Evaluation of Early Maturing Hybrids of Pearl millet in Arid Environment of Western Rajasthan

Sujit Kumar, Aman

Professor (GPB) & P.I., ICAR-AICRP on Pearl Millet, Agricultural Research Station, Swami Keshwanand Rajasthan
Senior Research Fellow (SRF), ICAR-AICRP on Pearl Millet, Agricultural Research Station, Swami Keshwanand

DOI: https://doi.org/10.17140/phoj-11-746 · Published 2026-07-17 · Licence: https://creativecommons.org/licenses/by/4.0 · © 2026 The Author(s)

Also available as PDF (figures and tables appear in the PDF).

Keywords: Pearl millet; genetic variability; grain yield; heritability; genetic advance; dry fodder yield; arid regions; phenotypic selection; Pearlmillet; geneticvariability; geneticadvance; grainyield; dryfodderyield; phenotypicselection

Introduction

Pearl millet stands as a vital coarse cereal in the

aridandsemi-aridtropics. Itvaluednotonlyforits

resilienceunderconditionsofhightemperature,low

rainfallandpoorsoilfertilitybutalsoforitsnutritional

profilethatisrichinenergy,protein,dietaryfiberand

minerals such as iron and zinc (Gupta et al., 2015;

Hassan etal.,2021;Karthik etal.,2024).Furthermore,

its versatility makes it suitable for both food and

fodder. WithinIndia,pearlmilletholdsaprominent

positionparticularlyinRajasthan, Gujarat, Haryana,

Maharashtra and parts of Uttar Pradesh (Satyavathi

et al., 2021). However, Western Rajasthan faces

extreme agricultural constraints including variable

rainfall, high evapo-transpiration, sandy soils and

frequent heat and moisture stress (Singh, 2025).

Despitethesechallenges,itremainsakeycropinthe

region which is essential to local farming systems.

Consequently,limitedirrigationandpoorsoilhealth

resultinproductivitylevelsthatfallwellbelowstate

and national averages (Singh, 2025; Yadav and Rai,

2013).ZoneIc(Bikaner,JaisalmerandChurudistricts)

reported4.23lakhhectaresofarea,2.44lakhtonnesof

productionandaproductivityof537kg/ha;incontrast,

Rajasthanasawholereported3.52lakhhectaresof

area,53.73lakhtonnesofproductionand1234kg/ha

productivity during the same period (Anonymous,

2025). This stark productivity gap underscores the

necessity for location-specific genotype selection

(YadavandRai,2013),asagro-climaticgradientsreveal

thatwesterndistrictsconsistentlylagbehindtherest

ofthestate.

All India Coordinated Research Project on Pearl

Milletfocusesonidentifyinghigh-yielding,stableand

adaptedgenotypesforvariousagro-climaticzonesto

addresstheseissues(Satyavathi etal.,2021).ZoneA1,

representingtherainfedregionsofRajasthan,Haryana

and Gujarat, exhibits notably low production and

productivity.Beyonditsgrainvalue,thecropprovides

essential fodder for livestock and supports dietary

diversityamongresource-poorpopulations(Karthik

etal.,2024).Economically,itservesasacriticalliveli-

hood source for smallholders (Naorem et al., 2023).

Thepresentinvestigationwasconductedtoidentify

promising early pearl millet entries suited to the

harshandaridenvironmentofZoneA1bycomparing

themwithstandardchecks. Suchevaluationisvital

because genotypes that thrive in one environment

may falter under the extreme moisture and heat

stresscharacteristicofwesternRajasthan.Therefore,

testinginthetargetenvironmentisessentialtoselect

genotypeswithsuperiorearliness,stabilityandyield.

MaterialsandMethods

Theexperimentalmaterialcomprising23pearlmillet

entries(IHT101toIHT123), includingfivestandard

checks,wasevaluatedattheAgriculturalResearchSta-

tion(ARS),SwamiKeshwanandRajasthanAgricultural

University(SKRAU),Bikaner,Rajasthan.Thetrialwas

conductedinaRandomizedBlockDesign(RBD)with

threereplications.Eachentrywasgrowninanetplot

measuring4.0m × 1.80m,maintainingacropgeom-

etryof60cm × 15cm. Theexperimentalsiteischar-

acterizedbysandysoilwithanalkalinereaction(pH

8.0).Theavailablenutrientstatusofthesoilwasvery

−1 lowinnitrogen(80kgha ),lowinphosphorus(18kg

−1 −1 ha )andmediuminpotassium(168kgha ). Rec-

ommendedagronomicpracticesfortheregionwere

followedthroughoutthecropseason.Basaldosesof

nitrogenandphosphoruswereappliedatsowingwhile

theremainingnitrogenwassuppliedthroughtopdress-

ingattheappropriatecropgrowthstage.

