Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Multivariate and stability analysis for yield and biochemical traits in radish (Raphanus sativus L.) genotypes from Sikkim Himalaya for functional food applications
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 19 February 2026

Multivariate and stability analysis for yield and biochemical traits in radish (Raphanus sativus L.) genotypes from Sikkim Himalaya for functional food applications

  • Kime Tare1,
  • Rajesh Kumar1,
  • Kunal Kaushik2,
  • Lomash Sharma1 &
  • …
  • Sandhya Lamichaney1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biotechnology
  • Genetics
  • Plant sciences

Abstract

Radish (Raphanus sativus L.) is an important root vegetable utilized worldwide. Highly genetic diverse germplasm of radish exists in Sikkim, but no high-yielding and climate resilient cultivar has been released so far, causing hindrance in its productivity especially under organic conditions. The present investigation was conducted to assess the existing genetic variability, and yield potential along with phytochemical constituents of sixty-one entries (fifty-seven radish genotypes and four checks) using augmented RCBD to identify genotypic performance under organic cultivation. Presence of high phytochemical composition can help to identify radish as a functional food. In the present investigation, augmented RCB design helped to handle a large number of genotypes with limited replications. The traits like total carotenoid, total carbohydrate, total sugar, reducing sugar, antioxidant capacity and total phenol content showed strong genetic potential for further selection. Character association for biochemical traits revealed that many of the traits had strong influence on each other. The genotypes were grouped into eight sub clusters nested within two macro-clusters. The findings provide an important insight towards phytochemical constituents present and their genotype-by-environment interaction in tested radish genotypes. The study concludes that genotypes SR24, SR14, SR50 and SR42 were found to be superior for their biochemical composition while genotypes SR56, SR39, and SR41 were found to be superior across years for both yield and biochemical constituents. The investigation presents the possibility of selection for radish genotypes suitable for organic farming in Sikkim Himalayan region, alongside a valuable source of medicinal value and functional food properties.

Similar content being viewed by others

Multi-model statistical approaches for assessing the stability of Cicer interspecific derivatives in the trans and upper gangetic regions of India

Article Open access 01 July 2025

A kabuli chickpea ideotype

Article Open access 31 January 2022

Response of Sorghum bicolor genotypes for yield and yield components and organic carbon storage in the shoot and root systems

Article Open access 25 April 2024

Data availability

All data generated or analysed during this study are included in this published article.

References

  1. Ahmad, M. H., Safdar, S., Kousar, S., Nadeem, M. & Asghar, Z. Functional foods and human health: An overview. Funct. Foods Phytochem. Health Promot. Potential https://doi.org/10.5772/intechopen.99000 (2021).

    Google Scholar 

  2. Gupta, E. & Mishra, P. Functional food with some health benefits, so called superfood: A review. Curr. Nutr. Food Sci. 17, 144–166. https://doi.org/10.2174/1573401316666200414150523 (2021).

    Google Scholar 

  3. Granado-Lorencio, F. & Hernández-Álvarez, E. Functional foods and health effects: A nutritional biochemistry perspective. Curr. Med. Chem. 23, 2929–2957. https://doi.org/10.2174/0929867323666160615105746 (2016).

    Google Scholar 

  4. Cartea, M. E., Francisco, M., Soengas, P. & Velasco, P. Phenolic compounds in Brassica vegetables. Molecules 16, 251–280. https://doi.org/10.3390/molecules16010251 (2011).

    Google Scholar 

  5. Sharma, R., Kumar, S., Kumar, V. & Thakur, A. Comprehensive review on nutraceutical significance of phytochemicals as functional food ingredients for human health management. J. Pharmacogn. Phytochem. 8, 385–395. https://doi.org/10.22271/phyto.2019.v8.i5h.9589 (2019).

    Google Scholar 

  6. Gómez-Campo, C. Morphology and morphotaxonomy of the Tribe Brassiceae. In Brassica Crops and Wild Allies, eds Tsunoda, S., Hinata, K. & Gómez-Campo, C. pp. 3–31 (Japan Scientific Societies Press, 1980).

  7. Singh, A., Sharma, S. & Dolly. Radish. In Antioxidants in Vegetables and Nuts—Properties and Health Benefits, pp. 209–235 (Springer, 2020). https://doi.org/10.1007/978-981-15-7470-2_10

  8. Gamba, M. et al. Nutritional and phytochemical characterization of radish (Raphanus sativus): A systematic review. Trends Food Sci. Technol. 113, 205–218. https://doi.org/10.1016/j.tifs.2021.04.045 (2021).

