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Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 5 (2020) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2020.905.146 Principal Component Analysis in Rainfed Green Gram Genotypes [Vigna radiata (L.) Wilczek] Champa Lal Khatik* Plant Breeding and Genetics, Agricultural Research Station, Fatehpur-Shekhawati, Sikar, Rajasthan, (SKN Agriculture University, Jobner), India *Corresponding author ABSTRACT Keywords principal component analysis, green gram, genotypes Article Info Accepted: 10 April 2020 Available Online: 10 May 2020 The present investigation entitled “Principal component analysis in rainfed green gram genotypes [Vigna radiata (L.) Wilczek]” was carried out to determine the relationship and genetic diversity among 16 green gram genotypes using principal component analysis for various characters during Kharif, 2019 at Agricultural Research Station, Fatehpur Shekhawati, Sikar (Rajasthan) under rainfed conduction. Principal component analysis (PCA) depicted that three components (PC1 to PC3) accounted for about more than 90% of the total variation for different characters. Out of total principal components retained V1, V2, V3 and V4 with values of 39.15%, 25.29%, 15.72% and 10.79 respectively. PCA based clustering showed that genotypes fall in to five different clusters showed genetic diversity between different genotypes. The Genotypes MSJ-118 and RMG-1094 which represents the mono genotypic cluster signifies that it could be the most diverse from other genotypes and it would be the suitable candidate for hybridization with genotypes present in other clusters to tailor the agriculturally important characters and ultimately to enhance the seed yield in green gram. Thus the results of principal component analysis revealed, wide genetic variability exists in these green gram genotypes. Hence these could be utilized as parental material in future breeding programme for green gram improvement. Introduction Green gram (Vigna radiata (L.) Wilczek) is one of the important pulse crops in arid region because of its short growth duration, adaptation to low water requirement and low soil fertility (Raturi et al., 2015). It is favored for consumption due to its easy digestibility and low production of flatulence. Pulses are extensively grown in tropical regions of the world as a major protein rich crop bringing considerable improvement in human diet (Muthuswamy et al., 2019 and Rahim et al., 2010). Average protein content in the seed is around 24 per cent. The protein is comparatively rich in the amino acid lysine but predominantly 1315 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 deficient in cereal grains (Baskaran et al., 2009 Garg et al., 2017 and Dhanajay et al., 2009). Presently, the yield of green gram is well below the optimum level compare to other pulses. Green gram (Vigna radiata (L.) Wilczek) is one of the chief pulse crops grown in India after chickpea and pigeon pea. In India, green gram is cultivated in 4.26 million ha with a production of 2.01 million tonnes and productivity of 472 kg/ha (AICRP on MULLaRP, 2018-19). The average yield of green gram is very low not only in India but in entire tropical and sub-tropical Asia (Pratap et al., 2012 and Kumar et al., 2005).Grouping of green gram genotypes based on genetic divergence for different characters will enable breeders for the better selection of parents during hybridization (Tripathi,2019). In plant breeding, genetic diversity plays an important role because hybrids between genetically diverse parents manifest greater heterosis than those between more closely related parents (Mahalingam et al., 2018). Some appropriate methods viz., factor analysis, cluster analysis and PCA helps in parental selection and genetic diversity identification. Recently PCA has been cited by various authors for the reduction of multivariate data into a few artificial varieties which can be further used for classifying material. The main objective of this study was to assess the potential genetic diversity and correlation by using cluster analysis-PCAbased methods for selection of parents in hybridization programme to obtain desirable segregants in advanced generation and to study the genetic parameters attributing to yield. The aim of present study was to