Evaluation of yield contributing characters and cluster analysis of soybean genotypes
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Abstract
A morphological divergence study among the twenty genotypes based on nine yield and yield contributing characters through the D2 statistic indicated the presence of substantial diversity by forming clusters with a wide range of inter-cluster distances. The soybean genotypes under investigation were divided into five clusters. Cluster I had the most genotypes, with 10, followed by clusters III and V, each with five and three genotypes. The relative divergence indicates how much each cluster varies from the others. Cluster I and Cluster III have the most significant order of divergence, followed by Cluster III and Cluster IV. The results revealed that the parents in these clusters are genetically heterogeneous. It's possible that a hybridization program obtained a significant heterotic response. Clusters I and II found the minimum inter-cluster distances, indicating limited genetic diversity. Cluster III had the maximum seed yield per plant cluster value. Individual performance was highest for the genotypes BINAsoybean-3, BINAsoybean-2, and Shohag for the trait seed yield per plant.
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Akand MMH, Mamun MAA, Ivy NA and Karim MA. Genetic variability of soybean genotypes under drought stress. Annals of Bangladesh Agriculture. 2018, 22 ;1: 79-93.
Mathur S. Soybean wonder legume. Beverage Food World. 2004, 31;1: 61–62.
Bretting PK and Widrlechner MP. Genetic markers and plant genetic resource management. In: Plant Breeding Reviews. (Ed.): J. Janick. John Wiley & Sons, Inc., New York. 1995, 13: 11-87. DOI: https://doi.org/10.1002/9780470650059.ch2
Guedira GLB, Thompson JA, Nelson RL and Warburton ML. Evaluation of genetic diversity of soybean introductions and North American ancestors using RAPD and SSR markers. Crop Sci. 2000, 40: 815-823. DOI: https://doi.org/10.2135/cropsci2000.403815x
Temesgen B. Major Challenging Constraints to Crop Production Farming System and Possible Breeding to Overcome the Constraints. Int. j. res. stud. agric. sci. 2020, 6;7: 27-46. http://dx.doi.org/10.20431/2454-6224.0607005 DOI: https://doi.org/10.20431/2454-6224.0607005
Bhandari HR, Bhanu AN, Srivastava K, Singh MN, Shreya and Hemantharanjan A. Assessment of genetic diversity in crop plants - an overview. Adv Plants Agric Res. 2017, 7;3:279-286. DOI: 10.15406/apar.2017.07.00255 DOI: https://doi.org/10.15406/apar.2017.07.00255
Arrones A, Vilanova S, Plazas M, Mangino G, Pascual L, Díez MJ, Prohens J and Gramazio P. The Dawn of the Age of Multi-Parent MAGIC Populations in Plant Breeding: Novel Powerful Next-Generation Resources for Genetic Analysis and Selection of Recombinant Elite Material. Biology. 2020, 9: 229. https://doi.org/10.3390/biology9080229 DOI: https://doi.org/10.3390/biology9080229
Mahalanobis PC. On the generalized distance in statistics. Proc Natl Inst Sci India. 1936, 2: 49-55.
Biswas G. Insect Pests of Soybean (Glycine Max L.), Their Nature of Damage And Succession With The Crop Stages. J Asiat Soc Bangladesh Sci. 2013. 39;1: 1–8. https://doi.org/10.3329/jasbs.v39i1.16027 DOI: https://doi.org/10.3329/jasbs.v39i1.16027
Ojo DK, Ajayi AO and Oduwaye OA. Genetic relationships among soybean accessions based on morphological and RAPDs techniques. Pertanika J. Trop. Agric. Sci..2012, 35;2:237–248.
Cui Z, Carter Jr. TE, Burton WJ and Wells R. Phenotypic diversity of modern Chinese and North American soybean cultivars. Crop Sci. 2001, 41; 6: 1954–1967. DOI: https://doi.org/10.2135/cropsci2001.1954
Iqbal Z, Arshad M, Ashraf M, Mahmood T and Waheed A. Evaluation of soybean [Glycine max (L.) Merrill] germplasm for some important morphological traits using multivariate analysis. Pak J Bot. 2008, 40; 6: 2323– 2328.