    Google Scholar 

  9. Manivannan, A., Kim, J. H., Kim, D. S., Lee, E. S. & Lee, H. E. Deciphering the nutraceutical potential of Raphanus sativus—a comprehensive overview. Nutrients 11(2), 402. https://doi.org/10.3390/nu11020402 (2019).

    Google Scholar 

  10. Singh, R., Avasthe, R., Babu, S., Yadav, G. S. & Kumar, A. (eds) Climate Resilient Cropping Systems for Sikkim[Technical Bulletin]. ICAR-National Organic Farming Research Institute, Tech. Bull. 2021/01 (2021).

  11. Di Renzo, L., De Lorenzo, A., Merra, G. & Gualtieri, P. Comment on: “A systematic review of organic versus conventional food consumption: Is there a measurable benefit on human health? Nutrients. Nutrients 12, 696. https://doi.org/10.3390/nu12030696 (2020).

    Google Scholar 

  12. Czech, A., Szmigielski, M. & Sembratowicz, I. Nutritional value and antioxidant capacity of organic and conventional vegetables of the genus Allium. Sci. Rep. 12, 18713. https://doi.org/10.1038/s41598-022-23497-y (2022).

    Google Scholar 

  13. Rubatzky, V. E. & Yamaguchi, M. World Vegetables: Principles, Production and Nutritive Values 2nd edn. (Chapman & Hall, UK, 1997). https://doi.org/10.1007/978-1-4615-6015-9.

    Google Scholar 

  14. Crisp, P. Radish, Raphanus sativus (Cruciferae). In Evolution of Crop Plants, 2nd edn., eds Smartt, J. & Simmonds, N. W. pp. 86–89 (Longman Scientific & Technical, 1995).

  15. Van Bueren, E. L. et al. The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: A review. NJAS Wageningen J. Life Sci. 58, 193–205. https://doi.org/10.1016/j.njas.2010.04.001 (2011).

    Google Scholar 

  16. Yang, Q. Radish genetic resources. Genebank Platform, CGIAR. (2019).

  17. Singh, B. K. Radish (Raphanus sativus L.): Breeding for higher yield, better quality and wider adaptability. In Advances in Plant Breeding Strategies: Vegetable Crops: Volume 8, Bulbs, Roots and Tubers, pp. 275–304 (Springer, 2021). https://doi.org/10.1007/978-3-030-66965-2_7

  18. Federer, W. T. Augmented (or Hoonuiaku) designs. Hawaiian Planters’ Record 55, 191–208 (1956).

    Google Scholar 

  19. Lin, C. S. & Poushinsky, G. A modified augmented design for an early stage of plant selection involving a large number of test lines without replication. Biometrics 39, 553–561. https://doi.org/10.2307/2531083 (1983).

    Google Scholar 

  20. Nowosad, K., Liersch, A., Popławska, W. & Bocianowski, J. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208, 187–194. https://doi.org/10.1007/s10681-015-1620-z (2016).

    Google Scholar 

  21. Kang, M. S. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron. 62, 199–252. https://doi.org/10.1016/S0065-2113(08)60569-6 (1997).

    Google Scholar 

  22. Borule, T. et al. Analysis of yield stability in diverse rice genotypes. J. Adv. Biol. Biotechnol. 27, 79–89. https://doi.org/10.9734/JABB/2024/v27i2701 (2024).

    Google Scholar 

  23. Meena, V. K., Sharma, R. K., Chand, S., Kumar, S. & Choudhary, K. Comparative study of stability models for identifying stable spring wheat genotypes in diverse conditions. Discov. Agric. 3, 1–24. https://doi.org/10.1007/s44279-025-00167-x (2025).

    Google Scholar 

  24. Anshori, M. F. et al. A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties. Front. Plant Sci. 15, 1462981. https://doi.org/10.3389/fpls.2024.1462981 (2024).

    Google Scholar 

  25. Piepho, H. P., Möhring, J., Melchinger, A. E. & Büchse, A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161, 209–228. https://doi.org/10.1007/s10681-007-9449-8 (2008).

