Submit or Track your Manuscript LOG-IN

SJA_36_4_1020_1032

 

 

 

Research Article

Genotype by Environment Interactions of Vegetative Growth Traits of Bread Wheat Genotypes

Hafsa Naheed* and Hidayat-Ur-Rahman

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

Abstract | Determining the amount of genotype by environment interaction (GEI) is an important step in identifying high yielding and stable genotypes for cultivar development. Keeping in view the significance of genotype and environment, a multi environment study was conducted to evaluate the response of genotypes to different cropping systems and environments. The performance of vegetative growth traits of the 40 exotic bread wheat lines was assessed across seven different environments during 2015-16 and 2016-17 growing seasons. The trials were conducted using RCB design at four locations for two years; Research Farm, of the University of Agriculture, Peshawar; Agriculture Research Station, Buner; Agriculture Research Station, Baffa, Mansehra; and Barani Agriculture Research Station, Jarma, Kohat (one year only). Data were recorded on leaf area, plant height, biological yield and straw yield. Highest plant height was recorded for G02, G31 and G11. The exotic lines included in the study generally had more plant height in Peshawar as compared to other locations. The highest maximum mean for leaf area was observed for G17 and CSA while minimum was observed for G03 followed by G33 and G25. Among the seven environments, genotypes on average produced larger flag leaves having maximum leaf area in Peshawar while lowest was observed in Mansehra. For straw yield of the 40 genotypes across seven environments, the highest straw yield of 12547 kg ha-1 was produced at Mansehra in 2016-17 and the lowest yield of 2440 kg ha-1 was recorded at Buner in 2017-18. Overall, the genotypes included in the study had more biological yield in Mansehra as compared to other locations. Line G06 and G17 produced maximum biological yield. The lines with desirable characteristics can be used in hybridization programs to combine these characteristics in single line, or to transfer them to other high yielding varieties.


Received | November 20, 2019; Accepted | August 24, 2020; Published | October 07, 2020

*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. 2020. Genotype by environment interactions of vegetative growth traits of bread wheat genotypes. Sarhad Journal of Agriculture, 36(4): 1020-1032.

DOI | http://dx.doi.org/10.17582/journal.sja/2020/36.4.1020.1032

Keywords | Bread wheat, GEI, Multi environment trial, Vegetative traits, Exotic lines


Introduction

Bread wheat (Triticum aestivum L.), is one of the most important grain crops used as a staple food in many countries. Currently, wheat is the most widely cultivated (approximately 220 million hectares) and consumed cereal crop. A significant population in many countries is largely dependent on wheat and it fulfills a large part of their nutritional requirements. Globally the consumption and demand of plant materials for use as food, feed and fuel is increasing with the increase in population; furthermore, the use of grain crops for biofuel production has placed an additional pressure on the global grain crops supply (Edgerton, 2009). Wheat is used for food, animal feed and an industrial raw material (Nhemachena and Kirsten, 2017). Consequently, both grain yield and biomass are important products of wheat crop. In Pakistan and other Asian countries wheat straw is an important source of animal feed, it is used in combination with green fodder as well as separately when green fodder is limited (Kumar et al., 2013).

The economic yield of grain crops depends on biological yield, which in turn depends on the crop growth rate and crop growth duration; growth rate is primarily affected by assimilates developed in the process of photosynthesis and on the efficiency with which assimilates are partitioned to different plant organs. The grain yield of a crop is therefore dependent upon different aspects of the developmental morphology mostly affecting the photosynthetic machinery and nutrient absorption mechanisms of the plant (Bueno, 1979). Balanced vegetative and reproductive growth and development are important for both biological and economic yields. Vegetative growth is crucial as photosynthetic machinery (source size and activity) is developed during vegetative phase and it also affects reproductive sink capacity and ultimately the seed yield. Plant height is one of the criteria for vegetative growth. Many wheat improvement programs have focused on plant height regulation for increasing grain yield (Wurschum et al., 2015). Flag leaf area is a reliable predictor of amount of assimilates synthesized and thus it is directly linked to crop growth, development and health (Alqudah and Schnurbusch, 2015). Flag leaf photosynthesis serves as the main supply of carbon for grain filling (Bishop and Bugbee, 1998). Flag leaf traits such as length, width and area are reported to be positively and significantly correlated to major yield contributing traits. Wheat genotypes having relatively larger flag leaf size tends to produce more grains spike-1 (Zhao et al., 2018). Among leaf morphology and its component traits flag leaf area is reportedly the most yield contributing trait, followed by flag leaf width and flag leaf length (Fan et al., 2015). Maximizing leaf area results in the increase of photosynthetic rate and could be considered an important trait for improvement of yield in wheat (Driever et al., 2014). In countries like Pakistan where yield of majority of crops including wheat is comparatively low, there is a dire need to develop new varieties having better agronomic traits and higher yield.

The performance of a particular variety in the field depends upon the genetics, the environment and the response of the genotype to the environment. The response of each genotype is different in different environments as genes of each genotype interact differently with aerial and below ground environmental factors to translate morphological and physiological aspect of crop growth differently. These variable responses of genotypes to environments are called genotype by environment interaction (GEI). The process of selecting superior lines is complicated by the presence of significant G × E interaction (Sohail et al., 2016), as in majority of the cases the phenotype is not the actual expression of the genotype and selection may not be very effective (Bondari, 1999). For effective and precise selection of genotypes; the genotypes are tested in different environments and multiple seasons (Ahmadi et al., 2012). Hence development of improved wheat varieties that can perform well in different agro-environmental conditions is one of the most cost effective and powerful approaches to increase crop production (Annicchiarico, 2002).

Knowledge of the magnitude and type of GEI is a pre requisite in any breeding program. Considering the importance of genotype × environment interactions, this multi-environment trial was conducted to understand the response of the genotypes to the diverse cropping systems and complexes of environments. The specific objectives of the study were to:

  1. Evaluate the performance of exotic wheat lines across the different environments.
  2. Assess G × E interaction of exotic wheat lines for vegetative growth traits.

