Himachal Journal of Agricultural Research Vol. 29 (1&2) : 52-58, 2003 Intra-specific variability and selection of promising type of aonla (Emblica officinalis L.) in Solan area of Himachal Pradesh

O.C. Sharma Dr Y. S. Parmar UHF Horticultural Research Station, Kandaghat , Solan 173215

Abstract

The nature and magnitude of intra-specific variability was assessed using non-hierarchical Euclidean cluster analysis in 61 seedling trees of aonla growing at Kandaghat area of Himachal Pradesh for 11 fruit characters. Minimum and maximum values of coefficients of variability were recorded for pulp percentage and pulp weight, respectively. All genotypes were grouped into seven clusters showing non-parallelism between geographic and genetic diversity. Genotypes of cluster 6 and 7 were highly diverse from each other having inter-cluster distance of 14.062. The mean value of most of the fruit characters was highest in cluster 6. Based on various important fruit characters from commercial point of view, 4 seedling trees viz. Tree No. 43, 44, 60 and 61 were found to be promising and will be now propagated vegetatively for further testing and release.

Introduction aonla introduced at various places did not perform well, therefore, an effort have been The aonla (Emblica officinalis L.) is made to utilize this intra-specific variability one of the richest source of vitamin C among and to overcome the adaptability problems. fruits except Barbadoes cherry. It belongs to family Euphorbiaceae and was originated in Materials and Methods Tropical South-East Asia, particularly in Central and Southern India (Firminger, The present investigations were 1947). Aonla is quite hardy, prolific bearer, undertaken during 2000, around Kandaghat highly remunerative even without much care area of district Solan in Himachal Pradesh. and is a crop of commercial importance. The The survey location was situated at the height fruits are mainly utilized as raw or used for of 1,200 to 1,400 meters above sea level and preparation of pickle and other products. In having 30° N latitude and 77° E longitude. In India, limited efforts have been made in the this area, the aonla trees of seedling origin direction of aonla improvement and mostly were found growing in neglected and grass the work was concentrated on selection of lands. Out of total population of 500 trees, better genotypes. In the country, there are the fruits were collected from 61 seedling few systematic orchards and mostly are of trees at the end of November, 2000 based on seedling origin are being grown which preliminary informations from farmers. A exhibit a tremendous heterozygosity for fruit sample size of 25 randomly selected fruits characters. Due to its seed propagation in was used for recording the data on various wild form, its adaptability have reached to fruit characters. Data were analysed as per marginal areas. Certain improved cultivars of method given by Panse and Sukhatme 52 (1985). Intra-specific divergence was studied clusters and the distribution from different by using non-hierarchical Euclidean cluster places was apparently random. Cluster 7 analysis (Spark, 1973). Based on various fruit contained the highest number of genotypes characters like fruit weight, length, diameter, (20) followed by cluster 1 having 14 trees. pulp weight, pulp percentage, seed : pulp Cluster 4 and 6 contained lowest number of ratio and TSS, the selection was made and trees (1) in each cluster (Table 2). Maximum chemical analysis of selected types was also mean values for fruit weight (21 g), fruit done as procedures of AOAC (1975). length (28.95 mm), fruit diameter (33.38 mm), fruit volume (20 cc), seed weight (1.04 Results and Discussion g), seed length (14.57 mm) and pulp weight (19.96 g) were observed in cluster 6. Cluster The coefficient of variability can be 4 contained maximum values of pulp arranged in a descending order as 40.37 per percentage (95.74 %), and seed : pulp ratio cent for pulp weight, 37.94 per cent for fruit (1:22.48), while maximum TSS (13.75° B) weight, 35.66 per cent for fruit volume, 25.55 and seed diameter (12.12 mm) was observed per cent for seed : pulp ratio, 17.19 per cent in cluster 2 (Table 3). Intra and inter cluster for TSS, 16.67 per cent for seed weight, distances are given in Table 4, which 10.80 per cent for fruit length, 9.44 per cent revealed that intra-cluster distance varied for seed length, 8.77 per cent for fruit between 0 to 1.78. The maximum inter- diameter, 5.83 per cent for seed diameter and cluster distance of 14.06 was observed 2.20 per cent for pulp percentage (Table 1). between cluster 6 and 7. The distribution pattern of 61 genotypes revealed that there were seven

Table 1. Range, mean, standard deviation and coefficient of variability for different fruit characters in seedling trees of Aonla

Characters Range Mean Standard Coefficient of deviation variability Fruit weight (g) 4.90-21.00 7.67 2.91 37.94

Fruit length (mm) 17.48-28.95 20.93 2.26 10.80

Fruit diameter (mm) 21.29-33.38 24.30 2.13 8.77 Fruit volume (cc) 4.40-20.00 7.32 2.61 35.66

Seed weight (g) 0.463-1.041 0.66 0.11 16.67

Seed length (mm) 9.23-14.75 10.70 1.01 9.44 Seed diameter (mm) 9.27-12.34 10.47 0.61 5.83 Pulp weight (g) 4.229-19.959 7.01 2.83 40.37 Pulp percentage 80.10-95.74 90.53 1.99 2.20

Seed: Pulp ratio 1:7.41-1:22.48 10.53 2.69 25.55

TSSoB 7-14 11.75 2.02 17.19

53 Table 2. Distribution of 61 seedling trees of Aonla on the basis of fruit characters

Characters No. of trees Tree No.

