Submit or Track your Manuscript LOG-IN

Stability Analysis of Bread Wheat Lines using Regression Models

SJA_37_4_1450-1457

Research Article

Stability Analysis of Bread Wheat Lines using Regression Models

Hafsa Naheed* and Hidayat-Ur-Rahman

Department of Plant Breeding and Genetics, Faculty of Crop Production Sciences, The University of Agriculture, Peshawar, Pakistan.

Abstract | Stability of performance is one of the most important characteristics of any variety. To evaluate stability of 40 bread wheat lines (30 introduced lines and 5 check cultivars), a multi-environment experiment was conducted at three locations for two years; The University of Agriculture, Peshawar; Agriculture Research Station, Baffa, Mansehra; Agriculture Research Station, Buner; and at one location for one year; Agriculture Research Station , Kohat, during 2016-17 and 2017-18. Data were recorded on tillers m-2, grains spike-1 and kernel weight. Analysis of variance indicated that the main environmental effects, genotypic effects as well as interaction effects were significant for all the traits studied, indicating that the performance of most of the genotypes was not the same across the environments. Various stability parameters; Eberhart and Russell, Perkin and Jinks and Finlay and Wilkinsons models were used to determine stability. Considering the Eberhart and Russell’s approach G.27, G.34, G.11, G.17, Siran and G.35 for tillers m-2 and G.22, G.23, G.31 for kernel weight had the bi (regression coefficient) values near to unity and had higher (coefficient of determination) Ri2. Based on the stability measure of Freeman and Perkin’s genotypes G.20, G.6 and G.29 for tillers m-2, G.11, G.10, G.34 for grains spike-1 and G.11, G.24, G.29, G.32 and G.21 for kernel weight, were having bi values almost equal to one while the Ri2 value were higher . Based on Finlay and Wilkinson’s model for tillers m-2 G.4, G.33 and G.5, for grains spike-1 G.30, G.24, G.2 and G.15, while for the weight of kernel G.19, G.21, G.28, G.25, G.11, G.29, G.32 and G.10 had bi value close to one. Near to unity bi value and higher Ri2 indicate that most of these genotypes performance is consistent for the mentioned traits under the tested environments.


Received | June 29, 2021; Accepted | September 07, 2021; Published | October 07, 2021

*Correspondence | Hafsa Naheed, Department of Plant Breeding and Genetics, Faculty of Crop Production Sciences, The University of Agriculture, Peshawar, Pakistan; Email: hafsaa.naheed@gmail.com

Citation | Naheed, H. and H.U. Rahman. 2021. Stability analysis of bread wheat lines using regression models. Sarhad Journal of Agriculture, 37(4): 1450-1457.

DOI | https://dx.doi.org/10.17582/journal.sja/2021/37.4.1450.1457

Keywords | Wheat, Stability, Regression models, Multi environment trials, G×E



Introduction

In Pakistan, bread wheat is the most consumed cereal crop. It is an essential diet component and constitutes around 60% of the daily diet intake. On average 135 kg of wheat is consumed per person per year in Pakistan. According to FAO’s estimates, majority (38%) of the world’s food shortage and mal-nourished people are living in South Asia (FAO et al., 2015). Food security in numerous countries including Pakistan largely relies on wheat; it is the most produced edible grain, which fulfills a major part of human nutritional requirements, and in some cases, it provides around one half of the human’s food calories. To ensure the highly increasing agricultural/food demands of the growing population, an increase in agriculture production can be achieved by increasing yield per unit area from the limited available land.

Pakistan produced 24.35 million tons of wheat from 8.68 million hectares during 2019 (FAOSTAT, 2019). Average wheat yield during 2019 was 2086 kg ha-1 in Pakistan whereas average wheat yield of the world was 3247 kg ha-1. In Pakistan the yield of most of the agricultural crops including bread wheat is comparatively low to the rest of the world; therefore, there is big scope to increase the yield and production of most of crops. One of the reasons of low wheat yield in Pakistan is the diverse agro ecosystems, where climate, soil texture and other environmental conditions vary significantly. The use of non-specific wheat varieties in these diverse agro zones make the situation even worse. Another reason is the inadequacy of improved wheat cultivars or the use of non-registered seeds.

