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PJAR_33_4_849_857

 

 

 

Research Article

Estimation of Different Genetic Parameters in Various Safflower (Carthamus tinctorius L.) Genotypes under Field Condition

Rao Wali Muhammad1, Hafiz Muhammad Wasif Ali2*, Amir Hamza1, Muhammad Qadir Ahmad2, Abdul Qayyum2, Waqas Malik2 and Etrat Noor2

1PARC Research and Training Station, Pakistan Agriculture Research Council, Multan, Pakistan; 2Department of Plant Breeding and Genetics, FAST, Bahauddin Zakariya University, Multan, Pakistan; 3Arid Zone Research Institute, Pakistan Agriculture Research Council, Bahawalpur, Pakistan.

Abstract | The study was carried out to investigate the genetic estimates regarding various economic traits i.e. days to flowering, plant height, days to maturity, no. of primary branches per plant, no. of secondary branches per plant, pods per plant, 1000-seed weight and yield per plant of 200 safflower accessions. Analysis of variance and principal component analysis were carried out to estimate the extent of variability for studied parameters and to partition the germplasm into various cluster groups on basis of their mean performance. A value of 74% was indicated by the first three PCs, which showed the highest variability between studied parameters. Eigen values >1 contributed variability between the genotypes under field condition. Minimum and maximum heritability estimates ranged between 77.4% - 99.0% for days to flowering and pods per plant, respectively. Genetic advance estimates ranged between 11.99-27.21% for number of primary branches per plant and yield per plant, respectively. However, coefficient of variance ranged between 2.67-19.31% for days to maturity and number of primary branches per plant. High broad sense heritability magnitudes predicted that studied parameters were under influence of additive genetic effects and less affected by environment. Thus, direct selection of accessions on basis of studied parameters could lead to genetic improvement of the material and these traits could also be helpful for potential improvement of yield in safflower (Carthamus tinctorius).


Received | February 17, 2020; Accepted | September 18, 2020; Published | October 17, 2020

*Correspondence | Hafiz Muhammad Wasif Ali, Department of Plant Breeding and Genetics, FAST, Bahauddin Zakariya University, Multan, Pakistan; Email: wasifdogar@gmail.com

Citation | Muhammad, R.W., H.M.W. Ali, A. Hamza, M.Q. Ahmad, A. Qayyum, W. Malik and E. Noor. 2020. Estimation of different genetic parameters in various safflower (Carthamus tinctorius L.) genotypes under field condition. Pakistan Journal of Agricultural Research, 33(4): 849-857.

DOI | http://dx.doi.org/10.17582/journal.pjar/2020/33.4.849.857

Keywords | Safflower, Genetic correlation, Heritability, Phenotypic correlation, PCA


Introduction

Safflower is famous oil seed crop of ancient world and belongs to compositae or Asteraceae family. It is a good source of high-quality oil mostly used for industrial, ornamental, biofuel and food purposes (Sehgal et al., 2009; Canavar et al., 2014). It is a multipurpose crop, mostly cultivated as cut flower, medicinal plant, vegetable crop, fodder crop, dye and oil extracting source for paint industry (Emongor, 2010; Emongor et al., 2015).

Safflower oil is rich source of vitamin ‘E’, polyunsaturated (linoleic acid) and monounsaturated (oleic acid) fatty acids, which are helpful in lowering blood cholesterol level (Baydar and Turgot, 1999; Arslan et al., 2003). Percent concentration of linoleum acid (70-87%) and oleic acid (11-87%) is much high as compared to olive oil, peanut, soybean, cotton seed and corn oil (Reza et al., 2013). Oil is also used in preparation of soft margarines and salad oil (Conge et al., 2007). Safflower seeds are rich in vitamins, minerals and tocopherols (Velasco et al., 2005). Petals of flower are used in manufacturing of dyes, food color and medicines (Istanbulluoglu, 2009; Emongor, 2010).

The crop was mainly cultivated in arid and semi-arid regions of the world with low irrigation, low fertilizer input and on marginal lands (Hojati et al., 2011). In past, safflower was cultivated on limited area, as minor crop (Canavar et al., 2014). Now, the scenario has changed. Efforts have been made to raise the cultivated area and productivity of the crop all over the world. Naturally, safflower is a temperate zone crop, but has capability of bearing temperature ranges from -7 to 40 oC with zero frost injury during vegetative and flowering growth periods. Crop is widely grown in more than 60 countries of the world, being resistance to many abiotic stresses. India, China, USA, Ethiopia, Kenya, Mexico, Argentina, Australia, Canada, Italy, Spain, Turkey, Iraq, Syria, Kazakhstan, Iran, Uzbekistan, Morocco, Israel, Russia and Pakistan are the commercial growers of the Safflower in the world (Emongor and Oagile, 2017).

