research article morphological characterization and

Research ArticleMorphological Characterization and Assessment ofGenetic Variability Character Association and Divergence inSoybean Mutants

M A Malek1 Mohd Y Rafii2 Most Shahida Sharmin Afroz3

Ujjal Kumar Nath4 and M Monjurul Alam Mondal1

1 Bangladesh Institute of Nuclear Agriculture Mymensingh 2202 Bangladesh2 Institute of Tropical Agriculture Universiti Putra Malaysia (UPM) 43400 Serdang Selangor Malaysia3 Cotton Development Board Farmgate Dhaka 1215 Bangladesh4 Bangladesh Agricultural University Mymensingh 2202 Bangladesh

Correspondence should be addressed to M A Malek malekbinagmailcom

Received 8 May 2014 Accepted 16 July 2014 Published 12 August 2014

Academic Editor Mirko Diksic

Copyright copy 2014 M A Malek et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Genetic diversity is important for crop improvement An experiment was conducted during 2011 to study genetic variabilitycharacter association and genetic diversity among 27 soybean mutants and four mother genotypes Analysis of variance revealedsignificant differences among the mutants and mothers for nine morphological traits Eighteen mutants performed superiorlyto their mothers in respect to seed yield and some morphological traits including yield attributes Narrow differences betweenphenotypic and genotypic coefficients of variation (PCV andGCV) formost of the characters revealed less environmental influenceon their expression High values of heritability and genetic advance with high GCV for branch number plant height pod numberand seedweight can be considered as favorable attributes for soybean improvement through phenotypic selection and high expectedgenetic gain can be achieved Pod and seed number and maturity period appeared to be the first order traits for higher yield andpriority should be given in selection due to their strong associations and high magnitudes of direct effects on yield Cluster analysisgrouped 31 genotypes into five groups at the coefficient value of 235 The mutantsgenotypes from cluster I and cluster II could beused for hybridization program with the mutants of clusters IV and V in order to develop high yielding mutant-derived soybeanvarieties for further improvement

1 Introduction

Cultivated soybean [Glycinemax (L)Merr] one of themajorcrops is used for animal feed and human foods [1] Unlikemost of the vegetable proteins soybean protein supplies allthe essential amino acids having cardio friendly oil whichfulfills 30 percent of world vegetable oil requirement and alsohas many therapeutic components namely lactose-free fattyacids antioxidants and folic acid vitamin B complex andisoflavones [2] Due to the versatile nature of this crop itscontribution to industrial agricultural andmedicinal sectorsis significantly increasing Rapid increase of populationtogether with gradual reduction of cultivable land has posedgreater challenges to human health in Bangladesh As a result

the present diet pattern in Bangladesh is highly imbalancedwith deficit consumption of both pulse and oils In thiscircumstance soybean can be the excellent source of balancediet to meet the nutritional deficiencies in Bangladesh Theaverage yield of soybean in Bangladesh is 164 tons per ha onlycompared toworld average yield of 30 tons per ha [3] Amongthe factors responsible for its lower yield in Bangladeshthe most important is the nonavailability of high yieldingvarieties

In soybean creation of genetic variation throughhybridization is a tedious process due to small and fragileflowers which make it very difficult to carry out the processof emasculation and injuring the parts of the flower andare prone to heavy flower shedding even under favorable

Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 968796 12 pageshttpdxdoiorg1011552014968796

2 The Scientific World Journal

conditionsThese coupled with complete self-fertility imposelimitations on the success of hybridization program [4] As aresult mutation breeding appears to play an important rolein creating genetic variability for improving this importantcrop

Kharkwal and Shu [5] reported that induced mutationbreeding is becoming more powerful and effective in breed-ing crop varieties to play a significant role for improvingworld food security in the coming years and decades Inducedmutations have generated a vast amount of genetic variabilityand are now widely used for the development of genescontrolling important traits and understanding the functionsand mechanisms of actions of these genes in plants [6]Mutation breeding is now playing an important role indeveloping new genetic resources and breakage of unwantedlinkages [7] Using mutation breeding genetic improvementof any yield attributes either qualitative or quantitative traithas been successfully achieved in soybean [8ndash16] and also inother oil crops like rapeseed-mustard [17ndash19] Furthermoremutation breeding requires less time to develop crop cultivarsas compared to the conventional breeding [20 21] The com-mercial utilization of approximately 3000 mutant-inducedandmutant-derived varieties strongly shows the contributionof mutation breeding to generating new germplasm for cropimprovement [22]

The information as well as assessment of genetic vari-ability in the existing germplasm of a particular crop issought as prerequisite [23ndash25] Furthermore heritability ofa plant trait is very important in determining the responseto selection because it implies the extent of transmissibil-ity of traits into next generations [26] In addition highgenetic advance coupled with high heritability estimate offersthe most effective condition for selection for a particulartrait [27]

Increased seed yield is the ultimate goal of the breedersBut seed yield itself is a product of interaction of manycomponent traits which influence yield directly or indirectlySo it is important to see the contribution of each of thetraits in order to give more attention to those having thehighest influence on yield Moreover understanding therelationship between yield and its component traits is ofgreat importance to a breeder for making the best use ofthese relationships in selecting desirable genotypes for yieldimprovement programs [28 29] As correlation alone cannotexplain relationships among the characters therefore thepath coefficient analysis has been used in different cropspecies for complete determination of the impact of theindependent variables on the dependent one and to finddirect and indirect effects [30]Therefore to identify the traitswhich have significant effect on yield for potential use inselection path analysis has beenwidely used in crop breedingprogram [31 32]

This study investigated the morphological variabilityamong 27 soybean mutants along with four mother vari-eties using quantitative morphological traits including yieldattributes For an effective breeding program for crop varietydevelopment through hybridization the analysis of geneticdiversity is one of the useful tools and plays a fundamentalrole in identification of parents [33 34] Moreover better

knowledge on genetic diversity could help to achieve long-term selection gain [35] As a traditional method morpho-logical traits are used to assess genetic divergence and classifyexisting germplasm materials However this technique alow level but powerful taxonomic tool has been utilizedfor the preliminary grouping of germplasm prior to theircharacterization using more precise marker technologiesAccording toDin et al [36] scientific classification of the plantstill relies on morphological traits Moreover this techniqueis easier cost effective and easy to score and requires less timeand finally it does not need any technical knowledge

From four mother genotypes (Sohag BARI Soybean-5Bangladesh Soybean-4 and BAU-S64) Bangladesh Instituteof Nuclear Agriculture (BINA) developed 27 true breedingsoybean mutants using gamma rays from the Co60 gammacell Among those mutants 18 promising mutants showedbetter performance in respect to seed yield per ha alongwith other morphological traits including important yieldattributes than the mother varietiesline In this researchwe evaluated the performances of those mutants along withmothers from January to June 2011 regarding morphologicalparameters and yield traits through the studies of genotypicand phenotypic variability character association and geneticdiversity among these mutants and mothers which have notyet been studied Such information will serve as a usefultool for establishing suitable breeding program for furthersoybean improvement

2 Materials and Methods

21 Experimental Site The experiment was carried out atthe experimental field of Bangladesh Institute of NuclearAgriculture (BINA) Mymensingh during January to June2011 Geographically the place is located at about 24∘751015840north latitude and 90∘501015840 east longitude The soil of theexperimental site is sandy loam having 006 nitrogen 105organic matter 185 ppm available phosphorus 028 meqexchangeable potassium 18 ppm sulphur and 68 pH

