CHAR AC TER IZA TION OF IN DIAN AND AUS TRA LIAN BAR LEY (
Hordeum vulgare
L.)
GE NO TYPES FOR AGRONOMICAL TRAITS
Sudhir Kumar, Nitish De, D.K. Baranwal* and Rakesh Deo Ranjan
De part ment of Plant Breed ing and Ge net ics, Bihar Ag ri cul tural Uni ver sity, Sabour, Bhagalpur 813210 (In dia). *Cor re spond ing au thor : [email protected].
ABSTRACT
An experiment was conducted for multivariate analysis of 25 Indian and Australian barley genotypes using ten investigating traits. All the traits showed significant genetic variability. High heritability with high genetic advance (GA) was found for yield per plot (gm) (YPP), plant height (cm) (PH), 1000 grain wt. (TKW) (g) and grains per spike (GPS) indicating the presence of additive genes effect and effectiveness of selection for improvement of these traits. The YPP revealed highly significant and positive genotypic correlation with harvest index (HI), grains per spike, plant height and 1000- grain wt. Maximum direct effect on yield per plot was exhibited by the HI observed via, GPS, PH, spike length (SL) and TKW. Based on cluster analysis, 25 barley genotypes were categorised into 6 clusters. The maximum cluster distance (386.78) was observed between cluster V and cluster VI. The proportionate contribution of the YPP and TKW towards genetic divergence was 40% and 27%, respectively. Highest cluster mean values for PH, awn length, GPS, HI, TKW and YPP was found in cluster 4. Principal component analysis revealed that first four principal components (PC1, PC2, PC3 and PC 4) contributed 74.34% of the total variations with the proportionate contribution values of 26.37, 19.7%, 16.7% and 11.6% respectively. The first PC has positive association with the GPS and YPP, while negative association with TKW and tiller per plant. Promising diverse parents identified based on multivariate analysis will be utilized in future hybridization programme.
Key words : Correlation co ef fi cient, diversity, genetic ad vance, heritability, path di a gram and principal components
Barley (Hordeum vulgare L.) is a fourth important cereal after Wheat, Rice and Maize in the world with a share of 7 per cent of the global cereal production. It has wider adaptability, better stress tolerance among the major cereals and importance for both human consumption and cattle feed along with its unique brewing properties. In India, total acreage of barley is about 0.70 Mha area with production of 1.74 MT and productivity 25.08 Q/ha (1). So there is an urgent need to enhance barley productivity under changing climate scenario. To fulfil the requirement, expanding the genetic variability of Indian barley with elite Australian barley lines will be major strategies for barley improvement. Grain yield being a complex trait, its degree of expression is depends upon component traits and their cumulative action. Degree and direction of association between two or more variables reveal correlation coefficient. Correlation studies provide better under-standing of yield component traits which would be beneficial for plant breeder during selection (2). Direct and indirect contribution of independent variables on dependent variables results path coefficient and it will help researchers to determine major yield components and understanding cause of relationship between two variables. Path coefficient analysis assists in indirect selection for barley improvement because direct selection is not effective for low heritable traits such as yield. Thus, the estimation of heritability and genetic advance is essential for a breeder which helps in understanding the magnitude, nature and interaction of genotype and environmental variation of the traits.
On the other hand assessment of genetic diversity of elite germplasms by multivariate analysis is more precise technique for choosing promising genetically diverse lines for desirable traits (3). By keeping these views in mind the present experiment was conducted to characterize 25 Indian and Australian elite barley genotypes through genetic variability parameters and multivariate analysis to classify the diverse parents for generating heterotic cross combination and transgressive segregants.
MATERIALS AND METHODS
The present experiment was conducted during Rabi season 2013-14 in experimental site of the Bihar Agriculture University, Sabour, Bhagalpur (India) (25o 15’ N and 84°4’ E, 45.75 m asl). Soil pH ranged from 6.5-7.5 as per soil report of the University. The average rainfall of this area is about 1150 mm and average relative humidity is 70 per cent as per meteorological data. Twenty five barley genotypes of diverse origin (India and Australia) were provided from the Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University Varanasi (U.P.), India (Table 1).The experiment was conducted in RCBD with three replications. The distance maintained between row to row and between plants to plant were 20 cm and 10 cm, respectively. All growing practices were applied as recommended to growers. Data were recorded on various parameters, viz., days to 50% heading, days to maturity, PH, tiller per plant, spike length, awn length, grains per spike, harvest index, 1000 grain wt. and yield per plant (3). Volume 11 (2) : 157-162, (2016) in Agriculture and Technology
Data from five plants of each genotype were averaged replication wise and their mean data was used for statistical analysis. Cluster and Principle component analysis of 25 barley genotypes based on yield and its attributing traits were assess by using statistical software Window stat version 8.6 from Indostat services. Clustering pattern among the 25 barley genotypes exhibiting dendrogram was assessed by using Tocher’s method (3) (Fig.-1). By this method, average intra and inter-cluster distance was estimated representing squared Euclidean distances considering yield and its nine contributing traits (Fig.-3). Average mean value of each cluster for ten studied traits has been represented in Table-6.