Cropping system of Pakistan is deficit in space for cultivation of both conventional and non-conventional oil seed crops and these are considered as minor crops. However, to meet the requirement of vegetable oil for humans, animals and industry, rearing of conventional oil seed crops over limited area is not fruitful. So, it is a dire need of the time to motivate the growers for sowing of non-conventional oil seed crops in Pakistan. Northern areas of Sindh and Baluchistan are suitable for cultivation of safflower as an oil seed crop. While, in Punjab and KPK provinces, arid and semi-arid regions have favorable environmental conditions for safflower production (Amjad, 2014). Pakistan expends a huge amount around US$ 1.5 billion to buy in the edible oil during 2018-19. The loss of foreign exchange reserves is much less than FY 2017-18, in which about US$ 3.0 billion were spent on import of edible oil. It is need of the time to enhance the cultivated area of the non-conventional oilseed crops like safflower and sunflower in Pakistan to meet the demand of annual vegetable oil of the country (Anonymous, 2018-19).

Present study was designed to investigate the importance of yield and its contributing parameters of a plant and to determine the high potential yielding germplasm based on different agronomic parameters by collecting and screening the national and international diverse genetic material. The selected genotypes may be included in further breeding programs enabling to help the safflower plant breeders to maintain and improve the genetic constitutions of the germplasm.

 

Materials and Methods

The genetic material was comprised of two hundred accessions (Table 1). Germplasm was collected from Institute of Agricultural Biotechnology and Genetic Resources (IABGR), NARC, Islamabad. Genetic material was evaluated for various yield contributing parameters during 2016-17 at experimental area of PARC Research and Training Station, Faculty of Agriculture, Bahauddin Zakariya University, Multan. Randomized Complete Block Design was implemented along with three replications by maintaining 15-20 cm interplant distance and row to row distance was 40-45cm. Balode et al. (2012) and Shinwari et al. (2014) screened 155 and 122 accessions of safflower for various screening purposes. All cultural practices were done as per requirement. Ten randomly chosen plants from each genotype were used to record data of the following parameters; days to flowering (DF), plant height (PH), pods per plant (PPP), no. of primary branches per plant (PBP), no. of secondary branches per plant (SBP), thousand grain weight (TGW), yield per plant (YPP) and days to maturity (DM).

Recorded data was put to estimate the analysis of variance (Steel et al., 1997) to check the existence of significant genetic variability. Heritability (h²) in the broad sense and genetic advance for all parameters were estimated according to the formulae as described by Allard (1960) and Falconer (1981), respectively. Principal component analysis was performed by using XLSTAT 2014.

 

Results and Discussion

Analysis of variance with genetic advance and heritability for 200 lines revealed valuable differences for all the traits under study (Table 2). Magnitude of genetic advance among studied parameters ranged between 11.99-27.21% for PBP and YPP, respectively. Whereas, coefficient of variance (CV) ranged from 2.67 to 19.71% for DM and PBP, respectively. Estimation of heritability ranged between 77.4% to 99.0% for DF and PPP in observed traits (Table 2).

 

Table 1: List of safflower germplasm.

Sr. No.

Accessions

Genus

Species

Origin

Sr. No.