 

Materials and Methods

This study was conducted to evaluate genotype × environment interaction and the performance of vegetative growth traits of 35 (Genotype, G1-G35) exotic bread lines and five check cultivars (CSA, Morocco, Atta-Habib, Ghanimat and Siran). The present multi-environment study was conducted at four locations during 2016-17 and three locations during 2017-18 wheat growing seasons of the Khyber Pakhtunkhwa province of Pakistan and the seven combinations of years and locations were considered as seven environments as given below:

 

Environment

Location

Year

E-01

Research Farm, The University of Agriculture, Peshawar

2016-17

E-02

Agriculture Research Station Baffa, Mansehra

2016-17

E-03

Agriculture Research Station, Amnawar, Buner

2016-17

E-04

Barani Agriculture Research Station, Jarma, Kohat

2016-17

E-05

Research Farm, The University of Agriculture, Peshawar

2017-18

E-06

Agriculture Research Station Baffa, Mansehra

2017-18

E-07

Agriculture Research Station, Amnawar, Buner

2017-18

 

Design and agronomic practices

This study was performed using RCB design having three replications at all environments except at E-07 where two replications were used due to limited land availability. Each experimental unit had four rows which were two meters long and row to row distance was 0.3 meter and hence the plot size was 2.4 m2. Ploughing, planking and seed bed preparation was done at proper moisture conditions. The application of recommended fertilizer i.e. 120 kg ha-1 nitrogen and 80 kg ha-1 phosphorous for wheat crop was achieved by applying full dose of DAP and half dose of urea at the time of sowing, the remaining half dose of urea was applied at the time of second irrigation. For planting the experiment recommended seed rate of 120 kg ha-1 was used. The calculated amount of seed for each plot (28.8 g) was uniformly distributed in four furrows and covered immediately. Uniform standard management practices were followed to raise the crop at all experimental sites.

Data were recorded on the following vegetative growth parameters:

Plant height: At physiological maturity, height of randomly selected representative plants from each experimental unit was measured from ground level to the tip of the spike excluding awns.

Flag leaf area: Length and width of randomly selected flag leaves in each experimental unit was measured. The leaf area was calculated using the following formula developed by Bari et al. (2010) for accurate estimate of leaf area of wheat.

1/LA= 0.001666 + 0.27934/LL -0.0079/LW + 0.43989/(LL*LW) + 0.01445/(LW2) +2.4645/(LL2)

Where;

LA is leaf area, LL is leaf length, and LW is leaf width.

Biological yield: The above ground mass from each plot was harvested separately and weighed after having dried in sun for a week to record biological yield plot-1. The following formula was used to calculate biological yield in kg ha-1.

Biological yield (kg ha-1) = 10,000 × biological yield plot-1 / plot size

Straw yield: Straw yield was calculated by subtracting grain yield from biological yield.

Statistical analysis

Analysis of variance: Data from all locations and years were analyzed as combined over the environments. The ANOVA appropriate for randomized complete block design was used for analysis of data to test the significance of genotypes, environments and genotypes × environments interaction. Least Significant Difference (LSD) was calculated to compare the means of lines with means of the check cultivars.

 

Results and Discussion

Plant height

Analysis of variance combined over years and locations revealed that differences among genotypes and environments were significant at P ≤ 0.001; interaction between genotypes and environments was also significant at P ≤ 0.001 for plant height (Table 1). These results show that the observable variation present in plant height of these lines is partly due to the differences in the genetic make-up of the lines, partly due to external factors and also due to the differential response of the genetic material of the different lines to the external environmental factors. Maximum portion of observable variation was due to environmental effect (68.31%), followed by GEI (12.32%) and genotypic effect (6.74%). The plant heights of these genotypes were greatly affected by the changes in below ground and aerial environments.

 

Table 1: Analysis of variance combined over 7 environments for biological yield and related traits of 40 genotypes of wheat planted in seven environments (at four locations during 2016-17 and three locations during 2017-18 in Khyber Pakhtunkhwa, Pakistan.

Trait

SoV§

Environments

Reps (E)

Genotypes

G×E

E. Error

DF

6

13

39

234

507

Plant Height (cm)

MS

9380**

159

142**

43**

16

% SS

(68.31)

(2.51)

(6.74)

(12.32)

(10.13)

Flag leaf area (cm-2)

MS

2406.61**

139.51

116.71**

35.52**

17.48

% SS

(38.02)

(4.78)

(11.98)

(21.88)

(23.34)

Straw yield (kg ha-1)

MS

9875867801**

63216596

104473330ns

485060600**

556150550

% SS

(89.09)

(0.57)

(0.94)

(4.38)

(5.02)

Biological Yield (kg ha-1)

MS

2967968236**

7294976

5276254*

3424799**

1815886

% SS

(89.8)

(0.48)

(1.04)

(4.04)

(4.64)

§ “SoV” is source of variation; “Reps (E)” is replications within environments; G×E is genotypes by environments interaction; “E. Error” is experimental error.

 

Across all the seven environments included in the study, the highest plant height was attained by G11 (109.2 cm) in E-01 while the lowest plant height was observed for G33 (66.4 cm) in E-03 (Table 2). Within environments, plant height of the genotypes ranged from 86.9 to 109.2 cm in E-01; 87 to 97 cm in E-02; 66.4 to 84.9 cm in E-03; 70 to 85.7 cm in E-04; 86.4 to 105.4 cm in E-05; 73.7 to 95.6 cm in E-06 and 71.5 to 86.7 cm in E-07. Mean plant height was 98.4 cm in E-01, 92.8 cm in E-02, 76.5 cm in E-03, 79.1 cm in E-04, 96.4 cm in E-05, 82.9 cm in E-06 and 80.4 cm in E-07. Mean values for plant height averaged over all locations and years ranged from 81.3 cm to 91 cm with a grand mean value of 86.9 cm. G11 exhibited maximum plant height of 91.0 cm averaged over all environments, followed by G31 (90.5 cm) and G02 (90.4 cm): G01 had minimum plant height of 81.3 cm; second and third plant height from bottom were recorded for G25 (81.8 cm) and G21 (82.1 cm).

Overall the genotypes included in the study had more plant height in Peshawar (E-01 and E-05) as compared to other locations (Figure 1).

