sleastsignificantdifference(LSD)
test.FurtherstatisticalanalyseswereperformedonlyusingtheF1populationmeans
(100genotypes)acrosssixenvironmentsasdescribedbelow.
Chapter3
90
2.6.1.Estimationofvariancecomponents
The variance components for genetic, environmental and experimental error were
estimated through the REML procedure to assess their contribution to the total
phenotypic variance of the traits cm, tE,ED,RUE, NUE, andwmax. Significance levels
weredeterminedwithalikelihoodratiotest(Morrell,1998),whichteststhechange
indeviationafterremovingtherespectivevariancecomponentfromthemodel.The
changeindeviationisapproximatelychi‐squaredistributed(Littelletal.,1996).Once
these variance components were estimated, phenotypic variance (σ2Ph) was
calculatedasper followingequation (Bradshaw,1994;FalconerandMackay,1996;
LynchandWalsh,1998):
2ε
2E
2G
2Ph (3.5)
where σ2G is genetic variance, σ2E represents environmental variance, and σ2ε is
experimentalerrorvariance.
2.6.2.Phenotypicandgeneticcoefficientsofvariation
Coefficientsofvariation(%)werecalculatedaccordingtothefollowingequation:
1002
CV (3.6)
where is the grandmeanof thepopulation, andσ2X is a variance component (i.e.σ2Phorσ2Gorσ2E).
2.6.3.GGEbiplotanalysisGGEbiplot analysiswas performed to analyse the inter‐relations among genotypes
and environments. GGE biplots were constructed by plotting the first principal
component (PC1) scores of the genotypes and the environments against their
respective scores for the second principal component (PC2). The environment‐
standardisedmethodofYan(2002)wasused.
2.6.4.Heritability
Estimatesofbroad‐senseheritability(H2)(%)werecalculatedbyusingtheestimated
variancecomponents(FalconerandMackay,1996;Hollandetal.,2003):
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
91
100
t
2ε2
G
2G2
n
H
(3.7)
where(nt=6)istheproductofnumberofblocksandenvironments.
2.6.5.Phenotypicandgeneticcorrelation
Phenotypic correlations were calculated using the Pearson Correlation Coefficient.
The genetic correlations were calculated using the following equation (Holland,
2006):
2G
2G
2G
Gji
ijijr
(3.8)
whereσ2Gijistheestimatedgeneticcovariancebetweentraits iand j;σ2Giandσ2Gjare
the genetic variances of traits i and j, respectively. The variance and covariance
componentswereestimatedfrommultivariateREMLanalyses(Meyer,1985;Holland,
2006).Thesignificanceofgeneticcorrelationswasdeterminedusingat‐testafteraz‐
transformationofthecorrelationcoefficients(SokalandRohlf,1995;Guttelingetal.,
2007).
2.6.6.Pathcoefficientanalysis
The inter‐associations between the important yield determining components were
ascertained across all six experiments byworking out the path coefficient analysis
following the procedure of Dewey and Lu (1959). This was accomplished by
partitioning the direct and indirect effects of various physiological traits upon the
final tuberdrymatter. The final tuber drymatter (i.e.wmax)was considered as the
response variablewhile traits cm,ED,Asum, RUE, andNUEwere assumed to be the
predictorvariables,whereAsumistheareaunderthewholecanopy‐covercurveand
reflects the capability of the crop to intercept solar radiation during the whole
growingseason,asquantified inChapter2.Thedirecteffectsofpredictorvariables
werethepathcoefficientscomputedthroughmultipleregression.Apathcoefficientis
a standardized regression coefficient (Li, 1975). Indirect effectswere computed as
the product of the correlation coefficient between two variables and the path
Chapter3
92
coefficient from the secondvariable to the response variable. Let variables x1 to x5
refer to cm, ED, Asum, RUE, and NUE, respectively. The total effect of a predictor
variable x1 correlatedwith other predictor variables x2, x3, x4, and x5 on response
variablez,wouldbegivenby,forexample:
rzx1=Pzx1+(Pzx2×rx1x2)+(Pzx3×rx1x3)+(Pzx4×rx1x4)+(Pzx5×rx1x5) (3.9)
whererzx1istotalcorrelationbetweenzandx1,Pzx1representspathcoefficientfrom
x1tozrx1xi(i=2,3,4and5)iscorrelationcoefficientbetweenvariablesx1andxi,and
Pzxidenotespathcoefficientfromxitoz.Thesamelogicwasappliedtocomputerzx2,
rzx3,…,rzx5.
2.6.7.QTLdetection
Theparental(SH,RH)geneticmapdescribedinChapter2wasusedforQTLmapping
of traits. Eighty‐eightgenotypesof our100F1 lineswere covered in theextended
ultra‐densegeneticmapof250 linesofSHRHpopulation (cf.Chapter2);dataofthese 88 lines were therefore used for detection of QTLs for model parameters,
derivedtraitsand(N,radiation)useefficiencies.QTLanalysiswasdoneindividually
forallsixexperiments(environments)usingGenstatversion14(Payneetal.,2009)
software.Formoredetailsaboutthemappingprocedure,seeMaterialsandMethods
ofChapter2.
3.Resultsanddiscussion
3.1.Modelperformanceindescribingtuberbulkingdynamicsofgenotypes
Themodelfortuberbulkingdynamics(i.e.combinedeqns3.1‐3.3)fittedwellforeach
genotypeofthepotatosegregatingpopulation,theparentsandthestandardcultivars
intheentiredataset,withR2valuesrangingfrom0.90to1.00(n=6).Theestimated
tuberbulkingcurvesforthetwoparents(SHandRH)andfourstandardcultivarsare
showninFig.3.2.Thetransformationofcalendardaysintothermaltimeresultedina
more stable parameter estimation (data not shown), as thermal time effectively
removed the confounding effect of diurnal and seasonal temperature fluctuations
during theexperimentalperiod.Overall, combinedeqns (3.1‐3.3)areveryuseful in
analysing the tuber bulking dynamics of a diverse set of potato genotypes under
variousenvironments.
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
93
Figure3.2.Observed(obs)pointsandfitted(fit)curvesofparents(SHandRH)and
fourstandardcultivarsforallsixexperiments(environments).
3.2.Modelparametersandsecondarytraits
Resultsofcombinedanalysisofvarianceshowedhighlysignificant(P<0.01)effectsof
genotype(including100F1genotypes,theparents,andstandardcultivars)acrossthe
experimental sites (i.e. environments) on all model parameters and derived traits.
However,asexpected,differencesbetweencultivarsandacrossenvironmentsinthe
onset of tuber bulking (tB) were very small, because as mentioned earlier tB was
calculatedbyeqn(3.4)wherewoandrmwerefixedduetolimiteddatapointsforthe
earlygrowthphase.Similarly,thetuberweight(wB)achievedattBwascalculatedin
relation to rm and cm (see Materials and Methods). Therefore, we will no longer
analysethevariationoftBandwB.
0
500
1000
1500
2000
2500
0 20 40 60 80 100
SH
0
500
1000
1500
2000
2500
0 20 40 60 80 100
RH
0
500
1000
1500
2000
2500
0 20 40 60 80 100
Première
0
500
1000
1500
2000
2500
0 20 40 60 80 100
Bintje
0
500
1000
1500
2000
2500
0 20 40 60 80 100
Seresta
0
500
1000
1500
2000
2500
0 20 40 60 80 100
Astarte
Tub
er d
ry m
atte
r (g
m-2
)
Chapter3
94
Thereweresignificantdifferences(P<0.05)amongthestandardcultivarsforcm,
tE, ED, and wmax (Table 3.1). Values of cm were comparatively higher for early
maturingcultivarslikePremièreandmid‐earlycultivarslikeBintjethanforlateones
likeAstarteandKarnico.
Thevalues for tEandEDwerehigher for latematuringcultivars than formid‐
late and early cultivars. Themaximum tuber drymatter yield (wmax), on the other
handwashigherforlatematuringcultivarsthanformid‐lateandearlycultivars.The
highervaluesofwmaxin latematuringcultivarsmightbedue to theircomparatively
higher tE and longer ED. This is in line with the result of Kooman and Rabbinge
(1996),whofoundthatcomparedwithlatecultivars,earlypotatocultivarsallocatea
larger part of the available assimilates to the tubers early in the growing season,
resulting in shorter growing periods and also lower yields. Moreover, cultivars
differenceswithrespecttotuberyieldingpotentialcouldbeattributedtovariationin
efficiencyofassimilatepartitioningtothetubers(bulkingrate)andmaturityperiod.
HammesandDeJager(1990)andGawronskaetal.(1990)reportedtheexistenceof
varietaldifferenceswithrespecttotherateofnetphotosynthesisanddrymatter
production.
Table 3.2 shows trait values of the F1 segregating population in comparison
withtheirparents(SHandRH).ThemeansoftheF1segregatingpopulationwerenot
withintherangesoftheparentalclonesforparameterstEandED.Widerangeswere
observed forallmodelparametersandderived traits.Mostof theparameterswere
Table3.1.Estimatedmeanvaluesofdifferenttraitsforfivestandardcultivars(listed
inorderofincreasinglylongercropcycle),asobtainedfromcombinedANOVAacross
sixenvironments.tdstandsforthermalday.
Cultivarcm
(gDMtd‐1)
tE
(td)
ED
(td)
RUE
(gDMMJ‐1)
NUE
(gDMg‐1N)
wmax
(gDMm‐2)
Première 55.7a 37.3c 16.1c 2.7a 70.4c 1122b
Bintje 48.2ab 47.9b 27.4b 2.4a 72.1c 1375ab
Seresta 36.7bc 62.4a 42.8a 2.4a 88.5b 1579a
Astarte 35.4bc 63.0a 43.5a 2.2ab 90.9b 1533a
Karnico 31.2c 63.7a 44.4a 1.9b 101.8a 1487a
LSD
(P<0.05)13.2 10.1 10.7 0.5 7.7 263
Meanswithinacolumnfollowedbydifferentlettersaresignificantlydifferent
accordingtoFisher’sMultipleRangeTest(P<0.05).
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
95
nearlynormallydistributeddisplayingtransgressivesegregation(Fig.3.3).
Environment had highly significant (P<0.01) effects on all model parameters
and derived traits. There were significant differences (P<0.05) between the
experiments for cm, tE, ED, and wmax (Table 3.3), within five standard cultivars.
This was at least partly due to the purposeful variation in availability of N across
trials (Chapter 2). Figure 3.4 illustrates the variation (the median, minimum and
maximumvalues)ofthesemodelparametersandderivedtraitsfortheF1population
per individual experiment. The ranges of the parameters cm, and wmax were
consistentlywiderinExp3thanintheotherexperiments(Fig.3.4).IncaseoftEand
ED,widerrangeswereobservedinExps4and5,respectively(Fig.3.4).Thiscouldbe
attributedtovariedavailabilityofNperexperiment(Chapter2) in interactionwith
genotypespecificbehaviour, causingdifferent trade‐offsbetweenrateandduration
oftuberbulkingandtuberfinaldrymatterproduction.
3.3.RadiationUseEfficiency(RUE)
TheRUEdifferedsignificantly(P<0.01)amongthegenotypes(standardcultivarsand
F1segregatingpopulation).ThevaluesofRUErangedbetween1.9and2.7gDMMJ‐1
(Table 3.1). The calculated values of RUE were within the range reported in the
literature for Solanum tuberosumgenotypes under temperate conditions (Spitters,
1988; Scott and Wilcockson, 1978; Allen and Scott, 1980; Khurana and McLaren,
1982; MacKerron and Waister, 1985a; Stol et al., 1991; Kooman and Haverkort,
1995).
Table3.2. Estimatedmean values of different traits (across six environments) for
two parents (SH and RH) andmean, minimum (Min), maximum (Max), and range
withintheF1population.tdstandsforthermalday.
Parameter SH RH ‡Mean(±S.E.) Min Max Range
cm(gtd‐1) 39.8 29.0 35.5(±4.2) 19.9 47.4 27.5
tE(td) 50.8 46.9 51.0(±2.9) 42.5 62.9 20.4
ED(td) 30.9 27.8 31.8(±3.3) 22.4 45.1 22.7
RUE(gDMMJ‐1) 2.7 2.4 2.1(±0.2) 1.5 2.6 1.1
NUE(gDMg‐1N) 69.6 67.2 68.2(±5.6) 54.4 81.6 27.2
wmax(gm‐2) 1219 847 955.6(±50.0) 830.4 1115.7 285.3‡MeanofF1segregatingpopulation(100genotypes)acrosssixenvironments.
Chapter3
96
Table3.3.Estimatedmeanvaluesofdifferenttraitsforeachindividualenvironment
as obtained from combined ANOVA across five standard cultivars. td stands for
thermalday.
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6 LSD
cm(gtd‐1) 30.6d 41.6c 46.1b 50.0a 30.9d 48.3a 2.2tE(td) 55.4d 42.9f 61.1b 59.4c 64.6a 53.0e 1.7
ED(td) 36.3d 22.8f 40.8b 38.8c 45.5a 32.5e 1.8
RUE(gDMMJ‐1) 2.1d 2.0e 2.8a 2.6b 2.0e 2.4c 0.09
NUE(gDMg‐1N) − − 102.5a 80.8c 69.4d 84.8b 1.5
wmax(gm‐2) 1086e 1003f 1834b 1954a 1341d 1555c 44
Meanswithinarowfollowedbydifferentlettersaresignificantlydifferentaccording
toFisher’sMultipleRangeTest(P<0.05).
Significant (P<0.05) differences were observed among the standard cultivars
withrespecttoRUE.TheRUEvalueswerecomparativelyhighforearlyandmid‐early
cultivars(PremièreandBintje,respectively) followedbymid‐late(Seresta)andlate
cultivars(Karnico).Theseresultswereexpectedbecausecumulativelightabsorption
tended to be greater for the later maturing cultivars, but on the other hand, they
exhibitedasmallerharvestindex(datanotshown).Owingtotheseoppositetrends
for cumulative light absorption andharvest index, tuber yield showed an optimum
relationshipwithmaturityclass.Earlymaturingcultivarsallocatealreadyinanearly
phase themajor part of their current assimilates to tuber growth, which is at the
expenseofcanopygrowth(Spitters,1988).Senescenceofleaves,withoutsignificance
formation of new leaves, causes an early senescence of foliage. Early cultivars had,
therefore,asmallercumulativelightabsorptionbutagreaterharvestindexandRUE.
On the other hand, late maturing cultivars maintain green, active foliage for an
extendedperiodoftime(SpittersandSchapendonk,1990).However,theinvestment
incanopygrowthisattheexpenseoftubergrowth.
Table3.2comparesthemean,andrangesofRUEbetweenthetwoparents(SH
andRH)andF1segregatingpopulation.RUEvaluesvariedfrom2.7gDMMJ‐1forSH
and 2.4 g DM MJ‐1 for RH parent, compared with mean (2.1 g DM MJ‐1) of F1
population.ThemeanRUEofF1populationwaslowerthanthevalueofeitherparent,
butthiswasassociatedwithawidevariationinthepopulationforRUEwithvalues
ranging from 1.5 g DM MJ‐1 to 2.6 g DM MJ‐1 (Table 3.2) and therefore displayed
transgressivesegregation(Fig3.3).Therearepossibilitiesthereforeforthebreeders
toexploitthisvariationforimprovingRUE.
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
97
Figure3.3. Distribution of five model parameters among F1 genotypes across six
experiments(environments).Thevaluesoftwoparents‘SH’and‘RH’areindicatedby
fullarrowanddashedarrow,respectively.
1.50
3.00
2.50
25
20
1.25
2.00
15
10
5
0
2.25
1.75
2.75
10
5
60 10 40 20 0
50 30
20
15
5
90 40 70 0
50
30
25
80 60
20
15
10
50 60
40
30
0 40
20
10
20 0
30 70 10
12.5
0.060 75
5.0
10.0
65 80
7.5
2.5
70
17.5
15.0
7.5
800
2.5
700 1100 900
20.0
17.5
0.01000 1200
5.0
15.0
12.5
10.0
tE NUE
wmax
Num
ber
of F
1 ge
noty
pes
ED
RUEcm
Chapter3
98
0
20
40
60
80
100
[g td-1] cm
0
20
40
60
80
100
120
[td] tE
0
20
40
60
80
100
1 2 3 4 5 6
[td] ED
0
1
2
3
4
5RUE[g DM MJ-1]
0
20
40
60
80
100
120
NUE[g DM g-1 N]
0
500
1000
1500
2000
1 2 3 4 5 6
[g m-2 ] wmax
Figure3.4.BoxplotsofgeneticmeansofanF1populationofdifferenttraitsinallsix
experiments. Theboxes span the interquartile rangeof the trait values, so that the
middle 50% of the data lay within the box, with a horizontal line indicating the
median. Whiskers extend beyond the ends of the box as far as the minimum and
maximumvalues.
Experiment no.
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
99
TheRUE showed significant (P<0.05)variationamongst environments for the
setofstandardcultivarsaswellasfortheF1segregatingpopulation.Forthestandard
cultivarsRUEmeanvalueswerehigherinExp3andlowerinExp5(Table3.3).For
the F1 population a similar trend was observed with wider and lower ranges of
variationinExp3andExp5,respectively(Fig.3.4)thanintheotherexperiments.We
surmise that such within and between experimental variations are the combined
result of differences in Asum associated with variation in maturity class and with
variedavailabilityofN(Chapter2).Plantnitrogenstatusandcropgrowthcycleboth
affectRUE,inadditiontotheeffectsofotherfactors(Green,1987;MuchowandDavis,
1988;SinclairandHorie,1989;Trapanietal.,1992).
TheRUEhasbeenusedtoexplaingenotypicdifferencesinpotatounderdiverse
environments (Sibma,1970,1977;VanderZaagandBurton,1978;MacKerronand
Waister, 1985a; Van der Zaag and Doornbos, 1987; Vander Zaag and Demagante,
1987;TrebejoandMidmore,1990;Komanetal.,1996).VanderZaagandDoornbos
(1987)concludedfromtheirtrialswith19cultivarsthatcultivardifferencesintuber
dry matter yield were mainly due to variation in RUE under varied growing
conditions.Itisconcludedthatanalysisofyieldvariationbetweenpotatogenotypes
can be obtained by interpreting that variation in terms of accumulated light
absorption,averageRUE,andharvestindex.3.4.NitrogenUseEfficiency(NUE)
Highlysignificant(P<0.01)effectsofgenotype(standardcultivarsandF1segregating
population) were observed for NUE (data not shown). Mean estimates for NUE
rangedbetween69.6to101.8gDMg‐1N(Table3.1).NUEvariedsignificantly(P<0.05)
among the standard cultivars. NUE values were comparatively higher for late
maturing cultivars like Karnico and Astarte than for mid‐early and early cultivars
suchasBintjeandPremière,respectively(seealsoZebarthetal.,2004).Theseeffects
weremainlyassociatedwithdifferencesinmaturitytype(VanKempenetal.,1996).
Latematuringcultivarscombinealongcanopycoverwithalongtuberbulkingperiod
(ED)andthereforeachievemoretuberdrymatteryield(wmax)perunitofNuptake
thanmidearlyandearlymaturingcultivars(Zebarthetal.,2008).Previousresearch
has also demonstrated that there is significant variation in crop uptake and use
efficiencyofNamongcommercialpotatocultivarsandadvancedclones(Kleinkopfet
al.1981;Lauer1986;Sattelmacheretal.,1990;PorterandSisson,1991a,b;Johnson
et al. 1995; Errebhi et al., 1998a,b, 1999; Sharifi et al., 2007; Zebarth et al., 2004,
2008).
MeancomparisonofNUEbetweentwoparents(SHandRH)andwithintheF1
populationisgivenby(Table3.2).NUEintheF1populationwas,onaverage,68.2g
Chapter3
100
DMg‐1Nwhichwasclosetothevaluesofthetwoparents(SH:69.6gDMg‐1N;RH:
67.2 g DM g‐1N). However, there was a very wide variation in NUEwithin the F1
population; it ranged from 54.4 g DM g‐1N to 81.6 g DM g‐1N. This wide range
suggested a transgressive segregation in F1 population for NUE (Fig. 3.3), which
could be further genetically manipulated for improving N use efficiency
characteristicsinpotato.
Therewassignificant(P<0.05)variationinNUEamongthesixenvironmentsfor
thestandardcultivars(Table3.3),aswellas fortheF1segregatingpopulation(Fig.
3.4); trendswereconsistent for the twogroupsofgenotypes.ThehighestNUEwas
recordedinExp3andthelowestinExp5,inlinewithlowertuberNuptake(11.3g
m‐2) observed in Exp 3 and higher tuber N uptake (15.3 gm‐2) observed in Exp 5
(Chapter2).ResponsestoNcanvarygreatlyfromsitetositeandfromyeartoyear.
Theydependonthecapacityof thesoil tosupplyNwhenthecropneeds it(Meyer,
1998)andonthecapacityofthecroptomakeefficientuseofthatN.
The NUE also varied within the experiments, with wide ranges of variation
observed in Exp 6 (Fig. 3.4). This might be due to the indirect effects of different
maturitygroupswithintheF1population.Thedifferentdurationsofthecropcycle,
asexpressedbymaturityclasses,isanobviousfactoronemightexpecttoaffecttheN
responsetoo(Vos,2009).
3.5.Phenotypic,geneticandenvironmentalvariances
Table 3.4 presents estimated values of phenotypic, genetic and environmental
variances for all the parameters and the derived traits in the F1 population. The
results revealed considerable phenotypic and genetic variances for all the traits
studied. All genetic and environmental components of variation were significant
(P<0.01)(Table3.4).
Thegeneticvariancecomponentcontributedamajorportiontothephenotypic
variance in traits cm, tE, andED (Table 3.4). The contribution of the environmental
variancetothephenotypicvariancewasrelativelylargeinwmax,RUE,andNUE(Table
3.4), probably because these traits were sensitive to nitrogen as we purposefully
appliedvarieddosesofnitrogenforcreatingcontrastingenvironments(seeMaterials
andMethods,Chapter2).Anumberofstudieshavealsoshownavaryingresponseto
ratesofNonthedrymatteryieldproductioninpotatocrops(e.g.PorterandSisson,
1991b;Maieretal.1994;Feibertetal.1998;Belangeretal.2000).
Estimates of phenotypic (CVPh) and genetic (CVG), and environmental (CVE)
coefficients of variation for traits across the six experiments are presented in
Table 3.5. Estimates of CVPh ranged from 19.5% to 54.7%. These estimates were
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
101
Table3.4.Variancecomponents fordifferenttraitswithintheF1populationacross
allsixexperiments.
Parameter σ2Ph σ2E σ2G σ2ε
cm(gtd‐1) 297.5 107.6** 120.6** 69.2tE(td) 253.9 51.1** 169.1** 33.7
ED(td) 301.0 65.1** 197.3** 38.6
RUE(gDMMJ‐1) 0.34 0.15** 0.09** 0.10
NUE(gDMg‐1N) 177.2 125.9** 20.7** 30.6
wmax(gm‐2) 47025 14706** 9902** 22417**Significantat1%.
σ2Ph=phenotypicvariance,σ2E=environmentalvariance,σ2G=geneticvariance,σ2ε=
residualvariance.
smallestforNUEandhighestforED.TraitswithhighCVPhexhibitlargetotalvariance
andareusefulasselectioncriteriainbreedingprovidedthetraitisalsoheritable.The
CVGestimateswerehigherthanCVEestimatesforalltraitsinvestigated.However,the
ratioofCVGoverCVEwasmuchgreaterintraitsNUE,te,andED,followedbyRUEand
wmax, whereas the lowest ratios were observed in cm. Our results indicated that
significant genetic variability existedwithin the F1 population formost traits. It is
therefore possible to utilise this wide genetic variability available for a breeding
programme aimed at improving tuber bulking dynamics, RUE,NUE, and ultimately
tuberdrymatterproduction.
Table 3.5. The phenotypic coefficient of variation (CVPh ), genetic coefficient of
variation (CVG), environmental coefficient of variation (CVE), and broad‐sense
heritability(H2)ofdifferenttraitswithintheF1populationacrossallsixexperiments.
Parameter ¶Mean CVPh(%) CVG(%) CVE(%) H2(%)
cm(gtd‐1) 35.5 48.6 30.9 29.2 91.3
tE(td) 51.0 31.3 25.5 14.0 96.8
ED(td) 31.7 54.7 44.3 25.5 96.8
RUE(gDMMJ‐1) 2.1 27.1 17.9 13.9 84.2
NUE(gDMg‐1N) 68.1 19.5 16.5 6.7 80.2
wmax(gm‐2) 957 22.7 12.7 10.4 72.6¶GrandmeanoftheF1segregatingpopulationacrossallsixexperiments.
Chapter3
102
3.6.GGEbiplotanalysisGGE biplot analysis allows visual examination of the relationships among the test
environments, genotypes, and the GE interaction (Yan, 2000). As N is the keyenvironmental variable affecting the important yield determining components of
potato(Honeycuttetal.,1996),herewediscussonlytheresultsfortraitNUEamong
the fourexperiments(Exps3‐4).TheGGEbiplotsrevealedthat the1standthe2nd
principalcomponentsaccountedfor84.04%oftheGEvariation(Fig.3.5).For any particular environment, genotypes can be compared by projecting a
perpendicular fromthegenotypesymbols to theenvironmentvector, i.e.genotypes
thatare furtheralonginthepositivedirectionof theenvironmentvectorarebetter
Figure3.5.GGEbiplot chart fornitrogenuseefficiency (NUE) inanF1 segregating
population.AECindicatesaverageenvironmentcoordinate(Yan,2001;YanandKang,
2003).Alinethatpassesthroughthebiplotoriginandaverageenvironmentindicates
themeanperformanceofgenotypes.
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
103
performing and vice versa. The biplot chart indicated that genotypes performed
exceptionallywell ineither loworhighinputenvironments(Fig.3.5).Thissuggests
that selection strategies can be developed for varieties with improved NUE. The
resultsfurthershowedthattwoenvironments(Exps4and5)weregroupedtogether.
Thissuggests that theseenvironmentswerehighlycorrelatedandrelativelysimilar
inthemannertheydiscriminateamonggenotypes.Theresultsfurtherrevealedthat
environments (Exps 4 and 5) fell close to the origin. This could mean that these
environments might have little variability across genotypes (Kroonenberg, 1997).
However,markersofenvironments(Exps3and6)werestandingfurthestapartfrom
the originwhichmight suggest that this environment causedmaximum variability
acrossthegenotypes.Theseenvironmentsmightthereforebethemaincontributorto
theoverallGEonaccountof their lowestandhighNavailability, respectively (seeTable2.1inChapter2)thantheotherenvironments.
3.7.Estimatesofbroad‐senseheritabilityAll estimates of broad‐sense heritability (H2) (eqn (3.7)) were very high and they
ranged from80.2 to96.8%(Table3.5). Inaprevioussectionweshowed that traits
likewmax, RUE, andNUEwere sensitive to environment.However, these traits also
had very high heritability estimates, which could mean that they can respond to
selection (Falconer and Mackay, 1996). High heritability estimates illustrate that
thesetraitshaveastronggeneticbasis,hencecouldbereliablyassessedandusedin
breeding for resource (radiation, N) use efficiency as well as tuber dry matter
productioninpotato.
3.8.Phenotypicandgeneticcorrelationsofmodelparametersandthesecondary
traits
Table 3.6 illustrates the phenotypic correlation coefficients among all the model
parametersandsecondary traitswithin theF1segregatingpopulationacrossall six
experiments.Allphenotypiccorrelationswerehighlysignificant(P<0.01)(Table3.6).
TheresultsshowedstrongnegativephenotypiccorrelationsbetweencmandtE(r=‐
0.83) and between cm andED (r= ‐0.84). These results suggest trade‐off between
tuberbulkingrateanddurationoftuberbulking.AsmentionedearliertBwasstable,
which means that ED was almost exclusively determined by tE; so, unsurprisingly
there was a strong positive correlation between tE and ED. The results further
revealed negative correlation (r = ‐0.37) between RUE and NUE. This suggested a
trade‐off between RUE and NUE, mainly caused by their negative and positive
Chapter3
104
Table 3.6. Phenotypic (lower triangle) and genetic (upper triangle) correlation
coefficientsamongallpairwisecomparisonsofdifferenttraitsacrosssixexperiments
ofanF1populationofpotato.tdstandsforthermalday.
Parameter cm tE ED RUE NUE wmax
cm(gtd‐1) − ‐0.91** ‐0.93** 0.95** ‐0.41** ‐0.20**
tE(td) ‐0.83** − 1.00** ‐0.91** 0.65** 0.66**
ED(td) ‐0.84** 1.00** − ‐0.93** 0.64** 0.64**
RUE(gDMMJ‐1) 0.69** ‐0.65** ‐0.65** − ‐0.75** ‐0.25**
NUE(gDMg‐1N) ‐0.28** 0.53** 0.52** ‐0.37** − 0.43**
wmax(gm‐2) ‐0.10** 0.55** 0.53** ‐0.02** 0.43** −**Significantat1%,NSNon‐significant.relationshipswithED,respectively(Table3.6).
Therewereweakbutnegative (r= ‐0.10)phenotypic correlationsbetweencm
andwmax(Table3.6).However, theresults indicatedstrongandpositivephenotypic
correlationsbetweentE,ED,andwmax(i.e.r=0.55and0.53,respectively).Fromthese
results, it seems that both rate and duration of tuber bulking are of importance in
determining final yield of potato crop. The positive role of NUEwas evident from
theseresultsduetoitspositivephenotypiccorrelationwithwmax(i.e.r=0.43).This
suggeststhatgenotypeswithhighNUEmayexhibithightuberyield.Theunderlying
relationships of wmax with important traits are elaborately discussed in the next
section.
Table 3.6 also illustrates the genetic correlation coefficients between the
model parameters and secondary traits within the F1 population. The results of
geneticcorrelationswereinlinewiththoseofphenotypiccorrelations.Asawholethe
coefficientvaluesforgeneticcorrelationswerecomparativelyhigherthanphenotypic
correlations.3.9.Pathcoefficientanalysis
Table 3.7 presents the results of path coefficient analysis describing thedirect and
indirect effects of different traits on tuber dry matter yield (wmax), while Fig. 3.6
presents the path coefficient structural model describing important relationships
among selected traits.The traitsAsum,RUEand cmhad thehighestdirect effects on
tuberdrymatteryield(wmax)(Table3.7).Ontheotherhand,very lowdirecteffects
onwmaxwereobservedfortraitsEDandNUE(i.e.0.06and‐0.04,respectively).The
results further illustrated that higher valueof direct effect ofAsum onwmaxwas the
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
105
Table3.7.Pathcoefficientanalysisofdirectandindirecteffectsofdifferenttraitson
thetuberdryyield(wmax)ofanF1population.tdstandsforthermalday.
Variable †Effect
Asum(td%)Directeffect 1.34
Indirecteffectvia cm ‐0.20ED 0.05
RUE ‐0.47NUE ‐0.02
Totalcorrelation 0.69cm(gtd‐1)
Directeffect 0.32Indirecteffectvia
Asum ‐0.85ED ‐0.05
RUE 0.47NUE 0.01
Totalcorrelation ‐0.10ED(td)
Directeffect 0.06Indirecteffectvia
Asum 1.21cm ‐0.27
RUE ‐0.46NUE ‐0.02
Totalcorrelation 0.53RUE(gDMMJ‐1)
Directeffect 0.69Indirecteffectvia
Asum ‐0.91cm 0.22ED ‐0.04
NUE 0.01Totalcorrelation ‐0.02
NUE(gDMg‐1N) Directeffect ‐0.04
Indirecteffectvia Asum 0.78cm ‐0.09ED 0.03
RUE ‐0.25Totalcorrelation 0.43
†Acrosssixenvironments.
Chapter3
106
AsumRUE
cm
Yield (wmax)
NUE
ED
1.34
0.59
-0.04
-0.37 -0.79
-0.720.52
0.90-0.28
-0.65 0.69
-0.84
0.320.06
0.69
Figure3.6.Pathcoefficientstructuralmodeldescribingdirectandindirecteffectsof
different traits on the tuber dry yield (wmax) across six experiments for an F1
segregatingpopulationofpotato.Thesolidlinerepresentsthecorrelationcoefficient
betweentwopredictorvariables;dashedlinerepresentsthepathcoefficientfromthe
predictorvariabletoresponsevariable(wmax).resultofsignificant,strongpositivecorrelationofAsumwithED(r=0.90)andNUE(r
= 0.59). On the other hand significant, strong, and positive correlations (r= 0.80)
between RUE and cmwere reflected in their higher values of direct effect onwmax.
Resultfurtherindicatedthattotalcorrelation(i.e.sumofdirectandindirecteffects)
betweenRUEandwmaxwasonly ‐0.02.Thiswasmainlydue to the strongnegative
indirecteffect(‐0.91)ofAsumonRUE.Thetotalcorrelationbetweencmandwmaxwas
alsolow(‐0.10). Inthiscasethestrongindirectnegativeeffect(‐0.85)ofAsumoncm
alsoplayeditsrole.
