Identification of the most stable genotypes in multi-environment trials by using nonparametric methods



Genotype performances in multi-environment trials are usually analyzed by different univariate and multivariate parametric models for assessing yield stability and genotype × environment (GE) interaction investigation. One of the alternative strategies can be nonparametric statistics approach which is particularly useful in situations where parametric statistics fail. For an estimation of yield stability of genotypes in various environments two new nonparametric stability statistics (NSi(1)and NSi(2)) have been used which are based upon the ranks of the genotypes in each environment. These statistics use median as a nonparametric central tendency, and two nonparametric index of statistical dispersion as interquartile range and inter-decile range. The NSi(1) and NSi(2) nonparametric stability statistics which presented here is similar to the nature and concept of environmental coefficient of variation. Results indicated that the most stable genotype based on the lowest values of these two nonparametric statistics, had the highest mean yield among studied genotypes. Plotting of mean yield versus NSi(1) and NSi(2) verified the above results and indicated that the highest mean yielding genotype is identified as the most stable genotype. These nonparametric statistics would be useful for simultaneous selection for mean yield and stability. They can be very helpful in selection for yield stability and determination of favorable genotypes in plant breeding programs.


genotypes; crop yield; statistical data; statistical methods; plant breeding; models

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