Flag leaf area

Highly significant differences were observed among the genotypes across the seven environments for flag leaf area. The main effect of environments as well as GEI were significant for flag leaf area (Table 1). The phenotypic variation present in the leaf area of the genotypes was influenced by genetic makeup, environment and the interaction between both; genotypes accounted for 11.98% of the variation and GEI accounted for 21.88% of the variation in the leaf area. Maximum variation in the trait was attributed to the environments (38.02%). This signifies that environmental changes had a dominant role and significantly affected the leaf area of the genotypes in this study.

 

 

Flag leaf area based on the average of each genotypes in the seven environments ranged from 16.7 cm2 for G03 at Buner in 2016-17 designated as E-03 to 50.7 cm2 for CSA at Peshawar in 2016-17 designated as E-01 (Table 3). Within environments, flag leaf area ranged from 28.2 cm2 to 50.7 cm2 in E-01; 20.5 cm2 to 36.0 cm2 in E-02; 16.7 cm2 to 32.8 cm2 in E-03; 20.5 cm2 to 43.4 cm2 in E-04; 19.8 to 32.6 cm2 in E-05; 23.0 cm2 to 40.5 cm2 in E-06 and 22.2 cm2 to 32.4 cm2 in E-07. Maximum flag leaf area was observed for CSA in E-01 (50.7 cm2), G02 in E-02 (36.0 cm2), G10 in E-03 (32.8 cm2), G01 in E-04 (43.4 cm2), G02 in E-05 (32.6 cm2), G15 in E-06 (40.5 cm2) and CSA in E-07 (32.4 cm2). Leaf area of the genotypes averaged over all the seven environments ranged from 25.2 cm2 (G03) to 34.3 cm2 (G17 and CSA). Maximum average leaf area was observed for G17 and CSA followed by G01 while minimum was observed for G03 followed by G33 and G25. Among the seven environments, genotypes in Peshawar during 2016-17 on the average produced larger flag leaves having and maximum leaf area, followed by Mansehra in 2017-18 and Kohat in 2016-17 (Figure 2). Flag leaf area was lowest in Mansehra in 2016-17.

 

 

Straw yield

F-values for environments and genotype × environment interaction sources of variation in combined ANOVA were highly significant for straw yield (Table 1) indicating that straw yield was highly influenced by the environments and the performance of genotypes changed with the changes in the environment. Analysis of variance revealed no significant differences among the average straw yields of the genotypes, which shows that differences among the straw yields of the genotypes did not reach statistically significant level. The GEI accounted for 4.38% of the total variation, however, its significance showed that the performance of the genotypes was not the same across environments; further partitioning in ANOVA showed that G × (irrigated versus rain-fed environments) interaction was also significant at the 1% level of probability. Environments sum of square contributed 89.09% to the total sum of squares signifying that mean straw yield was more influenced by environment and the phenotypic variation present in the trait is mostly due to the effect of the environments. Further partitioning of the environmental variance into single degree of freedom irrigated versus rain-fed environments contrast in ANOVA showed that the contrast was highly significant.

Perusal of the GEI means showed that straw yield of the genotypes in the seven environments, varied between 1013 kg ha-1 produced by G28 at Buner in 2017-18 (E-07) and 13722 kg ha-1 produced by G01 at Mansehra in 2016-17 (E-02) (Table 4). Within environments, straw yield of the genotypes ranged from 7583 to 13125 kg ha-1 in E-01; 10241 to 13722 kg ha-1 in E-02; 2433 to 6293 kg ha-1 in E-03; 2731 to 5672 kg ha-1 in E-04; 5193 to 10067 kg ha-1 in E-05; 8381 to 12381 kg ha-1 in E-06 and 1013 to 3900 kg ha-1 in E-07. Maximum straw yield was observed for G01 in E-01 and E-02 (13125 and 13722 kg ha-1 respectively), G16 in E-03 (6293 kg ha-1), G02 in E-04 (5672 kg ha-1), G01 in E-05 (10067 kg ha-1), G22 in E-06 (12381 kg ha-1) and G19 in E-07 (3900 kg ha-1). Though the F-test for the main effect of genotypes was not significant, average straw yield of the genotypes across all seven environments ranged from 6743 kg ha-1 to 8413 kg ha-1. Maximum mean straw yield was observed for G01 (8413 kg ha-1), followed by G06 (8241 kg ha-1) and G17 (8151 kg ha-1) while minimum was observed for G08 (6743 kg ha-1), followed by Morocco (6849 kg ha-1) and G28 (6907 kg ha-1).

 

 

Mean straw yield of the 40 genotypes across seven environments revealed that highest straw yield of 12547 kg ha-1 was produced at Mansehra in 2016-17 and the lowest yield of 2440 kg ha-1 was recorded at Buner in 2017-18 (Figure 3).

 

Table 2: Mean plant heights (cm) of 40 wheat genotypes planted in seven environments (four irrigated and three rain-fed) of Khyber Pakhtunkhwa, during 2016-17 and 2017-18.