The above resultswere further supportedby the strongnegative correlations
between RUE and Asum (r = ‐0.79) and Asum and cm (r = ‐0.72) (Fig. 3.6). These
suggested that genotypes with higher Asumexhibit slow tuber bulking rate in the
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
107
linearphaseandmaybelessefficientinconvertingtheradiationinterceptedintodry
matteryieldoftubers.Thiscouldberelatedwiththeassimilationofdrymatterand
itsdistributionwithintheplant.Higherinvestmentintermsofbiomassallocationto
vegetative organsmay give highAsum and thereby higher total biomass, but on the
other hand a relatively low proportion may be used for the production of tubers,
especiallyifthemaintenancerequirementsarehigh.Excessivevegetativegrowthcan
be compensated to only a limited extent by redistribution of dry matter from
vegetativepartstotubers(VanHeemst,1986).
BasedontheseresultsitcanbestronglyconcludedthatAsum,RUE,andcmcould
be the traits having the strongest influence on the temporal dynamics of yield
formationinpotato.Fromabreedingperspective,anidealgenotypeshouldhavean
optimalAsum, without compromising RUE and cm. Our path analysis between traits
indicated the direction andmagnitude of correlated responses to selection and the
relative efficiency of indirect selection. In addition, our results suggest that while
using these traits as a criterion for selection, other causal relationships must be
consideredsimultaneously.
3.10.QTLdetection
Intotal16QTLswereidentifiedonbothSHandRHparentalgenomesacrossallsix
environments. In the SH genome, three QTLs were associated with two linkage
groups(IandV).ThirteenQTLslinkedtosevenlinkagegroups(IB,II, III,IV,V,VIII,
andVIII) on theRH genome.Table 3.8 describes thenumber ofQTLsdetectedper
parentalchromosome.
Table3.9summarisesthelistofQTLsdetected,theirparentalchromosomesand
mappositions and their characteristics (i.e. additive effects and variance explained
(R2))foreachofthetraitinvestigatedforindividualenvironment.AllQTLsdetected
were significant at (P<0.05)with –log10(P) values ranging from3.52 to 67.99. The
total fraction of phenotypic variance explained by effects of individual QTL ranged
from<0.1% to79%.Thepercentageofphenotypic variancewasevenhigherwhen
consideringtheirglobaleffects(rangingfrom27%to71%)(Table3.9).
ThreeQTLsweredetectedforcm(Table3.9).TheseQTLswereassociatedwith
linkage groups (RHV andRHVIII). TwoQTLsweredetected inExps1 and3with
63%and35%associatedcombinedQTLphenotypicvariance,respectively.Theother
environmentsshowedonlyoneQTL.ThemajorQTL116_5_17onlinkagegroupRHV
wascommonlydetected inmostofenvironments.ThisQTLhadmaximumnegative
additiveeffectthatrangedfrom21.488gtd‐1 to53.663gtd‐1andexplained24%to
57%phenotypicvariance.Theother twoadditionalQTLs(188_8_83and189_8_83)
Chapter3
108
Table3.8.DistributionofQTLsdetectedonparentalgenomes.
SH RH
Linkage
groupNo.ofQTLs
Linkage
groupNo.ofQTLs
I 2 IB 1
V 1 II 3
III 1
IV 1
V 4
VIII 2
X 1
Total 3 13onlinkagegroupRHVIIIhadpositiveadditiveeffects(i.e.23.719gtd‐1and12.058g
td‐1)with7%to13%associatedphenotypicvariance,respectively.
TwoQTLsweredetectedfortEandEDassociatedwithtwolinkagegroups(RH
IBandRHV)(Table3.9).OneQTLwasdetectedinalltheenvironmentsapartfrom
Exp 4 where two QTLs were detected. The phenotypic variance associated with
combinedQTLsdetected inExp4wasabout70%.QTL116_5_17on linkagegroup
RHVwasdetectedconsistently inall theenvironmentswithassociatedphenotypic
variancerangingfrom62%to79%.Here,thisQTLhadamaximumnegativeadditive
effect that ranged from ‐24.133 td to ‐66.019 td. The additional QTL 48_1_71 on
linkagegroupRHIBhadpositiveadditiveeffectsrangingfrom9.561tdto10.369td.
However, phenotypic variance explained by
theseadditionalQTLswaslessthan1%.
InthecaseofRUE,atleastoneQTLwasdetectedinmostofenvironmentsapart
fromExps2and6,wheretwoQTLsandnoQTLsweredetected,respectively(Table
3.9).ThetwoQTLsfoundinExp2jointlyexplained47%ofthephenotypicvariance.
The major QTL (116_5_17) on linkage group RH V was consistently found in
environmentswithadditiveeffectsrangingfrom0.641gDMMJ‐1to1.732gDMMJ‐1
with24%to55%associatedphenotypicvariance.
In total seven individual QTLs were detected for NUE (Table 3.9). The
distributionofQTLsweresuchasthreewerefoundinExp3,twoinExps5and6and
oneinExp4(Table3.9).ThephenotypicvarianceassociatedwithmultipleQTLsper
environmentrangedfrom27%to56%.AmongtheQTLs,116_5_17atposition18.2
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
109
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
c m(gtd
‐1)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
24.97
38.833
0.57
0.63
189_8_83RHVIII
PAT/M
AGG_149.8__8_83
74.5
3.62
12.058
0.07
2116_5_17
RHV
EAGAMCTC_470__5_17
18.2
22.93
53.663
0.56
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
6.84
35.736
0.24
0.35
188_8_83RHVIII
PAG/M
AGC_298.8__8_83
76.1
4.01
23.719
0.13
4116_5_17
RHV
EAGAMCTC_470__5_17
18.2
24.54
51.958
0.57
5116_5_17
RHV
EAGAMCTC_470__5_17
18.2
17.74
21.488
0.48
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
18.8
34.06
0.50
t E(td)
1116_5_17
RHV
EAGAMCTC_470__5_17
18.2
67.62
‐55.624
0.79
2
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
36.78
‐54.458
0.68
3
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
19.376
‐38.459
0.63
4
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
34.7
‐38.968
0.65
0.70
48_1_71
RHIB
EACAMCGT_296.2__1_71
33.9
3.67
9.561
<0.1
5
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
51.11
‐24.133
0.63
6
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
52.33
‐49.912
0.74
Table3.9.Maincharacteristicsofquantitativetraitloci(QTL)identifiedfordifferenttraitswithinthe‘SHRH’population
perindividualexperiment(i.e.environment.).DatagivenintablearefromtheCIMmappingmethod.QTLsmarkedasbold
aredetectedonlybytheCIMmethod,otherwisebyboththeCIMandSIMmethods.Exp.,experiment;position,positionof
maximum
–log 1
0(P);a,additiveeffectofthepresenceofparentalalleleatamarker;R
2 ,theindividualcontributionofone
QTLtothevariationinatrait;globalR
2 ,thefractionofthetotalvariationexplainedbyQTLsofthesametraitwithinsingle
environm
ent;td,thermalday.Sym
bols‘−’and‘*’m
eanlackofQTLanddata,respectively.
Chapter3
110
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
ED(td)
1116_5_17RHV
EAGAMCTC_470__5_17
18.2
67.99
‐60.873
0.79
2116_5_17RHV
EAGAMCTC_470__5_17
18.2
37.54
‐60.247
0.68
3116_5_17RHV
EAGAMCTC_470__5_17
18.2
18.756
‐41.077
0.62
4116_5_17RHV
EAGAMCTC_470__5_17
18.2
36.23
‐43.013
0.66
0.71
48_1_71
RHIB
EACAMCGT_296.2__1_71
33.9
3.7
10.369
<0.1
5116_5_17RHV
EAGAMCTC_470__5_17
18.2
52.34
‐66.019
0.74
6116_5_17RHV
EAGAMCTC_470__5_17
18.2
52.35
‐52.988
0.74
RUE
1116_5_17RHV
EAGAMCTC_470__5_17
18.2
22.72
1.732
0.55
(gDMMJ‐1)
2116_5_17RHV
EAGAMCTC_470__5_17
18.2
14.87
1.259
0.43
0.47
121_5_46RHV
EAACM
CAG_231.8__5_46
52.6
6.74
0.402
0.15
3116_5_17RHV
EAGAMCTC_470__5_17
18.2
9.862
1.329
0.39
4116_5_17RHV
EAGAMCTC_470__5_17
18.2
16.32
1.135
0.45
5116_5_17RHV
EAGAMCTC_470__5_17
18.2
6.67
0.641
0.24
6
−−
−−
−−
−
Table3.9.(Continued)
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
111
Parameter
Exp.
QTL
Linkage
group
Markername
Position
(cM)
‐log
10(P)
aR2
GlobalR
2
NUE
1*
* *
* *
* *
(gDMg‐1N)
2*
* *
* *
* *
3116_5_17
RHV
EAGAMCTC_470__5_17
18.2
3.6
‐8.79
0.12
0.39
84_3_3
RHIII
PAT/M
AAC_298.3__3_3
04.29
8.881
0.11
204_10_34
RHX
EAACM
CCA_216.8__10_34
31.4
4.99
‐10.835
0.17
475_2_51
RHII
EAGTMCAC_249__2_51
50.9
3.83
‐8.352
0.15
56_1_2
SHI
PAC/MACT_232.4__1_2
6.6
3.293
‐6.291
0.10
0.27
68_2_32
RHII
PAT/M
AAC_570.4__2_32
32.2
4.686
‐8.005
0.16
6116_5_17
RHV
EAGAMCTC_470__5_17
18.2
18.91
‐30.187
0.48
0.56
179_5_77
SHV
PAC/MATA_201.4__5_77
77.2
3.7
10.299
0.03
wmax(gm‐2)
1117_5_37
RHV
EACAMCGT_250.1__5_37
35.7
3.82
274.016
0.15
2
131_5_55
RHV
PAC/MATA_99.4__5_55
62.6
4.36
246.053
0.18
3
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
15.84
‐630.531
0.44
0.53
81_2_75
RHII
PAC/MAGG_527.2__2_75
79.1
4.1
‐252.07
0.06
4
18_1_32
SHI
EAACM
CAG_187.9__1_32
36.3
3.52
244.584
0.13
5
−−
−−
−−
−
6
116_5_17
RHV
EAGAMCTC_470__5_17
18.2
8.94
‐458.44
0.29
0.41
99_4_35
RHIV
EACAMCTG_69.5__4_35
11.2
4.46
256.493
0.08
Table3.9.(Continued)
Chapter3
112
cMonthelinkagegroupRHVwasconsistentlydetectedinhalfoftheenvironments
explaining12%to48%phenotypicvarianceforNUE.ThiswasamajorQTLwithits
additiveeffectsrangingfrom‐8.79gDMg‐1Nto‐30.187gDMg‐1NinExps3and6,
respectively. These results indicated a threefold decrease in additive effects of this
QTLinthe lowNenvironment(i.e.Exp3;seeTable2.1inChapter2),whichmight
suggestthatallelesonpaternalchromosomeVatposition18.2cMmayreduceNUE
lessdrasticallyundersuchlowNconditions(Fig.3.4).Moreover,fouradditionalQTLs
were detected on linkage groups (RH II, RH III, SH I and SHV)with their additive
effects(‐8.005,8.881,‐6.291,and10.299gDMg‐1N,respectively).Interestingly,QTL
179_5_77atposition77.2 cMon thematernal (SH) linkagegroupVwasassociated
withapositiveadditiveeffectinExp6.Thiscouldsuggestthatallelesonthematernal
and paternal linkage groups V are associatedwith negative and positive effects on
NUE,respectively.
QTLmapping for tuberyield(wmax)perse showedsixQTLs(Table3.9).These
QTLswerescatteredoverfourparentallinkagegroups(SHI,RHII,RHIV,andRHV).
ThephenotypicvarianceexplainedbyQTLsrangedfrom(6%to44%).OneQTLwas
detectedinExps1,2,and4,whereastwoQTLsweredetectedinExps3and6.NoQTL
couldbedetectedinExp5.ThemultipleQTLsfoundinExps3and6jointlyexplained
53% and 41% of the phenotypic variance, respectively. Among the total QTLs
detectedforthiswmax,(116_5_17)onlinkagegroupRHVwasamajorQTLdetectedin
most of environments (i.e. Exps 3 and 6) with its additive effects ranging from
‐458.44 gm‐2 to ‐630.531 gm‐2with 29% to 44% associated phenotypic variance,
respectively(Table3.9).TheseresultsareinlinewithNUEandsuggestalsothatQTL
(116_5_17) at position 18.2 cM on paternal linkage group V is sensitive to the
environmentparticularlyNasnegativeeffectscausedbyallelesassociatedwiththis
QTLchangedinmagnitudewithrespecttoNavailability.Itisinterestingtonotethat
meanwmaxforExp3wasevenhigherthanExp6withhighNavailability(Table3.3).
As previouslymentioned, variability among the genotypeswas higher in Exp 3 for
most traits (Fig. 3.4). It would be expected that the some genotype were most
effective in thisenvironmentespecially forNUEas suggestedby thebiplotanalysis
(Fig.3.5).Thereforeafocusonwhyparticulargenotypesperformexceptionallywell
in loworhighinputsituationscouldenableselectionstrategiestobedevelopedfor
improvedvarieties.
InordertosummarisetheQTLmappingresults,QTLswithmajoreffectswere
associatedwithpaternal(RH)linkagegroupV,wheremaximumnumberoffourQTLs
wasdetected(Table3.8and3.9).OneparticularQTL(i.e.116_5_17)onpaternal(RH)
linkagegroupVatposition18.2cMwasdetectedforalltraitswithamajoradditive
effect and explainedmost of the total phenotypic variance (Table 3.10). Additional
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
113
QTLs with minor effects was mostly associated with only paternal (RH) linkage
groups for traits (cm, tE, and ED), whereas both maternal (SH) and paternal (RH)
linkagegroupswereassociatedwithtraitsNUEandwmaxforminorQTLs(Tables3.9
and3.10).OurresultsareinlinewiththosebyVandenBergetal.(1996),whoalso
reported thatmost of the QTLs had small effects, but a QTLwithmain effect was
found on chromosome V. It was further noted that paternal QTL (116_5_17) was
associatedwithitsnegativeadditiveeffectsformostofthetraitsincludingtuberyield
(wmax)perseexceptforcmandRUE,wherethisQTLshowedthepositiveeffects.This
indicatesthatRHallelesforthisQTLcausesynergisticeffectsduringtheearlyphase
ofplantgrowthwhenthetuberbulkingratesandRUEareattheirmaximum.
Itwasevident fromourresults thatvariation in tuberyield isassociatedwith
variationinbulkingrate,durationofcanopycover,andassociatedextentofradiation
interception (Fig. 3.6). However, these components are not physiologically
independent as genotypeswith a large tuber bulking ratemay effectively limit the
cropgrowthandduration(viaenhancedinternalplantcompetition)leadingtotheir
identified'earliness'.
We also observed co‐localisation of QTLs with many traits. For instance
clustering ofmanyQTLswere found on position18.2 cMon paternal (RH) linkage
group (Table 3.11).Heremost of the traits (e.g. cm, tE, andED)were tightly linked
withQTL116_5_17inmajorityoftheenvironments.ThiscouldmeanthatthisQTLis
playingapleiotropicroleindeterminingthesetraits.Thestronggeneticcorrelations
between these traits confirm these relations (Table 3.6). This may indicate the
difficulties of manipulating correlated traits simultaneously. However,
QTLs with similar behaviours could also be interesting targets for breeding
programmes as they aremore likely to be stable under various environments.Our
Table3.10.ListofparentallinkagegroupswithmajorandadditionalminorQTLs.
Parameter SHlinkagegroup RHlinkagegroup
MinorQTLs MajorQTLs MinorQTLs
cm − V VIII
tE − V IB
ED − V IB
RUE − V −
NUE I,V V II,III,X
wmax I V II,IV
Chapter3
114
resultsalsoindicatedanumberofindependentQTLsmainlyforNUE.TheseQTLsdid
notcoincidewithothertraits(Table3.12).TheseQTLshowever,withminoreffects
couldbeofgreatvalueforbreedingforNUE.
Several authors have indicated that some yield QTL coincide with those for
component traits, whereas other yield QTL map independently from component
traits (Xiao et al., 1995; Bezant et al., 1997). Few QTLs were expressed in one
environmentbutnotintheother(Table3.13).ThiswasmostlyevidentfortraitsRUE,
NUEandwmax.ThelowerrepeatabilityofsomeQTLsovertheenvironmentssuggests
QTLE interaction. Our results support this as for these traits the environmentalvariance component contributedmajorly to the total phenotypic variance for these
traits (Table 3.4). QTLs controlling such traits often show low stability (Veldboom
and Lee, 1996; Reymond et al., 2004). Several researchers have identified loci that
interactedwiththeenvironmentindifferentplantspeciese.g.yieldinbarley(Yinet
al.,1999a,b;Teulatetal.2001;Voltasetal.,2001).
OnlyoneQTL116_5_17onRHVshowedupinalltheexperiments(Table3.14).
ThisQTLwas thereforestableacross theenvironmentsandthereforedidnotshow
muchoftheQTLE.SuchQTLscouldbeusefulformarkerassistedbreeding.4.ConclusionsInthischapterwetriedtoelucidatetheonset,rate,anddurationoftuberbulkingas
yield‐determining, complex quantitative traits in a set of varieties covering awide
range ofmaturity types and a well‐adapted diploid F1 segregating population.We
presented a physiological and quantitative genetic analysis of the traits underlying
thedynamicsoftuberbulking.Ourmodeldescribedwellthetuberbulkingdynamics
andgaveinsightintothevitalunderlyingcomponenttraitsinfluencingthetuberdry
matter production. Our results showed that tuber bulking growth rate (cm) was
comparativelyhigherforearlymaturingcultivarsfollowedbylatecultivars.However,
theeffectiveperiodoftuberbulking(ED)waslongerforlatematuringgenotypes.Asa
result, tuberdrymatteryield (wmax)washigher in latematuringgenotypes than in
mid‐lateandearlygenotypes.
We also studied resource (radiation and N) use efficiencies and their
relationships with tuber dry matter yield production and with other important
physiological traits. The RUE values were higher for early maturing genotypes
followed by mid‐late and late genotypes. Mean NUE values were higher for late
maturing genotypes than formid early and early cultivars. Latematuring cultivars
have maximum canopy cover as well as a long tuber bulking period (ED) and
thereforethehighesttuberdrymatteryields(wmax)perunitofNuptake.
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
115
QTL
Linkage
group
Markername
Position
(cM)
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
Exp.6
48_1_71
RHIB
EACAMCGT_296.2__1_71
33.9
t E,ED
116_5_17RHV
EAGAMCTC_470__5_17
18.2
c m,tE,
ED,RUE
c m,tE,
ED,RUE
c m,tE,
RUE,
NUE,
wmax
c m,tE,
ED,RUE
c m.,t E,
ED,RUE
c m,tE,
ED,
NUE,
wmax
Table3.12.ListofindependentQTLs(i.e.QTLsdetectedonlyonceforaparticulartraitinanyenvironm
ent).
QTL
Linkage
group
Markername
Position
(cM)
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
Exp.6
6_1_2
SHI
PAC/MACT_232.4__1_2
6.6
NUE
18_1_32
SHI
EAACM
CAG_187.9__1_32
36.3
wmax
179_5_77
SHV
PAC/MATA_201.4__5_77
77.2
NUE
68_2_32
RHII
PAT/M
AAC_570.4__2_32
32.2
NUE
75_2_51
RHII
EAGTMCAC_249__2_51
50.9
NUE
84_3_3
RHIII
PAT/M
AAC_298.3__3_3
0
NUE
99_4_35
RHIV
EACAMCTG_69.5__4_35
11.2
wmax
117_5_37
RHV
EACAMCGT_250.1__5_37
35.7
wmax
121_5_46
RHV
EAACM
CAG_231.8__5_46
52.6
RUE
131_5_55
RHV
PAC/MATA_99.4__5_55
62.6
wmax
189_8_83
RHVIII
PAT/M
AGG_149.8__8_83
74.5
c m
188_8_83
RHVIII
PAG/M
AGC_298.8__8_83
76.1
c m
204_10_34
RHX
EAACM
CCA_216.8__10_34
31.4
NUE
Table3.11.Listofco‐localisedQTLs(i.e.QTLsweresameformorethanonetrait).
Chapter3
116
QTL Group MarkernamePosition(cM)
Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6
116_5_17 RHV EAGAMCTC_470__5_17 18.2 × × × × × ×
Table 3.13. Number of QTLs detected across six experiments (environments).
Symbols‘−’and‘*’meanlackofQTLanddata,respectively.
Parameter Exp.1 Exp.2 Exp.3 Exp.4 Exp.5 Exp.6
cm 2 1† 2 1 1 1
tE 1† 1 1 2 1 1
ED 1 1 1 2 1† 1
RUE 1† 2 1 1 1 −
NUE * * 3 1 2 2†
wmax 1 1 2† 1 − 2
TotalQTLs 6 6 10 8 6 7†QTLswithmaximumadditiveeffects.
Table3.14. List of stableQTLs across six experiments (i.e. SameQTLs detected in
majorityofenvironments).
Theprospectsofimprovingatargettraitbyselectingforcomponenttraitsare
determined by the genetic variation for the particular component traits and their
correlationwiththetarget trait.Highgeneticvariabilityalongwithhighheritability
wasrecordedformostofthetraits.Phenotypicandgeneticcorrelationswerehigh(r
>0.50)forthesetraits.Thephenotypicandgeneticcorrelationsamongmodeltraits
suggest a trade‐off between cm and ED and between RUE and NUE. Path analysis
showedthatcm,RUE,andAsumhadamajorinfluenceonwmax.
The genetic parameters found in this study indicated existence of significant
geneticvariabilitywithin theF1population formost traits studied. Itwaspossible,
therefore, to further analyse this wide genetic variability available, which could
potentiallybeexploited inbreedingprogrammesaimedat improving tuberbulking
dynamics,RUE,NUE,andultimatelytuberdrymatterproduction.
TheAFLPmarkerswerethereforeusedtoperformQTLmappingofthemodel
traitsandresource(radiation,N)useefficiencies.Intotal16QTLswereidentifiedon
both SH and RH parental genomes across all six environments. QTLs with major
Geneticvariationintuberbulkingandresourceuseefficiencyofpotato
117
effects were associatedmainlywith paternal (RH) linkage group V. One particular
QTL (116_5_17) on this linkage groupwas detected for nearly all the traitswith a
majoradditiveeffectandexplainedmostofthetotalphenotypicvariance.Additional
minorQTLsweremostlyassociatedwithonlypaternal(RH)linkagegroupsfortraits
(cm,tE,andED),whereasbothmaternal(SH)andpaternal(RH)linkagegroupswere
associatedwithtraitsNUEandwmaxforminorQTLs.
Ourquantitativeapproach in combinationwithAFLPmarkers identifiedQTLs
that could be useful in the development of marker assisted breeding strategies in
potato yield improvement. It was evident from our results that variation in tuber
yield is associated with variation in bulking rate, duration of canopy cover, and
associated extent of radiation interception. However, these components are not
physiologically independent as genotypes with a large tuber bulking rate may
effectively limit the crop growth and duration (via enhanced internal plant
competition)leadingtotheiridentified'earliness'.
CHAPTER4 Model‐basedevaluationofmaturitytypeofpotatousingadiversesetof
standardcultivarsandasegregatingdiploidpopulation*
M.S.Khan,H.J.vanEck,P.C.Struik
AbstractThe objective of this chapter is to evaluate the performance of the conventionalsystemofclassifyingmaturity type inpotatoand toprovideaconceptofmaturitytypebasedoncropphysiology.Wepresentanapproachinwhichphysiologicaltraitsareusedtoquantifyandassessmaturity typeunambiguously forasetofvarietiescoveringawiderangeofmaturityclassesandadiploidF1populationseparatingformaturity and well‐adapted to Dutch growing conditions, both grown in sixenvironments.Wedefinedphysiologicalmaturitybasedonfourtraits:thedurationofmaximumgreencanopy,theareaunderthegreencanopycoverprogresscurve,andtherateanddurationoftuberbulking.Theresultsindicatedthatphysiologicalmaturity typecriteria tended todefinematurity classes lessambiguously than theconventional criterion. Moreover, the conventional criterion was subject to morerandom noise and lacked stability and/or repeatability compared with thephysiological traits. The physiological maturity criteria also illustrated thephysiologicaltrade‐offsthatexistedbetweentheselectedtraitsandunderlinedthesubtle complexities in classifying maturity type. This study highlighted thecapabilities of differentmaturity type criteria in discriminating between differentmaturityclassesamongthelargesetofgenotypes.Ournewapproachinvolvingkeyphysiological traits could be beneficial in offering physiology‐based criteria to re‐definematuritytype.Animprovedcriterionbasedonimportantphysiologicaltraitswould allow relating the maturity to crop phenology and physiology. This newcriterionmaybeamenable to furthergeneticanalysisandcouldhelp indesigningstrategiesforpotatoideotypebreedingforgenotypeswithspecificmaturitytypes.Keywords:Potato(SolanumtuberosumL.),tuberbulking,earliness,maturitytype,breeding,segregatingpopulation,cultivarchoice,statisticaltools.
_____________________________________*AcceptedinPotatoResearch(2012).
Chapter4
120
1.IntroductionCropsundergosequentialdevelopmentalphasesfromemergencetosenescencethat
are characterised by their chronological age, but also by their phenology and
reproductivecapacity.Inmostannualcropplants,thetransitionfromthevegetative
tothereproductivephaseismarkedbytheonsetoffloweringandseedproduction;
hencefullyreproductiveplantsareconsideredmature(Bond,2000).However,inthe
case of potato (Solanum tuberosum L.), most genotypes maintain the capacity to
developnewleavesandcontinuetogrowthroughoutthemajorpartoftheirlifecycle
thankstothefactthatatleastsomevegetativemeristemsremainindeterminate(Vos
and Biemond, 1992; Struik and Ewing, 1995; Vos, 1995a; Almekinders and Struik,
1996; Fleisher et al., 2006). Progress tomaturity is therefore difficult to assess in
potato.
Cropdevelopmentinpotatoisacomplextraitthatmaybecomprisedofaseries
of phenological events such as completion of canopy growth, termination of
sympodialgrowth,theonsetoftuberformation,ortheonsetoftherapidincreasein
harvestindex,saggingofplants,senescenceofleaves,etc.(Struiketal.,2005;Struik,
2010). The sequential phases of growth determining the duration of phenological
stages of potato genotypes could be used to understand and define the concept of
maturitytypeandtoclearlyclassifygenotypes.
Theconventionallyuseddefinitionofmaturitytypeinpotato,however,israther
ambiguous (Haga et al., 2012). Many public and private institutions have tried to
define thematurity in potato and have come upwith their own definitions. Public
institutions such as the International Union for the Protection of New Varieties of
Plants (UPOV), and the authorities involved in granting plant breeders’ rights use
foliage maturity as descriptor among the many traits for DUS (Distinct, Uniform,
Stable) criteria.Maturity (Trait 36) in the UPOV document
(http://www.upov.int/edocs/tgdocs/en/tg023.pdf)isusingthecriterion“Thetimeof
maturity is reachedwhen80%of the leavesaredead.” Private institutions, such as
breeding companies and marketing boards release brochures to advertise the
specificitiesoftheircultivars.Maturitytypeindicationsinthesebrochuresareusually
based on the personal scale of the breeder. Furthermore, several databases on the
WorldWideWebareavailablesuchas“theEuropeancultivatedpotatodatabase”,
(http://europotato.org/display_character.php?char_no=18&character=Maturity).
Itisremarkable,thatmanyseemtoknowhowtoclassifyplantsintoaspecific
scale, and even to make conversion from one scale to another, without a proper
definition of this complex syndrome of developmental events. As a whole, the
conventional method or criterion of elucidating the maturity type used is mostly
basedonvisualobservationsmadeinthefieldatparticulartimeintervalsduringthe
Modelbasedanalysisofmaturitytypeofpotato
121
cropcycleandtransferringthatinformationtounit‐lessordinalscales.Thiscriterion
doesnothaveanybiologicalmeaningandinvolvesambiguityandmuchspeculation
in understanding the background of maturity, partly because of the different
viewpoints of growers and processors. For instance, the grower may consider
maturity as the onset of canopy senescence and/or when there is no further net
growth (Bodlaender and Reestman, 1968). The processor on the other hand may
consider the declining levels of reducing sugars in the tubers as an indication of
approaching maturity (Burton andWilson, 1970). Yet others think maturity to be
synonymouswithtuberdrymatterconcentration(BeukemaandvanderZaag,1979).
Onaccountofsuchdiscrepancies,theconventionalmaturitycriterionmayalso
notbe stable (changesof thematurity classof aparticular varietyhaveoftenbeen
reported), subject to personal bias and showing strong genotype‐by‐environment
(GE) interaction. Hence, there is a strong need for a clear and unambiguousdefinitionofthematuritytypeofpotatogenotypesbasedonaclearunderstandingof
potato physiology and of the GE. Crop physiologists, agronomists and breederswouldallprofitfromsuchacleardefinition.StudiesbyStruiketal.(2005)andStruik
(2010)haveshownthatredefiningthepotatomaturitybasedonphysiologicaltraits
ispromising.
Current state of the art in statistical knowledge and techniques has made it
possible to properly analyse large amounts of available physiological and genetic
information.Forinstance,clusteranalysisandpathcoefficientanalysisareuseful in
explorative data mining, information retrieval, and data summarisation. Cluster
analysis offers a meaningful grouping and/or sub‐grouping based on similarities
foundinthedata.Additionally,itofferswaystocharacteriseeachgroupintermsofa
clusterprototype, i.e.adataobject that is representativeof theotherobjects in the
group.Thismayhelpinpatternrecognition,targetingappropriatetreatment(s),and
studying typologies as well as finding the genetic relationships and/or diversity
amongthelargesetofgenotypes(Dunemannetal.,1994;Piluzzaetal.,2005;Arshad
et al., 2006;Ramet al., 2008). Inaddition,procedures likepath coefficient analysis
has proven to be useful in giving thorough understanding of relationships and
contributionof various characters to the target trait bypartitioning the correlation
coefficientintocomponentsofdirectandindirecteffects.Bhatt(1972)reportedthat
merelycorrelationstudiesdonotclearlyrevealsuchtypeofinformationandcanbe
misleadingifthehighcorrelationbetweentwotraitsisaconsequenceoftheindirect
effectofthetraits(DeweyandLu,1959).
Given the rich quantitative data set at hand from Chapters 2 and 3 and the
availabilityofstateoftheartstatisticaltools,thischapteraimstoevaluate,quantify,
andre‐definetheconceptofmaturitytypeofalargesetanddiversesetofgenotypes
Chapter4
122
ofpotato.Ourobjectiveistodevelopaphysiologicalapproachofassessingmaturity
typeduringthewholecourseofplantdevelopment.Thismethodshouldbereliable,
robust and should allow the phenotyping of large numbers of genotypes across
diverse environments. This study will also provide more insight into the genetic–
physiologicallinkageofmaturitytypeinpotato.
2.Materialsandmethods
2.1.Set‐upofexperiments
Datasetsforouranalysiscamefromsixfieldexperiments,performedinWageningen
(52˚Nlatitude),theNetherlands,during2002,2004,and2005withtwoexperiments
in eachyear.Theseexperimentsdiffered inenvironmental conditionsbecause they
werecarriedoutindifferentyears,ondifferentsoils,andunderdifferentNfertiliser
regimes(fordetails,seeChapter2).Theexperimentswereconductedunderstress‐
freegrowingconditionsandthestatisticaldesignsusedwererandomizedcomplete
block designs with two blocks. The planting material consisted of disease‐free,
uniform sized seed tubers of similar physiological quality from an F1 diploid
(2n=2x=24) potato population (100 genotypes) derived from a cross between two
diploid heterozygous potato clones, SH83‐92‐488 RH89‐039‐16, or simply the‘SHRH population’ (Rouppe van der Voort et al., 1997; Van Os et al., 2006). Thispopulationsegregatesformaturitytypeandiswell‐adaptedtoDutchconditions(Van
derWaletal.,1978;VanOijen,1991)thusmakingitidealforthisstudy.Asetoffive
standard cultivars (viz. Première, Bintje, Seresta, Astarte, and Karnico) covering a
widerangeofmaturitytypesunderDutchconditionswerealsoincludedinthisstudy
tobenchmarktheeffectsofmaturitytypeamongknowngenotypes.
2.2.Analyticalapproach
2.2.1.Selectionofphysiologicaltraits
Maturityisacomplexphenomenonaffectedbymanycomponentsofcropgrowthand
development. However, the variation in maturity between the genotypes could be
reflectedindifferingperiodsofattainingcriticalphysiologicalstagessuchascanopy
development,tuberinitiation,filling,andtheirtotalduration.InChapters2and3,we
developedphysiologicalapproachestoquantifythedynamicsofcanopydevelopment
Modelbasedanalysisofmaturitytypeofpotato
123
and of tuber bulking of potato during the entire crop cycle in the aforementioned
large setof genotypes.Thisworkyieldeda large setofbiologicallymeaningful and
geneticallyrelevantcomponenttraitsdirectlyrelatedtotheabilityof thegenotypes
to intercept photosynthetically active radiation and tuber dry matter production.