Breeding and testing of genotypes is a complex procedure and it becomes more complicated when the environmental conditions of the target region are diverse (Bondari, 1999). Considerable changes occur in the performance of a genotype when it is grown under different environmental conditions. These changes are usually the result of the varying environmental conditions at any specific site or season and is known as genotype × environment interactions (GEI) (Bassi and Garcia, 2017; Baye et al., 2011). This GEI affect the selection of genotypes and decreases the progress from selection (Sohail et al., 2016). Therefore, in plant breeding programs, genotypes are tested in different environments (Ahmadi et al., 2012; Casanoves et al., 2005). This study was designed to test the exotic genotypes in different environments, to see the effect of environmental changes and the response of the genotypes towards those changes. The genotypes that perform uniformly to some extent regardless of the environmental variations are desirable and these genotypes are considered as stable genotypes. Several different procedures are used to determine stability of genotypes. The simplest and most common among those are the procedures based on regression method (Dia, 2017). The main objective of the present study is to evaluate stability of wheat lines across different environments using three different stability parameters.

Materials and Methods

This study was conducted to evaluate the stability of 40 bread lines including five check cultivars (Atta Habib, CSA, Ghanimat, Morocco and Siran). The study was conducted at four different locations during 2016-17 wheat growing season and at three locations during 2017-18 wheat growing season. The experimental locations were Research Farm, The University of Agriculture, Peshawar, Agriculture Research Station Baffa, Mansehra, Agriculture Research Station, Amnawar, Buner for two seasons and Barani Agriculture Research Station, Jarma, Kohat for one growing season. RCB design with three replications was used for all the experimental trials.

Each plot had four rows of two-meter length, distance between the rows was kept 0.3 m (total plot area was 2.4 m2) and the seed rate was 28g per plot (120kg/ha). Data on tillers m-2, grains spike-1 and kernel weight were recorded on randomly selected plants from each experimental unit.

 

Data collected from separate trials was analyzed as combined over the environments using the following ANOVA outline;

Source of variation

DF

Mean Squares

F.Test

Environments (E)

e-1

EnMS

EnMS/ R(E)MS

Replications (Environments)

e(r-1)

R(E)MS

Genotypes (G)

g-1

GMS

GMS/GEMS

G × E

(g-1)(e-1)

GEMS

GEMS/EMS

Pooled Error

e(g-1)(r-1)

EMS

Total

(erg)-1

 

Upon significant GEI, the stability of the genotypes was analyzed using stability models suggested by Eberhart and Russell (1966), Freeman and Perkin’s (1971) and Finlay and Wilkinson (1963).

Results and Discussion

Tillers m-2

Significant differences were revealed by pooled analysis of variance for both the main effects, genotypes and environments as well as for interaction effects (Table 1). This shows that the performances of genotypes as well as the environments were different; the genotypes also had differential response to the changes in the environmental conditions. Similar results were reported by Ajmal et al. (2009). Considering Eberhart and Russell’s model of analysis no genotype had bi = 1, however, G.27, G.34, G.11, G.17, Siran and G.35 had bi value near to one (0.963, 0.971, 1.013, 1.023, 1.035 and 1.039, respectively); showing that most of these genotypes almost produced similar number of tillers under all the environments. For Atta Habib higher bi values were noted, G.5, G.33 and G.4, whereas, lower bi values were detected for G.16, G.15 and G.10. Lower values of bi indicate that these genotypes show more resistance to unfavorable environments (Yaghotipoor et al., 2017). Based on the Freeman and Perkin stability analysis approach; Morocco, G.20, G.6 and G.29 showed regression coefficient values of 0.966, 1.009, 1.011 and 1.049, respectively; these values are near unity and are coupled with higher values of coefficient of determination indicating good fit of the model, which suggests that the genotypes are stable and their tillering performance has not been significantly altered with changes in the environment. (Polat et al., 2016). G.5 had the maximum value of bi, followed by Atta Habib and G.4, showing comparatively larger change in tillering densities across the different environments, these can be recommended for favorable environments (Bassi and Garcia 2017; Ali et al., 2012). G.16 demonstrated minimum value of bi, which was followed by G.7, G.23 and G.15. Based on the Finlay and Wilkinson’s approach the regression coefficients for tillers m-2 of most of the genotypes was less than one, with only four genotypes having bi values more than one. The highest bi value was of Atta Habib had (1.192). Close to unity bi values were observed for Ghanimat (0.966), G.4 (1.064), G.33 (1.072) and G.5 (1.087); these genotypes can be considered phenotypically stable based on tillering capacity across the different environments. The smallest bi value was noted for G.16, followed by G.10, G.15 and G.19 (Table 2). Yaghotipoor et al. (2017) and Jhinjer et al. (2017) suggested that varieties with lower bi values can be recommended for environments with poor growing conditions.