22 Plant Materials Thirty-one soybean genotypes wereused as the experimental materials Among the genotypes 27were the true breeding M

6mutants and the other four were

the mother genotypes Sohag Bangladesh Soybean-4 (BDS-4) BARI Soybean-5 and BAU-S64 from which the mutantswere evolved The names of the 27 soybean mutants alongwith their respective mother genotype are listed in Table 1

23 Experimental Design and Setting the Experiment Theexperiment was laid out in a randomized complete blockdesign with three replicates Block-to-block and plot-to-plotdistances were maintained as 125 and 075m respectivelywith a plot size of 40m times 36m and line-to-line distance of30 cm Seedswere sownon 26December 2010 Each entrywasgrown in 12 rows keeping plant-to-plant distance of 8ndash10 cmin rows

24 Intercultural Operations Urea triple super phosphatemuriate of potash and gypsum were used as basal dose

The Scientific World Journal 3

Table 1 List of 27 soybean mutants with their mother varietiesline

Name of the mutant Mother varietyline Name of the mutant Mother varietylineSBM-01 Sohag SBM-18 BARI Soybean-5SBM-02 Sohag SBM-19 BARI Soybean-5SBM-03 Sohag SBM-20 BARI Soybean-5SBM-04 Sohag SBM-21 BARI Soybean-5SBM-05 Sohag SBM-22 BARI Soybean-5SBM-06 Sohag SBM-23 BARI Soybean-5SBM-08 Sohag SBM-24 SohagSBM-09 Sohag SBM-25 SohagSBM-10 Sohag SBM-26 SohagSBM-11 BDS-4 SBM-27 BAU S64SBM-12 BDS-4 SBM-28 BAU S64SBM-13 BDS-4 Sohag Mother varietySBM-14 BDS-4 BARI Soybean-5 Mother varietySBM-15 BDS-4 BDS-4 Mother varietySBM-16 BDS-4 BAU S64 Mother lineSBM-17 BARI Soybean-5Note BDS-4 Bangladesh Soybean-4

during final land preparation at 40 150 100 and 110 kg haminus1respectively Rhizobium inoculum for soybean was used at25 g per kg seeds Intercultural operations like weedingthinning application of pesticide and so forth were doneas recommended and when necessitated for proper growthand development of plants in each plot Harvesting was donedepending upon the maturity of the plants in each plot

25 Data Collection Data on plant height number of pri-mary branches and pods per plant number of seeds per podand seed yield per plantwere taken from 10 randomly selectedcompetitive plants from each plot Plants of each plot wereharvested when the plants and pods of each plot turned intoyellowish brown colour and almost all the leaves shed Plotseed yield was taken from the eight middle rows avoidingborder effects and plot seed yield was converted into kg perha (Table 2)

26 Statistical Analyses Analysis of variance (ANOVA) andleast significant difference (LSD) were computed for alltraits using SAS 91 for identification of significant differencebetween progenies Genetic parameters were estimated by theformula given by Burton [37] Burton and Vane [38] andJohnson et al [39] These parameters include the following

(i) 1205902G (an estimate of genotypic variance) = (MSG minusMSE)119903 where MSG is an estimate of mean square oftested accession MSE is an estimate of mean squareof error and 119903 refers to the number of replications

(ii) MSE is an estimate of 1205902E

(iii) 1205902P (an estimate of phenotypic variance) =1205902G (geno-typic component of variance) + 1205902E

(iv) PCV (phenotypic coefficient of variation) =radic1205902P119883times100 where 1205902P is the phenotypic component ofvariance and119883 is the mean of the trait

(v) GCV (genotypic coefficient of variation) =radic1205902G119883 times100 where 1205902G is the genotypic component of vari-ance and119883 is the mean of the trait

(vi) ℎ2B (an estimate of broad sense heritability) =1205902

G1205902

p where 1205902

G is the genotypic component ofvariance and 1205902P is the phenotypic component ofvariance

(vii) GA (genetic advance) is taken as percent of themean assuming selection of the superior 5 of theaccessions

(viii) GA (asof themean) =119870timesradic1205902P119883timesℎB2times100 where

119870 (the standardized selection intensity) = 206 (at 5selection intensity) 1205902P is the phenotypic componentof variance ℎ2B is the heritability in broad sense and119883 refers to the mean of the trait being evaluated

Genotypic and phenotypic correlation coefficients fordifferent characters were calculated in all possible combina-tions following the formula given by Miller et al [40] Pathcoefficient analysis was done following Dewey and Lu [24]also quoted by Singh and Chaudhury [41] and Dabholkar[42] For cluster analysis data were analyzed to determineEuclidean distance based on paired group method to deter-mine dissimilar groups of the mutants Two-dimensionalprincipal component analysis (PCA) graph was constructedusing PAST-multivariate software


Table 2 List of different traits and their description of measurement

Serial number Traits Method of measurement1 Days to flowering The number of days from sowing to flowering of 50 plants2 Days to maturity The number of days from sowing until approximately 90 pod turned into brownish colour3 Plant height (cm) The height from the base of the plant to the tip of last leaf4 Branches per plant (number) Total number of pod bearing primary branches in a plant5 Pods per plant (number) Total number of pods with seed in a plant6 Seeds per pod (number) Total number of seeds in a pod7 100-seed wt (g) One hundred seeds randomly counted and then weighed8 Seed yield per plant (g) Weighing the total number of seeds produced in a plant9 Seed yield (kg per ha) Weighing the seeds produced in a plot and then converted into kg per ha

Table 3 Mean square values for nine different phenological and morphological characters yield attributes and seed yield among 31 soybeangenotypes

Sources ofvariation DF Days to

floweringDays tomaturity

Plantheight (cm)

Branchesper plant(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(gm)

Seed yieldper plant

(g)

Seed yield(kg per ha)

Replication 2 312 3307 9749 0047 1087 0001 0128 0615 590291Genotypes 30 7506lowastlowast 2012lowastlowast 43908lowastlowast 4974lowastlowast 20388lowastlowast 0153lowastlowast 11735lowastlowast 4082lowastlowast 535273lowastlowast

Error 60 524 1784 2109 0289 1270 0011 0256 0326 40219lowastlowastSignificant at 1 level of probability

3 Results

31 Variability and Genetic Parameters among the MutantsANOVA showed that mean squares due to genotypes werehighly significant (119875 le 001) for all the nine characterslike days to flowering and maturity plant height numberof branches and pods per plant seeds per pod 100-seedweight seed yield per plant and seed yield per ha (Table 3)These results revealed highly significant genotypic variationsamong the genotypes for all these traits Phenotypic andgenotypic coefficients of variation (PCV and GCV) broadsense heritability and genetic advance were calculated forall the characters (Table 4) The highest PCV and GCV wereobserved for branches per plant (3811 and 3503 resp) andthe lowest PCV and GCV were recorded for days to maturity(722 and 635 resp) The PCV and GCV of plant height(1916 and 1791) pods per plant (1816 and 1659) 100-seed weight (1697 and 1643) and seed yield per ha (1406and 1261) were higher compared to days to flowering (836and 756) and days tomaturity (722 and 635) Results alsoshowed narrow differences between PCV and GCV for mostof the traits All the characters exhibited high heritabilitywhich ranged from 7740 in days to maturity to 9373in 100-seed weight Among the traits only days to maturityhad relatively low heritabilityThe genetic advance as percentof mean (GA) ranged from 1150 in days to maturity to6633 in branches per plant Among the traits number ofbranches per plant plant height 100-seed weight and podsper plant exhibited higher percentages of genetic advance