identify better combinations as selection criteria for developing high yielding fine green gram genotypes. Such type of findings may help green gram breeders and it could provide new opportunities for promoting the production of green gram with better yield. Materials and Methods The present investigation entitled “Principal component analysis in rainfed green gram genotypes [Vigna radiata (L.) Wilczek]” was under taken to study the different parameters of divergence. Sixteen genotypes of green gram were sown in randomized block design with three replications during Kharif, 2019 at research farm of Agricultural Research Station, Fatehpur-Shekhawati, Sikar (Rajasthan) under rainfed conduction. These genotypes of green gram were obtained from All India Coordinated Research Project on MULLaRP, RARI, Durgapur (Jaipur) is as under: 1.RMG-492 5.RMG-1087 9.RMG-1134 13.RMG-1147 2.RMG-975 6.RMG-1094 10.RMG-1137 14.RMG-1148 3.IPM-02-3 7.RMG-1098 11.RMG-1138 15.RMG-1152 4.MSJ-118 8.RMG-1132 12.RMG-1139 16.RMG-1154 Each genotype was given in a four row plot of 4 m length with a spacing of 30 cm between rows and 10 cm between plants. Ten plants were selected at random from each plot and data were recorded on 8 characters viz., plant height, pod length, number of seeds per pod, Test weight, seed yield per plot and seed yield per hectors whereas for days to 50% flowering and days to maturity data were recorded on whole plot basis. The data so obtained were subjected to analysis of variance and genetic divergence using cluster analysis-PCA-based methods. 1316 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 Results and Discussion Principal component analysis (PCA) reflects the importance of the largest contributor to the total variation at each axis of differentiation (Sharma, 1998). To understand variable independence and balanced weighting of characters, principal component analysis (PCA) was done to estimate effective contribution of different characters on the basis of respective variation (Table-1).Three principal components (PC1 to PC3) which were extracted from the original data and having latent roots greater than one accounting more than 90% of the total variation. Suggesting these principal component scores might be used to summarize the original eight variables in any further analysis of the data. Out of total principal components retained V1, V2, V3 and V4 with values of 39.15%, 25.29%, 15.72% and 10.79 (Table-1) respectively contributed more to the total variation. According to Chahal et al., (2002) and Hadavani et al., (2018) characters with lower absolute value closer to zero influence the clustering less than those with largest absolute value closer to unity within the first principal component. Accordingly, the first principal component (V1) had positive component loading from days to 50% flowering (0.528), days to maturity (0.270), pod length (0.191) and no. of seeds per pod (0.449) and negative loading for plant height (-0.428) followed by seed yield per plot (-0.353),test weight (-0.014) and seed yield kg per hectare (Table-1). The characters which load positively or negatively contributed more to the diversity and they were the ones that most differentiated the clusters. Table.1 Eigenvectors and eigene values of principal components for 8 characters of green gram genotypes PC 1 Vector 2 Vector 3 Vector 4 Vector (PC1) (PC2) (PC3) (PC4) Eigene Value (Root) 3.13230 2.02368 1.25790 0.86395 % Var. Exp. 39.15380 25.29599 15.72374 10.79940 Cum. Var. Exp. 39.15380 64.44978 80.17352 90.97292 1. D50%F 0.52870 0.06268 0.09757 0.17231 2. DM 0.27085 0.28276 0.64504 -0.06027 3. PH (cm) -0.42871 0.05651 0.40266 -0.27115 4. PL(cm) 0.19186 -0.57640 -0.06372 0.22409 5. No. of S/P 0.44940 0.24967 0.09027 0.34440 6. SY/Plot (g) -0.35346 -0.20077 0.14473 0.75830 7. TW(g) -0.01408 -0.45500 0.61164 -0.03680 8. SY(kg/ha) -0.31530 0.52059 0.07075 0.38464 Characters 1317 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 Table.2 The PCA scores of 16 genotypes of green gram PCA I PCA II PCA III Genotypes (X Vector) (Y Vector) (Z Vector) 1 | RMG-492 22.846 -10.636 54.930 2 | RMG-975 22.294 -12.615 56.403 3 | IPM-02-3 23.467 -13.328 58.283 4 | MSJ-118 24.342 -13.224 58.531 5 | RMG-1087 22.005 -13.213 57.716 6 | RMG-1094 25.162 -14.678 55.080 7 | RMG-1098 19.406 -12.754 55.639 