Grains spike-1

Combined analysis of variance based on seven different environments showed highly significant differences (p≤0.01) among genotypes and environments. Similar results of significant differences among genotypes and environments were also reported by Alemu et al. (2018) and Haydar et al. (2018). The interactions between the environments and genotypes likewise were highly significant for grains spike-1 (Table 1) which is similar with the findings of Trakanovas and Ruzagas (2006) and Temasgen et al. (2015). None of the genotypes had bi =1 coupled with higher coefficient of determination based on the Eberhart and Russell’s model. Only three genotypes G.24, G.23 and G.15 had bi values near to unity but they had lower Ri2 values which suggest that the model was not a good fit. Morocco, G.9 and G.12 showed higher values of bi accompanied with higher Ri2 values. Ali et al. (2012) suggested that genotypes with bi value more than unity are more responsive and recommended for better environments. Lowest value of bi was noted for G.18, followed by G.2, Atta Habib and G.3. These genotypes can be regarded as suitable for unfavorable environmental conditions (Thakur et al., 2019; Jhinjer et al., 2017). Based on the Freeman and Perkin’s model of stability, the bi value near to one was observed for G.11, G.10, G.34 and G.14. Ri2 value for these genotypes was observed to be low to medium (0.614, 0.253, 0.468 and 0.744, respectively). For Atta Habib Minimum value of bi was recorded, which was followed by G.3 and G.2. The Maximum value of bi was recorded for Morocco, G.9 and G.17. Based on Finlay and Wilkinson approach of stability, bi values of close to one was recorded for G.30, G.24, G.23 and G.15. The maximum value of bi was noted for G.9, which was followed by Morocco and G.32 while the minimum bi value of was observed for G.18 followed, by G.2 and Atta Habib (Table 3).

 

Table 1: Mean squares of the studied traits as combined over 7 environments planted in Khyber Pakhtunkhwa, Pakistan in 2016-18 growing seasons.

SoV

DF

Tillers

Grains spike-1

Kernel weight

Environments

6

923147***

920***

1631***

Reps(Environ)

13

12546

93

205

Genotypes

39

6904*

334***

433***

G×E

234

4343***

78***

46**

Expt'l Error

507

2169

52

38

Total

799

10122

81

74

 

Kernel weight

Highly significant differences were observed among the environments and the genotypes, as well as the interaction between them (Table 1) these results are in agreement with Alemu at al. (2018) and Ali et al. (2008). The three regression models; Finlay and Wilkinson’s model, Eberhart and Russell’s model and Freeman and Perkins model were used to analyze the stability genotypes based on the values of bi and Ri2. None of the genotypes had a perfect combination of bi = 1 and higher Ri2 values based on Eberhart and Russell’s approach of stability. Nevertheless, G.22, G.23, G.31, G.21, G.25, Siran, G.17, G.32 and G.19 had bi values close to one and the Ri2 values were moderate to high, therefore, these genotypes can be considered stable for kernel weight (Thakur at al., 2019;

 

Table 2: Means and regression coefficients (with R2) as stability measures of productive tillers m-2 of wheat genotypes.