32 Performance of the Mutants and Mothers Mean perfor-mances of the mutants along with the mothers for differentmorphological traits are shown in Table 5 The shortest time

required to flowering and maturity (58 and 116 days) wasobserved in mutant SBM-15 closely followed by SBM-16 (59and 116 days) and the longest (80 and 150 days) was requiredin BAU-S64 Results also showed that some of the mutantsrequired significantly lower flowering and maturity periodthan their respective mothers Most of the mutants fromSohag produced significantly lower plant height and lowernumber of branches per plant but 11 mutants producedsignificantly higher number of pods per plant and seedyield (per plant and ha) and only two mutants (SBM-08and SBM-10) gave significantly higher seed weight thanSohag On the other hand the mutants from BARI Soybean-5 and BDS-4 most of the mutants produced significantlytaller plant than their respective mothers and statisticallysimilar number of branches and pods per plant Amongfourmutants three (SBM-11 SBM-13 and SBM-14) producedsignificantly higher seed yield per plant and per ha thanmother variety Bangladesh Soybean-4 Among nine mutantsof BARI Soybean-5 six produced significantly higher 100-seed weight as well as seed yield per plant and per hathan mother Among the two mutants of BAU-S64 SBM-27produced significantly higher 100-seed weight as well as seedyield per plant and per ha than mother

33 Association among the Traits Genetic and phenotypiccorrelations were calculated (Table 6) followed by path coeffi-cient analysis to partition the correlation coefficients of traitswith yield per plant into direct and indirect effects (Table 7)Genotypic correlations were found to be higher than thephenotypic correlations in most of the cases Except for100-seed weight all other traits showed significant positivecorrelations with seed yield per plant and seed yield per haboth at genotypic and at phenotypic levels Besides these


Table 4 Estimation of genetic parameters of nine different phenological andmorphological characters yield attributes and seed yield among31 soybean genotypes

Characters Genotypicvariance

Phenotypicvariance Grand mean Heritability () GCV () PCV () GA ()

Days to flowering 2327 2851 6384 8162 756 836 1406Days to maturity 6111 7895 12315 7740 635 722 1150Plant height (cm) 13933 15942 6591 8774 1791 1916 3663Branches per plant (number) 1564 1851 357 8450 3503 3811 6633Pods per plant (number) 6373 7643 4813 8338 1659 1816 3120Seeds per pod (number) 0047 0058 196 8103 1106 1229 2051100-seed weight (g) 383 408 1191 9373 1643 1697 3276Seed yield per plant (g) 1252 1578 950 7934 1178 1351 2208Seed yield (kg per ha) 165018 205237 3221 8040 1261 1406 2329

100-seed weight also showed significant negative correlationswith all other traits except seed yield per plant Plant heightshowed highly significant positive correlation with branchesper plant and both traits also showed significant positivecorrelations with most of the other traits Days to floweringand days to maturity were positively and highly correlatedand both traits showed significant positive correlation withplant height branches per plant and pods per plant and nosignificant correlation with seeds per pod

Results of path coefficient analysis based on genotypiccorrelation of all the morphological traits indicated thatamong the traits seeds per pod had the highest directpositive effect (1450) on seed yield per plant followed by 100-seed weight (1350) days to maturity (1184) and pods perplant (0659) Days to flowering plant height and branchesper plant having significant positive correlation with yield(0646lowastlowast 0589lowastlowast and 0387lowast resp) contributed mainlytowards seed yield via days to maturity (1102 0736 and0459 resp) pods per plant (0253 0543 and 0528 resp)and seeds per pod (0405 1050 and 1150 resp) with negativedirect effects (minus0646 minus0258 and minus0285 resp) Pods perplant and seeds per pod contributed negatively towards seedyield via 100-seed weight (minus1040 and minus1168 resp)

34 Cluster Analysis Cluster analysis using all the ninemorphological traits grouped the 31 accessions into fivemajorgroups at the genetic distance of 2350 (Table 8 Figure 1) Itwas also found that among the five clusters cluster II was thelargest and consisted of 13 genotypes (12 mutants and BDS-4) and the second largest group was the clusters I and IIIand each consisted of eight genotypes The smallest groupwas clusters IV and V and each cluster contained only onemutant Mean values of nine different traits for six groupsamong 31 soybean genotypes are presented in Table 9 Resultsshowed that among the five clusters IV had the highestaverage means for all the traits except seeds per pod followedby clusters V and I On the contrary cluster III revealed thelowest means for all the traits

35 Principal Component Analysis (PCA) A two-dimen-sional principal component analysis was performed using

all the morphological traits The cluster analysis was mostlyconfirmed by the PCA analysis Two distant mutants suchas SBM-27 and SBM-28formed their individual clustergroupalone both in cluster (clusters IV and V) and in PCAanalyses (GIV andGV) (Figures 1 and 2 resp) Fourmutantsnamely SBM-02 SBM-06 SBM-09 and SBM-10 formedone group (GI) and BAU-S64 formed another group (GVI)with mutants SBM-11 SBM-13 and SBM-14 though theseseven mutants and BAU-S64 together formed single cluster(cluster I) in cluster analysis BDS-4 and SBM-12 formedone group (GVII) and 11 mutants formed another group(GII) though all these 12 mutants and Bangladesh Soybean-4together formed single cluster (cluster II) in cluster analysisSohag formed group with BARI Soybean-5 with other sixmutants both in cluster (cluster III) and in PCA analyses(GIII)

According to PCA the first four principal componentsaccounted for about 99999 of total variation for all themorphological traits and exhibited high correlation amongthe traits analyzed

4 Discussion

All the nine morphological traits showed highly significant(119875 le 001) variations indicating the presence of sufficientamount of genetic variability among the mutants for all thestudied traits In soybean genotypes significant variationshave also been reported earlier by other researchers forvarious morphological traits [43ndash46] Narrow differencesbetween PCV and GCV for most of the traits indicate lessinfluence of environmental factors on the expression of thesetraits and the chance of high selection gain The heritabilityestimates help the breeders in selection based on the basisof phenotypic performance Heritability and GA togetherwith GCV could provide the best image of the amount ofadvancement to be expected through phenotypic selection[39] So high values of heritability and GA () along withhigh GCV for the characters like plant height number ofbranches and pods per plant and 100-seed weight can beconsidered as favorable morphological traits for soybeanimprovement through effective phenotypic selection of these


Table 5 Mean performances of 27 soybean mutants and four mother varieties for nine different phenological and morphological charactersyield attributes and seed yield

Genotypes DF DM Plant height(cm)

Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper plant (g)

Seed yieldper ha (kg)