8 | RMG-1132 17.980 -15.340 60.584 9 | RMG-1134 19.831 -11.219 56.220 10 | RMG-1137 20.192 -13.971 56.584 11 | RMG-1138 19.386 -13.077 56.551 12 | RMG-1139 20.947 -15.390 58.473 13 | RMG-1147 18.470 -16.134 60.325 14 | RMG-1148 22.782 -14.661 59.039 15 | RMG-1152 23.823 -11.891 57.842 16 | RMG-1154 23.619 -12.379 58.908 Table.3 K means clustering for 8 characters of green gram genotypes K Mean Clustering Characters D50%F DM PH PL No. of SY/ TW SY (cm) (cm) S/P Plot (g) (g) (kg/ ha) 1 Cluster 40.500 61.667 41.875 7.708 10.667 217.917 32.800 605.323 2 Cluster 42.667 61.167 35.000 7.867 11.833 234.167 32.667 650.458 3 Cluster 37.333 59.833 44.208 7.658 10.833 280.000 30.758 777.774 4 Cluster 38.222 60.889 45.222 8.011 10.611 368.889 33.944 1020.572 5 Cluster 41.778 62.667 41.389 7.533 11.722 222.778 31.356 1318 618.826 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 Figure.1 Clustering of green gram genotypes by K means clustering method Figure.2 Three dimensional graph showing relative position of green gram genotypes based on PCA scores 1319 Int.J.Curr.Microbiol.App.Sci (2020) 9(5): 1315-1321 Hence, the major contributing characters for the diversity in the second principal component (V2) were days to flowering, days to maturity, plant height, no. of seeds per plant and seed yield kg per hectare (0.062, 0.282, 0.056, 0.249 and 0.520) while pod length, seed yield per plot and test weight (0.576, -0.200 and -0.455). Only pod length (0.063) load negative contributed and other characters positive contributed load for third principal component (V3). Similarly the characters days to flowering, pod length, no. of seeds per pod, seed yield per plot and seed yield kg per hectare (0.172, 0.224, 0.344, 0.758, 0.384) which load positively while days to maturity, plant height and test weight (-0.060, -0.271and -0.036) negatively in fourth principal component (V4) contributed more to the diversity and they were the ones that most differentiated the clusters. Similar results were obtained in finding of Mahalingam et al., (2020) and Thippani et al., (2017). The PCA scores for 16 genotypes in the first three principal components with eigen value more than one were computed and presented in Table-2. The PCA scores for 16 genotypes plotted in 3D (PCA I as X axis, PCA II as Y axis and PCA III as Z axis) scatter diagram (Fig.-2). On the PCA based clustering, 16 genotypes were grouped into 5 clusters in which maximum number of genotypes were fall in cluster 1 and 3 (4 genotypes) followed by cluster 4 and 5 (3 genotypes), whereas minimum number of genotypes were in cluster 2 (2 genotypes) (Table-3 and Figure1). On the basis of PCA, the maximum cluster distance was obtained for cluster 4 (5.455) followed by cluster 3 (4.385), cluster 1(3.461), cluster 5 (2.147) while minimum cluster distance was obtained for cluster 2 (1.393). These suggest that genotypes belonging to clusters separated by high statistical distance should be used in hybridization programme for obtaining a wide spectrum of variation among the segregants. Similar results were obtained in finding of Jakhar and Kumar, 2018 and Thippani et al., 2017. There is significant genetic variability among tested genotypes that indicates the presence of excellent opportunities to bring about improvement through wide hybridization by crossing genotypes with high genetic distance. The information obtained from this study can be used to plan crosses and maximized the use of genetic diversity and expression of heterosis. Hence these could be utilized as parental material in future breeding programme for green gram improvement. References AICRP on MULLarp, 2019. Project Coordinator Report- (2018-19) All India Coordinated Research Project on MULLaRP, ICAR- Indian Institute of Pulses Research, Kanpur-208204, Uttar Pradesh, India, Pp 35-39. Baskaran, L., Sundararmoorthy, P., Chidambaram, A.L.A. and Ganesh,. K.S. 2009. Growth and physiological activity of green gram (Vigna radiata (L.) Wilczek) under effluent stress. Bot. Res. Int., 2: 107-114. 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How to cite this article: Champa Lal Khatik. 2020. Principal Component Analysis in Rainfed Green Gram Genotypes [Vigna radiata (L.) Wilczek]. Int.J.Curr.Microbiol.App.Sci. 9(05): 1315-1321. doi: https://doi.org/10.20546/ijcmas.2020.905.146 1321