Accessions

Genus

Species

Origin

1

016173

Carthamus

tinctorius

India

101

016327

Carthamus

tinctorius

Afghanistan

2

016186

Carthamus

tinctorius

India

102

016329

Carthamus

tinctorius

Afghanistan

3

016188

Carthamus

tinctorius

India

103

016331

Carthamus

tinctorius

Afghanistan

4

016189

Carthamus

tinctorius

India

104

016333

Carthamus

tinctorius

Afghanistan

5

016190

Carthamus

tinctorius

India

105

016334

Carthamus

tinctorius

Afghanistan

6

016191

Carthamus

tinctorius

India

106

016335

Carthamus

tinctorius

Afghanistan

7

016192

Carthamus

tinctorius

India

107

016337

Carthamus

tinctorius

Afghanistan

8

016193

Carthamus

tinctorius

India

108

016338

Carthamus

tinctorius

Afghanistan

9

016194

Carthamus

tinctorius

India

109

016341

Carthamus

tinctorius

Iran

10

016195

Carthamus

tinctorius

Turkey

110

016342

Carthamus

tinctorius

Iran

11

016199

Carthamus

tinctorius

Kenya

111

016343

Carthamus

tinctorius

Iran

12

016200

Carthamus

tinctorius

Turkey

112

016345

Carthamus

tinctorius

Iran

13

016201

Carthamus

tinctorius

India

113

016346

Carthamus

tinctorius

Iran

14

016202

Carthamus

tinctorius

Afghanistan

114

016347

Carthamus

tinctorius

Ethiopia

15

016203

Carthamus

tinctorius

Afghanistan

115

016349

Carthamus

tinctorius

Portugal

16

016204

Carthamus

tinctorius

Iran

116

016351

Carthamus

tinctorius

Portugal

17

016205

Carthamus

tinctorius

Ethiopia

117

016353

Carthamus

tinctorius

Portugal

18

016206

Carthamus

tinctorius

Iran

118

016354

Carthamus

tinctorius

Portugal

19

016207

Carthamus

tinctorius

Australia

119

016356

Carthamus

tinctorius

Pakistan

20

016209

Carthamus

tinctorius

Morocco

120

016357

Carthamus

tinctorius

Pakistan

21

016210

Carthamus

tinctorius

Morocco

121

016358

Carthamus

tinctorius

Pakistan

22

016211

Carthamus

tinctorius

Spain

122

016359

Carthamus

tinctorius

Pakistan

23

016216

Carthamus

tinctorius

India

123

016360

Carthamus

tinctorius

India

24

016217

Carthamus

tinctorius

India

124

016361

Carthamus

tinctorius

India

25

016218

Carthamus

tinctorius

India

125

016362

Carthamus

tinctorius

India

26

016220

Carthamus

tinctorius

Pakistan

126

016364

Carthamus

tinctorius

India

27

016225

Carthamus

tinctorius

India

127

016365

Carthamus

tinctorius

India

28

016229

Carthamus

tinctorius

India

128

016366

Carthamus

tinctorius

India

29

016230

Carthamus

tinctorius

India

129

016367

Carthamus

tinctorius

India

30

016231

Carthamus

tinctorius

India

130

016368

Carthamus

tinctorius

India

31

016233

Carthamus

tinctorius

India

131

016369

Carthamus

tinctorius

India

32

016234

Carthamus

tinctorius

India

132

016373

Carthamus

tinctorius

India

33

016235

Carthamus

tinctorius

India

133

016374

Carthamus

tinctorius

Australia

34

016236

Carthamus

tinctorius

India

134

016375

Carthamus

tinctorius

Australia

35

016237

Carthamus

tinctorius

India

135

016377

Carthamus

tinctorius

Australia

36

016238

Carthamus

tinctorius

India

136

016379

Carthamus

tinctorius

Australia

37

016239

Carthamus

tinctorius

India

137

016381

Carthamus

tinctorius

Afghanistan

38

016240

Carthamus

tinctorius

India

138

016383

Carthamus

tinctorius

Ethiopia

39

016241

Carthamus

tinctorius

India

139

016386

Carthamus

tinctorius

Egypt

40

016242

Carthamus

tinctorius

India

140

016387

Carthamus

tinctorius

India

41

016243

Carthamus

tinctorius

India

141

016390

Carthamus

tinctorius

India

42

016245

Carthamus

tinctorius

India

142

016391

Carthamus

tinctorius

India

43

016246

Carthamus

tinctorius

India

143

016392

Carthamus

tinctorius

India

44

016247

Carthamus

tinctorius

India

144

016393

Carthamus