Genotype

Environments

Peshawar 2016-17

Mansehra 2016-17

Buner 2016-17

Kohat 2016-17

Peshawar 2017-18

Mansehra 2017-18

Buner 2017-18

Mean

E1

E2

E3

E4

E5

E6

E7

All Env’s

CSA

92.3

92.3

79.0

75.6

97.4

77.0

81.5

85.2

G01

86.9

93.7

69.8

71.7

86.4

85.9

71.5

81.3

G02

103.8

95.3

76.1

83.3

97.1

95.6

76.8

90.4

G03

105.1

92.3

72.6

74.1

94.8

86.4

74.5

86.3

G04

102.5

94.3

77.0

78.6

97.8

86.7

83.7

88.9

G05

103.0

88.0

84.9

83.3

94.6

73.8

81.2

87.2

G06

101.1

96.7

76.1

78.8

99.2

83.0

74.0

87.6

G07

107.4

89.0

76.3

80.0

105.4

87.4

83.3

90.2

G08

96.1

90.3

74.4

80.9

94.1

91.4

79.3

87.0

G09

100.8

92.7

77.1

85.0

99.6

89.2

82.8

89.9

G10

90.1

96.7

72.7

76.7

100.4

83.9

78.7

85.9

G11

109.2

97.0

78.4

78.8

102.0

86.0

82.5

91.0

G12

98.1

95.7

79.1

80.7

101.6

83.1

83.5

89.1

G13

100.9

90.0

77.3

83.3

100.9

86.8

82.7

89.1

G14

90.6

92.7

80.2

80.9

87.8

82.9

83.3

85.6

G15

99.7

94.3

78.6

82.7

88.3

88.8

84.3

88.3

G16

98.3

94.3

78.4

83.2

96.1

81.8

81.0

87.9

G17

108.1

91.0

75.7

85.7

97.8

81.3

81.2

89.1

G18

94.5

93.0

80.2

83.0

100.3

77.9

80.2

87.4

G19

102.7

94.3

78.6

80.1

102.3

83.6

84.8

89.7

G20

92.8

87.0

79.9

81.1

93.6

80.9

81.8

85.5

G21

91.5

94.3

72.5

75.1

89.0

75.3

74.7

82.1

G22

87.7

92.0

77.9

73.6

88.0

79.8

79.3

82.8

G23

99.3

88.3

78.1

73.0

94.4

73.7

77.2

83.7

G24

95.4

91.7

78.1

85.2

94.4

85.2

77.7

87.3

G25

89.9

94.3

72.4

72.4

88.4

75.8

78.0

81.8

G26

106.8

95.0

76.6

82.6

92.7

86.4

80.0

89.0

G27

89.0

90.3

77.6

77.9

92.9

78.6

82.3

84.2

G28

98.3

92.0

73.9

83.2

100.7

88.6

86.5

89.1

G29

104.2

96.3

81.9

83.8

98.4

85.0

73.8

89.8

G30

90.5

89.0

74.2

70.0

94.1

77.8

82.3

82.6

G31

105.0

97.0

77.1

80.3

95.3

90.9

86.7

90.5

G32

95.2

94.3

77.9

82.2

100.0

88.4

84.0

89.1

G33

93.2

91.7

66.4

74.7

96.1

81.3

74.7

83.0

G34

94.8

92.0

74.8

80.7

91.6

80.9

82.8

85.5

G35

100.5

96.3

75.9

79.6

100.1

81.6

81.0

88.2

Morocco

107.0

88.0

78.1

79.9

94.7

78.1

85.0

87.4

Atta Habib

101.7

87.3

76.5

74.8

101.0

76.9

77.8

85.5

Ghanimat

99.5

95.3

72.3

72.7

103.3

81.1

79.7

86.6

Siran

102.5

94.0

74.6

75.4

101.4

79.0

81.0

87.2

LSD’s§

7.7

6.2

6.0

7.0

6.3

6.6

7.6

2.5

§ LSD 5 % values for each of the seven environments. LSD 5% for GE is 6.7.

 

Table 3: Flag leaf area (cm2) of 40 wheat genotypes planted in seven environments (four irrigated and three rain-fed) of Khyber Pakhtunkhwa, during 2016-17 and 2017-18.

Genotype

Environments

Peshawar 2016-17

Mansehra 2016-17

Buner 2016-17

Kohat 2016-17

Peshawar 2017-18

Mansehra 2017-18

Buner 2017-18

Mean

E1

E2

E3

E4

E5

E6

E7

All Env’s

CSA

50.7

27.6

29.3

34.0

29.2

36.2

32.4

34.3

G01

38.8

35.1

21.7

43.4

32.1

34.0

30.5

33.8

G02

29.0

36.0

25.1

35.1

32.6

34.6

25.3

31.4

G03

35.5

21.2

16.7

28.6

20.6

28.5

25.7

25.2

G04

29.8

23.7

26.4

29.4

23.1

29.8

22.6

26.6

G05

38.6

21.7

30.9

35.4

24.5

31.6

24.7

29.9

G06

33.0

22.1

29.9

35.5

23.3

32.0

22.4

28.6

G07

41.6

24.0

27.2

34.4

27.2

39.7

28.2

31.9

G08

29.0

21.4

25.2

29.0

25.9

31.0

23.0

26.5

G09

35.8

22.2

27.4

33.5

25.9

33.6

23.5

29.1

G10

34.1

20.7

32.8

33.3

26.3

37.5

23.7

30.1

G11

28.2

23.5

26.0

21.6

29.8

32.7

25.0

26.8

G12

31.8

21.1

23.9

29.7

25.7

29.5

23.0

26.5

G13

38.4

24.4

28.7

25.2

23.7

34.4

22.8

28.5

G14

38.4

23.3

29.7

20.5

21.7

30.3

22.2

26.8

G15

41.1

22.5

22.5

33.2

21.4

40.5

25.5

29.7

G16

40.8

27.1

27.5

29.7

25.7

33.9

28.8

30.6

G17

45.2

29.5

32.3

37.2

27.1

38.9

27.4

34.3

G18

39.4

26.8

31.5

28.4

22.4

31.0

27.2

29.7

G19

40.9

28.5

23.7

28.8

26.5

33.4

24.3

29.7

G20

44.9

26.6

24.2

38.3

27.5

32.5

24.0

31.5

G21

33.0

25.4

23.4

34.3

19.8

30.5

27.5

27.7

G22

33.1

23.1

32.3

23.7

23.3

36.3

26.5

28.4

G23

33.9

24.4

22.7

24.9

21.1

33.0

25.9

26.6

G24

42.0

23.5

21.8

32.0

26.7

32.9

26.1

29.4

G25

32.8

21.3

25.2

28.0

20.5

29.1

24.8

26.0

G26

30.1

25.9

32.4

28.5

22.8

26.1

23.6

27.2

G27

37.2

23.9

27.0

25.2

21.7

34.3

28.6

28.2

G28

37.1

26.1

30.4

31.4

21.5

32.5

28.0

29.7

G29

38.5

28.3

27.1

28.3

25.9

38.8

31.9

31.2

G30

29.4

23.0

23.0

32.8

25.1

27.1

24.4

26.5

G31

33.5

26.8

31.7

28.6

26.3

31.7

30.5

29.9

G32

28.3

21.0

26.7

31.2

22.9

27.0

25.0

26.1

G33

33.8

21.3

24.6

27.3

21.4

23.0

26.9

25.4

G34

41.0

25.8

29.6

42.8

25.7

37.6

26.0

33.0

G35

38.2

23.5

21.2

26.7

23.5

32.7

26.8

27.5

Morocco

36.1

25.2

28.0

43.0

24.5

34.1

24.9

31.1

AttaHabib

40.4

22.6

22.3

30.0

27.4

33.4

27.7

29.2

Ghanimat

30.9

20.5

23.7

34.9

28.6

33.7

25.6

28.4

Siran

36.3

25.0

24.8

31.6

22.1

27.7

24.4

27.6

LSD’s §

7.1

5.6

7.8

7.9

5.9

7.1

5.6

2.6

§ LSD 5 % values for each of the seven environments. LSD 5% for GE is 6.9.

