Advertisement

Euphytica

, 215:63 | Cite as

Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes

  • Behrouz Vaezi
  • Alireza Pour-AboughadarehEmail author
  • Rahmatolah Mohammadi
  • Asghar Mehraban
  • Tahmasb Hossein-Pour
  • Ehsan Koohkan
  • Soraya Ghasemi
  • Hoda Moradkhani
  • Kadambot H. M. Siddique
Article
  • 115 Downloads

Abstract

Barley is the fourth largest grain crop globally with varieties suited to temperate, subarctic, and subtropical areas. The identification and subsequent selection of superior varieties are complicated by genotype-by-environment interactions. The main objective of this study was to use parametric and non-parametric stability measures along with a GGE biplot model to identify high-yielding stable barley genotypes in Iran. Eighteen barley genotypes (16 new genotypes and two control varieties) were evaluated in a randomized complete block design with four replications at five locations over three growing seasons (2013–2014, 2014–2015, 2015–2016). The combined analysis of variance indicated that the environment main effect accounted for > 69% of all variation, compared with < 31% for the combined genotype (G) and genotype-by-environment interaction effects. The mean grain yield of each genotype across the five test sites and three seasons ranged from 1900 to 2302 kg ha−1. Using Spearman’s rank correlation and principal component analyses, the stability measures were divided into three groups: the first included mean yield, TOP and b, which are related to the dynamic concept of stability, the second comprised θi, W i 2 , σ i 2 , CVi, \(S_{di}^{2}\), KR, and the non-parametric measures, S(i) and NP(i), which are related to the static concept of stability, and the third included θi and R2. The GGE biplot analysis indicated that, of the five test locations, Gonbad and Moghan had the most discriminating and representative environments. Hence, these locations are recommended as ideal test locations in Iran for the selection of superior genotypes. The numerical and graphical methods both produced similar results, identifying genotypes G12, G13, and G17 as the best material for rainfed conditions in Iran; these genotypes should be promoted for commercial production.

Keywords

Barley Genotype-by-environment interaction GGE biplot Parametric and non-parametric statistics Yield stability 

Notes

Acknowledgements

This research was supported by a grant and genetic material from the Dryland Agricultural Research Institute (DARI) of Iran. We would like to thank all members of the project who contributed to the implementation of the field work.

Supplementary material

10681_2019_2386_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 17 kb)