SBM-01 64 122 53 246 40 183 125 87 2675SBM-02 62 120 57 280 45 200 130 106 3663SBM-03 64 124 58 270 42 200 122 92 3126SBM-04 64 126 71 460 45 183 117 91 3015SBM-05 60 120 57 253 45 173 127 94 3202SBM-06 64 120 58 270 48 196 134 111 3498SBM-08 60 116 54 260 41 180 138 88 2913SBM-09 64 120 54 446 51 210 125 101 3418SBM-10 64 122 61 343 44 200 143 101 3518SBM-24 60 118 58 263 47 180 119 83 2772SBM-25 62 120 60 280 43 170 123 90 3017SBM-26 61 120 63 290 45 180 118 94 3107Sohag 66 125 65 526 38 186 128 82 2627SBM-11 66 122 81 610 65 233 76 97 3479SBM-12 66 122 86 560 64 236 77 94 3342SBM-13 62 120 87 550 65 253 79 103 3619SBM-14 62 121 87 626 64 240 77 108 3715BDS-4 68 128 76 576 61 230 78 89 3127SBM-15 58 116 59 213 43 180 119 83 2860SBM-16 59 116 58 326 46 180 134 87 3012SBM-17 60 118 55 280 51 176 137 92 3228SBM-18 61 118 53 283 36 180 131 80 2709SBM-19 62 120 65 240 44 200 116 90 3059SBM-20 62 119 65 210 45 180 128 90 3111SBM-21 60 118 66 230 42 203 124 92 3142SBM-22 61 122 67 260 45 180 132 93 3083SBM-23 60 120 57 300 42 176 132 88 2772BARI-5 66 126 54 260 41 196 114 82 2721SBM-27 76 145 85 480 55 206 132 136 4459SBM-28 74 143 82 440 55 190 134 116 4032BAU-S64 80 150 90 430 53 200 124 108 3824LSD005 374 690 655 049 582 024 083 078 284SE (plusmn) 090 147 216 024 148 004 036 021 766SD 500 819 1203 131 824 022 198 118 426CV 359 343 609 837 741 733 425 630 740Note BARI-S5 BARI Soybean-5 BDS-4 Bangladesh Soybean-4

traits and high expected genetic gain from selection for thesecharacters can be achieved This also indicates that thesecharacters are under the control of additive gene actionand would respond very well to continuous selection [47]However high heritability and GA () along with low GCVfor the rest of the traits like days to flowering and maturityseeds per pod and seed yield per plant and per ha indicatedthat expression of these traits is under the involvement ofnonadditive gene action and phenotypic selection of thesetraits might not be effective

In plant breeding creation of new plant type withimprovement characters leading to producing high yield isthe main objective In soybean the important yield attributesare the number of pods per plant seeds per pod and seedweight which determine the seed yield

In the present study it was observed that among the 27mutants 18 performed superiorly to their respective mothersin respect to seed yield per ha along with some othermorphological traits including yield attributes like numberof pods per plant and number of seeds per pod along with


Table 6 Genotypic (G) and phenotypic (P) correlation coefficients among nine morphological traits in 31 soybean genotypes

Characters Days tomaturity Plant height

Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering

G 0931lowastlowast 0659lowastlowast 0494lowastlowast 0385lowast 0279 minus0117 0646lowastlowast 0627lowastlowast

P 0966lowastlowast 0646lowastlowast 0485lowastlowast 0381lowast 0301 minus0090 0620lowastlowast 0627lowastlowast

Days tomaturity

G 0622lowastlowast 0388lowast 0286 0119 minus0004 0667lowastlowast 0629lowastlowast

P 0611lowastlowast 0381lowast 0290 0158 0032 0634lowastlowast 0626lowastlowast

Plantheight

G 0776lowastlowast 0824lowastlowast 0725lowastlowast minus0621lowastlowast 0589lowastlowast 0677lowastlowast

P 0771lowastlowast 0805lowastlowast 0696lowastlowast minus0615lowastlowast 0570lowastlowast 0668lowastlowastBranches perplant(number)

G 0801lowastlowast 0796lowastlowast minus0705lowastlowast 0387lowast 0457lowastlowast

P 0796lowastlowast 0763lowastlowast minus0700lowastlowast 0380lowast 0458lowastlowastPods perplant(number)

G 0864lowastlowast minus0774lowastlowast 0518lowastlowast 0640lowastlowast

P 0821lowastlowast minus0763lowastlowast 0508lowastlowast 0633lowastlowastSeeds perpod(number)

G minus0867lowastlowast 0398lowast 0509lowastlowast

P minus0818lowastlowast 0378lowast 0484lowastlowast

100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast

lowastlowast and lowast indicate significance at 1 and 5 level of probability respectively

Table 7 Partitioning of genotypic correlations into direct (bold) and indirect effects of eight morphological traits in 31 soybean genotypesby path analysis

Items Days toflowering

Days tomaturity Plant height Branch per

plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant

Days to flowering minus0646 1102 minus0170 minus0141 0253 0405 minus0157 0646lowastlowast

Days to maturity minus0601 1184 minus0161 minus0111 0188 0173 minus0005 0667lowastlowast

Plant height (cm) minus0425 0736 minus0258 minus0221 0543 1050 minus0836 0589lowastlowast

Branches per plant(number) minus0318 0459 minus0201 minus0285 0528 1150 minus0949 0387lowast

Pods per plant(number) minus0248 0338 minus0213 minus0228 0659 1250 minus1040 0518lowastlowast

Seeds per pod(number) minus0180 0141 minus0187 minus0227 0569 1450 minus1168 0398lowast

100-seed weight (g) 0076 minus00086 0161 0201 minus0510 minus1258 1350 0012Bold figures indicate the direct effectsResidual effect = minus00446lowast and lowastlowast indicate significant at 1 and 5 level of probability respectively

Table 8 Groups of 27 soybean mutants and four mother varieties according to cluster analysis from nine phenological and morphologicalcharacters yield attributes and seed yield

Cluster number Number of genotypes Percent Genotypes

I 8 258 BAU-S64 SBM-02 SBM-13 SBM-14 SBM-06 SBM-10 SBM-11SBM-09

II 13 420 SBM-12 SBM-05 SBM-17 BDS-4 SBM-03 SBM-26 SBM-20SBM-21 SBM-19 SBM-22 SBM-04 SBM-25 SBM-16

III 8 258 SBM-08 SBM-15 SBM-24 SBM-23 SBM-18 BARI-S5 SBM-01Sohag

IV 1 32 SBM-27V 1 32 SBM-28Note BARI-S5 BARI Soybean-5 BDS-4 Bangladesh Soybean-4


0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V

Figure 1 Dendrogram showing relationship among 31 soybean genotypes using nine phenological and morphological characters seed yieldand yield traits

Table 9 Mean values of nine different phenological and morphological characters yield attributes and seed yield for five groups revealed bycluster analysis among 31 soybean genotypes

Characters I II III IV VDays to flowering 655 6223 6188 7600 7400Days to maturity 12438 12100 12013 14500 14300Plant height (cm) 7188 6515 5663 8500 8200Branches per plant (number) 444 326 294 480 440Pods per plant (number) 5438 4754 4100 5500 5500Seeds per pod (number) 217 192 183 206 190100-seed weight (g) 1110 1179 1258 1320 1340Seed yield per plant (g) 1044 914 841 1360 1160Seed yield (kg per ha) 3592 3121 2756 4459 4032

higher 100-seed weight which contributed to the mutants inproducing higher seed yield These results are in agreementwith the results of Tulmann et al [48] Kundi et al [49]Hussain et al [50] and Ahire et al [51] who reportedimprovement in yield attributes in soybean mutants as aconsequence of mutagenesis