Most of these traits successfully explainedpart of the genetic variation inmaturity
typeandcouldbeusedtoenhancethebreedingeffortsaimingatelucidatingpotato
maturity.Inthisstudy,however,forthesakeofsimplicity,weselectedonlyfourtraits:
DP2,cm,ED,andAsumbasedontheirstronggeneticnatureandveryhighresponseto
direct selection (Chapter 6). DP2 is the duration of the phase with maximum and
constant canopy cover (in thermal days (td)). Asum is the area under whole green
canopycovercurve(intd%),cmistherateoftuberbulking(ingm‐2td‐1)andED is
thedurationoftuberbulking(intd).
2.2.2.Criteriaforre‐defininggenotypematuritytype
In the Netherlands, according to the conventional system, potato varieties are
officiallyclassifiedintofourmainmaturityclasses:veryearly,early/mid‐early,mid‐
late/late, and very late. However, for the sake of simplicity maturity classes
early/mid‐early and mid‐late/late were considered as early and late, respectively,
throughoutthetext,tables,andfigures.Genotypeswereallocatedtomaturityclasses
(veryearly,early,late,andverylate)basedoneachofthefourselectedphysiological
trait using the Ward’s minimum‐variance clustering method in SAS software (SAS
Institute Inc. 2004). A schematic representation of cluster analysis for identifying
differentmaturityclassesisdescribedbyFig.4.1.Theinputdataconsistedofmeans
of F1 genotypes, their two parents, and five standard cultivars across six
environments.
After the identification of the maturity classes, further sub‐clustering of
genotypes within each maturity class was done using the nearest neighbour
clusteringmethod.Eachsub‐clusterofgenotypeswithineachofthematurityclasses
was assigned different maturity scores according to the Netherlands Potato
ConsultativeFoundation(NIVAP,2007),forinstance,maturityscores8.0,8.5,9.0and
9.5intheclusterofveryearlygenotypes,maturityscores2.0,2.5,3.0,3.5forverylate,
etc.Table4.1describestherangesofscalesforeachmaturitytype.Finally,thiswhole
procedureresultedintore‐definedmaturitytypecriteriabasedonourfourselected
physiological traits. The scoring procedure was employed in order to make later
comparison possible between the conventional and newly re‐defined criteria of
genotype maturity type, now referred to as “physiological maturity type criteria”
throughout the remaining text. For the conventional criterion, we followed the
Chapter4
124
Figure4.1.AschematicrepresentationofclusteranalysisforsingletraitxwithinF1
population,wherex=DP2 orcmorED orAsum.Thedashed line showshypothetical
intersectionoffourmainclustersfortheselectionoffourmaturityclasses(veryearly,
early,late,andverylate).generalpracticeusedbybreeders, i.e.visualobservationsweremade inthe fieldat
particulartimeintervalsduringthecropcycleandtheinformationwastransferredto
unit‐lessordinalscales.FortheF1populationwehadthebreeders’assessments(i.e.
genotypematurityscales)basedontwoexperiments(environments)during2002.
Table4.1.Listofordinalscalevaluesfordifferentmaturityclasses.
Maturityclass Type Scalevalues
1 Veryearly 8.0,8.5,9.0,9.5
2 Early 6.0,6.5,7.0,7.5
3 Late 4.0,4.5,5.0,5.5
4 Verylate 2.0,2.5,3.0,3.5
Modelbasedanalysisofmaturitytypeofpotato
125
2.3.Statisticalanalysis
AllstatisticalanalyseswerecarriedoutusingGenstatcomputersoftware(Payneetal.,
2009). Path coefficient analysis was performed to ascertain the inter‐associations
between our selected physiological traits following the approach of Dewey and Lu
(1959).Thenon‐parametricMann‐WhitneyUtestwasusedtocheckthesignificance
ofdifferencesbetweentheconventionalandphysiologicalmaturitycriteriafortheF1
population. Descriptive statistics was performed and the relationships between
maturityscorespermaturitycriterion(i.e.conventionalandre‐definedcriteria)were
assessed using the Pearson correlation coefficient. Finally, regression analysis was
performedtocheckthecapabilityofconventionalandphysiologicalmaturitycriteria
to predict thematurity type of genotypes. As data consisted of ordinal scales, data
was log‐transformed prior performing the correlation and regression analysis to
satisfytestassumptions.
3.Resultsanddiscussion
3.1.Pathcoefficientanalysis
Wesurmisedbasedonourpreviousresults(seeChapters2and3)thatAsumcouldbe
seen as an expression of a series of underlying physiological, genetic, and
environmentalprocessesandinteractionsaffectingthematurityandyieldproduction
inpotato(cf.Vos,1995b;2009).Pathcoefficientanalysiswasthereforeaccomplished
bypartitioningthedirectandindirecteffectsofourselectedphysiologicaltraitsupon
Asum. Figure4.2presentsthepathcoefficientstructuralmodeldescribingimportant
relationshipsamongselectedphysiological traits.Amongtheselected traits,EDand
DP2hadthehighestdirecteffectsonAsum(0.69and0.51,respectively,seedashedlines
inFig.4.2).Ontheotherhand,cmshowedthelowest(0.22)directeffectonAsum.The
resultsfurtherindicatedastrongpositivecorrelationbetweenDP2andED(r=0.79)
andnegativecorrelationsbetweencmandDP2(r=‐0.63);cmandED(r=‐0.93)(Fig.
4.2).Theseresultssuggestthatgenotypeswithhighertuberbulkingrates(cm)exhibit
lower DP2 or ED and may mature early. Compared with late genotypes, early
genotypesallocatealargepartoftheiravailableassimilatestothetubersearlyinthe
growing season, leading to shorter crop cycle (Kooman and Rabbinge, 1996). In
conclusion,pathcoefficientanalysis indicated thatprogress tomaturity inpotato is
stronglyinfluencedbyourselectedphysiologicaltraits.Basedonthestrengthoftheir
directeffectsoncropmaturity,suchtraitscouldbecategorisedinfollowingorder:
ED>DP2>cm.
Chapter4
126
3.2.Descriptiveanalysisofdifferentmaturitytypedefiningcriteria
The first step in the analysis was to apply some standard descriptive statistical
methodstothedata.Table4.2showsthemeans,standarddeviations,andminimum,
maximumvaluesofgenotypesallocated inmaturityclassesunderconventionaland
ED
Asum(Maturity type)
DP2
0.79
0.51 0.69
- 0.93cm
0.22
-0.63
Figure4.2.Pathcoefficientstructuralmodeldescribingdirectandindirecteffectsof
different traits on the maturity type (defined by Asumi.e. area under whole green
canopy curve) across six experiments in an F1 population. DP2 represents the
durationofmaximumgreen canopyphase,cmandED aregrowth rateandeffective
durationofthe linearphaseoftuberbulking,respectively.Thesolid linerepresents
the correlation coefficient between twopredictor variables; dashed line represents
the path coefficient from the predictor variable to response variable (Asum). See
MaterialsandMethodsforfurtherdetails.
Modelbasedanalysisofmaturitytypeofpotato
127
Table4.2.Mean,minimum (Min),maximum (Max) values ofmaturity scores, and
percentage (%) of genotypes allocated to each maturity class for five different
maturitytypecriteriawithintheF1population(100genotypes).
Maturitytypecriterion1
andclass
Mean(±S.E.)2 Min Max%
Conventional
Veryearly 8.3(±0.1) 8.0 9.5 35.4
Early 7.0(±0.1) 6.0 7.5 53.8
Late 4.9(±0.1) 4.5 5.5 8.6
Verylate 3.0(±0.5) 2.5 3.5 2.2
DP2
Veryearly 8.3(±0.2) 8.0 9.0 7.3
Early 6.9(±0.1) 6.0 7.5 69.7
Late 4.8(±0.1) 4.0 5.5 16.5
Verylate 2.8(±0.2) 2.0 3.5 6.4
cm
Veryearly 8.6(±0.1) 8.0 9.5 18.5
Early 6.5(±0.1) 6.0 7.5 33.3
Late 4.9(±0.1) 4.0 5.5 34.3
Verylate 2.9(±0.1) 2.0 3.5 13.9
ED
Veryearly 8.7(±0.1) 8.0 9.5 52.8
Early 7.1(±0.1) 6.0 7.5 16.7
Late 4.6(±0.2) 4.0 5.5 10.2
Verylate 2.7(±0.1) 2.0 3.5 20.4
Asum
Veryearly 8.8(±0.2) 8.0 9.5 7.3
Early 7.0(±0.1) 6.0 7.5 62.4
Late 4.8(±0.1) 4.0 5.5 23.9
Verylate 2.9(±0.2) 2.0 3.5 6.41Conventional scale: based on breeders’ criteria; DP2 scale: based on duration of
maximumgreencanopycover;cmscale:basedontuberbulkingrate;EDscale:based
on effective duration of linear tuber bulking; Asumscale: based on the area under
wholegreenthecanopycurve.2MeanofF1segregatingpopulation(100genotypes)acrosssixenvironments.
Chapter4
128
physiological maturity type criteria. The mean values of maturity scaling varied
significantly (P<0.01) across fourmaturity classes (very early, early, late, and very
late) for eachmaturity type criterion. Examination of theminimum andmaximum
values illustrated that mean ranges of maturity classes were consistently similar
betweendifferentmaturitytypecriteria.Thiswasakindofcheckandprovedthatour
procedureofassigningthematurityscoreswassatisfactory.However,thepercentage
of genotypes allocated to eachmaturity classdifferedamongmaturity type criteria
(Table 4.2). Formaturity class ‘very early’, theDP2 andAsummaturity type criteria
allocatedminimum (7.3%) of total genotypes, while theED criterion allocated the
maximum(52.8%)numberofgenotypes.Incaseofmaturityclass‘early’,theEDand
DP2criteriaallocatedtheminimum(16.7%)andmaximum(69.7%)oftotalgenotypes,
respectively. For ‘late’ maturity class, the conventional criterion allocated the
minimum(8.6%)whereasthecriterionbasedoncmallocatedthemaximum(34.3%)
of total genotypes. On the other hand,minimum (2.2%) andmaximum (20.4%) of
totalgenotypeswereallocated tomaturity class ‘very late’by theconventionaland
EDmaturity–typecriteria,respectively.
These results illustrated the capabilities of these different maturity type
defining criteria in describing the extent of maturity type among the large set of
unknowngenotypes.
3.3.ComparisonofdifferentmaturitytypecriteriaThestatisticalcomparisonofdifferentmaturitytypecriteriafortheF1populationis
presented inTable4.3.Resultsof thenon‐parametricMann‐WhitneyUtestshowed
that there were significant (P<0.01) differences in maturity scores among the five
criteriaexceptbetweenconventionalandEDandbetweenDP2andAsum(Table4.3).
Figure 4.3 evaluates differences in maturity scales for standard cultivars. All
maturitycriteriagavedifferentscalestothestandardcultivars.Moreorlesssimilar
trend of maturity scaling was observed across the criteria following the order:
Première>Bintje>Seresta>Astarte>Karnico(Fig.4.3).
TheresultsindicatedfurthermorethatPremièrewasmarkedas‘veryearly’by
mostofthecriteria.However,accordingtoDP2andAsumcriteria,Premièrewasplaced
in the ‘early’ category. Bintje was marked as ‘early’ by all criteria, except the cm
criterionwhichputBintjeinthe‘veryearly’category.CultivarSerestawasmarkedas
‘late’ by the conventional,cmandED criteria, but the same cultivarwas considered
‘verylate’byDP2andAsumcriteria.AstarteandKarnicoweremarkedas‘verylate’by
all criteria except cm which considered these cultivars ‘late’. Apparently cm and
conventional criterion showed least and maximum resolution, respectively for
maturitytypeamongthestandardcultivars.
Modelbasedanalysisofmaturitytypeofpotato
129
Table 4.3. Mann‐Whitney U test for checking extent of variation among different
maturitytypecriteria.
Maturityclass1 Mann‐WhitneyUtest2
Conventionalscaleversus
DP2scale **
cmscale **
EDscale NS
Asumscale **
DP2scaleversus
cmscale **
EDscale **
Asumscale NS
cmscaleversus
EDscale **
Asumscale **
EDscaleversus
Asumscale **1Conventional scale: based on breeders’ criteria; DP2 scale: based on duration of
maximumgreencanopycover;cmscale:basedontuberbulkingrate;EDscale:based
oneffectivedurationoflineartuberbulking;Asumscale:basedontheareaunderthe
wholegreencanopyprogresscurve.2**Significantat1%,NS=non‐significant.
Theseresultshighlightedthecomplexitiesindefiningthematuritytypeinpotato.It
was notable that although conventionalmaturity criterionwas able to classify the
genotypes into different maturity classes, it could not offer a clear definition. In
contrast, results from physiological maturity type criteria were easily and clearly
interpretable. Our integrated approach involving different physiological traits
therefore could be very beneficial in offeringphysiology‐based criteria to re‐define
maturitytype.
Chapter4
130
Figure4.3.Comparisonofconventionalandphysiologicalmaturity typecriteria for
fivestandardcultivars.DP2representsthedurationofmaximumgreencanopyphase,
cmandEDaregrowthrateandeffectivedurationofthelinearphaseoftuberbulking,
respectively,Asumisareaunderwholegreencanopycurve.
3.4.Performanceandstabilityofconventionalmaturitytypecriterion
Our next objective was to check the performance of the conventional criterion in
termsofitscapacitytoclearlypickupgenotypesforeachphysiologicaltrait(i.e.DP2,
cm,ED,andAsum).Considerableoverlappingamongthematurityclasseswasobserved
intheconventionalcriterion(Fig.4.4).Theextentofstabilityand/orrepeatabilityof
maturity scores from the conventional criterionwere checked for the quantitative
physiologicaltraits.
Figure 4.5 compares the maturity scores from conventional method for one
physiological trait Asum. The results indicated that Asum values showed a lot of
variationbothwithinandthroughoutdifferentmaturityclasses.Forinstance,itwas
notedthattheconventionalcriterioncouldnotidentifygenotypeswithhighAsum(i.e.
very lategenotypes)asevident fromthe lackofmaturityscores intherangeof2‐4
(Fig.4.5).
These results indicated the weakness and lack of robustness of conventional
maturitycriterionindealingwithrathercomplexphysiologicalquantitativetraits.It
can be concluded that such criterion is not quite able to clearly interpret and
discriminate between maturity classes for the crucial growth related processes of
1
2
3
4
5
6
7
8
9
10
Conventional cm Asum
Mat
urity
sca
le
Maturity defining criteria
Premiére Bintje Seresta Astarte Karnico
cm ED AsumDP2
Modelbasedanalysisofmaturitytypeofpotato
131
Figure4.4.Boxplotsillustratingtherangesofgenotypesinfourmaturityclassesas
assessed by conventional criterion for physiological traits: DP2, cm, ED, and Asum,
withintheF1population(100genotypes).DP2representsthedurationofmaximum
green canopyphase,cmandED are growth rate andeffectivedurationof the linear
phaseoftuberbulking,respectivelyandAsumisareaunderwholegreencanopycurve.
Theboxesspantheinterquartilerangeofthetraitvalues,sothatthemiddle50%of
the data laywithin the box,with a horizontal line indicating themedian.Whiskers
extendbeyondtheendsoftheboxasfarastheminimumandmaximumvalues.VE=
veryearly;E=early;L=late;VL=verylate.
potato. An improved criterion based on important physiological traitswould allow
relating the maturity to crop phenology and physiology. Haga et al. (2012) used
flowerdevelopment, leafchlorophyllcontentand leafperoxidaseactivity toclassify
maturity of potato cultivars under temperate conditions. They observed that leaf
chlorophyllcontentwasthemostreliable indicatoramongthesethreevariables,an
indicator related toAsum as both reflect leaf senescence. However, leaf chlorophyll
content was mainly capable of discriminating between late and early‐medium or
medium‐late;itdidnotseparateearly‐mediumfrommedium‐latecultivarswell.
0
10
20
30
40
50D
P2
(td
)
0
10
20
30
40
50
60
c m (g
td
-1)
0
20
40
60
80
VE E L VL
ED
(td
)
0
2000
4000
6000
8000
VE E L VL
Asu
m (
td %
)
Maturity class
Chapter4
132
Figure 4.5. Extent of stability and/or repeatability of maturity scores from a
conventionalcriterionforquantitativetraits, forinstanceAsum(i.e.areaunderwhole
greencanopycovercurve)withintheF1population.
Our physiological based criteria of defining the maturity type could allow in
cleardiscriminatingbetweenallclasses.
3.5.Predictivecapabilityofdifferentmaturitytypecriteria
Differentmaturitycriteriawereanalysedfortheircapabilitiestopredictthematurity
in a large number of unknown genotypes across diverse environments. The
regression analysis was performed on the maturity scores from each maturity
criterion.Sofarourresultsindicatedthatnearlyallphysiologicaltraitssatisfactorily
definedthematuritytype.However,forthesakeofsimplicity,hereweconsideronly
onetraitAsumasasynonymtotheresponsevariable“maturity”.Figure4.6showsthe
results of regression between the observedAsum and theAsum (“maturity type”) as
predicted by regression analysis from the individual maturity criteria. The results
indicated thatalmostallmaturitycriteriapredicted thematuritywell, i.e.R2>0.50.
However, the conventional and cm based criteria predicted the maturity
comparatively less accurately with R2 values 0.68 and 0.51, respectively. Maturity
criterionbasedonEDgavethebest(R2=0.81)predictionsformaturitytypefollowed
0
2000
4000
6000
8000
1.0 2.5 4.0 5.5 7.0 8.5 10.0
Maturity score
Asu
m(t
d%
)
Modelbasedanalysisofmaturitytypeofpotato
133
Figure4.6.ComparisonofdifferentmaturitytypecriteriaforpredictingtheAsum(as
aproxyformaturitytype)withintheF1populationacrosssixenvironments.Panels
show regression analysis based on different maturity type criteria, i.e. (a)
conventional,(b)DP2(durationofmaximumgreencanopyphase),(c)cm(growthrate
during linear phase of tuber bulking), (d)ED (effective duration of tuber bulking).
NotethatvariableonY‐axis(i.e.Asum:areaunderwholegreencanopycovercurve)is
consideredequivalenttomaturity,seetext.
bycriterionbasedontraitDP2(i.e.R2=0.77).Theseresultswereinlinewithourpath
coefficientanalysisconclusions(Fig.4.2).Theseresultsconcludedthatoverall,most
of the physiological maturity type criteria were fully capable of reproducing the
maturitytypeofgenotypesacrossdiverseenvironments.
3.6.Direct/indirectselectionpossibilitiesformaturitytypeFigure 4.7 illustrates the relationships between maturity scores (Table 4.1) from
differentmaturity typecriteriawithin theF1populationacrossall sixexperiments.
Allcorrelationswerehighlysignificant(P<0.01)andpositive.
y = 0.6889x + 1198.7R² = 0.6889
1000
2500
4000
5500
7000 (a) y = 0.7692x + 881.46R² = 0.7691
1000
2500
4000
5500
7000 (b)
y = 0.511x + 1874.3R² = 0.5109
1000
2500
4000
5500
7000
1000 3000 5000 7000
(c) y = 0.8066x + 741.02R² = 0.8066
1000
2500
4000
5500
7000
1000 3000 5000 7000
(d)
Observed Asum (td %)
Pre
dict
ed A
sum (
td %
)
Chapter4
134
Figure4.7.Relationshipsbetweenmaturityscalevaluesfromdifferentmaturitytype
criteriawithinF1populationacrosssixenvironments.ValuesontheX‐axisandY‐axis
representlog‐transformedmaturityscales(cf.MaterialsandMethods).
The results indicated very strong phenotypic correlations between the Asum
criterionandDP2andED,andbetweencmandEDcriteria(r=0.89).Thisshowsthat
bothrateanddurationoftuberbulkingareimportantincontrollingthetotalduration
of crop growth and the capacity of the crop to intercept radiation, crucial in
determining final yield (Kooman andRabbinge, 1996). These results indicated that
maturity type of the genotypes based on Asum could be indirectly obtained by
selectinggenotypeswiththeproperDP2criterion.Ourresultsinpreviouschapters(2
and 3) showed that quantitative trait loci (QTL) for these physiological traits are
DP2 cm ED Asum
Con
vent
iona
l A
sum
E
D
c m
Maturity scale
Mat
urity
sca
le
Modelbasedanalysisofmaturitytypeofpotato
135
mostly co‐localised on chromosome V of the paternal (i.e. RH) genome in the F1
segregatingpopulationofpotatoandexplainmorethan50%ofthetotalphenotypic
variance.Moreover, our results in Chapter 2 indicated thatmaximumQTL additive
effectswereassociatedwithAsumthroughoutdifferentenvironments(seeFig.2.12),
illustrating thecloseassociationofmaturitywith thisphysiological trait.Therefore,
this locus could be used as an explanatory variable for further elucidating the
physiological as well as genetic aspects of maturity in potato. The conventional
criterion on account of its subjective nature does not explain maturity type any
further, inspiteof itshighcorrelationwithsomeof thephysiologicalmaturity type
criteria.
3.7.Applicationsofre‐definingmaturitytypebasedonphysiologicaltraits
Our study showed that re‐defining thematurity type on the basis of physiological
traits is useful. The new physiology‐based maturity definition could be applied in
plantbreedingandincropmanagement(Struik,2010).
Thephysiologicalmaturity criteria canplay a significant role indevising crop
escape strategies to cope with biotic stresses, such as late blight (Struik, 2010).
Strategieslikeadvancingthecropgrowthbeforethestressbecomestooharmfulmay
prove very useful. For instance, genotypes with early tuber set and faster tuber
bulking can better escape the disease severity. The physiological based maturity
criteriamayalso allow the selectionof thegenotypes formaturity type inorder to
breakthecorrelationbetweenresistanceandlatematurity.Characteristicsthatcould
beusefulindicatorsofcompetitiveabilityagainstweedsmayincludeagrowthhabit
thatallowsearlyattainmentoffullcanopycoveranditspresenceforlongerperiods
(i.e.genotypeswithhighDP2andlatematurity(e.g.highAsum)(JoenjeandKropff,1987;
Lotzetal.,1991;Grevsen,2003;Williamsetal.,2008).Thiscanhelppotatogrowersto
choosethosegenotypeswithahightolerancetoweedpressureandabletomaintain
highyieldsinthepresenceofweeds,especiallyinorganicagriculture.Inthiscontext,
inclusionofmaturitytypebasedonphysiologicalcharactersinbreedingprogrammes
couldallow the selectionof competitivegenotypes in field conditionswhereweeds
arealwayspresentaswellasenhancetheefficacyofoverallweedcontrol.
Thephysiologicalmaturity type criteriawouldalsogivemore insight into the
physiologicalnatureofgenotype’smaturitytypeandthereforewouldhelpinproper
planning of haulm killing. This would give the benefits of increased tuber quality
(Haldersonetal.,1985)andeconomicalsavings(costofdesiccant).
The physiological maturity type criteria take into account the quantitative
knowledge of the influence of environmental factors on the length of the growing
Chapter4
136
season and on drymatter partitioning, necessary for designing genotypes for such
environments(HaverkortandKooman,1997).Ourapproachthereforewouldhelpin
designingstrategiesforbreedingforgenotypeswithspecificmaturitytype.
4.Conclusions
Inthischapterwetriedtocreateacleardefinitionofmaturitytypeandtodevelopa
quantitative,reproduciblemethodtoassessthematuritytypeinalargesetofpotato
genotypes.Wepresentedanapproachwherebypotatophysiologicaltraitswereused
to explain thematurity type in a setof varieties coveringawide rangeofmaturity
typesandawell‐adapteddiploidF1segregatingpopulation.Thisprocedureresulted
in four re‐defined criteria of defining maturity type which we call “physiological
maturitytypecriteria”asopposedtotheconventionallyusedcriterion.
The physiological maturity type criteria were based on physiological and
quantitativeknowledgeandwereeasilyinterpretableintermsofphysiologicaltrade‐
offsthatexistedbetweentheselectedtraits.Theconventionalmaturitytypecriterion
could not offer this advantage on account of its subjective and ordinal nature. Our
results showed that the conventional maturity criterion showed considerable
overlappingamongthematurityclasseswithmaximumvariability.Besides, itcould
notofferacleardefinitionofmaturityonaccountofitssubjectivenature.Incontrast,
physiologicalmaturity type criteria tended toexplainmaturity type inpotatomore
clearlyandwerebettercapableofpredicting thematurity typeofgenotypesacross
diverseenvironmentsthantheconventionalcriterion.
This study gavemore understanding about the nature of genotype’smaturity
type,besides,italsohighlightedthecomplexityofdefiningmaturitytypeinapotato.
Our results however, showed that re‐defining the maturity type of a large set of
genotypesonthebasisofphysiologicaltraitsispossibleandmayproveuseful.Anew
physiology‐based maturity definition could offer wider applications in potato
breeding and crop management studies. This new criterion may be amenable to
further genetic analysis and could also help in designing strategies for potato
ideotype breeding for genotypes with specific maturity type for specific growing
conditionsand/orenvironments.
CHAPTER5 AnecophysiologicalmodelanalysisofyielddifferenceswithinasetofcontrastingcultivarsandanF1segregatingpopulationofpotato(SolanumtuberosumL.)grownunderdiverseenvironments*
M.S.Khan,X.Yin,P.E.L.vanderPutten,P.C.Struik
AbstractWetestedthecapabilityofthegenericecophysiologicalmodel‘GECROS’toanalysedifferences in tuber yield of potato in a set of cultivars covering awide range ofmaturitytypesandadiploidF1populationsegregatingformaturitytype.Themodelpredicts crop growth and development as affected by genetic characteristics andclimatic and edaphic environmental variables. The genotype‐specific model‐inputparametervalueswereestimatedandthemodelwasusedforpredictingthetuberyield of an F1 population, their parents and a set of cultivars in multiple fieldexperiments.Themodelyieldedareasonablygoodpredictionofdifferencesintuberyield across environments and across genotypes. Trends of growth and nitrogenuptake were adequately reproduced by the model. Model analysis identified thegenotypickey‐parametersaffectingtuberyieldproductionandNmax(i.e.totalcropNuptake)contributedmosttothedeterminationoftuberyield.GenotypeswithhigherNmaxandlowertuberNconcentrationexhibitedhighertuberdrymatteryield.Theseresultswerelargelyconfirmedbystatisticalpathcoefficientanalysis.GECROSmodelisausefultoolinanalysingthecontributionofindividualphysiologicaltraitstoyieldacross different environments. Such information can greatly facilitate thedevelopmentofpotatoideotypesforspecificenvironments.Keywords:Potato(SolanumtuberosumL.);ecophysiologicalcropmodel;GECROS;genotype‐by‐environment (GE) interaction; complex traits; yield; sensitivityanalysis;pathcoefficientanalysis;ideotype;plantbreeding._______________________*Tobesubmitted.
Chapter5
138
1.Introduction
Potato (Solanumtuberosum L.) is one of themost important andwidely cultivated
non‐cereal crops, inmany parts of theworld (Walker et al., 1999;Hijmans, 2001).
Duetoincreasingfooddemandandchangingdietspotatoisbecomingasubsistence
crop in many growing areas. There is a need to increase potato yield via genetic
improvement and/or altered cropmanagement. In order to efficiently improve the
targettraits,predictionofthephenotypiccharacteristicsofgenotypesundervarious
environmentalconditionsiscrucial(AssengandTurner,2007).
Mostagronomictraitsaregeneticallycomplex(DaniellandDhingra,2002;Lark
et al., 1995; Orf et al., 1999; Stuber et al., 2003); they have low heritability, are
strongly dependent on environmental changes and often show high genotype‐by‐
environment(GE)interactions(AllardandBradshaw,1964;Tardieu,2003;Cooperet al., 2002, 2005).There is a need todissect complex traits like yield into simpler
characters and to separate factors influencing a givenphenotypic trait and shifting
from highly integrated traits to genotype‐specific traits (Yin et al., 2002).
Ecophysiologicalcropgrowthmodelshavethepotentialtoassessacomplextraitata
higher organizational level, via integrating the information about processes at the
lowerlevel.Theirabilitytoincorporateknowledgeofphysiologicaltraitstosimulate
crop growth and yield as influenced by growing environment, and agronomic
practicessuggeststhepossibilityofusingmodelsasacropbreedingtool(Booteand
Tollenaar, 1994; Aggarwal et al., 1997; Bastiaans et al., 1997; Boote et al., 1998;
Uehara,1998;White,1998;Booteetal.,2001;Mavromatisetal.,2001,2002;Hammer
etal.,2002,2006;Tardieu,2003;Banterngetal.,2004;Hoogenboometal.,2004;Yin
etal.,2004;AssengandTurner,2007;Herndletal.,2007;Letortetal.,2007).
Oneof themain applicationsof thesemodels is todescribe thedifferences in
yieldpotentialofgenotypesbetweenorwithinabreedingpopulationonthebasisof
individual physiological parameters. These parameters could be considered as
quantitativetraitsandareamenabletofurtheranalysis(Yinetal.,2004;Quilotetal.,
2005),e.g. forevaluatinganddesigning ideotypes(Loomisetal.,1979;Kropffetal.,
1995; Boote et al., 1996; Cilas et al., 2006; Yin et al., 2003c, 2004; Yin and Struik,
2008). This is possible because these parameters, often regarded as ‘genetic
coefficients’,arespecifictoeachgenotypeandsupposedtobeconstantunderawide
range of environmental conditions (Boote et al., 2001; Tardieu, 2003; Bannayan,
2007). This model feature makes it possible to make predictions about the plant
developmentalprocessesofgenotypeinawiderangeofenvironments(Stam,1996;
Bindraban,1997;Hoogenboometal.,1997).Suchmodelscanquantifycropgenotype‐
phenotyperelationships(Yinetal.,2000b,2004;Reymondetal.,2003;Hammeretal.,
2006) and therefore are highly suitable for studying the GE (Shorter et al., 1991;
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
139
Hunt,1993;KropffandGoudriaan,1994;Hammeretal.,1996;Chapmanetal.,2002;
Yinetal.,2003c;Banterngetal.,2004,2006;Yinetal.,2004;Suriharnetal.,2007)
andcouldassistwithmulti‐locationevaluationofcropbreedinglines(Liuetal.,1989;
Aggarwaletal.,1995;Palanisamyetal.,1995;Piperetal.,1998;Booteetal.,2001;
Slafer, 2003; Banterng et al., 2004; Mayes et al., 2005). For potato, Kooman and
Spitters (1995) showed that simulation models can be useful for predicting tuber
yieldandgaininginsightintocropgrowthprocessesandcanhelptoexploreoptions
forcropimprovement.
However, traditionally, crop models have principally been used to study and
predictcropperformanceinresponsetoenvironmentalconditionsandmanagement
practices,whereasgenotypicimpactsoncropperformance(especiallyinthecontext
ofplantbreedingwherelargenumbersofgenotypesareinvolved)havereceivedless
attention.Thisispartlyduetotheconstraintsimposedbytime,resources,andlarge
numberofgenotypesthatmakesitdifficulttomeasuredetailedgrowthdynamicsto
fullyderivethegenotype‐specificmodelcoefficients(Anothaietal.,2008),andpartly
due to the restricted capabilities ofmodels to represent genetic differences (White
andHoogenboom,1996;Hoogenboometal.,1997).
To be useful, the physiological frameworks used for trait dissection and
modelling atwhole‐crop levelmust realistically capture the functional basis of the
geneticvariationforcomplextraitsofinterest(Yinetal.,2000b).Mostexistingcrop
models,whichwereconstructed todealmostlywithagronomic issues,arenotwell
structuredinthisregardforinstanceforcaptureanduseofnitrogen(N)(Jeuffroyet
al.,2002)andforcarbon(C)andNpartitioning(Dingkuhn,1996).Theyalsolackthe
ability to describe subtle complexities associated with the differences between
genotypes(WhiteandHoogenboom,1996;Yinetal.,2004).
Inthisrespect,cropphysiologicalmodesofactionofthecomplextraitmustbe
understood and quantified and the crop model must be sufficiently detailed to
simulate the consequenceson growth anddevelopment generatedby the genotype
(G),environment(E)andultimatelyGE(Hammeretal.,1996).Tobecomeeffectivetools for addressing GE, existing models have to be improved, both in terms ofmodelstructureandinputparameters(Yinetal.,2004;YinandStruik,2008).Studies
are thus needed to develop and/or test the potential capabilities of current crop
models for plant breeding and to specify the necessary modifications for such
applications.
The objectives of this study are (i) to examine the ability of a recent
ecophysiologicalcropgrowthmodel‘GECROS’toexplainyielddifferencesamong100
genotypes from an F1 segregating population, their parents and a set of standard
cultivars of potato, and (ii) to analyse the relative importance of individual
Chapter5
140
physiological traits in determining yield differences. These analyses could have a
strongimplicationinexploringtheextenttowhichourmodeldissectstheroleofG,E,
andGEon tuber yield production, and indesigning strategies for potato ideotypebreedingforspecificenvironments.