1 14 4,1,14,15,17,23,29,30,41,42,48,49,57,58

2 2 44,60

3 12 2,9,10,13,16,20,35,37,39,51,54,55

4 1 61

5 11 6,12,18,19,22,24,25,27,38,47,59

6 1 43

7 20 1, 3, 5, 7, 8, 21, 26, 28, 31,32,33,34,36,40,45,46,50,52, 53, 56

The number of genotypes falling in these two characters was also observed by Singh et al. clusters was 1 and 20, respectively. Cluster 1 (1987) and Chandra et al. (1998) in aonla and 3 had the minimum (1.47) inter-cluster varieties and seedling trees. The tendency of distance. The maximum genetic contribution genotype to occur in clusters, cutting across towards diversity was made by fruit weight geographic boundaries demonstrate that which gave 72.09 per cent value and the geographic isolation is not the only factor minimum was 0.00 per cent for TSS (Table causing genetic diversity. Intra- cluster 5). distance ranged from 0.000 -1.781. The Based on various characters like relatively low value of intra- cluster distance fruit weight, fruit length, fruit diameter, pulp depicted the presence of narrow range of weight, pulp percentage, seed : pulp ratio and genetic diversity within a cluster. The zero TSS; four seedling trees (Tree No. 43, 44, 60 value of intra- cluster distance is due to the and 61) were found to be promising and fact that only single plant entered in that were selected. All the selected trees bear fruit cluster. The inter-cluster distance was least of light green colour. The acidity in selected between cluster 1 and 3 which indicate that types ranged from 1.98 (Tree No. 43) to 2.17 these clusters are very close to each other and per cent (Tree No. 61), while ascorbic acid can not be used for hybridization programme. varied from 385 (Tree No. 61) to 475 mg/100 The maximum inter-cluster distance of g of fruit weight (Tree No. 43). Similarly 14.062 was observed between cluster 6 and 7. selected types have different percentage of These clusters contained 1 and 20 genotypes, sugars. Total sugar varied from 7.92 per cent respectively. The parents for hybridization in Tree No. 44 to 9.74 per cent in Tree No. could be selected on the basis of their large 43, while reducing sugars ranged from 5.02 inter-cluster distance for isolating useful per cent in Tree No. 44 to 5.98 in Tree No. 43 recombinants in the segregating generation. (Table 6). To improve various fruit characters, the In the present study a wide range of genotypes falling in cluster 6 and 7 can be intra-specific variation was observed in utilized for a hybridization programme as various fruit characters from different places. well as for introgressing their useful traits in Similar type of variation for various fruit the commercial aonla cultivars. As aonla is

54 Table 3. Cluster mean, standard deviation and coefficient of variability of 7 clusters in different fruit characters in seedling trees of Aonla

Character Parameters Clusters 1 2 3 4 5 6 7 Fruit weisght Mean 8.01 17.00 7.31 15.50 7.52 21.00 5.73 S.D. 0.55 0.71 0.60 0.00 0.52 0.00 0.59 C.V. 6.87 4.18 8.21 0.00 6.91 0.00 10.30 Fruit length Mean 21.45 27.02 20.86 28.48 20.95 28.95 19.20 S.D. 0.55 2.66 0.69 0.00 0.86 0.00 1.14 C.V. 2.56 9.84 3.31 0.00 4.11 0.00 5.94 Fruit diameter Mean 25.15 29.42 24.39 28.14 24.63 33.38 22.32 S.D. 0.76 0.24 0.86 0.00 1.01 .0.00 0.62 C.V. 3.02 0.01 3.53 0.00 4.10 0.00 2.78 Fruit volume Mean 7.73 14.80 7.11 12.80 7.25 20.00 5.54 S.D. 0.72 1.70 0.98 0.00 0.65 0.00 0.88 C.V. 9.31 11.49 13.78 0.00 8.97 0.00 15.88 Seed weight Mean 0.74 0.90 0.61 0.66 0.70 1.04 0.56 S.D. 0.03 0.14 0.04 0.00 0.05 0.00 0.06 C.V. 4.05 15.56 6.56 0.00 7.14 0.00 10.71 Seed length Mean 10.94 13.17 10.69 13.78 10.73 14.57 9.92 S.D. 0.33 1.03 0.47 0.00 0.58 0.00 0.44 C.V. 3.03 7.82 4.40 0.00 5.41 0.00 4.44 Seed diameter Mean 10.79 12.12 10.63 10.47 10.66 11.33 9.83 S.D. 0.25 0.31 0.26 0.00 0.44 0.00 0.29 C.V. 2.32 2.52 2.45 0.00 4.13 0.00 2.95 Pulp weight Mean 7.28 16.10 6.70 14.84 6.82 19.96 5.16 S.D. 0.55 0.57 0.60 0.00 0.51 0.00 0.56 C.V. 7.55 3.54 8.96 0.00 7.48 0.00 10.85 Pulp percentage Mean 90.75 94.71 91.61 95.74 90.67 95.04 89.79 S.D. 0.81 0.59 0.84 0.00 0.76 0.00 2.44 C.V. 0.008 0.006 0.006 0.00 0.008 0.000 2.717 Seed: pulp ratio Mean 1:9.98 1: 18.00 1:11.03 1:22.48 1:9.78 1:19.17 1:9.26 S.D. 0.91 2.12 1.21 0.00 0.89 0.00 1.01 C.V. 9.12 11.78 10.97 0.00 9.10 0.00 10.91 TSS Mean 12.32 13.75 12.83 13.50 8.82 13.00 11.95 S.D. 1.07 0.35 1.39 0.00 1.15 0.00 1.91 C.V. 8.69 2.55 10.83 0.00 13.04 0.00 15.98

