Euphytica

, Volume 171, Issue 1, pp 23–38

Phenotypic correlations, G × E interactions and broad sense heritability analysis of grain and flour quality characteristics in high latitude spring bread wheats from Kazakhstan and Siberia

Authors

    • Grain Quality Laboratory, Kazakh Research Center of Farming and Crop Sciences
    • Faculty of Engineering and Natural SciencesSabanci University
  • Aigul Abugalieva
    • Grain Quality Laboratory, Kazakh Research Center of Farming and Crop Sciences
  • Alexei Morgounov
    • CIMMYT-ICARDA
  • K. Abdullayev
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • L. Bekenova
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • M. Yessimbekova
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • G. Sereda
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • S. Shpigun
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • V. Tsygankov
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • Yu Zelenskiy
    • Kazakhstan-Siberia Network on Spring Wheat Improvement KASIB
  • Roberto Javier Peña
    • CIMMYT
  • Ismail Cakmak
    • Faculty of Engineering and Natural SciencesSabanci University
Article

DOI: 10.1007/s10681-009-9984-6

Cite this article as:
Gómez-Becerra, H.F., Abugalieva, A., Morgounov, A. et al. Euphytica (2010) 171: 23. doi:10.1007/s10681-009-9984-6
  • 232 Views

Abstract

Grain and flour samples of 42 high latitude spring bread wheat genotypes from Kazakhstan and Siberia evaluated in a multi-location trial were analyzed for grain concentrations of protein, zinc (Zn) and iron (Fe), as well as flour quality characteristics. The genotypes showed high grain protein concentrations (14–19%), but low dough strength was a common feature for most of them. Significant positive correlations were found between grain protein and flour protein, gluten, gliadin, gli/glu ratio, Zn, and Fe contents. Grain protein was also correlated positively with hardness, sedimentation, farinograph dough development time (DDT), stability time and ash content. Grain Fe concentration was positively associated with sedimentation, stability time, water absorption and valorimeter value, suggesting that improvements in micronutrient concentrations in the grain parallels enhancement in gluten strength. Interestingly, glutenin content correlated negatively with the concentrations of grain and flour protein, gluten, and minerals; and also with gluten deformation index (IDK), DDT, and stability time. Conversely, gliadin content showed strong positive correlations with the concentrations of grain and flour protein, gluten, and minerals. Gliadin also correlated positively, but in lesser magnitude, with DDT, stability time and IDK. Environment and G×E interaction were important sources of variation for some quality characteristics. This was reflected in the low broad sense heritability (H) values for traits related to flour strength, such as sedimentation, IDK, stability time and gliadin content. Breeding strategies, including three testing locations at the advanced selection stages, are adequate for the enhancement of most of the quality traits, but faster improvement in flour strength could be achieved with a larger number of locations.

Keyword

Flour strength Micronutrients Triticum aestivum

Introduction

Traditionally, breeding of common wheat (Triticum aestivum L.) concentrates largely on the improvement of protein quality due to the importance of protein in bread making, end-product quality, nutritional value, and economic impact (Suchy et al. 2007). To predict the quality of flour and dough, a number of physical, chemical and rheological characteristics must be determined. There are numerous tests to determine wheat quality for making different food products. Among them, grain and flour protein, ash content, falling number, flour colour, sedimentation, and dough rheology measured by farinograph, alveograph, and/or mixograph, are frequently used (Hruskova and Smejda 2003; Reese et al. 2007).

Grain protein content in wheat varies between 8 and 17 percent, depending on genetic make-up and external factors associated mainly with the crop production. A unique property of wheat flour is that its insoluble protein components, when in contact with water, form a viscoelastic protein mass known as gluten. Gluten, comprising 78–85% of the total wheat endosperm protein, is a very large complex composed mainly of polymeric (multiple polypeptide chains linked by disulphide bonds) and monomeric (single chain polypeptides) proteins known as glutenins and gliadins, respectively (Payne et al. 1987). Glutenins confer mainly elasticity, while gliadins confer mainly viscous flow and extensibility to the gluten complex. Thus, gluten is responsible for most of the viscoelastic properties of wheat flour doughs and is the main factor dictating the use of a wheat variety in bread and pasta making (Peña 2002). Gluten viscoelasticity related to end-use quality is commonly known as flour or dough strength.

Variations in grain protein content may significantly influence the dough strength properties of a wheat variety. Quantity alone, however, cannot always explain quality differences among wheat cultivars. Therefore, protein quality, in terms of the polymeric/monomeric protein ratio and the molecular size of the protein polymer (determined by the presence of specific glutenin subunits), is also important (Peña 2002; Reese et al. 2007). Wheat flour contains roughly the same amounts of glutenins and gliadins, and imbalance of the glutenin/gliadin ratio may change its viscoelastic properties. The glutenin fraction is, however, the major protein factor responsible for variations in dough strength among wheat varieties (Fu and Sapirstein 1996).

The farinograph is widely used to determine gluten quality-related properties. This instrument is used to predict dough mixing characteristics like water absorption, mixing time and stability. Farinograph parameters describe how flour behaves in a mixer during the development of its viscoelastic properties. Resistance to dough mixing is recorded as a curve while the dough is developing and eventually breaks down. The shape of the curve indicates the strength of the flour. A narrow curve that drops off rapidly indicates weak flour, whereas a wider, more level curve indicates stronger flour (LALLEMAND 1996).

Recently, nutritionists and plant breeders have been concerned about the widespread occurrence of micronutrient deficiencies in foods consumed in the developing world, especially in regard to zinc (Zn) and iron (Fe). Widespread consumption of cereal-based foods as the main sources of nutrients is a major reason for this, because cereal grains have inherently low concentrations of micronutrients, as well as being rich in compounds depressing the bioavailability of micronutrients, such as phytic acid (Pfeiffer and McClafferty 2007; Cakmak 2008). Consequently, the CGIAR (Consultative Group on International Agricultural Research, see http://www.cgiar.org) launched the Harvest-Plus initiative (http://www.harvestplus.org), a program focusing on breeding food crops with high micronutrient contents. Under this initiative, CIMMYT (Centro Internacional de Mejoramiento de Maiz y Trigo, http://www.cimmyt.org) and partner institutions, are attempting to develop high yielding, disease resistant wheat germplasm with enhanced levels of Fe and Zn. The concentrations of Zn and Fe in seed of modern wheat cultivars is on average 25 and 35 mg kg−1, respectively (Cakmak 2008). The aim is to increase the concentration of Fe and Zn in modern cultivars by nearly twofold through plant breeding. It is believed that such small changes in the concentrations will not alter the appearance, taste, texture, or baking/cooking quality of the desired foodstuffs (Bouis et al. 2000). However, there is little, if any, literature about the possible positive or negative effects of high grain micronutrient concentrations (i.e., Zn and Fe) on traditional milling and baking quality parameters in bread wheat. Recently, Guttieri et al. (2007), using a low phytic acid (LPA) wheat genotype derived from a low phytic acid mutant (Lpa 1-1) and a wild type (WT) genotype, investigated the effect of low phytic acid on nutritional and baking quality. They found that the LPA genotype produced flour with a longer time to mixograph peak and greater mixograph peak height than WT flours. However, the protein and mineral contents of the LPA and WT genotypes were similar, and the effects on quality properties were attributable only to the high inorganic phosphorus (Pi) and Mg concentrations expressed in the LPA genotypes.