Observationswererecordedontenquantitativechar-

acters, namely: days to 50% flowering, days to ma-

turity,plantheight(cm),paniclelength(cm),panicle

diameter(cm),1000-seedweight(g),populationathar-

vest(numberpernetplot),seedsetunderbagging(%),

grainyield(kgpernetplot)anddryfodderyield(kg

pernetplot).Grainandfodderyieldsrecordedonanet

plotbasiswereconvertedtohectareyieldusingacon-

versionfactorof1388.89.Inaddition,theincidenceof

majorpearlmilletdiseases, viz.,downymildew,smut

andergot,wasrecordedundernaturalfieldconditions

andexpressedaspercentageinfection. Theidentity

detailsofallentriesarepresentedinTable1.Genetic

variabilityparameterswerecomputedusingstandard

biometricalprocedurestoevaluatethemagnitudeof

variationavailableforselectionandgeneticimprove-

mentinpearlmillet.

StatisticalAnalysis

Therecordeddataweresubjectedtostatisticalanalysis

toestimatemeanperformanceandassesstheextent

ofgeneticvariabilityamongthehybridentries.OPSTAT

statisticalsoftwarewereusedtoanalyzetherecorded

dataat1%and5%levelofsignificance.

1.Phenotypeandgenotypevariability

The variability among genotypes was estimated us-

ingrange,mean,standarderror,phenotypicvariance,

genotypicvarianceandcoefficientsofvariation.The

variancecomponentswerecomputedfollowingstan-

dardproceduresandusedtoestimatephenotypicand

genotypiccoefficientsofvariation,heritabilityandge-

neticadvance.

Genotypicvariance(σg )wascalculatedas: σg = (MST − MSE)/r

where,MST=meansquareduetotreatments(geno-

types),MSE=errormeansquare,and r =numberof

replications.

Phenotypicvariance(σp )wasestimatedas:

2 2 2 σp = σg + σe

where, σe =environmental(error)variance.

AccordingtoSinghandChaudhary(1999),thepheno-

typiccoefficientofvariation(PCV)andgenotypicco-

efficientofvariation(GCV)werecalculatedusingthe

followingformulas:

√︂

PCV(%) = ( σp/X) × 100

√︂

GCV(%) = ( σg/X) × 100

where, isthegrandmeanofthetrait.

X ¯

The values of GCV and PCV were classified as low

(<10%), moderate (10–20%), and high (>20%)

following the criteria suggested by Deshmukh et al.

(1986).

2.Heritability,GeneticAdvanceandGeneticAdvanceas

PercentofMean

Heritabilityinthebroadsense(h )wasestimatedfor

eachtraitaccordingtoAllard(1960)as:

2 2 2 h (%) = (σg /σp) × 100

Theexpectedgeneticadvance(GA)underselectionwas

calculatedfollowingJohanson etal. (1955)assuming

aselectionintensityof5%:

GA = k × h × σp

where, k =standardizedselectiondifferential(2.06at

5%selectionintensity), h =heritabilityinbroadsense,

√︂

and σp =phenotypicstandarddeviation.

Geneticadvanceaspercentofmean(GAM)wascom-

putedas:

GAM(%) = (GA ¯) 100

where,GA=geneticadvanceand =grandmeanof

thetrait.

ResultsandDiscussion

TheANOVAresultsarepresentedinTable2.Themean

sumofsquareduetotreatmentsaresignificantforall

thecharactersstudiedwhichrevealedsignificantdif-

ferencesamongthegenotypesforallthetraitsstudied.

Significantgeneticvariabilityinbreedingmaterialisa

prerequisiteasitnotonlyprovidesthebasisforselec-

tionbutalsooffersvaluableinformationforidentifying

diverseparentsforuseinhybridizationprogrammes

(Gupta etal.,2015;Singh etal.,2016andBasavaraj et al.,2017).Themeanperformanceisgivenintable3for

eachcharacterstudied.

Perse performance

Allthetraitsstudiedexhibitedawiderangeofvaria-

tion,indicatingsubstantialgeneticdiversityandample

scopefortheirimprovementthroughselection.

