Ullah, H., Khalil, I. H., Durrishahwar, Iltafullah, Khalil, I. A., Qasim, M., Khan, S. M., Yan, J. and Ali, F. 2012. Selecting high yielding and stable mungbean [Vigna radiata(L.) Wilczek] genotypes using GGE biplot techniques. Can. J. Plant Sci. 92: 951-960. Multi-environment trials (MET) play a vital role in selecting genotypes for wider adoptability based on their superior performance across environments. The present study was carried out with the aim of selecting high-yielding and stable genotype(s). A set of 30 mungbean genotypes were evaluated in four environments comprising years (2007, 2008) and locations (Peshawar, Swat) in Pakistan. Combined analysis of variance was performed for seed yield to determine the effect of environment [consisting of year (Y), location (L), and L × Y interaction], genotypes and all possible interactions among these factors. Analysis of variance showed significant genotype × year (G × Y) and G × L interactions (P ≤ 0.01) exhibiting the influence of changes in environment (L and Y) on seed yield performance. The large yield variation due to environment (E), justified the selection of a genotype main effect genotype×environment (GGE) biplot as an appropriate method for analyzing MET data. GGE biplot arranged 30 genotypes in such a manner that they fell in four sectors based on their performance. Genotype'k' (NFM-11-3) performed well at PR07 and PR08, denoted as the first sector. In the second sector, mungbean genotype'y' (NFM-7-13) outclassed all other genotypes at ST07 and ST08. GGE biplot figured out the genotypes't' (NFM-14-5) and'e' (NFM-5-63-20) as the poor performing lines across location. GGE biplot identified ‘y’ (NFM-7-13) as the highest yielding genotype, followed by ‘k’ (NFM-11-3). Solely on yield performance, both of the genotypes were not statistically different however; the ranking made by GGE biplot was not only based on yield but on stability performance too. Similarly, Genotypes ‘Ad’ (NM-98) ‘m’ (NFM-12-6) ‘f’ (NFM-5-63-34) and ‘z’ (NFM-8-1) ranked 3rd, 4th, 5th and 6th as being stable and high-yielding across locations, respectively. Location ‘PR08’ was the most desirable environment as it lay closer to the “ideal” environment. While PR07, ST07 and ST08 were found undesirable regarding genotype differentiation as they were far away from the center of the concentric circle. The GGE biplot effectively identified the G × E interaction pattern of the data and explained which genotype performed extravagantly at which target environment.
analyse par double projection GGE
G × E Interaction
interaction G × E