Genotype

Mean

Eberhart & Russell

Freeman & Perkins

Finlay &Wilkinson

Value

Rank D

bi

Rank A

R2

bi

Rank A

R2

bi

Rank A

R2

G.1

254

8

0.839

10

0.9041

0.712

13

0.773

0.626

9

0.889

G.2

205

40

0.944

18

0.8924

0.811

19

0.812

0.685

16

0.905

G.3

209

39

0.887

14

0.9503

0.837

23

0.948

0.646

12

0.946

G.4

276

2

1.374

37

0.8690

1.293

38

0.917

1.064

37

0.927

G.5

292

1

1.411

39

0.8129

1.446

40

0.912

1.087

39

0.877

G.6

245

13

1.045

27

0.8644

1.011

33

0.866

0.769

23

0.841

G.7

230

30

0.861

13

0.9285

0.464

2

0.558

0.647

13

0.889

G.8

223

33

1.045

26

0.9227

0.807

18

0.868

0.772

25

0.922

G.9

254

11

0.838

9

0.7148

0.749

15

0.650

0.629

10

0.733

G.10

224

32

0.669

3

0.7113

0.616

6

0.634

0.467

2

0.772

G.11

228

31

1.013

22

0.8694

0.849

25

0.739

0.731

21

0.905

G.12

241

18

0.828

8

0.9559

0.671

10

0.899

0.632

11

0.926

G.13

245

14

1.147

32

0.9428

0.830

22

0.891

0.856

29

0.910

G.14

231

29

0.943

17

0.9222

0.682

11

0.821

0.704

17

0.889

G.15

215

38

0.668

2

0.6352

0.582

4

0.541

0.469

3

0.583

G.16

232

26

0.538

1

0.8513

0.357

1

0.601

0.396

1

0.869

G.17

271

4

1.023

23

0.9172

0.954

30

0.879

0.802

27

0.899

G.18

220

36

0.924

15

0.9594

0.755

16

0.780

0.678

15

0.951

G.19

235

24

0.710

4

0.7633

0.616

7

0.911

0.538

4

0.826

G.20

232

28

1.141

31

0.8863

1.009

32

0.929

0.862

30

0.935

G.21

242

16

1.141

30

0.9620

0.913

27

0.899

0.865

31

0.944

G.22

242

17

0.804

7

0.9475

0.661

9

0.830

0.600

7

0.927

G.23

233

25

0.767

6

0.8460

0.517

3

0.650

0.568

5

0.845

G.24

241

19

1.077

28

0.9689

0.948

29

0.916

0.817

28

0.943

G.25

240

20

0.853

11

0.6586

0.811

21

0.568

0.613

8

0.685

G.26

232

27

0.858

12

0.8951

0.609

5

0.745

0.649

14

0.841

G.27

240

21

0.963

20

0.9408

0.943

28

0.827

0.725

20

0.928

G.28

254

10

1.242

35

0.8264

1.182

35

0.815

0.950

35

0.725

G.29

236

23

1.219

33

0.9602

1.049

34

0.956

0.928

34

0.960

G.30

254

9

0.932

16

0.8459

0.786

17

0.843

0.739

22

0.842

G.31

215

37

0.766

5

0.9465

0.640

8

0.929

0.588

6

0.912

G.32

222

35

0.950

19

0.9405

0.811

20

0.902

0.714

19

0.898

G.33

245

15

1.381

38

0.9228

1.257

36

0.978

1.072

38

0.936

G.34

222

34

0.971

21

0.9386

0.840

24

0.815

0.710

18

0.920

G.35

247

12

1.039

25

0.9784

0.709

12

0.805

0.782

26

0.985

CSA

259

6

1.140

29

0.9419

0.904

26

0.881

0.871

32

0.913

Moroc

238

22

1.242

34

0.8983

0.966

31

0.763

0.915

33

0.955

AttaH

272

3

1.505

40

0.8867

1.304

39

0.949

1.192

40

0.895

Ghani

256

7

1.267

36

0.9784

1.263

37

0.977

0.966

36

0.963

Siran

259

5

1.035

24

0.9118

0.734

14

0.760

0.770

24

0.944

bi = 1 is stable, Rank A ranking in ascending order, Rank D ranking in descending order

 

Table 3: Means and regression coefficients (with R2) as stability measures of grains spike-1 of wheat genotypes.