2.Materialsandmethods
2.1.TheGECROSmodel
The model GECROS (Genotype‐by‐Environment interaction on CROp growth
Simulator) is a generic ecophysiological model that predicts crop growth and
development as affected by genetic characteristics and climatic and edaphic
environmental variables. For a detailed description of themodel, see Yin and Van
Laar(2005).Here,onlythekeyprocessesmodelled inGECROS(version2.0asused
byYinandStruik,2010)aresummarised.
Coupled modelling of CO2 diffusional (stomatal and mesophyll) conductance,
leaf photosynthesis and transpiration in dependence of leaf nitrogen content is
implemented according to Yin and Struik (2009). Subsequently, the results are
integrated for thewhole canopy,basedon the sun/shadeapproachofDePuryand
Farquhar(1997).TheGaussianintegrationisusedtoextendtheinstantaneousrates
intothedailytotalphotosynthesisandtranspiration.
Daily crop respiration is simulated based on the theoretical framework of
respiration components: (i) growth respiration, (ii) respiration for ammonium and
nitrateuptakeandnitratereduction;uptakeofother ions,phloem loading,and(iii)
residualmaintenancerespiration(CannellandThornley,2000).
Nitrogen demand ismodelled as themaximum value of the deficiency‐driven
and the growth activity‐driven demand. The first guarantees the actual plant N
concentration being above a critical value; the second is modelled based on the
optimum N‐C ratio for maximising the relative carbon gain, using the equation of
Hilbert (1990).TheactualNuptake is limitedby themaximumdailyNsupply rate
fromthesoil.
PartitioningofthenewlyproducedCandabsorbedNismodelledintwosteps:
first,betweentherootandtheshoot,andthenamongorganswithintheshoot.Root‐
shootpartitioning forC andN responds to environmental conditions, basedon the
root‐shoot functionalbalancetheory(e.g.Charles‐Edwards,1976)adjustedinorder
tomaximizerelativegrowthrate.
Intra‐shoot carbon partitioning to the structural stems and to the tubers are
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
141
determinedaccording to theirexpecteddailycarbondemands,whicharedescribed
by the differential form of a sigmoid function for asymmetric determinate growth
(Yinetal.,2003a). In themodel, thepotential tubersinksize iscalculated fromthe
estimatedamountofNavailablefortubergrowth.Theremainingshoot‐carbongoes
eithertotheleavesortotheCreservepool,dependingonwhetherthegreen‐surface
areaindexbecomesC‐orN‐limited.
The intra‐shoot N partitioning is based on pre‐defined maximum tuber N
concentration(nSO,whichisagenotype‐specificparameter,Table5.1)andminimum
N concentration in the stems. If the N requirements for the tubers and structural
stemsaremetfromthecurrentNuptake,theremainingshootNgoestotheleaves,
which include the photosynthetically active green surfaces of other organs. If the
requirementsforthetubersarenotmet,remobilisationofNfirst fromthereserves
and then from the leaves and the roots takesplace, until the reserves aredepleted
and theNconcentrations in the leavesand roots reach theirminimumvalues.This
remobilisationresults in leafandrootsenescence,reflectingthephenomenonofso‐
called ‘self‐destruction’ (Sinclair andDeWit,1975). If the tuberN requirementsare
notmetbyshootNandremobilization,thetuberNconcentrationdeclines.
Green‐surfaceareaindexismodelledastheminimumvalueofC‐limitedandN‐
limited areas. The underlying principle is described in detail by Yin et al. (2000c,
2003b).Assuch,senescenceofleavesiscomputedasafunctionofNreductioninthe
canopy.Similarly,senescenceofrootsdependsonNreductionintheroots.
Dailyphenologicaldevelopmentrate(i),whichhasaunitofd‐1, iscalculated
both for the pre‐tuberisation period and for the tuber bulking period, respectively,
using genotype‐specific parameters mV and mR, representing the pre‐tuberisation
phase and the duration between onset of tuber bulking and crop maturity,
respectively(Table5.1).Phenologicalresponsetotemperatureisbasedonaflexible
bell‐shapednon‐linearbetafunction(Yinetal.,1995).Thisfunctionisimplemented
onanhourlybasis toaccount for thediurnal temperature fluctuation.Valuesof the
cardinaltemperaturesinthisfunctionweretakenfromChapter2forpotato.
Inthemodel,plantheightisrequiredmainlyforcalculatingthecarbondemand
forstructuralstems.Plantheight ismodelled to followasigmoidpattern,assuming
thatmaximumplantheight(Hmax,Table5.1) isreachedatamidwaybetweentuber
bulking and cropmaturity. The increment of plant height is decreased if available
carbonassimilatesdonotmeetthepotentialcarbondemandforstemgrowth.
GECROSrunswithatimestepofoneday.Requireddailyweatherdatainclude
minimumandmaximumtemperature,globalsolarradiation,vapourpressure,wind
speed,andrainfall.AirCO2concentrationshouldbeprovided.Otherrequiredinputs
aredailysupplyofwaterandNavailableforcropuptake.
Chapter5
142
2.2.Experimentaldata
Datasets for our model analysis came from six field experiments, performed in
Wageningen(52˚Nlatitude),theNetherlands,withtwoexperimentsineachyear,i.e.
2002(Exps1and2),2004(Exps3and4),and2005(Exps5and6)(Fordetailssee
Chapters2and3).Theseexperimentsdifferedinenvironmentalconditionsbecause
they were carried out in different years, on different soils and under different
nitrogen (N) fertiliser regimes, thereby creating six contrasting environments. The
plantmaterialfortheseexperimentsconsistedof100F1diploid(2n=2x=24)potato
genotypes derived from a cross between two diploid heterozygous potato clones,
SH83‐92‐488 RH89‐039‐16, hereafter referred to as the ‘SH RH population’(RouppevanderVoortetal.,1997;VanOsetal.,2006).Thispopulationsegregates
formaturitytype(VanderWaletal.,1978;VanOijen,1991).Besides, fivestandard
cultivars (viz.Première,Bintje, Seresta,AstarteandKarnico)werealso includedon
thebasisoftheirmaturitytypeunderDutchconditions.
To evaluate model performance in representing growth dynamics, field
measurementswereperformedonfewgenotypesduringyear2004(Exps3and4),
and2005(Exps5and6).Atotaloffiveharvestswerecarriedoutatintervalsof1‐2
weeksduringthecropperiod.Atleastfourrepresentativeplantspersamplingpoint
were takenandmeasurementsweremadeonbothabove‐ andbelow‐groundplant
parts for dry matter and N content in different plant parts. To determine the dry
weight, the plants were divided into their constituents of living and dead leaves,
stems,stolon,andtubers,andsub‐samplesofeachportionweredriedat70 ˚C toa
constant weight. Roots were not measured since they are difficult to harvest
(Kooman, 1995). Further sub‐sampleswereused to determinenitrogen content by
meansofmicro‐Kjeldahldigestionanddistillation.
Tomeasurethemodelpredictabilityforthefullsetof100genotypes,tuberdry
matterwasmeasuredatonlythreeharvestsduringthegrowingperiodandtuberN
content was measured at maturity (see Materials and Methods, Chapter 3). These
datasetscreatedthebasisfortheestimationofgenotype‐specificparametersandfor
model testing as described later. In addition, green canopy cover (%)was visually
assessedonweeklybasisduringthewholecropcycleforallgenotypes(Chapter2).
2.3.Estimationofmodelparameters
Table 5.1 gives the description of genotype‐specific parameters of GECROS, which
include total crop N uptake at maturity (Nmax). Nmax per se, as an accumulative
quantityinthecroplifecycle,shouldnothavebeenconsideredasaninputparameter.
However, therewas not sufficient information about the soil andmodelling of soil
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
143
Table5.1.Listofgenotype‐specificparametersofGECROSmodel(SeeMaterialsand
Methods,section2.3).tdstandsforthermalday.
Trait Description Unit
mV Periodfromplantemergencetoonsetoftuberbulking td
mR Durationbetweenonsetoftuberbulkingandcrop td
Hmax Maximumplantheight m
nSO MaximumtuberNconcentration gNg‐1DM
Nmax TotalcropNuptakeatcropmaturity gNm‐2
processesisusuallyfullofuncertainties.Toreduceaninfluenceoflargeuncertainties
inpredictingedaphicvariablesforNsupply,wetookasimpleapproach,usingNmaxas
a model‐input parameter. The value of Nmax was estimated on the basis of the
assertion that the distribution of N over tubers and other components is probably
fairlyconservative(BiemondandVos,1992),assumingthatNaccumulationintubers
andhaulmaccountsfor77.5%and15%(ownmeasureddataonfewgenotypes),and
N accumulation in the roots for 7.5% (see data of Sharifi et al., 2005), of Nmax.
Procedures for estimation and correction for experiment‐specific parameter values
aredescribedbelow.
Of the five parameters, values of phenological parameters mV and mR were
derived from Chapters 2 and 3, where using the afore‐mentioned data sets, we
analysed the dynamics of potato canopy cover development and tuber bulking,
respectivelyasa functionof thermal time. Chapter2describes the threephasesof
canopycoverdevelopment(i.e.build‐upphase,maximumcoverphase,declinephase),
usingbiologicallymeaningfulcomponenttraits.Theendofthecanopybuild‐upphase
ismarkedbyt1atwhichthecanopycoverattainsitsmaximumlevel(seeFig.2.1in
Chapter 2). The end of canopy senescence (or crop maturity) is marked by te. In
Chapter3,wequantified the tubergrowthduringexponentialand linearphases, in
which tBcharacterises the onset of linear phase of tuber bulking (see Fig. 3.1 in
Chapter3).
Forfewgenotypeswithmoredataforgrowthdynamics,availabilityofsufficient
datapointsallowedtheestimationofmVastB.However,forothergenotypes,thepre‐
tuberisation phasemVis hard to estimate accurately. A sensitivity analysis showed
that this trait mattered little formodel prediction of tuber yield (see Results), we
assumedthatmV=t1.Becauset1issensitivetotheenvironmentalvariation(seeFig.
2.7),andExp6showedcomparativelylowervariationfort1(Chapter2),valuesoft1
Chapter5
144
basedonExp6wereusedforgenotype‐specificmV.ParametermRwascalculatedas
(te‐mv).Hereagain, inorder to reduce the randomnoise,mean teperF1genotypes
acrosssixexperimentswasusedforestimatingmR.
Notsurprisingly,valuesofplantheight,tuberNconcentration,andtotalcropN
uptake differed among the experiments (Fig. 5.1). To reduce the influence of
environmentalnoiseyetreflectthedifferencesamongtheexperiments,Hmax,nSO,and
Nmax were estimated as across‐experiment mean times a correction factor per
individual experiment. The correction factor was calculated as a slope of the
regression line between experiment‐specific parameter values versus its across‐
experiment mean. In the case of Exps 1 and 2, for which we lacked the data for
parameters nSO, Hmax and Nmax (Fig. 5.1), combined information from other
experimentsfortheseparameterswasused,withthecorrectionfactorobtainedfrom
theslopeoftheregressionbetweenobservedtuberyieldperExps1or2versusmean
observedtuberyieldacrosstheotherfourexperiments.
Cropswerenotsubjecttoanywaterstressduringthegrowth;so,ahighvalueof
dailywater supply from soil was given for simulation tomimic no drought stress.
DailyN supply from soilwas derived fromNmax, based on the fitted pattern of the
dynamicsoftheaccumulativecropNuptakeasobservedforthefewgenotypeswith
thedetailedmeasurements.
2.4.Modelanalysistoidentifymajoryield‐determiningtraits
Sensitivity analysis was performed to determine the importance of individual
genotype‐specific parameters in determining tuber yield potential. Data sets from
Exps3‐6wereusedforthispurpose.WefollowedtheapproachofYinetal.(2000b,
2005). First, the baseline simulation was conducted, where all genotype‐specific
parameter valueswere used as input for simulation. Then, parameterswere fixed,
oneatatime,attheiracross‐genotypemean.Theextent,towhichthepercentageof
varianceexplainedbythemodelwasdecreasedrelativetothepercentageexplained
by the baseline simulation, was used to rank the relative importance of the
parametersindeterminingtuberyields.
2.5.Statisticalpathcoefficientanalysis
Toconfirmtheresultofecophysiologicalmodelinganalysis,pathcoefficientanalysis
wasperformed,alsotocheckouttheimportantdirectandindirectinter‐associations
between the genotypic‐specific parameters and final tuber yield. This analysiswas
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
145
Figure5.1.BoxplotsofmeansofanF1populationforthreemeasuredparametersin
fourexperiments.Theboxesspanthe interquartilerangeofthetraitvalues,sothat
themiddle50%of thedata laywithin thebox,withahorizontal line indicating the
median. Whiskers extend beyond the ends of the box as far as the minimum and
maximumvalues.PointsweremissingforExps1and2becausethesetraitswerenot
measuredtherein(seethetext).
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
3 4 5 6
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.0
0.5
1.0
1.5
2.0
2.5
[m]
Plant N uptake
Plant height
[g N m-2]
Experiment no.
[g N g-1]
Tuber N conc.
Chapter5
146
performed on the mean of F1 genotypes across environments following the
procedureofChapter3.
3.Resultsanddiscussion
3.1.Modelparameters
The evaluation of genotype‐specific model parameters indicated that there were
strong genetic differences among the standard cultivars (Table 5.2). As expected,
valuesofmostoftheseparametersexceptnSOwerehigherforlatematuringcultivars
(Astarte and Karnico) than for mid‐late (Seresta), mid‐early (Bintje), and early
cultivars (Première). In contrast, values of nSO were higher for early maturing
cultivarslikePremièrefollowedbymid‐lateandlatecultivars(Table5.2).Ithasbeen
found in previous research that N uptake and use efficiency differ by cultivar
(Johnsonetal.,1995;Kleinkopfetal.,1981;Lauer,1986;PorterandSisson,1991a,b).
Early‐maturing cultivars stop leaf growth earlier than late‐maturing cultivars and
therefore exhibit less vegetative growth and a shorter tuber bulking period. This
pattern results in lower N use efficiencies and higher tuber N concentrations in
earliergenotypes(Vos,1997;Chapter3).
The results for theF1population indicated thatmost of theparameterswere
nearlynormallydistributed,apart frommRandHmax forwhich thedistributionwas
bimodal(Fig.5.2).TheF1populationdisplayedtransgressivesegregation,withmore
extremevaluesinthepopulationthantheirparents(SHandRH).
Table5.2.Estimatedmeanvaluesofgenotype‐specificparametersforfivestandard
cultivars(listedinorderofincreasinglylongercropcycle).tdstandsforthermalday.
(−)representslackofdata.SeeTable5.1forparameterdescriptions.
Cultivar mV
(td)
mR
(td)
Hmax
(m)
nSO
(gNg‐1DM)
Nmax
(gNm‐2)
Première 13.3 36.7 0.53 0.018 21.9
Bintje 14.4 51.1 0.82 0.017 25.8
Seresta 17.7 57.4 1.05 0.014 25.4
Astarte 15.1 65.1 1.14 0.013 24.6
Karnico − − 1.39 0.012 25.8
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
147
Figure5.2.Distributionofgenotype‐specificparametersofGECROSmodelfor100F1
genotypes.The valuesof twoparents ‘SH’ and ‘RH’ are indicatedby full arrowand
dashedarrow,respectively.SeeTable5.1forparameterdescriptions.
mV
mR Hmax
Nmax n SO
Num
ber
of F
1 ge
noty
pes
Chapter5
148
3.2.Modelperformanceinrepresentinggrowthdynamics
The GECROS model efficiently simulated the dynamics of important potato
physiologicalgrowthprocessesunderarangeofenvironmentalconditions.Herewe
presenttheresultsoftwoexperimentsof2004(i.e.Exps3and4).Theseexperiments
were low and high in N availability, respectively (Fig. 5.1; Chapter 2). Figure 5.3
presentsmodelledandobserveddataofplantandtuberNuptakeanddrymatterfor
the two parental genotypes. Model evaluation showed good agreements between
observedandsimulatedgrowthpatternoftheseprocesses.
TheNaccumulationintubersshowedanS‐shapedgrowthpattern, incontrast
to the pattern of plant N uptake (Fig. 5.3). The results further showed that at the
beginning of the season the tuber drymatterwas underestimatedwhile plant dry
matterwas estimated ratherwell, whereas at the end of the season the simulated
tuberdrymatteryieldscomparedwellwiththemeasureddata.Thecurrentdata(Fig.
5.3) also indicated that the tuber aswell asplantdrymatter is reducedwhenN is
limiting (i.e. Exp 3), most likely via limited leaf expansion and interception of
radiation(VosandBiemond,1992).
3.3.Modelperformanceinexplainingyielddifferencesamonggenotypes
To evaluate the model’s ability to reproduce the observed yield‐based variation,
differences in the actual and modelled yield were compared across different
environments for the F1 population, their parents and a set of standard cultivars.
Overall, the model adequately simulated differences in tuber yields among these
genotypes inallexperiments(Fig.5.4).However, therewerediscrepanciesbetween
simulatedandmeasuredtuberyields,especiallyinExp3,wherethepredictedtuber
yield was under‐estimated in high‐yielding, and over‐predicted in low‐yielding
genotypes.WiththeconceptusedinGECROSfortuberNaccumulation,thefractionof
tuber N is a very sensitive input parameter, not only affecting the final N
concentration,butalsotheharvest indexandthepotentialtuberyield(YinandVan
Laar,2005).
Themodelexplainedvariationsinthetuberyield,withR2rangingfrom35%
to 65% for the F1 population, and 80% to 96% for the parents and five standard
cultivars,amongtheExps3‐6(Fig.5.4).However,incaseofExps1and2,themodel
could not satisfactorily explain the variation in the tuber yield (Fig. 5.4). This is
because there was a lack of information about N availability and therefore
assumptions had to be made for defining the related input parameters for those
experiments(seeMaterialsandMethods).
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
149
Figure5.3.Modelled (lines) and observed (symbols) for genotypes (a) SHRH‐469
and(b)RH89‐039‐16, inExperiment3,and(c)SHRH‐469and(d)RH89‐039‐16, in
Experiment4,performedduring2004.
(a)
(b)
(c)
(d)
Thermal days (td)
0
2
4
6
8
10
12Plant Tuber
0
500
1000
1500Plant Tuber
0
5
10
15
20Plant Tuber
0
200
400
600
800
1000
1200Plant Tuber
0
5
10
15
20
25
30Plant Tuber
0
500
1000
1500Plant Tuber
0
10
20
30
40
0 20 40 60
Plant Tuber
0
500
1000
1500
2000
0 20 40 60
Plant Tuber
N u
ptak
e (g
m-2
)
Dry
mat
ter
(g m
-2)
Chapter5
150
Figure5.4.Comparisonbetweenpredictedandobservedtuberyieldfor(a)100F1
genotypes(smallcircles),(b)5standardcultivarsand2parentalgenotypes(SHand
RH), testedper each individual experiment (large circles).R2 valueswere obtained
from a linear regression; rRMSE is the relativeRMSE,calculated as the root‐mean‐
squareerrordividedbythemeanobservedtuberyield.
Observed yield (g m-2)
Pre
dict
ed y
ield
(g
m-2
)
0
500
1000
1500
2000
2500
Exp 1
(a) R2 = 0.02, rRMSE= 0.36(b) R2= 0.07, rRMSE= 0.37
0
500
1000
1500
2000
2500
Exp 2
(a) R2 = 0.08, rRMSE= 0.42(b) R2= 0.05, rRMSE= 0.46
0
500
1000
1500
2000
2500(a) R2 = 0.40, rRMSE= 0.23(b) R2= 0.88, rRMSE= 0.21
Exp 30
500
1000
1500
2000
2500(a) R2 = 0.50, rRMSE= 0.20(b) R2= 0.84, rRMSE= 0.09
Exp 4
0
500
1000
1500
2000
2500
0 1000 2000
(a) R2 = 0.35, rRMSE= 0.20(b) R2= 0.96, rRMSE= 0.09
Exp 50
500
1000
1500
2000
2500
0 1000 2000
(a) R2 = 0.652, rRMSE= 0.10(b) R2= 0.813, rRMSE= 0.11
Exp 6
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
151
Themodelalsopredictedwell(i.e.R2=86%)themeantuberyieldacrossfour
environments (i.e. Exps 3‐6) (Fig. 5.5a). Furthermore, the model satisfactorily
predicted variation in the mean tuber yield across the F1 genotypes among the
environments (Fig.5.5b).Themodelpredicted57%of the totalobservedvariation,
when all observations for the F1 genotypes in the four environmentswere pooled
(Fig.5.5c).
The model performance was also evaluated by calculating the relative root
meansquareerror(rRMSE;Figs5.4‐5.5).TherRMSEvaluesrangedfrom0.10to0.42
in F1 population and 0.09 to 0.46 for the two parents and five standard cultivars,
amongtheExps3‐6(Fig.5.4).Asexpected,therRMSEvalueswerehigherforthefirst
twoexperiments(Exps1and2)mainlyduetolackofgoodfitasdescribedearlier.In
otherexperiments,rRMSEvalueswerereasonablygood.Theresultsfurtherindicated
lowrRMSEvaluesforthemeantuberyieldacross‐environments(i.e.0.09),acrossthe
F1genotypesamongtheenvironments(i.e.0.06),andwhenallobservationsforthe
F1genotypesinthefourenvironmentswerepooled(i.e.0.17)(Fig.5.5).
Overall, our results indicated that using as few as five measured genotype‐
specific parameters (Table 5.1), the GECROS model showed a good potential in
explainingtheobservedyielddifferencesamonggenotypes.
3.4.Model‐basedsensitivityanalysistoidentifyimportantyield‐definingtraits
Ecophysiological models can serve as an important tool to determinemodel input
parameters critical for yield potential (Yin et al., 2000b). Sensitivity analysis was
performedtoevaluatethe importanceof individualgenotype‐specificparameters in
determining tuber yield potential. The results of the baseline simulation, in which
genotype‐specificvalueswereapplied forallmodel‐inputparameters,werealready
presented in the previous section for individual experiments (Fig. 5.4). When the
parameters were fixed, one at a time at their across‐genotype mean, the model
explainedpercentagevariancedroppedfromthebaselinesimulationformostofthe
parameters throughout the environments (Table 5.3). This reduction in variance
explainedwasmoreevidentforNmaxfollowedbynSOthroughouttheexperiments.For
other parameters, in some cases, the percentage variance accounted for was even
higher than that obtained from the baseline simulation. Thiswas evident formV in
Exps3,4,and5andHmax inExps3and6.Almostsimilar trendswererecorded for
observationsinacross‐environmentmeansaswellaswhendatawerepooled.These
results conclude that the relative importance of parameters for tuber yield
determinationcanberankedas:Nmax>nSO>mR>mVandHmax.Obviously,Nmaxand
nSO were the most important parameters as they contributed most to the
Chapter5
152
Figure5.5.Comparisonbetweenpredictedandobservedtuberyield(gm‐2)testedin
across‐environment mean of 100 F1 genotypes (n = 100 points) (a), in across‐
genotypemean of four environments (i.e. Exps 3‐6) (b), and when data from 100
genotypes tested in the four experiments are pooled (n= 400) (c).R2 valueswere
obtainedfromalinearregression;rRMSEistherelativeRMSE,calculatedastheroot‐
mean‐squareerrordividedbythemeanobservedtuberyield.
Observed yield (g m-2)
Pre
dict
ed y
ield
(g
m-2
)
0
500
1000
1500
2000
2500
0 1000 2000
a
R2=0.86, rRMSE= 0.09
0
500
1000
1500
2000
2500
0 1000 2000
b
R2=0.89, rRMSE= 0.06
0
500
1000
1500
2000
2500
0 1000 2000
c
R2=0.57, rRMSE= 0.17
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
153
Table5.3.Percentageoftheobservedtuberyieldvariationinfourexperiments(Exps3‐6)ofF1populationof
potatoaccountedforbytheGECROSmodelwhendifferentsetsofvaluesforfivegenotype‐specificparam
eterswere
used.SeeTable5.1forparameterdescriptions.
Set
of
para
met
ers
a
R2b
R
2c
R2d
mV
mR
Hm
ax
n SO
Nm
ax
E
xp. 3
E
xp. 4
E
xp. 5
E
xp. 6
+
+
+
+
+
0.
40
0.50
0.
35
0.65
0.
86
0.57
- +
+
+
+
0.44
0.
55
0.42
0.
59
0.90
0.
58
+
- +
+
+
0.35
0.
50
0.32
0.
56
0.81
0.
54
+
+
- +
+
0.48
0.
47
0.32
0.
68
0.90
0.
58
+
+
+
- +
0.21
0.
47
0.27
0.
52
0.62
0.
50
+
+
+
+
-
0.06
0.
0001
0.
02
0.24
0.
12
0.29
a The
‘+
’ sy
mbo
l in
dica
tes
the
para
met
er f
or w
hich
the
gen
otyp
e-sp
ecif
ic v
alue
was
use
d, a
nd ‘
-’ s
ymbo
l in
dica
tes
the
para
met
er f
or w
hich
the
gen
otyp
e m
ean
valu
e w
as u
sed
in t
he m
odel
sim
ulat
ion.
The
fir
st r
ow (
bold
sym
bols
and
tex
t)
indi
cate
s th
e re
sult
s of
the
base
line
sim
ulat
ion,
in w
hich
gen
otyp
e-sp
ecif
ic v
alue
s w
ere
appl
ied
to a
ll p
aram
eter
s.
b The
var
iati
on e
xpla
ined
per
eac
h ex
peri
men
t (nu
mbe
r of
poi
nts
n =
100
).
c The
var
iati
on e
xpla
ined
for
acr
oss-
envi
ronm
ent m
eans
in 1
00 F
1 ge
noty
pes
of p
otat
o (n
umbe
r of
poi
nts
n =
100
).
d The
var
iati
on ex
plai
ned
for
all
data
poi
nts
whe
n al
l th
e ob
serv
atio
ns f
or 1
00 F
1 ge
noty
pes
in t
he f
our
expe
rim
ents
wer
e
pool
ed (
n =
400
).
Chapter5
154
determinationoftuberyieldamongtheF1population(Table5.3).Lietal.(2006)also
performedasimilarsensitivityanalysisandreportedthatNavailabilitywasthekey
traitaffectingpotatotuberyield.CropNstatusaffectsphotosynthesiswhichinturnis
a functionof intercepted radiationby above‐groundvegetativeparts (Khurana and
McLaren,1982;Moll,1983;Marcelisetal.,1998).Thereisalinearrelationbetween
totalNuptakeandcanopygrowthanddevelopmentuntilamaximumlevelisreached
(Lemaire,1997).Thedurationoftheperiodwithfullcanopycoverislongerandthe
stageofdeclinestartslaterintimethehighertheNavailable(Chapter2).
The sensitivity analysis gave further insight into whether or not the relative
importanceofthefivemodel‐inputtraitsvarieswithenvironment.Suchinformation
about the relevance of different plant traits for yield under different growth
conditionscan,therefore,improvetheefficiencyofabreedingprogramme(Heuvelink
etal.,2007).However,ourresultsforexperiment‐specificsensitivityanalysisshowed
thattherelativeimportanceofthefivemodel‐inputtraitsvariedlittleamongthefour
experiments.
3.5.Pathcoefficientanalysis
Pathcoefficientanalysiswasperformed tounderstand theunderlying relationships
among the genotype‐specific parameters and with observed tuber yield (Fig. 5.6;
Table5.4).TheresultsrevealedthatparametersNmaxandnSOhadhighestsignificant
(P<0.01)positiveandnegativedirecteffects(i.e.0.91and‐0.47,respectively)onthe
finaltuberyield(Fig.5.6),whereasmV,mR,andHmaxhadleast(P>0.05)directeffects
onfinaltuberyield(i.e.‐0.05,‐0.10,0.00,respectively).Thesumofdirectandindirect
effects of parameters from path coefficient analysis revealed that there existed
significant (P < 0.01) positive effects of mR, Hmax, and Nmax on tuber yield (their
correlationcoefficientsas0.45,0.41,and0.81,respectively;Table5.4).
Path coefficient analysis has been very useful in partitioning the correlation
coefficient into components of direct and indirect effects and give thorough
understandingof actual causal relationships and contributionof various characters
on the target trait (Dewey and Lu, 1959; Samonte, 1998). Our results further
indicated thatrelationshipsofparameters(mR,Hmax)with tuberyieldwereactually
drivenbyindirecteffects,mainlythroughNmax(Table5.4).Theseresultsarelargelyin
line with our GECROS‐based sensitivity analysis, suggesting that genotypes with
higherNmaxmayexhibitmorefinaltuberyieldprovidednSOislow(Casaetal.,2005;
Chapter3).
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
155
Figure5.6.Pathcoefficientstructuralmodeldescribingdirectandindirecteffectsof
differentgenotype‐specificparameters(mR,mV,Hmax,nSO,andNmax;seeTable5.1for
theirdescriptions)onthetuberyieldacrosssixexperimentsforanF1populationof
potato. The solid line represents the correlation coefficient between two predictor
variables;dashed linerepresents thepathcoefficient fromthepredictorvariable to
responsevariable(tuberyield).**SignificantatP<0.01,NSNon‐significant(P>0.05).
4.Conclusions
Yield variation in terms of growth and development of the crop is complex, for it
involves the effect of external factors on all the physiological processes, the inter‐
relationships between different processes and their dependence on the genetic
constituentoftheplant.Inthisstudyweexploredtheabilityoftheecophysiological
model‘GECROS’toanalysedifferencesintuberyieldinasetofcultivarsofmaturity
types and a diploid F1 segregating population. The model described well the
dynamics of important growth related processes in potato and gave important
insights into theunderlying component traits and factors influencing the tuberdry
matterproduction.Usingfivemeasuredgenotype‐specificparameters(mV,mR,Hmax,
Chapter5
156
Table5.4.Pathcoefficientanalysisofdirectandindirecteffectsofgenotype‐specific
parametersonthetuberyieldofanF1populationofpotato.tdstandsforthermalday.
SeeTable5.1forparameterdescriptions.
Variable Effect†mV(td) Directeffect ‐0.052Indirecteffectvia
mR 0.008Hmax 0.000nSO ‐0.028Nmax ‐0.090
Totalcorrelation ‐0.161mR(td) Directeffect ‐0.098Indirecteffectvia
mV 0.004Hmax 0.000nSO 0.226Nmax 0.318
Totalcorrelation 0.451Hmax(m) Directeffect 0.000Indirecteffectvia
mV 0.002mR ‐0.089nSO 0.213Nmax 0.287
Totalcorrelation 0.413nSO(gNg‐1) Directeffect ‐0.473Indirecteffectvia
mV ‐0.003mR 0.047Hmax 0.000Nmax 0.131
Totalcorrelation ‐0.298Nmax(gNm‐2) Directeffect 0.908Indirecteffectvia
mV 0.005mR ‐0.034Hmax 0.000nSO ‐0.068
Totalcorrelation 0.811†Acrossexperiments
Anecophysiologicalmodelanalysisofyielddifferencesinpotato
157
nSO, and Nmax), GECROS showed a good potential in explaining the observed yield
differencesamongthegenotypes.Simulatedtrendsofgrowthwereinagreementwith
themeasureddata.Themodelyieldedreasonablepredictionsofdifferencesintuber
yield across environments and across genotypes. The simulation results suggested
thatgenotype‐specificparameterNmaxcontributedmosttothedeterminationoftuber
yield.Theseresultswerefurtherprovedbypathcoefficientanalysissuggestingthat
genotypes with higher Nmaxand low nSO may exhibit more final tuber yield. Such
information would greatly facilitate the development of ideotypes for specific
environment(s).
AsawholethisstudyhighlightedtheusefulnessofGECROScropgrowthmodel
for exploring the impact of new genotypes and the contribution of individual
physiologicaltraitsonyieldbysimulatingtheresponsesofgenotypesacrossdifferent
environments.Inaddition,ourmodelapproachshedlightonsomeofthemechanisms
causing genotype differences in tuber yield and provided support for important
aspects of hypothesis regarding N accumulation. The model analysis identified N
uptake and use efficiency as possible areas for improvements in tuber drymatter
production. Opportunities for improving the modelling of and understanding of N
dynamics and source‐sink dynamics in potato have become apparent during the
study. Further analysis of the genotypic parameters should be performed in
conjunctionwithmolecularmarkersinordertodeterminetheirgeneticcontrol.Such
informationwould guide research towards key processes affecting crop yields and
mayhelporientatebreedingprogrammesassuggested,e.g.byYinetal.(2004);Yin
andStruik(2008).
CHAPTER6
Generaldiscussion Breedingforhigh‐yieldinggenotypes:AchallengeBreeding for high‐yielding cultivars for specific and/or varied environments is a
majorchallengetoachieveworldfoodsecurityinthenewcentury.Thepaceatwhich
progress has been made in achieving higher crop yields has become stagnant
comparedwiththatrequiredbythegrowingdemands(Cassman,1999).Thepaceand
efficiencyof geneticprogressmustnow increase tomeet theprojecteddemand for
agriculturalproducts(FAO,2009).
Molecularbiologyhasdeliveredcommercial successes inenablingcropplants
to better resist pests and tolerate herbicides and, more recently, in improving
product quality.However, the challenge remains as to how to manipulate more
complex growth anddevelopment traits associatedwith crop adaptation and yield.