1. Days to 50% flowering: The mean days to 50%

floweringwas45.12dayswitharangeof42.33to50.00

days. The earliest flowering genotype was HHB67

(Imp.) (check) (42.33 days) followed by CZH 278

(43.00days)whereasRHB223(check)wasthelatest

floweringgenotype(50.00days).Basedonthecritical 2 ¯

difference(CD=1.77days),tenentriesareatparwith

theearliestfloweringentry.

2 ¯

2. Days to maturity: The overall mean for days to

maturitywas74.99daysandvariedfrom72.00to79.00

days. HHB67(Imp.) (check)maturedearliest(72.00

days)whereasRHB223(check)maturedlatest(79.00

days).Thedifferencebetweentheearliestandlatest

maturinggenotypesexceededtheCDvalue(2.18days),

indicatingsignificantvariationformaturityduration.

3.Plantheight: Plantheightrangedfrom116.00cm

to 178.33 cmwith ameanof 158.87cm. Thetallest

genotypewasRHB292(178.33cm)followedbyMPMH

52(177.67cm),whileRHB223(check)wastheshortest

genotype(116.00cm).Basedonthecriticaldifference

(CD=13.15cm),sixentriesareatparwiththetallest

entry.

√︂ 4.Paniclelength: Themeanpaniclelengthwas23.48

2 2

cm,rangingfrom20.67to26.67cm.Thelongestpan-

icleswererecordedinMPMH52and(26.67cmeach)

whereasDHBH-24305(IHT102)exhibitedtheshortest

panicles(20.67cm). Basedonthecriticaldifference

(CD=2.30cm),sevenentriesareatparwiththelongest

panicle.

5. Panicle diameter: Panicle diameter varied from

/X × 2.10to3.07cmwithameanof2.55cm.Themaximum

X ¯ paniclediameterwasobservedinBHB2501(3.07cm),

followedbyPA9070(3.03cm),whileRHB287recorded

theminimumdiameter(2.10cm).Basedonthecritical

difference(CD=0.32cm),threeentriesareatparwith

themaximumpaniclediameter.

6.1000-seedweight: Theaverage1000-seedweight

was9.78gandrangedfrom7.87to11.33g.Thehighest

testweightwasrecordedinPB1756(check)(11.33g),

followedbyPusaHybridBajra2501(IHT101)(11.03g),

whereasMPMH52andCZH280exhibitedthelowest

value(7.87g).Basedonthecriticaldifference(CD=1.01

g),sixentriesareatparwiththehighesttestweight.

7.Populationatharvest: Themeanplantpopulation

TestCode ProjectNo. Identity Pedigree

PusaHybrid

IHT101 MH2887 411A × PPMI1327 ICAR-IARI,NewDelhi

Bajra2501

IHT102 MH2888 DHBH-24305 DHLB-14A × DHLBI-1035

IHT103 Check PB1756 PSP91xPP107 ProtectionLtd.,

IHT104 MH2889 DHBH-24308 DHLB-36A × DHLBI-1035 ICAR-ICAR-AICRP-PM,Dhule

IHT105 MH2890 RHB284 ICMA02333 × RIB494 ICAR-AICRP-PM,SKNAU,Jaipur

IHT106 MH2891 RHB285 ICMA04888 × RIB20K86 ICAR-AICRP-PM,SKNAU,Jaipur

IHT107 MH2892 RHB287 ICMA99444 × RIB494 ICAR-AICRP-PM,SKNAU,Jaipur

IHT108 MH2893 RHB288 ICMA93333 × RIB3135-18 ICAR-AICRP-PM,SKNAU,Jaipur

IHT109 MH2894 RHB290 ICMA98222 × RIB15177 ICAR-AICRP-PM,SKNAU,Jaipur

IHT110 Check RHB223 ICMA96666xRIB3135-18 ICAR-AICRP-PM,SKNAU,Jaipur

IHT111 MH2895 RHB292 ICMA04999 × RIB15S076 ICAR-AICRP-PM,SKNAU,Jaipur

IHT112 MH2896 BHB2501 ICMA88004 × BIB-62 ICAR-AICRP-PM,SKRAU,Bikaner

IHT113 MH2897 BHB2502 ICMA98222 × BIB23-1 ICAR-AICRP-PM,SKRAU,Bikaner

IHT114 MH2898 MPMH52 ICMA95222 × MIR519-1 ICAR-AICRP-PM,PCUnit,Jodhpur

IHT115 MH2899 MPMH53 ICMA04999 × MIR519-1 ICAR-AICRP-PM,PCUnit,Jodhpur

IHT116 Check HHB67(Imp.) ICMA843-22xH77/833-2-202 ICAR-AICRP-PM,CCSHAU,Hisar

IHT117 MH2900 PA9070 PSP140 × PP125 CrystalCropPrtectionLtd.

