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Plant Science Today (PST)

Research Articles

Early Access

Multivariate analysis of barley (Hordeum vulgare L.) germplasm under temperate agro-climatic niches

DOI
https://doi.org/10.14719/pst.10976
Submitted
30 July 2025
Published
24-03-2026
Versions

Abstract

Barley (Hordeum vulgare L.) is a resilient cereal crop with significant global importance, particularly in temperate regions, where it thrives under a range of climatic stresses. This study assessed genetic variability, trait associations and population structure among 51 barley genotypes under temperate conditions using an augmented block design during the rabi season 2021–2022 at Mountain Research Centre for Field Crops (MRCFC), Khudwani- Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST) of Kashmir. Eight agronomically important traits namely, days to heading, days to maturity, plant height, tiller number per metre, spike length, grains per spike, 1000-grain weight and grain yield per plot, were evaluated using standard biometric and multivariate analytical approaches. Significant genetic diversity was observed, with high heritability and substantial genetic advance recorded for tiller number per meter and yield, indicating predominant additive gene action. Yield showed strong positive correlations with tiller number and grains per spike, highlighting their significance for selection. Path analysis confirmed the association pattern. Principal component analysis (PCA) identified 2 major components explaining almost 60 % of total variation, with plant height, tiller number per meter, grains per spike and yield being the dominant contributors. Clustering grouped genotypes into 5 clusters, with cluster V yielding highest. Large inter-cluster distances signified broad genetic divergence. Based on a combined selection index integrating PCA scores and yield performance, 6 superior barley genotypes were identified, among which IBYT-HI-2021-14, IBYT-HI-2021-12 and HUB113 exhibited the most consistent and outstanding agronomic performance under temperate conditions.