The inability to connect information at gene level to the expressed phenotype in a
manner that is useful for selection and plant breeding has restricted adoption of
molecular approaches in plant breeding (Miflin, 2000). Enhanced capabilities in
genotyping have not been matched by development of enhanced capabilities in
phenotyping or by development of enhanced approaches to link genotype and
phenotype(Camposetal.,2004).Thesituationisparticularlychallengingforcomplex
traits associated with crop adaptation and yield (e.g. grain or tuber drymatter or
resource use efficiencies) as they are regulated by multiple interacting genes and
environmental conditions, so that gene‐to‐phenotype relationships are not
straightforward (Liet al., 2001; Luoet al., 2001).Hence, new approaches must be
developed to accelerate breeding through improving genotyping and phenotyping
methodsandbyincreasingtheavailablegeneticdiversityinbreedinggermplasm.
Potato:AcomplexcropInpotato,breedingforyieldpotentialandrangeofadaptation(ordegreeofstability
inperformanceoverawiderangeofenvironments)isparticularlydifficultduetothe
complexity of the crop’s whole‐plant physiology (cf. Van Dam et al., 1996; Celis‐
Gamboaetal.,2003).Growthandyieldaredeterminedbyadiversityofcomplexcrop
factorsincluding(a)therateofleafappearance,individualleafgrowth,finalleafsize,
and the life span of individual leaves, (b) the number of lower and sympodial
branches, (c) the overall rate of canopy development (e.g. increasing of N supply
Chapter6
160
levels accelerates crop development and the timewhenmaximum canopy cover is
reached),(d)lightinterceptionbythecropovertime,(e)therateofphotosynthesis,
(f)theonsetoftuberisation,and(g)finaltuberyieldandharvestindex.Furthermore,
likemanyothercomplexquantitativetraits,eachdevelopmentalstageandcropfactor
in potato is likely to be regulated and controlled by a large set of interacting
expressedgenesthroughouttheplantgrowthandthestronginfluenceofgenotype‐
by‐environment (GE) interactions (Allard and Bradshaw, 1964; Jefferies andMacKerron, 1993; Bachem et al., 2000; Schittenhelm et al., 2006). All these
complexitiesmakethemanipulationofyielddeterminingtraitsandtheirpredictiona
challengingtaskinpotato.
The traditional system of potato breeding is a laborious and time‐consuming
process, and the probability of finding superior cultivars is very low. Breeding
generallyinvolvesevaluationandselection,basedonmanydifferenttraitsincluding
yield, disease resistance and quality, of the clonally propagated progeny of a cross
between two clones. These parent clones can be existing cultivars or clones with
introgression from wild species. Normally, it takes more than 10 to 15 years to
produceanewcultivar(Caligari,1992;Hunt,1993;HaverkortandKooman,1997).
Roleofmulti‐environmenttrialsPlant breeders are mostly interested in characterising genotypes in diverse
environments to understand how a particular genotype performs in different
environments. Individuals in genetic populations are studied in replicated
experimentsanddistributedover ‘discrete’ environmentswhichareoften locations
and/oryears.Themaintaskofdataanalysisistoestimateparameterswhichreveal
theeffectsofgeneticandenvironmentalfactorscontrollingtheexpressionofspecific
traits. The parameters concerning effectiveness of selection strategies of individual
genotypes, forexample,areaimedtoexamineperformanceoftraitsandcorrelation
between them across environments, variability of entries due to genetic and
environmental components, heritability, and genetic gain over generations of
selectionfornewsuperiorvarietiesingrowingregions(Tai,2007).Informationlike
thesecouldhelptobetterunderstandthetypeandsizeofgenetic(G),environmental
(E), and GE interaction components of variation, and the reasons for theiroccurrence; and if necessary, a strategy to develop genotypes for particular target
environments.
The G or E components of variation for any trait normally show average
differencesbetweengenotypesorenvironments,respectively.Purelyenvironmental
effects, reflecting the different ecological potential of sites and management
Generaldiscussion
161
conditions, are not of direct concern for the breeding or recommendation of plant
varieties. However, differences between genotypes may vary widely among
environmentsinthepresenceofGEinteractioneffects.Therefore,thereisaneedtoquantifythe impactsofGEinordertoexplorethepotentialopportunitiestowardsyieldimprovementunderthespecificand/orvariedenvironments.Thiscouldhelpto
betterunderstandthetypeandsizeofG,E,andGEcomponentsofvariation,andthereasonsfortheiroccurrence.Itcouldalsohelptodevelop,ifnecessary,astrategyto
designanidealgenotype(i.e.ideotypebreeding)inpotato.
Inpotato,however,very littlesystematic,quantitative information isavailable
about genotypic differences in response to contrasting environmental conditions
and/or input levels, e.g. temperature, precipitation, and nitrogen (N) fertilisation
regimes. A better understanding of the physiological, genetic and environmental
regulation of relationships between the main attributes of growth and yield
determiningcomponentswillfacilitatethebreedingofvarietiesabletoperformwell
underspecificsetsofenvironmentalconditions.Inordertoachievethisgoalweneed
useful tools and more concerted efforts to understand GE, and the benefits ofintegratingaccumulatedandexpensivedatasets.RoleofcropphysiologyBesidesrecentdevelopmentsingenomics(suchasgenomesequencing)thatwill
provide useful tools andmassive amounts of information to speed up the plant
breedingprocess(Stuberetal.,1999;Miflin,2000),anoptionforimprovingbreeding
efficiency is to develop and utilise a thorough understanding of underlying
morphologicalandphysiologicalprocessesthatdetermineimportanttraitslikeyield
(Bindraban,1997).
Cropphysiology,asapowerful, rapidlydevelopingandquantitativediscipline,
hastheabilitytocomplementplantbreedingasphysiologicalstudiesprovideaway
todissectcomplextraitsintosimplercomponentsthatmightbeunderseparateand
probablysimplergeneticcontrol(Yinetal.,2002).Anyincreaseinyieldmusthavea
physiological basis that, if targeted, should permit a significant advance in the
identification of superior genotypes (Richards, 1996). So far, however, only in a
limitednumberof instancesphysiologicalapproacheshavebeenappliedtosupport
crop improvementprogrammes(Sinclairetal.,2004).Thissituationmaychange in
the future if the links between physiology and genetics are established or
strengthened (Miflin, 2000). There is a growing awareness that inorder to better
analysephenotypesofcomplextraitsforanycropgenotypeunderanyenvironmental
scenario using increasingly available genomic information, integration of genetics
Chapter6
162
withquantitativecropphysiologyisrequired(Tardieu,2003).
Systemsapproaches:TheirroleinassistingbreedingAshighlightedinprevioussections,progressincropimprovementandparticularlyin
molecular approaches to plant breeding is limited by our ability to predict plant
phenotypebasedonitsgenotype,especiallyforcomplextraitslikeyield(Hammeret
al.,2010).Newapproachescouldcomplementtraditionalapproacheswithanalytical
selectionmethodologies to further improvecropyieldsand theoverallefficiencyof
thebreedingprocess.Suchnewapproacheswillbeofconsiderableinteresttoplant
breeders(Chapter1).
An important advance in crop physiology during the last four decades is the
development of dynamic process‐based crop growth models for predicting yields
undervariedenvironments(Booteetal.,1996).Process‐basedecophysiologicalcrop
growth models have been developed by integrating knowledge across multi‐
disciplinessuchasplantphysiology,ecology,agronomy,soilscience,andmeteorology
(Loomisetal.,1979).
Asquantitativecropmodelsrepresentcausalitybetweencomponentprocesses
andyield,theycanpredictcropperformancebeyondtheenvironmentsforwhichthe
model parameters were estimated. In this respect, systems approaches based on
physiologicallysoundcropgrowthmodelscanquantifyandintegratecropresponses
togenetic,environmental,andmanagementfactors.Suchanintegratedapproachcan
guide research on questions such as bridging the gap between genotype and
phenotypeandtopotentiallyresolveGEinteractions(Chenuetal.,2009;Messinaetal., 2009;Hammeret al., 2010;TardieuandTuberosa,2010;YinandStruik,2010),
andinproposingideotypesforparticulartargetenvironments(Hammeretal.,1999;
Yinetal.,2000a,2004;YinandStruik,2008).Thisoffersamajorpotentialofapplying
modelling based approaches in plant breeding strategies (Jackson et al., 1996;
Chapter1).
YieldimprovementinpotatoTuber yield is still themost important trait to be selected for, since it remains the
basisonwhichthegrowerobtainsaneconomicreturn.Yieldproductioninpotatocan
be explained in terms of an integrated response of distinct plant physiological
processes(seeequation1.1,Chapter1).Physiologicaltraitsexplainingthedynamics
ofcanopycoverandtuberbulkingareveryusefulastheyareimportantdeterminants
ofyield(Hodges,1991).The totalbiomassproductionandaccumulationdependon
Generaldiscussion
163
the intercepted photosynthetically active radiation (PAR), which is directly
proportional to the crop green canopy cover (Burstall and Harris, 1983; Spitters,
1988; Tekalign and Hammes, 2005). Differences in the rate of dry matter
accumulation can be attributed to differences in light interception caused by
variability in growth and duration of maximum green canopy cover and its
senescence,andincanopy‐levelefficiencyofutilisationofinterceptedradiationasa
result of changes in the functionality of leaf photosynthesis during tuber bulking
(Pashiardis,1987;Spitters,1988;VanDeldenetal.,2001).Thetuberyieldisthenthe
productof interceptedradiationandradiationuseefficiency(RUE)anddetermined
bythefractionoftotalbiomassthatispartitionedtothetubers,influencedbytherate
anddurationoftuberbulking(VanderZaagandDoornbos,1987;Spittersetal.,1989;
Struik et al., 1990). All this shows that yield production in potato is the result of
interactions among several crop characters which are greatly influenced by both
geneticandenvironmentaland/ormanagementfactors.
Thegenetic improvementofyieldcanbeunderstoodmoremechanisticallyby
partitioning theyield into component traits (Chapters2 and3) andanalysing their
inter‐relationships(Bezantetal.,1997;Arausetal.,2008).Itisaveryusefulapproach
for the breeder to split a complex trait intomore simple ones, provided sufficient
geneticvariationexistsforthesetraits;thattheyhaveclearlyhigherheritabilitythan
thecomplextrait,andtheirassessmentwouldbeeasierthanthatoftuberyielditself.
Untilnow,limitedeffortshavebeenmadetostudythephysiologicalandgenetic
basisofvariabilityamongtraitsdeterminingcropperformance.Thekeyphysiological
and genetic processes leading to yield development and the factors affecting these
processesarestillnotwellunderstood.Furthermore,limitedinformationisavailable
on the temporal dynamics of important above‐ and below ground developmental
processes in potato under diverse environmental conditions on a large set of
contrasting genotypes. Majority of studies have focussed on one, or a very limited
number of genotypes and/or cultivars. This has made more difficult the
understandingofcomplexregulatorymechanismsunderlyingthedynamicsoftuber
formationandotherdevelopmentalprocesses.ThesisapproachKeeping in view the above discussion, this thesis aims to develop an approach to
quantify and predict the temporal dynamics of yield formation of individual
genotypesfromalargepopulationandtoestimateparameterswhichcouldrevealthe
effects of genetic and environmental factors on the important plant processes
controllingyieldformationinpotato.Besides,thisstudyalsoexploresthequestionof
Chapter6
164
whether using a crop growth and development modelling framework can link
phenotypiccomplexitytounderlyinggeneticsystemsinawaythatmaycontributeto
new insights into molecular breeding strategies as well as into possibilities to
indirectly select for higher yield. We approach this question by physiological
dissection and integrative modelling of complex traits in potato. Figures 1.2‐1.3
(Chapter1),presentaschematicrepresentationoftheoverallapproachemployedin
thisthesis.Likemostgeneticstudiesinpotato,wechoseadiploid(2n=2x=24)F1
segregatingpopulation(i.e.SHRH)inordertoavoidthecomplexityoftetrasomicinheritance(BradshawandMackay,1994;Meyeretal.,1998).Thispopulationiswell
adapted to the growing conditions of the Netherlands and segregates formaturity
(Van der Wal et al. 1978; Van Oijen 1991). Besides, we also used five standard
cultivarswithverydifferentmaturitytypes(i.e.Première,Bintje,Seresta,Astarte,and
Karnico)asstandardgenotypes.Thedatasetsusedinthesisareprimarilybasedon
six contrasting field experiments carried out in Wageningen (52˚ N latitude), the
Netherlands, during 2002, 2004, and 2005. These experiments differed in
environmental conditions because they were carried out in different years, on
different soils and under different N fertiliser regimes, thereby creating six
contrasting environments. For more details about the plant material used and
experimentaldetails(seeMaterialsandmethods,Chapter2).
Inthefollowingsections,Idiscussthemainfindingsandoverallcontributionof
thisthesis.
AnapproachforanalysingthedynamicsofcanopycoverandtuberbulkingAs mentioned in the previous section, intercepted radiation has been found to be
linearly correlatedwith the quantity of drymatter producedwhich, in the case of
potatoes, depends on the leaf canopy size, rate, and theduration over the growing
season to intercept radiant energy (Van der Zaag, 1984; MacKerron and Waister,
1985a,b;VanderZaagandDoornbos,1987;FahemandHaverkort,1988;VanDelden
etal.,2001).Tuberbulkinginpotato,ontheotherhandisthedeterminantprocessin
the formation of harvestable products of potato crop. It marks the onset of the
production phase of the potato crop after a dynamic sequence of several complex
physiological events (Ewing and Struik, 1992; Jackson, 1999; Struik et al., 2005).
Quantitativeunderstandingofbothcanopyandtuberbulkingdynamics is therefore
imperativetoexplainpotatoproductionunderparticularconditions.
Research has addressed many aspects of potato canopy growth and
development(e.g.FleisherandTimlin,2006;Fleisheretal.,2006;VosandBiemond,
1992);however,insightsintotherelationshipsamongenvironment,nutritional,and
Generaldiscussion
165
otherfactors,particularlywithrespecttocanopycoveranditsrelationwithwhole‐
plant physiology are still lacking (Almekinders and Struik, 1996). Despite the
importance of potato tubers as a source of food and a means of propagation, the
initiation,growthanddevelopmentoftubersandthefactorsaffectingtheseprocesses
are not well understood, especially in the context of large number of contrasting
genotypes and diverse multi‐environments under field conditions. Until now, few
studies have quantitatively and systematically explored the temporal dynamics of
tuber bulking for a wide range of genotypes, with the exception of the work by
Spitters(1988)andCelis‐Gamboaetal.(2003).
InChapter2,wethereforedevelopedaquantitativeapproachtobreakdownthe
timecourseofcanopycoverduringtheentirecropcycleasafunctionofthermaltime
into component traits. Canopy cover development pattern was subdivided into
distinctstagesusingabetafunctionbasedonYinetal.(2003,2009).Themodel(eqns
2.1‐2.3,Chapter2)describesthreephasesofcanopydevelopment(i.e.canopybuild‐
up phase, maximum cover phase, and canopy decline phase) through multiple
parameters defining the timing, rate, duration and area under green canopy curve
((seeFig.2.1inChapter2);Table6.1).Thesetraitswerebiologicallymeaningfuland
directlyrelatedtotheabilityoftheadaptedgenotypestointerceptPARandthusto
createhightuberyields.Manyofthesetraitsshowedsignificantgeneticvariation.
Inaddition,Chapter3presentsaquantitativeapproachtoanalysethedynamics
oftuberbulkingduringtheentirecropcycleanditsvariabilityamonga largesetof
potato genotypes and to break the time course and its variation down into
biologically meaningful and genetically relevant component traits. Tuber bulking
results from two basic processes, tuber initiation and tuber growth. Tuber growth
normally follows a sigmoid pattern as a function of time, including an early
acceleratingphase,alinearphase,andamaturationphase(seeFig.3.1inChapter3).
TheseprocessescanbedescribedbyusingtheexpolinearfunctionofGoudriaanand
Monteith (1990) as described by eqn 3.1 (Chapter 3). However, because of its
curvilinear nature the true growth rate in the linear phase is under‐estimated.We
thereforemodified the approach of Goudriaan andMonteith (1990) and quantified
thesephasestogetherwithapiece‐wiseexpolinearfunction(i.e.combinedeqns3.1‐
3.3;Chapter3).Severalparametersweregenerateddescribingtherateandduration
oflinearphaseoftuberbulking(Table6.1).
Toaccountfortheinfluenceofdailyandseasonaltemperaturefluctuationson
tuber growth, all time variables and durationwere expressed as thermal days (td)
followingour approach inChapter2.According to the results, ourmodel approach
successfully described the sigmoidal time‐course curve of canopy growth (Fig. 2.2,
Chapter 2) and the dynamics of tuber bulking (Fig. 3.2, Chapter 3) of individual
Chapter6
166
genotypes from a large set of population across varied environments via
agronomicallymeaningfultraits(asdiscussedlater).QuantificationofresourceuseefficienciesProductivity increase can also be accompanied by an increase in the genotype’s
resource use efficiency. Efficient utilisation requires both effective capture of
resources and efficient conversion into useable biomass. In potato, plant growth
includingcanopyproduction forefficient light interceptionandphotosynthesis (see
earliertext)dependsonadequatenutrition,andoptimisedfertiliserinputs,especially
N.BothradiationandNarethereforeessentialresourcesforefficientcropproduction.
Thevariationindrymattercontentamongcultivarscanalsopartlybeattributedto
the variation in efficiency of diverting of more dry matter to the tubers (Spitters,
1988; Struik et al., 1990). Therefore the interpretation of plant growth in terms of
accumulatedinterceptedPAR,Nuptakeandtheefficiencywithwhichtheseresources
are used for dry matter production should receive adequate attention in potato
breedingprogrammes.
The aim of this thesis was also to produce tools and materials suitable for
breeding new crop varieties with improved resource (i.e. radiation and N) use
efficiencies and to study how they are influenced by genetic and environmental
influences. Nonetheless, interventions in breeding based on understanding of the
geneticandphysiologicalbasisofcropperformancehavethepotentialtoaccelerate
geneticgainsforyieldandresourceuseefficiencies.Wethereforeanalysedradiation‐
andNuseefficiencies(RUEandNUE,respectively),andtheirrelationshipswithour
estimatedphysiologicalmodeltraits.
ThetermRUEreferstotheslopeoftherelationbetweentotaldrymatter(gm‐2)
and cumulative intercepted radiation (MJ m‐2) (Haverkort and Harris, 1987).
Similarly,NUE is generallydefined as the ratiobetween total plantdryweight and
plantNuptake.Inthisthesis,however,duetolackofdetailedmeasurementsoftotal
plant biomass for our large set of genotypes, radiation andN use efficiencieswere
estimatedonthebasisof tuber‐dryweight foreach individualgenotype/cultivarby
dividing the total tuber dry matter at maturity (g DM m−2) by the cumulative
intercepted PAR (MJ PARintm−2) and total tuber N uptake (g N m−2), respectively
(Chapter3).
Theresultsindicatedthatthereexistedasignificant(P<0.01)widevariationfor
bothRUEandNUEwithintheF1populationwithvaluesrangingfrom1.5gDMMJ‐1
to 2.6 g DM MJ‐1 and 54.4 g DM g‐1N to 81.6 g DM g‐1N, respectively (Table 3.2,
Chapter 3). Thesewide variations (see Fig. 3.3, Chapter 3) suggested transgressive
Generaldiscussion
167
Table6.1.DescriptionofparametersreproducedfromChapters2and3.tdstandsfor
thermalday.
Trait Description Unit
Canopycoverdynamics
tm1 Inflexionpointduringcanopybuild‐upphase td
t1,DP1 Periodfromplantemergencetomaximumcanopycover td
(momentanddurationsinceemergence,respectively)
t2 Onsetofcanopycoversenescence td
te Timeofcompletesenescenceofcanopycoverorcrop td
vmax Maximumlevelofcanopycover %
cm1 Maximumcanopygrowthrateduringbuild‐upphase %td‐1
c1 Averagecanopygrowthrateduringbuild‐upphase %td‐1
c3 Averagecanopysenescencerateduringdecline‐phase %td‐1
DP2 Totaldurationofmaximumcanopycoverphase td
DP3 Totaldurationofcanopysenescencephase td
A1 AreaundercanopycurveforDP1 td%
A2 AreaundercanopycurveforDP2 td%
A3 AreaundercanopycurveforDP3 td%
Asum Areaunderwholegreencanopycurve td%
Tubergrowth
tB Onsetoflinearphaseoftuberbulking td
tE Endoflinearphaseoftuberbulking td
ED Totaldurationoftuberbulking td
cm Rateoftuberbulking gtd‐1
wmax Maximumtuberdrymatteratcropmaturity gm‐2
segregation.
The continuous selection in most breeding programmes has very much
narrowed down the genetic base for important resource use efficiencies in most
moderndaypotatocultivars (Brown,1993;Love,1999).This thesis concluded that
there are enough possibilities to exploit the available variation in the SH RHbreeding population and may offer more efficient screening possibilities for
Chapter6
168
improvingresource(radiationandN)useefficiencies.Inthecomingsectionswewill
discussthebasisofthisvariation.
GeneticanalysisTheprospectsofimprovingatargettraitbyselectingforcomponenttraitsismostly
determinedbytheircorrelationwiththetargettrait(e.g.yield)aswellasamountof
heritableor genetic variation for theparticular component traits relative to thatof
non‐heritableor environmental variation, andby thenature andmagnitudeofGEinteraction. Therefore, an understanding of such relationships within potato
germplasmisimportanttoestablishabroadgeneticbaseforbreedingpurposes.
Results of the current thesis demonstrated that standard cultivars exhibited
significant (P<0.05) differences with respect to component traits controlling the
dynamicsofcanopycoverandtuberbulking,resource(radiation,N)useefficiencies,
andfinaltuberyield.Notsurprisingly,inmostcases,performanceofthecommercial
potatocultivarswassuperiortothatofthediploidF1population(Tables2.2;3.1‐3.2).
Tetraploid potatoes are typicallymore vigorous and high yielding (DeMaine, 1984;
Hutton,1994).Thesomewhatdecreasedvigourandyield indiploidsmaybedueto
theirploidyreductionandinbreedingdepression(Kotch,1987).
Estimates of different components of variance can be used to formulate the
mostefficientbreedingstrategyforimprovedgenotypes.Studyofsourcesofvariation
amongthetestedF1genotypesindicatedthattherewassignificant(P<0.01)genetic
variation for most traits determining the course of canopy development, tuber
bulking,and(radiation,N)resourceuseefficiencies(Chapters2and3).
The results further indicated that some genotypes performed best across the
environments. Table 6.2 presents themeans of top 10% of the out‐performing F1
genotypesfor fewselectedphysiological traits.Suchgenotypescouldbeveryuseful
forselectionandfurthertestinginbreedingprogrammes.
Heritabilityofa trait isakeycomponent indetermininggeneticadvance from
selection (Nyquist, 1991). The results indicated high broad‐sense heritability (H2)
estimates across environments for most traits. For instance, >80%H2values were
recorded for traits (t2, te, DP2, A2, Asum, cm, tE, ED, RUE, and NUE) (cf. Tables 2.6
(Chapter2)and3.5 (Chapter3)).Theseresultssuggested that thesearerepeatable
traits and strongly expressed across a range of environmental conditions. On the
otherhand lowbroad‐senseheritabilityaswellasminimumCVGestimates fort1or
DP1andc3indicatedthattotaldurationofcanopybuild‐upphaseandrateofcanopy
senescence, respectively is sensitive to the environment (mainly N) (Table 2.4,
Chapter2).
Generaldiscussion
169
No.
Genotype
t e(td)
Genotype
DP2(td)
Genotype
Asum(td%)
Genotype
c m(gtd
‐1)
1SHRH83‐L9*
79.5
SHRH83‐L9
32.6
SHRH34‐H6*
6146.4
SHRH11‐F10*
61.6
2SHRH34‐H6*
79.3
SHRH‐136
32.4
SHRH83‐L9*
5836
SHRH53‐J8*
58.1
3SHRH‐136*
75.4
SHRH‐150
28.3
SHRH‐136*
5739.3
SHRH118‐C7*
53.5
4SHRH29‐H2
73.1
SHRH‐148
27.5
SHRH‐148
5156.9
SHRH51‐J7*
53.5
5SHRH‐148
73.0
SHRH73‐B11
27.1
SHRH67‐K9*
5092.7
SHRH‐465
53.0
6SHRH9‐F8
71.2
SHRH‐428
26.1
SHRH29‐H2
5065.6
SHRH86‐L12*
52.0
7SHRH73‐B11
71.0
SHRH71‐K12
25.5
SHRH9‐F8
5061.0
SHRH17‐G5*
50.8
8SHRH67‐K9*
71.0
SHRH179‐E5
24.7
SHRH73‐B11
5022.4
SHRH138‐C10*
50.4
9SHRH111‐N12
70.6
SHRH9‐F8
24.5
SHRH‐150
4992.2
SHRH‐500*
50.3
10
SHRH179‐E5
70.1
SHRH89‐M3
24.3
SHRH179‐E5
4984.6
SHRH35‐H7
48.8
No.
Genotype
ED(td)
Genotype
RUE
(gDMMJ‐1)
Genotype
NUE
(gDMg‐1 N)
Genotype
wmax
(gm
‐2)
1SHRH83‐L9
68.0
SHRH‐477*
2.78
SHRH‐136*
78.7
SHRH42‐H12*
1215.9
2SHRH‐136
66.4
SHRH11‐F10
2.77
SHRH‐406*
78.2
SHRH‐406*
1177.9
3SHRH34‐H6
63.1
SHRH33‐H5*
2.77
SHRH71‐K12*
78.1
SHRH11‐F10
1166.2
4SHRH29‐H2
60.8
SHRH23‐G9*
2.64
SHRH114‐O3*
77.5
SHRH53‐J8*
1163.8
5SHRH67‐K9
60.3
SHRH‐500
2.61
SHRH34‐H6*
77.3
SHRH34‐H6
1158.0
6SHRH9‐F8
58.0
SHRH35‐H7*
2.59
SHRH‐148*
76.4
SHRH179‐E5
1122.0
7SHRH‐416
56.5
SHRH97‐M9*
2.56
SHRH17‐G5*
74.8
SHRH89‐M3*
1116.1
8SHRH87‐M1
54.2
SHRH95‐M7*
2.55
SHRH67‐K9*
74.2
SHRH6‐F5
1115.8
9SHRH71‐K12
53.2
SHRH‐465*
2.55
SHRH‐150*
73.8
SHRH97‐M9
1107.3
10
SHRH179‐E5
51.7
SHRH53‐J8
2.47
SHRH‐478*
73.3
SHRH83‐L9*
1094.0
Table6.2.ListoftoptenbestperformingF1genotypesforspecificphysiologicalmodeltraits.Traitvaluesindicategenotype
meanacrossenvironments.FordescriptionoftraitsseeTable6.1.*denotesparticulargenotypesthatperform
edwellinlowN
environm
ent(i.e.Exp3).
Chapter6
170
GE interaction is an extremely important issue in potato breeding as suchinteraction impedesplantbreedingprogress for complex traits suchasyieldand is
consideredtobeamongthemajorfactorslimitingresponsetoselection(Tarnetal.,
1992;Bradshaw,1994;Kang,1998).Thedevelopmentofshootsandtubersinpotato
isstronglyinfluencedbybothgeneticandenvironmentalfactors.Inthisstudy,itwas
noted that proportion of GE interaction variancewas greater than the G variancecomponentfortraitsdeterminingthecanopybuild‐upandsenescencephases,seeFig.
2.2(Chapter2).Thesetraitsmainlyincludedmaximumcanopycover(vmax),ratesof
canopygrowthandsenescence(cm1,c1,andc3),duration(DP1)andareaundercanopy
cover(A1)whencanopycoverreaches itsmaximum,duration(DP3)andareaunder
canopycover(A3)ofcanopysenescence(Table2.3,Chapter2).Thiscouldmeanthat
these traits may show a range of phenotypic expressions when subject to diverse
environmentsduetoGEinteraction.ThelargertheGEinteractioncomponent,thesmaller theheritabilityestimate; thus,selectionprogresswouldbereducedaswell.
However, themoderate (50% <H2< 70%) heritability estimates formost of these
traitsindicatedthatthesetraitsareresponsivetoselection(Table2.6,Chapter2).
Our results further indicated that some important physiological traits were
stable across the environments on account of their G component of variance being
higherthantheGEinteraction.Thesetraitsincludedonsetofcanopysenescence(t2),cropmaturity (te), duration (DP2) and area under canopy cover (A2), when canopy
coverisatmaximumlevel(or100%),andtotalareaundergreencanopycover(Asum)
(Table2.3,Chapter2).
Incaseoftraitsrelatedtothedynamicsoftuberbulkingandresource(radiation
andN)useefficiencies,thenatureofthedatasets(i.e.lackofindependentreplication
of estimations) did not allow estimating the GE component of variation for thesetraits. However, highCVG estimates for these traits alongwith high (>80%) broad‐
senseH2 (asdiscussedearlier) indicatedstronggeneticbasis for these traits(Table
3.5,Chapter3).
Studyofsourcesofvariationfortuberyieldperseontheotherhand,indicated
high contribution of environmental (E) variance in its total observed phenotypic
variance(Table3.4,Chapter3).Thisisnotsurprisingasyieldisacomplextraitand
thenetresultofmanyplantphysiologicalprocessesandtheirrelationshipswiththe
environmentparticularlyNasdiscussedinthelatersections.
For a successful breeding programme, genetic diversity and variability in a
populationplaysavitalrole(Omoiguietal.,2006).Thisthesisclearlyindicatedthat
thereexistsstronggeneticdiversityintheSHRHgermplasmfornearlyallthemodeltraits. There are opportunities to further analyse and exploit this wide genetic
variabilityavailable,whichcouldpotentiallybeusedinbreedingprogrammesaimed
Generaldiscussion
171
at improving canopy, as well as tuber bulking dynamics and resource (radiation, N) use
efficiencies and ultimately tuber dry matter production. RoleofenvironmentPlant breeders are mostly interested in characterising genotypes in diverse
environmentstounderstandhowaparticularsetofgenotypesperformdifferentlyin
different environments. A thorough understanding of the relationship between the
crop and the environment may help to increase potato productivity for specific
environments.
Inthisthesis,environmentrepresentsacombinationofyear,site,anddifferent
N availability regimes (Chapter 2). Typically, N is the key environmental variable
affecting the important yield determining growth and development processes of
potato(Honeycuttetal.,1996).Nmanagementisthereforeahighpriorityinpotato
cropping systems. In this study we lacked precise information about amount of
mineral N becoming available during the course of crop growth. However, crop N
uptake can be used as a site‐specific indicator of N that is “available” to the crop
(Sullivanetal.,2008).Therefore,toassesstheroleofN,totalamountofNuptakeby
tuberswas used as an indicator ofN availability per each environment (Table 2.1,
Chapter2).
Our results further indicated good correlations among the environments for
mostofphysiologicaltraits,e.g.Asum.However,thecorrelationsvariedmuchforsome
traits like t1 orvmax. It is obvious that traitsmore sensitive to environment and/or
GE interaction show more heterogeneity among the environments (Fig. 6.1).Rankingof genotypes changed across the environments (Table6.2)withmaximum
variabilityobservedwithin theF1populationunder lowNenvironment(i.e.Exp3)
(Table2.1,Figs.2.3‐2.4(Chapter2);Table3.4(Chapter3)).Suchchangesingenotype
rankinghavebeendefinedascross‐overGEinteractions(Baker,1988).Cross‐overtypes of interactions are important to breeders and production agronomists for
identifying adapted traits and may enable selection strategies for developing
improvedvarieties.
Plantbreedersandgeneticistshavealong‐standinginterestininvestigatingand
integrating G and GE interaction in selecting superior genotypes for specificapplicationsandforparticulartargetenvironmentswithhighestpossibleeconomical
yieldandresourceuseefficiencies(KangandPham,1991).Itwasremarkabletonote
thatsomegenotypes ‐besidesperformingbest inNsufficientenvironments–were
alsohighlyrankedunderresourcepoorconditions(i.e.Exp3withlowNavailability).
Chapter6
172
Figure6.1.H
eatmapsrevealingcorrelationsbetweensixenvironm
entsfortraits(a)t 1(b)vmax,and(c)A
sum
withinF1populationofpotato.FordescriptionoftraitsseeTable6.1.
(a)
(b)
(c)
Generaldiscussion
173
Table6.2 indicatessuchgenotypes for fewselectedphysiologicalmodel traits.
For instance genotypes SHRH34‐H6 and SHRH‐136 showedmaximumvalues for te
(timeofcropmaturity),Asum(areaunderwholegreencanopycurve)whereassome
other genotypes (e.g. SHRH11‐F10, SHRH53‐J8) indicated high cm (rates of tuber
bulking). It was further noted that the majority of best performing genotypes
indicatedhighresource(radiationandN)useefficienciesunderlowNenvironment
(Chapter 3; Table 6.2).Moreover, some of the best yielding genotypes also yielded
relativelyverywellunderlowNconditions(e.g.SHRH42‐H12,SHRH‐406,SHRH53‐J8,
and SHRH83‐L9; Table 6.2). Such genotypes therefore could be very useful for
breedingforwidelyadaptedpotatolines.
