SpringerLink
Log in

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

  • Published:
Euphytica Aims and scope Submit manuscript

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 2 i , σ 2 i , 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Adugna W, Labuschagna MT (2003) Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.). Euphytica 129:211–218

    Article  CAS  Google Scholar 

  • 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 17:1825–1838

    Google Scholar 

  • 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 119:225–230

    Article  Google Scholar 

  • Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23

    Article  Google Scholar 

  • Dehghani H, Ebadi A, Yousefi A (2006) Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron J 98:388–393

    Article  Google Scholar 

  • 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 6:385–394

    Article  Google Scholar 

  • 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 66:963–972

    Article  CAS  Google Scholar 

  • Dia M, Wehner TC, Arellano C (2016) Analysis of genotype × environment interaction (G × E) using SAS programming. Agron J 108:1838–1852

    Article  Google Scholar 

  • 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 31:75–87

    Article  Google Scholar 

  • 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 42:489–496

    Article  Google Scholar 

  • Eberhart SAT, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40

    Article  Google Scholar 

  • Falconer DS (1990) Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet Res 56:57–70

    Article  Google Scholar 

  • Finlay KW, Wilkinson GN (1963) Adaptation in a plant breeding programme. Aust J Agric Res 14:742–754

    Article  Google Scholar 

  • Flores F, Moreno MT, Cubero JI (1998) A comparison of univariate and multivariate methods to analyze G9E interaction. Field Crop Res 56:271–286

    Article  Google Scholar 

  • Fox P, Skovmand B, Thompson B, Braun HJ, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47:57–64

    Article  Google Scholar 

  • Francis TR, Kannenberg LW (1978) Yield stability studies in short-season maize: I. A descriptive method for grou** genotypes. Can J Plant Sci 58:1029–1034

    Article  Google Scholar 

  • GENSTAT (2008) GENSTAT 12th edn. VSN International Ltd. http://www.vsni.co.uk. Accessed July 2009

  • Huehn M (1979) Beitrage zur erfassung der phanotypischen stabilitat. EDV Med Biol 10:112–117

    Google Scholar 

  • Huehn M (1990) Nonparametric measures of phenotypic stability. Part 1: theory. Euphytica 47:189–194

    Google Scholar 

  • Jamshidmoghaddam M, Pourdad SS (2013) Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 180:321–335

    Google Scholar 

  • Kang MS (1988) A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res Commun 16:113–115

    Google Scholar 

  • 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 18:789–803

    Google Scholar 

  • Khalili M, Pour-Aboughadareh A, Naghavi MR (2016) Assessment of drought tolerance in barley: integrated selection criterion and drought tolerance indices. Environ Exp Biol 14:33–41

    Article  Google Scholar 

  • Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: where do we stand? Crop Sci 26:894–900

    Article  Google Scholar 

  • Loss SP, Siddique KHM (1994) Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv Agron 52:229–276

    Article  CAS  Google Scholar 

  • 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 19:264–270

    Article  Google Scholar 

  • Mohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159:419–432

    Article  Google Scholar 

  • 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 61:92–101

    Article  Google Scholar 

  • Nassar R, Huehn M (1987) Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics 43:45–53

    Article  Google Scholar 

  • 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 57:1–9

    Google Scholar 

  • Pinthus JM (1973) Estimate of genotype value: a proposed method. Euphytica 22:121–123

    Article  Google Scholar 

  • Plaisted RL (1960) A shorter method for evaluating the ability of selections to yield consistently over locations. Am Potato J 37:166–172

    Article  Google Scholar 

  • Plaisted RI, Peterson LC (1959) A technique for evaluating the ability of selection to yield consistently in different locations or seasons. Am Potato J 36:381–385

    Article  Google Scholar 

  • 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:e1211

    Article  Google Scholar 

  • Purchase JL (1997) Parametric analysis to describe GE interaction and yield stability in winter wheat. Dissertation, University of the Orange Free State

  • 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 17:101–107

    Article  Google Scholar 

  • SPSS Inc. Released (2007) SPSS for Windows, Version 16.0. Chicago, SPSS Inc.

  • SAS (2011) Base SAS 9.1 procedures guide. SAS Institute Inc, Cary

  • Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29:237–245

    Article  CAS  Google Scholar 

  • 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 68:78–88

    Article  Google Scholar 

  • Tai GCC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190

    Article  Google Scholar 

  • Thennarasu K (1995) On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Dissertation, University of New Delhi

  • 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 45:500–511

    Article  Google Scholar 

  • 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 64:597–611

    Article  Google Scholar 

  • 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 122:477–490

    Article  Google Scholar 

  • Ward JH (1963) Hierarchical grou** to optimize an objective function. J Am Stat Assoc 58:236–244

    Article  Google Scholar 

  • Wricke G (1962) Übereine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Zeitschr F Pflanzenz 47:92–96

    Google Scholar 

  • XLSTAT (2017) Data analysis and statistical solution for Microsoft Excel. Addinsoft, Paris

    Google Scholar 

  • Yan W, Hunt LA (2001) Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci 41:19–25

    Article  Google Scholar 

  • Yan W, Kang MS (2002) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton

    Book  Google Scholar 

  • Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86:623–645

    Article  Google Scholar 

  • Yan W, Cornelius PL, Crossa J, Hunt LA (2001) Two types of GGE biplots for analyzing multi-environment trial data. Crop Sci 41:656–663

    Article  Google Scholar 

  • Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–655

    Article  Google Scholar 

  • Zhang Z, Lu C, **ang ZH (1998) Stability analysis for varieties by AMMI Model. Acta Agron Sinica 24:304–309

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Yasuj, Iran

    Behrouz Vaezi

  2. Department of Agronomy and Plant Breeding, Tehran University, Karaj, Iran

    Alireza Pour-Aboughadareh

  3. Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gonbad, Iran

    Rahmatolah Mohammadi

  4. Ardabil Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREEO), Ardebil, Iran

    Asghar Mehraban

  5. Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam, Kohdasht, Iran

    Tahmasb Hossein-Pour

  6. Kohgiluyeh and Boyerahmad Metrological Organization, Synoptic Metrological Management, Yasuj, Iran

    Ehsan Koohkan

  7. Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Dezful, Iran

    Soraya Ghasemi

  8. Department of Plant Breeding, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

    Hoda Moradkhani

  9. The UWA Institute of Agriculture, The University of Western Australia, LB 5005, Perth, WA, 6001, Australia

    Kadambot H. M. Siddique

Authors
  1. Behrouz Vaezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Pour-Aboughadareh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rahmatolah Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asghar Mehraban
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tahmasb Hossein-Pour
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ehsan Koohkan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Soraya Ghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hoda Moradkhani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kadambot H. M. Siddique
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Pour-Aboughadareh.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 17 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R. et al. Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes. Euphytica 215, 63 (2019). https://doi.org/10.1007/s10681-019-2386-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10681-019-2386-5

Keywords

Search

Navigation