Nội dung

Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 03 (2021) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2021.1003.221 Genetic Variability, Correlation and Path Analysis for Grain Yield and its Components in Soybean Vasundhara Dangi* and Kamal K. Sharma Department of Plant Breeding and Genetics, JNKVV College of Agriculture, Rewa, M.P. India *Corresponding author ABSTRACT Keywords Soybean, Heritability, Genetic advance, Correlation, Direct effect Article Info Accepted: 15 February 2021 Available Online: 10 March 2021 Present investigation of geneticvariability, correlation, path and genetic divergence analysis for yield and yield contributing characters in 30genotypes of soybean was carried out during kharif, 2019 at JNKVV, College of agriculture (Ganj Basoda, M.P.). The genotypes MAUS-71, SL-525 and JS 71-05 found highest yielding among all the 30 genotypes. Highest estimates of PCV were observed for primary branching per plant, secondary branching per plant, grain yield per plant, harvest index and seed yield per plot, and higher GCV were observed for primary branches per plant, secondary branches per plant, grain yield per plant, harvest index and seed yield per plot. High estimates of heritability was recorded for primary branches per plant, plant height, secondary branches per plant, number of pod plant, day to 1 stpod initiation and grain yield per plant and high genetic advance was recorded for primary branches per plant, secondary branches per plant, grain yield per plant. The correlation analysis revealed that grain yield per plant had significant positive association with harvest index, number of seed per pod, secondary branching per plant and primary branching per plant. Path coefficient analysis at phenotypic level revealed that number of seed per pod was observed the maximum positive direct effect on grain yield per plant followed by number of pod per plant, secondary branches per plant, 1st pod initiation, days to 50% flowering and number of seeds per plant. Introduction Soybean (Glycine max L. Merrill) crop belong to family legumineceae its chromosome number is 2n=40. It is a legume crop that grows in tropical, sub tropical and temperate climates. Soybean is also known as the “Golden bean” or “Miracle crop” or “Wonder crop” because of its multiple uses. It is an excellent source of good quality vegetable protein (40%) and edible oil (20%). It is native of China and was introduced to India in 1968 from USA (Nagata 1970).Soybean is used for green vegetable, soybean milk and oil which is used for food preparation as well as in several industrial products. Soybean is high in protein content and this may find a place in Indian diet. Soybean may be used as green vegetable if a variety is found suitable for vegetables. Madhya Pradesh is the leading state in the country for soybean production which contributes about 86 per cent of the 1776 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 total production of the country. Due to the highest acreage (6.6 Lakh hectares) and production (5.94 Lakh metric tonnes), the state is known as “Soybean state”. The presence of genetic variability in source population is primary prerequisite for making selection for crop improvement programme. Therefore study of variability in terms of estimating coefficient of variation, heritability and genetic advance to find out response to selection. The variance component analysis suggested by Jinks and Hayman (1953). Hayman (1954) and the combining ability analysis of Griffing (1956) and graphical analysis of Hayman (1956) provide useful information on the nature of inheritance of quantitative characters and also help in identifying the superior parents and the combination