Wheat protein content and baking quality, as well as grain Fe and Zn concentrations, depend on genotypic and environmental factors (Triboi et al. 2000; Cakmak et al. 2004; Morgounov et al. 2007). High temperature has a greater impact on protein content than either soil moisture or nitrogen fertilization (Campbell and Davidson 1979; Selles and Zentner 1998). There can also be differences between wheat cultivars in protein response to increasing temperatures (Blumenthal et al. 1993). Several studies indicate that as temperatures rise there is an increase in the ratio of gliadin to glutenin polymers (Blumenthal et al. 1993; Ciaffi et al. 1996; Panozzo and Eagles 2000; Zhu 2001). These studies indicated that changes in protein quality composition with increasing temperature are attributable to increased synthesis of gliadins at the expense of glutenins. It has been recognized that variations in protein content and composition significantly modify wheat quality (Rousset et al. 1985; Bietz 1988; Jarvis 2006).

In the present study we aimed to: (1) characterize phenotypic relationships among quality traits, (2) study relationships between grain micronutrient concentrations and quality traits; (3) determine the effects of genotype, environment and genotype × environment (G×E) interaction on grain and flour quality traits; (4) calculate the broad sense heritability for each characteristic; and (5) identify the most outstanding genotypes for important quality parameters across environments.

Materials and methods

Plant material and field experiments

The 4th and 5th KASIB-SBWBYT (Kazakhstan Siberia Network Spring Bread Wheat Breeding Yield Trials) is a set of 42 genotypes including varieties and advanced breeding lines (Table 1). The nursery was evaluated in 2003 and 2004 across 12 locations as a yield trial in a randomized complete block design with two replications. Several agronomically important traits were also recorded. Each entry was grown in 2.4 m2 plots. Plots were managed conventionally, following established local practices. Quality data for grain and flour was collected only in 2004 at the following 9 locations in Kazakhstan (KZ) and the Siberian region of Russia: Kostanay (KZ), Karaganda (KZ), Pavlodar (KZ), Aktobe (KZ), Shortandy (KZ), Almaty (KZ), Ust-Kamenogorsk (KZ), Kurgan (Russia) and Chelyabinsk (Russia). However, data from the Kurgan and Chelyabinsk locations were excluded from the analysis due to lack of balance in the grain quality data; whereas the Almaty location had incomplete flour quality data and was not included in the analysis of flour quality traits. Data from weather stations closest to the field sites were obtained from the Global Summary of the Day Database (Lott 1998).
Table 1

Genotype code, variety and mean values for 21 quality characteristics, including grain yield and grain Fe and Zn concentrations (Morgounov et al. 2007)