IHT118 MH2901 CZH278 ICMA04999 × CZI2022/7 ICAR-CAZRI,Jodhpur

IHT119 MH2902 CZH279 ICMA843-22 × CZI2023/4 ICAR-CAZRI,Jodhpur

IHT120 Check MPMH35 ICMA194555xMIR1252 ICAR-AICRP-PM,PCUnit,Jodhpur

IHT121 MH2903 CZH280 ICMA083333 × CZI2023/4 ICAR-CAZRI,Jodhpur

IHT122 Check 86M94 9PHGF39AxM444R PioneerOverseasCorp.,Hyderabad

IHT123 MH2904 RVBH-2253 ICMA97111 × 24110 ICAR-AICRP-PM,RVSKVV,Gwalior

SourceofVariation DegreeofFreedom Daysto50%flowering Daystomaturity PlantHeight(cm.) PanicleLength(cm.) PanicleDiameter(cm.) 1000-Seedwt.(g) Populationatharvest/no.netplot Seedsetunderbagging(%) GrainYield(kg/netplot) DryFodderYield(kg/netplot)

Replication 2 0.04 0.08 0.12 0.14 0.1 Treatment 22 13.56** 12.83** 664.87** 12.02** 0.23** Error 44 1.15 1.74 63.43 1.93 0.04

*,**–Significantat5%and1%levelofsignificance

atharvestwas72.38plantspernetplot,rangingfrom

67.00to77.33plants.Thehighestpopulationwasob-

servedinRHB223(IHT110),BHB2501(IHT112),and

RHB287(IHT107)(77.33plants), whilePusaHybrid

Bajra2501(IHT101)recordedthelowestpopulation

(67.00plants).

8.Seedsetunderbagging: Themeanseedsetunder

baggingwas50.94%,withvaluesrangingfrom43.33%

to 58.33%. The highest seed set was observed in

DHBH-24305(IHT102)andCZH279(IHT119)(58.33%),

whilePB1756(IHT103)recordedthelowestseedset

(43.33%).

9.Grainyield: Grainyieldpernetplotaveraged0.55

kgandrangedfrom0.35to0.83kg.Thehighestgrain

yieldwasrecordedbyPusaHybridBajra2501(0.83kg),

followedbyBHB2501,MPMH5386M94(check)(0.75

kgeach)andRHB292(0.72kg).Basedonthecritical

−1 difference(CD=0.15kgplot ),thegrainyieldsofBHB

2501,MPMH53,86M94andRHB292werestatistically

atparwiththehighestyieldinggenotype.Thelowest

grainyieldwasobservedinRHB223(check)(0.35kg).

Origin

ICAR-ICAR-AICRP-

PM,Dhule

CrystalCrop

Hyderabad

Table 2. AnalysisofVariance(ANOVA) Characters 0.12 0.05 0.12 0.01 0.014 3.04** 31.83** 49.11** 0.05** 0.445** 0.37 23.59 41.16 0.01 0.067 10. Dryfodderyield: Dryfodderyieldrangedfrom

2.12to3.57kgpernetplot,withameanof2.54kg.The

highestdryfodderyieldwasrecordedinMPMH52(3.57

kg),followedbyRHB223(check)andMPMH53(3.27kg

each).RHB223andMPMH53werestatisticallyatpar

withMPMH52.Thelowestfodderyieldwasobserved

inPusaHybridBajra2501(2.12kg).

Noincidenceofdownymildew,smutorergotwasob-

servedinanyofthetestedentriesthroughoutthecrop

growth period under natural field conditions. Thus,

allgenotypesexhibitedfreedomfromthesemajordis-

easesduringthecourseofexperimentation.

Geneticvariabilityparameters

Table4depictstheestimatesofgenotypiccoefficient

ofvariation(GCV),phenotypiccoefficientofvariation

(PCV),heritability(broadsense),geneticadvance(GA)

and genetic advance as per cent of mean (GAM) for

differentcharactersstudiedinpearlmillet.

Estimatesofphenotypiccoefficientsofvariationwere

higherthangenotypiccoefficientofvariationwhich

TestCode Identity Daysto50%flowering Daystomaturity PlantHeight(cm.) PanicleLength(cm.) PanicleDiameter(cm.)