Genotype

Mean

Eberhart & Russell

Freeman & Perkins

Finlay &Wilkinson

Value

Rank D

bi

Rank A

R2

bi

Rank A

R2

bi

Rank A

R2

G.1

52.1

10

0.318

8

0.015

-0.420

5

0.048

0.321

8

0.017

G.2

45.6

37

-0.443

3

0.086

-0.492

4

0.141

-0.531

2

0.092

G.3

42.2

40

-0.316

4

0.041

-0.724

2

0.233

-0.349

4

0.040

G.4

51.9

12

1.469

32

0.516

1.195

33

0.483

1.387

27

0.490

G.5

51.4

15

1.236

24

0.772

0.861

26

0.887

1.222

23

0.747

G.6

46.6

33

1.347

27

0.192

0.040

9

0.000

1.751

35

0.271

G.7

56.9

3

-0.119

6

0.006

0.308

14

0.039

-0.135

6

0.009

G.8

46.0

35

1.564

33

0.554

0.824

23

0.179

1.657

34

0.534

G.9

46.7

32

2.773

39

0.834

1.970

39

0.691

3.075

40

0.831

G.10

46.1

34

1.220

23

0.238

0.869

27

0.190

1.430

31

0.233

G.11

49.8

19

1.200

22

0.342

0.826

24

0.431

1.139

21

0.297

G.12

50.1

18

2.174

38

0.924

1.671

37

0.712

2.126

38

0.918

G.13

53.6

6

1.617

34

0.666

1.344

35

0.548

1.587

33

0.699

G.14

47.3

29

1.264

26

0.736

1.009

29

0.712

1.325

25

0.740

G.15

53.1

8

1.039

19

0.362

0.159

11

0.005

1.017

20

0.390

G.16

50.3

17

0.362

9

0.145

1.397

36

0.543

0.323

9

0.112

G.17

51.8

13

2.040

37

0.403

1.895

38

0.508

1.989

36

0.388

G.18

45.6

36

-0.454

2

0.092

-0.177

8

0.017

-0.516

3

0.093

G.19

47.5

28

1.163

21

0.197

0.855

25

0.164

1.344

26

0.217

G.20

54.0

4

1.406

30

0.590

0.396

17

0.052

1.217

22

0.537

G.21

53.1

9

0.192

7

0.015

-0.670

3

0.230

0.079

7

0.003

G.22

50.7

16

1.443

31

0.669

1.204

34

0.542

1.396

29

0.644

G.23

53.9

5

1.010

18

0.371

0.558

20

0.129

1.005

18

0.417

G.24

51.5

14

0.912

16

0.539

0.234

13

0.203

0.955

17

0.583

G.25

48.2

26

1.379

29

0.519

0.626

21

0.176

1.398

30

0.489

G.26

43.9

39

0.574

12

0.097

0.392

16

0.174

0.641

15

0.085

G.27

49.1

21

0.530

10

0.154

0.358

15

0.107

0.577

12

0.174

G.28

47.1

31

0.621

14

0.555

0.180

12

0.023

0.689

16

0.567

G.29

47.1

30

-0.280

5

0.084

-0.266

6

0.126

-0.311

5

0.093

G.30

44.3

38

0.951

17

0.376

0.792

22

0.340

1.010

19

0.343

G.31

48.5

25

0.582

13

0.184

0.491

19

0.234

0.588

13

0.158

G.32

48.6

24

2.005

36

0.549

1.107

31

0.381

2.103

37

0.515

G.33

47.6

27

1.347

28

0.392

1.007

28

0.372

1.391

28

0.363

G.34

48.8

23

1.667

35

0.619

1.022

30

0.436

1.549

32

0.588

G.35

53.3

7

0.561

11

0.589

0.427

18

0.251

0.520

11

0.558

CSA

63.8

1

0.642

15

0.079

-0.195

7

0.009

0.404

10

0.044

Moroc

57.0

2

3.457

40

0.822

1.978

40

0.431

2.926

39

0.778

AttaH

49.6

20

-0.755

1

0.104

-0.993

1

0.244

-0.698

1

0.092

Ghani

49.1

21

1.244

25

0.369

1.144

32

0.284

1.312

24

0.360

Siran

52.1

11

1.057

20

0.468

0.057

10

5.9

0.612

14

0.001

bi = 1 is stable, Rank A ranking in ascending order, Rank D ranking in descending order

 

Table 4: Means and regression coefficients (with R2) as stability measures of kernel weight (mg) of wheat genotypes.