Genotypic coefficients of variation (GCV) and phenotypic coefficients of variation (PCV) were estimated according to (4) while heritability in broad sense (h2 b.s.) was estimated according to (5). The genetic advance (GA) and genetic advance as per cent of mean (GAPM) were calculated according to (6) however, correlation coefficient analysis by (2) and path coefficient analysis was accessed by (7).
RESULTS AND DISCUSSION
The analysis of variation (ANOVA) revealed that all the
traits show significantly higher variability. The difference between GCV and PCV were higher for harvest index followed by yield per plant indicating more environmental influence on these traits. PCVs were slightly higher than GCVs for all concern traits which indicate the environmental influence on traits expression as earlier reported by (8) (Table-2). The PCV analysis revealed that yield per plot exhibited highest PCV (59.03) followed by yield per plant (34.37), tiller per plant (27.87), harvest index (26.32) similar with the studies of (9). The maximum value of GCV was found for yield per plot (58.13) followed by yield per plant (31.9), tiller per plant (24.87), grain per spike (22.97) showed conformity with the findings of Chand et al (2008). The estimates of heritability (broad sense) and genetic advance expressed as per cent of mean have been presented in Table-2. High heritability estimates was associated with high estimates of genetic advance (GA) for yield per plot (gm) (97%; 102.71), plant height (cm) (97%; 16.73), 1000 grain weight (gm) (88%; 11.25) and grains per spike (81%; 16.12) which in fact demonstrate the presence of additive genes effect i.e. essential for effective selection for these traits. Such results showed similarity with findings of (11). The estimate of genetic advance as per cent of mean was noticed maximum for yield per plot (117.94), yield per plant (61.01), tiller per plant (46.63) and grain per spike (42.48) as earlier reported by (9).
The yield per plant envisaged highly significant and positive genotypic correlation coefficient (rg) with harvest index (0.77**), grains per spike (0.46**), plant height (0.33**) and 1000-grain wt. (0.27*). Plant height showed highly significant and positive genotypic correlation coefficient (rg) with awn length (0.70**), spike length (0.46**), days to maturity (0.38**), harvest index (0.32**), grains per spike (0.31**) and 1000 grain wt. (0.27*). Days to 50 % heading exhibited negative significant genotypic correlation with 1000 grain wt, (-0.32**) and harvest index (-0.27*). The 1000 grain weight showed positive significant genotypic correlation with tiller per plant (0.36**), harvest index (0.29*) and plant height (0.27*) however negative correlation with days to 50 % heading (-0.32**) and grains per spike (-0.37**) which is supported by report of (12).
Path analysis partitions correlation coefficient into direct and indirect effect which proves the cause and effect relationship (Table 4). Maximum direct effect on yield per plot was exhibited by harvest index (0.95) observed via, grain per spike (0.42), plant height (0.31), spike length (0.25) and 1000 grain wt. as same as observed by Carpici et al., (2012) and Talebi, R. (2012). The genotypic correlation matrix shows that harvest index which has significant direct effect on yield per plot and positively correlate with grain per spike (0.44) and Table-1 : List of used 25 Indian and Australian Barley genotypes.
Sl. No.
