Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran

By H. Dehghani, A. Ebadi, A. Yousefi. 2006

238 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/91957d0c49ef8db9f5ad6377c7af2282dab82292

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In Plant Science

Citations in the corpus

GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition (2013, methodology): “Geographical properties and mean yield of the 18 lentil genotypes, studied in four locations Code Location Position in Iran Altitude (meter) Longitude Latitude Soil Texture Rainfall (mm) Yield (kg ha-1) 1 Gorgan Northeast 45 55° 12’ E37° 16’ N Silty Clay Loam 367 767 2 Kermanshah West 1351 47° 19’ E”
Genotype × Environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia* (2017, background): “When genotype by environment interaction is due to predictable factors variation, then rice breeder has the choice of developing genotypes either for a specific environment or genotypes that perform well under different conditions (Dehghani et al. 2006).”
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, background): “contribute to total GEI variations (Saeed et al. 1984; Dehghani et al. 2006; Jalata 2011).”
Analysis of Genotype × Environment Interaction (G×E) Using SAS Programming (2016, methodology): “For multivariate stability models, the additive main effects and multiplicative interaction (AMMI) model and genotype main effects plus G ́E (GGE) model with a graphical display has gained in popularity for analyzing multiple-environment trial data (Casanoves et al., 2005; Dehghani et al., 2006).”
A Framework for Identification of Stable Genotypes Basedon MTSI and MGDII Indexes: An Example in Guar (Cymopsis tetragonoloba L.) (2021, methodology): “Multivariate techniques were used to assess genotypic stability across divergent environments as univariate method proved inefficient in stability assessment [47].”
Integrating Multivariate and Univariate Statistical Models to Investigate Genotype–Environment Interaction of Advanced Fragrant Rice Genotypes under Rainfed Condition (2021, background): “[18] reported, when the genotype by environment interaction resulted from predictable variation factors, breeders can either develop broadly adapted genotypes that perform well under a wide range of conditions or select genotypes for specific environments.”
GGE biplot and AMMI analysis of barley yield performance in Iran (2017, background): “In this regard, several studies on grain yield stability and adaptation have been done on barley in different regions of Iran (Dehghani et al. 2006; Mortazavian et al. 2014; Mohammadi et al. 2015; Khalili and Pour-Aboughadareh 2016), but these studies deal with different plant materials, locations a”
GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. (2012, methodology): “However, no single method can adequately explain cultivar performance across environments (Dehghani et al., 2006).”
Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment (2008, background): “Another model that has gained importance in investigating the role of genotype, environment and GEI effects in yield-trial experiments is the genotype main effects and genotype 9 environment interaction (GGE) biplot (Yan et al. 2000, 2001, 2007; Yan and Kang 2002; Yan and Tinker 2005; Samonte et al.”
Multivariate analyses of genotype x environment interaction of popcorn (2009, methodology): “Dehghani et al. (2006) evaluated the effects of genotype and genotype x environment interaction on grain yield of 19 barley (Hordeum vulgare L.) genotypes via the GGE Biplot method.”