G.2078Page1GlobalResearchjournalofNaturalScience&Technology(GRJNST)Volume:04-Issue2(2026),2078ISSNP:2790-7643ISSNE:2790-7651www.grjnst.nethttps://doi.org/10.53762/grjnst.04.02.29EvaluationofGeneticParametersandCombiningAbilityforDroughtToleranceinCotton(Gossypiumhirsutum)Received:27February2026.Accepted:20March2026.Published:31March2026-un-Zaibstitute,MultanCottonResearchInMuhammadIhsanUllahCottonResearchInstitute,Multan-60000,PakistanSaeedAhmedCottonResearchInstitute,Multan-60000,PakistanShoaibLiaqat(CorrespondingAuthor)CottonResearchInstitute,Multan-60000,Pakistanshoaibliaqat87@gmail.comNisa-60000,PakistanWajadNazeer(CorrespondingAuthor)DepartmntofPlantBreedingandGenetics,GhaziUniversity,DeraGhaziKhan-32200wajidpbg@yahoo.comAmnaBibiCottonResearchInstitute,Multan-60000,PakistanSadiaHakeemCottonResearchInstitute,Multan-60000,PakistanHammadHussnainCottonResearchInstitute,Multan-60000,PakistanJavedIqbalCottonResearchInstitute,Multan-60000,PakistanNadiaHussainAhmadCottonResearchInstitute,Multan-60000,PakistanSadiaKanwalCottonResearchInstitute,Multan-60000,PakistanMuhammadLuqmanCottonResearchInstitute,Multan-60000,PakistanGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29Copyright©2026GRJNST.ThisarticleispublishedunderanOpenAccessmodel.ItismadeavailabletothepublicunderthetermsoftheCreativeCommonsAttribution4.0International(CCBY4.0)license,whichpermitsunrestricteduseanddistributionG.2078AbstractPage2Globalclimatechangeespeciallywatershortageandheatwaveshavedrasticallyreducedcottonproduction.Itisnecessarytodevelopclimateresistantcoupledwithhighyieldercottonvarietiessuitableforvariousenvironments.Forthispurpose,tencottonveritieswithdifferentmorphological,yieldparameters,anddroughttolerance/susceptibilitywereselectedandhybridizedusingline×testermatingdesign.Tostudytheinheritancepattern,twenty-fivecombinationsalongwiththeirparentsweregrowninthefieldfollowingaugmenteddesignwithtwodifferenttreatmentsi-enormalandwater-deficit.Understressenvironment,theGCAvariance(σ2gca)washigherthantheSCAvariance(σ2sca)forCLCuD,numberofbolls,bollweight,seedindex,fiberlength,andmicronaierevaluethatshowsadditivetypeofgeneactionofinvestigatedtraits.LineL5(VH-327),testerT5(FH-Lalazar)andcrosse18(L4xT3)andcross21(L5xT1)performedbestforyieldandyieldcontributingtraitsunderwaterdeficitconditionenvironment.Cross18(L4xT3),21(L5xT1)and22(L5xT2)hadthehigherSCAforthebollsnumberandcottonyield(15-20%higherfromparents)bothundernormalandwaterdeficitcondition.Bothundernormalandwaterdeficitcondition,thebest-performinggenotypesandcombinations(15to20%increaseinseedcottonyield)maybeusedinthecottonbreedingprogramtoenahancetheseedcottonyieldanddroughttolerance.Keywords:Hybridization,GCAeffects,SCAeffects,drought,cottonGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078IntroductionPage3Globalclimatechangeduetofrequentandintenseweatherconditions,hasledtotheoccurrenceofabioticandbioticstressorswhichhaveadetrimentalimpactonplantgrowth,yieldandquality(Zafaretal.2022).Abioticstresses,specifically,impedethephysiologicalandmetabolicactivitiesofplantsatdifferentgrowthstages.Bothhumanactivitiesandclimaticfactorssuchasheat,drought,humidityanderraticrainfallpatternshaveinfluencedcropproductivity(Zhuetal.2023).Cotton(Gossypiumspp.)isanimportantcropthatisgrowncommerciallyinChina,USA,India,Pakistan,Brazil,AustraliaandUzbekistanforitsfiber,fuelandfeedmaking(Kashifetal.2023).Probably,cottoncoversmorethan33millionhectaresoflandinmorethan100countriesandfulfillsthe31%demandforfiberallovertheworld(Ijazetal.2024).Cottoncontributesalmost0.8%ofGDPalongwith5.5%ofagriculturalvalueadditionandiscultivatedintheSouthernregionofPunjabandsomedistrictsoftheSindhprovince(Mahmoodetal.2021).Atdifferentplantdevelopmentalandreproductionphases,watershortageandhighdayandnighttemperatureshavevulnerableeffectsoncottonplantswhichemphasizesthecrucialimportancefordevelopingdrought-tolerantcultivars.Duringtheyear,2021,about35%lossincottonproductionwasreportedinPakistanduetowaterdeficitcondition.Deficiencyinirrigationwaterhasnegativeeffectsonmorphological,physiologicalandbiochemicalprocessesatdifferentgrowthstagesandresultsinyieldreduction(Bozorovetal.2018).Ariseinbothmaximumandminimumtemperaturesgivesrisetowaterevaporationandresultsinsaltstressthatinduceswatershortage/availabilitytotheplants(Saleemetal.2021).Atthecelllevel,waterdeficitGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page4conditionresultedinahigherconcentrationofreactiveoxygenspecies(ROS)thatleadtooxidativestressduetotheruptureofthecellmembrane(Jansetal.2021).Further,itcausesareductionincelldivision,leafsurfacearea,metabolicadaptation,rootandstemcellproliferation.Thesemechanismsresultedinlimitedaccesstoresourcesandultimatelyinhibitedplantgrowthandreproduction.Evenatthebolldevelopmentstage,waterdeficiencyforashortperiodcouldresultinmaximumlossinseedcottonyield(Nazetal.2022).Ithasalsobeenreportedthatprolongedexposuretowaterdeficitconditionlowersplantgrowthandproductivityandultimatelycausesplantdeath(Ullahetal.2019).Managementpracticessuchasmoreirrigationmightbeusedtocontrolthewaterdeficitconditionbutinthecurrentsituation,thereisadireneedtoboostthecottonyieldpotentialthroughthedevelopmentofdrought-resistantvarietieswithlimitedwaterresources(Rehmanetal.2022).Toovercomethisproblem,varioustechniqueshavebeenusedtodevelopclimate-resilientcottongermplasmforeffectiveandlong-lastingsolutions(Rahmanetal.2020).Exploitationofgeneticvariabilityinexistinggermplasmandscreeningbasedonmorpho-physiologicalandbiochemicalcharacterscouldleadtothedevelopmentofcottongenotypeswithsignificanttoleranceagainstabioticstresses(Farooqetal.2023).Forplantbreeders,combiningabilityestimatesareanimportantfactorthatcanhelptoachievethedesiredtargetsthroughhybridizationandselectionschemes.Asreported,fortheselectionofdesiredparents,combiningabilitytechniqueshasbeenbestutilized(Liaqatetal.2023).Significantgeneticvariabilityandheterosisarethemaincomponentsofasuccessfulbreedingprogram.ParentgenotypescouldbeidentifiedfromtheF1populationthroughselectionandthentheirperformance-basedevaluation.Forselection,estimationofcombiningabilityandpossiblegeneactionofparentallines,GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page5Line×testeranalysis,provedtobethewiselyadoptedmethodindifferentbreedingprograms(Ammaretal.2023).Itnotonlyprovidestheestimationofspecificcombining(SCA)andgeneralcombiningability(GCA)butalsothemajorgeneticcomponents.Inthecurrentstudy,line×testermatingdesignwasutilizedforthehybridizationofdifferentcottongenotypeswithcontrastingtraitstoinvestigatetheinheritancepatternandenhancethedroughttoleranceandseedcottonyield.Themainobjectivesofthisstudyweretodevelopthebreedingmaterialofcotton,undertakingthemorphologicaltraitscontributingtodroughttoleranceandinheritancepatternandassociationindifferentcombinationsunderdifferentwaterlevelconditions.MaterialsandMethods2.1PlantmaterialanddesignofexperimentalTencommercialcottonvarietieswereselectedforhybridizationpurposes.Line×testermatingdesignvizfivelinesandfivetesterswasemployed,and25crossesweredeveloped(Kempthorne,1957).ThecharacteristicsoftheparentvarietiesalongwithpassportinformationispresentedinTable01.TheexperimentalwasconductedatCottonResearchInstituteMultan(30°08′55″N,71°26′30″E)duringthecottongrowingseason2022-23.Thematerialwassownfollowingaugmenteddesignundernormal(irrigationwasappliedat8-10daysintervalstill165daysaftersowing)andwaterdeficitconditions(oneirrigationwasskippedcomparedtoNormal,aftertheseedlingestablishment).Weatherparametersincludingmaximumandminimumtemperature(°C),rainfall(mm),andrelativehumidity(%),wererecordedusingtheGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page6automaticweatherstationwithdataloggerCR1000X(CampbellScientificInc.,UT,USA)installedat~750mdistancefromtheexperimentalarea.2.2DatarecordingforinvestigatedtraitsCottonleafcurldisease(ClCuD)wasscoredfortenplants(avoidingborderplants)perreplication(30plantsperentry)followingtheseveritygrade0(Asymptomatic),1(slightthickeningofprimaryveins),2(secondaryandtertiaryveinsthickening),3(veinthickening,enationorleafcurlingorboth),4(stuntingofgrowthalongwithleafenationandcurling).Thediseaseindex(%)wascalculatedaccordingtotheformulabyLiaqatetal.(2023).Diseaseindex=(Sumofalldiseaseratings/totalplants)×(100/maximumgrade)Plantheight(PH;cm),andyieldcomponentsincludingnumberofbolls(NB),bollweight(BW;g),seedindex(SI;g),ginningoutturn(GOT;%),seedcottonyield(SCY;g),andqualitytraitsrelatedtofiberi.e.,fiberlength(FL;cm),strengthoffiber(FS;g/tax),micronaire(Mic;μg/inch)wererecorded.Seedindexwascomputedbycountingtheonehundredseeds,followedbyweighingusinganalyticalweightbalance.FiberqualitytraitswererecordedusingUsterHighVolumeInstrumnetSpectrum1(UsterTechnologiesAG(HQ),Switzerland).Table1:PassportoflinesandtestersusedforhybridizationLinesVarietiesCharacteristics123VH-259Tall,lowinput,mediumbollsize.VH-282Compactplantgrowth,earlymaturing,(>30mm)longstaplelengthVH-363Heattolerant,singlestem,shortstature,earlymaturing.