IHT101 PusaHybridBajra2501 45.33 75.67 176.67 21.33 2.87 IHT102 DHBH-24305 47.33 77.33 164.67 20.67 2.47 IHT103 PB1756(C) 43.67 73.00 155.00 22.00 2.53 IHT104 DHBH-24308 48.00 78.00 161.67 22.00 2.63 IHT105 RHB284 44.00 74.00 148.33 24.00 2.57 IHT106 RHB285 43.33 74.00 167.00 23.33 2.47 IHT107 RHB287 43.67 73.67 158.33 22.67 2.10 IHT108 RHB288 44.00 73.67 174.33 24.67 2.33 IHT109 RHB290 47.67 77.67 152.67 22.33 2.90 IHT110 RHB223(C) 50.00 79.00 116.00 21.00 2.17 IHT111 RHB292 44.67 74.33 178.33 24.00 2.73 IHT112 BHB2501 44.67 74.33 151.67 21.33 3.07 IHT113 BHB2502 44.33 74.33 151.67 22.33 2.63 IHT114 MPMH52 45.00 74.67 177.67 26.67 2.30 IHT115 MPMH53 44.00 74.00 173.33 26.00 2.63 IHT116 HHB67(Imp.)(C) 42.33 72.00 155.33 26.00 2.17 IHT117 PA9070 43.67 73.67 167.33 26.67 3.03 IHT118 CZH278 43.00 72.67 165.00 23.33 2.27 IHT119 CZH279 43.33 73.33 159.67 25.00 2.20 IHT120 MPMH35(C) 48.67 78.33 145.67 21.00 2.87 IHT121 CZH280 45.33 75.33 141.67 26.33 2.57 IHT122 86M94(C) 43.33 73.33 172.67 22.00 2.43 IHT123 RVBH-2253 48.33 78.33 139.33 25.33 2.63

GrandMean 45.12 74.99 158.87 23.48 2.55 Range 42.33-50.00 72.00-79.00 116.00-178.33 20.67-26.67 2.10-3.07

C.D. 1.77 2.18 13.15 2.30 0.32 SE(m) 0.62 0.76 4.60 0.80 0.11 SE(d) 0.88 1.08 6.50 1.14 0.16 C.V.(%) 2.38 1.76 5.01 5.92 7.67

Characters GCV

Daysto50%flowering 4.509

Daystomaturity 2.565

PlantHeight(cm.) 8.912

PanicleLength(cm.) 7.812

PanicleDiameter(cm.) 10.053

1000-Seedwt.(g) 9.639

Populationatharvest/no.netplot 2.289

Seedsetunderbagging(%) 3.196

DryFodderYield(kg/netplot) 13.996

GrainYield(kg/netplot) 21.18

depicts the environmental influence on characters.

The high estimates of PCV and GCV was found for

grain yield as 26.71 and 21.18, respectively. These

findings are consistent with the results reported by

Chaudhary etal. (2012),Gupta etal. (2015)andRamya

etal. (2022).ModerateestimatesofPCVandGCVwas

recordedfordryfodder(17.30and14.00)andpanicle

diameter(12.65and10.05).Certaincharactersshowed

moderate estimates for PCV such as seed set under

bagging(12.99),1000-seedweight(11.50)andplant

height(10.23). Thesefindingsareinagreementwith

thereportsofSingh etal. (2014a),Kumar etal. (2015),

Sharma etal. (2018)andRamya etal. (2022).Although

thesecharacteralongwithreamingcharactersstudied

depictedlowestimatesforGCV.

Heritability is an important genetic parameter that

measures the proportion of phenotypic variation

attributable to genetic factors and indicates the

likelihoodoftransmittingtraitsfromparentstotheir

offspring. Basedonheritabilityestimates, traitsare

categorizedashavinglow(<30%),moderate(30–60%)

or high (>60%) heritability (Robinson et al., (1949).