Genotype

Mean §

Eberhart & Russell

Freeman & Perkins

Finlay & Wilkinson

Value

Rank D

bi

Rank A

R2

bi

Rank A

R2

bi

Rank A

R2

G.1

26.05

40

1.60

37

0.72

1.48

35

0.63

2.55

40

0.77

G.2

27.15

39

1.13

27

0.58

0.89

24

0.29

1.87

39

0.65

G.3

46.96

4

-0.22

1

0.06

-0.02

2

0.00

-0.16

1

0.05

G.4

40.64

27

0.58

6

0.61

0.54

9

0.69

0.60

6

0.62

G.5

41.07

23

0.66

9

0.56

0.18

3

0.10

0.63

9

0.55

G.6

42.67

18

0.63

8

0.08

0.60

13

0.05

0.63

8

0.10

G.7

40.89

24

0.40

4

0.38

-0.23

1

0.15

0.37

3

0.33

G.8

41.83

20

0.57

5

0.73

0.60

12

0.42

0.53

5

0.71

G.9

40.75

26

0.84

14

0.63

0.85

23

0.42

0.76

13

0.54

G.10

43.97

11

1.25

29

0.57

1.34

32

0.88

1.09

27

0.52

G.11

45.24

8

1.10

26

0.61

0.91

25

0.76

1.04

24

0.67

G.12

46.31

5

0.86

15

0.85

0.47

7

0.39

0.73

11

0.83

G.13

43.38

14

0.81

13

0.53

0.46

6

0.21

0.70

10

0.49

G.14

38.71

35

1.31

32

0.71

1.53

38

0.66

1.50

35

0.75

G.15

44.17

10

1.35

33

0.79

1.52

37

0.55

1.16

31

0.75

G.16

43.36

15

0.80

12

0.15

0.83

21

0.37

0.83

15

0.22

G.17

40.27

29

1.04

22

0.60

1.64

39

0.70

1.15

30

0.64

G.18

43.61

13

1.37

34

0.72

0.28

4

0.03

1.22

32

0.71

G.19

45.99

6

1.08

24

0.70

0.73

16

0.39

0.94

19

0.73

G.20

41.63

21

0.61

7

0.26

0.35

5

0.09

0.62

7

0.29

G.21

43.12

16

0.97

19

0.69

1.15

29

0.62

0.95

20

0.69

G.22

40.82

25

0.91

16

0.62

0.48

8

0.05

0.83

16

0.56

G.23

39.41

32

0.92

17

0.54

0.80

20

0.58

0.92

18

0.56

G.24

44.24

9

1.28

30

0.64

0.94

26

0.44

1.11

29

0.59

G.25

36.38

37

0.99

20

0.52

0.55

10

0.14

1.04

23

0.52

G.26

48.20

1

0.38

3

0.16

0.62

14

0.41

0.32

2

0.16

G.27

40.00

30

1.71

38

0.90

1.42

33

0.56

1.65

36

0.89

G.28

47.67

2

1.20

28

0.58

0.78

18

0.24

0.97

21

0.59

G.29

47.34

3

1.29

31

0.81

0.99

27

0.59

1.05

25

0.82

G.30

43.83

12

1.75

39

0.76

2.15

40

0.84

1.68

37

0.73

G.31

45.41

7

0.94

18

0.79

0.85

22

0.35

0.82

14

0.80

G.32

39.08

34

1.07

23

0.63

1.02

28

0.60

1.06

26

0.61

G.33

39.74

31

1.83

40

0.95

1.49

36

0.51

1.86

38

0.97

G.34

41.52

22

1.40

35

0.88

1.45

34

0.55

1.36

34

0.88

G.35

39.19

33

0.35

2

0.05

0.59

11

0.24

0.50

4

0.09

CSA

37.61

36

1.09

25

0.56

0.78

17

0.38

1.10

28

0.53

Moroc

33.64

38

0.75

10

0.16

0.78

19

0.12

0.75

12

0.14

AttaH

42.39

19

0.80

11

0.32

1.19

31

0.51

0.88

17

0.36

Ghani

42.68

17

1.55

36

0.78

1.17

30

0.52

1.35

33

0.74

Siran

40.61

28

1.02

21

0.79

0.70

15

0.45

1.04

22

0.78

bi = 1 is stable, Rank A ranking in ascending order, Rank D ranking in descending order

 