Generally estimates of genotypic correlation coefficientswere found to be higher than their respective phenotypiccorrelation coefficients (Table 6) which are in agreementwith the results of Weber and Moorthy [52] and Anand andTorrie [53] Weber and Moorthy [52] also explained theirresult of low phenotypic correlation due to the masking ormodifying effect of environment on the genetic associationamong the traitsThe genotypic correlations of pods per plant

and seedspod with days to flowering and maturity werepositive and the correlation between these two traits wasvery high (0864lowastlowast) indicating that late maturing genotypeshave more number of pods per plant and seeds per podand consequently give higher seed yield Seed weight alwaysshowed negative correlations with other desirable yield traits[54 55] which indicates that the increase in one trait wouldresult in the reduction of the other that is simultaneousincrease or decrease of both traits would be difficult Thestrong negative correlation of seed weight with other yieldtraits indicated that it would be very difficult to identify asoybean genotype having higher seed weight simultaneouslywith higher number of pods per plant and seeds per podrather an increase in one trait would result in the reduction


05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15

∙SBM-20∙SBM-26 ∙SBM-25

∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09

minus05minus1minus15minus2minus25minus3

minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5

Figure 2 Two-dimensional plot of PCA showing relationships among 31 soybean genotypes using morphological and yield related traitsNote BDS-4 Bangladesh Soybean-4 BARI-S5 BARI Soybean-5

of the others Significant positive correlations of days toflowering and maturity plant height branches and podsper plant seeds per pod and seed weight with seed yield(Table 6) indicate that in selecting high yielding genotypesthese characters should be given more emphasis as the bestselection criteria These results also are in agreement withthe results reported by others in soybean [30 45 53 55ndash58]Machikowa et al [57] also reported that days to floweringand maturity were highly and positively correlated withyield components in soybean Highly significant and positivecorrelation between seed yield per plant and yield per haindicates that in soybean individual plant yield contributedsignificantly towards yield per unit area Significant positivecorrelation of plant heightwith days tomaturity indicates thatgenotypes with taller plants tend to longer maturity period

In soybean positive direct effects of number of podsper plant [54 55 59] and days to maturity [30] on seedyield were also reported and showed similarity with thepresent results The direct effect of 100-seed weight on seedyield was also positive (1350) having high negative indirecteffect through seeds per pod (minus1258) and pods per plant(minus0521) Therefore the negative indirect effects of 100-seedweight with these traits will be a problem in combiningthese important characters for high seed yield Among thetraits indirect effects through pods per plant seeds perpod and days to maturity were found to be important andthese results agreed partially with the findings of Iqbal etal [60] and Machikowa and Laosuwan [55] who reportedhigh indirect effects through pods per plant and maturityperiod Therefore days to maturity is also suggested to bean important selection criterion in soybean for seed yieldFaisal et al [30] and Harer and Deshmukh [61] also reportedsimilar results and suggested greater emphasis on longer

duration during selection Present results also suggest thatsoybean yield could be increased through the selection ofhigher number of pods per plant with higher number ofseeds per pod and longer maturity period Therefore insoybean pod number per plant and seeds per pod and daysto maturity can be considered as the major and effectivecharacters influencing the seed yield in soybean Both thecorrelation and path analyses indicate that pod number perplant and seeds per pod and days to maturity appeared to bethe first order yield components and priority should be givenduring selection due to having strong associations as well ashigh direct effects on seed yield

Clustering analysis based on nine morphological traitsgrouped 31 soybean genotypes into five different clustersand indicates that 31 soybean genotypes exhibited notablegenetic divergence in terms of morphological traits There-fore classification in this study based on morphologicaltraits is in agreement with previous report Formation ofdifferent number of clusters using morphological charactersin diverse soybean genotypes was also reported [45 62 63]The dendrogram tends to group some of the mutants withsimilar morphological traits into the same cluster Similarresults were also reported in soybean and other crops by Cuiet al [62] Yu et al [64] Iqbal et al [63] Abdullah et al [65]Latif et al [66] and Rafii et al [67]

Results revealed that among 13 mutants from Sohag andnine mutants from BARI Soybean-5 only three (SBM-08SBM-10 and SBM-24) from Sohag and only three (SBM-15 SBM-18and SBM-23) from BARI Soybean-5 formedcluster with mother varieties Sohag and BARI Soybean-5respectively and others formed distinct clusters other thanthe mother genotypes Similarly among four mutants fromBangladesh Soybean-4 only one (SBM-12) formed cluster


with mother and both mutants SBM-27 and SBM-28 fromBAU-S64 formed two individual clusters Present resultsconfirm that inducedmutations are contributing significantlyto creating genetic variations in crop plants The first fourprincipal components accounted for 99999 of the totalvariation Cluster analysis using dendrogram and PCA fol-lowing two-dimensional method played complementary roleto each other with little inconsistencies in respect of numberof genotypes in cluster formation To obtain greater heterosisgenotypes having distant clusters could be used as parents forhybridization program Dendrogram and two-dimensionalPCA graph clearly indicated that mutants SBM-27 and SBM-28 made two individual groups (clusters IV and V resp)and were far away from the other three clusters Thereforethe mutants from cluster I and cluster II could be usedfor hybridization program with the mutants of clusters IV(SBM-27) and V (SBM-28) in order to develop high yieldingmutant-derived soybean varieties

5 Conclusion

In plant breeding generation of new genotypes from theexisting ones with improvement in plant traits is the mainobjective The present study revealed the presence of highlevels of variations for nine different morphological traitsincluding yield attributes and seed yield among the newlydeveloped 27 mutants along with four mother genotypes ofsoybean These mutants could be served as raw materialsfor further genetic improvement of different characters ofthe soybean Among the nine traits plant height number ofbranches and pods per plant and 100-seed weight exhibitedhigh values of genotypic coefficient of variation broad senseheritability and genetic advanceTherefore these traits can beconsidered as favorable attributes for soybean improvementthrough effective phenotypic selection and high expectedgenetic gain can be achieved for these characters Most ofthe traits showed positive correlations between each otherwhich will assist in the combined improvement of thesetraits by selecting only highly heritable and easily measurablephenotypic traits In addition both the correlation and pathcoefficient analyses indicated that pod number per plant andseeds per pod and days to maturity appeared to be the firstorder traits for higher seed yield in soybean and priorityshould be given in selection due to strong associations as wellas high magnitudes of direct effects on seed yield Clusteranalysis using all the nine different traits grouped 27 soybeanmutants and four mother genotypes into five main clustersThese results also confirm that not only the geographicalbackground but also induced mutations significantly con-tribute to creating genetic variations The first four principalcomponents accounted for about 99996 of total variationfor all the morphological traits This study indicated thepresence of high levels of genetic diversity among themutantsfor evaluated characters

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of the paper

Acknowledgment

The financial support obtained from the Research andDevelopment Project of Bangladesh Institute of NuclearAgriculture Bangladesh (BINA) to carry out the researchwork is fully acknowledged

References

[1] T E Carter R L Nelson C H Sneller and Z Cui ldquoGeneticdiversity in soybeanrdquo in Soybeans Improvement Productionand Uses H R Boerma and J E Specht Eds AgronomyMonographs no 16 ASA-CSSA-SSSA Madison Wis USA 3rdedition 2004

[2] SMathur ldquoSoybean wonder legumerdquo Beverage FoodWorld vol31 no 1 pp 61ndash62 2004

[3] SAIC SAARC Agricultural Statistics of 2006-07 SAARC Agri-cultural Information Centre (SAIC) Dhaka Bangladesh 2007