Estimation of heritability assists plant breeders in

identifying and selecting superior genotypes from

genetically diverse populations. Traits exhibiting

high heritability are generally less influenced by

environmentalfactorsandcanbeimprovedeffectively

throughselection. Inthepresentstudy,mostofthe

traits exhibited high (62.89%–78.27%) heritability

1000-Seedwt.(g) Populationatharvest/no.netplot Seedsetunderbagging(%) GrainYield(kg/netplot) DryFodderYield(kg/netplot)

11.03 67.00 53.33 0.83 2.12 9.73 76.67 58.33 0.47 2.25 11.33 77.00 43.33 0.58 2.20 9.93 71.33 55.00 0.46 2.63 9.17 68.67 53.33 0.44 2.28 9.07 72.00 48.33 0.55 2.43 8.47 77.33 46.67 0.45 2.80 10.63 73.33 51.67 0.55 2.75 10.63 77.00 53.33 0.52 2.50 10.13 77.33 51.67 0.35 3.27 10.30 71.67 46.67 0.72 2.43 11.00 77.33 55.00 0.75 2.22 9.63 71.67 48.33 0.46 2.35 7.87 70.00 51.67 0.60 3.57 8.40 68.33 48.33 0.75 3.27 9.37 71.00 48.33 0.46 2.18 10.93 70.67 55.00 0.58 2.62 10.23 73.33 53.33 0.53 2.20 9.87 68.33 58.33 0.59 2.35 10.70 72.00 50.00 0.43 2.57 7.87 70.33 46.67 0.50 2.23 9.40 72.00 50.00 0.75 2.72 9.17 70.33 45.00 0.42 2.43

72.38 50.94 0.55 2.54 7.87-11.33 67.00-77.33 43.33-58.33 0.35-0.83 2.12-3.57

1.01 N/A N/A 0.15 0.43 0.35 2.80 3.70 0.05 0.15 0.50 3.97 5.24 0.07 0.21 6.27 6.71 12.59 16.27 10.17

Table 4. GeneticVariabilityParameters

PCV Heritability(%) GA GAM(%)

5.097 78.271 3.707 8.218

3.109 68.061 3.269 4.359

10.226 75.965 25.422 16.002

9.803 63.492 3.01 12.822

12.647 63.194 0.419 16.463

11.496 70.302 1.628 16.648

7.091 10.423 1.102 1.522

12.993 6.053 0.825 1.62

17.3 65.458 0.592 23.327

26.708 62.892 0.192 34.602

estimates. These results are in agreement with

previousfindingsreportedbyKumar etal. (2015)and

Sharma et al. (2018), Bika and Shekhawat (2015),

Yahaya(2015),Abdulhakeem etal. (2019)andRamya

et al. (2022) for related traits. Low heritability was

recorded for the population at harvest per plot and

seedsetunderbagging,indicatingagreaterinfluence

ofenvironmentalfactorsontheexpressionofthistrait.

Similarfindingswerereportedby(Rama etal.,2005;

Kumar etal., 2016)asheritabilityisagoodindexof

transmissionofcharactersfromparentstoitsprogeny.

Geneticadvanceisanimportantparameterthatindi-

catestheexpectedimprovementinatraitachieved

throughselection. Inpresentstudyhighheritability

andgeneticadvanceaspercentageofmean(>20)was

recordedfordryfodderyieldandgrainyieldindicating

predominanceofadditivegeneactionforthesechar-

acters.Traitsdisplayingbothhighheritabilityandhigh

geneticadvanceasapercentageofthemeanarepri-

marilyunderadditivegenecontrol, renderingthem

highlyreliablefordirectphenotypicselection(Sang-

wan etal.,2019).1000-Seedwt.,Paniclediameter,Pan-

iclelengthandplantheightexhibitedhighheritability

coupled with moderate genetic advance as percent-

ageofmean.SimilarresultswerereportedbySaksh-

mana etal. (2003),Sumathi etal. (2010),Vinodhana

etal. (2013),Kumar etal. (2014)andBasavara etal.

(2017).traitsexhibitinghighheritabilityalongsidelow

geneticadvanceareofteninfluencedbyenvironmental fluctuations, necessitating more nuanced breed-

ingapproacheslikeheterosisorrecurrentselectionto

achievefurthergains(Ahmad,2018).Furthermore,the

presenceofhighheritabilitycoupledwithsignificant

geneticadvanceobservedforkeyyield-relatedtraits

indicatesthatphenotypicselectionwillbeparticularly

efficientfordevelopingsuperiorgenotypes(Ayesha et

al.,2019;Subbulakshmi etal.,2022).