 

Table 4: Straw yield (kg ha-1) of 40 wheat genotypes planted in seven environments (four irrigated and three rain-fed) of Khyber Pakhtunkhwa, during 2016-17 and 2017-18.

Genotype

Environments

Peshawar 2016-17

Mansehra 2016-17

Buner 2016-17

Kohat 2016-17

Peshawar 2017-18

Mansehra 2017-18

Buner 2017-18

Mean

E1

E2

E3

E4

E5

E6

E7

All Env’s

CSA

10083

13352

3642

3611

8574

11821

2496

7912

G01

13125

13722

2436

4676

10067

11019

1558

8413

G02

11639

13130

3156

5672

5193

11533

2183

7767

G03

10708

11944

5502

2731

5789

11144

1846

7357

G04

9444

11759

3189

3808

5759

10992

3338

7077

G05

10236

12222

3502

4240

7111

10619

2858

7476

G06

11208

12704

4349

4006

9319

11558

2700

8241

G07

11528

12778

4637

3826

7626

10891

1788

7872

G08

10069

10815

3193

3960

5852

9708

2033

6743

G09

9167

12815

4353

4094

8970

8705

2713

7487

G10

9778

13000

5747

3614

8274

9348

2392

7703

G11

10181

11759

4644

3724

6470

11131

1963

7383

G12

9292

10241

4924

4697

8259

10792

1913

7422

G13

9667

12370

3773

3772

8193

11350

2975

7666

G14

10694

12852

4680

3946

5778

10376

2792

7528

G15

10167

13685

4553

3390

5707

11559

3221

7681

G16

10264

12148

6293

4739

7389

10678

3096

8036

G17

10750

13407

5168

4286

8300

10997

2150

8151

G18

10014

13204

5944

4039

7756

10728

1954

7948

G19

10875

13537

4771

4192

8915

9208

3900

8115

G20

9875

12037

4592

3549

7304

10231

2588

7397

G21

9028

12889

3891

3993

7774

10172

1692

7331

G22

9389

11759

4397

3140

7263

12381

2821

7531

G23

10403

12778

4702

3853

7393

11326

2433

7811

G24

10000

13389

2433

4382

8259

9173

1971

7343

G25

8681

13056

3964

4056

6370

10014

3196

7241

G26

10333

12167

4494

3783

6600

10997

1771

7433

G27

8958

12481

4461

4303

6563

11181

3008

7493

G28

7583

12815

4549

3313

5978

11135

1013

6907

G29

9417

13074

4889

4317

5541

11203

1425

7408

G30

9264

12815

3276

4096

8133

11342

3138

7653

G31

10042

13019

3876

3294

7600

10972

3188

7639

G32

9917

12926

4320

4085

6622

10519

1996

7458

G33

9319

12944

3813

3456

6078

10672

1183

7061

G34

10847

12278

3736

4419

7372

10758

2513

7663

G35

11000

11222

4664

4203

8615

10939

2788

7875

Morocco

7903

11218

2607

4226

7978

10126

2404

6849

AttaHabib

10569

12000

3462

3571

8000

10914

2396

7517

Ghanimat

9975

13093

3233

4104

6726

10385

3067

7434

Siran

10056

12481

4864

3588

8222

8381

3142

7453

LSD’s §

1941

1677

1951

902

2099

1764

620

651

§ LSD 5 % values for each of the seven environments. LSD 5% for GE is 1722.

 

Table 5: Biological yield (kg ha-1) of 40 wheat genotypes planted in seven environments (four irrigated and three rain-fed) of Khyber Pakhtunkhwa, during 2016-17 and 2017-18.