55 Table 4. Average inter and intra-cluster distance in seedling trees of Aonla on the basis of different fruit characters (bold figures represent intra-cluster distances)

Clusters I 2 3 4 5 6 7 1 0.995 2 7.818 1.239 3 1.473 8.457 1.200 4 8.094 3.500 8.189 0.000

5 1.833 8.713 2.249 8.771 1.183

6 11.194 4.057 11. 933 5.826 11.939 0.000 7 3.319 10.806 2.522 10.344 3.102 14.062 1. 781

Table 5. Percentage contribution of different fruit characters towards diversity in seedling trees of Aonla

Character Per cent contributions

Fruit weight 72.09

Fruit length 10.36

Fruit diameter 7.82

Fruit volume 3.58

Seed weight 2.34

Seed length 1.55

Seed diameter 1.26

Pulp weight 0.54

Pulp percentage 0.35

Seed: pulp ratio 0.10

TSS 0.00

Per cent variation explained by first components=96.1906

56 Table 6. Fruit characters of some selected promising seedling trees

Tree Numbers Characters 43 44 60 61

Fruit weight (g) 21.00 17.50 16.50 15.50

Fruit length (mm) 28.95 25.14 28.90 28.48

Fruit diameter (mm) 33.38 29.59 29.25 28.14

Fruit volume (cc) 20.00 16.00 13.60 12.80

Seed weight (g) 1.041 1.000 0.805 0.660

Seed length (mm) 14.57 13.90 12.45 13.78

Seed diameter (mm) 11.33 11.90 12.34 10.97

Pulp weight (g) 19.959 16.500 15.695 14.840

Pulp percentage 95.04 94.29 95.12. 95.74

Seed: pulp ratio 1: 1917 1 :16..50 1 :22.48 1:22.48

TSS (oB) 13.0 13.5 13.5 14.00

Acidity (%) 1.98 2.06 2.13 2.17 Ascorbic acid (mg/ 100 g of 475 464 420 385 fruit) Reducing sugars (%) 5.98 5.02 5.41 5.14

Total sugar (%) 9.74 7.92 8.95 8.85 less exploited, but important fruit crop and mostly the improvement had took place Similar selection study was also conducted in through selection. These four selected types Meghalaya and 3 promising types were of aonla seems to be promising and to avoid selected (Chandra et al., 1998). But, the fruit adaptability problems of exotic types, these characters in present study are much higher will be now vegetatively multiplied for than Meghalaya study, which indicate great further multilocational testing and release. deal of intra-specific variability in aonla. Thus in present study, area offer a great scope for improvement through selection.

57 References

A.O.A.C. 1975. Official methods of analysis Panse, V.G. and Sukhatme, P.V. 1985. analytical chemist. Washington, D.C. U.S.A. Statistical methods for agricultural workers. I.C.A.R., New Delhi, India. Chandra, R.; Srivastava, R.; Govind, S.; Hore, D.K and Singh, A.S. 1998. Collection Singh, B.P.; Singh, I.P.; Singh, S.P. and of genetic diversity of aonla (Emblica Kumar, K.A. 1987. Physico-chemical officinalis L.) from Garo hills of Meghalaya. composition of different cultivars of aonla. Indian Journal of Hill Farming 11(1&2): Indian Food Packer 41(2): 7-10. 116-123. Spark, D.N. 1973. Euclidean cluster analysis. Firminger, T.A. 1947. Firminger's mannual Algorithm As 58. Applied Statistics 22: 126- of gardening for India (8th edition). Thacker 130. Spink Co. Ltd., Calcutta.

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