In addition, focus onwhy particular genotypes perform exceptionally well in
low or high input situations could enable selection strategies to be developed for
improvedvarieties.
Overall,theseresultsindicatethatNavailabilitymightbeoneofthekeydriving
factors for causing trade‐offs between the physiological traits in different
environments.Moreover,resultssuggestedthatNavailabilityanditsinteractionwith
genotype’smaturitytypemainlycontributetotheGEinteractionofthegrowthanddevelopment related processes in potato (see Fig. 3.5, Chapter 3). This thesis
therefore allowed a partial understanding of the environmental causes of the
observedGEinteractions.
UnderstandingmaturityinpotatoCropsundergosequentialdevelopmentalphasesfromemergencetosenescencethat
arenotonlycharacterisedbytheirchronologicalage,butalsobytheirphenologyand
reproductive capacity. Inmost annual crop plants, the transition to thematurity is
markedbytheonsetoffloweringandseedproduction(Bond,2000).However,inthe
caseofpotato,progresstomaturityisdifficulttoassess.Thisismainlybecausemost
potatogenotypespossess indeterminategrowthpatternandthereforemaintain the
capacity todevelopnew leaves and continue to grow throughout themajorpartof
theirlifecycle(StruikandEwing,1995;AlmekindersandStruik,1996;Fleisheretal.,
2006).
The sequential phases of growth determining the duration of phenological
stagesofpotatogenotypescouldbeusedtodefinethematuritytypeofgenotype(s).
In order to better understand the maturity type in potato and to study the
consequencesofmaturity typeon the canopycoverand tuberbulkingdynamicsas
wellasresource(radiation,N)useefficiencies,asetofcultivarscoveringawiderange
ofmaturitytypeswereincludedinthisthesis,seeMaterialandMethods(Chapter2).
Chapter6
174
Theresults indicated that lengthof thecanopybuild‐upphase (t1orDP1)was
conservative. Genotypic differences during early growth stages of potato are less
distinct (Spitters, 1988). The duration of maximum canopy cover (DP2) and the
canopy decline phase (DP3)varied greatly with maturity type, with late maturing
genotypeshavinglongerDP2andDP3andthusaccumulatedhigherareaunderwhole
greencanopycurve(Asum)(Table2.2,Chapter2).Theamountoflightinterceptedisin
proportiontotheareaunderthewholegreencanopycurve(Vos,1995a;Fig.6.2).
y = 0.1037x + 80.16R² = 0.9728
0
200
400
600
800
1000
0 2000 4000 6000 8000
Cum
ulat
ive
PAR
int (
MJ
m-2
)
Asum (td %)
Figure6.2.RelationshipbetweencumulatedPARinterceptionandareaunderwhole
greencanopycurve(Asum)inanF1populationofpotatoacrossenvironments.
Thetuberbulkingrate(cm)washighestforearlymaturinggenotypesfollowed
bymid‐lateandthenlategenotypes.Latematuringgenotypeshadlongestperiodof
tuber bulking (ED) followed by mid‐late and early genotypes. As a result crop
matured(tE) laterandtuberyield(wmax)washigher in lategenotypesthan inearly
genotypes(Table3.1,Chapter3).ThiswasinlinewithKoomanandRabbinge(1996)
and Spitters (1988), who reported that, comparedwith late cultivars, early potato
cultivarsallocatea largerpartof theavailableassimilates to thetubersearly in the
growing season, resulting in shorter growingperiods and also lower yields.On the
otherhand, latematuring cultivars combine a long canopy coverwith a long tuber
bulking period (ED) and therefore achievemore tuber drymatter yield (wmax) per
Generaldiscussion
175
unitofNuptake thanmid‐earlyandearlymaturingcultivars (Zebarthetal.,2008).
Figure6.3illustratestherelationshipbetweencropmaturityandfinaltuberyieldfor
asetofstandardcultivarsand(SHRH)segregatingpopulation.The results were further evaluated for relationship between maturity and
resource(radiation,N)useefficiencies.RUEvalueswerehighest forearlymaturing
genotypesfollowedbymid‐lateandlategenotypes(Table3.1,Chapter3).Thismight
berelatedwiththeleafage,whichislinkedwiththepartitioningofdrymatter.Ifthe
foliage of a crop remains green for a longer period, for example in late‐maturing
cultivars, andnonew leavesare formed in the latepartof thegrowing season, the
leavescanbecomesooldthattherateofphotosynthesisdeclinestowardstheendof
the season resulting into low RUE values (Van der Zaag and Doornbos, 1987). In
contrary,resultsindicatedhighNUEestimatesforpotatocultivarswithlatermaturity
(Zebarthetal.,2003,2004).Thereducedcanopysenescenceandgreaterpartitioning
ofN to above groundpartsmost likely contributed to thehighmeasuredvaluesof
NUEforlatecultivars(Sharifietal.,2005).
This thesis indicated that most of the physiological model traits (either
individually or in combination) elaborately explained the genetic variation in
maturitytype.Ourapproachcouldbeusefulinelucidatingtheroleofmaturitytypein
potato.
Thedefinitionofmaturitytypeasagenotypictraitinpotato,however,israther
ambiguousandcurrentlytherearesomanyunclearinterpretations.Manypublicand
privateinstitutionshavetriedtodefinethematuritytypeinpotatoandhavecomeup
with theirowndefinitions.The conventionalmethodor criterionof elucidating the
maturity type used is mostly based on visual observations made in the field at
particular time intervals during the crop cycle and transferring that information to
unit‐less ordinal scales. This criterion does not have any biological meaning and
involves ambiguity and much speculation in understanding the background of
maturity,partlybecauseofthedifferentviewpointsofgrowersandprocessors.The
added physiological and quantitative knowledge gained in Chapters 2 and 3 were
thereforeusedtoevaluatetheconventionalsystemofmaturitytypeandtoquantify
and re‐define the concept ofmaturity type on physiological basis for a large set of
genotypes. Four traits (DP2, cm,ED, andAsum,)were selected among the large set of
physiologicaltraitsbasedontheirhighdirectresponsetoselection(Table6.3),are‐
definedphysiologicalbasedmaturitycriteriawasdevelopedandcomparedwiththe
conventionally used criterion (Chapter 4). The results indicated that the
conventional‐criterion based maturity scores showed more chances of errors and
lack of repeatability in maturity scoring throughout the different maturity classes
(veryearly, early/mid‐early,mid‐late/late, andvery late).Moreover, themethodof
Chapter6
176
Figure 6.3. Final tuber yields of 100 F1 genotypes, 5 standard cultivars, and 2
parentalgenotypes(SHandRH),measuredineachindividualexperiment.
Thermal days (td)
Tub
er y
ield
(g
m-2)
0
500
1000
1500
2000
2500
Exp 1 Exp 2
0
500
1000
1500
2000
2500
Exp 3 Exp 4
0 25 50 75 100
Exp 60
500
1000
1500
2000
2500
0 25 50 75 100
Exp 5
F1 Première Bintje Seresta Astarte Karnico SH RH
Generaldiscussion
177
classification lacked the capability to clearly interpret and discriminate between
maturityclassesforthecrucialgrowthrelatedprocessesofpotato(e.g.Asum)(seeFig.
4.5,Chapter4).
The re‐defined criteria of maturity type based on physiological traits on the
other hand tended to define maturity classes less ambiguously and with a clear
conceptualbasis.Thisstudythereforeindicatedthatre‐definingthematuritytypeof
alargesetofgenotypesispossibleonthebasisofphysiologicaltraitsandmayprove
veryuseful.
Thephysiological traits related to the capability of genotype to interceptPAR
and its rate and duration of tuber bulkingwere indicated crucial formaturity and
therefore could be used to quantify variation inmaturity type among sets of new,
unknownpotatogenotypes(Chapter4).
Thenewphysiology‐basedmaturitydefinitionwouldnotonlyallowrelatingthe
maturitytocropphenologyandphysiologybutcouldalsoofferwiderapplicationsin
potatobreedingandcropmanagementstudies.Onesuchapplicationwouldbetohelp
indesigningbreedingstrategiesforpotatoideotypeswithspecificmaturitytypefor
specificgrowingconditionsand/orenvironments.Inter‐relationshipsamongthephysiologicaltraitsYield is a complex trait associated with many inter‐related components (Yan and
Wallace, 1995). The knowledge of quantitative relationships among physiological
charactersdeterminingyieldcanprovideuseful insightsintohowdifferencesofthe
complex trait and thesignificance in correlationsbetweencomponentandcomplex
traits(e.g.yield)comeabout(Chapter1).Inaddition,determiningthephysiological
traitsmostinvolvedintheformationofayieldcomponentcouldgiveinsightinto
thepossibilitiesofmanipulatingthesizeornumberofthecomponent.
Although information about the correlation of agronomic and morphological
characters with yields is helpful in the identification of the components of this
complexcharacter,yetthesedonotprovidepreciseinformationontherelative
importanceofdirectandindirectinfluencesofeachofthecomponentcharacters.
Manyresearchers(e.g.Bhatt,1972)havereportedthatmerelycorrelationstudiesdo
not clearly reveal such type of information and can be misleading if the high
correlation between two traits is a consequence of the indirect effect of the traits
(Dewey and Lu, 1959). Procedures like path coefficient analysis permit a thorough
understanding of relationships and contribution of various characters to the target
traitbypartitioningthecorrelationcoefficientintocomponentsofdirectandindirect
effects.
Chapter6
178
Path coefficient analysis was therefore performed to quantify the inter‐
relationshipsofourphysiologicalmodeltraitsandtoindicatewhethertheirinfluence
isdirectlyreflectedinthetuberyield(wmax)or iftheytakesomeotherpathwaysto
produceaneffect.Theresults revealed that traits likeAsum followedbyRUEandcm
exerted the highest direct influence on wmax (Table 3.7; Fig. 3.6,
Chapter3).
TheresultsindicatedthathighdirecteffectsofAsumonwmaxweremainlydueto
significant (P<0.05), strong positive phenotypic correlations betweenAsum,ED, and
NUE(Fig.3.6,Chapter3).Ontheotherhandsignificant,strong,positivephenotypic
correlations (r=0.69)betweenRUEandcmwerereflected in theirhighervaluesof
directeffectonwmax.However, results indicated that totaleffects (i.e. sumofdirect
and indirect effects) of traits cm andRUE onwmaxwere reducedmainly due to the
strong, negative, indirect effects ofAsum on these traits (Table 3.7, Chapter 3). The
resultsfurtherindicatedthateffectsoftraitsEDandNUEonthetuberyieldconsisted
mostly of positive indirect influencesmainly throughAsum, suggesting a correlated
responseinselection(Table3.7,Chapter3).
These findings thereforesuggested thatselection forAsum,RUE,andcmshould
be emphasised in breedingprogrammes for improving tuber yields.Donald (1968)
proposedthatanidealgenotypeisthatwithaspecificcombinationofcharacteristics
favourableforgrowthandyieldproductionbasedontheknowledgeofplantandcrop
physiology and morphology. Our results further indicated that an ideal potato
genotype from a breeding perspective is characterised by green canopy cover that
intercepts solar radiation for as long as possible (i.e. with higherAsum) during the
availablegrowingseasontoaccumulateasmuchdrymatteraspossiblemaintaininga
maximumcapacitytodivertdrymattertothetubers(i.e.withgreaterperiodoftuber
bulking(ED))withoutcompromisingtheoptimallevelsofgrowthrates(cm)andRUE
ensuringhighestpossibleeconomicaltuberyields.Figure6.4schematicallyhighlights
varioussituationsofpotatoideotypesunderbothoptimumandresourcepoor(lowN)
conditions.Researcherscouldthenstrivetoobtainanoptimumcombinationofyield
components thatwouldbestsuit therequirement forhighyieldingability inpotato
genotypesforanyparticularenvironment.IndirectselectionforyieldinpotatoInatraditionalcropbreedingprogramme,elite linesare inter‐crossedandthenthe
highest yielding linesare selected.However, yield selection is empiricaldue to low
heritabilityandahighgenotype–environment interaction (Reynoldsetal.,1999). It
alsorequires theevaluationofa largenumberofadvanced lines in fieldyieldtrials
Generaldiscussion
179
(a)
(b)
0
500
1000
1500
2000
0 250 500 750 1000
Cumulative PARint (MJ m-2)
Can
opy
cove
r (%
)
Tub
er d
ry m
atte
r (g
m-2)
0
500
1000
1500
2000
0 250 500 750 1000
0
20
40
60
80
100
0 25 50 75 100
0
20
40
60
80
100
0 25 50 75 100
Thermal days (td)
Figure6.4.Schematicrepresentationofthecourseofgreencanopycoverandtuber
drymatterproductionof5standardcultivars,and2parentalgenotypes(SHandRH)
for(a)lowNsituation(Exp3)and(b)optimumNsituation(Exp6).
over several years and locations (Ball and Konzak, 1993). Potato breeding
programmes therefore constantly need new strategies to improve efficiency by
increasingthefrequencyofselectedgenotypesandreducingtimeandcosts.
An alternative strategy that gives a high indirect response to selection for
averageyieldovertheproductionenvironmentshasthepotentialforscreeninglarge
numbers of genotypes in breeding programmes for identifying and selecting high‐
yielding lines (Shorter et al., 1991; Cooper et al., 1995; Marti et al., 2007). In this
context,thebestapproachwouldbetoselectforcomponenttraitsthatareputatively
relatedwith ahigheryieldpotential and/or to the improvedbehaviourof the crop
whengrowninaparticularenvironment.Thisisknownasanalyticalorphysiological
Chapter6
180
breeding(Chapter1).
Oneofthekeyobjectivesofthisthesiswastoidentifyphysiologicaltraitswhich
couldbeusefulasaselectioncriteriontoimprovecropyieldinpotato.Therefore,in
this section,ouraimwas toestimate theexpecteddirect selectionresponse (R) for
tuberyieldandalso forphysiological traits, correlatedselectionresponse(CR),and
efficiencyofindirectselection(CR/R)fortuberyieldfromphysiologicaltraits.Mean
values of F1 population were used to estimate the genetic correlations between
physiologicaltraitsandmeasuredtuberyield(Chapters2and3);estimatesforR,CR,
andCR/RwereestimatedfollowingthemethodsdescribedbyFalconer(1996)as:
x2xiHR (6.1)
yGyx rHiHCR (6.2)
xGy // HrHRCR (6.3)
where, i istheselectiondifferential(i=1.76at10%selectionintensity); 2xH and x
aretheheritabilityandphenotypicstandarddeviationvaluesfortraitx,respectively;
HxandHyarethesquarerootoftheheritabilityofthetraitxandy,respectively; Gr is
thegeneticcorrelationbetweentraitxandy, y isthephenotypicstandarddeviation
fortraity.
Theheritabilityandgeneticcorrelationswereestimatedaspereqns3.7and3.8,
respectively(cf.Chapter3).ValuesofR,CR,andCR/Rwereexpressedaspercentage
ofthegenotypesmeanforeachtraitinordertoallowcomparisonamongtraitswith
differentunits(Table6.3).
Theresults indicatedhigh(>50%)expectedordirectresponsetoselectionfor
physiologicalmodel traits:DP2,A2,Asum,cm,tE,andED.However, itwasnotablethat
direct selection response to tuber yieldperse (wmax)was very low (i.e. only 29%)
(Table 6.3). This was also supported by relatively lower proportion of genetic
componentofvarianceinthetotalphenotypicvarianceobservedfortuberyieldper
se(Chapter3).Theseresultsshowedtheaddedvalueandsuperiorityofphysiological
traitsovertuberyieldperse.
The results furthermore indicated that correlated response towmax based on
most physiological traits was higher than the direct selection for wmax. Using an
alternative indirect selection tool for yield is appropriate if the genetic correlation
between the target trait (e.g. yield) and component traits is very high, and the
correlatedresponseintheunselectedtraitbasedontheselectedtraitishigherthan
Generaldiscussion
181
Table 6.3. Genetic correlation coefficients (rG) between physiological traits and
measuredtuberyield(wmax),expectedselectionresponse(R)forphysiologicaltraits
and forwmax, correlated selection response (CR) and efficiency of indirect selection
(CR/R)forwmaxestimatedfromphysiologicaltraitsofpotato.Estimationswerebased
onF1populationmeanacrossexperiments.tdstandsforthermalday.Fordescription
aboutparameters,seeTable6.1.
Trait Unit rG R CR CR/R
tm1 td 0.24** 0.31 0.06 0.29
t1/DP1 td ‐0.05NS 0.20 ‐0.01 ‐0.09
t2 td 0.70** 0.22 0.22 0.57
te td 0.75** 0.27 0.25 0.57
vmax % 0.84** 0.18 0.23 0.94
cm1 %td‐1 0.53** 0.48 0.15 0.57
c1 %td‐1 0.46** 0.48 0.14 0.42
c3 %td‐1 0.17** 0.27 0.03 0.40
DP2 td 0.85** 0.98 0.27 0.73
DP3 td 0.15** 0.39 0.04 0.20
A1† td% − − − −
A2 td% 0.84** 1.07 0.27 0.69
A3 td% 0.41** 0.44 0.11 0.46
Asum td% 0.78** 0.49 0.26 0.59
cm gtd‐1 ‐0.20** 0.78 ‐0.06 ‐0.16
tE td 0.66** 0.53 0.22 0.49
ED td 0.64** 0.93 0.21 0.48
RUE gDMMJ‐1 ‐0.25** 0.41 ‐0.08 ‐0.22
Nuptake gm‐2 0.85** 0.29 0.24 0.91
NUE gDMg‐1N 0.43** 0.28 0.13 0.39
wmax gm‐2 0.29 **Significantat1%,NSNon‐significant.†Estimation was not possible due to zero genetic variance ( 2
G ), see Table 2.3,
Chapter2.
thedirectresponsetoselectionoftheunselectedtrait(Falconer,1996).Ourresults
furtherindicatedsignificant(P<0.01)andstronggeneticcorrelationsbetweentuber
Chapter6
182
yieldandmostofphysiologicalmodeltraits(Table6.3).Forinstance,traitssuchast2,
te,vmax,cm1,DP2,A2,Asum,tE,ED,Nuptake,anduseefficiencyshowedveryhigh(>0.80)
genetic correlations with wmax. The results therefore suggested that high indirect
responsecouldbeobtainedbyselectinggenotypesforthesetraitstoimprovetuber
yieldinpotato.However,ourresultsalsosuggestedthatwhileusingthesetraitsasa
criterion for selection, their causal physiological inter‐relationships and trade‐offs
must be considered simultaneously (see Fig. 3.6, Chapter 3). Finally, the results
concluded that indirect selection efficiency of most physiological traits was higher
thanthedirectselectionefficiency for tuberyield.Theselectionefficiency fromour
physiological traits ranged up to 94% (Table 6.3). However, it was noted that
efficiencyof indirect selectionwasnothigh for traits likecmandRUEdespite their
high direct physiological relationship with tuber yield determination (Fig. 3.6,
Chapter3).Thiswasmainlyduetotheweakphenotypic(Table3.6,Chapter3)aswell
asgeneticcorrelations(Table6.3)withtuberyieldobservedforthesetraits,mainly
influencedbythestrongnegativeindirecteffectsofAsum(asexplainedintheprevious
section).
Accordingtoourresults,therelativeimportanceoftraitsbasedontheir(>50%)
indirectselectionefficiencyfortuberyielddeterminationcanberankedas:vmax>N
uptake>DP2>A2>Asum>t2>te>cm1.Thesefindingsstronglyconcludedthatmost
physiological traits can be used successfully as indices of selection for yield
improvementinpotato.
Thisthesisthusindicatedthedirectionandmagnitudeofcorrelatedresponses
toselectionfortuberyieldandtherelativeefficiencyofindirectselection.Inaddition,
our results suggest that the development of such a selection indexmust integrate
several key physiological traits, their inter‐relationships and repeatability (high
heritability) for assessing yield in breeding programmes (Baker, 1986; Bouman,
1995).
QTLmappingofphysiologicaltraitsWhile environmental characterisation and physiological knowledge help to explain
andunravelgeneandenvironmentcontextdependencies,theanalysisofgeneeffects
on yield in diverse environments unmasks some of the key effects of genetic and
environmental variability on the target trait.The phenotype of most plant
characteristics varies quantitatively as it is under the influence both of the
environment and of genetic factors encoded at quantitative trait loci (QTLs)
(Gelderman,1975).Understandingthebasisoftheinteractionresponsesatthewhole
plant level (Reynolds et al., 2005; Raven et al., 2005; Trethowan et al., 2005) and
Generaldiscussion
183
identifyingQTLandmolecularmarkersassociatedwithdesirabletraits(Priceetal.,
2002;Nigametal.,2005;Habashetal.,2007)arelikelytohavealargeimpactonthe
researchaimedatimprovingyieldinpotato.
Many studies have identifiedQTLs for different traits in potato under normal
conditions for various agronomic, andquality traits (e.g.VandenBerget al., 1996;
Van Eck et al., 1994; Schäfer‐Pregl et al., 1998; Bradshaw et al., 2008). However,
knowledgeaboutgeneticbasisofimportantphysiologicaltraitscloselylinkedtothe
temporaldynamicsofyieldformationandresource(radiationandN)useefficiencies
isstilllimitedandhardlyanyQTLshavebeenidentifiedforthesetraitsinpotato.In
otherwords,physiologicalaspectsofcomplexquantitativetraitsinpotatohavesofar
received little attention from geneticists in QTL analysis although there are some
recentstudiesthatusestatisticalmodelstoanalysedatabasedonsemi‐quantitative
scalesdescribingcanopydevelopment(e.g.Hurtadoetal.,2012).
In this study, our molecular dissection of traits determining the dynamics of
canopy cover, tuber bulking, and resource (radiation, N) use efficiencies identified
severalQTLs,themappingpositionofeachidentifiedQTL,theinteractionofQTLwith
environment,andthemagnitudeofQTLeffectonexplaininggeneticvarianceinboth
SHandRHparentalgenomes(Chapters2and3).
The QTL results clearly showed that one particular chromosomal position at
18.2cMonpaternal(RH)linkagegroupVwascontrollingnearlyallthetraits(Tables
2.9and3.10),renderingthepopulationonwhichthisresearchwasdonelesssuitable,
albeititisagronomicallywelladapted.Forexample,itwasnotablethatQTLfortraits
likete,t2,DP2,A2,Asum,cm,tE,ED,andtuberyieldpersewereco‐localisedinmajorityof
theenvironments.ThisQTLwasassociatedwithmajoradditiveeffectsonmostofthe
traits and explained more than 50% of the phenotypic variance. Strong genetic
correlations among most of these traits (Chapters 2 and 3) and with tuber yield
(Table6.3) further supported these results and suggestedpleiotropicnatureof the
QTLformostofthetraits.
It iswellknownthatlinkagegroupVharbourstheQTLforplantmaturityand
vigourinpotato(Collinsetal.,1999;Oberhagemannetal.,1999;Viskeretal.,2003;
Bradshaw, et al., 2008). Our results also confirmed this fact as most of the traits
linkedtothischromosomewererelatedwithmaturity, for instanceDP2,cm,ED,and
Asum(Chapter3).Inotherwords,thisthesisgaveaclearpictureaboutmaturityina
SH RH segregating population of potato and indicated that maturity is mainlyexpressedfromthepaternal(RH)sideonlinkagegroupV.Itwasfurthernotablethat
this linkage group is mainly controlling earliness in genotypes on account of the
negative additive effects associatedwith amajorQTL foundhere formost of traits
includingtuberyieldperse(Chapters2and3).Thisinformationcouldbeveryuseful
Chapter6
184
in elucidating the genetic basis of yield determination in early and late maturing
genotypes.
TheQTL‐by‐environment(QTLE)interactionphenomenonwasevidentmainlyfortraitsdeterminingthecanopybuild‐upandsenescencephases,maximumcanopy
cover (vmax) ratesof canopygrowthand senescence (cm1,c1, andc3), duration (DP1)
andareaundercanopycover(A1)whencanopycoverreachesitsmaximum,duration
(DP3) and area under canopy cover (A3) of canopy senescence, resource (radiation
andN)useefficienciesandtuberyieldperse.Theseresultswereexpectedduetothe
presenceofrelativelylowergeneticvariancethantheirGEorEcomponentoftotalphenotypicvarianceinthesetraits,asdescribedinprevioussections(seeTables2.3
(Chapter 2) and 3.4 (Chapter 3)). Figure 6.5 illustrates such genetic complexity in
tuberyieldperse.SuchinconsistenciesinQTLbehaviourcancausemajorproblemsin
thedetectionofQTLs and their effectiveuse inmolecularbreeding.However, such
QTLs could be useful for marker assisted breeding for specific environments. Our
thesis as a whole provided a better understanding of the genetic background of
importantphysiologicalcomponentstraitsdeterminingmaturitytypeaswellastuber
yieldinpotato.
Figure 6.5. QTLE additive effects for tuber yield (g m‐2) parental (SH and RH)
genomes for six experiments. Letters above or below the bars indicate different
linkagegroups.
-700-600-500-400-300-200-100
0100200300400
1 2 3 4 5 6
Add
itive
eff
ects
Experiment no.
SH RH
V
V
V
V IVI
II
Generaldiscussion
185
AnecophysiologicalmodeltoanalysegeneticvariationintuberyieldAs already highlighted in previous sections, yield variation in terms of growth and
developmentofthecropiscomplex,foritinvolvestheeffectofexternalfactorsonall
thephysiologicalprocesses, the inter‐relationshipsbetweendifferentprocessesand
theirdependenceon thegenetic constituentof theplant.Oneof theapplicationsof
processed‐based ecophysiological crop growthmodels is their capability to explain
differencesintheyieldpotentialofcultivarsonthebasisof individualphysiological
parameters,andtheuseof thisknowledge forevaluatinganddesigningplant types
(Kropffetal.,1995;Yinetal.,2003c).Thisispossiblebecause,astheybecomemore
mechanistic and comprehensive, crop‐growth models can be used to mimic the
geneticcharacteristicsofplants(Chapter1).
However, a major limitation of current crop models in accounting for GEinteractions is the low resolution and accuracy of themodel in comparison to the
subtle differences between genotypes commonly observed inmanywell‐conducted
multi‐environmenttrials(WhiteandHoogenboom,1996).Acontrastingpointofview
is that crop physiological frameworks that are more readily aligned with plant
breeders’modesofactionarerequired(e.g.Shorteretal.,1991;Yinetal.,2004;Yin
andStruik,2008).Studieswheresimulationanalysesofvariationinatraithavebeen
confirmedinthefieldarerare.Certainly,itseemslogicalthatifcropmodelsaretobe
incorporatedintoacropimprovementprogramme,itisessentialthattheparameters
areeasilyandsimplyobtainedsothatbreederscanusethemandapplythemwithout
substantialinvestmentintimeanddatacollection.
YinandVanLaar(2005),alongtheaforementionedlinesofthinking,presented
a crop model, GECROS (Genotype‐by‐Environment interaction on CROp growth
Simulator), to overcome some of the weaknesses of earlier crop models. GECROS
capturestraitsofgenotype‐specificresponsestoenvironmentbasedonquantitative
descriptions of complex traits related to the phenology, root system development,
photosynthesis, stomatal conductance, and stay‐green traits. GECROS uses new
algorithms to summarise current knowledge of individual physiological processes
and their interactions and feedbackmechanisms (Chapter 5). It attempts tomodel
eachsub‐processataconsistentlevelofdetail,sothatnoprocessisoveremphasised
or requires too many parameters and similarly no process is treated in a trivial
manner,unlessunavoidablebecauseof lackofunderstanding.GECROSalso tries to
maintain a balance between robustmodel structure, high computational efficiency,
and accurate model output. The model can be used for examining responses of
biomass and dry matter production in arable crops to both environmental and
genotypiccharacteristics.
In this study (Chapter5),we thereforeexamined thepotentialof theGECROS
Chapter6
186
crop growth model in predicting yield differences of individual genotypes in a
segregatingpopulationofpotato,theirparentsandasetofcultivarscoveringawide
range of maturity types. We focused mainly on the genotype‐specific input
parameters of themodel. Table 5.1 (Chapter 5) gives the description of genotype‐
specific parameters of GECROS, which includemV andmR (i.e. period from plant
emergencetoonsetoftuberbulkinganddurationbetweenonsetoftuberbulkingand
cropmaturity,respectively);Hmax,plantheight;Nmax,totalcropNuptakeatmaturity;
nSO,maximumpotatoseedtuberNconcentration.Calibrationdataforthemodelinput
parameters were obtained from our extensive data sets of Chapter 2 and 3.
Procedures for estimation and correction for experiment‐specific parameter values
aredescribedinChapter5.
Themodeldescribedwellthedynamicsofimportantgrowthrelatedprocesses
in potato and gave important insights into the underlying component traits and
factors influencing the tuberdrymatterproduction.Using as fewas fivemeasured
genotype‐specific parameters (Table 5.1, Chapter 5), the model showed a good
potential in explaining the observed yield differences among genotypes. Simulated
trendsofgrowthwereincloseagreementwiththemeasureddata(Fig.5.3,Chapter
5). The model yielded reasonable predictions of differences in tuber yield across
environmentsandacrossgenotypes(Figs.5.4and5.5).
Simulationmodelscanbeveryusefultoevaluatecriticaltraitsneededforhigh
yield potential (Penning de Vries, 1991; Kooman and Haverkort, 1995; Yin et al.,
2000b). Themodel based sensitivity analysis indicated thatNmax andnSOwere the
most important parameters of GECROS model as they contributed most to the
determinationoftuberyield(seeChapter5,section3.4).Theresultssuggestedthat
genotypeswithhigherNmaxmayexhibitmorefinaltuberyield(Fig.6.6),providednSO
isloworinotherwordsgenotypeswithhighNUE(Casaetal.,2005;Chapter3).Our
path coefficient analysis also further indicated that relationships of most
physiological traits with tuber yield are actually driven mainly through plant’s
capabilitytouptakemaximumN(Table5.4,Chapter5).Theseresultsfurtherproved
ourearlierdescribedresultsthatplantNuptakecouldbeproposedasacriterionto
indirectlyselectfortuberyield.
This thesis therefore confirmed the usefulness of an ecophysiological model
‘GECROS’inexploringtheimpactofnewgenotypesandthecontributionofindividual
physiologicaltraitsonyieldbysimulatingtheresponsesofgenotypesacrossdifferent
environments. In addition, ourmodel analysis allowed us to identify the genotypic
key parameters and shed light on some of the vital physiological mechanisms
responsible for genotypic differences in tuber yield. The concepts captured in the
modeldrawmoreattentiontoNuptakeanduse
Generaldiscussion
187
Figure6.6.RelationshipsbetweenmeasuredplantNuptakeandtuberdrymatterin
F1populationofpotatoacrossenvironments.efficiency,aspossibleareasforimprovementsintuberdrymatterproduction.
The identification of both genotype‐specific key parameters and main
physiological processes involved in tuber yield formation should be useful from a
geneticpointof view. They couldguide research towards thesekeyprocesses and
orientatebreedingprogrammesassuggestedbyYinetal.(2004).Furtheranalysisof
the genotypic parameters should be performed in conjunction with molecular
markersinordertodeterminetheirgeneticcontrol.Suchinformationwouldgreatly
facilitatethedevelopmentofideotypesforspecificenvironments.
ConclusionsTheworkpresentedinthisthesisintendstoincreaseourphysiologicalknowledgeof
those factors applicable to plant breeding determining the yield of potato. We
presented a robust physiological framework for quantitatively dissecting the
phenotypic variation in potato growth and development to aid understanding of
underlying causes while simultaneously providing means to predict emergent
phenotypicconsequencesbyintegratingeffectsofvariationincomponentfactorsand
processesleadingtoyieldformationinpotato.
y = 45.777x + 106.04R² = 0.6513
0
500
1000
1500
2000
0 5 10 15 20 25 30
Tub
er
dry
ma
tter
(g m
-2)
N uptake (g m-2)
Chapter6
188
Thisthesisindicatedthatyieldpotentialinpotatocanbeenhancedbyselecting
formorphological, physiological, and phenological traits. Once this is accepted, the
challenge is in finding which traits to modify and the optimum phenotype or
expressionforthesetraits.Itwasconcludedthatparametersofthegrowthfunctions
determining thetemporaldynamicsofcanopycoverandtuberbulkinghaveaclear
meaningwithregardstotheprocessesofresourcecapturebytheplant,thusallowing
amore easy interpretation of the value andmagnitude of the growth components
associatedwithvariationintuberyieldamongcultivarsand/orgenotypes.However,
thesecomponentsarenotphysiologicallyindependentasgenotypeswithahightuber
bulking rate may effectively limit the crop growth and duration (via enhanced
internalplantcompetition)leadingtotheiridentified'earliness'.
In this context, this study also highlighted such trade‐offs among key
physiologicaltraitscausingsubtlecomplexitiesindefiningthematuritytypeinpotato.
Our results indicated that re‐defining the maturity type of a large set of new
genotypesbasedonphysiologicaltraitsispossibleandwouldproveuseful.Thenew
physiology‐based maturity definition could allow relating the maturity to crop
phenologyandphysiologyandcouldhelpindesigningpotatoideotypeswithspecific
maturitytypeforspecificgrowingconditionsand/orenvironments.