[4] D Kavithamani A Kalamani C Vanniarajan and D UmaldquoDevelopment of new vegetable soybean (Glycinemax LMerill)mutants with high protein and less fibre contentrdquo ElectronicJournal of Plant Breeding vol 1 no 4 pp 1060ndash1065 2010

[5] M C Kharkwal and Q Y Shu ldquoThe role of induced mutationsin world food securitrdquo in Induced Plant Mutations in theGenomics Era Q Y Shu Ed pp 33ndash38 Food and AgricultureOrganization of the United Nations Rome Italy 2009

[6] Q Liang ldquoPrefacerdquo in Induced PlantMutations inGenomics Erap 1 Food and Agriculture Organization of the United States2009

[7] Q Y Shu and P J L Lagoda ldquoMutation techniques for genediscovery and crop improvementrdquo Molecular Plant Breedingvol 2 pp 193ndash195 2007

[8] J R Wilcox G S Premachandra K A Young and VRaboy ldquoIsolation of high seed inorganic P low-phytate soybeanmutantsrdquo Crop Science vol 40 no 6 pp 1601ndash1605 2000

[9] K K Kato and R G Palmer ldquoGenetic identification of a femalepartial-sterile mutant in soybeanrdquo Genome vol 46 no 1 pp128ndash134 2003

[10] B S Ahloowalia M Maluszynski and K Nichterlein ldquoGlobalimpact of mutation-derived varietiesrdquo Euphytica vol 135 no 2pp 187ndash204 2004

[11] M C Kharkwal R N Pandey and S E Pawar ldquoMutationbreeding for crop improvementrdquo in Plant BreedingmdashMendelianto Molecular Approaches H K Jain and M C Kharkwal Edspp 601ndash645 Narosa Publishing House NewDelhi India 2004

[12] B G Zhu and Y R Sun ldquoInheritance of the four-seeded-podtrait in a soybean mutant and marker-assisted selection for thistraitrdquo Plant Breeding vol 125 no 4 pp 405ndash407 2006

[13] I Cervantes-Martinez M Xu L Zhang et al ldquoMolecularmapping of male-sterility loci ms2 and ms9 in soybeanrdquo CropScience vol 47 no 1 pp 374ndash379 2007

[14] D Sandhu J L Alt C W Scherder W R Fehr and M KBhattacharyya ldquoEnhanced oleic acid content in the soybeanmutant M23 is associated with the deletion in the Fad2-1a geneencoding a fatty acid desaturaserdquo Journal of the American OilChemistsrsquo Society vol 84 no 3 pp 229ndash235 2007

[15] F Yuan H Zhao X Ren S Zhu X Fu andQ Shu ldquoGenerationand characterization of two novel low phytate mutations in soy-bean (Glycine max L Merr)rdquoTheoretical and Applied Geneticsvol 115 no 7 pp 945ndash957 2007


[16] M H Khan and S D Tyagi ldquoInduced morphological mutantsin soybean [Glycine max (L) Merrill]rdquo Frontiers of Agriculturein China vol 4 no 2 pp 175ndash180 2010

[17] M L Das A Rahman and M A Malek ldquoTwo early maturingandhigh yielding rapeseed varieties developed through inducedmutationrdquoBangladesh Journal of Botany vol 28 no 1 pp 27ndash331999

[18] M A Malek H A Begum M Begum M A Sattar M RIsmail and M Y Rafii ldquoDevelopment of two high yieldingmutant varieties of mustard [Brassica juncea (L) Czern]through gamma rays irradiationrdquo Australian Journal of CropScience vol 6 no 5 pp 922ndash927 2012

[19] M A Malek M R Ismail F I Monshi M M A Mondal andM N Alam ldquoSelection of promising rapeseed mutants throughmulti-location trialsrdquo Bangladesh Journal of Botany vol 41 no1 pp 111ndash114 2012

[20] S N Bolbhat and K N Dhumal ldquoInduced macromutations inhorsegram [Macrotyloma uniflorum (Lam) Verdc]rdquo LegumeResearch vol 32 no 4 pp 278ndash281 2009

[21] J G Manjaya ldquoGenetic improvement of soybean variety VLS-2 through induced mutationsrdquo in Induced Plant Mutations inGenomics Era pp 106ndash110 Food and Agriculture Organizationof the United States 2009

[22] T Ishige ldquoSummary of the FAOIAEA international sym-posium on induced mutations in plantsrdquo in Induced PlantMutations in Genomics Era T Ishige Ed pp 11ndash12 Food andAgriculture Organization of the United States 2009

[23] H A Al-Jibouri P A Miller and H A Robinson ldquoGenotypicand environment variances and covariance in an upland cottoncross of inter specific originrdquo Agronomy Journal vol 50 pp633ndash636 1958

[24] D R Dewey and K H Lu ldquoA correlation and path coefficientanalysis of component of crested wheatgrass seed productionrdquoAgronomy Journal vol 51 pp 515ndash518 1959

[25] A Appalaswamy and G L K Reddy ldquoGenetic divergence andheterosis studies of mungbean (Vigna radiata (L) Wilczek)rdquoLegume Research vol 21 pp 115ndash118 2004

[26] H Surek and N Beser ldquoSelection for grain yield and yieldcomponents in early generations for temperate ricerdquo PhilippineJournal of Crop Science vol 28 no 3 pp 3ndash15 2003

[27] A S Larik and L S Rajput ldquoEstimation of selection indicesin Brassica juncea L and Brassica napus Lrdquo Pakistan Journal ofBotany vol 32 no 2 pp 323ndash330 2000

[28] A A Ismail M A Khalifa and A K Hamam ldquoGeneticstudies on some yield traits of durum wheatrdquo Asian Journal ofAgricultural Science vol 32 pp 103ndash129 2001

[29] P Kumar and R S Shukla ldquoGenetic analysis for yield andits attributed traits in bread wheat under various situationsrdquoJawaharlal NehruKrishi VishwaVidyalaya Research Journal vol36 pp 95ndash97 2002

[30] M A M Faisal M Ashraf A S Qureshi and A GhafoorldquoAssessment of genetic variability correlation and path analysesfor yield and its components in soybeanrdquo Pakistan Journal ofBotany vol 39 no 2 pp 405ndash413 2007

[31] S AMohammadi BM Prasanna andNN Singh ldquoSequentialpath model for determining interrelationships among grainyield and related characters in maizerdquo Crop Science vol 43 no5 pp 1690ndash1697 2003

[32] A R Biabani and H Pakniyat ldquoEvaluation of seed yield-relatedcharacters in sesame (Sesamum indicum L) using factor andpath analysisrdquo Pakistan Journal of Biological Sciences vol 11 no8 pp 1157ndash1160 2008

[33] S J Kwon W G Ha H G Hwang et al ldquoRelationship betweenheterosis and genetic divergence in ldquoTongilrdquo-type ricerdquo PlantBreeding vol 121 no 6 pp 487ndash492 2002

[34] M SMazidM Y RafiiMMHanafiHA RahimM Shaban-imofrad andMA Latif ldquoAgro-morphological characterizationand assessment of variability heritability genetic advance anddivergence in bacterial blight resistant rice genotypesrdquo SouthAfrican Journal of Botany vol 86 pp 15ndash22 2013

[35] M A Chowdhury B Vandenberg and T Warkentin ldquoCultivaridentification and genetic relationship among selected breedinglines and cultivars in chickpea (Cicer arietinum L)rdquo Euphyticavol 127 no 3 pp 317ndash325 2002