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page745Tester12345VH-369Tall,broadleaf,latematuringVH-327Hightolerantagainstsuckinginsect/pests,CLCuDandlodgingtolerant,creamypollen.MNH-886Compactplantshape,earlymaturing,MNH-814Tall,Boldbollsize,spreadinggrowthhabitMNH-1016Tall,semi-compact,lowinput,goodbollopening.FH-142Boldbollsize,highyielder,CLCuDtolerant.FH-LalazarHeattolerant,spreadinggrowthhabit,bigbollsize.2.3StatisticalanalysisVariabilityamonglines,testersandF1hybridswasestimatedthroughanalysisofvarianceusingpackage“agricolea”atasignificancelevelP<0.05(MendiburuanddeMendiburu,2019).Genotype-trait-environement(GGE)biplotwasusedtoevaluatetheperformanceofgenotypesundernormalandstressconditions.Fortheestimationofgeneticcomponentsandcombiningability,line×testeranalysiswasperformed.AllthestatisticswereperformedusingRsoftwareversion3.6.3.Results3.1WeatherconditionsTemperature,rainfall,andhumidityareimportantparameterswhilestudyingdroughttolerance.Duringcottongrowingseason,rangeofmaximumtemperaturewasrecordedform21°Cto48°Cwhilerangeforminimumwas9°Cto32°C.MaximumrainfallwasobservedduringJulyi.e.57mm(Figure1).Maximumtemperature(48°C)GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page8wasrecordedduringMay,whilehighhumidity(upto90%)wasobservedduringJuneduetocontinuousrainfall(~30mm).Figure1:Dailyaverageweatherparametersduringcottongrowingseason,2022-23.3.2EvaluationofparentsandF1material3.2.1AnalysisofvarianceSufficientvariationsamongtreatments,parentsvscrosses,parents,crosses,andtheirinteractionwereobservedforallthetraitsunderobservation(Table2)exceptfewnon-sinficanttrends.Forinstance,linesshowednon-significantdifferencesforbollweight,andtestersvariednon-significantlyforPH,GOT,FL,andMic,undernormalconditions.Whileunderwaterdeficitconditions,allsourcesofvariationsvariedsignificantly(Table3).Table2:Sumofsquaresformorphologicalandqualityrelatedtraitsoftenparentsand25F1crossesundernormalcondition.SourceDFCLCuDPHNBBWSCYGOTSIFLFSMic.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page9SourceDFCLCuDPHNBBWSCYGOTSIFLFSMic.Rep.(R)02205.9N.S1.50N.S1.48N.S8.49N.S5.15N.S122.2N.S1.48N.S1.48N.S1.48N.S3.88N.STrt.(T)34728.3*Parents(P)Crosses(C)09517.2*24812.4*PvsC01612.7*Lines(L)041233.2*1.30*1.47*1.29*5.86*2.48*Tester(T)1400.2*7.09N.S04L×T16560.2*1.14*1.37*3.89*1.76*9.52*4.19*1.72*1.16*1.64*1.44*1.47*7.68*0.78N.S2.86*1.30*6.18*3.39*6.82*1.59*2.42*7.09*2.40*5.99*9.62*4.87*2.75*1.45*1.86N.S3.20*5.52*7.44*5.03*1.61*1.58*9.35*1.25*3.61*2.68*3.91*5.04*1.12*1.39N.S2.71*2.46*3.11*2.06*5.97*6.78*1.02*1.15*0.30*0.31*0.30*0.25*0.60*0.11N.S0.27*Error680.228.829.100.012.480.049.109.109.100.04*=Significantatp≤0.05,N.S=Non-significantTable3:Sumofsquaresformorphological,yield,andfibertraitsoftenparentsand25F1crossesunderwaterdeficitcondition.SourceDFCLCuDRep.(R)02236.7N.SPHNBBWSCYSIGOTFLFSMic.195.8N.S195.8N.S5.60N.S112.1N.S8.15N.S272.1N.S172.1N.S138.6N.S9.72N.SGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page10Trt.(T)34589.0*639.8*Parents(P)Crosses(C)09558.8*612.1*24549.5*675.4*109.6*973.4*968.5*PvsC01180.7*33.8*528.Lines(L)04117.3*582.1*Tester(T)04260.0*124.6*L×T16143.7*611.5*1*940.4*305.7*453.3.2.31*0.59*0.52*0.68*0.79*0.81*2.86*0.10*112.7*670.9*989.0*855.1*184.3*188.8*551.5*0.71*0.88*0.70*0.47*1.39*1.38*0.36*2.66*3.31*3.01*11.1*9.85*5.49*0.67*2.61*26.7*0.10*2.62*22.6*0.064.05*29.04**0.15*44.1*8.53*0.86*17.8*31.6*0.496.91*13.09**0.17*0.65*2.91*0.07*Error6820.512.211.00.3617.80.802.652.287.640.114*=Significantp≤0.05,N.S=Non-significant3.2.2.Estimatesofgeneticcomponents,heritabilityandmidparentheterosisUndernormalconditions,theGCAwaslessthanSCAvarianceofalltraitsthatrepresentthenon-additivetypeofgeneactionundernormalwatercondition.Also,theratioforGCAvariancetoSCAvariancewaslowerthan1,indicatinghighercontributionofdominancegeneticvariance(Table4).Comparatively,underwaterdeficitconditioncondition,thevaluesofGCAvarianceforCLCuDindex,NB,BW,SI,FL,andmic.wereobservedhigherthantheSCAvariance,representingtheinfluenceofadditivetypeofgeneactionforthesetraitsunderwaterdeficitenvironment(Table5).RatiooftheGCAvariancetoSCAvariancewaslowerthan1forallthestudiedtraitsexceptGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page11micronaierevalue.Broadsenseheritability(H2)andnarrowsenseheritability(h2)showedasignificantvariatonamongthetwodifferentenvironments(Table4&5).Undernormalcodition,BWhadmaximum(0.99)whileFSpossessedminimum(0.77)H2value.Incaseofh2,BW,PHandFShadmaximumvaluewith0.75.0.74and0.64,respectively.Underwaterdeficitcondition,FS(0.88)andBW(0.79)possessedmaximumH2valuewhileintermsofh2,BWhadmaximumvalueof0.79also.Table4:Geneticcomponentsofthemorphological,yieldandfibertraitsundernormalcondition.GeneticcomponentsCLCuDPHNBBWSCYSIGOTFLFSMic.44.889.4202.4-0.03145.70.370.750.569.70.0256.0-28.8372.10.10313.1-0.008-0.08-0.085.40-0.016.33.78149.50.04110.60.020.040.020.940.07354.7481.9786.50.54375.10.972.181.6929.30.106.33.75149.60.04110.60.020.040.0250.90.005186.6380.8385.80.42800.60.361.060.904.10.080.030.0090.380.090.130.050.030.020.210.0625.215.1589.10.01442.50.090.160.113.70.02Cov.H.S.(lines)Cov.H.S.(tester)Cov.H.S.(average)F.S.Cov.(average)σ2gcaσ2scaσ2gca/σ2scaF=0AdditivegeneticvarianceGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page12GeneticcomponentsCLCuDPHNBBWSCYSIGOTFLFSMic.F=1AdditivegeneticvarianceF=0VariancetoduedominanceF=1Varianceduetodominance12.67.5299.20.08221.20.040.080.051.80.01373.3761.7775.90.85160.10.732.121.808.30.17186.6380.8385.80.42800.60.361.060.904.10.08H2h20.990.210.840.970.990.890.910.950.940.770.990.740.240.750.530.330.240.210.640.61Table5:Geneticcomponentsofthemorphological,yieldandfibertraitsunderwaterdeficitcondition.GeneticComponentsCLCuDPHNBBWSCYSIGOTFLFSMic.CovH.S.