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Selection indices for yield components in mung bean (Vigna radiata (L.) R. Wilczek) during summer season.

Mung bean (Vigna radiata (L.) R.Wilczek) known as green-gram is one of the most important pulse crops of India. Mung bean is considered as hardiest of all pulse crops. It is a self-polli-nated crop. It is a photo and thermo insensitive crop and is grown in kharifand summer seasons in northern India. Mung bean provides about 25 % protein, restores and maintains the soil fertility by fixing atmospheric nitrogen, and also fits well in different cropping systems. High protein, easy digestibility and low flatulence production made the crop acceptable to the people world over (Prasanna et al. 2013). Keeping in mind the dietary importance of this crop and its low productivity, there is ample scope of genetic improvement in this crop. Cultivation of summer mung bean is now becoming more and more popular with farmers in northern India as their practices enable them to use the land and the water resources which otherwise would have remained unutilized during summer season. Moreover, summer mung can be fitted very well in different cropping systems. (Rao et al. 2009). The yield is a complex character and the multiplicative end product of many quantitative traits (Whitehouse et al. 1958). The different components of yield very often exhibit considerable degree of association among themselves and with yield. Thus, selection for yield alone will not be desirable. However, such an improvement would be more reliable if indirect selection was made based on another correlated trait. Path analysis permits the examination of direct effects of various characters on yield as well as their indirect effects via other component traits. And construct suitable selection indices for obtaining high genetic gain for seed yield per plant. This can be done using selection index, which is multiple regressions of genotypic values on phenotypic values of several traits (Falconer, 1989). The use of selection index is superior in improving complex traits (Hazel and Lush, 1943).

Certain desired plant characteristics are considered while selecting for particular genotype with varying given to different traits for arriving on decisions. The better way of exploiting genetic correlation with several traits having high heritability is to construct an index which combines information on all the characters associated with yield. This suggest the use of selection index, which gives proper weight to each of the two or more characters to be considered. Selection index was proposed for the first time by Smith (1936) on the basis discriminant function of Fisher (1936). Hazel and Lush (1943) and Robinson et al. (1951) showed that the selection based on such an index is more efficient than selecting individually for the various characters. Keeping these facts in view, the present study was undertaken in order to construct selection indices for efficient selection in mung bean breeding programme.


The experimental material for the present study consisted of 50 mung bean genotypes received from Central Arid Zone Research Institute (CAZRI)-Jodhpur. The experiment was carried out at Instructional Farm, College of Agriculture, J.A.U., Junagadh (Gujarat) during summer 2014. The genotypes were evaluated in randomized block design (RBD) with three replications. Each entry was planted as a single row of 3 m length, keeping plant to plant distance of 10 cm and row to row spacing of 30 cm. The recommended cultural practices were adopted for the proper growth and stand of the crop. The data were recorded five randomly selected plants from each replication for plant height, number of primary branches per plant, number of clusters per plant, number of pods per plant, length of pod, number of seeds per pod, 100-seed weight (g), biological yield per plant (g), harvest index (%) and seed yield per plant (g). Discriminant function analysis described by Dabholkar (1992) was used to construct the selection indices involving six characters, seed yield per plant ([X.sub.1]), number of primary branches per plant ([X.sub.2]), 100-seed weight ([X.sub.3]), biological yield per plant ([X.sub.4]), harvest index ([X.sub.5]) and days to maturity ([X.sub.6]). For computing selection index, seed yield per plant was considered as the dependent variable with the relative efficiency of 100 per cent. The model suggested by Robinson et al. (1951) was used for the construction of genetic advance as well as selection indices and development of a required discriminant function using six characters along with seed yield per plant.


Selection indices for grain yield and other characters were constructed and examined to identify their relative efficiency in the selection of superior genotypes. The results on selection indices, discriminant function, expected genetic gain and relative efficiency are presented in Table 1. The basis for the development of the selection indices has been provided by Smith (1936), Hazel (1943) and Robinson et al. (1951). Hazel and Lush (1943) stated that the superiority of selection based on index increases with an increase in the number of characters under selection. A total of thirty one selection indices (Table 1) based on five characters constructed in all possible combinations revealed that the selection efficiency was high over straight selection when selection was based on individual components. The selection based on individual yield contributing character like biological yield per plant was more rewarding than straight selection for seed yield during summer season. It gave higher expected genetic advance and relative efficiency (GA= 12.58g; RI=940.51%)as compared to that for seed yield for which the genetic advance and relative efficiency (GA=1.34g; RI=100.00%) was considerably lower in kharif season. The best selection index identified for four characters viz., seed yield per plant, biological yield per plant, length of pod and plant height followed by an index of three characters viz., biological yield per plant, length of pod and plant height and an index of two characters viz., involving biological yield per plant and length of pod. The discriminant function method of making selection in plants appeared to be the most useful than the straight selection for seed yield alone and hence, due weightage should be given to the important selection indices while making selection for yield advancement in mung bean. The observations from the study of Sable et al. (2001), Patel et al. (2007), Bertini et al. (2010) and Ullah et al. (2012) support the above conclusions.