Ozturk and Korkut, 2017). Lower values of bi were observed for G.3, G.35 and G.26 while G.33, G.30 and G.27 had higher values of bi. Genotypes with higher bi values perform better in environments with high inputs as they are more responsive (Haydar et al., 2018; Bassi and Garcia, 2017). Freeman and Perkin’s model for stability indicated that G.11, G.24, G.29, G.32 and G.21 can be considered stable genotypes as they had near to one bi value. G.30 had the maximum value of bi (2.15) while G.7 had the minimum value of bi (-0.23). Genotypes having lower value of bi perform fairly better in low yielding environments (Jhinjer et al., 2017; Polat et al., 2016). According to the stability model of Finlay and Wilkinson G.19, G.21, G.28, Siran, G.25, G.11, G.29, G.32 and G.10 had consistent performance for the weight of kernel. Minimum bi was noted for G.3, followed by G.26 and G.7 while the maximum bi value was documented for G.1, followed by G.2 and G.33 (Table 4).

Conclusions and Recommendations

Significant differences were found among the genotypes and environments for all the studied traits. Genotypes by environment interactions were also highly significant for the studied traits showing that performance of these genotypes varied in different environments. G.16 and G.2 were capable of producing more productive tillers under low yielding environments while G.5, Atta Habib, and G.33 performed better under favorable environments in terms of the tillering capacity. G.21 had more grains spike-1 under the low yielding environments while CSA and Morocco produced more grains spike-1 in the favorable environments. G.3 produced bigger kernels compared to the other genotypes in the low yielding environments while under favorable environments G.29 and G.33 produced heavier kernels. The above mentioned genotypes can be used as parents in a breeding program to develop genotypes having higher wheat yield under low yielding environments and also under favorable environments.

Novelty Statement

This study focuses on the importance of GEI and regression models of stability analysis.

Author’s Contribution

Hafsa Naheed: Experiment conduction, data analysis, interpretation and write up.

Hidayat ur Rahman: Research supervision, designing of experiment, provision of research material, review and editing.

Conflict of interest

The authors have declared no conflict of interest.

References

Ahmadi, J., B. Vaezi and M.H. Fotokian. 2012. Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed conditions. J. Plant Physio. Breed., 2(1): 43-54.

Ajmal, S., N. Zakir and M.Y. Mujahid. 2009. Estimation of genetic parameters and character association in wheat. J. Agric. Biol. Sci., 1(1): 15-18.

Alemu, G., H. Mohammed and D. Asnake. 2018. Analysis of genotype by environment interaction for agronomic traits of bread wheat (Triticum aestivum L) genotype in Ethiopia. J. Agri. Res., 3(8): 000191. https://doi.org/10.23880/OAJAR-16000191

Ali, A., M.A. Masood and M.A. Zahid. 2012. Identifying the most promising genotypes in lentil for cultivation in a wide range of environments of Pakistan using various yield stability measures. Pak. J. Bot., 44(6): 1919-1922.

Ali, Y., B.M. Atta, J. Akhter, P. Monneveux and Z. Lateef. 2008. Genetic variability, association and diversity studies in wheat (Triticum aestivum L.) germplasm. Pak. J. Bot., 40(5): 2087-2097.

