Final SPH -JHS Coverpage 17-1 Jan 2022 single J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 This is an open access article d istributed under the terms of Creative Commons Attribution-NonCommer cial-ShareAl ike 4.0 International License, which permits unrestricted non-commercial use, d istribution, and reproduction in any med ium, provide d the original author and source are credited. Original Research Paper INTRODUCTION Bael (Aegle marmelos(L) Correa) belongs to the family Rutaceae and is an important underutilized indigenous fruit crop of India and has high medicinal and nutritional values. Since pre-historic times, it was found as wild in Sub-Himala yan tract and dry deciduous forests of Central and Southern Indian region. Therefore, a large number of landraces are available in different diversity regions (Pandey et al., 2013) Each tree is genetically different from others as most of them are of seedling origin. Traditionally, morphological characters have been used to identify and characterize the bael. However, there is a high level of genetic variability which can sometimes be used accurately to distinguish each tree. When the morphological traits are used for determining diversity and relationships among plant species, they are not sufficient because of environmental influences. Thus, the usefulness of molecula r ma rker s ha s been investigated as a means of cha ra cterizing and discriminating against different species more precisely (Benharr ant et al., 2002). The introduction of molecular biology techniques, such as DNA-based markers, allows for direct comparison of different genetic materials independent of environmental influences. The viability and purity of accessions can be analysed by utilization molecular markers. This process can increase both the quantity and quality of plant (Mujeeb et al., 2017) Molecular characterization would be more rewarding in terms of accurate identification and characterization of most closely related trees at the intra-specific level. The degree of similarity between the banding patterns provides information about genetic similarity and relationships between the samples studied. The application largely depends on the type of markers employed, distribution of markers in the genome, type of loci they amplify, level of polymorphism and reproducibility of the products (Virk et al., 2001 and Fernandez et al., 2002). Among the molecular markers, RAPD and ISSR markers have been extensively used to study genetic diversity and relationship. These markers can detect polymorphism in a single reaction. The main objective of the study was to characterise bael trees using morphological molecular markers, to evaluate the genetic diversity and relationship. Studies on genetic variability and relationship of bael (Aegle marmelos (L) Correa) using morphological and molecular markers Amulya R.N.1*, Nagarajappa Adivappar2, Shivakumar B.S.3 and Satish K.M.4 1,2Zonal Agricultural and Horticultural Research Station, KSNUAHS, Shivamogga - 577 204, Karnataka, India 3Department of Fruit Science, College of Horticulture, KSNUAHS, Mudigere - 577 132, Chikkamagalur district, Karnataka, India 4 Department of Bio-technology, College of Agriculture, KSNUAHS, Navile, Shivamogga - 577 204, Karnataka, India *Corresponding Author E-mail : amulyahiriyur@gmail.com ABSTRACT Bael (Aegle marmelos (L) Correa) is an important underutilized fruit crop of India. A total