likely to yield superior recombinants. The study of correlations further provides an indication about the various associations existing between yield and its components. The studies of correlation and path coefficient analysis are required to formulate the selection criteria for adopting soybean improvement programs. It is necessary to find out the list of yield per plant. The study of genetic diversity is useful tool in quantifying the degree of divergence between biological population at genotypic level and to assess relative contribution of different components to the total divergence, both at the inter and intra cluster levels. The path coefficient analysis which is a standardised partial regression coefficient originally proposed by Wright (1921) and further elucidated by Li (1956) and Dewey and Lu (1959) permits the separation of the correlation coefficient into direct and indirect effects. Materials and Methods In the present study, the estimates of genetic variability, correlation and path analysis for grain yield and its components in soybean and it’s 30 genotypes for 11 characters including yield and contributing traits during kharif2019 at, instructional farm, College of Agriculture, Ganj Basoda (M.P). The design adopted was Randomized Block Design with three replications. Each plot consisted of10 rows of 3 m length with a spacing of 22.5 x 10 cm. the fertilizer dose of 40:40:0 kg NPK/ha and seeds were sown by hand dibbling. Observations were recorded on five plants for 11 yield component characters viz.,Days to 50% flowering, Days to 1st pod initiation, Primary branching per plant, Secondary branching per plant, Number of pods per plant, Number of seeds per pod, Pod length (cm), Plant height (cm), Yield per plant (g), Seed yield per plot (kg) and Harvest index (%). Genetic parameters, correlation coefficients were computed according to the method suggested by Singh and Chaudhry (1979). Path coefficients were worked out by the methods used by Dewey and Lu (1959). Results and Discussion Genetic parameters The genetic coefficient of variation provides a measure to compare the genetic variability present among various quantitative and qualitative traits. The moderate magnitude of genotypic coefficient of variation (GCV) was recorded for harvest index, grain yield per plot and secondary branching per plant. Similar finding were observed by Bhairav et al., (2006) and karad et al., (2005) (Table 1). The phenotypic coefficient of variation was found to be highest for primary branching per plant and moderate magnitude of phenotypic coefficient of variation (PCV) was recorded for secondary branching per plant, grain yield per plant, harvest index, seed yield per plot, days to 50% flowering, number of pods per 1777 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 plant, plant height and number of seeds per pod. Similar finding were observed by Jain and Ramgiry (2000). findings observed by Ganeshmurthy and seshadri (2004) and Dilnesaw et al., (2013). Correlation studies The highest heritability was recorded for primary branching per plant, plant height, secondary branches per plant, number of pod plant, day to 1st pod initiation and grain yield per plant. The results are in accordance with reports of earlier work reported by Karad et al., (2005), Kumar (2003) and Malik et al., (2006). The highest genetic advance was recorded for primary branches per plant, secondary branches per plant, grain yield per plant and moderate values of plant height, number of pods per plant, seed yield per plant, number of seed per pod and day to 50% flowering. The present result was supported by the The phenotypic and genotypic correlation coefficients between yield and yield components and inter-relationship