Code

Genotype

Yield

Gprt

Wet gluten

Gli

Glu

Gli/Glu

Glu/Gli

Hard

Vitre

IDK

G1

Omskaya 34

2.52

17.09

42.67

30.88

22.50

1.38

0.73

97.86

80.83

90.00

G2

Omskaya 35

2.86

15.72

39.27

30.06

22.90

1.32

0.76

89.43

83.55

81.88

G3

Lutescens 148-97-16

2.38

17.36

41.73

31.45

22.26

1.42

0.71

88.00

81.67

92.50

G4

Chernyava 13

2.60

16.06

41.27

30.97

23.01

1.35

0.74

84.57

77.83

94.17

G5

Sonata

2.86

15.59

40.53

31.20

23.68

1.32

0.76

96.57

81.67

102.50

G6

Niva 2

2.61

15.51

40.60

29.71

23.11

1.29

0.78

88.29

80.67

91.67

G7

Golubkovskaya

2.71

15.06

38.93

29.80

23.32

1.28

0.78

89.29

81.17

96.67

G8

Iren

2.34

16.94

42.87

31.91

22.84

1.40

0.72

92.29

76.50

88.33

G9

Irgina

2.01

18.61

47.93

33.21

22.22

1.49

0.67

92.14

84.33

96.67

G10

Krasnoufimskaya 90

2.26

17.41

44.73

31.63

22.20

1.43

0.70

88.30

76.00

94.27

G11

Sibirskaya 12

2.64

15.59

38.55

30.01

23.06

1.30

0.77

99.03

77.75

86.88

G12

Sibirskaya 123

2.57

15.71

39.47

30.82

23.21

1.33

0.75

93.41

78.83

87.50

G13

Novosibirskaya 15

2.25

17.07

43.83

31.51

22.51

1.40

0.71

88.51

75.15

83.88

G14

Novosibirskaya 29

2.44

18.19

46.33

32.50

22.10

1.47

0.68

87.21

77.33

85.00

G15

Udacha

2.79

15.31

38.20

31.02

23.64

1.32

0.76

88.29

79.00

83.33

G16

Lutescens 509

2.66

14.90

40.27

32.00

24.46

1.31

0.76

86.73

83.67

85.83

G17

Lutescens 574

2.82

14.72

38.20

30.67

23.97

1.28

0.78

87.98

81.17

80.83

G18

Lutescens 424

2.65

14.87

38.47

31.67

24.04

1.32

0.76

83.57

81.83

90.00

G19

Altaiskaya 50

2.11

15.25

38.53

31.01

23.69

1.31

0.76

85.36

78.83

85.83

G20

Aria

2.85

15.27

40.67

30.81

23.49

1.32

0.76

83.64

73.67

92.50

G21

Tertsia

2.97

15.44

39.80

31.17

23.82

1.31

0.76

99.00

84.83

96.67

G22

Fora

2.04

15.47

39.46

31.47

23.58

1.34

0.75

82.15

63.02

89.27

G23

Chelyaba

2.58

18.65

46.33

31.06

21.54

1.44

0.69

90.07

78.67

90.00

G24

Chebarkulskaya

2.68

15.87

42.40

30.27

22.92

1.32

0.76

86.93

84.67

87.50

G25

Astana

2.70

16.84

44.07

31.32

22.56

1.39

0.72

84.07

77.33

90.83

G26

Bayterek

2.73

15.55

40.13

30.53

23.21

1.32

0.76

89.43

80.83

85.83

G27

Shortandynskaya 95

2.85

16.00

41.93

31.51

23.34

1.35

0.74

91.36

82.67

91.67

G28

Lutescens 13

2.92

15.66

40.67

31.78

23.39

1.36

0.74

88.64

86.17

82.50

G29

Lutescens 54

3.16

14.69

38.73

30.90

22.65

1.37

0.73

48.43

46.33

87.50

G30

Eritrospermum 78

2.54

15.95

40.00

31.08

23.16

1.35

0.74

82.64

74.50

73.33

G31

Nadezhda

2.19

14.07

36.47

31.11

24.60

1.27

0.79

86.57

83.83

80.83

G32

No. 18

2.15

14.20

36.67

31.43

24.84

1.27

0.79

86.43

84.33

75.00

G33

Eritrospermum 727

2.55

15.73

39.47

31.37

23.26

1.35

0.74

80.21

81.33

72.50

G34

Lutescens 29-94

3.01

14.46

38.93

30.01

24.02

1.25

0.80

89.86

82.17

95.00

G35

Lutescens 30-94

3.11

14.54

35.53

29.96

23.54

1.28

0.79

70.93

71.50

71.67

G36

Lutescens 53-95

2.78

15.21

37.00

30.31

23.41

1.30

0.77

88.86

78.00

85.83

G37

Stepnaya 1

2.82

14.79

37.00

29.74

23.49

1.27

0.79

80.79

69.50

81.67

G38

Aktubinka

2.62

14.77

38.40

30.62

23.94

1.28

0.78

88.36

84.50

85.83

G39

Aktube 32

2.76

13.99

34.87

28.96

24.01

1.21

0.83

83.29

64.50

77.50

G40

GVK 1860/8

2.58

14.80

39.47

30.98

24.16

1.29

0.78

88.00

84.15

103.33

G41

GVK 1369/2

2.85

14.48

38.81

30.78

23.97

1.29

0.78

84.60

81.21

105.19

G42

GVK 1857/9

2.77

14.47

40.47

30.46

23.78

1.28

0.78

81.43

80.55

100.88

Mean

 

2.63

15.66

40.23

30.94

23.32

1.33

0.75

86.73

78.48

88.11

Max

 

3.16

18.65

47.93

33.21

24.84

1.49

0.83

99.03

86.17

105.19

Min

 

2.01

13.99

34.87

28.96

21.54

1.21

0.67

48.43

46.33

71.67

LSD (0.05)

 