of 25 bael trees were selected from 356 bael trees of Sakharayapattana in Chikkamagalur district, Karnataka, India based on the fruit morphological traits (fruit weight, pulp weight, skull thickness, seed weight per fruit, No. of seeds per fruit, No. of locules per fruit, No. of seeds per locule, pulp wt. : seed wt.). These 25 trees were evaluated for phenotypic and genotypic variations using random amplified polymorphic DNA (RAPD) and inter-simple sequence repeats (ISSR) markers. RAPD and ISSR markers showed significant polymorphism among the trees. Jaccard’s genetic similarity value of RAPD and ISSR was found in the range of 0.00–0.95 and 0.06–0.56, respectively suggesting a moderate level of genetic diversity. The present study revealed that molecular markers can be successfully utilized for determining genetic diversity and relationship of bael trees for further varietal improvement. Keywords: Bael, genetic variability, morphology and molecular markers 89 Studies on genetic variability and relationship of bael J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 MATERIALS AND METHODS Among 356 trees, 76 fruiting trees were subjected to study of variation in fruit morphological traits like fruit weight, pulp weight, skull thickness, seed weight per fruit, No. of seeds per fruit, No. of locules per fruit, No. of seeds per locule, pulp wt. : seed wt. Based on the fruit morphological traits the best 25 trees were selected for molecular marker analysis. Plant material (leaves) of 25 bael trees were collected for genomic DNA isolation using standardized cetyl trimethyl ammonium bromide (CTAB) extraction protocol (Benharrant et al.,2002) and thenthe DNA was quantified using a spectrophotometer and the quality of the DNA was checked on 0.7% agarose gel. RAPD-PCR Amplification Twelve RAPD primers were used for RAPD analysis of 25 bael trees. PCR amplification was carried out using 1X Taq buffer solution and 1 U Taq DNA polymerase (Bangalore Genie Pvt. Ltd.), 1.25 mM MgCl2, 0.8 mM dNTP mix, 5 µM of a single decamer primer and 50 ng genomic DNA and the volume made up to 20 µl using sterilized double-distilled water. The a mplifica tion wa s per for med in VWR Peqla b thermocycler with initial pre-denaturation at 94 °C for 4 min followed by 40 cycles of denaturation at 92 °C for 2 min, at annealing temperature (Table 1.) for 1 min, and extension at 72 °C for 2 min. Final extension was performed for 5 min at 72 °C. Amplification Table 1. List of RAPD primers and their annealing temperatures Primer Marker sequence Annealing (5’ to 3’) temperature (°C) OPA-02 TGCCGAGCTG 37 OPN-03 GGTACTCCCC 37 OPN-12 CACAGACACC 37 OPM-05 GGGAACGTGT 37 OPM-06 CTGGGCAACT 38 OPX-17 GACACGGACC 36 OPM-12 GGGACGTTGG 38 OPM-15 GACCTACCAC 36 OPM-20 AGGTCTTGGG 38 OPB-1 GTTTCGCTCC 36 OPA-08 GTGACGTAGG 36 OPA-1 CAGGCCCTTC 38 products were separated by electrophoresis on 1.5 % Agarose gel stained with ethidium bromide at 80 V. Bands were visualized and photographed in a gel documentation unit. ISSR-PCR Amplification Sixteen primers, which gave the best amplification results with the sample DNA, were selected for ISSR- PCR analysis. PCR-amplification was carried out using 1X Taq buffer solution and 1 U Taq DNA polymerase (Bangalore Genie Pvt. Ltd.), 1.40 mM MgCl2, 0.8 mM dNTP mix, 8 µM of a single decamer primer and 50 ng genomic DNA and the volume made upto 25 µl using sterilized double-distilled water. The a mplifica