Conclusion

Thesuccessofcropbreedingprogrammeslargelyde-

pendsontheavailabilityofgeneticvariabilityandthe

inheritance of desirable traits, while the analysis of

geneticvariationhelpsbreedersformulateeffectivese-

lectionstrategiesfortraitimprovement.Inthepresent

study,thetraitsexhibitedvaryinglevelsofvariability,

heritabilityandgeneticadvanceasapercentageofthe

meanamongthestudiedgenotypes. Traitsshowing

highvaluesofphenotypiccoefficientofvariation(PCV)

andgenotypiccoefficientofvariation(GCV)indicatea

highlevelofgeneticvariability. Highheritabilityval-

uesindicatethatthetraitsunderstudyarelargelycon-

trolledbygeneticfactorsandarelessaffectedbyenvi-

ronmentalinfluencesintheirexpression.Thepresence

ofhighvariabilitysuggeststhepotentialforeffective

selectionandimprovementoftraits.Traitsexhibiting

highheritabilityalongwithhighgeneticadvanceasa

percentageofthemeanindicatethepredominanceof

additivegeneaction,suggestingthatthesetraitscan

beeffectivelyimprovedthroughselection.Incontrast,

traitswithmoderateheritabilityandlowgeneticad-

vanceasapercentageofthemeanarepredominantly

governedbynon-additivegeneaction,implyingthat

directselectionforthesetraitsmaybelesseffective.