0.28

0.87

2.86

1.12

0.71

0.05

0.03

7.51

6.90

10.54

Code

Genotype

Fprt

Sed

White

DDT

Sta

MTI

Abs

Val

Ash

Zn

Fe

G1

Omskaya 34

15.66

72.12

54.77

3.19

1.38

111

57.62

52.50

0.66

G2

Omskaya 35

14.50

72.62

55.19

2.44

0.88

121

54.90

44.50

0.68

29

47

G3

Lutescens 148-97-16

15.09

52.37

55.34

3.06

0.94

193

57.90

42.50

0.68

32

48

G4

Chernyava 13

14.40

64.00

52.70

2.94

0.59

151

56.05

44.00

0.70

G5

Sonata

14.60

57.75

51.30

2.81

0.93

146

57.38

41.75

0.66

G6

Niva 2

14.46

70.00

56.56

2.75

1.06

101

55.38

49.38

0.62

G7

Golubkovskaya

14.14

59.75

57.66

2.50

0.73

134

53.94

42.71

0.63

27

41

G8

Iren

15.96

72.63

52.93

3.00

0.97

127

56.44

47.15

0.65

32

48

G9

Irgina

16.07

74.25

56.69

3.07

0.97

130

57.13

47.72

0.66

G10

Krasnoufimskaya 90

15.73

64.75

54.99

2.91

1.08

120

56.30

48.47

0.66

G11

Sibirskaya 12

14.85

60.50

52.87

2.46

1.05

109

57.05

49.80

0.65

G12

Sibirskaya 123

15.02

74.25

54.21

3.06

1.06

101

57.35

51.25

0.64

G13

Novosibirskaya 15

16.48

86.88

53.30

3.39

1.44

92

58.52

55.11

0.68

29

46

G14

Novosibirskaya 29

16.11

87.62

57.56

3.38

1.38

61

58.12

57.50

0.67

G15

Udacha

13.95

67.88

54.89

2.75

0.82

70

60.30

52.88

0.61

G16

Lutescens 509

14.45

76.25

56.07

2.68

1.06

76

59.92

52.11

0.62

G17

Lutescens 574

14.46

70.00

55.55

3.00

1.19

76

60.15

53.88

0.62

29

47

G18

Lutescens 424

14.41

78.25

56.31

3.44

1.56

76

62.08

56.75

0.64

29

45

G19

Altaiskaya 50

15.09

79.38

54.77

2.86

1.04

116

57.87

48.15

0.65

28

46

G20

Aria

14.06

75.38

57.74

3.44

1.31

75

59.02

56.38

0.64

-

-

G21

Tertsia

14.60

64.88

53.36

2.94

1.25

101

60.53

53.00

0.64

29

46

G22

Fora

15.04

67.38

53.50

4.05

1.37

94

57.92

57.89

0.68

33

47

G23

Chelyaba

16.57

78.25

56.83

4.25

1.81

43

58.45

65.38

0.67

32

56

G24

Chebarkulskaya

14.89

65.12

55.45

3.13

1.31

64

56.25

57.38

0.64

G25

Astana

14.90

82.12

59.59

3.69

1.37

71

57.30

56.50

0.59

26

39

G26

Bayterek

13.87

74.37

59.69

2.73

1.11

95

58.78

51.29

0.60

25

41

G27

Shortandynskaya 95

14.19

73.38

58.01

2.88

1.07

79

57.30

52.38

0.60

31

45

G28

Lutescens 13

14.47

74.75

52.92

2.56

1.00

83

56.15

51.38

0.68

27

43

G29

Lutescens 54

13.09

61.75

69.01

2.56

1.13

84

52.27

49.75

0.67

26

40

G30

Eritrospermum 78

15.04

79.38

56.27

2.94

1.25

70

58.12

55.00

0.67

29

48

G31

Nadezhda

14.39

63.25

56.16

3.06

1.15

65

56.05

55.75

0.62

G32

No. 18

14.32

67.38

55.34

2.63

1.03

65

56.67

53.00

0.65

G33

Eritrospermum 727

14.55

84.12

59.25

3.31

1.25

48

57.62

58.88

0.63

29

47

G34

Lutescens 29-94

13.65

76.12

58.56

3.38

1.39

75

58.67

56.00

0.62

24

43

G35

Lutescens 30-94

13.05

74.12

64.79

2.00

0.75

63

52.10

51.00

0.63

G36

Lutescens 53-95

14.38

74.75

58.75

2.25

0.83

58

55.05

53.88

0.61

25

43

G37

Stepnaya 1

13.15

76.25

62.60

2.31

0.69

56

56.00

53.62

0.59

24

40

G38

Aktubinka

13.65

58.63

61.09

2.81

1.44

95

57.53

50.75

0.55

24

42

G39

Aktube 32

13.32

76.25

59.76

2.63

1.05

74

59.07

52.50

0.62

23

40

G40

GVK 1860/8

14.74

67.75

58.24

3.49

1.03

100

60.55

52.54

0.64

G41

GVK 1369/2

14.32

53.75

60.54

3.38

1.31

136

57.75

50.37

0.63

26

43

G42

GVK 1857/9

14.06

48.75

60.61

3.88

1.21

131

57.62

51.00

0.63

26

45

Mean

 

14.61

70.46

56.95

3.00

1.12

94

57.41

51.99

0.64

28

45

Max

 

16.57

87.62

69.01

4.25

1.81

193

62.08

65.38

0.70

33

56

Min

 

13.05

48.75

51.30

2.00

0.59

43

52.10

41.75

0.55

23

39

LSD (0.05)

 

0.54

7.94

1.69

0.50

0.35

19

1.14

4.21

0.03

13

6

Yield = grain yield (t/ha), Gprt = grain protein content (%), Gluten (%), Gli = gliadin (% of total protein), Glu = glutenin (% of total protein), Gli/Glu = gliadin to glutenin ratio, Glu/Gli = glutenin to gliadin ratio, Hard = hardness, Vitre = vitreousness (%), IDK = Gluten deformation index, Fprt = flour protein content (%), Sed = sedimentation (ml), White = whiteness, DDT = dough development time (minutes), Sta = stability time (minutes), MTI = mixing tolerance index (BU), Abs = water absorption (%), Val = valorimeter, Ash = flour ash content (%), Zn = Zn-grain concentration (mg kg−1), Fe = Fe-grain concentration (mg kg−1)

Grain and flour quality traits

All grain and flour quality analyses (except estimations of Fe and Zn grain concentrations) were carried out in the Grain Quality Laboratory, Kazakh Research Center of Farming and Crop Sciences. Total protein content of the grain was determined by the Kjeldahl method (N × 5.7% db). Gluten content (% of wet gluten) was determined by using a mechanical gluten washing apparatus (MOK-1 M) and distilled water (Republic of KZ standard), where gluten proteins and globulin fractions (insoluble in distilled water) are retained while the albumin is washed out. Grain hardness was measured by the single kernel characterization system (SKCS), using a 4100 instrument (Perten, Sweden). Grain vitreousness was expressed as the percentage of grains having an endosperm with transparent (non starchy) appearance. Gliadins were extracted in 70% ethanol, and glutenins in NaOH, using the Osborne protein fractionation protocol (Osborne 1924).

Flour whiteness was estimated according to the GOST standard (Russian Federation State Standards) method 26361-84 (GOST 1986). This consists of measuring the reflectance ability of a compact, flat flour sample using a BLIK-R3 photoelectric apparatus. Flour sedimentation was determined using a 2% acetic acid solution in water. Flour protein was measured by near-infrared (NIR) transmittance spectroscopy, using an Inframatic 8620, instrument (Perten, Sweden) previously calibrated to flour protein contents obtained by the Kjeldahl method. The gluten deformation index (IDK) was determined on 4-g flour samples using the IDK-1 apparatus and graded by gluten compressibility with scores ranging from 0 to 120 as follows: 45–75 = strong gluten; 20–40 and 105–120 = weak; 80–100 = medium.

The farinograph was used according to the standard AACC method (AACC 1995) to determine flour water absorption (to centre the curve at 500 Brabender Units (BU)), dough development time, stability to overmixing, dough mixing tolerance index (MTI, modified according to the Republic of KZ standard; that is, the difference in BU from the 500 BU line to the top of the curve after a total time of 15 minutes), and valorimeter value (an empirical quality score, where higher values indicate stronger flour).

Ash contents in wheat and flour were determined according to standard AACC procedure (AACC 1995) and expressed as a percentage of ash in the sample on an 11% moisture basis for wheat and a 14% moisture basis for flour.

Analyses for Fe and Zn grain concentrations of 25 selected genotypes were carried out at Waite Analytical Services, University of Adelaide, Australia, based on the nitric/perchloric acid digestion method using an inductively coupled plasma optical emission spectrometer (ICP-OES) (Zarcinas et al. 1987). Results of grain micronutrient concentrations of 25 selected genotypes were reported by Morgounov et al. (2007). The “concentration” effect was estimated by the method proposed by McDonald et al. (2008), where grain Zn and Fe yields were plotted against grain yield. Genotypes that have inherently low or high grain Zn and Fe concentrations at a given yield level were identified by being located below or above the regression line, respectively.

Data analyses

Replicated data from multi-environment trials for each characteristic were subjected to analysis of variance to separate the main effects of genotype, environments and G×E interaction. Variance components \( (\sigma^{2}_{\text{G}} ,\,\sigma^{2}_{\text{GL}} \,{\text{and}}\,\sigma^{2}_{\text{E}} ) \)were calculated by REML (residual maximum likelihood) analysis of mixed models; where environments and replications were considered fixed effects, while genotypes and G×E interactions were random effects. Genotype means were compared using least significant difference (LSD). Phenotypic relationships among traits were investigated using correlation and linear regression analysis. All calculations were made using the different commands of GenStat Discovery Edition 3 (Buysse et al. 2004).