tion wa s per for med in VWR Peqla b thermocycler 2 min at 94°C, followed by 40 cycles each of 1 min at 94°C (denaturation), 1 min at 55°C (a nnea ling for ISSR pr imer s), 2 min a t 72°C (extension) followed by one final extension of 7 min at 72°. Amplification products were separated by electrophoresis on 1.5 % Agarose gel stained with ethidium bromide at 80 V. Bands were visualized and photogra phed in a gel documentation unit a nd analyzed. Data Analysis Amplified bands generated from RAPD and ISSR- PCR amplification were scored based on the presence (1) or absence (0) of bands for each primer and used to calculate a genetic similarity matrix using software NT SYS-pc ver sion 2. 1. Cluster a na lysis wa s performed for molecular data using the ‘‘unweighted pair group method using arithmetic means’’ (UPGMA) a lgor ithm, fr om which dendr ogr ams depicting similarity among trees were drawn and plotted using NTSYS-pc software. RESULTS AND DISCUSSION The variations in fruit morphological traits among the trees are depicted in Table 2. Significant maximum fruit weight was observed in tree SB-353 (320.00 g) and minimum fruit weight was observed in tree SB- 115 (54.30 g). Pulp weight was found significantly ma ximum in tree SB-353 (202.40 g) wher ea s, minimum pulp weight was observed in SB-71 (22.53 g) and it was on par with the tree SB-148. The difference in fruit weight might be attributed to an increase in pulp weight, seed weight, skull weight of trees. The findings are in agreement with the results of earlier researches (Pandey et al., 2008, Pandey et 90 Amulya et al T re e N o. Fr ui t w ei gh t ( g) Pu lp w ei gh t ( g) Sk ul l t hi ck ne ss ( m m ) Se ed w ei gh t ( g) N o. o f se ed s / f ru it N o. o f lo cu le s / f ru it N o. o f se ed s / l oc ul e Pu lp w t. : S ee d w t. SB -3 53 32 0. 00 20 2. 40 4. 95 19 .8 0 40 .0 0 10 .6 7 3. 75 10 .2 2 SB -3 51 13 6. 00 58 .3 7 6. 82 8. 80 16 .0 0 11 .0 0 1. 46 6. 64 SB -1 47 99 .1 0 36 .5 0 4. 40 7. 70 10 .0 0 8. 00 1. 25 4. 74 SB -9 0 11 0. 30 45 .1 0 6. 92 0. 12 1. 00 9. 00 0. 11 39 2. 17 SB -1 11 14 6. 20 48 .1 0 5. 33 8. 70 44 .0 0 10 .0 0 4. 40 5. 53 SB -3 3 15 3. 50 57 .1 0 3. 96 18 .0 0 56 .0 0 8. 00 7. 06 3. 18 SB -8 0 85 .9 0 27 .7 0 5. 54 13 .2 2 26 .0 0 9. 00 2. 90 2. 10 SB -3 50 91 .2 0 39 .5 0 3. 89 5. 40 40 .0 0 9. 00 4. 49 7. 33 SB -2 88 95 .5 0 30 .8 0 5. 51 3. 50 33 .0 0 10 .0 0 3. 32 8. 81 SB -1 61 12 3. 30 47 .3 0 5. 47 15 .3 0 34 .0 0 8. 00 4. 28 3. 09 SB -2 16 2. 00 46 .0 0 4. 09 12 .0 0 46 .0 0 10 .0 0 4. 64 3. 85 SB -1 48 65 .7 0 22 .6 0 4. 06 2. 40 15 .0 0 7. 00 2. 14 9. 40 SB -1 6 15 7. 20 48 .0 0 4. 48 20 .5 0 62 .0 0 9. 00 6. 89 2. 34 SB -9 1 12 5. 10 46 .0 7 3. 87 0. 15 1. 00 9. 00 0. 11 32 2. 12 SB -6 6 12 7. 30 35 .8 0 7. 10 9. 96 33 .0 0 9. 00 3. 67 3. 59 SB -1 19 0. 90 49 .0 0 5. 60 25 .8 0 84 .0 0 10 .0 0 8. 45 1. 90 SB -7 3 15 9. 60 65 .5 0 4. 89 15 .5 0 26 .0 0 7. 67 3. 40 4. 85 SB -9 75 .7 0 27 .7 0 4. 82 4. 71 13 .0 0 9. 00 1. 46 5. 90 SB -1 15 54 .3 0 10 .4 7 3. 99 5. 38 22 .0 0 10 .0 0 2. 24 1. 92 SB -2 73 96 .2 0 34 .0 0 6. 67 7. 00 17 .0 0 11 .0 0 1. 55 4. 92 SB -2 72 91 .9 0 33 .5 0 4. 23 7. 10 22 .0 0 9. 00 2. 46 4. 72 SB -1 46 10 5. 50 42 .3 0 5. 40 3. 60 9. 00 11 .0 0 0. 83 11 .8 6 SB -7 1 56 .0 0 22 .5 3 4. 02 5. 60 3. 00 7. 00 0. 43 4. 04 SB -3 54 15 0. 00 73 .0 0 4. 18 9. 60 34 .0 0 12 .0 0 2. 83 7. 62 SB -1 75 13 6. 00 53 .4 2 5. 93 5. 20 25 .0 0 10 .0 0 2. 