Thus, the current study revealed that the index which includes more than one characters, gave high genetic advance, suggesting the utility of constructing of selection indices for effecting simultaneous improvement in several characters. Hazel and Lush (1943) stated that the superiority of selection based on index increases with an increase in the number of characters under selection. Smith (1936), Rao (1974), Dobariya et al. (2008), Babariya et al. (2014) and Gupta et al. (2015) also were with the same opinion that inclusion of characters one by one in the function resulted in increasing genetic advance and the selection indices improve the efficiency than the straight selection for yield alone.

The relative efficiency (RE%) of various selection indices presented in Table 3 indicated that when relative efficiency of single character index was measured over straight selection for seed yield per plant, the efficiency was declined to less than 100 per cent. This observation indicated that the indirect selection through individual traits over straight selection for seed yield per plant alone would not be effective.

It is interesting to note that selection efficiency (Table 2) improved with an increase in number of characters in combination with yield. For example, average selection efficiency of 293.84%, when one character included in selection function. Similarly, the selection efficiency was 555.05% for two characters, 797.96 for three characters, 1032.98% for four characters and 1262.10% for five characters selection indices improve the selection efficiency than the straight selection for yield alone with an increase in the number of characters under selection.

Some of the selection indices with high relative efficiency listed in Table 1 indicated that the highest efficiency was observed with a combination of five characters (1262.10%). Selection indices with five characters, i.e. seed yield per plant ([X.sub.1]) and four yield components viz., biological yield per plant ([X.sub.2]), number of primary branches per plant ([X.sub.3]), number of Seed per pod ([X.sub.4]) and plant height ([X.sub.5]), therefore, appear to be more useful. It can be seen that seed yield per plant, biological yield per plant and number of seed per pod were the characters being commonly involved in more number of the combinations, the next being number of primary branches per plant and plant height in order (Table 3).

Keeping in view, the basic idea of saving time and labour in a selection programme, it would be desirable to base the selection of few characters. In the present study, selection index based on five characters gave maximum genetic gain and high efficiency over straight selection, but practically it is more cumbersome to use in the selection exercise. However, in practice, the plant breeder might be interested in maximum gain with minimum number of characters. In the present study, selection index based on three characters (Biological yield per plant + Number of seed per pod + Plant height) showing genetic gain (14.18%) and selection efficiency (1128.82%) comparable to some extent of those based on four or more characters, which is more desirable and practically possible to use breeder than the index that includes more number of characters.


In the conclusion, based on the discriminant function analysis for selection indices suggested that the selection efficiency in general was higher over straight selection, when the selection was based on yield contributing characters and not directly for seed yield per plant. The relative selection efficiency further increased with the inclusion of two or more characters. The best relative efficiency was obtained with four character combinations. It was noted that biological yield per plant was part of the all the character combinations formulated for selection in mung bean.


(1.) Babariya, C. A., Dobariya, K. L., Sapovadiya, M. H. and Vavdiya, P. A. Discriminant function method of selection in groundnut (Arachis hypogaea L.). Electronic Journal of Plant Breeding, 2014; 5(1):112-116.

(2.) Bertini and Candida H. C. M. Multivariate analysis and selection index for identification of cowpea genotypes. Acta Sci. Agron., 2010; 32(4): 613- 619.

(3.) Dabholkar, A. R. Elements of Biometrical Genetics. Concept Publishing Company, New Delhi, 1992; pp. 444-478.

(4.) Dobariya, K. L., Odedra, R. K., Jivani, L. L., Ponkia, H. P. and Khanpara, M. D. Selection indices for yield improvement in groundnut (Arachis hypogaeaL.). Intl. J. of Bioscience Reporter, 2008; 6(2): 299-330.

(5.) Falconer, D. S. Introduction to quantitative genetics, 3rd Ed., John Wiley and Sons. Inc., 1989; New York.

(6.) Fisher, R. A. The use of multiple measurements in taxonomic problems. Ann. Eugen., 1936; 7:179.

(7.) Gupta, R. P., Vachhani, J. H., Kachhadia, V. H., Vaddoria, M. A. and Praveen Kumar. Selection indices in Virginia groundnut (Arachis hypogaea L.). Electronic Journal of Plant Breeding, 2015; 6(2):560-565.