References

1. AbdulhakeemA.AhmedF.O.OmoniyiA.M.KayodeO.I.YusufD.O.A.. (2019).Geneticdiversitystudiesformorphologicaltraitsinpearlmillet (Pennisetumglaucum L.)landracesofNorthern Nigeria. GSCBiologicalandPharmaceuticalSciences,7(2),060–070
2. AhmadH.B.SadaqatH.A.TahirM.H.N.SadiaB.. (2018).Studyofsomedirectandin-direct selectionindicesin B.campestris L. PakistanJournalofAgriculturalSciences,55(2)
3. AllardR.W.. (1999). PrinciplesofPlantBreeding. JohnWiley&Sons
4. Anonymous(2025). RajasthanAgriculturalStatistics–AtaGlance,(2018-19).StatisticalCell,DirectorateofAgriculture,Jaipur,Rajasthan
5. AyeshaM.D.BabuD.R.BabuJ.D.P.RaoV. S.. (2019).Geneticparametersforgrainyieldand nutritionalqualitytraitsinfoxtailmillet[Setaria italica (L.)Beauv.]. InternationalJournalofCurrent MicrobiologyandAppliedSciences,8(2),4–9
6. BasavarajP. S.BiradarB.D.SajjanarG.M.. (2017).Geneticvariabilitystudiesforquantitative traitsofrestorer(R)linesinpearlmillet[Pennisetumglaucum (L.)R.Br.]. InternationalJournalof CurrentMicrobiologyandAppliedSciences,6(8), 3353–3358
7. BikaN. K.ShekhawatS. S.. (2015). Genetic study in pearl millet (Pennisetum glaucum (L.) R.Br.) for green fodder yield and related traits. Electronic Journal of Plant Breeding, 6(2), 600–602
8. DeshmukhS.N.BasuM.S.ReddyP.S.. (1986). Geneticvariability,characterassociationandpath coefficientsofquantitativetraitsin Virginia bunch varietiesofgroundnut
9. GuptaP.C.AgarwalV.P.NarayanS.GuptaN. K.SinghG.. (2015).Variabilityandassociation analysisofphysiologicalandyieldattributesin diversegenotypesofpearlmilletunderaridconditions. In Souvenir of International Conference onEmergingTrendsinBiotechnologyandScience withSpecialReferencetoClimateChange (February18–20,2015)
10. HassanZ. M.SebolaN. A.MabelebeleM.. (2021).Thenutritionaluseofmilletgrainforfood andfeed: areview. Agriculture&FoodSecurity, 10(1),16
11. JohnsonH.W.RobinsonH.F.ComstockR.. (1955).Estimatesofgeneticandenvironmental variabilityinsoybeans
12. KNG.SDS.. (2022).Studiesongeneticdiversity,variabilityandcontributionoftraitsforgreen fodderyieldandqualitytraitsinNapiergrass(Pennisetumpurpureum L.Schumach.). JournalofCurrentCropScience&Technology,109
13. KumarM.GuptaP.C.KumarM.. (2015).Geneticvariability,heritabilityandgeneticadvance studiesforyieldanditscontributingtraitsinpearl millet(Pennisetumglaucum (L.)R.Br.)hybrids. IndianResearchJournalofGeneticsandBiotechnology,7(2),271–273
14. Kumar,R.,Harish,S.,Dalal,M.S.,Malik,V.,Devart, K., Chugh, P., Garg, &Raj, K.(2014).Studieson variability,correlationandpathanalysisinpearl millet(Pennisetumglaucum (L.)R.Br.)genotypes. ForageResearch,40(3),163–167
15. LakshmanaD.SurendraP.GurumurthyR.. (2003).Geneticvariabilitystudiesinpearlmillet. ResearchonCrops,4,355–357
16. Naorem,A.K.,Saritha,M.,Kumar,S.,Panwar,N. R., Meena, K. K., Patel, A.,... & Ram, B. (2023). Pearlmillet(Pennisetumglaucum L.)researchin India: ascientometricjourneythroughthelast twodecades(2000–2022). Ann.AridZone,62,1–17
17. Ramya, A. R., Ahamed, M. L., Ratna Babu, D., AshokaRani,Y.,SrinivasaRao,V.,&Srivastava,R. K.(2022).Geneticvariability,heritabilityandgeneticadvanceforyieldandyieldcomponenttraits inpearlmillet(Pennisetumglaucum (L.)R.Br.). The AndhraAgriculturalJournal,69(4),488–492
18. RobinsonH.F.ComstockR.F.HarveyP.H.. (1949). Estimates of heritability and degree of dominanceincorn. Agron.J.,41,353–359
19. Sangwan,S.,Yashveer,S.,Kumar,R.,Hemender, Sharma,S.,&Redhu,N.(2019).Multitraitanalysisrevealssubstantialdiversityinpearlmillet [Pennisetumglaucum (L.)R.Br.]inbredlines
20. SatyavathiC.T.AmbawatS.KhandelwalV.SrivastavaR. K.. (2021). Pearl millet: a climateresilientnutricerealformitigatinghiddenhunger andprovidenutritionalsecurity. FrontiersinPlant Science,12,659938
21. SharmaB.ChughL.K.SheoranR.K.SinghV. K.SoodM.. (2018). Study on genetic variability,heritabilityandcorrelationinpearlmillet germplasm. JournalofPharmacognosyandPhytochemistry,7(6),1983–1987
22. SinghG.. (2025).EffectofenvironmentalconditionsonpearlmilletinWesternRajasthan. InternationalJournalofGeography,GeologyandEnvironment
23. SinghR.K.ChaudharyB.D.. (1999). Biometrical GeneticsAnalysis.Kalyani,NewDelhi
24. SinghS.YadavY.P.YadavH.P.VartD.YadavN.. (2014).Studiesongeneticvariabilityandtrait associationforgrainyieldanditscomponentsin pearlmillet(Pennisetumglaucum (L.)R.Br.). ForageResearch,40(2),91–94
25. SinghV.K.SharmaV.PaswanS.K.ChaudharyM.SharmaB.ChauhanM. P.. (2016). Study ongeneticvariability,heritabilityandgeneticadvanceforyieldanditscontributingtraitsinlinseed (Linumusitatissimum L.). Curr.Adv.Agric.Sci,8(2), 192–194
26. SumathiP.SumanthM.VeerabadhiranP.. (2010).Geneticvariabilityfordifferentbiometrical traitsinpearlmilletgenotypes(Pennisetumglaucum (L.)R.Br.). ElectronicJournalofPlantBreeding,1(4),437–440
27. T.R.,Karthik,SudhaB.,SimhiSamyuktaS.M.,DineshKumarReddy,VinodhiniS.M.,&PavithraG. (2024).Pearlmillet(Pennisetumglaucum): aclimateresilientandnutritionallysignificantcrop forglobalfoodsecurity. InternationalJournalof EnvironmentandClimateChange,14(10),381–93. https://doi.org/10.9734/ijecc/2024/v14 i104493 https://doi.org/10.9734/ijecc/2024/v14
28. TerfaG.N.GurmuG.N.. (2020).Geneticvariability,heritabilityandgeneticadvanceinlinseed (Linumusitatissimum L.)genotypesforseedyield andotheragronomictraits. OilCropScience,5(3), 156–160
29. VinodhanaN.SumatiP.SatyaM.. (2013). Geneticvariabilityandinterrelationshipamong morpho-economictraitsofpearlmillet(Pennisetumglaucum (L.)R.Br.)andtheirimplicationsin selection. InternationalJournalofPlant,Animal andEnvironmentalSciences,3,145–149
30. YadavO.P.RaiK.N.. (2013).GeneticimprovementofpearlmilletinIndia. AgriculturalResearch, 2(4),275–292
31. YahayaY.. (2015). Correlation and heritability in pearl millet (Pennisetum glaucum (L.) R.Br.). AfricanJournalofAgronomy,3(3),257–258