Genotype

Environments

Peshawar 2016-17

Mansehra 2016-17

Buner 2016-17

Kohat 2016-17

Peshawar 2017-18

Mansehra 2017-18

Buner 2017-18

Mean

E1

E2

E3

E4

E5

E6

E7

All Env’s

CSA

14444

17778

5309

5000

12222

17500

3742

11212

G01

15417

17222

3547

5833

13148

15278

3400

10907

G02

13611

16296

4622

6528

7037

16111

3658

9997

G03

15556

15556

8236

3889

8519

16944

3313

10636

G04

13333

15185

4900

5556

8704

15833

4950

10022

G05

14583

16111

5147

5833

10185

16111

4271

10623

G06

15139

16852

6638

5417

12593

17778

3988

11561

G07

16528

16852

6937

5139

11296

16667

3546

11367

G08

14583

14444

4904

5556

8704

14722

3296

9767

G09

12917

17037

6776

5417

12593

13889

4513

10745

G10

14028

16852

8747

4722

12037

15000

3854

11093

G11

14583

15370

6756

5417

9630

15833

3371

10475

G12

14306

13519

7436

6528

11481

15833

4367

10802

G13

14028

16296

5596

5694

10741

16111

5425

10812

G14

14722

16667

7236

5556

8519

15278

4867

10683

G15

13750

18148

7264

5000

7963

16944

4854

10846

G16

13333

16111

9782

6389

10741

15556

4879

11275

G17

16111

17407

7612

5833

11852

15556

3871

11543

G18

15139

17407

8678

5694

10741

15556

3650

11347

G19

16111

17778

6638

5833

12407

13889

5842

11483

G20

14444

15556

7103

5139

10556

15000

4179

10588

G21

14306

16667

6402

5417

10556

15278

3063

10600

G22

13472

15370

6774

4306

10000

17778

4617

10617

G23

15000

16481

6880

5139

10926

15833

5013

11040

G24

15972

17593

3744

5833

12037

14444

3671

10811

G25

12639

17407

6231

5417

8333

14722

4875

10200

G26

15139

16481

7272

5278

9259

15556

3829

10731

G27

12917

16481

7039

5556

9630

16389

4792

10681

G28

11944

16852

6838

4583

7778

16111

2663

9882

G29

13611

17037

7267

5972

8148

16111

2871

10509

G30

13611

16852

5031

5417

11296

16389

5129

10802

G31

15000

17407

5964

4861

11296

16111

5188

11115

G32

14444

16852

6476

5694

9074

15833

3242

10580

G33

15694

16667

5647

5833

9630

15000

5000

10771

G34

16111

15000

7264

5833

12037

16944

5304

11509

G35

11019

15023

3851

5278

10556

14167

4583

9442

Morocco

15000

17037

4833

5000

9815

16111

4438

10613

AttaHabib

14028

17222

6036

4583

8148

15556

2304

10066

Ghanimat

15000

15741

5329

5000

11667

17222

4217

10915

Siran

15000

16296

7998

5139

12037

17222

5146

11568

LSD’s §

2319

2062

2811

1221

2877

1927

732

840

§ LSD 5 % values for each of the seven environments. LSD 5% for GE is 221.

 

Biological yield

Probability of F-value for genotypes showed significant differences (P≤0.05) among biological yields of the genotypes (Table 1) showing that the genotypes produced different amount of above ground biomass. Main effects of environments and interaction effects were highly significant for biological yield. Maximum contribution to the total sum of squares was made by environments (89.80%), followed by GEI (4.04%) and genotypes (1.04%). The total variation present in the above ground biomass was mainly due to the effects of the environmental conditions. Significant GEI suggested that fluctuating external factors had dissimilar effects on the genotypes and thus the same genetic makeup expressed differently in different environments.

Across all the environments included in the study, highest biological yield was observed for G15 (18148 kg ha-1) in E-02 while lowest biological yield was observed for Atta Habib (2304 kg ha-1) in E-07 (Table 5). In E-01 biological yield ranged from 11019 kg ha-1 to 16528 kg ha-1 with a grand mean of 14414 kg ha-1, in E-02 from 13519 kg ha-1 to 18148 kg ha-1 with a grand mean of 16473 kg ha-1, in E-03 it ranged from 3547 kg ha-1 to 9782 kg ha-1 with a grand mean of 6418 kg ha-1, in E-04 from 3889 kg ha-1 to 6528 kg ha-1 with a grand mean of 5403 kg ha-1, in E-05 from 7037 kg ha-1 to 13148 kg ha-1 with a grand mean of 10347 kg ha-1, in E-06 from 13889 kg ha-1 to 17778 kg ha-1 with a grand mean of 15854 kg ha-1 and in E-07 from 2304 kg ha-1 to 5842 kg ha-1 with a grand mean of 4194 kg ha-1. Mean values for biological yield averaged over all locations and years ranged from 9442 kg ha-1 to 11568 kg ha-1 with a grand mean of 10756 kg ha-1. The check cultivar Siran exhibited maximum biological yield averaged over all environments (11568 kg ha-1), followed by G06 and G17 (11561 and 11543 kg ha-1, respectively). G35 had minimum above ground biomass followed by G08 and G28 (9442, 9767, 9882 kg ha-1, respectively).

Overall, the genotypes included in the study had more biological yield in Mansehra as compared to other locations. A difference of 3782 kg ha-1 was observed in the average biological yield of Mansehra and Peshawar whereas a difference of 10857 kg ha-1 and 10760 kg ha-1 was observed between Mansehra and Buner and Mansehra and Kohat, respectively (Figure 4). Considering the mean biological yield of all the genotypes across the environments highest biological yield was produced in Mansehra during 2016-17 followed by Mansehra in 2017-18, while lowest average biological yield was produced in Buner in 2017-18.

 

 

The increase in agriculture production in recent past few decades has generally been in pace with the demands however the yields of a number of crops are reaching a plateau (Raines, 2011). The current scenario of rapidly increasing population and climate change effects is resulting in more pressure on the production of agriculture crops. One important element of plant productivity that is not much used directly to select for improved yield is photosynthesis (Raines, 2011). Photosynthetic products are the primary elements of plant productivity, increasing photosynthetic rate is considered an important trait for increasing biomass and biological yield (Parry et al., 2011; Zhu et al., 2010). Previous studies show that agronomic traits such as plant height, harvest index and total biomass have also contributed to improvement in grain yield (Gao et al., 2017). In the present study, exotic bread wheat lines were evaluated for vegetative growth traits, as total biomass is a function of the total photosynthetic activities of the plant (Driever et al., 2014) and flag leaf area has been identified as valuable trait in breeding programs and a major contributor to grain yield (Alqudah and Schnurbusch, 2015; Driever et al., 2014).

Breeding and selection for ideal plant traits to increase crop production is challenging due to the presence of genotype × environment interactions (Sohail et al., 2016; Sharma, 1993). For assessing performance of genotypes, they are generally tested over a range of different environments to evaluate the variation in performance in diverse set of conditions (Ahmadi et al., 2012) as the accuracy of independent field trials is low (IRRI, 2006) and the G × E interactions hampers the actual performance of the genotypes. In the present multi-environment trial, pooled analysis of variance showed that the main effects of genotype and environment were significant for plant height, leaf area and biological yield and for the straw yield the main effect of the environment was significant whereas the main effect of the genotypes was statistically not significant (Table 1). These results indicate that the genetic makeup of the genotypes as well as the environmental conditions were responsible for the variation observed in the vegetative growth traits of the genotypes. For all the studied traits main effect of the environments was an important source of variation as indicated by the higher contribution of environment sums of squares to the total sum of squares. This shows that the environments in which the genotypes were tested were diverse and had significantly affected the performance of the genotypes.