(8.) Hazel, L. N. and Lush, J. L. The genetic basis for constructing selection indexes. Genetics, 1943; 28(6): 476-490.

(9.) Hazal, L.N. The genetic basis for constructing selection indices. Genetics, 1943; 28: 476-490.

(10.) Patel, G.M.; Pithia, M.S.; Vachhani, J.H. and Bhatiya, V. J. Selection indices in mung bean. Nat. J. of Plant Improvement, 2007; 9 (1): 50-53.

(11.) Prasanna, B.L.; Rao, P.J.M.; Murthy, K.G.K.; Kiran Prakash, K.; Yamini, K.M. and Srividhya A. Genetic divesity and Molecular characterization of mung bean genotypes. IJABPT, 2013; 4 (4):151-160.

(12.) Rao, S.D.; Soami Pyari, D. and Biyan, C. S. Retrospective and prospective of agricultural systems models in the eco-village paradigm. Proceedings of Joint International Conference on Applied Systems Research and XXXIII National Systems Conference(ASR-NSC-2009), DEI Agra: 136-140.

(13.) Robinson, H.F., Comstock, R.E. and Harvey, P.H. Genotypic and phenotypic correlations in corn and their implications in selection. Agron. J., 1951; 43: 282-287.

(14.) Sable, N. H.; Khorgade, P. W.; Narkhede, M. N. Genetic parameters and formulation of selection indices in chickpea, Annuals of Plant Physiology. 2001; 14(1): 83-87.

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(16.) Ullah, H.; Hussain, I.K.; David A. L.; Nayab, N. and Imdadullah. Selecting Mungbean genotypes for Fodder production on the basis of degree of indeterminacy and Biomass. Pak. J. Bot., 2012; 44 (2): 697-703.

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Indu *, R. Niyaria, S.S. Raghuwanshi and A. Saxena

Department of Genetics and Plant Breeding, College of Agriculture, Junagadh Agricultural University, Junagadh, Gujarat-362 001, India.

(Received: 04 July 2016; accepted: 20 September 2016)

* To whom all correspondence should be addressed.
Table 1. Selection index, Discriminant function, Expected genetic
advance in yield and Relative efficiency from the use of
different Selection indices in mung bean in summer season.

S.     Selection Index                   Discriminant
No.                                      Function

1      2                                 3

1      [X.sub.1] Seed yield per          0.7628 [X.sub.1]
       plant (gm)

2      [X.sub.2] Biological yield        0.8745 [X.sub.2]
       per plant (gm)

3      [X.sub.3] Number of primary       0.8672 [X.sub.3]
       branches per plant

4      [X.sub.4] Number of seed          0.9089 [X.sub.4]
       per pod

5      [X.sub.5] Plant height            0.8646 [X.sub.5]

6      [X.sub.1].[X.sub.2]               0.954 [X.sub.1] + 0.872

7      [X.sub.1].[X.sub.3]               0.690 [X.sub.1] + 1.225

8      [X.sub.1].[X.sub.4]               0.593 [X.sub.1] + 1.057

9      [X.sub.1].[X.sub.5]               1.089 [X.sub.1] + 0.861

10     [X.sub.2].[X.sub.3]               0.842 [X.sub.2] + 1.831

11     [X.sub.2].[X.sub.4]               0.860 [X.sub.2] + 1.043

12     [X.sub.2].[X.sub.5]               0.885 [X.sub.2] + 0.948

13     [X.sub.3].[X.sub.4]               1.467 [X.sub.3] + 0.865

14     [X.sub.3].[X.sub.5]               1.015 [X.sub.3] + 0.860

15     [X.sub.4].[X.sub.5]               1.217 [X.sub.4] + 0.799

16     [X.sub.1].[X.sub.2].[X.sub.3]     0.625 [X.sub.1] + 0.852
                                         [X.sub.2] + 2.204 [X.sub.3]

17     [X.sub.1].[X.sub.2].[X.sub.4]     0.735 [X.sub.1] + 0.867
                                         [X.sub.2] + 1.112 [X.sub.4]

18     [X.sub.1].[X.sub.2].[X.sub.5]     1.163 [X.sub.1] + 0.866
                                         [X.sub.2] + 0.946 [X.sub.5]

19     [X.sub.1].[X.sub.3].[X.sub.4]     0.498 [X.sub.1] + 1.394
                                         [X.sub.3] + 1.028 [X.sub.4]