tinctorius

India

45

016249

Carthamus

tinctorius

India

145

016396

Carthamus

tinctorius

India

46

016250

Carthamus

tinctorius

India

146

016397

Carthamus

tinctorius

India

47

016252

Carthamus

tinctorius

India

147

016398

Carthamus

tinctorius

India

48

016253

Carthamus

tinctorius

India

148

016402

Carthamus

tinctorius

Israel

49

016254

Carthamus

tinctorius

India

149

016407

Carthamus

tinctorius

Iran

50

016259

Carthamus

tinctorius

Iran

150

016408

Carthamus

tinctorius

Iran

51

016260

Carthamus

tinctorius

Egypt

151

016409

Carthamus

tinctorius

Iran

52

016261

Carthamus

tinctorius

Egypt

152

016410

Carthamus

tinctorius

Iran

53

016262

Carthamus

tinctorius

Egypt

153

016411

Carthamus

tinctorius

Iran

54

016264

Carthamus

tinctorius

Egypt

154

016412

Carthamus

tinctorius

Iran

55

016265

Carthamus

tinctorius

Pakistan

155

016413

Carthamus

tinctorius

Iran

56

016266

Carthamus

tinctorius

Pakistan

156

016414

Carthamus

tinctorius

Iran

57

016267

Carthamus

tinctorius

Pakistan

157

016415

Carthamus

tinctorius

Iran

58

016268

Carthamus

tinctorius

Pakistan

158

016416

Carthamus

tinctorius

Iran

59

016269

Carthamus

tinctorius

Pakistan

159

016419

Carthamus

tinctorius

Iran

60

016270

Carthamus

tinctorius

Pakistan

160

016420

Carthamus

tinctorius

Iran

61

016271

Carthamus

tinctorius

Egypt

161

016421

Carthamus

tinctorius

Iran

62

016272

Carthamus

tinctorius

Egypt

162

016423

Carthamus

tinctorius

Iran

63

016273

Carthamus

tinctorius

Egypt

163

016425

Carthamus

tinctorius

Iran

64

016274

Carthamus

tinctorius

Egypt

164

016426

Carthamus

tinctorius

Turkey

65

016276

Carthamus

tinctorius

Egypt

165

016428

Carthamus

tinctorius

Afghanistan

66

016278

Carthamus

tinctorius

India

166

016430

Carthamus

tinctorius

Afghanistan

67

016279

Carthamus

tinctorius

Egypt

167

016431

Carthamus

tinctorius

Afghanistan

68

016280

Carthamus

tinctorius

Egypt

168

016432

Carthamus

tinctorius

India

69

016281

Carthamus

tinctorius

Iran

169

016434

Carthamus

tinctorius

India

70

016283

Carthamus

tinctorius

Iran

170

016435

Carthamus

tinctorius

India

71

016284

Carthamus

tinctorius

Iran

171

016436

Carthamus

tinctorius

India

72

016285

Carthamus

tinctorius

Iran

172

016438

Carthamus

tinctorius

India

73

016287

Carthamus

tinctorius

Iran

173

016439

Carthamus

tinctorius

India

74

016288

Carthamus

tinctorius

Iran

174

016441

Carthamus

tinctorius

Sudan

75

016289

Carthamus

tinctorius

Iran

175

016442

Carthamus

tinctorius

Sudan

76

016290

Carthamus

tinctorius

Iran

176

016443

Carthamus

tinctorius

Sudan

77

016291

Carthamus

tinctorius

Iran

177

016446

Carthamus

tinctorius

Russia

78

016292

Carthamus

tinctorius

Iran

178

016447

Carthamus

tinctorius

Egypt

79

016293

Carthamus

tinctorius

Iran

179

016451

Carthamus

tinctorius

Egypt

80

016295

Carthamus

tinctorius

Iran

180

016453

Carthamus

tinctorius

Egypt

81

016296

Carthamus

tinctorius

Iran

181

016458

Carthamus

tinctorius

India

82

016297

Carthamus

tinctorius

Iran

182

016459

Carthamus

tinctorius

India

83

016298

Carthamus

tinctorius

Iran

183

016460

Carthamus

tinctorius

India

84

016299

Carthamus

tinctorius

Iran

184

016464

Carthamus

tinctorius

India

85

016301

Carthamus

tinctorius

Iran

185

016465

Carthamus

tinctorius

India

86

016303

Carthamus

tinctorius

Iran

186

016466

Carthamus

tinctorius

India

87

016304

Carthamus

tinctorius

Iran

187

016467

Carthamus

tinctorius

India

88

016306

Carthamus

tinctorius

Iran

188

016469

Carthamus

tinctorius

India

89

016308

Carthamus

tinctorius

Iran

189

016470

Carthamus

tinctorius

Turkey

90

016310

Carthamus