[36] R Din M Y Khan M Akmal et al ldquoLinkage of morphologicalmarkers in Brassicardquo Pakistan Journal of Botany vol 42 no 5pp 2995ndash3000 2010

[37] G W Burton ldquoQuantitative inheritance in grassesrdquo in Proceed-ings of the 6th International Grassland Congress pp 277ndash283Ames Iowa USA 1952

[38] G Burton and D E Vane ldquoEstimating heritability in tallfescue (Festuca arundinacea) from replicated clonal materialrdquoAgronomy Journal vol 45 pp 478ndash481 1953

[39] H W Johonson H F Robinson and R E ComostockldquoGenotypic and phenotypic correlations in soybeans and theirimplication in selectionrdquo Agronomy Journal vol 47 pp 477ndash483 1955

[40] P A Miller J C Williams H P Robinson and R E Com-stock ldquoEstimation of genotypic and environmental variancesand covariances in upland cotton and their implications inselectionrdquo Agronomy Journal vol 50 pp 126ndash131 1958

[41] R K Singh and B D Chudhary Biometrical Methods inQuantitative Genetic Analysis Kalyani New Delhi India 1985

[42] A R Dabholkar Elements of Biometrical Genetics AshokKumar Mittal Concept Publishing New Delhi India 1992

[43] V N Gohil HM Pandya andD RMehta ldquoGenetic variabilityfor seed yield and its component traits in soybeanrdquo AgriculturalScience Digest vol 26 no 1 pp 73ndash74 2006

[44] M Tavaud-Pirra P Sartre R Nelson S Santoni N Texier andP Roumet ldquoGenetic diversity in a soybean collectionrdquo CropScience vol 49 no 3 pp 895ndash902 2009

[45] D K Ojo A O Ajayi and O A Oduwaye ldquoGenetic relation-ships among soybean accessions based on morphological andRAPDs techniquesrdquo Pertanika Journal of Tropical AgriculturalScience vol 35 no 2 pp 237ndash248 2012

[46] M A Malek L Rahman M Y Rafii and M A SalamldquoSelection of a high yielding soybean variety Binasoybean-2from collected germplasmrdquo Journal of Food Agriculture andEnvironment vol 11 no 2 pp 545ndash547 2013

[47] V G Panse ldquoGenetics of quantitative characters in relation toplant breedingrdquo Indian Journal of Genetics and Plant Breedingvol 17 pp 318ndash328 1957

[48] N A Tulmann A Neto and T C Pieixoto ldquoEarly maturingand good yield mutants in soybean (Glycine max (L) Merr) inBrazilrdquoMutation Breeding Newsletter vol 36 p 9 1990

[49] R S Kundi M S Gill T P Singh and P S Phul ldquoRadiationinduced variability for quantitative traits in soybean (Glycinemax (L) Merrill)rdquoCrop Improvement vol 24 pp 231ndash234 1997

[50] S M Hussain P S Bhatnagar and P G Karmakar ldquoRadiationinduced variability for seed longevity of soybean variety NRC-7rdquo Soybean Genetic Newsletter vol 25 p 83 1998

[51] D D Ahire R J Thengane J G Manjaya M George andS V Bhide ldquoInduced mutations in soybean (Glycine max (L)Merrill) Cv MACS 450rdquo Soybean Research vol 3 pp 1ndash8 2005


[52] C R Weber and B R Moorthy ldquoHeritable and non-heritablerelationships and variability of oil content and agronomiccharacters in the F

2generation of soybean crossesrdquo Agronomy

Journal vol 44 pp 202ndash209 1952[53] S C Anand and J H Torrie ldquoHeritability of yield and other

traits and interrelationship among traits in the F3and F

4

generations of three soybean crossesrdquo Crop Science vol 3 pp508ndash511 1963

[54] M Arshad N Ali and A Ghafoor ldquoCharacter correlation andpath coefficient in soybean Glycine max (L) Merrillrdquo PakistanJournal of Botany vol 38 no 1 pp 121ndash130 2006

[55] T Machikowa and P Laosuwan ldquoPath coefficient analysis foryield of early maturing soybeanrdquo Songklanakarin Journal ofScience and Technology vol 33 no 4 pp 365ndash368 2011

[56] H D Voldeng E R Cober D J Hume C Gillard and M JMorrison ldquoFifty-eight years of genetic improvement of short-season soybean cultivars in Canadardquo Crop Science vol 37 no 2pp 428ndash431 1997

[57] T Machikowa A Waranyuwat and P Laosuwan ldquoRelation-ships between seed yield and other characters of differentmaturity types of soybean grown in different environments andlevels of fertilizerrdquo ScienceAsia vol 31 pp 37ndash41 2005

[58] J P Aditya P Bhartiya and A Bhartiya ldquoGenetic variabilityheritability and character association for yield and componentcharacters in soybean (G max (L) Merrill)rdquo Journal of CentralEuropean Agriculture vol 12 no 1 pp 27ndash34 2011

[59] R A Ball R W McNew E D Vories T C Keisling and L CPurcell ldquoPath analyses of population density effects on short-season soybean yieldrdquo Agronomy Journal vol 93 no 1 pp 187ndash195 2001

[60] S Iqbal T Mahmood M Tahira M Ali M Anwar andM Sarwar ldquoPath coefficient analysis in different genotypes ofsoybean (Glycinemax (L)Merril)rdquoPakistan Journal of BiologicalScience vol 6 pp 1085ndash1087 2003

[61] P N Harer and R B Deshmukh ldquoGenetic variability correla-tion and path coefficient analysis in soybean (Glycine max (L)Merrill)rdquo Journal of Oilseeds Research vol 9 no 1 pp 65ndash711992

[62] Z Cui T E Carter Jr J W Burton and R Wells ldquoPhenotypicdiversity of modern Chinese and North American soybeancultivarsrdquo Crop Science vol 41 no 6 pp 1954ndash1967 2001

[63] Z Iqbal M Arshad M Ashraf T Mahmood and A WaheedldquoEvaluation of soybean [Glycine max (L) Merrill] germplasmfor some important morphological traits using multivariateanalysisrdquo Pakistan Journal of Botany vol 40 no 6 pp 2323ndash2328 2008

[64] C Y Yu S W Hu H X Zhao A G Guo and G LSun ldquoGenetic distances revealed by morphological charactersisozymes proteins and RAPD markers and their relationshipswith hybrid performance in oilseed rape (Brassica napus L)rdquoTheoretical and Applied Genetics vol 110 no 3 pp 511ndash5182005

[65] N Abdullah M Y Rafii Yusop M Ithnin G Saleh and M ALatif ldquoGenetic variability of oil palm parental genotypes andperformance of itsprogenies as revealed by molecular markersand quantitative traitsrdquo Comptes Rendus Biologies vol 334 no4 pp 290ndash299 2011

[66] M A Latif M Rafii Yusop M Motiur Rahman and MR Bashar Talukdar ldquoMicrosatellite and minisatellite markersbasedDNAfingerprinting and genetic diversity of blast and ufraresistant genotypesrdquo Comptes Rendus Biologies vol 334 no 4pp 282ndash289 2011

[67] M Y Rafii M Shabanimofrad M W Puteri Edaroyati and MA Latif ldquoAnalysis of the genetic diversity of physic nut Jatrophacurcas L accessions using RAPD markersrdquo Molecular BiologyReports vol 39 no 6 pp 6505ndash6511 2012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


conditionsThese coupled with complete self-fertility imposelimitations on the success of hybridization program [4] As aresult mutation breeding appears to play an important rolein creating genetic variability for improving this importantcrop