50 10 .3 1 F va lu e ** ** - - * - - ** S. E m ± 5. 31 1. 14 0. 06 0. 29 0. 71 0. 43 0. 20 15 C D @ 5 % 15 .0 8 3. 24 0. 18 0. 82 2. 02 1. 21 0. 56 42 .6 1 ** S ig ni fi ca nt @ 5 % a nd 1 % , * Si gn if ic an t @ 5 % , - N on s ig ni fi ca nt Ta bl e 2. F ru it m or ph ol og ic al t ra its o f 25 b ae l t re es J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 91 al., 2013 and Mitra et al., 2010).The maximum number of seeds per fruit was found in tree SB-1 (84.00) and minimum in SB-90 and SB-91. The difference in seed weight ma y be a ttr ibuted to differences in the number and size of seeds among the trees. The results are in conformity with the earlier findings (Pandey et al., 2008, Pandey et al., 2013; Singh and Misra, 2010). Pulp weight : Seed weight was found maximum in SB- 90 (392.17) and it was minimum in SB-1 (1.90). The decrease in seed number per locule has a positive correlation with higher pulp content. Findings are in agreement with the results of earlier researches (Pandey et al., 2013 and Singh and Misra, 2010). The traits like skull thickness, seed weight per fruit, no. of locules per fruit and no. seeds per locule were observed non-significant among the trees. RAPD analysis The simplicity of laboratory assay for RAPD markers makes them an attractive method for obtaining intraspecific distinctions. This technique is already used for cultivar identification and genetic variability analysis of several underutilized fruit crops like tamarind (Diallo et al., 2007) and bael (Nayak et al., 2013). In this study, a set of RAPD primers were used for distinguishing the superior trees of bael. The comparatively higher percentage of polymorphic bands detected in the present study indicated that RAPD fr a gments a r e moder a tely polymor phic a nd particularly informative in the estimation of the genetic relationship of bael trees studied. The polymerase chain reaction of bael genomic DNA using 12 selected RAPD primers generated a total of 1,399 amplified bands (Table 3.). The highest number of bands was observed with primer OPX-17. The size of amplified fragments ranged between 300 and 1800 bp and the lowest number of bands was observed with primer OPN-03. The size of amplified fragments ranged between 500-900 bp. Comparatively, moderate level of polymorphic information content (0.39 to 0.77) value was seen in selected polymorphic primers. The highest PIC value (0.77) was observed for primer OPM-12 whereas, the lowest PIC value (0.39) was observed for OPM-06. It was observed that DNA primers showed an average PIC value of >0.5, which confirms that the primers are highly informative. The maximum average number of bands across trees was found for primer OPX-17 (7.88) while minimum was in primer OPN-03 (1.68).The highest genetic similarity coefficient of 0.95 was found between the SB-147 and SB-90 may be due to their same place of origin. The trees SB-175 and SB-66, SB-9 and SB-1 showed the lowest similarity coefficient (0.00). But the molecular diversity was not in agreement with most of the morphological diversity as reported in Colocasia esculenta (Singh et al., 2012). Comparatively high a mplitude of the genetic similar ity coefficient esta blished in the pr esent study confir ms the Table 3. List of RAPD primers, their sequence and generated bands Primer Marker sequence Range of Total No. Average no. of PIC (5’ to 3’) amplicon size of bands across value (bp) bands trees OPA-02 TGCCGAGCTG 