20     [X.sub.1].[X.sub.3].[X.sub.5]     1.141 [X.sub.1] + 0.865
                                         [X.sub.3] + 0.857 [X.sub.5]

21     [X.sub.1].[X.sub.4].[X.sub.5]     0.646 [X.sub.1] + 1.336
                                         [X.sub.4] + 0.821 [X.sub.5]

22     [X.sub.2].[X.sub.3].[X.sub.4]     0.838 [X.sub.2] + 2.014
                                         [X.sub.3] + 0.902 [X.sub.4]

23     [X.sub.2].[X.sub.3].[X.sub.5]     0.862 [X.sub.2] + 1.740
                                         [X.sub.3] + 0.917 [X.sub.5]

24     [X.sub.2].[X.sub.4].[X.sub.5]     0.856 [X.sub.2] + 1.121
                                         [X.sub.4] + 0.904 [X.sub.5]

25     [X.sub.3].[X.sub.4].[X.sub.5]     0.693 [X.sub.3] + 1.286
                                         [X.sub.4] + 0.790 [X.sub.5]

26     [X.sub.1].[X.sub.2].[X.sub.3].    0.441 [X.sub.1] 1.034
       [X.sub.4]                         [X.sub.4] + 0.847 [X.sub.2] +
                                         2.385 [X.sub.3]

27     [X.sub.1].[X.sub.2].[X.sub.3].    0.962 [X.sub.1] 0.934
       [X.sub.5]                         [X.sub.5] + 0.854 [X.sub.2] +
                                         1.797 [X.sub.3]

28     [X.sub.1].[X.sub.2].[X.sub.4].    0.892 [X.sub.1] 0.943
       [X.sub.5]                         [X.sub.5] + 0.859 [X.sub.2] +
                                         1.164 [X.sub.4]

29     [X.sub.1].[X.sub.3].[X.sub.4].    0.732 [X.sub.1] 0.829
       [X.sub.5]                         [X.sub.5] + 0.942 [X.sub.3] +
                                         1.271 [X.sub.4]

30     [X.sub.2].[X.sub.3].[X.sub.4].    0.843 [X.sub.2] 0.901
       [X.sub.5]                         [X.sub.5] + 1.684 [X.sub.3] +
                                         1.119 [X.sub.4]

31     [X.sub.1].[X.sub.2].[X.sub.3].    0.691 [X.sub.1] + 0.846
       [X.sub.4].[X.sub.5]               [X.sub.2] + 1.946 [X.sub.3]
                                         1.116 [X.sub.4] + 0.936

S.     Expected    Relative
No.    Genetic    Efficiency
       Advance       (%)

1         4           5

1        1.26       100.00

2        9.25       736.47

3        0.77        61.31

4        2.44       194.19

5        4.74       377.23

6       10.15       808.36

7        2.00       159.08

8        3.62       288.30

9        5.81       462.18

10       9.78       778.82

11      10.77       857.64

12      12.23       973.81

13       3.23       256.93

14       5.10       406.21

15       7.02       559.16

16      10.74       854.78

17      11.79       939.01

18      13.30      1059.24

19       4.26       339.49

20       6.25       497.77

21       8.14       648.01

22      11.35       903.82

23      12.75      1015.13

24      14.18      1128.82

25       7.45       593.55

26      12.41       988.38

27      13.85      1102.55

28      15.29      1217.36

29       8.60       684.95

30      14.72      1171.66

31      15.85      1262.10

Table 2. Average selection efficiency of
different combination of characters in
Mung bean

No. of characters    Selection
in the index         efficiency (%)

One                  293.84
Two                  555.05
Three                797.96
Four                 1032.98
Five                 1262.10

Table 3. Highest selection efficiency with characters
combination in mung bean

S.     Characters                                           Selection
No.                                                       efficiency (%)

1      Biological yield per plant                             940.51
2      Biological yield per plant + Plant height              973.81
3      Biological yield per plant + Number of seed            857.64
         per pod
4      Biological yield per plant + Number of seed           1128.82
         per pod + Plant height
5      Seed yield per plant + Biological yield per           1059.24
         plant + Plant height
6      Seed yield per plant + Biological yield per           1217.36
         plant + Number of seed per pod + Plant height
7      Biological yield per plant + Number of primary        1171.66
         branches per plant + Number of seed per pod +
         Plant height
8      Seed yield per plant + Biological yield per           1262.10
         plant + Number of primary branches per plant
         + Number of seed per pod + Plant height
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Author:Indu; Niyaria, R.; Raghuwanshi, S.S.; Saxena, A.
Publication:Journal of Pure and Applied Microbiology
Article Type:Report
Date:Dec 1, 2016
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