Phenotypic stability analysis of barley promising lines in the cold regions of Iran

Authors

1 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran

2 Field and Horticultural Crops Research Department, Agricultural and Natural Resources Research and Education Center of Khorasan Razavi province, Agricultural Research, Education and Extension Organization, Mashhad, Iran.

3 Field and Horticultural Crops Research Department, Agricultural and Natural Resources Research and Education Center of West-Azarbayjan province, Agricultural Research, Education and Extension Organization, Urmia, Iran.

4 Field and Horticultural Crops Research Department, Agricultural and Natural Resources Research and Education Center of Ardabil (Moghan) province, Agricultural Research, Education and Extension Organization, Ardabil, Iran.

5 Field and Horticultural Crops Research Department, Agricultural and Natural Resources Research and Education Center of Markazi province, Agricultural Research, Education and Extension Organization, Arak, Iran.

6 Field and Horticultural Crops Research Department, Agricultural and Natural Resources Research and Education Center of Hamedan province, Agricultural Research, Education and Extension Organization, Hamedan, Iran.

Abstract

Development of high-yielding new barley promising lines with wide adaptation across a wide range of diverse environments is a key goal of barley breeding program in the cold regions of Iran. The main objective of the current study was to use different stability analysis approaches to analyze phenotypic stability for selecting high-yielding with yield stability barley promising lines adapted to the cold regions of Iran as well as to investigate the relationships among different stability parameters and grain yield. Eighteen barley promising lines and two check cultivars; Bahman and Jolgeh were evaluated using randomized complete block design with three replications at six research stations during 2015–2017 cropping seasons. The AMMI analysis of variance indicated that the environment, genotypes and their interaction accounted for 53.60, 5.77 and 24.59% of the total variations, respectively. The first six interaction principal components (IPCA1 to IPCA6) were highly significant, revealing differential responses of the tested lines to different environments and the necessity of stability analysis. In total, 18 parametric and non-parametric statistics were used to analyze the data. According to PCA-based biplot and correlation heat-map, the stability statistics were classified into two main groups (CI and CII): CI comprised mean grain yield, θi, TOP and bi, which are referred to the dynamic concept of stability, and CII included S1, S2, S3, S6, NP1, NP2, NP3, NP4, CV, ASV, Wi2, σ2, θ(i), Sdi2 and KR, which are referred to static concept of stability.In general, the parametric and non-parametric stability statistics indicated similar results, identifying the promising line G8 (Makouee/Jolge) as high-yielding with yield stability. Therefore, this promising line can be recommended for being grown and commercialized in the cold regions of Iran.

Keywords

References

Abdipour, M., Vaezi, B., Younessi-Hamzekhanlu, M. and Ramazani. S. H. R. 2017. Nonparametric phenotypic stability analysis in advanced barley (Hordeum vulgare L.) genotypes. J. Crop Sci. Biotech. 20: 305-314.
 
 
Adugna, W., and Labuschagna, M. T. 2003. Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.). Euphytica 129: 211-218.
 
 
Ahmadi, A., Mohammadi, A. and Najafi Mirak, T. 2012a. Targeting promising bread wheat (Triticum aestivum L.) lines for cold climate growing environments using AMMI and SREG GGE biplot analyses. J. Agric. Sci. Tech. 14: 645-657.
 
 
Ahmadi, J., Vaezi, B., Shaabani, A., and Khademi, K. 2012b. Multi-environment yield trials of grass pea (Lathyrus sativus L.) in Iran using AMMI and SREG GGE. J. Agric. Sci. Tech. 14: 1075-1085.
 
 
Ahmadi, J., B. Vaezi, Shaabani, A., Khademi, K., Fabriki-Ourang, S., and 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.
 
 
Anonymous. 2018. Agricultural Statistics. http://dpe.maj.ir/Index.aspx?lang=1&sub=65#Becker, H. C., and Leon, J. 1988. Stability analysis in plant breeding. Plant Breed. 101: 1-23.
 
 
Bhargava, A., Shukla, S. and. Ohri, D. 2007. Evaluation of forage yield and leaf quality traits in Chenopodium Spp. in multiyear trials. Euphytica 153: 199-213.
 
 
Ebdon, J. S., and Gauch, H. G. 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.
 
 
Eberhart, S. A. T., and Russell, W. A.. 1966. Stability parameters for comparing varieties. Crop Sci. 6:36-40.
 
 
Finlay, K. W., and G. N. Wilkinson, G. N. 1963. Adaptation in a plant breeding programme. Aust. J. Agric. Res. 14: 742-754.
 
 
Fischbech, G. 2002. Contribution of barley to agriculture: A brief overview. Pp. 1-14. In: G. A. Slafer, et al. (eds.) Barley science, recent advances from molecular biology to agronomy of yield and quality. Food Product Press, New York.
 
 
Fox, P., Skovmand, B., Thompson, B., Braun, H. J. and Cormier, R. 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47: 57-64.
 
 
Francis, T. R., and. Kannenberg, L. W. 1978. Yield stability studies in short-season maize: I. a descriptive method for grouping genotypes. Can. J. Plant Sci. 58: 1029-1034.
 
 
GENSTAT. 2008. GENSTAT 12thedition. 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.
 
 
Huehn, M. 1990. Nonparametric measures of phenotypic stability. Part 1: theory. Euphytica 47: 189-194.
 