tinctorius

Iran

190

016471

Carthamus

tinctorius

Iran

91

016312

Carthamus

tinctorius

Turkey

191

016474

Carthamus

tinctorius

Iran

92

016313

Carthamus

tinctorius

Turkey

192

016478

Carthamus

tinctorius

Iran

93

016314

Carthamus

tinctorius

Turkey

193

016479

Carthamus

tinctorius

Iran

94

016316

Carthamus

tinctorius

Turkey

194

016482

Carthamus

tinctorius

Iran

95

016317

Carthamus

tinctorius

Turkey

195

016483

Carthamus

tinctorius

USA

96

016318

Carthamus

tinctorius

Spain

196

016484

Carthamus

tinctorius

USA

97

016320

Carthamus

tinctorius

Germany

197

016489

Carthamus

tinctorius

China

98

016324

Carthamus

tinctorius

Iraq

198

016492

Carthamus

tinctorius

China

99

016325

Carthamus

tinctorius

Iraq

199

016495

Carthamus

tinctorius

China

100

016326

Carthamus

tinctorius

Iraq

200

016501

Carthamus

tinctorius

USA

 

Table 2: Means and analysis of variance (ANOVA) for eight traits among 200 safflower genotypes.

Parameters

MS (R)

MS (V)

MS(E)

Means ± SE

h2 (%)

GA (%)

CV (%)

DF

9.512

50.028

11.294

121.97

77.4

22.22

2.755

PH

8.202

567.962

13.287

104.77

97.7

19.6

3.479

DM

9.052

87.593

16.453

151.70

81.2

17.33

2.673

PPP

1.872

224.181

2.223

38.31

99.0

16.51

3.892

PBP

0.140

4.902

0.246

2.57

95.0

11.99

19.31

SBP

1.415

74.672

1.1369

8.54

98.5

23.51

12.48

TGW

5.612

157.537

4.012

37.78

97.5

14.73

5.301

YPP

15.247

1248.901

20.240

83.82

98.4

27.21

5.367

DF: days to flowering; PH: plant height; DM: days to maturity; PPP: pods per plant; PBP: number of primary branches per plant; SBP: number of secondary branches per plant; TGW: thousand grain weight; YPP: yield per plant; MS(R): mean square of replications; MS(V): mean square of varieties; MS(E): mean square of errors; h2: heritability; GA: genetic advance; CV: coefficient of variability.

 

Table 3: Genotypic (rg) and phenotypic (rp) correlation between various morpho-physiological traits of safflower.

Traits

DF

PH

DM

PPP

PBP

SBP

TGW

YPP

DF

G

1.000

-0.0279*

0.9275*

0.0325*

-0.0992*

-0.0371*

0.0679*

-0.0168*

P

1.000

-0.0246

0.7489**

0.0271

-0.0770

-0.0300

0.0561

-0.0619

PH

G

1.0000

-0.1590*

0.6102*

-0.783*

-0.0690*

0.1032*

0.6551*

P

1.0000

-0.1399

0.5999*

-0.0784*

-0.0677*

0.0993

0.6423**

DM

G

1.0000

-0.0989*

-0.0055

-0.0085

-0.0670*

-0.1165*

P

1.0000

-0.0847

-0.0019

-0.0047

-0.0582

-0.1051

PPP

G

1.0000

-0.0005

-0.0694*

0.1099*

0.7520*

P

1.0000

0.0014

-0.0670

0.1082

0.7437**

PBP

G

1.0000

0.7807*

-0.1782*

-0.0826*

P

1.0000

0.7541**

-0.1666

-0.0791

SBP

G

1.0000

-0.1445*

-0.1388

P

1.0000

-0.1432

-0.1367

TGW

G

1.0000

0.1231*

P

1.0000

0.1222

YPP

G

1.0000

P

1.0000

For abbreviations, See Table 2; *Significant; ** Highly significant.

 

Genotypic and phenotypic correlations were observed among eight (8) morpho-physiological parameters in safflower. Days to flowering (DF) showed negatively significant interrelationship with PH, PBP, SBP and YPP, while positive significant interrelationship was observed among DF, DM, PPP and TGW. Plant height (PH) showed negatively significant genotypic and phenotypic interrelations with DM, PBP and SBP. However, there was positive and significant genotypic interrelation among PH, PPP, TGW and YPP. Negatively significant genotypic and phenotypic interrelations were present among days to maturity and other yield related parameters. Pods per plant (PPP) had negatively significant interrelation with PBP and SBP, while positively significant correlation was observed among PPP, TGW and YPP. Results revealed the existence of positive correlation among PBP and SBP, while both traits showed negative interrelation with TGW and YPP. However, TGW showed positive interrelation with YPP.

