GGE biplot analysis of yield perfomance and stability of durum wheat genotypes in multi envi-ronment trials in Algeria
DOI:
https://doi.org/10.30835/2413-7510.2018.152126Keywords:
GGE biplot, durum wheat, yield, adaptability, genotype-environment interaction, stabilityAbstract
Introduction. In the arid conditions of Algeria, the productive potential of durum wheat varieties is not fully fulfilled. Under such unfavorable conditions, selection of the best genotypes is complicated, especially if they are evaluated only in one environment. Therefore, for a more effective assessment of the performance and adaptive potential of promising genotypes, an environmental variety trial was conducted at four experimental stations of the Technical Institute of Field Crops (Algeria).
The purpose of this study was to evaluate the yield and stability of durum wheat genotypes using the GGE biplot analysis to select genotypes with a high performance and phenotypic stability.
Materials and Methods. The study was conducted in four testing sites with full randomization in four replicas in 2008–09 and 2009–10. Twenty durum wheat genotypes, commercial varieties and breeding lines from national and CIMMYT-ICARDA breeding programs were taken as the test material.
Results and Discussion. The yields of all the genotypes were significantly influenced by the growing conditions; this factor accounted for 88.2% of the total variance of yield, while the genotype effect and the genotype-environment interaction effect accounted for 2.9% and 8.9%, respectively. The GGE biplot represented most of the variation caused by the effects of genotype and the genotype-environment interaction in the first two principal components - 69.9%. The “which-won-where” GGE biplot showed that the 8 environments under investigation formed 3 mega-environments in which genotypes G20, G19 and G9 were the most productive. Genotypes G19, G3, G12, G20, and G21 gave the highest average yields. The yield of genotype G21 was the most variable, whereas genotypes G20 and G12 had high and stable performance. Comparison with the “ideal” genotype showed that genotypes G19, G20 and G12 were the best in terms of yield and its stability. Analysis of the differentiating ability and representativeness of the environments demonstrated that environment E4 (Khroub09/10) was ideal for testing genotypes. This environment was the most informative and representative, that is, it is optimal for selecting the best genotypes. Environments E2 (Guelma 09/10) and E8 (Tiaret 08/09) were not informative regarding differentiation of genotypes.
Conclusions. The genotype-environment interaction was evaluated in detail using the GGE blot analysis, which allowed selecting the most productive and stable genotypes, as well as genotypes with broad and specific adaptability.
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