This study also highlighted the characterisation of the role of environments
mainlyN.Overall,resultsconcludedthatNavailabilityisthedrivingfactorforcausing
trade‐offs between the physiological traits and different environments. Moreover,
resultssuggestedthatNavailabilityanditsinteractionwithgenotype’smaturitytype
mainly contribute to the GE interaction of the growth and development relatedprocessesinpotatoinalargesetofF1segregating(SHRH)potatogenotypes.Thisthesis thereforeallowedapartialunderstandingof theenvironmentalcausesof the
observedGEinteractions.Ourquantitativeapproachincombinationwithmarkersofthewidelyavailable
and easy to use AFLP marker system identified QTLs that could be useful in the
development of marker assisted breeding strategies in potato yield improvement.
The QTL results showed that nearly all the physiological traits co‐localised at one
particular chromosomalpositionat18.2 cMonpaternal (RH) linkagegroupVwith
major effects. This QTL was associated withmajor additive effects onmost of the
traitsandexplainedmore than50%of thephenotypicvariance.Thissuggested the
pleiotropic nature of theQTL formost of the traits determining cropmaturity and
tuberyields.AnumberofQTLsfortraitswerenotdetectedwhentuberyieldperse
was subjected toQTL analysis. The phenotypic variance explained by theQTLs for
tuberyieldpersewasalsolowerthanforothertraits.
Our results also confirmed previous studies that most of the traits linked to
Generaldiscussion
189
linkagegroupVwererelatedwithmaturity. Itwas furthernotable that this linkage
groupismainlycontrollingearlinessingenotypesonaccountofthenegativeadditive
effectsassociatedwithamajorQTLfoundhereformostoftraitsincludingtuberyield
perse.
Thecapabilityofanecophysiologicalmodel‘GECROS’wastestedinthisthesisto
analysedifferencesintuberyieldofpotatoandanalysetheimportanceofgenotype‐
specificmodel‐inputparametertowardstuberyieldproduction.Themodelyieldeda
reasonably good prediction of differences in tuber yield across environments and
acrossgenotypes.Trendsofgrowthandnitrogenuptakewereadequatelyreproduced
bythemodel.TheGECROSmodelbasedsensitivityanalysisindicatedthattotalcrop
NuptakeandtuberNconcentrationwerethemostimportantparametersofGECROS
model as they contributed most to the determination of tuber yield. The results
concluded that GECROS model is a useful tool in analysing the contribution of
individualphysiologicaltraitstotuberyieldbysimulatingtheresponsesofgenotypes
acrossdifferentenvironments.Furtheranalysisof thegenotypicparameters should
be performed in conjunction with molecular markers. They could guide research
towardskeyprocessesandorientatebreedingprogrammesassuggestedbyYinetal.
(2004).
To conclude, breeding efforts aimed at meeting ongoing challenges, such as
breakingyieldbarriersandimprovingperformanceunderdiverseenvironments,are
more likely toachievesuccess ifphysiologicalunderstanding isusedtocompliment
traditional breeding approaches. This thesis yielded estimates for agronomically
relevant crop physiological and genetic characteristics and/or traits that are
promising fordefining futurebreeding strategies inpotato.High genetic variability
along with high heritability for most of these traits indicated that a more general
breeding goal, increased tuber drymatter yield by indirectly selection for optimal
combinationofimportantphysiologicaltraitscanbeachieved.Resultsalsoindicated
that while using these traits as a criterion for selection, the causal physiological
relationshipsandtrade‐offsmustbeconsideredsimultaneously.
Theapproachdescribedinthisthesisispromisingfordefiningfuturebreeding
strategiesinpotatoandtheinformationobtainedmayhelpindesigningideotypesfor
specificand/ordiverseenvironmentsaswellas inmarkerassistedselection(MAS).
AsmentionedinChapter1,currentapproachestoMASforcomplextraits lackgood
predictive power due to complex interactions among genes and/or environments.
One strategy to overcome these difficulties would be to combine QTL and
ecophysiological models, i.e. a step forward towards QTL‐based crop modelling.
However,smalldatasets forQTLmapping(i.e.only88genotypes,cf.Materialsand
Methods,Chapter2)andlowernumbersofindependentQTLsfoundforsingletraits
Chapter6
190
(seeTables2.8 (Chapter2)and3.9 (Chapter3))didnotallowachieving thisnovel
goal. In this context, future research should focus on this area and further steps
should be taken in linking cropmodellingwith QTLmapping for physiological and
genotypictraitsgeneratedbythisthesis.Wehopethatthestrongfoundationsetby
thisthesiswouldhelp inachievingbroadergoalofproceedingtowardsmodelbased
plantbreeding(Chapter1).Ontheselines,afollowupPhDresearchiscurrentlywell
underway including the same SH RH population as well as a very large set ofcultivars are grown in experiments in which nitrogen supply is included as an
experimentalfactor.
However,makingsignificantcontributionsintheseareasrequiresmuchcloser
collaborative research efforts between physiologists and plant breeders. Until such
research is conducted, and the benefits of shifting resources into the systems
approach can beweighed against reducing the resources put into the conventional
empiricalapproach,widespreadacceptanceofthenewmethodsisunlikely.
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SummaryThesustainedincreaseinglobalfoodproductionisunprecedented.Amajorchallenge
forplantandcropscienceisdiscoveringnewwaystocontinuetoincreasecropyield
in a sustainablemanner. Potato (Solanumtuberosum L.) is among theworld’smost
important cropswith awell‐recognised role in humannutrition, food security, and
national economyofmany countries.Due to increasing fooddemand and changing
dietspotatoisbecomingasubsistencecropinmanyregions.However,theagronomy
and whole crop physiology of tuber yield production are difficult as each
developmentalstageinpotatoislikelytoberegulatedandcontrolledbyalargesetof
interacting expressed genes and because the genotype‐by‐environment (GE)interactions are strong. Breeding for highly‐yielding crop cultivars for specific
environments and the prediction of their behaviour are major challenges. New
technologies must be developed to accelerate breeding through improving
genotyping and phenotyping methods and by increasing the available genetic
diversityinbreedinggermplasm.
Interventions in breeding based on understanding of the genetic and
physiologicalbasisofcropperformancehavethepotentialtoaccelerategeneticgains
for yield and resource use efficiency. The genetic improvement of yield can be
understoodmoremechanisticallybyinvestigatingandinterpretingtherelationships
between the main attributes of growth. Such studies may shed light on potential
futurealternativesforsustainableyieldimprovement.
Cropmodellingcanplayasignificantpartinsystemapproachesbyprovidinga
powerful tool for phenotype prediction and yield scenario analysis; to explore the
impact of new genotypes and the contribution of individual physiological traits on
yield by simulating genotypes under various environmental scenarios and
management options.Modellingmay also prove useful in understanding GE, thushelpingtospeedupthecurrentlystagnantpaceofcropimprovement.Nevertheless,
insightsintothephysiologicalprocessesleadingtoyielddevelopmentandthefactors
affectingtheseprocessesarestillunderdeveloped.
This thesis aims to develop an approach to quantify and predict the yield of
individual genotypes and to estimate parameters which may reveal the effects of
genetic and environmental factors on important plant processes controlling tuber
yield variation. The analysis conducted in this thesis is based primarily on data
collected from six contrasting field experiments carried out inWageningen (52˚N
latitude), theNetherlands,during2002,2004and2005.Theplantmaterialusedin
thisstudyconsistedof100F1diploid(2n=2x=24)potatogenotypesderivedfroma
Summary
220
crossbetweentwodiploidheterozygouspotatoclones,SH83‐92‐488RH89‐039‐16.Five standard cultivars (Première, Bintje, Seresta, Astarte, and Karnico) and the
parentalcloneswerealsoincludedinthisstudy.
The thesis is composed of six chapters. Chapter 1 gives an overview of
complications indealingwithquantitativetraits likeyieldandhighlights theroleof
systems approaches via incorporating crop modelling and crop physiological
knowledge in potato breeding programmes. It presents the possible opportunities
fromsuchcombinedeffortstowardsreinforcingthegeneticanalysisofcomplextraits.
Chapter2presentsaquantitativemodelapproachbasedonthebetafunctionto
analysethetimecourseofcanopycoverduringtheentirecropcycleasafunctionof
thermaltime.Themodeldescribesthecanopydevelopmentdynamicsinthreephases
(build‐up phase, maximum cover phase, canopy decline phase) through five
parameters defining the timing and duration of the phases and maximum canopy
cover (vmax). These five parameters were further used to derive secondary traits,
related to rate of increase or decrease of canopy cover, and the area underwhole
green canopy cover curve (Asum). The latter trait Asum was directly related to the
ability of the genotypes to intercept photosynthetically active radiation (PAR) and
thustorealizetuberyield.Themodelsuccessfullydescribedquantitativedifferences
in canopydynamicsof adiversesetof genotypes ina segregatingF1populationof
potatoundervariedenvironments.
Theresults indicated that lengthof thecanopybuild‐upphase (t1orDP1)was
conservative,butthedurationofmaximumcanopycover(DP2)andthedeclinephase
(DP3)varied greatly,with later genotypeshaving similarDP1, butlongerDP2 andDP3
and thus generate higher values of Asum. Strong positive phenotypic and genetic
correlations were observed between DP2 and vmax, indicating that genotypes with
longerDP2couldbeindirectlyobtainedbyselectinggenotypeswithmaximumvalue
ofcanopycover(vmax).Formosttraitsquantifiedinthemodel,highgeneticvariability
andhighheritabilitywererecorded.Geneticvariancecontributedgreatlytothetotal
phenotypicvarianceformosttraits,forinstance,timeofonset(t2)orendofcanopy
senescence(te),DP2,andAsum,indicatingtheirstronggeneticbackgroundandstability
acrossenvironments.
SeveralQTLsweredetectedacross theexperiments for themodelparameters
and derived traits determining the canopy dynamics. One particular chromosomal
positionat18.2cMonpaternal (RH) linkagegroupVwascontrollingnearlyall the
traits and showed its pleiotropic nature. This QTL had major additive effects and
explained 74%of the phenotypic variance.Most of the traits linked to this linkage
group were related with earliness. Such information could be very useful in
identifyingthegeneticbasisofmaturitytypeinpotato.Thischapteryieldedestimates
Summary
221
foragronomically relevantcropcharacteristics thatcouldbepromising fordefining
future breeding strategies for traits influencing the radiation interception and
therebyfinalyield.
Chapter3describesaquantitativemodelapproachtoanalysethedynamicsof
tuberbulkingduringtheentirecropcycleasafunctionofthermaltimeusingapiece‐
wise expolinear function. The model breaks down tuber growth into parameters
related with the growth rate and effective duration of the linear phase of tuber
bulking.Furthermore, radiation‐ and nitrogen use efficiencies (RUE and NUE,
respectively), and their relationships with the model parameters are also studied.
Rateoftuberbulking(cm)washighestforearly‐maturinggenotypesfollowedbymid‐
lateandthenlategenotypes.Ontheotherhand,late‐maturinggenotypeshadlongest
effectivedurationoftuberbulking(ED)followedbymid‐lateandearlygenotypes.As
aresult,finaltuberyield(wmax)washigherinlategenotypesthaninearlygenotypes.
The results further illustrated that RUE values were highest for early‐maturing
genotypesfollowedbymid‐lateandlategenotypeswhereasNUEwashighestinlate‐
maturinggenotypesfollowedbymid‐earlyandearlygenotypes.Thephenotypicand
geneticcorrelationssuggestedphysiologicaltrade‐offsbetweencmandEDaswellas
between RUE and NUE. High genetic variability along with high heritability was
recorded for most traits which illustrated their strong genetic background. Path
coefficientanalysisshowedthatRUE,cm,andAsum(previouslydefinedinChapter2),
hadamajor influenceonwmax.SixteenQTLsweredetected fortraitsstudiedinthis
chapter, explaining the phenotypic variance by up to 79%. In accordance with
Chapter2,QTLscontrollingmostof thetraitsweredetectedatposition18.2cMon
paternal(RH)linkagegroupVwithmajoradditiveeffectsandexplainedmostofthe
totalphenotypicvariance.OurresultsindicatedthatanumberofQTLsfortraitswere
notdetectedwhentuberyieldpersewassubjectedtoQTLanalysis.Thephenotypic
varianceexplainedby theQTLs for tuberyieldpersewasalso lower than forother
traits.Thissuggests thatcomplextraits likethesearecontrolledbyQTLsthatoften
show low stability across environments. Overall, parameters found in this chapter
indicatedthatthereareopportunitiesforimprovingtuberdrymatteryieldinpotato
by selection for optimal combination of important physiological traits likeRUE, cm,
andAsum.
Chapter4addressestheissueofmaturitytypeinpotato,ascurrentlythereare
somanyunclear interpretations.This chapter tries to create a clear and consistent
definition of the concept of maturity type and presents an approach to assess the
maturitytypeinalargesetofpotatogenotypesinarathersimpleandreproducible
way. The added physiological and quantitative knowledge gained from Chapters 2
and3wereusedtoquantifymaturitytypeunambiguouslyforasetofvarietiesanda
Summary
222
segregating population across diverse environments. The re‐defined physiological
basedmaturitycriteriaarebasedonfourtraits:DP2,cm,ED,andAsum.There‐defined,
four physiological maturity criteria were compared with the conventionally used
criterion. The results indicated that physiological maturity type criteria tended to
definematurityclasseslessambiguouslyincomparisontotheconventionalcriterion.
Moreover, theconventionalcriterionwassubject tomorerandomnoiseand lacked
stabilityand/orrepeatabilitywithrespecttophysiologicalquantitativetraits.There‐
defined criteria also clearly illustrated the physiological trade‐offs that existed
between the selected traits and underlined the subtle complexities in defining the
maturity type. The new definition would not only allow relating maturity to crop
phenologyandphysiologybutcouldalsoofferwiderapplicationsinpotatobreeding
and cropmanagement studies. One such applicationwould be to help in designing
strategiesforpotatoideotypebreedingforgenotypeswithspecificmaturitytypes.
Chapter 5 sheds light on some of the key mechanisms causing genotype
differences in tuberyieldandprovidessupport for importantaspectsofhypothesis
regarding nitrogen accumulation. It tests the capability of the generic
ecophysiological model ‘GECROS’ to analyse differences in tuber yield of potato
withinanF1population,theparents,andasetofcultivars.Themodelpredictscrop
growth and development as affected by genetic characteristics and climatic and
edaphic environmental variables. The genotype‐specific model‐input parameter
valueswereestimatedandthemodelwasusedforpredictingthetuberyieldofafore‐
mentionedplantmaterial inmultiple field experiments. Themodelwas reasonably
good in predicting the differences in tuber yield across environments and across
genotypes.Trendsofgrowthandnitrogenuptakewereadequatelyreproducedbythe
model.Modelanalysis identified thegenotypickey‐parametersaffecting tuberyield
productionandNmax(i.e.totalcropNuptake)contributedmosttothedetermination
oftuberyield.TheresultsshowedthatgenotypeswithhigherNmaxandlowertuberN
content exhibited higher tuber dry matter yield. Such information can greatly
facilitatethedevelopmentofpotatoideotypesforspecificenvironments.Theresults
confirmedthattheGECROSmodelisusefulforexploringtheimpactofnewgenotypes
and the contribution of individual physiological traits on yield by simulating the
responsesofgenotypesacrossdifferentenvironments.
Chapter6 finally discusses themain findings and overall contribution of this
thesis. It indicatesways to enhance the power ofmolecular breeding strategies as
wellasideotypebreedinginpotato.Itstressesupontheuseofcombinedknowledge
from the fields of crop physiology, modelling, and genetics in elucidating and
understanding the complex traits. Such an integrated approach can reinforce the
geneticanalysisofcomplextraitslikeyield,therebyimprovingbreedingefficiency.In
Summary
223
this context, future research should focus on this area and further steps should be
taken in linking cropmodellingwith QTLmapping for physiological and genotypic
traits generated by this thesis. The strong base set by this thesis could help in
achieving this broader goal ofmodelbased plant breeding. However, tomake such
significantstrides,muchclosercollaborative researcheffortsbetweenphysiologists
andplantbreedersaredirelyneeded
SamenvattingWereldwijdblijftdevraagnaarvoedseltoenemen.Eengroteuitdagingvoorplant‐en
gewaswetenschappen ishetontdekkenvannieuwemanierenomopeenduurzame
manierdegewasopbrengstenteblijvenverhogen.Deaardappel(Solanumtuberosum
L.) is één van de belangrijkste gewassen in de wereld. Het gewas speelt een
belangrijke rol in de humane voeding, voedselzekerheid, en de nationale economie
van vele landen. Door de toenemende vraag naar voedsel en veranderende
voedingspatronenwordtdeaardappelinveleregio’seenbelang‐rijkerbasisvoedsel.
In het algemeen zijn agronomie en gewasfysiologie van opbrengstvorming lastig
aangezien elk ontwikkelingsstadium waarschijnlijk wordt gereguleerd en
gecontroleerddooreengrotereekstotexpressiegebrachteenopelkaarinwerkende
genen en omdat de genotypemilieu (GE) interacties sterk zijn. Veredeling voorhoog‐opbrengenderassenvoorspecifiekemilieusenhetvoorspellenvanhungedrag
zijn daarbij belangrijke uitdagingen. Nieuwe technologieën moeten worden
ontwikkeldomhetveredelingsprocesteversnellen.Denkaanverbeterdemethoden
voorgenotyperingen fenotypering, alsmedemodellendiehet fenotypevoorspellen
uit het genotype. Daarmee kan bestaande en nieuwe genetische diversiteit beter
benutwordeninhetveredelingsproces.
Inzicht in de genetische en fysiologische basis van opbrengstvorming kan
bijdragenaanmeerefficiënteselectiemethodenvoordegewenstegenetischeaanleg,
in het bijzonder voor eigenschappen die bijdragen aan betere benutting van de
hulpbronnen lichtenstikstof (RUE,NUE).Degenetischeopbrengst‐verbeteringkan
meermechanistischwordenbegrependoorhetonderzoekeneninterpreterenvande
relaties tussen de belangrijkste groeikarakteristieken. Door het onderzoeken en
interpreteren van de relaties tussen de belangrijkste processen tijdens de groei en
ontwikkelingvandeplantingenetischdiversmateriaalkanmeerspecifiekgestuurd
wordenindeveredelingvanopbrengst‐componenten.Dergelijkestudieskunnenlicht
werpen op mogelijke toekomstige alternatieven voor duurzame
opbrengstverbetering.
Gewasmodellering kan een belangrijke rol spelen in systeembenaderingen
omdatmodellenkrachtige instrumentenzijnvoorhetvoorspellenvanhet fenotype
en voor het analyseren van opbrengstscenarios.Modellen kunnen ook behulpzaam
zijn bij verkennen van het opbrengstpotentieel van nieuwe genotypen en van de
bijdrage van individuele fysiologische kenmerken aan deze opbrengst, omdat ze
gebruikt kunnen worden voor het simuleren van de groei van genotypen onder
verschillendemilieu‐scenario's en teelttechniek.Modellering kanooknuttig zijn bij
Samenvatting
226
hetbegrijpenvanGE.Tochzijndeinzichtenindefysiologischeprocessendieleidentotdeopbrengstvormingende factorendievaninvloedzijnopdezeprocessennog
onderontwikkeld.
Dit proefschrift beoogt een aanpak te ontwikkelen voor het kwantificeren en
voorspellenvandeopbrengstvoor individuelegenotypenenvoorhet schattenvan
relevante parameters. Het gaat daarbij om parameters die de effecten kunnen
blootleggen van genetische factoren en omgevingsfactoren op belangrijke
plantprocessendiegezamenlijkdevariatie inknolopbrengstbepalen.Deanalyse in
dit proefschrift is hoofdzakelijk gebaseerd op gegevens die zijn verzameld in zes
contrasterende veldexperimenten uitgevoerd nabij Wageningen (52˚
Noorderbreedte),Nederland, inde jaren2002,2004en2005.Hetplantmateriaal in
deze studie bestond uit een diploïde F1 populatie (2n = 2x = 24) van 100
nakomelingen verkregen uit een kruising tussen twee heterozygote diploïde
aardappelklonen, SH83‐92‐488 en RH89‐039‐16. Vijf standaardrassen (Première,
Bintje, Seresta,AstarteenKarnico)endeoorspronkelijkeouderklonenwerdenook
opgenomenindezestudie.
Hetproefschriftbestaatuitzeshoofdstukken.Hoofdstuk1geefteenoverzicht
van complicaties in het omgaan met kwantitatieve kenmerken zoals opbrengst en
benadrukt de rol van de systeembenaderingen via het inpassen van
gewasmodellering en gewasfysiologische kennis in aardappelveredelings‐
programma's. Het presenteert de mogelijkheden van dergelijke gecombineerde
inspanningenomtekomentoteengenetischeanalysevancomplexeeigenschappen.
In Hoofdstuk 2 wordt een kwantitatieve modelbenadering op basis van de
bètafunctie gepresenteerd om het verloop van de bodembedekkingsgraad te
analyseren tijdens de gehele gewascyclus als functie van de thermotijd. Hetmodel
beschrijft de dynamiek van de ontwikkeling van de bodembedekkingsgraad in drie
fasen (opbouwfase, fase vanmaximale bodembedekking, afstervingsfase). Het doet
dat doormiddel van vijf parameters die samen aanvang, eind en duur van de drie
fasen bepalen alsmede de maximale bedekkingsgraad (vmax). Deze vijf parameters
werden verder gebruikt om secundaire kenmerken te schatten, gerelateerd aan
snelheid van toe‐ of afname van de bedekkingsgraad, en de oppervlakte onder de
totale curve van de bodembedekking tegen de thermotijd (Asum). Deze laatste
eigenschap, Asum, was direct gerelateerd aan het vermogen van de genotypen om
fotosynthetischactievestraling(PAR)teonderscheppenendusomknolopbrengstte
realiseren.Hetmodelbeschreefmetsucceskwantitatieveverschillenindynamiekin
bodembedekkingvandezegenetischdiversesetvangenotypenineenuitsplitsende
F1populatievanaardappelgeteeldonderuiteenlopendeomstandigheden.
De resultaten gaven aan dat de lengte van de opbouwfase van de bodem‐
Samenvatting
227
bedekking (t1 of DP1) conservatief was, maar de duur van de maximale
bodembedekkingsgraad (DP2) en de duur van de fase van afsterving (DP3) liepen
uiteen,waarbijlateregenotypenvergelijkbareDP1,maarlangereDP2enDP3haddenen
dushogerewaardenvoorAsumgenereerden.Erwerdensterke,positievefenotypische
en genetische correlaties gevonden tussen DP2 en vmax, hetgeen aangeeft dat
genotypenmeteenlangereDP2 indirectzoudenkunnenwordenverkregendoorhet
selecteren van genotypen met een hoge waarde voor de maximale
bodembedekkingsgraad (vmax). Voor de meeste met het model gekwantificeerde
eigenschappen was de F1 populatie uitermate variabel in vergelijking met de
standaardrassen. Voor demeeste eigenschappenwerd een hoge erfelijkheidsgraad
waargenomen; met andere woorden de genetische variantie droeg in belangrijke
mate bij aan de totale fenotypische variantie. Dit was bijvoorbeeld het geval voor
tijdstipvanaanvang(t2)ofheteindevanloofafsterving(te),DP2,enAsum,duidendop
eensterkegenetischebasisenstabiliteitovermilieus.
Over de verschillendemilieuswerden voor demodelparameters en afgeleide
kenmerken die de bodembedekkingsdynamiek bepalen verschillende QTLs
gedetecteerd. De ouder RH89‐039‐16 bleek heterozygoot voor een bijzonder
genetische locus op 18.2 cM op chromosoom 5. De uitsplitsende allelen van deze
ouderbepaalden indenakomelingenbijnaallegemeteneigenschappenen toonden
zo een pleiotroop karakter. De andere ouder SH83‐92‐488 splitste voor deze locus
nietuit.Deallelenvandezelocus(QTL)hadgroteadditieveeffectenenverklaardetot
74%vande fenotypischevariantie.Erwas sprakevangenetischekoppeling tussen
QTLsvoorvroegheidenmodelparameters.Dergelijkeinformatiekanzeernuttigzijn
bij het identificeren en karakteriseren van de genetische factoren betrokken bij
vroegrijpheid in aardappel en daaraan gerelateerde kenmerken. Dit hoofdstuk
leverde schattingen op voor landbouwkundig relevante gewaskenmerken die
veelbelovendkunnenzijn voorhetbepalenvan toekomstigeveredelingsstrategieën
voor eigenschappen die de lichtonderschepping, en daarmee uiteindelijk de
opbrengst,beïnvloeden.
Hoofdstuk 3 beschrijft een kwantitatieve modelbenadering waarmee de
dynamiekvandeknolgroeigedurendedegehelegewascycluswerdgeanalyseerdals
functie vande thermotijdmetbehulp van een in trajectenopgedeelde expolineaire
functie te analyseren. Het model ontleedt knolgroei tot parameters die betrekking
hebbenopdegroeisnelheidendefeitelijkeduurvandelineairefasevanknolvorming.
Bovendienwerdendegebruiksefficiëntievanstralingenstikstof(respectievelijkRUE
en NUE), en hun relaties met de modelparameters bestudeerd. Snelheid van
knolvorming (cm) was het hoogst voor de vroegrijpe genotypen gevolgd door
middellateendanlategenotypen.Anderzijds,laatrijpegenotypenhaddendelangste
Samenvatting
228
knolvormingsduur (ED) gevolgd door middellate en vroege genotypen.
Dientengevolge was de uiteindelijke knolopbrengst (wmax) hoger voor de late
genotypendanvoordevroegegenotypen.Erwerdtevensgevondendatdewaarden
voorRUEhethoogstwarenvoorvroegrijpegenotypen,gevolgddoormiddellateen
late genotypen, terwijl NUE juist het hoogst was in late genotypen, gevolgd door
middelvroege en vroege genotypen. De waargenomen fenotypische en genetische
correlatiessuggererendateromfysiologischeredeneneenafruilbestaattussencmen
ED.Hetzelfdegeldt tussenRUEenNUE.Voordemeestekenmerkenwerdeenhoge
genetischevariatie eneenhogeerfelijkheidsgraadgevonden, suggererenddat ze in
sterkemategenetischbepaaldzijn.PadcoëfficiëntanalysetoondeaandatRUE,cm,en
Asum(eerderbeschreveninHoofdstuk2)eengroteinvloedopwmaxhadden.Voorde
eigenschappendieindithoofdstukwerdenbestudeerdwerden16QTLsgedetecteerd,
diemaximaal 79%vande fenotypische variantie verklaarden.Overeenkomstigmet
wat in Hoofdstuk 2werd gevonden, beheerste de locus op positie 18.2 cM van de
vaderlijke(RH)koppelingsgroepVhetgrootstedeelvandeeigenschappen.Vooralle
eigenschappen vertoonde deze locus grote additieve effecten en verklaarde het
grootstedeelvandetotalefenotypischevariantie.Onzeresultatenlatenziendateen
aantal QTLs die voor de deeleigenschappen werden gedetecteerd niet werden
gevondenwanneer knoldrogestofopbrengstpersewerd onderworpen aan eenQTL
analyse. De fenotypische variantie verklaard door de QTLs voor
knoldrogestofopbrengst per se was ook lager dan voor andere eigenschappen. Dit
suggereert dat complexe eigenschappen als knoldrogestofopbrengst worden
gecontroleerd door QTLs die vaak slechts in beperkte mate stabiel zijn over de
milieus.Overhet geheel genomengavendeparameters indithoofdstukaandat er
kansenzijnomdeknoldrogestofopbrengst inaardappel teverbeterendoorselectie
opeenoptimalecombinatievanbelangrijkefysiologischeeigenschappen,zoalsRUE,
cm,enAsum.
Hoofdstuk 4 gaat in op de kwestie van vroegheid in aardappel, omdat
momenteel veel onduidelijk is over dit concept. Dit hoofdstuk probeert een
eenduidige en consistente definitie van het begrip vroegheid te geven en geeft een
benaderingomvroegheidtebepalenineengrotereeksaardappelgenotypen,opeen
vrij eenvoudige en reproduceerbare manier. De nieuwe fysiologische en
kwantitatieve kennis opgedaan in de Hoofdstukken 2 en 3 werd gebruikt om
vroegheid eenduidig te kwantificeren voor een set van rassen en een uitsplitsende
populatie geteeld in diverse milieus. De nieuwe definitie van vroegheid werd
gebaseerdopvierverschillendefysiologischekenmerken:DP2,cm,EDenAsum.Devier
nieuwe, fysiologische vroegheidscriteria werden vergeleken met het gebruikelijke
criterium.Deresultatengavenaandatdefysiologischevroegheidscriteriadoorgaans
Samenvatting
229
de vroegheidsklassen op een minder dubbelzinnige manier definieerden dan het
conventionelecriterium.Bovendiengafhetconventionelecriteriummeerruisenwas
het minder stabiel en minder herhaalbaar dan de fysiologische kenmerken. De
nieuwe criteria blijken overigens ook de fysiologische wisselwerkingen te
onderstrepen die bestonden tussen de geselecteerde eigenschappen en
onderstreepten tevens de complexiteit van vroegheid bij aardappel. De nieuwe
definitie maakt het niet alleen mogelijk vroegheid te relateren aan fenologie en
fysiologie, maar kan ook breed worden toegepast in de aardappelveredeling en
onderzoeknaardeteeltechniekvanaardappel.Eénvandietoepassingenzoukunnen
zijn het ontwerpen van strategieën voor veredeling van aardappelideotype voor
specifiekevroegheids‐klassen.
Hoofdstuk5werptlichtopenkelevandebelangrijkstemechanismendieertoe
leiden dat genotypen verschillen in knoldrogestofopbrengst en biedt houvast voor
belangrijke veronderstellingen aangaande de ophoping van stikstof. Het test de
mogelijkheid om met het generieke gewasgroeimodel GECROS ecofysiologische
verschillen in knoldrogestofopbrengst van nakomelingen uit een F1 populatie, de
ouders, en een reeks cultivars te analyseren. Het model voorspelt de groei en
ontwikkelingvangewassenonder invloedvangenetischekenmerkenenbodem‐en
klimaatfactoren. De genotype‐specifieke model‐input parameterwaarden werden
geschatenhetmodelwerdgebruiktvoorhetvoorspellenvandeknolopbrengstvan
voornoemdplantmateriaalinverschillendeveldexperimenten.Hetmodelvoorspelde
de verschillen in knoldrogestofopbrengst over omgevingen en tussen genotypen
tamelijk goed. Het model reproduceerde op adequate wijze de trends in groei en
stikstofopname. Met behulp van de modelanalyse konden de belangrijkste
genotypische parameters worden geïdentificeerd die van invloed zijn op de
knoldrogestofopbrengst.DeNmax(demaximaletotaleNopname)droeghetmeestbij
aan de uiteindelijke knoldrogestofproductie. De resultaten toonden aan dat
genotypen met een hogere Nmax en een lager N‐gehalte in de knol een hogere
knoldrogestofopbrengsthadden.Dergelijkeinformatiekanaanzienlijkbijdragenaan
deontwikkelingvanaardappelideotypenvoorspecifiekeomgevingen.Deresultaten
bevestigdendathetmodelGECROShandigisvoorhetverkennenvanhetbelangvan
nieuwe genotypen en van de bijdrage van individuele fysiologische eigenschappen
aandeopbrengstdoorhetsimulerenvandereactiesvangenotypeninverschillende
milieus.
Hoofdstuk6 behandelt tot slot de belangrijkste bevindingen en de algemene
bijdrage van dit proefschrift aan de wetenschap. Het geeft manieren aan om
moleculaire veredelingsstrategieën en ideotypeveredeling in de aardappel te
versterken. Het benadrukt de noodzaak om kennis op de terreinen van de
Samenvatting
230
gewasfysiologie, modellering, en de genetica te combineren teneinde complexe
eigenschappen beter te begrijpen. Een dergelijke geïntegreerde aanpak kan de
genetische analyse van complexe eigenschappen zoals opbrengst versterken en
verhoogt aldus de efficiëntie van het veredelen. In dit verband dient toekomstig
onderzoek zich te richten op dit gebied en dienen verdere stappen te worden
genomenomgewasmodellerentekoppelenaanQTLmappingvandefysiologischeen
genotypische eigenschappendie in dit proefschrift zijn beschreven.Dit proefschrift
legteenstevigebasisvoorhetbrederedoelvandoorgewasmodellenondersteunde
plantenveredeling. Echter, dergelijke stappen kunnen alleen gezet worden als de
samenwerkingtussenfysiologenengeneticigeïntensiveerdwordt.
Acknowledgements
Thisthesisisasteptowardsunderstandingofgeneticvariationamongalargesetof
potatogenotypes.Thisstudy isapartofanextensiveresearcheffort toexplorethe
possibilities to link crop physiology, modelling, and genetic analysis for a better
understandingofpotatogrowthanddevelopmentthatmayhelpinunderstandingthe
yield dynamics as well as in designing ideotypes for specific and/or diverse
environments. This thesis has resulted into four years and more of field and
laboratory experimentation, literature review, data collection and modelling and
geneticanalysis. I sincerelyhope that this thesiswill, albeitmodestly, contribute to
thescientificknowledgeofpotatoscience.