    Google Scholar 

  26. Pathy, T. L. & Mohanraj, K. Estimating best linear unbiased predictions (BLUP) for yield and quality traits in sugarcane. Sugar Tech. 23, 1295–1305. https://doi.org/10.1007/s12355-021-01011-4 (2021).

    Google Scholar 

  27. Rabiei, B., Valizadeh, M., Ghareyazie, B. & Moghaddam, M. Evaluation of selection indices for improving rice grain shape. Field Crops Res. 89, 359–367. https://doi.org/10.1016/j.fcr.2004.02.016 (2004).

    Google Scholar 

  28. Jackson, M. L. Soil Chemical Analysis (Prentice Hall of India, India, 1973).

    Google Scholar 

  29. Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38. https://doi.org/10.1097/00010694-193401000-00003 (1934).

    Google Scholar 

  30. Subbaiah, B. V. & Asija, G. L. A rapid procedure for the estimation of available nitrogen in soil. Curr. Sci. 25, 258–260 (1956).

    Google Scholar 

  31. Bray, R. H. & Kurtz, L. T. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59, 39–46. https://doi.org/10.1097/00010694-194501000-00006 (1945).

    Google Scholar 

  32. Metson, A. J. Methods of chemical analysis for soil survey samples, Soil Bureau Bulletin No. 12, 208 pp. (New Zealand Dept. of Scientific and Industrial Research, 1956).

  33. Association of Official Analytical Chemists & Cunniff, P. Official Methods of Analysis of the Association of Official Analytical Chemists, 15th ed. (AOAC International, 1990).

  34. Ebell, L. F. Variation in total soluble sugars of conifer tissues with method of analysis. Phytochemistry 8, 227–233. https://doi.org/10.1016/S0031-9422(00)85818-5 (1969).

    Google Scholar 

  35. Teixeira, G. G. & Santos, P. M. Simple and cost-effective approaches for quantification of reducing sugar exploiting digital image analysis. J. Food Compos. Anal. 113, 104719. https://doi.org/10.1016/j.jfca.2022.104719 (2022).

    Google Scholar 

  36. Mushtaq, M. W. et al. Spectrophotometric determination of Vitamin C in underground vegetables and kinetic modelling to probe the effect of temperature and pH on degradation of Vitamin C. Pak. J. Bot. 54(5), 1771–1775 (2022).

    Google Scholar 

  37. Braniša, J., Jenisová, Z., Porubská, M., Jomová, K. & Valko, M. Spectrophotometric determination of chlorophylls and carotenoids: an effect of sonication and sample processing. J. Microbiol. Biotechnol. Food Sci. 3, 61–64 (2014).

    Google Scholar 

  38. Pérez-Patricio, M. et al. Optical method for estimating the chlorophyll contents in plant leaves. Sensors 18, 650. https://doi.org/10.3390/s18020650 (2018).

    Google Scholar 

  39. Oyaizu, M. Studies on products of browning reaction: antioxidative activities of products of browning reaction prepared from glucosamine. Jap. J. Nutr. Dietet. 44, 307–315. https://doi.org/10.5264/eiyogakuzashi.44.307 (1986).

    Google Scholar 

  40. Jelodarian, S., Ebrahimabadi, A. H., Khalighi, A. & Batooli, H. Evaluation of antioxidant activity of Malus domestica fruit extract from Kashan area. Avicenna J. Phytomed. 2, 139 (2012).

    Google Scholar 

  41. Official Methods of Analysis, 18th edn. AOAC INTERNATIONAL (2005).

  42. Official Methods of Analysis, 21st edn., Appendix D [Appendix]. AOAC INTERNATIONAL (2020). http://eoma.aoac.org/app_d.pdf

  43. Lowry, O. H., Rosebrough, N. J., Farr, A. L. & Randall, R. J. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193, 265–275. https://doi.org/10.1016/S0021-9258(19)52451-6 (1951).

    Google Scholar 

  44. Gallik, S. Determination of the anthocyanin concentration in table wines and fruit juices using visible light spectrophotometry. Cell Biol. 2, 1–12 (2012).

    Google Scholar 

  45. Keser, S., Celik, S., Turkoglu, S., Yilmaz, O. & Turkoglu, I. Hydrogen peroxide radical scavenging and total antioxidant activity of hawthorn. Chem. J. 2, 9–12 (2012).