References

  1. Adugna W, Labuschagna MT (2003) Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.). Euphytica 129:211–218CrossRefGoogle Scholar
  2. Ahmadi J, Vaezi B, Shaabani A, Khademi K, Fabriki-Ourang S, Pour-Aboughadareh A (2015) Non-parametric measures for yield stability in grass pea (Lathyrus sativus L.) advanced lines in semi warm regions. J Sci Tech 17:1825–1838Google Scholar
  3. Alwala S, Kwolek T, McPherson M, Pellow J, Meyer D (2010) A comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field Crop Res 119:225–230CrossRefGoogle Scholar
  4. Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23CrossRefGoogle Scholar
  5. Dehghani H, Ebadi A, Yousefi A (2006) Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron J 98:388–393CrossRefGoogle Scholar
  6. Dehghani H, Sabaghpour SH, Sabaghnia N (2008) Genotype × environment interaction for grain yield of some lentil genotypes and relationship among univariate stability statistics. Span J Agric Res 6:385–394CrossRefGoogle Scholar
  7. Dehghani MR, Majidi MM, Saeidi G, Mirlohi A, Amiri R, Sorkhilalehloo B (2015) Application of GGE biplot to analyse stability of Iranian tall fescue (Lolium arundinaceum) genotypes. Crop Pasture Sci 66:963–972CrossRefGoogle Scholar
  8. Dia M, Wehner TC, Arellano C (2016) Analysis of genotype × environment interaction (G × E) using SAS programming. Agron J 108:1838–1852CrossRefGoogle Scholar
  9. Dyke GV, Lane PW, Jenkyn JF (1995) Sensitivity (stability) analysis of multiple variety trails, with special reference to data expressed as proportion or percentage. Expl Agric 31:75–87CrossRefGoogle Scholar
  10. Ebdon JS, Gauch HG (2002) Additive main effect and multiplicative interaction analysis of national turf grass performance trials: I. interpretation of genotype × environment interaction. Crop Sci 42:489–496CrossRefGoogle Scholar
  11. Eberhart SAT, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40CrossRefGoogle Scholar
  12. Falconer DS (1990) Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet Res 56:57–70CrossRefGoogle Scholar
  13. Finlay KW, Wilkinson GN (1963) Adaptation in a plant breeding programme. Aust J Agric Res 14:742–754CrossRefGoogle Scholar
  14. Flores F, Moreno MT, Cubero JI (1998) A comparison of univariate and multivariate methods to analyze G9E interaction. Field Crop Res 56:271–286CrossRefGoogle Scholar
  15. Fox P, Skovmand B, Thompson B, Braun HJ, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47:57–64CrossRefGoogle Scholar
  16. Francis TR, Kannenberg LW (1978) Yield stability studies in short-season maize: I. A descriptive method for grouping genotypes. Can J Plant Sci 58:1029–1034CrossRefGoogle Scholar
  17. GENSTAT (2008) GENSTAT 12th edn. VSN International Ltd. http://www.vsni.co.uk. Accessed July 2009
  18. Huehn M (1979) Beitrage zur erfassung der phanotypischen stabilitat. EDV Med Biol 10:112–117Google Scholar
  19. Huehn M (1990) Nonparametric measures of phenotypic stability. Part 1: theory. Euphytica 47:189–194Google Scholar
  20. Jamshidmoghaddam M, Pourdad SS (2013) Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 180:321–335Google Scholar
  21. Kang MS (1988) A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res Commun 16:113–115Google Scholar
  22. Khalili M, Pour-Aboughadareh A (2016) Parametric and non-parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. J Agric Sci Tech 18:789–803Google Scholar
  23. Khalili M, Pour-Aboughadareh A, Naghavi MR (2016) Assessment of drought tolerance in barley: integrated selection criterion and drought tolerance indices. Environ Exp Biol 14:33–41CrossRefGoogle Scholar
  24. Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: where do we stand? Crop Sci 26:894–900CrossRefGoogle Scholar
  25. Loss SP, Siddique KHM (1994) Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv Agron 52:229–276CrossRefGoogle Scholar
  26. Madakemohekar AH, Prasad LC, Lal JP, Prasad R (2018) Estimation of combining ability and heterosis for yield contribution traits in exotic and indigenous crosses of barley (Hordeum vulgare L.). Res Crop 19:264–270CrossRefGoogle Scholar
  27. Mohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159:419–432CrossRefGoogle Scholar
  28. Mohammadi R, Haghparast R, Amri A, Ceccarelli S (2010) Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Pasture Sci 61:92–101CrossRefGoogle Scholar
  29. Nassar R, Huehn M (1987) Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics 43:45–53CrossRefGoogle Scholar
  30. Oyekunle M, Haruna A, Badu-Apraku B, Usman IS, Mani H, Ado SG, Olaoye G, Obeng-Antwi K, Abdulmalik RO, Ahmad HO (2016) Assessment of early-maturing maize hybrids and testing sites using GGE biplot analysis. Crop Sci 57:1–9Google Scholar