(lines)CovH.S.(tester)CovH.S.(average)F.S.Cov(average)-1.75-1.9532.40.04860.90.070.611.141.010.02164.027.5173.60.18888.70.060.320.418.530.0610.11.5912.80.01109.30.0080.050.090.650.02182.6-162.2126.40.29-119.50.080.892.0515.80.04GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page13σ2gcaσ2sca10.11.59512.850.01109.350.0050.0550.0950.650.02-87.9204.9-217.2-0.08411.1-0.140.65-0.541.570.01σ2gca/σ2sca-0.110.07-0.05-0.120.26-0.030.08-0.170.412.0F=0AdditivegeneticvarianceF=1AdditivegeneticvarianceF=0VarianceduedominancetoF=1Varianceduedominanceto40.56.3951.50.05437.40.030.230.392.610.0820.23.1925.70.02218.70.010.110.191.300.04175.8-409.8-434.1-0.17-822.3-0.29-1.31-1.08-87.9-204.9-217.2-0.08-411.1-0.14-0.65-0.54-3.15-0.03-1.57-0.01H2h20.730.730.280.590.790.630.510.520.710.880.200.180.500.790.480.400.470.450.640.20CLCuD;Cottonleafcurldisease(%),PH;Plantheight(cm),NB;Numberofbolls,BW;Bollweight(g),SCY;Seedcottonyield(g),SI;Seedindex(g),GOT;Ginningoutturn(%),FL;Fiberlength(cm),Fiberstrength(g/tax),Mic;Micronaire(μg/inch),σ2GCA,GCAvariance;σ2SCA,SCAvariance;H2,broadheritability;h2,narrowheritabilityGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page143.2.3EstimatesofGCAofparentsGCAeffectsbothunderbothconditionsi.e.,thenormal&waterdeficit,havebeenpresentedintheTable7and8,respectively.CLcuDhadnegativeandlowervaluesforL5undernormalconditions,L2underwaterdeficitconditions,andT5,T3inbothconditions.Undernormalconditions,L5hadhigherandpositiveGCAeffectsforNB,SCY,FL,andFS,whileL4forPH,BW,andGOT.Comparatively,T5showedpositiveandhigherGCAeffectsforbollweight,GOT%,fiberlength,strength,T3fortheSCY,T4hadhigherGCAformic(Table6).Underwaterdeficitconditioncondition,L2hadhighestpositiveGCAvaluesfortheNB,SCY,andSI,L5fortheBW,GOT,FL,andmic.Amongtesters,T5showedsignificantlypositiveGCAforBW,SI,FL,andFS,andT3forNBandSCY(Table7).Table6:Generalcombiningabilityestimatesforlinesandtesterundernormalcondition.GCAEffectsCLCuDPHNBBWSCYSIGOTFLFSMic.LinesL18.59-0.6-41.1-0.11-130.1--0.01-1.10.380.19-1.08-7.6-23.70.12-45.11.17-1.50.3-4.30.19L2L33.593.54-16.5-27.1-88.4-0.30L4-0.6015.41.40.2992.5L5-15.139.590.5171.2-0.01GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.290.5-0.6-0.9-0.011.050.52.4-0.11-0.020.83.9-0.27-0.35-0.39-0.05G.2078Page15GCAEffectsCLCuDPHNBBWSCYSIGOTFLFSMic.S.E.0.120.020.030.030.040.040.020.030.020.01Testers4.20.17-0.20T10.71-1.647.6T2-0.42-7.6-18.2T3-6.429.423.4-0.30-0.08-0.38-28.8117.2T415.574.4-21.20.05-40.8-2.2-0.07-2.8-0.030.2-0.052.20.14-0.31-0.01-0.05-0.110.10-0.420.001-0.06-0.26-0.20T5-9.42-4.6-31.60.71-51.80.350.520.502.50.02S.E.0.120.020.020.0.30.030.050.020.020.020.02Table7:Generalcombiningabilityestimatesforlinesandtesterunderwaterdeficitcondition.GCAEffectsCLCuDPHNBBWSCYSIGOTFLFSMic.LinesL12.2910.6-2.5-7.3-0.24-0.030.18-0.070.05-0.10L2L3-3.97-2.59.30.0237.10.39-0.170.401.27-0.083.02-4.9-10.5-53.4-0.3-0.91-0.38-2.4-0.14-1.60GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page16GCAEffectsCLCuDPHNBBWSCYSIGOTFLFSMic.L4-0.97-3.1-2.40.18-1.20.11-0.190.64-0.11-0.35L5-0.370.046.20.1924.90.101.311.360.630.29S.E.3.21.32.50.153.40.200.420.300.710.08T14.33-7.4-6.66T23.0-10.1-8.40T3-12.08.525.02T418.335.2-1.46-0.09-0.04-0.54-0.07Testers-10.80.07-0.30-25.10.07-0.4962.1-0.29-0.41-3.8-0.070.06-2.180.96-0.07-0.64-0.45-0.09-19.2-0.280.250.091.800.14T5-13.66S.E.2.73.81.2-8.480.68-6.80.430.941.093.30-0.121.80.153.30.200.350.300.560.083.2.4.Specificcombiningability(SCA)effectsforhybridsUndernormalcondition,crossL2×T4,showedthehighestpositiveSCAvalueforNB,SCY,andmicvalues.CrossL2×T3exhibitedthehigherSCAvalueforGOTwhilecrossL2×T5forFS.CrossL4×T1hadthehighesnegativevaluesforCLCuD(Table8).Underwaterdeficitstress,L3×T5showedhighestpositiveSCAvaluesforNB,SCY,SI,andFL(Table9).CrossL2×T1showedthehigherSCAvalueforBW.CrossL3×T4hadthehigherSCAvalueforFS.Table8:SCAestimatesforlines×testerundernormalcondition.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page17CrossesCLCuDPHNBBWSCYSIGOTFLFSMic.L1×T1L1×T2L1×T312.280.6-52.8-0.83-54.2-0.710.12-0.083.42-8.415.00.3428.80.020.02-0.38-3.50-0.16-0.66-0.104.429.6-7.6-0.45-92.2-0.27-0.160.251.470.21L1×T4-7.57-5.422.00.5065.80.46-0.380.310.470.21L1×T5L2×T1L2×T2L2×T3-12.573.623.40.4451.80.500.40-0.102.232.2827.6-49.20.22-14.20.52-0.320.952.23-0.16-0.263.42-16.46.60.10-21.24.42-3.4-7.0-0.19-87.2-0.03-0.03-1.620.75-1.12-0.201.50-0.004-3.78-0.38L2×T4-2.5711.636.60.56150.80.101.48-0.540.210.51L2×T5L3×T1L3×T2L3×T3-7.57-19.413.0-0.69-28.22.86-33.4-61.60.42-50.2-0.55-0.71-1.04-1.162.470.330.22-0.54-1.240.370.690.23-1.71-12.49.2-0.89-37.20.020.42-0.7444.2815.616.6-0.09-3.2-0.27-0.56-0.401.03-0.04GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page18CrossesCLCuDPHNBBWSCYSIGOTFLFSMic.L3×T4L3×T512.2810.612.2-0.134.80.06-0.280.150.03-0.24-17.7119.623.60.7085.80.900.201.53-0.50-0.32L4×T1-18.71-5.4-29.2-0.7748.81.02-0.580.351.790.17L4×T22.4220.67.60.30-23.2-0.73-0.080.851.23-0.16L4×T3-11.57-6.432.00.70145.80.560.060.490.070.05L4×T4L4×T5L5×T1L5×T216.428.6-10.4-0.43-136.2-0.49-1.02-1.64-0.92-0.2411.42-17.40.010.20-35.21.2810.6192.80.9669.8-0.35-0.11-0.30-0.06-2.160.56-0.680.73-7.5716.6-38.40.1452.80.721.26-0.48-0.120.17-0.100.23L5×T3-1.57-15.4-34.00.0436.80.02-0.72-0.341.210.15L5×T4L5×T5-18.57-25.4-60.4-0.49-85.226.4213.6-60.0-0.65-74.2-0.13-0.49-1.841.710.21-0.240.74-0.20-2.02-0.02S.E.0.270.050.050.060.570.120.050.050.040.03Table9:SCAestimatesforlines×testerunderwaterdeficitcondition.CrossesCLCuDPHNBBWSCYSIGOTFLFSMic.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29L1×T4L1×T5L2×T1L2×T2L2×T3G.2078Page19CrossesCLCuDPHNBBWSCYSIGOTFLFSMic.L1×T1L1×T2-2.09.800.01-0.23-19.120.29-0.13-0.03-0.860.022.66-4.20-3.910.24-4.850.15-0.680.11-0.540.05L1×T31.0-21.20-13.60.11-20.45-7.6618.804.08-0.18-5.786.0-3.2013.490.0650.21-0.07-0.34-0.030.350.31-0.02-0.48-0.01-0.300.540.030.150.251.343.661.802.740.3331.680.050.070.150.75-5.06.135.81-0.0215.28-0.010.190.090.18-5.015.809.12-0.0513.680.18-0.110.130.76L2×T43.0-25.86-3.380.043.340.21-0.180.27-0.73L2×T5L3×T1L3×T23.332.135.66-9.86-14.30-11.65-0.30-63.98-0.12-45.78-0.44-0.390.020.035.33-2.20-5.910.02-14.850.33-0.51-0.65-0.96-0.20-1.06-0.39-0.40L3×T38.66-14.2--0.10-57.12-0.15---GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29-0.09-0.14-0.17-0.040.100.240.150.12G.2078Page20CrossesCLCuDPHNBBWSCYSIGOTFLFSMic.L3×T4L3×T5L4×T1L4×T216.34-10.020.8014.08-0.0434.210.42-0.060.420.840.040.290.281.420.03-9.665.4619.820.2483.5470.540.030.750.88-2.00.138.340.0116.3470.40-0.260.230.612.66-5.53-0.58-0.04-3.387-0.39-0.110.550.37-0.26-0.08-0.14L4×T3-7.3319.1313.720.0651.6800.260.040.410.720.02L4×T5L5×T1L5×T2L5×T3L5×T4L5×T5L4×T45.66-12.53-8.780.02-15.321.0-1.20-12.70-0.06-49.32-5.33-1.860.530.00716.88-5.665.804.60-0.217.813-0.07-0.19-0.35-0.090.37-0.040.291.11-0.76-1.44-0.43-0.270.030.17-0.14-0.360.560.040.380.142.660.467.17-0.0112.210.04-0.43-0.09-0.169.0-1.20-5.990.15-16.450.26-0.800.510.200.08-0.20-0.66-3.20-6.310.06-20.450.14-0.160.08-0.99-0.06S.E.1.162.211.910.352.710.510.940.871.590.19GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page213.2.5MidparenthetersosisMidparenthetersosisofalltraitsunderbothenvironmentalconditionswascalsulatedandpresentedinTable10&11,respectively.Combinations,L1×T4andL2×T4,revealedthepositiveandhighlysignificantheterosisforNB,BWandSCYunderwaterdefcitconditionincontrasttonormalcondition.While,crossL3×T4showedpositiveandhighlysignificantheterosisforNB,BWandSCYbothundernormalandwaterdeficitconditions.MaximumandhighlysifnificantpossitiveheterosiswasobservedforSCYinthecrossL3×T5,forNBincrossL2×T1,forBWinL3×T4underwaterdeficitenvironment.Undernormalcondition,themaximumandsignificantposssitiveheterosisforFLwasobservedinL4×T3,forFSinL2×T1,formicronaiervaluincrossL2×T5.allsourcesofvariationsvariedsignificantly(Table3)deficitcondition,maximumpositiveandsignificantheterosisforFLwasrecordedincrossL2×T2,forFSincrossL1×T5,formicronaiervalueincombinationL2×T4.Table10:Midparentheterosisofcrossesundernormalcondition.5.26**4.26**CrossesCLCuDL1×T1L1×T2L1×T3L1×T4L1×T5L2×T1-5.88**-6.66*PHNBBWSCYSIGOTFLFSMic.8.80**4.57**9.80**3.57**-15.44**-14.07**-19.44**-17.07**-5.66**6.89**16.21**25**-4.42**0.63*-0.31**-3.13**-6.15**-3.42**1.63*-1.52NS1.11**-1.31**-4.13**-5.15**-0.53**0.64**-2.08**8.47**-27.84**2.23**-20-3.42**1.0**0.8**10.60**3.09**-14.2**14.28**10.75NS32.18**48.83**0.23*-0.12*0.1**11.94**6.12**-57.14*1.81**0*26.82**28.57**4.72**1.59*-0.52**13.66**-2.08**GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078L2×T2L2×T3L2×T4L2×T5L3×T1L3×T2L3×T3L3×T4L3×T5L4×T1L4×T2L4×T3L4×T4L4×T5L5×T1L5×T2L5×T3L5×T4Page22-3.22NS-0.78**-15.38**0**-18.84**-5.37**-2.85**17.02**-12.5NS18.51**58.73**6.32**64.70**16.93**1.44*5.66**0NS0NS-21.42**23.07**6.17**27.81**4.21**-2.21**6.83**-9.67**-6.59**6.32**11.11**6.01**6.36**3.30**0**-45.45NS15.38**90.88**-24.13**11.86**27.93**49.05**2.59NS-3.37**7.69*-0.50**5.60**0.51**-1.15**0NS0**-25**-1.44**-16**9.85**15.55**20**-16.41**-6.77**7.5**5.88**1.61**20**7.14**16.09**18.70**38.09**5.88**21.73**2.36**18.21**19.14**4.29**-0.85**0.689**13.04**-1.142NS-6.16**10.37**1.84**0**-0.24**0.35**-0.122*1.41**-4.41NS9.37**0**1.54**1.36**0.43**50**-77.77**7.69**-66.66**23.07**38.66**82.60**18.56**0.84**6.9**-2.78**-1.7**-3.22**50.79**6.45**130.98**34.2**-0.36**5.4**4.72**20**6.25**3.12**43.75**66.67**1.96*0.60**9.09**-0.68**-50**-8.57**26.55**78.70**25.45NS21.91**0.98**6.90**-0.64**71.42**13.33**-32.43**34.22**-1.36**1.28**71.42**-18.18**-26.22**47.22**50**7.5**3.03**-0.35**4.67**-1.58**0.63**-4.94**-3.03**-60**-84.62**8.47**-81.82**12.28**488.40**28**94.28**9.20**0.24**-1.42**3.40**-3.37**55.71**9.09**71.64**11.76**1.70**1.027**-2.6**2.27**-10.76**40.52**15.85**63.88**5.52**-2.85**0.68**3.7**-1.12**GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29-6**-11.11**-8.0**-10.89**G.2078L5×T5-76.47***=Significant,**=HighlySignificant,N.S=Non-significant-5.45**15.70**15.88**-1.74**1.92**-6.09NS6.7**Page235**-6.66**Table11:Midparentheterosisofcrossesunderwaterdeficitcondition.NBBWSCYSIGOTFLFSMic.