    Google Scholar 

  46. Tamboli, F. A. et al. Estimation of total carbohydrate content by phenol sulphuric acid method from Eichhornia crassipes (Mart.) Solms. Asian J. Res. Chem. 13(5), 357–359. https://doi.org/10.5958/0974-4150.2020.00067.X (2020).

    Google Scholar 

  47. Lin, Y. T., Liang, C. & Chen, J. H. Feasibility study of ultraviolet activated persulfate oxidation of phenol. Chemosphere 82, 1168–1172. https://doi.org/10.1016/j.chemosphere.2010.12.027 (2011).

    Google Scholar 

  48. Panse, V. G. & Sukhatme, P. V. Statistical methods for agricultural workers, 4th edn., p. 347 (ICAR, 1984).

  49. Burton, G. W. & De Vane, D. E. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agron. J. 45, 478–481. https://doi.org/10.2134/agronj1953.00021962004500100005x (1953).

    Google Scholar 

  50. Johnson, H. W., Robinson, H. F. & Comstock, R. E. Estimates of genetic and environmental variability in soybeans. Agron. J. 47, 314–318. https://doi.org/10.2134/agronj1955.00021962004700070009X (1955).

    Google Scholar 

  51. Hanson, C. H., Robinson, H. F. & Comstock, R. E. Biometrical studies of yield in segregating populations of Korean lespedeza. Agron. J. 48, 268–272. https://doi.org/10.2134/agronj1956.00021962004800060008x (1956).

    Google Scholar 

  52. Lush, J. L. Heritability of quantitative characters in farm animals. Proc. 8 th Int. Congress Genet. 1948, 356–375 (1949).

    Google Scholar 

  53. Al-Jibouri, H., Miller, P. A. & Robinson, H. F. Genotypic and environmental variances and covariances in an upland cotton cross of interspecific origin. Agron. J. 50, 633–636. https://doi.org/10.2134/agronj1958.00021962005000100020x (1958).

    Google Scholar 

  54. Searle, S. R. Phenotypic, genetic and environmental correlations. Biometrics 17, 474–480. https://doi.org/10.2307/2527838 (1961).

    Google Scholar 

  55. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).

    Google Scholar 

  56. Smith, H. F. A discriminant function for plant selection. Ann. Eugenics 7, 240–250. https://doi.org/10.1111/j.1469-1809.1936.tb02143.x (1936).

    Google Scholar 

  57. Samyuktha, S. M., Geethanjali, S. & Bapu, J. R. Genetic diversity and correlation studies in chickpea (Cicer arietinum L.) based on morphological traits. Electron. J. Plant Breed. 8, 874–884 (2017).

    Google Scholar 

  58. Singh, B. K. et al. Pigmented radish (Raphanus sativus): Genetic variability, heritability and inter-relationships of total phenolics, anthocyanins and antioxidant activity. Indian J. Agric. Sci. 87, 1600–1606 (2017).

    Google Scholar 

  59. Lone, R. A. et al. Genetic variability and correlation studies in winter wheat (Triticum aestivum L.) germplasm for morphological and biochemical characters. Int. J. Pure Appl. Biosci. 5, 82–91. https://doi.org/10.18782/2320-7051.2489 (2017).

    Google Scholar 

  60. Fufa, N., Tsagaye, D., Ali, A., Wegayehu, G. & Fikre, D. Assessing genetic variability and heritability in garlic (Allium sativum L.) genotypes for bulb yield and related traits. Cross Curr. Int. J. Agric. Vet. Sci. 7, 1–8; https://doi.org/10.36344/ccijavs.2025.v07i01.001 (2025).

  61. Manzoor, A. et al. Morphological characterization and analysis of genetic variability in radish (Raphanus sativus) genotypes for important qualitative and quantitative traits. Brazil. Arch. Biol. Technol. 67, e24230627. https://doi.org/10.1590/1678-4324-2024230627 (2024).

    Google Scholar 

  62. Lawal, B., Shittu, O. K., Oibiokpa, F. I. & Mohammed, H. African natural products with potential antioxidants and hepatoprotective properties: A review. Clin. Phytosci. 2, 23. https://doi.org/10.1186/s40816-016-0037-0 (2016).

    Google Scholar 

  63. Das, A. K. et al. A comprehensive review on antioxidant dietary fibre enriched meat-based functional foods. Trends Food Sci. Technol. 99, 323–336. https://doi.org/10.1016/j.tifs.2020.03.010 (2020).