References

  1. 1. Schreiber M, Mascher M, Wright J, Padmarasu S, Himmelbach A, Heavens D et al. A genome assembly of the barley “transformation reference” cultivar Golden Promise. G3. 2020;10:1823–27. https://doi.org/10.1534/g3.120.401010
  2. 2. Kumbhar RA, Yang K, Baloch SNM, Memon S, Liu Y, Zhao H, et al. Exploring the significance of β-glucan in grains of hulless barley. Plant Mol Biol Rep. 2025;43:1–13. https://doi.org/10.1007/s11105-025-01542-0
  3. 3. Reiners S, Hebestreit S, Wedekind L, Kiehntopf M, Klink A, Rummler S, et al. Effect of regular consumption of traditional and roasted oat and barley flakes on blood lipids and glucose metabolism—a randomized crossover trial. Front Nutr. 2023;10:1095245. https://doi.org/10.3389/fnut.2023.1095245
  4. 4. Geng L, Li M, Zhang G, Ye L. Barley: A potential cereal for producing healthy and functional foods. Food Qual Saf. 2022;6:12. https://doi.org/10.1093/fqsafe/fyac012
  5. 5. Perera WNU, Abdollahi MR, Zaefarian F, Wester TJ, Ravindran V. Barley, an undervalued cereal for poultry diets: Limitations and opportunities. Animals. 2022;12(19):2525. https://doi.org/10.3390/ani12192525
  6. 6. Anonymous. International Production Assessment Division (IPAD), USDA. 2025. https://ipad.fas.usda.gov/countrysummary/default.aspx?id=IN&crop=Barley
  7. 7. Capasso G, Santini G, Petraretti M, Esposito S, Landi S. Wild and traditional barley genomic resources as a tool for abiotic stress tolerance and biotic relations. Agriculture. 2021;11(11):1102. https://doi.org/10.3390/agriculture11111102
  8. 8. Akbari M, Sabouri H, Sajadi SJ, Yarahmadi S, Ahangar L. Classification and prediction of drought and salinity stress tolerance in barley using GenPhenML. Sci Rep. 2024;14(1):17420. https://doi.org/10.1038/s41598-024-68392-w
  9. 9. Sun L, Lai M, Ghouri F, Nawaz MA, Ali F, Baloch FS, et al. Modern plant breeding techniques in crop improvement and genetic diversity: from molecular markers and gene editing to artificial intelligence—A critical review. Plants. 2024;13(19):2676. https://doi.org/10.3390/plants13192676
  10. 10. Deepika K, Lavuri K, Rathod S, Yeshala CM, Jukanti AK, Reddy SN, et al. Multivariate analysis of geographically diverse rice germplasm for genetic improvement of yield, dormancy and shattering-related traits. Plant Genet Resour. 2021;19(2):144–52. https://doi.org/10.1017/S1479262121000186
  11. 11. Zewodu A, Mohammed W, Shiferaw E. Analysis of genetic diversity and population structure of Ethiopian barley (Hordeum vulgare L.) accessions using SSR markers. PLoS One. 2024;19(6):e0305945. https://doi.org/10.1371/journal.pone.0305945
  12. 12. Marone D, Russo MA, Mores A, Ficco DB, Laidò G, Mastrangelo AM, et al. Importance of landraces in cereal breeding for stress tolerance. Plants. 2021;10(7):1267. https://doi.org/10.3390/plants10071267
  13. 13. Noirot M, Hamon S, Anthony F. The principal component scoring: A new method of constituting a core collection using quantitative data. Genet Resour Crop Evol. 1996;43:1–6. https://doi.org/10.1007/BF00126744
  14. 14. Assefa A, Mekonnen D, Bantayehu M. Path coefficient, genetic divergence and principal component analysis on common bean (Phaseolus vulgaris L.) genotypes in Sekota, northwestern Ethiopia. Int J Food Agric Nat Resour. 2024;5(4):105–13. https://doi.org/10.46676/ij-fanres.v5i4.415
  15. 15. Güngör H, Türkoğlu A, Çakır MF, Dumlupınar Z, Piekutowska M, Wojciechowski T, et al. GT biplot and cluster analysis of barley (Hordeum vulgare L.) germplasm from various geographical regions based on agro-morphological traits. Agronomy. 2024;14(10):2188. https://doi.org/10.3390/agronomy14102188
  16. 16. Haines LM. Augmented block designs for unreplicated trials. J Agric Biol Environ Stat. 2021;26:409–27. https://doi.org/10.1007/s13253-021-00445-3
  17. 17. Aravind J, Mukesh Sankar S, Wankhede DP, Kaur V. Analysis of augmented randomised complete block designs (R package). CRAN. 2025. https://cran.r-project.org/package=augmentedRCBD
  18. 18. Matin MQ, Amiruzzaman M, Billah MM, Banu MB, Naher N, Choudhury DA. Genetic variability and path analysis studies in barley (Hordeum vulgare L.). Int J Appl Sci Biotechnol. 2019;7(2):243–47. https://doi.org/10.3126/ijasbt.v7i2.24663