-4**3.7**-20**4.76**CrossesCLCuDPHL1×T1L1×T2L1×T3L1×T4L1×T5L2×T1L2×T2L2×T3L2×T4L2×T5L3×T1-44.4**-18.18**4.34**-26.31**-11.1**12**10**27.45**-20.58**0NS-5.26**1.84NS-18.18**31.14**8.82**42.85**-6.32**1.06NS-6.1**-1.15NS-2.713**-4.8**-5.88NS6.93NS-18.51**10.34**15.78**-25**-11.76**-3.44**-7.40**-2.59**3.0NS-1.84**-0.95NS1.96NS36.17**23.52**68.75**0.61NS0.18NS0.18NS4.83**0NS11.80**15.38**28**1.19**1.29NS-1.8NS10.86**-2.97NS-40**88.75**5.55**100**17.5**1.25NS8.22**0NS-59.09**34.94**0**34.61**3.0**-5.3**11.36**-3.27**-7.07NS-4.081NS-25.92**15.315**3.22**32.25**7.8**7.1**-0.18NS-1.51**-5.05NS-36.84**50.7**13.88**90**-2.13**4.00*1.48NS-4**14.85**-41.6**40.77**-7.3**33.33**-2.6**-0.07NS-0.54NS3.7**12.24**-56.25**21.495**13.3**GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.2953.84**6.5**0.37NS0.58**-7.91**13.6**G.2078Page24L3×T2L3×T3L3×T4L3×T5L4×T1L4×T2L4×T3L4×T4L4×T5L5×T1L5×T2L5×T3L5×T4L5×T520**-22.22**-9.44**-13.33**-24.05**9.09**1.36NS2.53**-3.87**10.63*12.5**-4.347**65.38**12**84.6**5.0**-0.76NS-2.04**-7.9**3.15NS33.33**46.66**-73.33**0NS-64.70**9.80**51.02**16.66**33.3**4.054**-0.24NS1.50NS-0.72NS1.03NS70.2**34.28**213.0**26.79**-0.61NS9.023**-5.1**-2.12NS44.08**29.82**113.7**40.14**-0.746NS5.22**-4.27**-7.69*20**-45.45**-20**-16.53**36.842**38.36**4.89**-16.92**31.60**74.468**128.5**23.74**0.24NS10.90**-3.54**0.882NS-3.31**5.01**-6.79**-7.69NS68.42**22.44**-51.14**29.82**-29.03**-4.05**0.12NS-2.35**-6.57**-11.3*100**-18.64**-22.22**43.28**52.72**7.18**0.48NS6.0**0NS-4.85NS-60**23.404**60.572**23.94**108.88**11.6**-0.37NS3.9**-2.7**-1.07NS-77.7**13.72**-71.42**56.950**4.22**63.17**8.7**0.36NS5.4**-4.89**2.17NS-18.03**46.52**14.75**65.51**7.6**-2.52NS1.73**-2.74**-1.07NS9.090**-2.2**21.21**-1.40**19.14**38.46**-5.45**21.160**-6.17**28.7**-6.14**8.61**-3.03**-11.76**-1.09NS3.44**-0.66NS-7.4NS-5.19**0NS*=Significant,**=HighlySignificant,N.S=Non-significant3.2.6Proportionalcontributionoflines,testers,andtheirinteractionsTheproportionalcontributionoflines,testersandtheirinteractionsundernormalandwaterdefictconditionshavebeenpresentedinFig.3a&3b.Undernormalcondition,linescontributedhigherforthetraits;SCY,SI,GOT,FL,whilethecontributionofcrosseswashigherforCLCuD,PH,BW,andmic.UnderwaterdeficitGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page25condition,testerscontributedsignificantlyhigherforCLCuD,NB,BWandmicvalue.LinescontributedhighertowardsGOT,FL,andFS.CrossesshowedhighercontributiontoPH,SCY,andSI.Figure2:Proportionalcontributionoflines,testers,andtheirinteraction(line×tester)forthemorphological,yield,andfiberqualitytraitsundernormal(a)andwaterdeficit(b)conditions.3.2.7.PerformanceofparentsandtheirhybridsundernormalandwaterdeficitconditionsUndernormalcondition,PC1andPC2explained33.8%and20.6%ofthetotalvariability,respectively(Fig.3a).Overall,hybrids(H21;L5xT1,H18;L4xT3,H23;L5xT3)showedhighestvaluesforseedcottonyieldandnumberofbollsunderbothnormalandwaterdeficitconditionenvironments.ParentshadhighervaluesforClCuDcomparedtohybrids.Cross15(L3xT5)hadhighestbollweight,cross6(L2xT1)hadhighestseedindex.Line5hadhighestnumberofbolls,whileT3showedhighestGOT%.Underwaterdeficitconditionenvironment,PC1explainedthe41.8%whilePC218.1%ofthevariability(Fig.3b).Line5wasfoundattheextremeoftheplotforthenumberofbolls.Tester,T3,wasalsohigherforGOT%sameasundernormalcondition.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page26Figure3:Biplotanalysisoflines,testersandhybridsformorphological,yieldandfibertraitsundernormal(a)andwaterdeficit(b)conditions.3.2.8Meanperformanceofcrossesformorphological,yieldandfibertraitsThedataformorphological,yieldandfiberparametersunderbothwaterenvironmentswererecorded.MeandataofcrosseshavebeenpresentedinFig.4.Cross20(L4xT5)and21(L5xT1)showedhighermenvaluesforbollweight.Cross15(L3xT5),16(L4xT1)and22(L5xT2)weresignificantlyhighforseedindex.IncaseofCLCuD,fiberlength,strengthandmicronairevalue,cross22(L5xT2),23(L5xT3)and24(L5xT4)wereobservedwithmaximummeanvaluesbothundernormalandwaterdeficitcondition.