    Google Scholar 

  64. Zeng, Y. et al. Preventive and therapeutic role of functional ingredients of barley grass for chronic diseases in human beings. Oxidative Med. Cell. Longevity 2018, 3232080. https://doi.org/10.1155/2018/3232080 (2018).

    Google Scholar 

  65. Rolland, F., Moore, B. & Sheen, J. Sugar sensing and signaling in plants. Plant Cell 14, 185–205. https://doi.org/10.1105/tpc.010455 (2002).

    Google Scholar 

  66. Thomas, J. A., Jeffrey, A. C., Atsuko, K. & David, M. K. Regulating the proton budget of higher plant photosynthesis. Proc. Natl. Acad. Sci. USA 102, 9709–9713. https://doi.org/10.1073/pnas.0503952102 (2005).

    Google Scholar 

  67. Rodriguez-Saona, L. E., Giusti, M. M. & Wrolstad, R. E. Anthocyanin pigment composition of red-fleshed potatoes. J. Food Sci. 63, 458–465. https://doi.org/10.1111/j.1365-2621.1998.tb15764.x (1998).

    Google Scholar 

  68. Khoo, H. E., Azlan, A., Tang, S. T. & Lim, S. M. Anthocyanidins and anthocyanins: Colored pigments as food, pharmaceutical ingredients, and the potential health benefits. Food Nutr. Res. 61, 1361779. https://doi.org/10.1080/16546628.2017.1361779 (2017).

    Google Scholar 

  69. Mezzomo, N. & Ferreira, S. R. Carotenoids functionality, sources, and processing by supercritical technology: A review. J. Chem. 2016, 3164312. https://doi.org/10.1155/2016/3164312 (2016).

    Google Scholar 

  70. Xiao, S. & Li, J. Study on functional components of functional food based on food vitamins. In Journal of Physics: Conference Series, Vol. 1549, No. 3, p. 032002 (IOP Publishing, 2020). https://doi.org/10.1088/1742-6596/1549/3/032002

  71. Lutz, M., Fuentes, E., Ávila, F., Alarcón, M. & Palomo, I. Roles of phenolic compounds in the reduction of risk factors of cardiovascular diseases. Molecules 24, 366. https://doi.org/10.3390/molecules24020366 (2019).

    Google Scholar 

  72. Kurina, A. B., Kornyukhin, D. L., Solovyeva, A. E. & Artemyeva, A. M. Genetic diversity of phenotypic and biochemical traits in VIR radish (Raphanus sativus L.) germplasm collection. Plants 10, 1799 (2021).

    Google Scholar 

  73. Iloki-Assanga, S. B. et al. Solvent effects on phytochemical constituent profiles and antioxidant activities, using four different extraction formulations for analysis of Bucida buceras L. and Phoradendron californicum. BMC. Res. Notes 8(1), 396. https://doi.org/10.1186/s13104-015-1388-1 (2015).

    Google Scholar 

  74. Kuperman, F. M. & Kalacheva, L. I. The morphological and physiological classification of Raphanus sativus. Vestn. Sel’skokhozyaistvennoi Nauki 11, 37–43 (1972).

    Google Scholar 

  75. Lee, O. N. & Park, H. Y. Assessment of genetic diversity in cultivated radishes (Raphanus sativus) by agronomic traits and SSR markers. Sci. Hortic. 223, 19–30. https://doi.org/10.1016/j.scienta.2017.05.025 (2017).

    Google Scholar 

  76. Tsehaye, A., Fikre, A. & Bantayhu, M. Genetic variability and association analysis of Desi-type chickpea (Cicer arietinum L.) advanced lines under potential environment in North Gondar, Ethiopia. Cogent Food Agric. 6(1), 1806668. https://doi.org/10.1080/23311932.2020.1806668 (2020).

    Google Scholar 

  77. Singh, B. et al. Genetic association analysis in Asiatic radish (Raphanus sativus L.). Indian J. Plant Genet. Resour. 15, 121–124 (2002).

    Google Scholar 

  78. Singh, A. K., Ahmed, N. & Narayan, R. Genetic variability and characters association in radish under temperate conditions. Haryana J. Hort. Sci. 34, 346–384 (2005).

    Google Scholar 

  79. Ullah, M. Z., Hasan, M. J., Rahman, A. H. & Saki, A. I. Genetic variability, character association and path coefficient analysis in radish (Raphanus sativus L.). Agric. 8, 22–27. https://doi.org/10.3329/agric.v8i2.7573 (2010).