  31. Pinthus JM (1973) Estimate of genotype value: a proposed method. Euphytica 22:121–123CrossRefGoogle Scholar
  32. Plaisted RL (1960) A shorter method for evaluating the ability of selections to yield consistently over locations. Am Potato J 37:166–172CrossRefGoogle Scholar
  33. Plaisted RI, Peterson LC (1959) A technique for evaluating the ability of selection to yield consistently in different locations or seasons. Am Potato J 36:381–385CrossRefGoogle Scholar
  34. Pour-Aboughadareh A, Yousefian M, Moradkhani H, Poczai P, Siddique KHM (2019) STABILITYSOFT: a new online program to calculate parametric and non- parametric stability statistics for crop traits. Appl Plant Sci 7:e1211CrossRefGoogle Scholar
  35. Purchase JL (1997) Parametric analysis to describe GE interaction and yield stability in winter wheat. Dissertation, University of the Orange Free StateGoogle Scholar
  36. Purchase JL, Hatting H, Van Deventer CS (2000) Genotype × environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. S Afr J Plant Soil 17:101–107CrossRefGoogle Scholar
  37. SPSS Inc. Released (2007) SPSS for Windows, Version 16.0. Chicago, SPSS Inc.Google Scholar
  38. SAS (2011) Base SAS 9.1 procedures guide. SAS Institute Inc, CaryGoogle Scholar
  39. Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237–245CrossRefGoogle Scholar
  40. Subira J, Alvaro F, del Mora Garcia, Luis F, Royo C (2015) Breeding effects on the cultivar × environment interaction of durum wheat yield. Eur J Agron 68:78–88CrossRefGoogle Scholar
  41. Tai GCC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190CrossRefGoogle Scholar
  42. Thennarasu K (1995) On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Dissertation, University of New DelhiGoogle Scholar
  43. Vaezi B, Pour-Aboughadareh A, Mohammadi R, Armion M, Mehraban A, Hossein-Pour T, Dorri M (2017) GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Reas Commun 45:500–511CrossRefGoogle Scholar
  44. Vaezi B, Pour-Aboughadareh A, Mehraban A, Hossein-Pour T, Mohammadi R, Armion M, Dorri M (2018) The use of parametric and non-parametric measures for selecting stable and adapted barley lines. Arch Agron Soil Sci 64:597–611CrossRefGoogle Scholar
  45. Van Eeuwijk FA, Cooper M, DeLacy IH, Ceccarelli S, Grando S (2001) Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials. Euphytica 122:477–490CrossRefGoogle Scholar
  46. Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244CrossRefGoogle Scholar
  47. Wricke G (1962) Übereine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Zeitschr F Pflanzenz 47:92–96Google Scholar
  48. XLSTAT (2017) Data analysis and statistical solution for Microsoft Excel. Addinsoft, ParisGoogle Scholar
  49. Yan W, Hunt LA (2001) Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci 41:19–25CrossRefGoogle Scholar
  50. Yan W, Kang MS (2002) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca RatonCrossRefGoogle Scholar
  51. Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645CrossRefGoogle Scholar
  52. Yan W, Cornelius PL, Crossa J, Hunt LA (2001) Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci 41:656–663CrossRefGoogle Scholar
  53. Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–655CrossRefGoogle Scholar
  54. Zhang Z, Lu C, Xiang ZH (1998) Stability analysis for varieties by AMMI Model. Acta Agron Sinica 24:304–309Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Behrouz Vaezi
    • 1
  • Alireza Pour-Aboughadareh
    • 2
    Email author
  • Rahmatolah Mohammadi
    • 3
  • Asghar Mehraban
    • 4
  • Tahmasb Hossein-Pour
    • 5
  • Ehsan Koohkan
    • 6
  • Soraya Ghasemi
    • 7
  • Hoda Moradkhani
    • 8
  • Kadambot H. M. Siddique
    • 9
  1. 1.Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education CenterAgricultural Research, Education and Extension Organization (AREEO)YasujIran
  2. 2.Department of Agronomy and Plant BreedingTehran UniversityKarajIran
  3. 3.Golestan Agricultural and Natural Resources Research and Education CenterAgricultural Research, Education and Extension Organization (AREEO)GonbadIran
  4. 4.Ardabil Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREEO)ArdebilIran
  5. 5.Lorestan Agricultural and Natural Resources Research and Education CenterAgricultural Research, Education and Extension Organization (AREEO)Ilam, KohdashtIran
  6. 6.Kohgiluyeh and Boyerahmad Metrological Organization, Synoptic Metrological ManagementYasujIran
  7. 7.Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO)DezfulIran
  8. 8.Department of Plant BreedingKermanshah Branch, Islamic Azad UniversityKermanshahIran
  9. 9.The UWA Institute of AgricultureThe University of Western AustraliaPerthAustralia

Personalised recommendations