  19. 19. Karaman M, Seydoşoğlu S, Vyhnánek T, Ali MA, Elshikh MS. Interpretation of yield and quality traits of barley (Hordeum vulgare L.) varieties by heatmap technique. J King Saud Univ Sci. 2024;36(9):103382. https://doi.org/10.1016/j.jksus.2024.103382
  20. 20. Haile J, Legesse H, Rao CP. Genetic variability, character association and genetic divergence in barley (Hordeum vulgare L.) genotypes grown at Horo district, western Ethiopia. Sci Technol Arts Res J. 2015;4(2):1–9. https://doi.org/10.4314/star.v4i2.1
  21. 21. Dinsa T, Mekbib F, Letta T. Genetic variability, heritability and genetic advance of yield and yield-related traits of food barley (Hordeum vulgare L.) genotypes in mid Rift Valley of Ethiopia. Adv Crop Sci Technol. 2018;6(5):1000401. https://doi.org/10.4172/2329-8863.1000401
  22. 22. Kumar K, Singh PN, Gaur SC, Nageshwar HP, Gautam U, Tarkeshwar MY, et al. Unravelling variability, heritability and genetic advance for yield and its component traits in Indian barley (Hordeum vulgare L.) landraces and diallelic lines. Int J Adv Biochem Res. 2025;9(1):797–801. https://doi.org/10.33545/26174693.2025.v9.i1j.3642
  23. 23. Mohtashami R. Correlation study of important barley agronomic traits and grain yield by path analysis. Biol Forum. 2015;7(1):211–19.
  24. 24. Amardeep L, Singh V, Malik P. Studies on correlation and path coefficients analysis in barley (Hordeum vulgare L.). Bull Environ Pharmacol Life Sci. 2017;6(12):75–80.
  25. 25. Kumar M, Shekhawat SS. Genetic variability in barley (Hordeum vulgare L.). Electron J Plant Breed. 2013;4(4):1309–12.
  26. 26. Setotaw TA, Belayneh SG, Gwinner R, Ferreira JL. Developing selection criteria based on an ontogenetic path analysis approach to improve grain yield in barley. Genet Mol Res. 2014;13(2):4635–46. https://doi.org/10.4238/2014.June.19.2
  27. 27. Tawfiq SI, Abdulkhaleq DA, Hama SJ, Abdulqader SH. Correlation and path analysis in barley under rainfall conditions. J Zankoy Sulaimani. 2016;18(3 Pt A):1–7. https://doi.org/10.17656/jzs.10538
  28. 28. Mirosavljević M, Momčilović V, Pržulj N, Ristov N, Aćin V, Čanak P, et al. Variation of agronomic traits associated with breeding progress in winter barley cultivars. Zemdirbyste. 2016;103(3):267–72. https://doi.org/10.13080/z-a.2016.103.034
  29. 29. Marzougui S, Chargui A. Estimation of correlation, regression and heritability among barley (Hordeum vulgare L.) accessions. J New Sci. 2018;60(2):3838-43.
  30. 30. Fatemi R, Yarnia M, Mohammadi S, Khalil Vand E, Mirashkari B. Evaluation of adaptability, yield potential and relationship between traits in barley varieties and lines in West Azerbaijan. Environ Stress Crop Sci. 2019;12(3):709–24.
  31. 31. Tanaka R, Nakano H. Barley yield response to nitrogen application under different weather conditions. Sci Rep. 2019;9(1):8477. https://doi.org/10.1038/s41598-019-44876-y
  32. 32. Hailu A, Alamerew S, Nigussie M, Assefa E. Correlation and path coefficient analysis of yield and yield-associated traits in barley (Hordeum vulgare L.) germplasm. Adv Crop Sci Technol. 2016;4(2):100216. https://doi.org/10.4172/2329-8863.100216
  33. 33. Derbew S. Multivariate analysis of hulled barley (Hordeum vulgare L.) landraces of southern Ethiopia. Cogent Food Agric. 2020;6(1):1841357. https://doi.org/10.1080/23311932.2020.1841357
  34. 34. Neykov N, Doneva M, Chavdarov P, Alexiev I. Correlation, path-coefficient and principal component analysis of yield and productivity-related traits of winter barley accessions with Bulgarian origin. Bulgarian J Agric Sci. 2022;28(4):658–61.
  35. 35. Sarma RN, Roy A. Genetic divergence in early maturing pigeonpea. Indian J Genet Plant Breed. 1994;54(2):184–87.
  36. 36. Abebe K, Mekonnen F, Zewdu D. Genetic variability, heritability and genetic advance for yield and yield-related traits of food barley (Hordeum vulgare L.) recombinant inbred lines at Woreilu district, south Wollo, Ethiopia. Agric Biol Res. 2024;40(1):787–91. https://doi.org/10.21203/rs.3.rs-3437891/v1
  37. 37. Elakhdar A, El-Naggar AA, El-Wakeell S, Ahmed AH. Integrating univariate and multivariate stability indices for breeding climate-resilient barley cultivars. BMC Plant Biol. 2025;25(1):76. https://doi.org/10.1186/s12870-024-05530-6

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