    Google Scholar 

  80. Yousuf, M., Ajmal, S. U., Munir, M. & Ghafoor, A. Genetic diversity analysis for agro-morphological and seed quality traits in rapeseed (Brassica campestris L.). Pak. J. Bot. 43, 1195–1203 (2011).

    Google Scholar 

  81. Huang, T. et al. Evaluation of genetic variation of morphological and clubroot-resistance traits of radish and metabonomic analysis of clubroot-resistant cultivar. Sci. Hortic. 321, 112272. https://doi.org/10.1016/j.scienta.2023.112272 (2023).

    Google Scholar 

  82. Mohammadi, S. A. & Prasanna, B. M. Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop Sci. 43, 1235–1248. https://doi.org/10.2135/cropsci2003.1235 (2003).

    Google Scholar 

  83. Raihan, M. S. & Jahan, N. A. Genetic variability assessment in selected genotypes of radish (Raphanus sativus L.) using morphological markers. J. Res. Opinion 6, 2495–2501 (2019).

    Google Scholar 

  84. Ali, S. et al. Groundnut genotypes’ diversity assessment for yield and oil quality traits through multivariate analysis. SABRAO J. Breed. Genet. 54, 565–573. https://doi.org/10.54910/sabrao2022.54.3.9 (2022).

    Google Scholar 

  85. George, R. A. T. & Evans, D. R. A classification of winter radish cultivars. Euphytica 30, 483–492. https://doi.org/10.1007/BF00034013 (1981).

    Google Scholar 

  86. Saroj, R. et al. Unraveling the relationship between seed yield and yield-related traits in a diversity panel of Brassica juncea using multi-traits mixed model. Front. Plant Sci. 12, 651936. https://doi.org/10.3389/fpls.2021.651936 (2021).

    Google Scholar 

  87. Tudu, V. K., Kumar, A. & Rani, V. Assessment of genetic divergence in Indian mustard (Brassica juncea L. Czern. & Coss.) based on yield-attributing traits. J. Pharmacogn. Phytochem. 7(1S), 2093–2096. https://doi.org/10.3329/bjb.v50i1.52669 (2018).

    Google Scholar 

  88. Ahmad, R., Shah, M. K., Ibrar, D., Javaid, R. A. & Khan, N. Assessment of genetic divergence and its utilization in hybrid development in cultivated onion (Allium cepa L.). J. Anim. Plant Sci. 31, 175–187 (2021).

    Google Scholar 

  89. Hassan, Z. et al. Phenotypic characterization of exotic tomato germplasm: An excellent breeding resource. PLoS ONE 16, e0253557. https://doi.org/10.1371/journal.pone.0253557 (2021).

    Google Scholar 

  90. Wu, X. et al. Lipophilic and hydrophilic antioxidant capacities of common foods in the United States. J. Agri. Food Chem. 52(12), 4026–4037. https://doi.org/10.1021/jf049696w (2004).

    Google Scholar 

  91. Kallithraka, S., Mohdaly, A. A., Makris, D. P. & Kefalas, P. Determination of major anthocyanin pigments in Hellenic native grape varieties (Vitis vinifera): Association with antiradical activity. J. Food Compos. Anal. 18, 375–386. https://doi.org/10.1016/j.jfca.2004.02.010 (2005).

    Google Scholar 

  92. Zoecklein, B. W., Fugelsang, K. C., Gump, B. H. & Nury, F. S. Carbohydrates: reducing sugars. In Production Wine Analysis, pp. 114–128 (Springer, 1990). https://doi.org/10.1007/978-1-4615-8146-8_6

  93. Khatri, D. & Chhetri, S. B. B. Reducing sugar, total phenolic content, and antioxidant potential of Nepalese plants. Biomed Res. Int. 2020, 7296859. https://doi.org/10.1155/2020/7296859 (2020).

    Google Scholar 

  94. Kar, P. K., Srivastava, P. P., Awasthi, A. K. & Urs, S. R. Genetic variability and association of ISSR markers with some biochemical traits in mulberry (Morus spp.) genetic resources available in India. Tree Genet. Genomes 4, 75–83. https://doi.org/10.1007/s11295-007-0089-x (2008).