Plant height is a measure of vertical growth and it is needed to place leaves at different positions for proper light interception and photosynthesis, however, very tall varieties are prone to more lodging under irrigated conditions and very short varieties do not perform well under limited water conditions. Moreover, wheat genotypes with plant height reduced to a certain level has increased genetic gains in wheat and has significantly contributed to increased wheat productivity globally (Tshikunde et al., 2019; Zhang et al., 2016). Plant height in this study was affected by environment, genotypes and G x E interaction. Plant height in different environments ranged from 76 to 98 cm; average height of the different genotypes ranged from 83 to 91 cm, in the GE two-way table plant height ranged from 66 to 109 cm. G02, G31 and G11 produced maximum mean plant height (Table 2). Measuring the leaf area of plants is important for measuring growth and vigor of plants and leaf area especially the flag leaf area is the primary source of energy and mass exchange between the atmosphere and plants (Fang and Liang, 2008). Leaf area is measured in plants to evaluate processes such as canopy evapo-transpiration, biomass accumulation and photosynthesis (Ahmad et al., 2015). Flag leaf area is important as it contributes major part of assimilates for grain filling and thus contributes to yield. Leaf area is considered to be an indicator of crop growth, development, and plant health, and has a strong relationship with these traits in wheat and barley (Alqudah and Schnurbusch, 2015). Several studies report that appropriate flag leaf size could promote development of high grain yield potential (Zhao et al., 2018). Leaf area of the genotypes as averaged over all seven environments ranged from 25.2 to 34.3 cm2. Maximum mean leaf area was observed for G17 and CSA followed by G01 while minimum was observed for G03 followed by G33 and G25 (Table 3).

Wheat straw is an important component of the crop, it serves as a main source of animal feed in areas/time of year where green fodder is not available; it is also used in combination with green forage to fulfill the nutritional requirements of the animals (Kumar et al., 2013) especially in Pakistan and other Asian countries. Cereals straw is recognized as a significant source of renewable energy, (Zajac et al., 2013), in some advanced countries like USA, there is an increasing demand for wheat straw by livestock farmers to be used in feed rations (Gross, 2016). Wheat straw also serves as an important raw material for bioethanol (renewable fuel), the use of which can reduce the production of carbon dioxide and also lessen the dependency on fossil fuels (Dai et al., 2016). Wheat straw can also be used for animal bedding, paper making to save trees, cap making, basket making, composting, packing material and mushroom cultivation. In the present study, though the seven single site ANOVAs for the different environments showed significant differences among straw yields of the genotypes (as shown by LSDs in each column for environment in Table 4); main effect of the genotypes in combined ANOVA for straw yield was not significant but the GEI for straw yield was significant indicating that differences were found among the straw yields of the genotypes in the different environments and rankings of the straw yields of the genotypes were different in the different environments with different genotypes producing maximum and higher straw yields in different environments as shown in each column of Table 4, However, the environmental conditions at each site had much greater effect on straw yield of the genotypes (Figure 3).

The above ground biomass known as biological yield is a useful selection criteria for improving grain yield in wheat and it has a high economic value as well (Jimenez-Berni et al., 2018; Sharma, 1993). In developing countries genotypes having high biological yield are more preferred by farmers as such genotypes produce more grains as well as non-grain plant parts (Sharma, 1992). G06 and G17 among the tested lines exhibited maximum above ground biomass averaged over all environments. Biological yield of the genotypes differed significantly across environments. Biological yield ranged from 18148 kg ha-1 for G15 in E-02 to 2304 kg ha-1 for check line Atta Habib in E-07. The trend of the biological yield produced by the genotypes in different environments showed that majority of the genotypes produced higher biomass in Mansehra and lower in Buner (Figure 4). The reason for low dry matter accumulation in genotypes in Buner could be due to the scarcity of water in early vegetative growth stages.

 

Conclusions and Recommendations

Significant differences were found among the genotypes for plant height, leaf area and biological yield whereas there were non-significant differences among the straw yield of the genotypes. Main effects of the environments were also highly significant for all the traits. GEI were significant for all the studied traits showing that performance of the lines varied under different environments. It can be concluded from these results that genetic makeup, environmental differences as well as their interaction were responsible for the phenotypic differences in then genotypes. This has an important implication for wheat breeders and these can be exploited by breeders for variety development and varietal improvement programs. Exotic lines G06 and G17 produced more biological yield; G11, G31 and G02 produced taller plants and G17 and G01 produced larger leaves. Grain yield can be improved by increasing biological yield as it is the product of biological yield and harvest index. These lines can be used in crossing programs to combine desirable traits in single line, or to transfer these traits to other high yielding and superior varieties.

 

Novelty Statement

This study highlights the importance of otherwise neglected straw yield and other vegetative growth traits of bread wheat.

 

Author’s Contribution

Hafsa Naheed: Conducted the experiments, collected, interpreted and analysed the data and wrote the manuscript.

Hidayat-Ur-Rahman: Supervised the research, provided research material, reviewed the manuscript.

Conflict of interest

The authors have declared no conflict of interest.

 

References

Ahmad, S., H. Ali, A. Rehman, R.J.Z. Khan, W. Ahmad, Z. Fatima, G. Abbas, M. Irfan, H. Ali, M.A. Khan and M. Hasanuzzaman. 2015. Measuring leaf area of winter cereals by different techniques: A comparison. Pak. J. Life Soc. Sci., 13(2):117-125.

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 Physiol. Breed., 2(1): 43-54.

Alqudah, A.M. and T. Schnurbush. 2015. Barley leaf area and leaf growth rates are maximized during the pre-anthesis phase. Agronomy., 5(2): 107-129. https://doi.org/10.3390/agronomy5020107

Annicchiarico, P., 2002. Genotype x Environment interactions challenges and opportunities for plant breeding and cultivar recommendations. Food Agric. Organ. U. N. Rome.

Bari, A., A. Ayub and P. Shah. 2010. Nitrogen management for wheat and its residual effect on maize crop. VDM Verlag Dr. Muller GmbH and Co. KG. Germany.

Bishop, D.L. and B.G. Bugbee. 1998. Photosynthetic capacity and dry mass partitioning in dwarf and semi-dwarf wheat (Triticum aestivum L.). J. Plant Physiol., 153(5-6): 558–565. https://doi.org/10.1016/S0176-1617(98)80204-6

Bondari, K., 1999. Statistical analysis of genotype by environment interaction in agricultural research. Exp. Stat. Coastal Plain Station, Univ. Georgia, Tifton, GA., pp. 31793-0748.