Yu CY, Hu SW, Zhao HX, Guo AG and Sun GL. Genetic distances revealed by morphological characters, isozymes, proteins and RAPD markers and their relationships with hybrid performance in oilseed rape (Brassica napus L.). Theor Appl Genet. 2005, 110;3: 511–518. DOI: https://doi.org/10.1007/s00122-004-1858-7
Abdullah N, Rafii Yusop MY, Ithnin M, Saleh G and Latif MA. Genetic variability of oil palm parental genotypes and performance of its'progenies as revealed by molecular markers and quantitative traits. C R Biol. 2011, 334; 4: 290–299. DOI: https://doi.org/10.1016/j.crvi.2011.01.004
Latif MA, Rafii Yusop M, Rahman MM and Talukdar MRB. Microsatellite and minisatellite markers Based DNA fingerprinting and genetic diversity of blast and ufra resistant genotypes. C R Biol. 2011, 334; 4: 282–289. DOI: https://doi.org/10.1016/j.crvi.2011.02.003
Rafii MY, Shabanimofrad M, Edaroyati MWP and Latif MA. Analysis of the genetic diversity of physic nut, Jatropha curcas L. accessions using RAPD markers. Mol Biol Rep. 2012, 39; 6: 6505–6511. DOI: https://doi.org/10.1007/s11033-012-1478-2
Shu QY and Lagoda PJL. Mutation techniques for gene discovery and crop improvement. Mol Plant Breed. 2007, 2: 193–195.
Wilcox JR, Premachandra GS, Young KA and Raboy V. Isolation of high seed inorganic P, low-phytate soybean mutants. Crop Sci. 2000, 40; 6: 1601–1605. DOI: https://doi.org/10.2135/cropsci2000.4061601x
SAIC, SAARC Agricultural Statistics of 2006-07, SAARC Agricultural Information Centre (SAIC), Dhaka, Bangladesh, 2007.
Kharkwal MC and Shu QY. The role of inducedmutations in world food security in Induced Plant Mutations in the Genomics Era, Food and Agriculture Organization of the United Nations, Rome, Italy. 2009, 33–38.
Yang W, Wu T, Zhang X, Song W, Xu C, Sun S, Hou, W, Jiang B, Han T and Wu C. Critical Photoperiod Measurement of Soybean Genotypes in Different Maturity Groups. Crop Science. 2019, 59;5: 2055–2061. https://doi.org/10.2135/cropsci2019.03.0170 DOI: https://doi.org/10.2135/cropsci2019.03.0170
Borrell JS, Goodwin M, Blomme G, Jacobsen K, Wendawek AM, Gashu D, Lulekal E, Asfaw Z, Demissew S, and Wilkin P. Enset‐based agricultural systems in Ethiopia: A systematic review of production trends, agronomy, processing and the wider food security applications of a neglected banana relative. Plants people planet. 2020, 2;3: 212–228. https://doi.org/10.1002/ppp3.10084 DOI: https://doi.org/10.1002/ppp3.10084
Bhuiyan MSH, Malek MA, Sarkar MA, Islam M, and Akram MW. Genetic Variance And Performance Of Sesame Mutants For Yield Contributing Characters. Malays J Sustain Agric. 2019, 3;2: 27-30. http://doi.org/10.26480/mjsa.02.2019.27.30 DOI: https://doi.org/10.26480/mjsa.02.2019.27.30
Liu S, Zhang M, Feng F & Tian Z. Toward a “Green Revolution” for Soybean. Mol Plant. 2020, 13; 5: 688–697. https://doi.org/10.1016/j.molp.2020.03.002 DOI: https://doi.org/10.1016/j.molp.2020.03.002
Feng L, Raza MA, Li Z, Chen Y, Khalid MHB, Du J, Liu W, Wu X, Song C, Yu L, Zhang Z, Yuan S, Yang W and Yang F.). The influence of light intensity and leaf movement on photosynthesis characteristics and carbon balance of Soybean. Front Plant Sci. 2019, 9: 1952. https://doi.org/10.3389/FPLS.2018.01952 DOI: https://doi.org/10.3389/fpls.2018.01952
Sulistyo A, Purwantoro and Sari KP. Correlation, path analysis and heritability estimation for agronomic traits contribute to yield on soybean. IOP Conf Ser: Earth Environ Sci. 2018, 102: 012034. https://doi.org/10.1088/1755-1315/102/1/012034 DOI: https://doi.org/10.1088/1755-1315/102/1/012034
Diers BW, Specht J, Rainey KM, Cregan P, Song Q, Ramasubramanian V, Graef G., Nelson R, Schapaugh W, Wang D, Shannon G, McHale L, Kantartzi SK, Xavier A, Mian R, Stupar RM, Michno JM, An YQC, Goettel W and Beavis WD. Genetic Architecture of Soybean Yield and Agronomic Traits. G3: Genes Genomes Genet. 2018, 8; 10: 3367–3375. https://doi.org/10.1534/g3.118.200332 DOI: https://doi.org/10.1534/g3.118.200332
Ghanbari S, Nooshkam A, Fakheri BA and Mahdinezhad N. Assessment of Yield and Yield Component of Soybean Genotypes (Glycine Max L.) in North of Khuzestan. J Crop Sci Biotechnol. 2018, 21; 5:435–441. https://doi.org/10.1007/s12892-018-0023-0 DOI: https://doi.org/10.1007/s12892-018-0023-0