among them were estimated and presented in the (Table 2). Grain yield was found to be positively and significantly associated with harvest index, number of pod per plant, secondary branching per plant, and primary branching per plant, day to 1st pod initiation at phenotypic level indicating the importance of these traits for yield improvement in soybean. The present result was supported by the findings observed by Chand (1999), Iqbal et al., (2003), Mukheker et al., (2004). Table.1 Estimation of genetic parameters for different quantitative characters in soybean PCV GCV h2 (bs) % GA as% of mean 55.00 12.19 8.36 47 11.81 53.70 65.78 7.61 6.15 65 10.23 3.35 2.18 4.23 18.61 16.95 83 31.81 SB 6.50 4.78 9.26 16.98 14.28 70 24.75 5 NPP 40.19 31.94 49.31 11.72 9.68 68 16.45 6 NSP 2.36 1.97 3.02 10.87 8.68 63 14.28 7 PL (cm) 3.71 3.26 4.36 9.24 5.15 31 5.91 8 PH (cm) 59.97 49.67 73.79 11.02 9.58 75 17.16 9 GYPP (g) 15.01 12.09 22.04 15.96 12.81 64 21.17 10 HI (%) 32.69 27.18 47.09 15.94 12.65 63 20.68 11 SYPP (kg) 223.28 177.42 289.65 15.43 10.46 46 14.61 S.No. Character Mean Range Minimum Maximum 1 DTF 44.40 37.00 2 DPI 60.86 3 PB 4 1778 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 Table.2 Genotypic and phenotypic correlation between grain yield and its components in soybean S.N. Characters 1 DFF 2 3 4 5 6 7 8 9 10 DPI PBPP SBPP NPPP NSSP PL (cm) PH (cm) HI (%) SYPP (KG) DFF DPI PBPP SBPP NPPP NSSP PL (cm) PH (cm) HI (%) SYPP (kg) YPP (g) 1 0.0633 0.2036 0.0865 0.0542 0.2252 * -0.2149 * 0.0496 -0.0465 -0.0528 -0.0055 G 1 0.1290 0.2767 0.2584 0.0751 0.4342 -0.4685 0.0745 -0.0583 -0.1093 -0.0189 P 1 0.3029 ** 0.2422 * 0.2918 ** 0.2426 * 0.0432 -0.1014 -0.1794 0.0284 0.2241* G 1 0.3636 0.3276 0.3893 0.3099 -0.1924 -0.1406 0.1772 -0.2500 0.2210 P 1 0.8255 *** 0.7286 *** -0.0761 -0.0915 -0.4675*** 0.4728 *** -0.3354 ** 0.4908*** G 1 0.8938 0.7813 -0.2184 -0.5973 0.5150 -0.4989 0.5327 P 1 0.8581 *** 0.0034 -0.0587 -0.3014 ** 0.6372 *** 0.1897 0.6584*** G 1 0.9008 -0.0842 -0.2612 -0.4156 0.6509 -0.3844 0.6916 P 1 -0.0724 -0.0678 -0.2627 * 0.7484 *** -0.1405 0.7758*** G 1 -0.1402 -0.2928 -0.3327 0.7364 -0.3160 0.7667 P 1 -0.0197 0.0589 0.0744 -0.0090 0.1057 G 1 0.0809 0.1099 0.0997 0.1149 0.0757 P 1 0.0422 0.0570 0.5537 *** -0.0214 G 1 0.1118 -0.0215 0.3576 -0.2131 P 1 0.1905 0.7633 *** -0.2689 G 1 -0.2451 0.9857 -0.3544 P 1 -0.0263 0.9596*** G 1 -0.1553 0.9702 P 1 -0.1314 G 1 -0.3462 P -0.1511 1779 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 Table.3 Phenotypic path analysis table S.N Character DFF DPI PB SB NPP NSP PL (cm) PH (cm) HI (%) SYPP GYPP (g) (kg) 1 DFF 0.0340 0.0022 0.0069 0.0029 0.0018 0.0077 -0.0073 0.0017 -0.0016 -0.0018 -0.0055 2 DPI 0.0024 0.0384 0.0116 0.0093 0.0112 0.0093 0.0017 -0.0039 0.0069 -0.0011 0.2241 3 PB -0.0276 -0.0411 -0.1358 -0.1121 -0.0989 0.0103 0.0124 0.0635 -0.0642 0.0455 0.4908 4 SB 0.0040 0.0113 0.0386 0.0468 0.0401 0.0002 -0.0027 -0.0141 0.0298 -0.0089 0.6584 5 NPP 0.0081 0.0438 0.1092 0.1287 0.1499 -0.0108 -0.0102 -0.0394 0.1122 -0.0211 0.7758 6 NSP 0.0067 0.0073 -0.0023 0.0001 -0.0022 0.0299 -0.0006 0.0018 0.0022 -0.0003 0.1057 7 PL (cm) 0.0105 -0.0021 0.0045 0.0029 0.0033 0.0010 -0.0487 -0.0021 -0.0028 -0.0270 -0.0214 8 PH (cm) -0.0049 0.0100 0.0459 0.0296 0.0258 -0.0058 -0.0041 -0.0982 0.0187 -0.0750 -0.2689 9 HI (%) -0.0399 0.1539 0.4056 0.5466 0.6420 0.0639 0.0489 -0.1635 0.8578 -0.0226 0.9596 10 SYPP 0.0010 0.0005 0.0065 0.0037 0.0027 0.0002 -0.0107 -0.0147 0.0005 -0.0193 -0.1314 (kg) RSQUARE=0.9452 RESIDUALEFFECT=0.2340 1780 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 Path coefficient analysis The direct and indirect effects of different yield components on grain yield worked out through path analysis at phenotypic