Principal component analysis

Results of PCA showed that on basis of eigen value, data is considered up to three principal components. It was noted that 74 % variability of the total variation lies in three PC’s. First PC has 30.6 % variability, while PC2 and PC3 has 22.9 % and 20.5 % variability of the total existing variation of the data. These PC’s are orthogonal with each other. In first PC, four parameters i.e. PH, PPP, TGW and YPP were correlated with each other in negative direction, while remaining traits showed positive correlation with each other. In PC2, five yield related parameters viz; PH, PPP, PBP, SBP and YPP were negatively correlated with each other, while remaining traits were positively interrelated with each other. Under PC3, only one parameter (TGW) showed negative correlation with other parameters, which is a valuable yield index. The component with eigenvalues > 1 contribut­ed 74% (Table 4) of the total variability among accessions of safflower for various morph-physiological traits. Two hundred accessions of safflower have been divided into eight cluster groups on the basis of their performance for studied parameters. Cluster 7 contain maximal (41) number of accessions, while cluster number 5 and 6 consist of lowest (15) number of accessions each. Cluster number 1 and 3 consist of 34 and 28 accessions respectively. While cluster number 4, 2 and 8 comprised of 26, 21 and 20 accessions respectively (Table 5).

Results of ANOVA revealed the presence of high genetic variability among all accessions of safflower for studied parameters and proved that data was fit for further statistical analysis. Existence of variation is useful for various genetic analysis and ultimately helpful in selection and improvement of crop (Kose et al., 2018). The basic purpose of correlation studies was to observe a common re­lationship between different characters and their level of the involvement to the yield (Panhwar et al., 2003). Plant parameters viz., DF, DM, PBP and SBP showed negative impact on yield and grain yield reduces with increase in number of DF, DM, PBP and SBP. However, plant attributes like PH, PPP and TGW had positive effect on yield of the crop as yield increases with increase in magnitude of these traits. It was observed that grain yield had significant interaction with PPP, PH and TGW (Ahmadzadeh, 2013; Kose et al., 2018). To intensify the crop yield, magnitude of plant attributes like PPP and TGW must be increased because these parameters had direct influence on YPP (Elfadl et al., 2010; Eslam et al., 2010; Safavi, 2011). Based upon the results of correlation, it is suggested that genotypes having higher magnitude of PH, branches per plant and grain weight will be selected for future breeding program to enhance yield (Kose et al., 2018). If value of ‘r’ (correlation) is near to 1, interrelation among two variables is positive and traits are highly dependent on each other. If ‘r’ is nearly zero among different variables no interdependency is observed, while ‘r’ with negative sign among variables proved negative relation among variables (Katar, 2013).

 

Table 4: Principal Component Analysis (PCA) of germplasm.

PC 1

PC 2

PC 3

Eigen values

2.448

1.832

1.640

Proportion of variance

30.600

22.903

20.497

Cumulative variance

30.600

53.503

74.000

Eigen vectors

Variables

PC 1

PC 2

PC 3

DF

0.108012

0.674046

0.644409

PH

-0.808049

-0.111115

0.199538

DM

0.255235

0.634851

0.640596

PPP

-0.831564

-0.104359

0.308155

PBP

0.287557

-0.685164

0.554232

SBP

0.331518

-0.656911

0.549469

TGW

-0.263355

0.214418

-0.143479

YPP

-0.874539

-0.068448

0.222903

 

Estimation of heritability is a promising indication about the transmittance of various parameters from parents to progeny. Appraisal of heritability is very helpful in selection of suitable genotypes/ accessions among various environmental and field conditions from a heterogeneous breeding population (Tahernezhad et al., 2018). Based upon percent (%) magnitude, heritability could be classified into low (0-30%), medium (30-60%) and high (>60%) (Reddy et al., 2013). Results revealed that magnitude of heritability was greater than 70% for all studied parameters (Table 2), indicating high transmittance percentage. It is also predicted that these parameters

 

Table 5: Cluster-wise accession membership.

Cluster No.