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page27Figure4:Meanperformanceofcrossesundernormal(N)andwaterdeficit(WD)conditions.GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078DiscussionPage28Globalclimatechangethreatenscottonyield(Lokaetal.2020).A5oCriseintemperatureenhancetheriskofbothfloodsanddroughts(Soongetal.2020).Boththeincreasingglobalpopulationandfiberdemandhavecreatedachallengetodevelopstress-resistantcultivars.Cottoncropisexposedtovariousclimate-drivenbioticandabioticstresses(Haroonetal.2023).Thiscallsforassessmentofvariabilityandcreatenewgeneticcombinationstoselectdroughtandheattolerantgermplasmforfuturebreedingprograms.Thisstudyfoundthatthegeneticvariabilitywasbettermanifestedinhybridsastheyrevealedsignificantvariationsforallthetraitsundertwodifferentenvironmentalconditions(Table&3)(Kamaraetal.2021).GeneticdversitywasfurtherportionedintodifferentcomponentsduetoSCAandGCA(Griffing,1956)whichprovidesabetterunderstandingofhowthesecharactershavebeencontrolled(Imtiazetal.2023).SCAandGCAvarianceswerefoundsignificantunderbothwateredenvionmentsthatrevealedthecontributionofadditiveandnon-additivegeneaction.Earlier,invariousstudies,bothtypeofgeneactionshavebeenreportedintheinheritancepatternofmorphological,yield,andfibertraits(Abroetal.2009;Imranetal.2012).Itwasobservedthatundernormalirrigationcondition,theGCAvariancewaslessthantheSCAvarianceindicatingthesuperiorityofnon-additivegeneactionforallthetraits(Table4).Previously,thedominatingroleofthenon-additivegenesforSI,fiberqualityandSCYhasbeenreported(Zareetal.2014;Alietal.2016).Non-additivegeneactionsupportstheopportunityfortheselectionofsuchgenotypesorcombinationsfortheimprovementofthespecifictraitinahybrid.Whileunderwaterdeficitconditioncondition,traitslikeCLCuD,NB,BW,SI,FL,andmicvalueexhibitahigherGCAvariancethantheSCAvariancewhichrepresentstheadditivetypeofgeneaction(Table5)asalsosupportedbyJavidetal.2014.SufficintheterosisisalsoneededGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page29foranybreedingprogramme.Crosses,L1×T4,L2×T4,L3×T4,L3×T5,L2×T1,L3×T4wereobservedwithwithmaximumheterosisfordifferenttraitsunderbothtreatments.Theseresultsarerelatedwithpreviosfindingsandmaybeselectedtodesignthebreedingstategyforthetargetedtraits(Monicashreeetal.2017;Maqbooletal.2017;Sajjadetal.2016).Forasuccessfulbreedingprogramandgenepyramiding,severalcrossingcyclesfollowedbyselectionofgoodcombiningparentsareimportant(Desokyetal.2021).Amonglines,L5(VH-327),hadthehighestvaluesfortheNB,SCY,FLandFSbothundernormalandwaterdeficitconditionenvironments(Table6)whichindicatedthatthislinecanbeusedfortheimprovementofthesetraits.While,T5(FH-Lalazar)showedhigherGCAeffectsforBW,GOT%,FL,FSundernormalwhilehighervaluesforBW,SI,FLandFSunderwaterdeficitcondition(Table7).Previously,thedominantroleofgenesregardingthegeneticmechanismforthesetraitshasbeenreported(Imranetal.2012;Shakeeletal.2012).Specificcombiningabilityestimatesisusedtoidentifythecrossesthatperformwellinspecificcombinationsforthedesiredtrait(Liaqatetal.2023).Underwaterdeficitcondition,crossL3×T5,possessedhigherSCAvalueforNB,SCY,SI,andFL.Suchcrossescanproducetransgressivesegregants,hencegivingrisetonewgeneticcombinationsandsignificantlyimprovingthespecifictraits.Underwaterdeficitcondition,line5possessedahighervaluefortheNB,Cross18(L4xT3)andcross21(L5xT1)wasthebestperformingforSCYandtester,T3,wasalsohigherforGOT%andpresentontheextremeofbiplot(Fig.3b).Theselines,crossedandtestersrevealedsignificantpotentialformorphologicalandyieldtraitsunderwaterdeficitconditionenvironmentandimportantfrombreedingpointofGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page30view.Emphasisonseednumberperboll(Seedindex)andbollnumberscouldbeanimportantcriterionwhilebreedingfordrought-tolerantcottongenotypes(UrRehmanetal.2007).ConclusionChangingclimatescenariohasbecomeaseriousthreattocottonyieldandproduction.Thisstudyprovidesvaluableinsightsforthegeneticcomponentsunderlyingmorphological,yield,andfibertraitsofdifferentcottonvarietiesandtheirinteractionunderwaterdeficitcondition.Hybridizationindifferentcombinationsresultedinincreasedseedcottonyieldanddroughttolerance.Generalandspecificcombiningabilityestimatesrevealedtheinheritancepatternoflines,testersandtheirinteractionsformorphologicalandyieldtraitsundernormalandwaterstressconditions.VH-327,FH-Lalazarandcross18(L4xT3)andcross21(L5xT1)werethebestperformingforyieldcontributingtraitsunderwaterdeficitcondition.Theselinesandtestershavethepotentialtobeutilizedinbreedingprogrammespecificallyunderwaterstressenvironment.CrossL3×T5possessedthehigherSCAeffectandmaybeexploitedforthedevelopmentofahybridforspecifictraits.Cross18(L4xT3),21(L5xT1),22(L5xT2)and23(L5xT3)exhibitedthemaximumseedcottonyieldandnumberofbollsunderbothnormalandwaterdeficitconditionconditionthatisalmost15-20%higherthantheparentalgenotypes.Ourinvestigationnotonlyprovidesanunderstandingoftheinheritancepatternofdroughttolerancebutalsoprovidestheopportunitytodesignthebreedingstrategyspecificallyforthedrought-stressenvironment.Abbreviations:DF;Degreeoffreedom,CLCuD;Cottonleafcurldisease(%),PH;Plantheight(cm),NB;Numberofbolls,BW;Bollweight(g),SCY;Seedcottonyield(g),SI;GRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page31Seedindex(g),GOT;Ginningoutturn(%),FL;Fiberlength(cm),FS;Fiberstrength(g/tax),Mic;Micronaire(μg/inch).DeclarationsConflictofinterest:Weconfirmthatthispaperhasnotbeenpublishedanywhere.Ethicalapproval:Alltheauthorshavenoconflictofinterestwithanyinstitution,journaletc.Funding:Nospecificfundingwasreceivedforthisresearch.ReferencesAbroS,KandhroMM,LaghariS,etal.Combiningabilityandheterosisforyieldcontributingtraitsinuplandcotton(GossypiumhirsutumL.).PakJBot.2009;41(4):1769-1774.AliI,ShakeelA,SaeedA,etal.Combiningabilityanalysisandheteroticstudiesforwithin-bollyieldcomponentsandfibrequalityincotton.JAPS:JAnimPlantSci.2016;26(1):156-162.AmmarA,AliZ,SaddiqueMAB,etal.GeneticanalysisandexpressionprofilingofTaHSP90Atranscriptsconferheattoleranceinwheat.SABRAOJBreedGenet.2023;55(3):653-670.http://doi.org/10.54910/sabrao2023.55.3.5.BozorovTA,UsmanovRM,YangH,HamdullaevSA,etal.Effectofwaterdeficiencyonrelationshipsbetweenmetabolism,physiology,biomass,andyieldofupland(GossypiumhirsutumL.).JAridLand.2018;10:441-456.cottonhttps://doi.org/10.1007/s40333-018-0009-y.DesokyESM,MansourE,AliMM,etal.Exogenouslyused24-epibrassinolidepromotesdroughttoleranceinmaizehybridsbyimprovingplantandwaterproductivity2021;10(2):354.https://doi.org/10.3390/plants10020354.environment.Plants.aridaninFarooqMA,ChatthaWS,ShafiqueMS,etal.Transgenerationalimpactofclimatic14:987514.production.FrontPlantSci.2023;onchangeshttps://doi.org/10.3389/fpls.2023.987514.cottonGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page32GriffingBRUCE.Conceptofgeneralandspecificcombiningabilityinrelationtodiallelcrossingsystems.AustJBiolSci.1956;9(4):463-493.HaroonM,AnasM,NaurinI,etal.Autoimmunityinplants;apowerfulweaponinKingdomPlantaetocombatstresses.IntJBiosci.2023;12(3):159-164.https://doi.org/10.47278/journal.ijab/2023.059.IjazA,AnwarZ,AliA,etal.UnravelingthegeneticandmolecularbasisofheatstressinGenet.2024;15:1296622.cotton.Fronthttps://doi.org/10.3389/fgene.2024.1296622.ImtiazM,ShakeelA,KhanAI,UlRehmanMSN.Identificationofpotentialplantmaterialandgeneticanalysisfordroughttoleranceinuplandcottonbasedonphysiologicalindicators.PakJBot.2023;55:1215-1227.JansY,VonBlohW,SchaphoffS,MullerC.Globalcottonproductionunderclimatechange–Implicationsforyieldandwaterconsumption.HydrolEarthSystSci.2021;25:2027-2044.https://doi.org/10.5194/hess-25-2027-2021,2021.JavaidA,AzharFM,KhanIA,RanaSA.GeneticbasisofsomeyieldcomponentsinGossypiumhirsutumL.PakJAgriSci.2014;51(1):143-146.KamaraMM,IbrahimKM,MansourE,KheirAM,etal.Combiningabilityandgeneactioncontrollinggrainyieldanditsrelatedtraitsinbreadwheatunderheatstressand2021;11(8):1450.https://doi.org/10.3390/agronomy11081450.conditions.Agron.normalKashifM,SangY,MoS,etal.DecipheringthebiodesulfurizationpathwayemployingmarinemangroveBacillusaryabhattaistrainNM1-A2accordingtowholegenomeanalyses.Genomics.2023;115(3):110635.sequencinghttps://doi.org/10.1016/j.ygeno.2023.110635.transcriptomeandKempthorneO.Thecontributionsofstatisticstoagronomy.AdvAgron.1957;9:177-204.https://doi.org/10.1016/S0065-2113(08)60113-3.LiaqatS,TipuALK,AhmadG,etal.Geneticdiversityamongcotton(gossypiuml.)Germplasmforyieldandfibretraitsunderaridclimatichirsutumcondition.AgricSciJ.2023;5(1):1-7.https://doi.org/10.56520/asj.v5i1.187.LokaDA,OosterhuisDM.Physiologicalandbiochemicalresponsesoftwocotton(GossypiumhirsutumL.)cultivarsdifferinginThermotolerancetohighnightGRJNST,Volume:04-Issue2(2026)/ISSNP:2790-7643ArticleID:2078https://doi.org/10.53762/grjnst.04.02.29G.2078Page33temperaturesduringhttps://doi.org/10.3390/agriculture10090407.Anthesis.Agriculture.2020;10(9):407.MahmoodT,WangX,AhmarS,etal.Geneticpotentialandinheritancepatternofphenologicalgrowthanddroughttoleranceincotton(GossypiumhirsutumL.).Front2021;12:705392.https://doi.org/10.3389/fpls.2021.705392.PlantSci.MaqboolMA,AslamM,KhanMS,etal.Evaluationofsinglecrossyellowmaizehybridsforagronomicandcarotenoidtraits.IntJAgricBiol.2017;19:1087-1098.https://doi:10.17957/IJAB/15.0389ImranM,ShakeelA,AzharFM,etal.Combiningabilityanalysisforwithin-bollyieldcomponentsinuplandcotton(GossypiumhirsutumL.).GenetMolRes.2012;11(3):2790-2800.DOIhttp://dx.doi.org/10.4238/2012.August.24.4NazR,GulF,ZahoorS,etal.Interactiveeffectsofhydrogensulphideandsiliconenhancedroughtandheattolerancebymodulatinghormones,antioxidantdefenceenzymesandredoxstatusinbarley(HordeumvulgareL.).PlantBiol.2022;24:684-696.https://doi.org/10.1111/plb.13374.RahmanMHU,AhmadI,GhaffarA,etal.Climateresilientcottonproductionsystem:acasestudyinPakistan.In:AhmadS,HasanuzzamanM(eds)CottonProductionandUses.Springer,Singapore.2020;447-484.https://doi.org/10.1007/978-981-15-1472-2_22.RehmanT,TabassumB,YousafS,etal.Consequencesofdroughtstressencounteredduring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