    Google Scholar 

  95. Khodadadi, M., Dehghani, H., Fotokian, M. H. & Rain, B. Genetic diversity and heritability of chlorophyll content and photosynthetic indexes among some Iranian wheat genotypes. J. Biodiv. Environ. Sci. 4, 12–23 (2014).

    Google Scholar 

  96. Luximon-Ramma, A., Bahorun, T. & Crozier, A. Antioxidant actions and phenolic and vitamin C contents of common Mauritian exotic fruits. J. Sci. Food Agric. 83, 496–502. https://doi.org/10.1002/jsfa.1365 (2003).

    Google Scholar 

  97. Pathak, R., Singh, M. & Henry, A. Genetic diversity and interrelationship among clusterbean (Cyamopsis tetragonoloba) genotypes for qualitative traits. Indian J. Agric. Sci. 81, 402–406 (2011).

    Google Scholar 

  98. Ebdon, J. S. & Gauch, H. G. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci. 42, 489–496. https://doi.org/10.2135/cropsci2002.4890 (2002).

    Google Scholar 

  99. Mustapha, M. & Bakari, H. R. Statistical evaluation of genotype by environment interactions for grain yield in millet (Pennisetum glaucum (L.) R. Br.). Int. J. Eng. Sci. 3, 7–16 (2014).

    Google Scholar 

  100. Mohamed, M. Genotype by environment interactions for grain yield in bread wheat (Triticum aestivum L.). J. Plant Breed. Crop Sci. 5, 150–157. https://doi.org/10.5897/JPBCS2013.0390 (2013).

    Google Scholar 

  101. Strefeler, M. S. & Wehner, T. C. Comparison of six methods of multiple trait selection for fruit yield and quality traits in three fresh-market cucumber populations. J. Amer. Soc. Hort. Sci. 111, 792–798 (1986).

    Google Scholar 

  102. Mallikarjunarao, K., Singh, P. K., Vaidya, A., Pradhan, R. & Das, R. K. Genetic variability and selection parameters for different genotypes of radish (Raphanus sativus L.) under Kashmir valley. Ecol. Environ. Conserv. 21, 361–364 (2015).

    Google Scholar 

  103. Fayezizadeh, M. R., Ansari, N. A., Sourestani, M. M. & Hasanuzzaman, M. Biochemical compounds, antioxidant capacity, leaf color profile and yield of basil (Ocimum sp.) microgreens in floating system. Plants 12, 2652 (2023).

    Google Scholar 

  104. Milligan, S. B. & Kang, M. S. A mixed-model approach. In Crop Improvement: Challenges in the Twenty-First Century, p. 353 (Publisher—check edition; 2024).

Download references

Acknowledgements

We sincerely acknowledge Department of Horticulture, Sikkim University for extending the all the necessary facilities for conducting present study.

Funding

No funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Horticulture, Sikkim University, Gangtok, Sikkim, 737102, India

    Kime Tare, Rajesh Kumar, Lomash Sharma & Sandhya Lamichaney

  2. Department of Horticulture, Assam Agriculture University, Jorhat, Assam, 785013, India

    Kunal Kaushik

Authors
  1. Kime Tare
    View author publications

    Search author on:PubMed Google Scholar

  2. Rajesh Kumar
    View author publications

    Search author on:PubMed Google Scholar

  3. Kunal Kaushik
    View author publications

    Search author on:PubMed Google Scholar

  4. Lomash Sharma
    View author publications

    Search author on:PubMed Google Scholar

  5. Sandhya Lamichaney
    View author publications

    Search author on:PubMed Google Scholar

Contributions

K.T. investigated, visualized and validated the research, curated data and written–original draft. R. K. conceptualized the methodology and supervised the research. K. K. done the formal analysis and handled the software. L. S. handled the software, and edited the manuscript. S. L. done the formal analysis, handled the software and edited the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Rajesh Kumar.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tare, K., Kumar, R., Kaushik, K. et al. Multivariate and stability analysis for yield and biochemical traits in radish (Raphanus sativus L.) genotypes from Sikkim Himalaya for functional food applications. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38280-6

Download citation

  • Received: 19 September 2025

  • Accepted: 29 January 2026

  • Published: 19 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38280-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Radish
  • Sikkim Himalaya
  • Genetic variability
  • Augmented RCBD
  • Organic cultivation
  • Nutritional profiling
  • Functional food
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research