Accessions

I

A5, A9, A23, A24, A29, A35, A50, A52, A53, A54, A55, A78, A79, A86, A90, A96, A99, A100, A102, A105, A108, A109, A114, A118, A121, A122, A131, A137, A141, A150, A162, A163, A185, A189

II

A33, A41, A48, A58, A59, A73, A85, A97, A101, A106, A110, A119, A142, A143, A144, A164, A172, A175, A177, A182, A190

III

A14, A15, A16, A17, A28, A32, A37, A43, A47, A56, A57, A69, A70, A74, A84, A91, A92, A94, A98, A104, A111, A112, A115, A117, A126, A128, A132, A138

IV

A2, A3, A10, A13, A27, A36, A38, A40, A60, A63, A64, A65, A83, A87, A93, A95, A103, A107, A113, A124, A129, A133, A134, A135, A136, A139

V

A7, A8, A11, A18, A20, A21, A26, A34, A42, A49, A62, A68, A71, A72, A82

VI

A1, A6, A12, A19, A22, A25, A30, A31, A39, A61, A66, A67, A123, A127, A140

VII

A4, A44, A45, A46, A51, A75, A76, A77, A80, A81, A88, A89, A116, A120, A125, A130, A145, A148, A149, A152, A153, A154, A155, A156, A158, A159, A160, A161, A165, A166, A169, A173, A174, A178, A179, A186, A191, A193, A196, A199, A200

VIII

A146, A147, A151, A157, A167, A168, A170, A171, A176, A180, A181, A183, A184, A187, A188, A192, A194, A195, A197, A198

 

are less influenced by environment and highly suitable for early selection due to presence of additive nature of genetic inheritance. These results are in accordance with the findings of Arslan (2007), Sirisha (2009) and Elfadl et al. (2010).

PCA is a multivariate analysis technique, which is usually used to develop coordinated axis of an orthogonal and to estimate the relative importance of classified variables. This technique is characterized by conversion of complex plant data analysis into simple form (Slavkovic et al., 2004). Maximum variation was observed among first three PC’s and it contained 74% of the total variability. Ahmadzadeh (2013) reported that 72.92 percent of the total variation was found in first three PC’s, while Kose et al. (2018) found 65.4 percent of total variation in first two PC’s. If the eigen values are greater than one, then diversity is not found in the traits and values are less than one, then diversity is found in all the characters. Negative eigen values were ignored because these values have no importance while positive values considered diversity is found in the characters. Cluster analysis classified the germplasm into eight groups on basis of similarity in their mean performance for observed parameters. Cluster analysis is a helpful technique to categorize the germplasm into well-defined subgroups and groups depending upon resemblance and deviation among mean performance of observed parameters (Biljana and Onjia, 2007).

 

Conclusions and Recommendations

Significant genetic variability was observed among germplasm regarding yield and its related attributes. The highest estimates of genetic advance and heritability for all traits showed their significance in selection of particular parents to be used in future breeding program. Higher heritability magnitude showed the predominance of additive genetic effects for studies traits, due to which direct and early selection is useful. From the results of correlation, it was concluded that three parameters viz., plant height, pods per plant and thousand grain weight had positively significant genotypic interrelationship with yield per plant. So, genotypes having higher value of PH, PPP and TGW can be selected to develop high yielding safflower varieties for Pakistan.

 

Acknowledgments

This study supported by the PARC, Government of Pakistan. We pay our gratitude to Dr. Yusuf Zafar (T.I), worthy chairman PARC and IABGR (Institute), NARC, Islamabad for supplying seeds of safflower accessions. We are thankful to Dr. Sohail (Department of Statistics), who provided computer programs for statistical analysis.

 

Novelty Statement

Safflower is a good source of high-quality oil and mostly used for food, industry, ornamental purpose and as a biofuel. Pakistan expands a huge amount of foreign exchange on import of oil. By evaluation of available germplasm in the country, we will be able to develop high yielding safflower varieties with less input resources. This will not only helpful to meet the oil requirement of the country but also to improve health of the people and save the foreign exchange.

 

Author’s Contributions

Rao Wali Muhammad and Abdul Qayyum planed the proposal and conducted the whole study. Methodology and analysis were carried out by Muhammad Qadir Ahmad and Waqas Malik. Hafiz Muhammad Wasif Ali completed the manuscript write up. Overall assessment of write up was done by Amir Hamza. Statistical analysis was accomplished by Etrat Noor.

Conflict of interest

The authors have declared no conflict of interest.

 

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