Bassi, F.M. and M.S. Garcia. 2017. Adaptation and stability analysis of ICARDA durum wheat elites across 18 countries. Crop Sci., 57: 2419-2430. https://doi.org/10.2135/cropsci2016.11.0916

Baye, T.M., T. Abebe and R.A. Wilke. 2011. Genotype-environment interactions and their translational implications. Per. med., 8(1): 59-70. https://doi.org/10.2217/pme.10.75

Bondari, K. 1999. Statistical analysis of genotype by environment interaction in agricultural research. Experimental statistics, Coastal Plain Station, University of Georgia, Tifton, GA. 31793-0748.

Casanoves, F., J. Baldessari and M. Balzarini. 2005. Evaluation of multi environment trials of peanut cultivars. Crop Sci., 45: 18-26. https://doi.org/10.2135/cropsci2005.0018

Dia, M., T.C. Wehner and C. Arellano. 2017. RGxE: An R program for genotype x environment interaction analysis. Am. J. Plant Sci., 8: 1672-1698. https://doi.org/10.4236/ajps.2017.87116

Eberhart, S.A. and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci., 6: 36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x

FAO, IFAD and WFP. 2015. The state of food insecurity in the world 2015: Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome, FAO.

FAOSTAT production book. Food and Agriculture Organization, United Nations, Rome.

Finlay, K.W. and G.N. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res., 14: 742-754. https://doi.org/10.1071/AR9630742

Freeman, G.H. and J.M. Perkins. 1971. Environmental and genotype-environmental components of variability. VIII. Relations between genotypes grown in different environments and measures of these environments. Heredity, 27: 15-23. https://doi.org/10.1038/hdy.1971.67

Haydar, F.M. A., M.N. Uddin, U.K. Roy, G.M. Uddin and A.B. Siddique. 2018. Stability analysis of yield performance in wheat (Triticum aestivum L.). Plant Environ. Dev. 7(2): 10-15.

Jhinjer, R.K., G.S. Mavil, A. Malhotra, N. Sood, B. Singh, B. Kaur, V. Kumar, I. Bhagat and V.S. Sohu. 2017. Stability analysis for grain yield and micronutrients in bread wheat genotypes. Adv. Res., 11(1): 1-8. https://doi.org/10.9734/AIR/2017/35461

Ozturk, I. and K.Z. Korkut. 2017. Stability parameters for yield and yield component of the bread wheat genotypes under various drought stress condition. J. Tekirdag Agric. Fac., 77-82.

Polat, P.O. K., E.A. Cifci1 and K. Yagdı1. 2016. Stability performance of bread wheat (Triticum aestivum L.) lines. J. Agric. Sci. Tech., 18: 553-560.

Sohail, Q., H. Naheed and R. Mohammadi. 2016. Breeding and genetic enhancement of dryland crops. Pp 257-296. In: M.Farooq and K.H. M. Siddique (Ed). Innovations in dryland agriculture. Springer International Publishers AG, Switzerland. https://doi.org/10.1007/978-3-319-47928-6_10

Yaghotipoor, A., E. Farshadfar and M. Saeidi. 2017. Evaluation of phenotypic stability in bread wheat accessions using parametric and non-parametric methods. J. Anim. Plant Sci., 27(4): 1269-1275.

Tarakanovas, P. and V. Ruzgas. 2006. Additive main effect and multiplicative interaction analysis of grain yield of wheat varieties in Lithuania. Agron. Res., 4(1): 91-98.

Temesgen, M., S. Alamerew and F. Eticha. 2015. GGE biplot analysis of genotype by environment interaction and grain yield stability of bread wheat genotypes in South East Ethiopia. World J. Agric. Sci., 11(4): 183-190.

Thakur, P., L.C. Prasad, R. Prasad, Omprakash, K. Chandra and K. Rashmi. 2019. Stability analysis for yield and related traits over four environments in wheat (Triticum aestivum L.). Plant Arch., 19(2): 3541-3545.

To share on other social networks, click on any share button. What are these?

Sarhad Journal of Agriculture

December

Vol. 37, Iss. 4, Pages 1098-1499

Featuring

Click here for more

Subscribe Today

Receive free updates on new articles, opportunities and benefits


Subscribe Unsubscribe