Genotypes Origin (India/ Australia)
Row types (Two Row/Six Row)
1. Jyoti India Six
2. C-138 India Six
3. DL-88 India Six
4. Lakhan India Six
5. Azad (K125)# India Six
6. RS 6 India Six
7. Geetangali India Six
8. Karan 4 India Six
9.. Karan 19 India Six
10. Karan 521 India Six
11. Karan 16 India Six
12. Alfa 93 India Two
13. DWRUB 73 India Two
14. DWRUB 52 India Two
15. DWR 28 India Two
16. Clipper Australia Two
17. Henley Australia Six
18. Prestige Australia Two
19. Hormal Australia Two
20. Beecher Australia Six
21. Rihani Australia Six
22. Morac-9-75 Australia Six 23. V. Morles Australia Six
24. Yardu Australia Six
25. Maria Australia Six
1000 grain wt. (0.29) while negative with tiller per plant (0.35) and days to 50% heading (0.27) (Table 3).
The 25 barley genotypes were categorized into six cluster using Tocher’s method and their Euclidean distance using D2 statistics depicted in Fig.-1 and 3. Eleven genotypes were classified in cluster III accounting 44 per cent of total genotypes which is maximum number of genotypes in any cluster however six genotypes categorized in cluster II and five genotypes categorized in cluster I followed by remaining three clusters have one genotype in each cluster (Fig.-1). Average inter-cluster distance was found maximum (386.78) between cluster V (RS 6) and cluster VI (Karan 4) followed by between cluster III and IV (308.00). So, the crossing between these two highly diverse cluster parents would be fruitful for
getting heterotic cross combination. The intra cluster distance is minimum in cluster I (23.46) which contains 5 genotypes. It indicated that cluster I genotypes are genetically related. Out of the ten traits, only three traits like yield per plot, 1000 grain wt. and plant height contributed 41%, 27% and 20% respectively out of total divergence (Fig.-2). Highest cluster mean values for yield plot, yield per plant, harvest index, awn length,1000 grain weight (g) and plant height, was found in Cluster IV having only one genotype (Lakhan) (Table-6). Cluster V containing only genotype RS 1 represented highest cluster mean value for number of grain per spike.
The Principal component (PC) analysis was performed to study yield components in 25 barley genotypes (Table-7). The first four PCs having Eigen Table-2 : Grand mean, coefficient of variation, genetic advance and heritability for yield and yield attributing characters.
Character Mean Vp Vg PCV (per
cent)
GCV (per cent)
ECV (per cent)
H2b.s (per cent)
GA GAPM
(%) Days to 50% heading (Days) 71.80 17.47 12.91 5.82 5.0 2.97 74.0 6.37 8.87 Days to maturity (Days) 111.65 15.81 9.28 3.56 2.73 2.29 59.0 4.81 4.32 Plant height (cm) 64.86 69.62 67.76 12.86 12.70 2.11 97.0 16.73 25.80
Tiller per Plant 8.00 4.786 3.96 27.33 24.87 11.33 83.0 3.73 46.63
Spike length (cm) 9.34 0.96 0.72 10.46 9.09 5.16 76.0 1.52 16.28
Awn length (cm) 9.68 0.85 0.45 9.53 6.96 6.51 53.0 1.01 10.47
Grains per spike 37.99 94.22 75.94 25.59 22.97 11.28 81.0 16.12 42.48 Harvest Index (%) 36.03 89.86 55.96 26.32 20.76 16.16 63.0 12.16 33.75 1000- grain wt. (g) 38.25 38.48 33.88 16.22 15.22 5.62 88.0 11.25 29.42 Yield per Plant (g) 10.32 12.57 10.83 34.37 31.90 12.78 86.0 6.29 61.01
Where Vp : Phenotypic variance; Vg : Genotypic variance; PCV : Phenotypic coefficient of variation; GCV : Genotypic coefficient of variation; h2 : Heritability (Broad sense %); GA : Genetic advance and GAPM : Genetic advance per cent of mean.
Table-3 : Phenotypic (rp) and Genotypic (rg) correlation coefficients.