Pursuing thisPhDprojectwithsomany interesting facetswas indeeda lovely
andmemorableexperienceforme.Itwasjustlikeclimbingahighpeak,stepbystep,
accompaniedwithchallenges,hardships,encouragement,andhope.NowwhenIfind
myselfatthetopenjoyingthebeautifulscenery,Irealisethatitwasinfactpossible
duetoexcellentteamworkthatgotmeherefinally.
ThecompletionofmydissertationandsubsequentPhDhasbeenalongrichand
learning journey. This journey actually started in 2007, when my dream to attain
highereducationfromawellrenownedworldleadinguniversitybecamearealitydue
to a scholarship award from Higher Education Commission (HEC), Government of
Pakistan under “Overseas PhD Scholarship Program Phase‐II”. I was very lucky to
cometotheWageningenUniversity(WUR),theworld’sleadingacademicinstitution.I
couldnothavesucceededwithoutthe invaluablesupportofseveral.Hence, Iwould
hereliketotakethisopportunitytoextendmymany,manysincerethankstoallthe
personsandinstitutionswhohavecontributedinthedifferentaspectsofthisstudy.
Myappreciationismuchdeeperthanafewwordscansay.
Firstandforemost,Iwouldliketoexpressmydeepandsinceregratitudetomy
multidisciplinaryPhDsupervisoryteammadeupofProf.dr.ir.PaulC.Struik(Centre
forCropSystemsAnalysis,WUR(CSA)),Prof.dr. ir.FredA.vanEeuwijk(Biometris,
WUR), Dr. Xinyou Yin (CSA), and Dr. ir. Herman J. van Eck (Laboratory of Plant
Breeding,WUR).Theteamwasapillarofsupportall theway fromthebeginning. I
couldnothavewishedforbettercollaboratorsandcoaches.Eachofyouhadaunique
support and efforts towards my success, which will always be remembered and
appreciated.Thankyouverymuchforembarkingwithmeonthislongjourney.Your
relentlesscontributionsandinsighthavebeenofgreatvaluetome.Youhaveallmade
itpossibleformetocommenceandcompletethisenormoustask.This is inspiteof
thedifficultchallengesandpredicamentsthatwewereallfacedwith.Toallthefour
supervisors, I have learned a lot under your tutelage and grown not just as a
Acknowledgements
232
researcher,butaspersontooandforthisIamindebtedtoyouall.
Aboveall,myspecialandsinceredeeply felt thanks tomyhonorificpromotor
Prof.dr.ir.PaulC.Struik,whoacceptedmeashisPhDstudentwithoutanyhesitation
when I requested him for an acceptance letter. Thereafter, he offeredme somuch
advice,patiently supervisingme,andalwaysguidingme in the rightdirection.This
thesiswould not have been possiblewithout the help, support and patience ofmy
principlesupervisorProf.PaulC.Struik,nottomentionhisadviceandunsurpassed
knowledgeofpotatophysiology.Heguidedmethroughoutmystudyperiod.Itisnot
sufficienttoexpressmygratitudewithonlyafewwords.Hewasinvolvedinalmost
allaspectsofplanningandoperationalaspectsoftheresearchcarriedout,resulting
inmanyfruitfuloutcomes.HewastheheadoftheCropPhysiologychairgroupatmy
departmentCSA.Nevertheless,hewasalwaysavailablewheneverIneededhisadvice.
Healwaysguidedmewithhisquickresponses,sharpanalyses,andnolessimportant,
great mentorship. Whenever necessary, he kept me going with a few stimulating
words.
DearPaul,Iamverygratefulforyourunreservedsupport,concern,day‐to‐day
guidance, enthusiasm, and keen interest in the progress of the research and
numerous constructive discussions throughout my study, which were always
encouragingand forsparingmuchofyourprecious timeeditingand improving the
manuscripts and chapters of my thesis. The door of your office was always open,
encouragingmetoenterwithoutanxiety.Yourswiftnessingoingthroughdrafts,even
during your journeys to a faraway destination is somuch appreciated. I was very
luckyhavingyouasmysupervisor.Paul, thankyouverymuchforhelpingmeinall
my queries, for editing themanuscripts, for you always helpful suggestions during
ourprojectmeetings, for yourworthy letters on gettingmyextensions and solving
administrativeissues.Thanksalsoforyourpatienceandfornevergivingupwithme.
You will remain my best role model for a scientist, mentor, and teacher. I am
privilegedtohavehadtheopportunitytobeyourstudentasItrulybelievethatyou
are the best supervisor I ever could have had to pursue my doctoral studies. My
special thanks for introducing me to the fascinating world of “potato science”, for
sharing with me your incredible knowledge and ideas about the potato crop and
physiology. Your enthusiasmand love for science is contagious.Honestly, Iwish to
workwithyouagaininthefuture! Ialsowishtoextendmysincereheartfeltthanks
to yourwife Prof. dr. ir. EdithT. Lammerts vanBueren for her kindness and great
hospitality.
Iamdeeplygratefultomyco‐promotorProf.dr.ir.FredA.Eeuwijkforsharing
hisknowledgeandexperienceontheappliedstatisticalgenetics,forhisdetailedand
constructivecomments,andforhisoverall inputandimportantsupportthroughout
Acknowledgements
233
this work. Prof. Fred provided countless valuable comments and suggestions to
improvethequalityofthisthesis.
DearFred,thanksalotforyourcriticalviewsonmyresearchworkduringour
teammeetingsandpositiveandveryhelpfulcommentsonmydraftmanuscripts,for
ourdiscussionswhichwerealwaysacontinuoussourceofinspirationformeandalso
foryourmotivation.Iamdeeplygratefulforyourpositivefeedbacks,expertguidance
on statistical genetics, insightful comments and suggestions throughout the study
whichwere essential to strengthening the finalwork. Thank you for being patient
withme.Itwasanhonourworkingwithyou!
Iwillforeverbethankfultomydailysupervisordr.XinyouYin,forhisexcellent
guidance duringmy PhD study.Workingwith Yin, I learned that Imust look after
every detail; to be precise and concrete. Throughout my thesis‐writing period, he
providedencouragement,soundadvice,goodteaching,andlotsofgoodideas.Iwould
have been lost without him. His detailed comments on my draft manuscripts
improvedconsiderablymyability towritescientificpaperswhilehisoverallefforts
guaranteed high quality of this thesis. Xinyou is a great teacher and mentor. He
encouragesandexpectedmetothinkmoreindependentlyaboutmyexperimentsand
results.
Dear Yin, I greatly admire your high level of expertise in the field of crop
modellingandyetmodesty,yourcapacityofunderstandingandanalysingasituation
in the breadth of your knowledge. Your door was always open for me to discuss
whateverdoubtsIhadinmymind.Youwerealwaysnexttome,anytimeoftheday,
providing ample scientific knowledge and vital advices for my life. You were
instrumentalinthemodellingexercises,thanksforintroducingmetothewonderful
galaxyofcropmodelling.Youconstantlychallengedmetogetthebestoutofmeand
thiswasalwaysanencouragingexperienceforme.Ourconversationsenlightenedmy
wayofthinking,andsoIwouldliketogivemysincerethanksforyourgeneroushelp.
Youremainedasupporterandprovidedinsightanddirectionrightuptotheend.For
this,Icannotthankyouenough.Iamforevergrateful.Ialsothankyourfamilyforall
the hospitality and nice time duringNewYear dinners. Thank you Yin; it’s a great
pleasureworkingwithyou!
Iwouldalsoliketogiveaheartfelt,specialthankstomyseconddailysupervisor
Dr. ir.HermanJ.vanEckforhisknowledgeableadvice,encouragementandpositive
perspectivesatalltimes.Hehasbeenmotivating,encouraging,andenlightening.He
remainedagreatsupport.Hehasbeenhelpfulinprovidingadvicemanytimesduring
my PhD period. Dr. Herman has provided very insightful discussions about the
research.IamalsoverygratefultoHermanforhisscientificadviceandknowledgeon
potatobreedingandgenetics,andformanyinsightfuldiscussionsandsuggestions.
Acknowledgements
234
Dear Herman, thank you very much for your valuable comments and
suggestions.Yourwideknowledgeaboutthepotatogeneticsandyourlogicalwayof
thinking has been a good basis for the present thesis. I wish I could have learned
moreaboutfascinatingscienceofpotatogeneticsfromyou.
Performingatopqualityresearchisnotpossibleintheabsenceofqualitydata
sets.IwasreallyfortunateenoughtohaveIng.PeterE.L.vanderPutteninmyteam.
Peterneedsaspecialwordofthanks.Hehasbeenindispensableinmyexperimental
work.Heisaverydedicatedworkerandhehadalwaysthepatiencetoexplaintome
whateverneeded.
DearPeter, I reallyadmireall yourhardworkanddedication in settingupof
fieldexperiments,collectionandcompilingdata.Therichdatayoucollectedisindeed
astrongfoundationofthisthesisandhelpedmetofocusmoreonmymodellinggoals.
Iamverygrateful toyouforprovidingmethenecessarydatafilesandteachingme
thepracticalaspectsofpotatoexperiments. Iwouldhavebeenunabletomake it to
whereIamtoday!
Ialsowishtoextendmydeepfeltthanksandappreciationtotheotherexternal
membersofmythesiscommitteeProf.dr.ir.M.K.vanIttersum,Dr.J.‐P.Goffart,Prof.
dr. ir. A.J. Haverkort, Dr. ir. E. Heuvelink whose helpful suggestions increased the
readabilityandqualityofthisthesis.Thankyouverymuchfortakingyourprecious
timeinreadingmybook!
IwanttoextendmymanythankstopeoplefromBiometrisfortheiradviceand
helpinsolvingstatisticalproblemsonacoupleoftimeswhendirelyneeded.Special
thanks to Dr. Marcos Malosseti, Mr. Paul Keizer, and Dr. Martin Boer for their
excellentco‐operationandassistancewiththeGenstatprocedures.Specialthanksto
Dr.HansJansenforintroducingmetoJoinMapandforgenerouslyhelpingwiththe
QTLanalysis inGenstat. IwouldalsoliketoacknowledgethenicesupportofICTof
Wageningen UR for their prompt assistance and for providing the necessary
softwares.TheWURlibrarystaffofalsodeservesspecialthanksfortheirunreserved
assistanceandco‐operation.
I am also grateful to all the staffmembers of theWageningen Plant Sciences
Experimental Centre (Unifarm), without the continuous support and assistance of
whommyresearchworkwouldhavebeenpracticallyimpossible.
IspentallmytimeatthedepartmentofCentreforCropSystemsAnalysis(CSA).
IconsidermyyearsspentatCSAtobeacruciallearningperiodandmorethanthata
great privilege. Tome CSA is also synonymouswith “the Centre of Excellence”, an
entity inWageningenURwith itsownflavouranduniqueness inscience;providing
topqualityresearchsupportandtrainingonmultifacetedaspectsofplantproduction,
fromgenotypestocropsystemsanalysisandproductionchains.Iamdeeplygrateful
Acknowledgements
235
toallthestaffmembersofCSAfortheirencouragement,support,andexpertadvice
whenever required. Thank you very much for offering me such an excellent
atmosphere, for making me feel at home, and for being so helpful and friendly.
Thanks for all the nice social activities, PV events, for the Dutch dinners, and
particularlyfunwiththetypicalDutchgames.IkzalmijnverblijfbijCSAaltijdblijven
herinnerenalseenvandebestetijdendieikooitgehadheb.
My life was easier in the Netherlands thanks to the professionalism and
dedicationoftheCSAstaffmembers.MyspecialthanksgotoMrs.JennyElwood,Mr.
EugeneGoosens,Mr.SjaakTijnagel,Mrs.WampievanSchowenburgandMrs.Sjanie
vanRoekel,fortheirhelpintheadministrativematters,Mrs.GijsbertjeBerkhoutand
Mr.Alex‐JandeLeeuwfortheirassistanceinfinancerelatedmatters,Dr.Ing.Aadvan
Ast,Mrs.Ans (J.E.)Hofman, and Ing.HenrietteDrenth for theirkindassistanceand
help on the technical issues.Many thanks for taking over all theworries frommy
head so that I could just concentrate onmy studieswith total dedication. Bedankt
allemaalvoorjullieadviesenhulp!
DearJenny,thankyouverymuchforyourwarmthandkindnessandhelpingme
withmyinitialdaysofPhDstudies.DearSjanieandWampie,thankyouverymuchfor
alwayslisteningtomyproblemsandforshowingalotofcare,help,andsupport,this
meanalottomeandIamverygrateful forthat.Sjanie, ikspreekhierbijmijngrote
waarderinguitvoorjeaanmoeding,supportenhetaltijdbereidzijnommijmeteen
groteglimlachtehelpen.Bedanktvoorallehulpenbedanktommijtestimulerenom
meerNederlandstepraten!Wampie, ikdankjouhartelijkvoordesupportdejeme
altijdgaf,vooraljehulpvoorde‘IND’en‘Zorgtoeslagzaken’.
Iamalsograteful todr.ClaudiusvandeVijver(coordinatorof theC.T.deWit
Graduate School for ProductionEcology andResource Conservation) for his advice
and assistance during the course of my PhD studies and for organising excellent
informative PhD related programmes, PE&RC weekends, PE&RC days, etc. I really
enjoyedmyselfbeingparticipantoftheseprogrammesandlearnedalotabouthowto
organisemyPhDstudieswell.
IwouldliketothankallthecolleaguesandfriendsoftheCSAanddepartment
PlantProductionSystems (PPS) for thegood chatswehadduring coffeeand lunch
breaks,andseminarsandgatheringsandcolleaguesforprovidingastimulatingand
funenvironments. Ihavereallyenjoyedsharingexperienceswithyouallduringthe
coffeebreaksandPVevents.
IamverygratefultotheformerandcurrentstaffmembersoftheCSA:Prof.dr.
ir.NielsP.R.Anten,Prof.dr.HolgerMeinke,Prof.dr.Huub(J.H.J.)Spiertz,Dr. ir. Jan
Vos, dr. ir. Wopke van derWerf, Dr. ir. Willemien J.M. Lommen, Dr. ir. Tjeerd‐Jan
Stomph,Dr.ir.LammertBastiaans,Dr.JochemB.Evers,Dr.GerhardBuck‐Sorlin,Dr.
Acknowledgements
236
CorA.Langeveld,Dr.PepijnvanOort,Dr.ir.Felix(J.J.A.)Bianchi,andtomycolleagues
andfriendsfromPPS:Prof.dr.ir.HermanvanKeulen,Prof.dr.KenGiller,Prof. dr. ir.
Martin van Ittersum, Dr. ir. Bert Janssen, Dr. ir. P.A. Leffelaar, Dr. Nico de Ridder, Ir.
JoostWolf, Ing. Elco Meuter, Ing. Bert Rijk, Dr. Ir. Gerrie van de Ven, Dr. ir. Maya
Slingerland,Dr.MarkvanWijk,Dr.PytrikReidsma,Mr.MarcelLubbers,Mrs.Riavan
Dijk,andMrs.CharlotteSchilt.
Iamgratefultoallfellow(formerandcurrent)PhDsandfriendsfrombothCSA
andPPS,forbeingthesurrogatefamilyduringthemanyyears.Thanksaredueto(in
alphabetical order): Aart van der Linden, Abdoul Aziz Niane, Aisha Abdulkadir,
AmbechaGemechis,AndréMonteiro,Dr.ArgyrisKanellopoulos,AshwiniNarasimhan,
BenjaminKibor,BennoBurema,CharlesBucagu,ChristiaanBiemond,ClaraVenegas,
DechassaLemessa,Dr.DonaldGaydon,EssegbemonAkpo,EstherRonner,Dr.Gisella
Cruz García, Junfei Gu, Herman Berghuijs, Inga Kurnosova, Joyce Challe, Julienne
Fanwoua, JunqiZhu, LennyvanBussel,Dr.MadeleineFlorin,MarciaThaisdeMelo,
Marieke Sassen, Maryia Mandryk, Masood Awan, Mazhar Ali Khan, Meike van
Opeusden, Dr. Michiel de Vries, Murilo de Arruda, Dr. Myriam Adam, Nicodème
Fassinou,NiteenKadam,NoméSakané, Dr.Rik Schuiling,RobertOkello, Sanderde
Vries, Dr. Sander van Delden, Sheida Sattari, Dr. Sinead O’ Keeffe, Sjaak Aben, Dr.
Sotiris Archontoulis, Dr. Susana Akrofi, Tommie Ponsioen,Wenfeng Cong,Wenjing
Ouyang,WietskevanderStarre,WillemWesterhuis,andYangYu.Thankyouall for
makingourgroupsmorevibrantandenjoyabletowork.
Iwishtoextendmysincerethanksandappreciationtomydearcolleagueand
best friend Junfei Gu for the great friendship and for helping me get through the
difficult times, and for all the emotional support, camaraderie, and caring he
provided.IwishyoulotsofsuccessandgoodluckwithyourPhDstudies.Thanksto
Dr.Gerhardforourendlessscientificdiscussionsrangingfromcropmodellingtovast
scientific matters; thanks also to your wife Evelyne and cute Coquinette for the
excellenthospitality.DearDr.JanVosandProf.dr.ir.HuubSpiertz,Iamverygrateful
to the great hospitality offered by you and your families. Dear Joost, I will always
remember our interesting talks and discussions during the coffee breaks and
sometimes in the late hours. Thanks for your encouragement, support, and expert
guidanceondiversescientificmatters.
Iwould liketothankmyoffice‐matesDr.GisellaCruzGarcía,MazharAliKhan
andMasoodAwan,forbeingveryhelpfulandsupportive.DearGisella,thankstoyou
andyourhusbandPaul,forthegreathospitalityandcare.DearAliandAwan,thanks
for always standing bymy side in times of need. I really felt like at homewith your
company.IwishyougreatsuccesswithyourrespectivePhDprojects.
IwarmlythankMarjoleinTiemens‐HulscherandCesarAndresOspinaNietofor
Acknowledgements
237
becomingmyparanymphs. Your presence and support onmy special day is highly
appreciated.Thegreatthingisthatweallsharethesameinterestsandambitions,i.e.
toexplorethefascinatingworldofpotatoscience.Inthisrespect,Iamreallyproudof
youbeingmyparanymphs!DearCesar,thanksforbeingagreatcolleagueandfriend.
IalsowishyoulotsofsuccessandgoodluckwithyourPhDproject.
Iwouldalsoliketothankmycolleaguesandfriendsfromotherdepartmentsof
WUR, particularly (in alphabetical order): AhmedAbd‐El‐Haliem,Dr. Anna Finkers,
Dr. Anitha Kumari, Dr. Arwa Shahin, Bas Allema, Dr. Bas Engel, ChristosKissoudis,
DennisOonincx, Dr. Fradin Emilie, Dr.Gerard vanderLinden,Dr.HenkSchouten, Jet
Vervoort, Jimmy Ting Hieng Ming and wife, Mansoor Karimi, Mohamad Arif,
Mohamed El Soda, Dr. Nasim Irani, Nasr Ahmed, Partha Sarathy, Paula Ximena
Hurtado,PaulinaKerbiriov,QiiWeicong, Dr.SameerJoshi,SuxianZhu,TarekSoliman,
Thijs van Dijk and Yusuf Sen. Thank you very much for your support and great
interaction.
Imetwithmanyinterestingpeoplefromaroundtheworldandmademanynew
friends during my studies in Wageningen. I consider Wageningen “the city of life
sciences” truly my second home now. I really enjoyed and learned about a wide
spectrumofcultureshere. Iwould liketo thankmyfriendsandcolleagues fortheir
lovely friendship and support and making my time in Wageningen the most
memorable in my life. Many thanks to you my good friends, particularly (in
alphabetical order): Alba Tros, Alberto Franco Solís, Aleksander Banasik, Alisa
Shlyuykova,AntonAgarev,AtivanderHoning,AzizPatwary,BenoitCarréres,Daniel
Kinpara, Diego Chacon, Dimitris Mitsopoulos, Dmitry Zolotarev, Elena Sokolova,
Eliana de Souza, Dr. Fareeduddin Noori, Habibullah Asad, Hamed Rashidi, Inga
Kurnosova,JanneDenolf,KhanWaliKamran,KunHan,LiyakatMujawar,LolaRomera,
Manuel Jesùs, Mariya Tarazanova, Maya Lievegoed, Rahatullah Mohmand, Romi
Gaspric,SandraDelgado,SatyaSriram,SergeyKleymenov,Dr.SouravBhattacharjee,
Tanya Huizer, Tooryalay Nasery, Victoria Naipal,Wasil Akhter,Wei Qin andmany
more.Icouldnotincludeeverybody’snameasthelistisverylong.Yoursalutations,
smiles, jokes, encouragements, social, and academic discussions were simply
infectious.
DearAlberto,thanksforyourwarmthandunselfishfriendship,forallthegood
time we had together, for you great sense of humour. I immensely enjoyed your
company.IwishyougreatsuccessandgoodluckwithyourPhDstudies.
Thanks tomyTurkish friends for yourwarmwelcome and hospitality onmy
visittoTurkey,especially:Prof.Dr.MehmetEminÇalışkan,Dr.FundaArslanoğlu,Dr.
ErdoğanÖztürk,andDr.YasinBedrettinKaran.Manythanksforyourbestwishesand
encouragementevenwhenfaraway,çokteşekkürederim!
Acknowledgements
238
IwouldalsoliketothankmyPakistanifriendsandfamiliesinWageningenand
elsewhereintheNetherlands.Myheartiestgratitudeto(inalphabeticalorder):Abbas
Gul, Abid Maan, Akmal Nazir, Dr. Farrakh Mehboob, Farooq Ahmad, Ghulam Abbas,
GhulamMustafa, Hafiz Sultan, Haider Ali, Hamid Shah, HashimDurrani, Hazrat Ali,
Imran Bhatti, Imran Haider, Kashif Khan, Dr. Luqman Aslam, Mateen Ahmad, Dr.
MazharAli,Dr.MazharIqbalZafar,MubarakAli,MuhammadImtiaz,Dr.Muhammad
Irshad, Dr. Muhammad Jamil and family, Muhammad Rashid, Muhammad Sohail,
MunawarHussain and family, Dr. NadeemKhan, Dr.Nazir AhmadKhan,Noorullah
Khan,SaadGilani,Dr.SabazAliKhan,SadeeqUrRahman,SaifMalik,Dr.SajidRehman
and family,ShafqatQaisrani,SuhailYousaf,ZakirDahri,andZeshanHassan. I spent
mostjoyousandpleasanttimewithyouinWageningen.Iwishyoumuchsuccessand
goodluckwithyourcurrentandfutureendeavours.
Isharedaspecial friendshipwithDr.NazirAhmadKhan,NoorullahKhan,and
Dr.SabazAliKhan.Iwillrememberourinterestingculturaldinnerevenings,ourtalks
overpracticallyanythingonthisearth,especiallyoverhistory,politics,Pashtomusic,
younameit,althoughImustadmitourfavouritetopicwashowtoimproveanduplift
theeducationsystemofPakistan.
Iwould like to conveymyheartfelt thanks tomyhome institute, Agricultural
Research Institute (ARI), D.I.Khan for their companionship and encouragement. I
thankthestaffofARI for theirgeneroussupport. Iconsidermy initial jobperiodat
ARIasacruciallearningperiodofmylife.
My sincere thanks go to all the institutions andorganisations,which gaveme
theirindispensablegeneroussponsoring,withouttheirsupportandfinancialhelp,it
would not have been possible for me to pursue and to complete this PhD project
successfully.MysincerethanksareduetotheHECPakistan,forprovidingmeafully
fundedscholarshiptofulfilmydreamofhighereducationintheNetherlands.Iwould
liketoacknowledgethefinancial,academic,andtechnicalsupportofentireHECteam
during the course ofmy studies. Iwant to thank theNetherlands Organization for
InternationalCooperationinHigherEducation(NUFFIC)fortheirunreservedsupport
and excellent assistance and co‐operation. I am thankful to the European Union
SeventhFrameworkProgrammeunderproject (NUE‐CROPSFP7‐CP‐IP222645) for
supportingmystudies.IamverygratefultomyhostdepartmentintheNetherlands
i.e. CSA for offering me necessary financial support during the end phase of my
studies.
Ofcoursenoacknowledgmentswouldbecompletewithoutgivingthankstomy
parents.ThecontinuoussupportandencouragementIreceivedfrommyfamilyback
in Pakistan was a great source of strength. Without their encouragement and
understandingitwouldhavebeenimpossibleformetocompletelydevotemyselfto
Acknowledgements
239
myPhDstudies. Iwish toexpressmyeverlastingsenseofgratitudeand lovetomy
affectionate parents, for their endless patience, relentless support, understanding,
andencouragementall thewaythroughoutmystay intheNetherlands.Yourcaring
wordsofencouragementandconstantassurancehavehelpedmegetthroughsomeof
themostdifficulttimes.Icouldnothavedoneitwithoutyou!Mostimportantofallis
myfatherDr.RahmatUllahKhan,whostimulatedmecontinuouslyduringmystudies.
Hehasbeena constant inspiration.Mymother, especially,playedavery important
roleduringmyPhD,providingmewithherample love,moral supportandshowed
greatpatienceandunderstandingduringalltheseyears.Shewasagreatrolemodel
ofresilienceandstrength.Iowemyeveryachievementtobothofthem.Thisthesisis
alsodedicated toyoubothasa small tokenof appreciation foreverything thatyou
havedoneformealltheseyears.Myspecialgratitudeisduetomygrandmotherfor
herblessings,tomybelovedyoungerbrotherYasirKhan,mysistersMariyaKhanand
UmaimaKhan,mybrother‐in‐lawsDr.KashifWaseemKhan,AdnanKhanand their
families, for their loving unequivocal support throughout, as always, forwhichmy
mereexpressionofthankslikewisedoesnotsuffice.
MystudytooklongtimeandImayhaveneglectedtoacknowledgeotherpeople
whohavehelpedmealongtheway.Ifso,pleaseacceptmyapologies,andknowthat
youareappreciated.
ToallthoseI’vemetalongthewayinsomewayaffectedme,mysincerethanks!
MuhammadSohailKhan
Wageningen,September2012
241
CurriculumvitaeMuhammadSohailKhanwasbornon6thJune1981
inKhyberPakhtunkhwa,Pakistan.Hecompletedhis
basic edu cation in 1998 and a fewmonths later he
entered as a student at the Faculty of Agriculture,
Gomal University, Dera Ismail Khan, Khyber
Pakhtunkhwa,Pakistan. In2002,heobtainedhis4‐
yrB.Sc.(Hons)Agriculturediplomawithdistinction.
HecontinuedhisstudiesinthesameuniversityforhisMastersandobtainedhis2‐yr
M.Sc.(Hons)Agriculturediplomawithdistinction(GoldMedal)in2004.Hechoseto
specialize in the field of Horticulture during his Masters studies and worked on
evaluatingtheeffectsofdifferentauxinsonthegrowthandestablishmentofDamask
Rose(RosadamascenaMill.)cuttingsindifferentgrowingmedia.
SoonaftercompletinghisMasters,hebrieflyworkedasaResearchAssistantat
the Arid Zone Research Institute Dera Ismail Khan, Pakistan Agricultural Research
Council (PARC). His work mostly involved developing improved production and
propagationtechnologiesforaridhorticulturalfruits.In2005,hewasappointedasa
ResearchOfficerattheHorticultureSectionofAgriculturalResearchInstitute(ARI),
DeraIsmailKhanundertheauspicesofAgriculturalResearchGovernmentofKhyber
Pakhtunkhwa, Pakistan. He was mainly involved in the conservation of elite
indigenous fruit germplasm as well as multi‐environment evaluation of different
exotic cultivars of important regional fruits: Datepalm (Phoenixdactylifera),Mango
(MangiferaindicaL.),Ber(ZizyphusjujubaL.),Falsa(Grewiaasiatica),etc. It is there
thathebecamefascinatedwithstudyingtheroleofG×Einteractiononplantgrowth
andphysiology.
Inearly2007,hewasawardedan“OverseasPhDScholarship”fromtheHigher
Education Commission of Pakistan (HEC) in collaboration with the Netherlands
Organization for International Cooperation in Higher Education (NUFFIC). A few
monthslater,hecametoWageningenUniversity,theNetherlandstopursuehisPhD
study under the supervision of Prof. dr. ir. Paul C. Struik (Centre for Crop Systems
Analysis, (CSA)),Prof.dr. ir.FredA.vanEeuwijk (Biometris),Dr.XinyouYin (CSA),
andDr.ir.HermanJ.vanEck(LaboratoryofPlantBreeding).HisPhDresearchwason
“Assessing genetic variation in growth and development of potato (Solanum
tuberosumL.)”whichresultedintotheoutcomeofthisthesis.AfterdefendinghisPhD
thesis,hewilljoinhisparentinstituteinPakistan.Hehasdevelopedastronginterest
incropphysiology,quantitativegenetics,andmodel‐basedplantbreeding.Hewould
liketoinvesthisfutureresearcheffortsexcellingintheseareas.
PE&RCPhDEducationCertificate
With the educational activities listed below the PhD
candidate has complied with the educational
requirementssetbytheC.T.deWitGraduateSchoolfor
ProductionEcologyandResourceConservation(PE&RC)
whichcomprisesofaminimumtotalof 32ECTS(=22
weeksofactivities)
Writingofprojectproposal(6ECTS)
- Genotype‐by‐environment (G×E) interaction on the dynamics of yield
formationinpotato(SolanumtuberosumL.)(2012)
Writingofprojectproposal(4.5ECTS)
- Assessing genetic variation in growth and development of potato (Solanum
tuberosumL.)(2008)
Post‐graduatecourses(6ECTS)
- Artofmodelling;PE&RC(2008)
- Modernstatisticsforthelifesciences;Biometris(2009)
- Multivariateanalysis;PE&RC(2010)
Deficiency,refresh,brush‐upcourses(3ECTS)
- Cropecology(2008)
- Systemsanalysis,simulationandsystemsmanagement(2008)
Competencestrengthening/skillscourses(6.6ECTS)
- Informationliteracy,includingintroductiontoEndnote;WGS(2008)
- Projectandtimemanagement;WGS(2008)
- Scientificwriting;CENTA(2009)
- Techniquesforwritingandpresentingscientificpapers;WGS(2009)
- Presentationskills;CENTA(2010)
- Howtowriteaworld‐classpaper;LibraryWageningenUR(2011)
PE&RCAnnualmeetings,seminarsandthePE&RCweekend(1.2ECTS)
- PE&RCWeekend(2008)
- PE&RCDay(2010)
PE&RCPhDEducationCertificate
244
Discussiongroups/localseminars/otherscientificmeetings(5.8ECTS)
- SENSEContextsymposium;WageningenUR,theNetherlands(2007)
- Ontheevolutionofplant‐pathogeninteractions: fromprinciplestopractices;
WageningenUR,theNetherlands(2008)
- 40YearstheoryandmodelatWageningenUR,theNetherlands(2008)
- Plant roots from gene to ecosystem; Radboud University, the Netherlands
(2008)
- Ecologyandexperimentalplantsciences2;WageningenUR, theNetherlands
(2009)
- Biometrisstatisticalgeneticsmeeting(2009‐2012)
- The potato root system; Plant Breeding, Wageningen UR, the Netherlands
(2010)
- YoungPSGmeetings(2010)
- Plantsciencesseminars(2010‐2012)
- PlantBreedinginthegenomicsera;WageningenUR,theNetherlands(2011)
Internationalsymposia,workshopsandconferences(5.6ECTS)
- InternationalEucarpiaCongress,“PotatobreedingaftercompletionoftheDNA
sequenceofthepotatogenome”;WageningenUR,theNetherlands(2010)
- International symposium on agronomy and physiology of potato, “Potato
Agro‐physiology‐2010”;Nevşehir,Turkey(2010)
Lecturing/supervisionofpractical’s/tutorials(0.6ECTS)
- Researchmethodsincropscience;2days(2010)
Supervisionof4MScstudents;7days
- Model based estimation of parameters describing the dynamics of canopy
covergrowthinalargesetofpotato(SolanumtuberosumL.)genotypes
- Estimationofbetathermaltime
- Non‐linearregressioninSAS
- MixedmodelanalysisinGenstat
245
Funding
The research work described in this PhD thesis was co‐funded by the European
Community financial participation under the Seventh Framework Programme for
Research, Technological Development and Demonstration Activities, for the
Integrated Project NUE‐CROPS FP7‐CP‐IP 222645. The views expressed in this
publicationarethesoleresponsibilityoftheauthoranddonotnecessarilyreflectthe
views of the European Commission. Neither the European Commission nor any
personactingonbehalfoftheCommissionisresponsiblefortheusewhichmightbe
made of the information contained herein. Scholarship grant to the author was
grantedbytheHigherEducationCommission(HEC),GovernmentofPakistanunder
“OverseasPhDScholarshipProgramPhase‐II”andCentreforCropSystemsAnalysis
(CSA).
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