Assessment of genotype × trait interaction of rye genotypes for some morphologic traits through GGE biplot methodology

Samaneh Yari, Naser Sabaghnia, Mokhtar Pasandi, Mohsen Janmohammadi

Abstract


Effective interpretation of the data on breeding programs is important at all stages of plant improvement and the genotype by trait (GT) biplot was used for two-way wheat dataset as genotypes with multiple traits. For this propose, 18 rye genotypes with specific characteristics were evaluated in randomized block design with four replications. The GT biplot for rye dataset explained 61% of the total variation of the standardized data (the first two principal components explained 40 and 21% respectively). The polygon view of GT presented for 11 different traits of rye cultivars showed six vertex cultivars as G1, G3, G6, G8, G11 and G13 whose genotype G8 had the highest values for most of the measured traits. Generally based on vector view, ideal genotype and ideal tester biplots, it was demonstrated that the selection of high seed yield will be performed via seed number per spike, first internode weight, number of spike per area and harvest index. These traits should be considered simultaneously as effective selection criteria evolving high yielding rye cultivars because of their large contribution to seed yield. The genotypes G8 and G7 following to genotypes G3, G18 and G19 could be considered for the developing of desirable progenies in the selection strategy of rye improvement programs.


Keywords


genotype-by-trait, principal components, trait associations

Full Text:

PDF

References


Bhutta W.M., 2006. Role of some agronomic traits for grain yield production in wheat (Triticum aestivum L.) genotypes under drought conditions. Revista UDO Agrícola 6(1): 11–19.

Crossa J., Cornelius P.L., Yan W. 2002. Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Sci. 42(2): 619–633.

Del-Blanco I.A., Rajaram S., Kronstad W.E. 2001. Agronomic potential of synthetic hexaploid wheat-derived populations. Crop Sci. 41(3): 670–676.

Dogan R., Senyigit E. 2016. Correlation and path coefficient analysis of yield and yield components in hexaploid triticale (X Triticosecale Wittmack) Genotypes under Mediterranean Conditions. J. Biol. Environ. Sci. 10(28): 21–27.

FAO, 2016. FAO Stat. data of Food and Agriculture Organization of the United Nations. http://faostat. fao.org/.

Karpenstein-Machan M., Maschka R. 1996. Investigations on yield structure and local adaptability of triticale, hybrid-rye and population-rye based on data of regional cultivar trails. Agribiol. Res. 49: 130–143.

Korzun V., Malyshev S., Voylokov A.V., Börner A. 2001. A genetic map of rye (Secale cereal L.) combining RFLP, isozyme, protein, microsatellite and gene loci. Theor. Appl. Genet. 102(5): 709–717.

Koutis K., Mavromatis A.G., Baxevanos D., Koutsika-Sotiriou M. 2012. Multienvironmental evaluation of wheat landraces by GGE biplot analysis for organic breeding. Agric. Sci. 3(1): 66–74.

Okuyama L.A., Federizzi L.C., Neto J.F., 2004. Correlation and path analysis of yield and its components and plant traits in wheat. Ciência Rural 34(6): 1701–1707.

Rubio J., Cubero J.I., Martín L.M., Suso M.J., Flores F. 2004. Biplot analysis of trait relations of white lupin in Spain. Euphytica 135(2): 217–224.

Sabaghnia N., Dehghani H., Alizadeh B., Mohghaddam M. 2010. Genetic analysis of oil yield, seed yield, and yield components in rapeseed using additive main effects and multiplicative interaction biplots. Agron. J. 102(5): 1361–1368.

Seibel W., Weipert D. 2001. Bread baking and other food uses around the world. [In:] Rye: Production Chemistry and Technology. W. Bushuk, Ed., American Association of Cereal Chemists: St. Paul, MN.

Y an W. 2001. GGE biplot – A Windows application for graphical analysis multienvironment trial data and other types of two-way data. Agron. J. 93(5): 1111–1118.

Y an W., Hunt L.A., Sheng Q., Szlavnics Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40(3): 597–605.

Y an W., Kang M.S., Ma B., Woods S., Cornelius P.L. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47(5): 643–655.

Y an W., Rajcan I. 2002. Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Sci. 42(1): 11–20.

Y an W. 2014. Crop Variety Trials: Data Management and Analysis. John Wiley & Sons Press. 18. Y an W., Kang M.S. 2003. GGE Biplot Analysis: A Graphical Tool for Geneticists, Breeders, and Agronomists. CRC Press.

Z ečević V., Knežević D., Mićanović D. 2004. Genetic correlations and path-coefficient analysis of yield and quality components in wheat, Triticum aestivum L. Genetika 36(1): 13–21.




DOI: http://dx.doi.org/10.17951/c.2017.72.1.37-45
Date of publication: 2018-07-16 14:19:12
Date of submission: 2018-07-16 14:18:15


Statistics


Total abstract view - 1221
Downloads (from 2020-06-17) - PDF - 0

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Samaneh Yari, Naser Sabaghnia, Mokhtar Pasandi, Mohsen Janmohammadi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.