Broad sense heritability (H) estimates were calculated as intraclass correlations for four breeding scenarios: (1) unreplicated plot: one location with one replication (l = r = 1), (2) one location with three replications (l = 1,r = 3), (3) three locations and two replications (l = 3,r = 2), and (4) multi-locations (5 to 8 locations, according to the number of locations where genotypes were evaluated) with two replications each (5 ≤ l ≤ 8, r = 2). Heritabilities were estimated as:
$$ {\rm H}_{lr} = \sigma^{2}_{\text{G}} / {\left({\sigma^{2}_{\text{G}} + \sigma^{2}_{\text{GL}} /l\,+\,\sigma^{2}_{\text{E}} /lr} \right) = \sigma^{2}_{\text{G}}/\sigma^{2}_{\text{P} {lr}} }$$
where the variance component \( \sigma^{2}_{\text{G}} \) equaled the genotypic variance, \( \sigma^{2}_{\text{GL}} \) was the genotype × location, \( \sigma^{2}_{\text{E}} \) was the residual, and \( \sigma^{2}_{\text{Plr}} \) was the phenotypic variance for the given selection criterion.

Results

Phenotypic means and ranges were calculated for all grain and flour quality characteristics (Table 1). In general, the genotypes had relatively low grain yields (range 2.01–3.16 t/ha, mean = 2.63 t/ha), and had high grain proteins (range 14–18.7%, mean = 15.7%) and flour proteins (range 13–16.6%, mean = 14.6%). Glutenin was lower (range 21.5–24.8% of total protein; mean = 23.3%) than gliadin (range 29–33.2% of total protein; mean = 30.9%); consequently, low values for the glu/gli ratio (range 0.67–0.83; mean = 0.75) were obtained. Variation in sedimentation values ranged 1.8-fold between the lowest and highest scores, from 49 to 88 ml. Based on the sedimentation test, 24 genotypes with values over 70 ml were considered to have strong gluten. In contrast, according to the IDK test, only 4 (Lutescens 30-94, Eritrospermum 727, Eritrospermum 78 and No. 18) of the 42 genotypes with scores between 45 and 75 had strong gluten. For this test, the majority of genotypes fell within the 80–100 range indicating medium gluten strength. Four genotypes had IDK values between 100 and 105, indicating weak gluten. In regard to dough mixing properties, dough development times (DDT) ranged from 2 to 4.25 min (mean = 3 min); and farinograph stability times ranged from 0.6 to 1.8 min (mean = 1.1 min). MTI values ranged between 43 and 193 BU, whereas valorimeter varied from 42 to 65.

Associations among traits

Correlation coefficients between all traits are shown in Table 2. Significant positive correlations were found between grain protein and flour protein, gluten, gliadin, gli/glu ratio, and concentrations of Zn and Fe. Grain protein was also positively correlated, but to a lesser extent, with grain hardness, sedimentation, DDT, stability and flour ash content. Sedimentation associated best with valorimeter values (r = 0.53, P ≤ 0.001), followed by grain and flour protein. Hardness showed a significant relationship with vitreousness (r = 0.74, P ≤ 0.001). Genotypes with white soft grain were also those with the highest grain yields (r = 0.45, P ≤ 0.002). The correlations between gluten content and micronutrient concentrations were weaker (r = 0.45, P ≤ 0.023 for Zn; and r = 0.53, P ≤ 0.007 for Fe) than the associations between grain protein content and Zn and Fe concentrations (r = 0.52, P ≤ 0.008; and r = 0.64, P ≤ 0.001, respectively). There were significant negative correlations between glutenin content and grain protein, wet gluten, flour protein, ash, and Zn and Fe concentrations. Also, there were weak negative correlations between glutenin and IDK, DDT and stability. In contrast to glutenin, gliadin content showed strong and positive correlations with grain protein, gluten, flour protein, ash, Zn, and Fe concentrations. Gliadin levels were also positively correlated, but in lesser magnitudes, with DDT, stability time and IDK. Grain Zn and Fe concentrations correlated well with grain and flour protein, wet gluten, gliadin, DDT and flour ash. Furthermore, grain Fe concentration was positively associated with sedimentation, flour stability, water absorption and valorimeter value.
Table 2

Phenotypic correlations between quality traits, grain yield, and Zn and Fe grain concentrations

 

Gprt

Gluten

Gli

Glu

Gli/Glu

Glu/Gli

Hard

Vitre

IDK

Fprt

Sed

White

DDT

Sta

MTI

Abs

Val

Ash

Zn

Fe

Yield

−0.41

(0.007)

−0.35

(0.021)

−0.50

(0.001)

0.16

(0.324)

−0.40

(0.008)

0.40

(0.010)

−0.26

(0.090)

−0.15

(0.350)

0.06

(0.727)

−0.65

(0.000)

−0.12

(0.438)

0.45

(0.002)

−0.32

(0.041)

−0.12

(0.433)

−0.18

(0.252)

−0.14

(0.368)

−0.03

(0.833)

−0.29

(0.061)

−0.62

(0.001)

−0.49

(0.012)

Gprt

 

0.91

(0.000)

0.57

(0.000)

−0.88

(0.000)

0.93

(0.000)

−0.93

(0.000)

0.31

(0.047)

0.11

(0.501)

0.15

(0.342)

0.83

(0.000)

0.30

(0.054)

−0.33

(0.033)

0.34

(0.026)

0.23

(0.138)

0.18

(0.245)

0.03

(0.853)

0.06

(0.716)

0.44

(0.004)

0.52

(0.008)

0.64

(0.001)

Gluten

  

0.63

(0.000)

−0.77

(0.000)

0.89

(0.000)

−0.89

(0.000)

0.27

(0.084)

0.18

(0.264)

0.36

(0.018)

0.79

(0.000)

0.22

(0.168)

−0.28

(0.072)

0.50

(0.001)

0.35

(0.024)

0.20

(0.215)

0.10

(0.509)

0.09

(0.567)

0.37

(0.015)

0.45

(0.023)

0.53

(0.007)

Gli

   

−0.25

(0.108)

0.75

(0.000)

−0.75

(0.000)

0.11

(0.478)

0.20

(0.200)

0.13

(0.423)

0.61

(0.000)

0.21

(0.179)

−0.32

(0.039)

0.35

(0.025)

0.22

(0.167)

0.10

(0.533)

0.26

(0.090)

0.08

(0.617)

0.35

(0.022)

0.60

(0.001)

0.53

(0.006)

Glu

    