 
Jamshidi-Moghaddam, M., and Pourdad, S. S. 2013. Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 180: 321-335.
 
 
Kang, M. S. 1988. A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res. Commun. 16: 113-115.
 
 
Kang, M. S., J. Miller, D. and Darrah, L. L. 1987. A note on relationship between stability variance and ecovalence. Heredity 78: 107-112.
 
 
Khalili, M., and 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.
 
 
Langridge, P., and Barr, A.. 2003. Preface to better barley faster: the role of marker-assisted selection. Aust. J. Agric. Res. 54: 1-4.
 
 
Lin, C. S., Binns, M. R. and Lefkovitch, L. P. 1986. Stability analysis: where do we stand? Crop Sci. 26: 894-900.
 
 
Maechler, M., Magnusson, A. and Steffen, M. 2014. gplots: various R programming tools for plotting data. Available: http://CRAN.R-project.org/package=gplots.
 
 
Mahfoozi, S., Hosseini-Salekdeh, G., Mardi, M. and Karimzadeh, G. 2008. Freezing resistance from the lab to the field in wheat: what should we breed for? The 10th National Crop Science Cong. 18–20 Aug, 2008. Seed and Plant Improvement Institute, Karaj. Iran.
 
 
Mut, Z., Aydin, N., Bayramoglu, H. O.  and Ozcan, H. 2009. Interpreting genotype × environment interaction in bread wheat (Triticum aestivum L.) genotypes using nonparametric measures. Turk. J. Agric. For. 33: 127-137.
 
 
Nassar, R., and Huehn, M. 1987. Studies on estimation of phenotypic stability: tests of significance for non-parametric measures of phenotypic stability. Biometrics 43: 45-53.
 
 
Plaisted, R. I., and. Peterson, L. C. 1959. A technique for evaluating the ability of selection to yield consistently in different locations or seasons. Am. Potato J. 36: 381-385.
 
 
Plaisted, R. L. 1960. A shorter method for evaluating the ability of selections to yield consistently over locations. Am. Potato J. 37: 166-172.
 
 
Poehlman, J. M. 1985. Adaptation and distribution. Pp. 1-17. In: D. C. Rasmusson (ed.) Barley. Agronomy Monograph No. 26, ASA. Madison WI.
 
 
Pour-Aboughadareh, A., Yousefian, M. Moradkhani, H.,  Poczai, P. and Siddique, K. H. M. 2019. STABILITYSOFT: a new online program to calculate parametric and non- parametric stability statistics for crop traits. Appl. Plant Sci. 7: e1211.
 
 
Purchase, J. L., Hatting, H. and Van Deventer, C. S. 2000. Genotype × environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. S. Afr. J. Plant Soil. 17: 101-107.
 
 
Segherloo, A. E., Sabaghpour, S. H., Dehghani, H. and Kamrani, M. 2008. Nonparametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica 162: 221-229.
 
 
Shukla, G. K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29: 237-245.
 
 
STATISTA. 2019. Barley production worldwide 2008/2009-2018/2019. https://www.statista.com/statistics/271973/world-barley-production-since-2008/
 
 
Taheripourfard, Z., Izadi-Darbandi, A., Ghazvini, H., Ebrahimi, M., Mortazavian, S. M. M. and Abdipour, M. 2017. Identifying superior barley (Hordeum vulgare L.) genotypes using GGE-biplot across warm and moderate environments under irrigated conditions in Iran. Crop Breeding Journal. 7: 23-35.
 
 
Thennarasu, K. 1995. On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Ph.D. Thesis. Uni. of New Delhi. India.
 
 
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.
 
 
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Mehraban A., Hossein-Pour, T., Koohkan, E. Ghasemi, S.,  Moradkhani, H., and Siddique, K. H. M. 2019. Integrating different stability models to investigate genotype × environment interaction and identify stable and high-yielding barley genotypes. Euphytica 215:63.
 
 
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., M. Armion, A. Mehraban, Hossein-Pour, T. and Dorri, M. 2017. GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Res. Commun. 45: 500-511.
 
 
Van Eeuwijk, F. A., Cooper, M., DeLacy, I. H., 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.
 
 
Warnes, G. R., Bolker, B., Bonebakker, L., Gentlemean, W. Huber, A. Liaw, T. Lumley, M. and Wricke, G. 1962. Ubereine Methode zur Erfassung der okologischen Streubreite in Feldversuchen. Zeitschr F Pflanzenz 47: 92-96.
 
 
XLSTAT. 2017. Data analysis and statistical solution for Microsoft Excel. Addinsoft, Paris.
 
 
Yan, W., and Tinker, N. A. 2006. Biplot analysis of multi-environment trial data: principles and applications. Can. J. Plant Sci. 86:.623-645.
 
 
Yousefi, A., and Ghazvini, H. 2002. Barley cultivar 'Makouee', suitable for cultivation in the cold regions of Iran. Publication of Deputy of Extension Office, Agricultural Research, Education and Extension organization, Tehran, Iran. 8 pp.
 
 
Zadoks, J .C., Chang, T. T., and Konzak, C. F. 1974. A decimal code for the growth stages of cereals. Weed Research 14: 415-421.
 
 
Zhang, Z., Lu, C. and Xiang, Z. H. 1998. Stability analysis for varieties by AMMI Model. Acta Agron. Sinica 24: 304-309.
 
 
Crop Breeding Journal
Volume 8, Issue 2
October 2018
Pages 17-29

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