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Interpretation of genotype × environment interaction for grain yield of barley using the GGE biplot method | ||
| Journal of Plant Physiology and Breeding | ||
| دوره 10، شماره 2، 2020، صفحه 109-119 اصل مقاله (864.74 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jppb.2020.13670 | ||
| نویسندگان | ||
| Behrouz Vaezi1؛ Hamid Hatami Maleki* 2؛ Ali Ahmadi3؛ Asghar Mehraban4؛ Rahmatollah Mohammadi5؛ Zeinab Sabzi6؛ Naser Sabaghnia2 | ||
| 1Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Yasuj, Iran | ||
| 2Department of Plant Production and Genetics, Faculty of Agriculture, University of Maragheh, Maragheh, Iran. | ||
| 3Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Khorram-Abad, Iran. | ||
| 4Ardabil Agricultural and Natural Resources Research Center, Agricultural Research, Education and Extension Organization (AREEO),Parsabad, Iran | ||
| 5Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran | ||
| 6Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ilam, Iran | ||
| چکیده | ||
| The identification of the most favorable cultivar(s) with high yield and stable performance is usually done based on the analysis of the genotype × environment (GE) interaction. The yield stability of 16 barley lines with two check varieties was studied in a randomized complete block design with four replications across three years at five locations in a multi-environment trial layout. The dataset was analyzed with a GGE (genotype main effect (G) + GE interaction) biplot method. Results indicated that the first two principal components (PCs) explained 81, 78 and 71% of the GGE sum of squares for 2017, 2018 and 2019 growing seasons, respectively. According to the average environment coordinate abscissa, G2, G13 and G18 were the best genotypes in terms of grain yield in years 2017 and 2018 while genotypes G2, G7 and G14 were the highest yielding genotypes in 2019. When both yield performance and stability were considered simultaneously, the G2 and G13 genotypes in 2017 and G2, G8 and G13 in 2018, were closer to the ideal genotype. In 2019, G2, G7 and G14 were the best in terms of grain yield and stability. In the "which-won-where pattern", the five locations in 2017 fell into four sectors with different winning genotypes as G2, G5, G14 and G13. In 2018, the five locations fell into three sectors in which G2, G4 and G17 were the highest yielding genotypes while in 2019, locations were positioned in four sectors and G2, G7, G10 and G13 were chosen as the winning genotypes. However, for practical use of the “which-won-where” pattern, the mean performance of genotypes over three years in the five test locations was taken into account. Although the results revealed six mega-environments, by neglecting small differences, we can assume only one mega-environment in which G2 (the check variety Khorram) was the best performing genotype. | ||
| کلیدواژهها | ||
| Barley؛ GGE biplot؛ Grain yield؛ Multi-environment trials؛ Singular value decomposition | ||
| مراجع | ||
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Ahmadi J, Vaezi B and Fotokian MH, 2012. Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-Biplot under rain-fed conditions. Journal of Plant Physiology and Breeding 2: 43-54.
Bustos-Korts D, Romagosa I, Borras-Gelonch G, Casas AM, Slafer GA and Van Eeuwijk F, 2018. Genotype by environment interaction and stability reaction. In: Meyers R. (eds.). Encyclopedia of Sustainability Science and Technology. Springer, Germany.
Dehghani H, Ebadi A and Yousefi A, 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal 98: 388-393.
Dehghani H, Sabaghnia N and Moghaddam M, 2009. Interpretation of genotype-by-environment interaction for late maize hybrids’ grain yield using a biplot method. Turkish Journal of Agriculture and Forestry 33: 139-148.
Dia M, Wehner TC, Hassell R, Price DS, Boyhan GE, Olson S, King S, Davis AR and Tolla GE, 2016. Genotype × environment interaction and stability analysis for watermelon fruit yield in the United States. Crop Science 56: 1645-1661.
FAO, 2017. FAOSTAT. Food and Agriculture Organization of the United Nations. http://faostat.fao.org/.
Karimzadeh R, Mohammadi M and Sabaghnia N, 2013. Site regression biplot analysis for matching new improved lentil genotypes into target environments. Journal of Plant Physiology and Breeding 3: 51-65.
Le Marie CA, York LM, Strigens A, Malosetti M, Camp KH, Giuliani S and Hund A, 2019. Shovelomics root traits assessed on the EURoot maize panel are highly heritable across environments but show low genotype-by-nitrogen interaction. Euphytica 215: 173.
Mattos PHC, Oliveira RAJ, Filho CB, Daros E, Verissimo, MAA, 2013. Evaluation of sugarcane genotypes and production environments in Parana by GGE biplot and AMMI analysis. Crop Breeding and Applied Biotechnology 13: 83-90.
Oghan HA, Sabaghnia N, Rameeh V, Fanaee HR and Hezarjeribi E, 2016. Univariate stability analysis of genotype × environment interaction of oilseed rape seed yield. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64: 1625-1634.
Pankin A, Altmüller J, Becker C and Von Korff M, 2018. Targeted resequencing reveals genomic signatures of barley domestication. New Phytologist 218: 1247-1259.
Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A and Kale S, 2018. Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea. Scientific Reports 8: 1-11.
Sabaghnia N, Karimizadeh R and Mohammadi M, 2012. Genotype by environment interaction and stability analysis for grain yield of lentil genotypes. Zemdirbyste 99: 305-312.
Yan W, 2001. GGEBiplot-A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93: 1111-1118.
Yan W, 2014. Crop variety trials: data management and analysis. John Wiley & Sons, USA.
Yan W, 2019. LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Science Reports 9: 7130.
Yan W and Hunt LA, Sheng Q and Szlavnics Z, 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science 40: 596–605.
Yan W and Kang MS, 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL, USA.
Yan W and Rajcan I, 2002. Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Science 42: 11-20.
Yan W and Tinker NA, 2005. An integrated system of biplot analysis for displaying, interpreting, and exploring genotype × environment interaction. Crop Science 45: 1004-1016.
Yan W and Wu HX, 2008. Application of GGE biplot analysis to evaluate genotype (G), environment (E), and G × E interaction on Pinus radiata: a case study. New Zealand Journal of Forestry Science 38: 132-142. | ||
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