200-1400 102 4.08 0.74 OPN-03 GGTACTCCCC 500-900 42 1.68 0.56 OPN-12 CACAGACACC 100-1000 196 7.84 0.58 OPM-05 GGGAACGTGT 300-1000 150 6.00 0.45 OPM-06 CTGGGCAACT 300-900 154 6.16 0.39 OPX-17 GACACGGACC 300-1800 197 7.88 0.47 OPM-12 GGGACGTTGG 300-750 78 3.12 0.77 OPM-15 GACCTACCAC 300-1200 61 2.44 0.61 OPM-20 AGGTCTTGGG 600-1000 136 5.44 0.50 OPB-1 GTTTCGCTCC 500-1200 111 4.44 0.58 OPA-08 GTGACGTAGG 600-1000 55 2.20 0.65 OPA-1 CAGGCCCTTC 300-1200 117 4.68 0.50 Studies on genetic variability and relationship of bael J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 92 occurrence of considerable genetic variability among bael trees. However, variation was higher than that reported for 25 cultivars of mango (range 0.69-0.89) (Rajwana et al., 2008). A dendrogram (Fig 1.) was constructed from values of similarity coefficients generated from RAPD data. The trees were divided into six major genotypic groups at a 0.446 similarity coefficient, containing 6 clusters respectively, based on the unweighted pair group method using arithmetic average cluster analysis. The trees SB-2, SB-351, SB- 161, SB-353 placed in a distinct cluster while other clusters subdivided into sub-clusters. Cluster ‘a’ consists of 19 trees, where these trees separated from each other at 0.57 similarity coefficients forming a distinct cluster for SB-175. This cluster was further divided at 0.614 forming a distinct cluster for SB-80. Cluster ‘b’ comprised of two trees SB-123 and SB- 273. It was observed that SB-147 and SB-90 were placed very closely at a similarity co-efficient of 0.95. ISSR analysis Polymerase chain reaction of bael genomic DNA using 16 selected ISSR primers generated a total of 1,496 amplified bands (Table 4.). The highest number of bands was observed with primer UBC-807 and the lowest number of bands was observed with primer UBC-890. Compa r a tively higher polymor phic information content (0.83 to 0.99) was shown by selected polymorphic primers. The highest PIC value (0.99) was observed in primer UBC-888 whereas, lowest PIC value (0.83) was observed in UBC-815. Average number of bands across trees were found maximum in primer UBC-807 (7.28) while minimum in primer UBC-890 (1.60). The highest genetic Fig. 1. Dendrogram deviding the 25 trees of bael based on Jaccard genetic similarity coefficient from analysis. Table 4. List of ISSR primers, their sequence and generated bands Primer Marker sequence Total No. Average No. PIC value (5’ to 3’) of of bands bands across trees UBC 807 AGA GAG AGA GAG AGA GT 182 7.28 0.90 UBC 810 GAG AGA GAG AGA GAG AT 125 5.00 0.88 UBC 811 GAG AGA GAG AGA GAG AC 59 2.36 0.97 UBC 815 CTC TCT CTC TCT CTC TG 63 2.52 0.83 UBC 824 TCT CTC TCT CTC TCT CG 97 3.88 0.94 UBC 825 ACA CAC ACA CAC ACA CT 137 5.48 0.94 UBC 834 AGA GAG AGA GAG AGA GYT 103 4.12 0.95 UBC 836 AGA GAG AGA GAG AGA GYA 76 3.04 0.96 UBC 840 GAG AGA GAG AGA GAG AYT 65 2.60 0.98 UBC 841 GAG AGA GAG AGA GAG AYC 108 4.32 0.92 UBC 842 GAG AGA GAG AGA GAG AYG 117 4.68 0.94 UBC 859 TGT GTG TGT GTG TGT GRC 88 3.52 0.98 UBC 888 BDB CAC ACA CAC ACA CA 56 2.24 0.99 UBC 889 DBD ACA CAC ACA CAC AC 96 3.84 0.84 UBC 890 VHV GTG TGT GTG TGT GT 40 1.60 0.97 UBC 891 HVH TGT GTG TGT GTG TG 57 2.28 0.96 Amulya et al J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 93 similarity coefficient of 0.56 between the SB-1 and SB-73may be due to their same place of origin and occurrence of an intense gene flow between these trees. But