Kharkwal and Shu [5] reported that induced mutationbreeding is becoming more powerful and effective in breed-ing crop varieties to play a significant role for improvingworld food security in the coming years and decades Inducedmutations have generated a vast amount of genetic variabilityand are now widely used for the development of genescontrolling important traits and understanding the functionsand mechanisms of actions of these genes in plants [6]Mutation breeding is now playing an important role indeveloping new genetic resources and breakage of unwantedlinkages [7] Using mutation breeding genetic improvementof any yield attributes either qualitative or quantitative traithas been successfully achieved in soybean [8ndash16] and also inother oil crops like rapeseed-mustard [17ndash19] Furthermoremutation breeding requires less time to develop crop cultivarsas compared to the conventional breeding [20 21] The com-mercial utilization of approximately 3000 mutant-inducedandmutant-derived varieties strongly shows the contributionof mutation breeding to generating new germplasm for cropimprovement [22]

The information as well as assessment of genetic vari-ability in the existing germplasm of a particular crop issought as prerequisite [23ndash25] Furthermore heritability ofa plant trait is very important in determining the responseto selection because it implies the extent of transmissibil-ity of traits into next generations [26] In addition highgenetic advance coupled with high heritability estimate offersthe most effective condition for selection for a particulartrait [27]

Increased seed yield is the ultimate goal of the breedersBut seed yield itself is a product of interaction of manycomponent traits which influence yield directly or indirectlySo it is important to see the contribution of each of thetraits in order to give more attention to those having thehighest influence on yield Moreover understanding therelationship between yield and its component traits is ofgreat importance to a breeder for making the best use ofthese relationships in selecting desirable genotypes for yieldimprovement programs [28 29] As correlation alone cannotexplain relationships among the characters therefore thepath coefficient analysis has been used in different cropspecies for complete determination of the impact of theindependent variables on the dependent one and to finddirect and indirect effects [30]Therefore to identify the traitswhich have significant effect on yield for potential use inselection path analysis has beenwidely used in crop breedingprogram [31 32]

This study investigated the morphological variabilityamong 27 soybean mutants along with four mother vari-eties using quantitative morphological traits including yieldattributes For an effective breeding program for crop varietydevelopment through hybridization the analysis of geneticdiversity is one of the useful tools and plays a fundamentalrole in identification of parents [33 34] Moreover better

knowledge on genetic diversity could help to achieve long-term selection gain [35] As a traditional method morpho-logical traits are used to assess genetic divergence and classifyexisting germplasm materials However this technique alow level but powerful taxonomic tool has been utilizedfor the preliminary grouping of germplasm prior to theircharacterization using more precise marker technologiesAccording toDin et al [36] scientific classification of the plantstill relies on morphological traits Moreover this techniqueis easier cost effective and easy to score and requires less timeand finally it does not need any technical knowledge

From four mother genotypes (Sohag BARI Soybean-5Bangladesh Soybean-4 and BAU-S64) Bangladesh Instituteof Nuclear Agriculture (BINA) developed 27 true breedingsoybean mutants using gamma rays from the Co60 gammacell Among those mutants 18 promising mutants showedbetter performance in respect to seed yield per ha alongwith other morphological traits including important yieldattributes than the mother varietiesline In this researchwe evaluated the performances of those mutants along withmothers from January to June 2011 regarding morphologicalparameters and yield traits through the studies of genotypicand phenotypic variability character association and geneticdiversity among these mutants and mothers which have notyet been studied Such information will serve as a usefultool for establishing suitable breeding program for furthersoybean improvement

2 Materials and Methods

21 Experimental Site The experiment was carried out atthe experimental field of Bangladesh Institute of NuclearAgriculture (BINA) Mymensingh during January to June2011 Geographically the place is located at about 24∘751015840north latitude and 90∘501015840 east longitude The soil of theexperimental site is sandy loam having 006 nitrogen 105organic matter 185 ppm available phosphorus 028 meqexchangeable potassium 18 ppm sulphur and 68 pH

22 Plant Materials Thirty-one soybean genotypes wereused as the experimental materials Among the genotypes 27were the true breeding M

6mutants and the other four were

the mother genotypes Sohag Bangladesh Soybean-4 (BDS-4) BARI Soybean-5 and BAU-S64 from which the mutantswere evolved The names of the 27 soybean mutants alongwith their respective mother genotype are listed in Table 1

23 Experimental Design and Setting the Experiment Theexperiment was laid out in a randomized complete blockdesign with three replicates Block-to-block and plot-to-plotdistances were maintained as 125 and 075m respectivelywith a plot size of 40m times 36m and line-to-line distance of30 cm Seedswere sownon 26December 2010 Each entrywasgrown in 12 rows keeping plant-to-plant distance of 8ndash10 cmin rows

24 Intercultural Operations Urea triple super phosphatemuriate of potash and gypsum were used as basal dose













G1205902

p where 1205902












Plantheight (cm)


Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(gm)

Seed yieldper plant

(g)




3 Results
















4 Discussion





Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)










Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













G1205902

p where 1205902












Plantheight (cm)


Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(gm)

Seed yieldper plant

(g)




3 Results
















4 Discussion





Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)










Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







Plantheight (cm)


Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(gm)

Seed yieldper plant

(g)




3 Results
















4 Discussion





Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)










Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology












4 Discussion





Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)










Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)










Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Branches perplant

(number)

Pods perplant

(number)

Seeds perpod

(number)

100-seed wt(g)

Seed yieldper

plant (g)


Days toflowering



Days tomaturity



Plantheight









100-seedwt (g)

G 0012 minus0129P 0004 minus0120

Yield perplant (g)

G 0986lowastlowast

P 0962lowastlowast





plantPods perplant

Seeds perpod

100-seed wt(gm)

Yield perplant















0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


0 4 8 12 16 20 24 28 32

1120

960

800

640

480

320

160D

istan

ce

BAU

-S6

4SB

M-0

2SB

M-1

3SB

M-1

4SB

M-0

6SB

M-1

0SB

M-1

1SB

M-0

9SB

M-1

2SB

M-0

5SB

M-1

7BD

S-4

SBM

-03

SBM

-26

SBM

-20

SBM

-21

SBM

-19

SBM

-22

SBM

-04

SBM

-25

SBM

-16

SBM

-08

SBM

-15

SBM

-24

SBM

-23

SBM

-18

BARI

-S5

SBM

-01

Soha

gSB

M-2

7SB

M-2

8

235

I II III IV V








05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


05 1 15

Component 1

06

12

18

24

Com

pone

nt 2

GI

GIII

GIV

GV

GVI

GVII∙SBM-12

∙SBM-13∙SBM-14

∙SBM-11

∙SBM-28

∙BDS-4

GII

∙BAU-S64

∙SBM-27

∙SBM-10∙SBM-06

∙SBM-04

∙SBM-01

∙SBM-08

∙SBM-18∙SBM-03

∙SBM-05∙SBM-17

∙SBM-22∙SBM-24

∙SBM-23

∙SBM-15


∙SBM-16∙SBM-21

∙SBM-19

∙SBM-02

∙SBM-09


minus3

minus06

minus12

minus18

minus24

∙Sohag

∙BARI-S5









5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



5 Conclusion




Acknowledgment


References


























































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology











































4























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article morphological characterization and

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