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IJSTR >> Volume 6 - Issue 11, November 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Assessing The Representative And Discriminative Ability Of Test Environments For Rice Breeding In Malaysia Using GGE Biplot

[Full Text]

 

AUTHOR(S)

Yusuff Oladosu, M.Y. Rafii, Usman Magaji, Norhani Abdullah, Asfaliza Ramli, Ghazali Hussin

 

KEYWORDS

Genotype by Environment interaction, stability analysis, GGE biplot, test location, grain yield.

 

ABSTRACT

Identification of outstanding rice genotype for target environments is complicated by genotype × environment interactions. Using genotype main effect plus genotype by environment interaction (GGE) Biplot software, fifteen rice genotypes were evaluated at five locations representing the major rice producing areas in peninsula Malaysia in two cropping seasons to (i) identify ideal test environment for selecting superior rice genotype, and (ii) identify discriminative and representative ability of test locations. Genotypes, locations, years, and genotypes by environment interaction effect revealed high significant difference (P < 0.01) for number of tillers per hill, grains per panicle, grain weight per hill, and yield per hectare. Grain yield per hectare had a non-repeatable crossover pattern that formed a complex and single mega-environment. Based on the crossover pattern, a set of cultivars were selected for the whole region on the merit of mean performance and their stability analysis. The tested environments were divided into two mega-environments. An ideal test environment that measures the discriminative and representative ability of test location reveal that environment Sekinchan SC is the best environment, while Kedah KD and Penang PN can also be considered as favorable environment whereas Serdang SS and Tanjung Karang TK were the poorest locations for selecting genotypes adapted to the whole region. This study serves a reference for genotypes evaluation as well as identification of test locations for rice breeding in Malaysia.

 

REFERENCES

[1] Oladosu, Y., Rafii, M. Y., Abdullah, N., Hussin, G., Ramli, A., Rahim, H. A., & Usman, M. (2016). Principle and application of plant mutagenesis in crop improvement: a review. Biotechnology & Biotechnological Equipment, 30(1), 1-16.

[2] Oladosu Y, Rafii MY, Abdullah N, Magaji U, Miah G, Hussin G, Ramli A, 2017. Genotype × Environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agric. Scand., Sect. B, 1-17.

[3] Najim MMM, Lee TS, Haque MA, Esham M, 2007. Sustainability of rice production: A Malaysian perspective. JAS. 3:1−12.

[4] Cooper M, Byth DE, 1996. Understanding plant adaptation to achieve systematic applied crop improvement–a fundamental challenge. M. Cooper, G.L. Hammer (Eds.), Plant Adaptation and Crop Improvement, CABI Publishing, Wallingford, pp. 5−23.

[5] Yan W, Pageau D, Frégeau-Reid J, Durand J., 2011. Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Sci. 51:1603−1610.

[6] Yan W, Tinker NA, 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci. 86:623−645.

[7] Gauch H, Zobel RW. 1997. Identifying mega-environments and targeting genotypes. Crop Sci.37: 311−326.

[8] Braun HJ, Rajaram S, Ginkel M, 1996. CIMMYT's approach to breeding for wide adaptation. Euphytica. 92:175−183.

[9] Peterson CJ, Pfeiffer WH, 1989. International winter wheat evaluation: Relationships among test sites based on cultivar performance. Crop Sci. 29:276−282.

[10] Malosetti M, Ribaut JM, Van Eeuwijk FA, 2013. The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front. Physiol. 4:44.

[11] Yan W, Kang MS, 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC press.

[12] Oladosu Y, Rafii MY, Abdullah N, Malek MA, Rahim HA, Hussin G, Kareem I, 2015. Genetic variability and diversity of mutant rice revealed by quantitative traits and molecular markers. Agrociencia. 49:249−266.

[13] Dia M, Wehner TC, Arellano C, 2015. Analysis of genotype × environment interaction (GxE) using SAS programming. http://cuke.hort.ncsu. edu/cucurbit/wehner/software.html (accessed 27 May. 2017).

[14] R Core Team 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/

[15] RStudio. 2014. RStudio: Integrated development environment for R (Computer software v0.98.1074). http://www.rstudio.com/ (accessed 30 Dec. 2016).

[16] Badu-Apraku B, Akinwale RO, Obeng-Antwi K, Haruna A, Kanton R, Usman I, Oyekunle M, 2013. Assessing the representativeness and repeatability of testing sites for drought-tolerant maize in West Africa. Can J Plant Sci. 93:699−714.

[17] Dia M, Wehner TC, Hassell R, Price DS, Boyhan GE, Olson S, Tolla GE, 2016. Genotype × environment interaction and stability analysis for watermelon fruit yield in the United States. Crop Sci. 56:1645−1661.

[18] Oladosu Y, Rafii MY, Abdullah N, Abdul Malek M, Rahim HA, Hussin G, Kareem I, 2014. Genetic variability and selection criteria in rice mutant lines as revealed by quantitative traits. Scientific World J. http://dx.doi.org/10.1155/2014/190531

[19] Fan XM, Kang MS, Chen H, Zhang Y, Tan J, Xu C, 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron J. 99:220−228.

[20] Annicchiarico P, 2002. Genotype × environment interactions: Challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper No. 174. FAO, Rome

[21] Yan W, Hunt LA, Sheng Q, Szlavnics Z, 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40:597−605.

[22] Yan W, 2002. Singular-value partitioning in biplot analysis of multi environment trial data. Agron J. 94:990−996.

[23] Dia M, Wehner TC, Hassell R, Price DS, Boyhan GE, Olson S, Juarez B, 2016. Value of Locations for Representing Mega-Environments and for Discriminating Yield of Watermelon in the US. Crop Sci. 56:1726−1735.

[24] Yan W, Kang MS, Ma B, Woods S, Cornelius PL, 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47:643−653.

[25] Lin CS, Binns MR, 1994. Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breed Rev. 12:271−297.

[26] Luo J, Pan YB, Xu L, Zhang H, Yuan Z, Deng Z, Que Y, 2014. Cultivar evaluation and essential test locations identification for sugarcane breeding in China. Scientific World J. http://dx.doi.org/10.1155/2014/302753

[27] Luo J, Zhang H, Deng ZH, Xu LP, Xu LN, Yuan ZN, Que YX, 2013. Analysis of yield and quality traits in sugarcane cultivars (lines) with GGE-Biplot. Acta Agron Sin. 39:142−152.