−0.83

(0.000)

0.82

(0.000)

−0.02

(0.886)

0.19

(0.221)

−0.10

(0.544)

−0.61

(0.000)

−0.21

(0.178)

0.05

(0.738)

−0.21

(0.185)

−0.16

(0.325)

−0.18

(0.266)

0.25

(0.112)

0.00

(0.997)

−0.43

(0.004)

−0.30

(0.143)

−0.44

(0.028)

Gli/Glu

     

−0.99

(0.000)

0.07

(0.668)

−0.03

(0.857)

0.13

(0.402)

0.76

(0.000)

0.26

(0.091)

−0.21

(0.180)

0.34

(0.027)

0.23

(0.148)

0.18

(0.261)

−0.02

(0.897)

0.05

(0.776)

0.51

(0.001)

0.54

(0.006)

0.59

(0.002)

Glu/Gli

      

−0.07

(0.639)

0.02

(0.914)

−0.14

(0.388)

−0.77

(0.000)

−0.27

(0.081)

0.23

(0.146)

−0.35

(0.021)

−0.25

(0.115)

−0.17

(0.295)

0.00

(0.999)

−0.06

(0.708)

−0.50

(0.001)

−0.53

(0.006)

−0.60

(0.001)

Hard

       

0.74

(0.000)

0.27

(0.082)

0.47

(0.002)

0.00

(0.990)

−0.75

(0.000)

0.09

(0.554)

0.05

(0.760)

0.25

(0.114)

0.42

(0.005)

−0.10

(0.513)

−0.03

(0.832)

0.23

(0.278)

0.22

(0.288)

Vitre

        

0.20

(0.207)

0.25

(0.106)

−0.07

(0.674)

−0.49

(0.001)

0.07

(0.677)

0.03

(0.832)

0.15

(0.330)

0.34

(0.026)

−0.08

(0.617)

−0.16

(0.297)

0.10

(0.638)

0.21

(0.307)

IDK

         

0.17

(0.284)

−0.50

(0.001)

−0.13

(0.404)

0.39

(0.010)

0.08

(0.633)

0.59

(0.000)

0.18

(0.245)

−0.31

(0.046)

0.07

(0.647)

0.03

(0.897)

−0.13

(0.551)

Fprt

          

0.28

(0.075)

−0.59

(0.000)

0.50

(0.001)

0.38

(0.013)

0.17

(0.273)

0.22

(0.154)

0.15

(0.337)

0.52

(0.000)

0.68

(0.000)

0.73

(0.000)

Sed

           

−0.02

(0.902)

0.07

(0.655)

0.24

(0.125)

−0.61

(0.000)

0.19

(0.229)

0.53

(0.000)

0.01

(0.929)

−0.01

(0.962)

0.11

(0.590)

White

            

−0.15

(0.333)

0.01

(0.941)

−0.32

(0.039)

-0.36

(0.018)

0.16

(0.316)

−0.44

(0.003)

-0.69

(0.000)

-0.54

(0.006)

DDT

             

0.74

(0.000)

−0.02

(0.914)

0.50

(0.001)

0.52

(0.000)

0.19

(0.235)

0.46

(0.020)

0.53

(0.007)

Sta

              

−0.34

(0.029)

0.46

(0.002)

0.71

(0.000)

−0.01

(0.936)

0.16

(0.455)

0.42

(0.035)

MTI

               

−0.08

(0.599)

−0.83

(0.000)

0.36

(0.020)

0.31

(0.125)

0.05

(0.812)

Abs

                

0.36

(0.019)

−0.11

(0.482)

0.20

(0.347)

0.28

(0.183)

Val

                 

-0.17

(0.271)

0.01

(0.969)

0.30

(0.144)

Ash

                  

0.62

(0.001)

0.60

(0.002)

Zn

                   

0.78

(0.000)

Numbers in parentheses indicate probability levels. Trait nomenclature as in Table 1

Genotype × environment interactions and broad sense heritabilities

The relationship between the effects of genotype and environment and their interactions on quality parameters was investigated by analysis of variance (Tables 3, 4). With regard to the grain quality and gluten-related traits (Table 3), genotype was the most important source of variation for Gli/Glu and Glu/Gli ratios, hardness and gluten content (explaining 45, 43, 47 and 39% of the G + E + G×E sums of squares, respectively). Genotype was also important for glutenin, grain protein and vitreousness (explaining 21, 35 and 24% of the G + E + G×E sums of squares, respectively). Environment accounted for 63, 50 and 62% of the G + E + G×E sums of squares for glutenin, grain protein and vitreousness, respectively, being the most important source of variation for those traits. Environment was also important for gliadin and gluten contents (explaining 36 and 37% of the G + E + G×E sums of squares, respectively). The G×E interaction consistently accounted for more than 14% of the G + E + G×E sums of squares, being an important source of variation for gliadin, hardness, Gli/Glu and Glu/Gli ratios, and gluten (explaining 38, 32, 26, 28 and 25% of the G + L + G×E sums of squares, respectively). However, only in the case of gliadin, was the G×E larger than the genotypic effect (1.5-fold). Broad sense heritabilities (H) values on a plot basis were low for all grain quality traits (Table 3), ranging from 0.17 to 0.49, but increased with increased numbers of replications and test environments.
Table 3

Analyses of variance and broad sense heritabilities (H) for grain quality and gluten-related traits of 42 high latitude spring bread wheat cultivars from Kazakhstan and Siberia

Source of variation

Gprt

Gluten

Gliadin

Glutenin

Gli/Glu

Glu/Gli

Hardness

Vitreousness

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

Genotypes

41

398***

41

2,018.5***

41

184.9***

41

148.1***

41

1.0452***

41

0.3200***

41

18,390***

41

12,783***

Environments

6

561***

5

1,931.0***

6

265.0***

6

450.1***

6

0.6639***

6

0.2141***

6

8,178***

5

32,928***

G×E interaction

246

167***

205

1,290.3***

246

280.3***

246

111.5***

246

0.6082***

246

0.2038***

246

12,529***

205

7,523***

Error

287

73.1

246

547.3

287

97.3

287

53.2

287

0.2626

287

0.0910

287

4,305

246

2,814

Broad sense heritability

 

    H plot

0.49

 

0.36

 

0.17

 

0.33

 

0.40

 

0.38

 

0.36

 

0.38

 

    H mean (1 location; 3 reps)

0.59

 

0.46

 

0.24

 

0.43

 

0.50

 

0.47

 

0.46

 