Character Days to
50% heading
Days to maturity
Plant height
Tiller/ Plant
Spike length
Awn length
Grains/ Spike
Harves t Index %
1000 grain weight
Yield per Plant Days to 50% heading (Days) rp 1.00 0.36** 0.10 0.00 -0.04 -0.01 -0.10 -0.33** -0.28* -0.03 rg 1.00 0.40** 0.12 0.00 0.00 0.04 -0.09 -0.27* -0.32** 0.06 Days to maturity (Days) rp 0.36** 1.00 0.30** 0.05 0.22 -0.13 -0.02 -0.08 0.15 0.13 rg 0.40** 1.00 0.38** 0.04 0.50 0.09 0.12 -0.15 0.24* 0.17 Plant height (cm) rp 0.10 0.30** 1.00 0.06 0.41** 0.54** 0.29* 0.28* 0.27* 0.32**
rg 0.12 0.38** 1.00 0.07 0.46** 0.70** 0.31** 0.32** 0.27* 0.33** Tiller/ Plant rp 0.00 0.05 0.06 1.00 0.11 0.26* -0.43** -0.23 0.34** -0.27* rg 0.00 0.04 0.07 1.00 0.18 0.54** -0.52** -0.35** 0.36** -0.34** Spike length (cm) rp -0.04 0.22 0.41** 0.11 1.00 0.44** 0.35** 0.23 0.12 0.16
rg 0.00 0.50** 0.46** 0.18 1.00 0.46** 0.38** 0.26* 0.18 0.12 Awn length (cm) rp -0.01 -0.13 0.54** 0.26* 0.44** 1.00 0.19 0.06 0.10 0.05 rg 0.04 0.09 0.70** 0.54** 0.46** 1.00 0.11 0.05 0.18 0.09 Grains/ Spike rp -0.10 -0.02 0.29* -0.43** 0.35** 0.19 1.00 0.41** -0.24* 0.40**
rg -0.09 0.12 0.31** -0.52** 0.38** 0.11 1.00 0.44** -0.37** 0.46** Harvest Index (%) rp -0.33** -0.08 0.28* -0.23 0.23 0.06 0.41** 1.00 0.33** 0.73** rg -0.27* -0.15 0.32** -0.35** 0.26* 0.05 0.44** 1.00 0.29 0.77** 1000 grain wt. (g) rp -0.28* 0.15 0.27* 0.34** 0.12 0.10 -0.24* 0.33** 1.00 0.29* rg -0.32** 0.24* 0.27* 0.36** 0.18 0.18 -0.37** 0.29* 1.00 0.27*
Yield/Plant (g) rp 1.00
Table-4 : Direct and Indirect effects of agronomical traits in 25 studied barley genotypes.
Character Days to 50% heading
Days to maturity (Days)
Plant height (cm)
Tiller/ Plant (count)
Spike length (cm)
Awn length
(cm)
Grains/ Spike (number)
Harvest Index
%
1000 grain wt.
(gm)
Genotypic Correla-tion with Yield/Plot Days to 50% heading 0.27 0.11 0.03 0.00 0.00 0.01 -0.02 -0.07 -0.09 0.0643 Days to maturity 0.23 0.59 0.22 0.02 0.29 0.05 0.07 -0.09 0.14 0.1685 Plant height (cm) -0.08 -0.24 -0.64 -0.04 -0.29 -0.45 -0.20 -0.21 -0.17 0.3286 Tiller per Plant 0.00 -0.01 -0.01 -0.20 -0.04 -0.11 0.10 0.07 -0.07 -0.3417 Spike length (cm) 0.00 -0.30 -0.28 -0.11 -0.61 -0.28 -0.23 -0.16 -0.11 0.1249 Awn length (cm) 0.03 0.06 0.51 0.39 0.33 0.72 0.08 0.04 0.13 0.0938 Grains per Spike -0.03 0.04 0.11 -0.18 0.14 0.04 0.36 0.16 -0.13 0.4580 Harvest Index (%) -0.26 -0.14 0.31 -0.33 0.25 0.05 0.42 0.95 0.28 0.7747 1000 grain wt. (g) -0.09 0.07 0.08 0.11 0.05 0.05 -0.11 0.08 0.29 0.2680
R square =0.234
Table-5 : Inter and Intra Cluster Distances : Tocher Method.
Cluster I Cluster II Cluster III Cluster IV Cluster V Cluster VI
Cluster I 23.46 60.48 82.16 152.02 68.88 208.79
Cluster II 50.77 153.06 85.45 118.98 161.19
Cluster III 78.71 308.00 168.82 283.88
Cluster IV 0.00 231.81 131.53
Cluster V 0.00 386.78
Cluster VI 0.00
Table-6 : Cluster Mean : Average of studied traits of each cluster based on Tocher’s method
Days to 50% heading
Days to maturity
Plant height
Tillers/ plant
Spike length
Awn length
Grains/ Spike
Harvest Index
%
1000 grain weight
Yield/P lant
Cluster I 71.40 110.60 65.53 6.99 8.93 9.43 38.87 36.97 37.33 11.07
Cluster II 72.94 113.67 73.60 8.87 10.28 10.47 43.47 38.22 39.82 12.09
Cluster III 71.27 111.18 58.18 8.09 8.96 9.26 34.09 33.68 38.84 9.28
Cluster IV 71.33 110.00 79.90 7.70 8.80 10.93 38.40 44.33 41.70 12.68
Cluster V 73.67 113.67 73.30 6.17 9.95 9.50 49.83 43.90 29.27 11.02
Cluster VI 71.33 109.67 59.10 9.17 9.80 9.67 30.10 27.80 32.37 4.30
Table-7 : Principal component analysis for ten traits in 25 barley genotypes.