levels are presented in the (Table 3). Path coefficient analysis at phenotypic level revealed that harvest index registered the maximum positive direct effect (0.8578) on grain yield per plant followed by number of pod per plant (0.1499), secondary branches per plant (0.0468),1st pod initiation (0.0384), days to 50% flowering (0.0340) and number of seeds per plant (0.0299). While substantial negative direct effects on grain yield per plant were contributed by primary branches per plant (0.1358), plant height (-0.0982), pod length (0.0487) and seed yield per plot (-0.0193). Similar result reported by Sharma et al., (1983), Chettri et al., (2003) and Inderjit Singh (1999). References Bhairav B, Sharma SP and Ranwah BR. (2006). Genetic variability, heritability and genetic advance in Soybean. National Pl Improvement, 8 (1): 94-95. Chand P.1999. Association analysis of yield and its components in soybean (Glycine max(L) Merrill). Madras Agric J., 86 (79): 378-381. Chettri M and Nath R. 2005. Studies on genetic variability in Soybean (Glycine max L. Merrill) in the mid hills of Darjeeling district. J. Interacademicia, 9(2): 175-178. Dewey JR, and Lu KH (1959). A correlation and path co-efficient analysis of components of crested wheat seed production. Agron. J., 51:515-8. DilnesawZinaw, AhadiSeltene and Getahun Addis 2013. Genetic variability and heritability of soybean (Glycine max (L.) Merritt) genotypes in Paste district, Metekel zone, Benishangule Gumuz regional state, north-western Ethiopia Wudpecker, Journal of Agricultural Research. Vol.219. pp. 240 - 245. Ganesamurthy K. and Seshardri P. 2004. Genetic divergence in Soybean (Glycine max (L.) Merrill). Madras Agric. Univ., 89 (1-3): 18-21. Inderjit Singh, Phul PS and Singh I. (1999). Correlation and path coefficient analysis in soybean. Legume Res., 22(1): 67-68. Iqbal ST,MahmoodTahira, Ali M, Anwar M and Sarwar M. 2003. Path coefficient analysis different genotypes of soybean (Glycine max (L) Merrill). Pak J. Biol. Sci., 6 (12): 1085-1087. Jain PK and Ramgiry SR. 2000. Genetic variability of metric traits in Indian genotype of soybean (Glycine Max (L.) Merrill). Advances in Plant Sciences. 13 (1): 27-131. Karad SR, Harer PN, Kadam DB, and Shinde RB. 2005. Phenotypic variation in Soybean soybean (Glycine max (L.) Merrill. Maharastra AgrioUnt, 30 (3): 365-367. Kumar A. 2003. Genetic analysis of yield and its components in Soybean, M.Sc. (Ag), Thesis. IGKV, Raipur. pp. 49-51. Kumar A, Pandey A and Pattanayak A. (2015). Assessment of genotypic variation in soybean [(Glycine max (L.) Merrill]. Legume Research. 38 (2): 174177. Li CD. (1956). The concept of path coefficient and its impact on population genetics. Biometrics. 12:190-210. Malik MFA, Ashraf M, Qureshi AS and Ghafoor A. 2006. Utilization of diverse genotype for soybean yield improvement. Asian JPL, Sol 5.663567. MukhekarGD, BangarND and Lad DB. (2004). Character association and path coefficient analysis to soybean (Glycine max (L.) Merrill). Maharashtra agric. Univ, 29(3): 256-258. 1781 Int.J.Curr.Microbiol.App.Sci (2021) 10(03): 1776-1782 Sharma SK and Abraham MJ. 1983. Genetic Variability in Indigenous soybean [Glycine max (L.) Merrill] of north eastern hills region. India). Oilseeds Res, 5c2): 93-100. Singh RK and Choudhary BD.1985. Biometrical methods in quantitative genetic analysis. Kalyani Publishers, New Delhi, India. pp – 54. Wright S, (1921). Correlation and causation. J. Agric. Res., 20; 257-287. How to cite this article: Vasundhara Dangi and Kamal K. Sharma. 2021. Genetic Variability, Correlation and Path Analysis for Grain Yield and its Components in Soybean. Int.J.Curr.Microbiol.App.Sci. 10(03): 1776-1782. doi: https://doi.org/10.20546/ijcmas.2021.1003.221 1782