the molecular diversity was not in agreement with most of the morphological diversity as reported in Colocasia esculenta (Singh et al. , 2012). Comparatively high amplitude of the genetic similarity coefficient established in the present study confirms the occurrence of considerable genetic variability among bael trees. A dendrogram was constructed from values of similarity coefficients generated from ISSR data. According to the dendrogram (Fig. 2.), the trees were divided into nine major genotypic groups at a 0.30 similarity coefficient, containing nine clusters respectively, based on unweighted pair group method using arithmetic average cluster analysis. The trees SB-354, SB-351, SB-175, SB-353 placed in a distinct cluster while other clusters sub divided in to sub- clusters. Cluster ‘a’ consists of five trees, where these trees separated from each other at 0.57 similarity coefficients forming a distinct cluster for SB-175. This cluster was further divided at 0.33 forming a distinct the trees were divided into nine major genotypic groups at a 0.51 similarity coefficient, containing nine clusters respectively, based on unweighted pair gr oup method using arithmetic average cluster analysis. The treesSB-353, SB-80, SB-175, SB- 123, SB-273, SB-161, SB-2, SB-351 placed in a distinct cluster while other clusters sub divided in to sub-clusters. Cluster ‘a’ consists of four trees, where these trees separated from each other at 0.59 Fig. 2. Dendrogram of 25 trees of bael based on Jaccard genetic similarity coefficient ISSR markers analysis. cluster for SB-147. Cluster b comprised of two trees SB-350 and SB-288. Cluster c comprised of three trees SB-161, SB-2 SB-148. Cluster d, e, f comprised of two, six and three trees respectively. At a similarity co-efficient of 0.56, it was observed that SB-1 and SB- 73 were placed very closely. RAPD and ISSR combined analysis A dendrogram was constructed using values of similarity coefficients generated from RAPD and ISSR data. According to the dendrogram (Fig. 3.), Fig. 3. Dendrogram of 25 trees of bael generated based on combined RAPD and ISSR data similarity coefficients. Cluster ‘b’ comprised of three trees SB-350, SB-288 and SB-148. Cluster ‘c’ comprised of four trees SB-16, SB-91, SB-272 and SB-146. Cluster ‘d’ and ‘e’ comprised of four and two trees respectively. At similarity co-efficient of 0.70 it was observed that SB-1 and SB-73 were placed very closely. CONCLUSION Both the molecular markers analysis showed a high degree of variation among the selected bael trees. The present study revealed that both the molecular markers can be successfully utilized for inferring genetic diversity and genetic relationship of bael tr ees. T he simila rity between SB-1 a nd SB-73 confirmed the importance of these ma rkers for distinguishing the bael trees based on environmental and genetic factors. Findings of this study indicate that identification of trees from various locations mainly based on morphological characteristics may have encountered the mismatches and mistakes. This indicates the importance of characterisation of trees both at morphological and molecular level for efficient maintenance and exploitation of precious germplasm and to determine groups of high genetic similarity and dissimilarity, which is the key for Studies on genetic variability and relationship of bael J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 94 es t a b lis hin