Traits/Components PC1 PC 2 PC 3 4 PC PC 5
Eigen Value 2.90 2.16 1.84 1.28 0.83
% Variance explained 26.37 19.67 16.70 11.60 7.52
Cum. Variance explained 26.37 46.04 62.74 74.34 81.86
Eigen Vector
Days to 50% heading 0.16 0.06 0.30 0.57 0.01
Days to maturity -0.08 -0.52 -0.14 0.20 0.50
Plant height 0.17 -0.45 0.43 -0.13 0.04
Tillers/ plant -0.47 0.06 0.27 0.17 -0.14
Spike length -0.26 -0.25 -0.26 0.50 -0.05
Awn length -0.41 0.34 -0.09 0.27 -0.17
Grains/ Spike 0.33 -0.06 -0.53 0.21 -0.22
Harvest Index (%) 0.11 0.50 0.20 0.02 0.41
1000 grain weight -0.50 -0.06 -0.04 -0.14 0.45
values greater than one accounted for 74.34% of total variation. This finding was in agreement with that of Meena et al. (2014). Moreover, two dimensional ordinations of 25 barley genotypes on PC axis 1 and 2 are represented in Fig.-4 which revealed scattered diagram of genotypic distribution pattern on axis. Interestingly, distribution of genotypes along the two axes in the PC plot was consistent with the grouping of these genotypes obtained using cluster analysis. The first PC has positive association with grain per spike and yield per plant, while negative association with awn length, tiller per plant and 1000 grain wt. The second PC has positive association with harvest index (HI %), awn length while negative association with days to maturity and plant height. The third PC has positive association with yield per plant, plant height and tiller per plant while negative association with grain per spike and days to maturity. The fourth PC has positive association with days to 50% heading, spike length and yield per plant while negative association with plant height. The traits of barley that demonstrated positive association with PCs have major role in genetic diversity analysis and revealed total genetic variation are in agreement with findings of (3). For future experiment, the top priority as selection parameters of the genotypes should be on the basis of those traits which have high heritability with high genetic advance such as 1000 grain weight, grain per spike grain yield per plant etc and diverse genotypes identified in the present study contributing maximum in genetic diversity such as yield per plant, 1000 grain weight may be utilized for attempting heterotic cross combination.
Fig.-2 : Graphical representation of proportionate contribution of studied major traits (in parentheses value) towards genetic divergence.
CONCLUSION
The correlation study revealed that grain yield per plant had strong positive association with harvest index, grain per spike and plant height. It indicated that grain yield of barley can be improved by selecting genotypes having higher performances for the above traits. Path-coefficient analysis revealed that harvest index followed by awn length, days to maturity, tiller per plant, spike length and grain per spike may serve as effective selection attributes during crop improvement programme in barley. The studied 25 barley genotypes were categorized into six cluster using Tocher’s method. Crossing between diverse cluster V (RS 6) and Cluster VI (Karan 4) would be effective to bring desirable segregants. Out of ten studied traits, only three traits like yield per plot, 1000 grain wt. and plant height contributed 41%, 27% and 20% respectively towards total divergence. Distribution of genotypes along the two axes in the PC plot was found consistent with the clustering pattern. Based on PC analysis, grain per spike tiller per plant, 1000 grain wt., harvest index, days to maturity and yield per plant were found major contributing traits for crop improvement.
ACKNOWLEDGEMENT
We acknowledge the Department of Genetics and Plant Breeding, Banaras Hindu University, Varanasi (India) for providing us these elite barley genotypes and Department of Plant Breeding and Genetics, Bihar Agricultural
University, Sabour (India) for providing basic facility to conduct the experiment. The manuscript is index as BAU communication no.43/2015 as per the University rule.
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Received : February-2016 Revised : March-2016 Accepted : March-2016