Bueno, A., 1979. Leaf area estimation, growth analysis, and yield evaluation in grain sorghum (Sorghum bicolor L. Moench.). Retrospective Theses and Dissertations, Iowa State University Capstones.

Dai, J., B. Bean, B. Brown, W. Bruening, J. Edwards, M. Flowers, R. Karow, C. Lee, G. Morgan, M. Ottman and J. Ransom. 2016. Harvest index and straw yield of five classes of wheat. Biomass Bioenergy, 85: 223-227. https://doi.org/10.1016/j.biombioe.2015.12.023

Driever, S.M., T. Lawson, P.J. Andralojc, C.A. Raines and M.A.J. Parry. 2014. Natural variation in photosynthetic capacity, growth, and yield in 64 field-grown wheat genotypes. J. Exp. Bot., 65: 4959–4973. https://doi.org/10.1093/jxb/eru253

Edgerton, M.D., 2009. Increasing crop productivity to meet global needs for feed, food, and fuel. Plant Physiol., 149: 7–13. https://doi.org/10.1104/pp.108.130195

Fan, X., F. Cui, C.H. Zhao, W. Zhang, L. Yang, X. Zhao, J. Han, Q. Su, J. Ji, Z. Zhao and Y. Tong. 2015. QTL for flag leaf size and their influence on yield-related traits in wheat (Triticum aestivum L.). Mol. Breed, 35: 1–16. https://doi.org/10.1007/s11032-015-0205-9

Fang, H. and S. Liang. 2008. Leaf area index models. Encycl. Ecol., pp. 2139-2148. https://doi.org/10.1016/B978-008045405-4.00190-7

Gao, F., D. Ma, G. Yin, A. Rasheed, Y. Dong, Y. Xiao, X. Xia, X. Wu and Z. He. 2017. Genetic progress in grain yield and physiological traits in Chinese wheat cultivars of southern Yellow and Huai Valley since 1950. Crop Sci., 57: 760–773. https://doi.org/10.2135/cropsci2016.05.0362

Gross, P., 2016. What is the nutrient value of wheat straw? Michigan State University, MSU Extension https://www.canr.msu.edu/news/whats_the_nutrient_value_of_wheat_straw accessed 5 September 2019

IRRI, 2006. Rice breeding course. Int. Rice Res. Inst., https://www.knowledgebank.irri.org/ricebreedingcourse/bodydefault.htm#Lesson_8_Correlation%20among%20traits%20implications%20for%20screening.htm accessed 21 November 2018

Jimenez-Berni, J.A., D.M. Deery, P. Rozas-Larraondo, A.T.G. Condon, G.J. Rebetzke, R.A. James, W.D. Bovill, R.T. Furbank and X.R. Sirault. 2018. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front. Plant Sci., 9: 237-251. https://doi.org/10.3389/fpls.2018.00237

Kumar, S., A.S. Dhindwal and R.K. Ary. 2013. Dry matter and straw yield in wheat as influenced by preceding crops, planting methods and irrigation levels. Forage Res., 39(2): 88-92.

Nhemachena, C.R. and J. Kirsten. 2017. A historical assessment of sources and uses of wheat varietal innovations in South Africa. SA. J. Sci., 113: 1–8. https://doi.org/10.17159/sajs.2017/20160008

Parry, M.A.J., M. Reynolds, M.E. Salvucci, C.A. Raines, P.J. Andralojic, X.G. Zhu, G.D. Price, A.G. Condon and R.T. Furbank. 2011. Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency. J. Exp. Bot., 62(2): 453–467. https://doi.org/10.1093/jxb/erq304

Raines, C.A., 2011. Increasing photosynthetic carbon assimilation in C3 plants to improve crop yield: Current and future strategies. Plant Physiol., 155: 36–42. https://doi.org/10.1104/pp.110.168559

Sharma, R.C., 1992. Analysis of phytomass yield in wheat. Agron. J., 84: 926-929. https://doi.org/10.2134/agronj1992.00021962008400060003x

Sharma, R.C., 1993. Selection for biomass yield in wheat. Euphytica, 70: 35-42. https://doi.org/10.1007/BF00029638

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

Tshikunde, N.M., J. Mashilo, H. Shimelis and A. Odindo. 2019. Agronomic and physiological traits, and Associated Quantitative Trait Loci (QTL) affecting yield response in wheat (Triticum aestivum L): A Review. Front. Plant Sci., 10: 1428-1443. https://doi.org/10.3389/fpls.2019.01428

Wurschum, T., S.M. Langer and C.F.H. Longin. 2015. Genetic control of plant height in European winter wheat cultivars. Theor. Appl. Genet., 128: 865–874. https://doi.org/10.1007/s00122-015-2476-2

Zajac, T., A. Oleksy, A. Stoklosa, A. Klimek-Kopyral and J. Macuda. 2013. Vertical distribution of dry mass in cereals straw and its loss during harvesting. Int. Agrophys., 27: 89-95. https://doi.org/10.2478/v10247-012-0072-0

Zhang, Yu, W. Xu, H. Wang, H. Dong, X. Qi, M. Zhao, Y. Fang, C. Gao and L. Hu. 2016. Progress in genetic improvement of grain yield and related physiological traits of Chinese wheat in Henan Province. Field Crops Res., 199: 117–128. https://doi.org/10.1016/j.fcr.2016.09.022

Zhao, C., Y. Bao and X. Wang. 2018. QTL for flag leaf size and their influence on yield-related traits in wheat. Euphytica, 214(11): 209-215. https://doi.org/10.1007/s10681-018-2288-y

Zhu, X.G., S.P. Long and D.R. Ort. 2010. Improving photosynthetic efficiency for greater yield. Annu. Rev. Plant Biol., 61: 235–261. https://doi.org/10.1146/annurev-arplant-042809-112206

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

Sarhad Journal of Agriculture

September

Vol.40, Iss. 3, Pages 680-1101

Featuring

Click here for more

Subscribe Today

Receive free updates on new articles, opportunities and benefits


Subscribe Unsubscribe