0.48

 

    H mean (3 locations; 2 reps)

0.79

 

0.69

 

0.46

 

0.67

 

0.73

 

0.71

 

0.69

 

0.71

 

    H mean (multilocations; 1 rep)

0.87

 

0.77

 

0.60

 

0.78

 

0.82

 

0.81

 

0.80

 

0.79

 

*** P ≤ 0.001

Table 4

Analyses of variance and broad sense heritabilities (H) for flour quality traits of 42 high latitude spring bread wheat cultivars from Kazakhstan and Siberia

Source of variation

Fprot

Sed

IDK

DDT

Stability

MTI

Abs

Val

Whiteness

Ash

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

df

SS

Genotypes

41

240***

41

26,619***

41

15,793***

41

73.9***

41

20.4***

41

336,129***

41

1,367***

41

7,515***

41

3,977***

41

0.31***

Environments

7

220***

7

7,681***

5

11,880***

7

55.2***

7

11.3***

7

39,615***

7

330***

7

4,690***

7

860***

7

0.10***

G×E interaction

287

87.1***

287

18,668***

205

17,584***

287

73.2***

287

36.2***

287

109,780***

287

386***

287

5,250***

287

843***

287

0.21***

Error

328

41.6

328

9,166

246

6,530

328

32.6

328

15.2

328

45,713

328

175

328

2,180

328

451

328

0.09

Broad sense heritability

    H plot

0.53

 

0.36

 

0.23

 

0.28

 

0.16

 

0.56

 

0.60

 

0.36

 

0.67

 

0.36

 

    H mean (1 location; 3 reps)

0.63

 

0.46

 

0.30

 

0.36

 

0.22

 

0.66

 

0.69

 

0.46

 

0.75

 

0.46

 

    H mean (3 locations; 2 reps)

0.82

 

0.69

 

0.54

 

0.60

 

0.42

 

0.84

 

0.86

 

0.69

 

0.89

 

0.70

 

    H mean (multilocations; 1 rep)

0.90

 

0.82

 

0.64

 

0.75

 

0.60

 

0.91

 

0.92

 

0.82

 

0.94

 

0.82

 

*** P ≤ 0.001

In the case of flour quality traits (Table 4), genotype was the most important source of variation (as indicated by the proportion of the G + E + G×E sums of squares from ANOVA) for whiteness (70%), flour protein (44%), water absorption (66%), sedimentation (50%), valorimeter (43%), MTI (69%), ash content (50%) and DDT (37%). Genotype also was important for IDK and stability (explaining 35 and 30% of the G + E + G×E sums of squares, respectively). In contrast, environment was never the most important source of variation for flour quality characteristics. However, environment accounted for 40, 26, 27 and 27% of the G + E + G×E sums of squares for flour protein, IDK, valorimeter and DDT, respectively. The G×E interaction was responsible for most of the G + E + G×E variation for IDK and stability (accounting for 39 and 53% of the sums of squares, respectively), being also important for sedimentation (35%), valorimeter (30%), MTI (23%), ash content (34%) and DDT (36%). However, only in the case of stability time, was the G×E larger than the genotypic effect (1.8-fold). Broad sense heritabilities (H) on a plot basis for flour quality traits, were generally higher than those for grain quality traits (range 0.16–0.67, Table 4). As for grain quality, H was lower for traits where G×E was the most important source of variation (i.e., stability time and IDK).

From Table 1, and based on single-location data analyses (not shown), it was possible to identify the most widely adapted genotypes for flour strength characteristics. In the case of sedimentation value, genotypes Astana, Novosibirskaya 29, Novosibirskaya 15, Eritrospermum 727, Lutescens 574, Chelyaba and Shortandynskaya 95 dominated the ranking across locations. For DDT, the genotypes Fora, Chelyaba, GVK 1860/8 and GVK 1857/9 were superior. Genotypes Chelyaba, Eritrospermum 727, Fora, Novosibirskaya 29, Astana, Tertsia, Lutescens 574 and Shortandynskaya 95 had the longest mixing times across locations; whereas for MTI, genotypes Novosibirskaya 29, Chelyaba, Eritrospermum 727 and Stepnaya 1 consistently gave the lowest scores.

Discussion

High grain and flour proteins are common features of wheat growns under dry high latitude environments in northern Kazakhstan and Siberia (Shegebaev 1997). However, despite the high protein contents (supposedly indicators of high quality), other grain and flour quality parameters related to flour strength exceeded the optimal ranges set for traditional quality tests that determine desirable characteristics for the baking industry.

The large variation observed on flour strength-related parameters suggests that specific factors related to gluten protein composition also affected flour strength. For instance, higher glutenin to gliadin ratios were shown to correspond with greater dough strength (MacRitchie 1985). In addition, there is evidence that along with high- (HMW) and low- (LMW) molecular weight glutenin composition, gliadins play an important role in wheat flour quality (Peña et al. 2004; Wesley et al. 2001).

Classifications of strong gluten genotypes as indicated by sedimentation and IDK tests were different, the latter test identifying many genotypes as having medium rather than strong gluten strength. However, both tests identified extreme genotypes with weak or strong glutens. Better classifications were obtained using sedimentation along with farinograph stability time. The fact that many genotypes in the study showed low farinograph stability times, and DDT and valorimeter values, as well as high MTI scores, was indicative of weak gluten flours. Consequently, the resulting doughs could not be mixed for long times or stretched extensively without breaking because of insufficient elasticity and extensibility. This could have resulted from low glutenin contents, although the possible role of excessive gliadins cannot be ruled out (Payne et al. 1991; Metakovsky et al. 1997; Wesley et al. 2001).