g b r eeding s t r a t egies in genet ic improvement programme of bael. ACKNOWLEDGEMENT Authors are thankful to the Director of Research, University of Agricultural and Horticultural Sciences, Shivamogga for providing financial assistance under the staff research project No. 5.20. REFERENCES Benha r r at, H. , Ver onesi, C. , T heodet, C. a nd Thalouam, P. 2002.Orobanche species and population discrimination using inter simple sequence repeat (ISSR). Weed Res., 42 :470– 474. Diallo, B.O., Joly, H.I., Mckey, D., Mckey, M.H. and Chevalier, M.H. 2007. Genetic diversity of Tamarindus indica populations: Any clues on the origin from its current distribution. Afr. J. Biotechnol., 6: 853-860. Fernandez, M.E., Figueiras, A.M. and Benito, C. 2002. The use of ISSR and RAPD markers for detecting DNA polymor phisms, tr ee identification and genetic diversity among barley cultivars with known origin. Theor. Appl. Gen., 104 :845–851. Mitra, S.K., Maity, C.S., Mani, D. and Ghosh, B. 2010. Genetic r esour ces of ba el (Aegle marmelos Correa) – a potential underutilized fruit. Acta Hort., 864:49-52. Mujeeb, F., Bajpai, P., Pathak, N. and Verma, S.R. 2017. Genetic diversity analysis of medicinally important horticultural crop Aegle marmelos by ISSR markers. Methods Mol. Biol., 1620:195- 211. Nayak, D., Singh, D.R., Sabarinathan, P., Singh, S. a nd Na ya k, T. 2013. Random a mplified polymorphic DNA (RAPD) markers reveal genetic diversity in bael (Aegle marmelos Correa) trees of Andaman Islands, India. Afr. J. Biotechnol., 12:6055-6060. Pa ndey, D. , Shukla , S. K. a nd Kuma r, A. 2008.Variability in bael accessions from Bihar and Jharkhand. Indian J. Hort., 65 :226-229. Pandey, D., Tandon D.K., Hudedamani, U. and Tripathi, M. 2013.Variability in bael (Aegle marmelos Corr.) trees from eastern Uttar Pradesh. Indian J. Hort., 70:170-178. Rajwana, I.A., Tabbasam, N., Malik, A.U. and Malik, A.S. 2008. Assessment of genetic diversity among mango (Mangifera indica L.) trees using RAPD markers. Sci. Hort., 117 :297-301. Singh, S., Singh, D.R., Faseela, F., Kumar, N., Damodaran, V. and Srivastava, R.C. 2012. Diversity of 21 taro (Colocasia esculenta L. Schott) trees of Andaman Islands. Genet. Resour. Crop Evo., 59: 821-829. Singh, V.P. and Misra, K.K. 2010. Analysis of genetic variability and heritability in bael (Aegle marmelos Correa) germplasm. Prog. Agric., 10: 132-134. Virk, P.S., Zhu, J., Newburg, H.J., Bryan, G.J., Jeckson, M.T. and Ford-Lloyd, B.V. 2001. Effectiveness of different classes of molecular markers for classifying and revealing variation in rice germplasm. Euphytica, 112: 275–284. Amulya et al J. Hortl. Sci. Vol. 17(1) : 88-94, 2022 (Received: 16.02.2022; Revised: 29.05.2022; Accepted: 22.06.2022) 00 A Final SPH -JHS Coverpage First 2 pages.pdf 00 Content and in this issue.pdf 01 Mohan Kumar G N.pdf 02 Meera Pandey.pdf 03 Biradar C.pdf 04 Varalakshmi B.pdf 05 Vijayakumari N.pdf 06 Barik S.pdf 07 Sajid M B.pdf 08 Ranga D.pdf 09 Usha S.pdf 10 Manisha.pdf 11 Amulya R N.pdf 12 Akshatha H J.pdf 13 Adak T.pdf 14 Sujatha S.pdf 15 Gowda P P.pdf 16 Subba S.pdf 17 Dhayalan V.pdf 19 Ahmed S.pdf 20 Vishwakarma P K.pdf 21 Deep Lata.pdf 22 Udaykumar K P.pdf 23 Nayaka V S K.pdf 24 Sahel N A.pdf 25 Bayogan E R V.pdf 26 Rathinakumari A C.pdf 27 Yella Swami C.pdf 28 Saidulu Y.pdf 29 Sindhu S.pdf 30 Neeraj.pdf 31 Sivaranjani R.pdf 32 Rashied Tetteh.pdf 34 Sangeetha G.pdf 35 Shareefa M.pdf 36 Last Pages.pdf