Among trait associations, the negative relationships between glutenin content and most of the flour strength parameters has at least two explanations; firstly, cross-contamination among flour protein fractions (because each of the Osborne fractions is a complex mixture of polypeptides that may overlap in solubilities (Gianibelli et al. 2001)), and secondly and more likely, the effects of high temperatures on the composition of the developing gluten protein. Increases in temperatures above 30–32°C, and increased duration of such temperatures during the first 2–3 weeks after anthesis, results in dough weakening due to increased gliadin relative to glutenin content (Blumenthal et al. 1991; Corbellini et al. 1997; Dupont and Altenbach 2003). Increased gliadin to glutenin ratio is a major reason for decreases in breadmaking quality (Bushuk et al. 1978). Thus, as temperatures exceed 30–32°C, there is a decline in gluten-related quality parameters, such as loaf volume and loaf weight. Don et al. (2005) demonstrated that heat stress in the early stages of glutenin synthesis (between 16 days after anthesis (DAA) and 25 DAA) negatively affected the formation of the glutenin macropolymer (GMP). Peck et al. (2008) made similar observations in a series of field experiments conducted in southern Australia. Genotypes in the present experiments were exposed to high temperatures during the 16–25 DAA period at all test locations (Fig. 1a–h). Those heat stress conditions may have caused significant increases in the amounts of gluten protein soluble in 70% ethanol, as well as increases in glutenin particle size, with consequent decreases in glutenin quantity (Don et al. 2005), explaining the low glu/gli ratios (Dupont and Altenbach 2003). Moreover, increased glutenin particle size led to increased DDT, and decreased GMP led to lower dough stability (Don et al. 2005), as observed in the present study.
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Fig. 1

Daily maximum, minimum and mean air temperature (°C), rainfall (mm), and days to anthesis per location in 2004: a Chelyabinsk, b Shortandy, c Ust-Kamenogorsk, d Kostanay, e Kurgan, f Aktobe, g Karaganda and h Pavlodar

The weaker correlations between gluten and micronutrient (Zn and Fe) concentrations compared to the associations between total grain protein content and Zn and Fe concentrations, suggests a possible role of non-gluten protein fractions in binding Zn and Fe (Ozturk et al. 2006). Nevertheless, the strong correlation between gliadin and grain Zn concentration (r = 0.60, P ≤ 0.001) may also suggest that grain Zn in wheat is bound to sulfur-rich low molecular weight (30–50 kDa) prolamins (γ and α gliadins, and B and C type LMW glutenins) (Gokce 2006).

The poor positive correlation between gliadin and flour strength-related traits (e.g., sedimentation, DDT, stability time and IDK) and the negative association between glutenin and the same set of traits, could indicate the presence of specific gliadin alleles (e.g., Gli-B1c, Gli-D1 g alleles) in explaining—at least partially—superior dough mixing properties. However, further analyses of the electrophoretic compositions of storage proteins of the genotypes included in this study are needed to confirm this hypothesis.

Positive correlations of Zn and Fe concentrations with grain protein, flour protein, gluten, gliadin and DDT, and the positive associations between grain Fe concentration and sedimentation, flour stability, water absorption and valorimeter value, suggest that improvements in grain micronutrient concentrations could be associated with protein-related factors contributing to flour strength. Our results showed that among the evaluated quality properties, only flour colour (whiteness) and glutenin (and the glu/gli ratio) were negatively associated with high grain concentrations of Fe and Zn (Table 2). Nevertheless, in the worst example in our study, the glutenin content in a high Fe and Zn genotype (Chelyaba) was only 3% lower than that of the genotype with the highest glutenin content (No. 18). This implies no large changes in glutenin content due to higher micronutrient concentrations in the grain. Furthermore, glutenin contents estimated from linear regression models (Fig. 2a, b), where values between 60 and 70 mg kg−1 for Fe and 50–60 mg kg−1 for Zn were used as predictor variables, were never less than 21%. It is important to note that the conclusions drawn from the associations of Zn and Fe with the other traits were not merely the results of “concentration” effects. The identification of some genotypes with high grain Zn and Fe levels at given grain yield levels (Fig. 3), indicates genotypic differences in grain Zn and Fe concentrations that were independent of the grain yield potential of the genotypes (McDonald et al. 2008).
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Fig. 2

Linear regression between glutenin and micronutrient concentrations: a Glutenin vs. Fe; b Glutenin vs. Zn

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Fig. 3

The relationships of grain Zn yield (above) and grain Fe yield (bottom) with grain yield for 42 high latitude bread wheat cultivars from Kazakhstan and Siberia grown at six locations in 2004

Traits where the genotype effect was an important source of variation, exhibited greater broad sense heritability (H) values than those where environment and G×E explained most of the G + E + G×E variation (i.e., gliadin content and stability time) (Tables 3,4). The fact that the genotypic effects were always larger than the G×E interactions (except for gliadin and stability time) implied there was no need for G×E partitioning in most cases. This could have happened because some quality traits (such as grain protein, sedimentation, glutenin, hardness) derive from limited numbers of genes/loci (strong genotypic effect) where negligible or non crossover G×E interactions were present. Among the four breeding scenarios used for the estimation of H, the one including 3 test locations with 2 replicates increased the repeatability of the trait estimates by 46 to 89%. Although this breeding strategy gives lower H values than those from the multi-location (between 5 and 8 locations) strategy, it has the advantage that fewer resources are needed. However, for a faster improvement in gluten strength (e.g., sedimentation and stability time), selection based on a larger number of locations might be preferred.

Interestingly, we identified a few genotypes that were among the best for several flour strength characteristics. For example, genotype Chelyaba was among the best for sedimentation, DDT, stability and MTI. Novosibirskaya 29 and Eritrospermum 727 were similarly superior for sedimentation, stability and MTI. Importantly, Omskaya 35, Lutescens 574, Chelyaba and Shortandynskaya 95 with sedimentation values over 70 ml, were among the outstanding genotypes with high grain Zn and Fe concentrations independent of “concentration” effects (Fig. 3), indicating that improvements in wheat quality (e.g., flour strength) and grain micronutrient (Zn and Fe) concentrations can be achieved simultaneously. Currently, development of new wheat genotypes with high Zn and Fe concentrations is a priority objective. Genotypes having high protein, Zn and Fe levels could be used directly in breeding programs aimed at further improvements of wheat for Zn, Fe and protein.

The present characterization of wheat genotypes for quality traits, including Zn and Fe concentrations, as well as their associations and adaptability patterns, constitute a starting point towards the development of new breeding populations for the improvement of nutritional and quality characteristics in high latitude wheat form northern Kazakhstan and Siberia. However, the narrow phenotypic diversity of some flour strength-related traits, along with the possible negative effects of heat stress on glutenin content, and hence overall quality, may suggest that the introduction of heat tolerant and high glutenin (or high glu/gli ratio, which seems to be more genotype dependent) genotypes would be advantageous.

Acknowledgments

The authors are especially grateful to all institutions, scientists and technicians involved in the Kazakhstan-Siberian Network on Spring Wheat Improvement (KASIB) for excellent execution of the multi-location trials. The support of HarvesPlus is gratefully acknowledged. We thank Dr. Robert McIntosh for his helpful corrections of the English manuscript.

Copyright information

© Springer Science+Business Media B.V. 2009