SIMULTANEOUS SELECTION OF MOST STABLE AND HIGH YIELDING GENOTYPES IN BREEDING PROGRAMS BY NONPARAMETRIC METHODS

Abstract

Explaining genotype by environment (GE) interaction is important in breedingprograms because environmental effects are very often greater than genotypiceffects in multi-environment trials. Statistical methods that select for high yield andstability have been proposed, but have not been compared for their usefulnessespecially for nonparametric methods. We compared fourteen nonparametricmethods used for analyzing GE interaction at a set of experimental lentil data (11genotypes at 20 environments). Nonparametric methods consist of six Huehn’sstatistics (S1, S2, S3, S4, S5 and S6), four Thennarasu’s statistics (NP1, NP2, NP3and NP4), tow Sabaghnia’s statistics (NS1 and NS2), Kang’s RS andnonparametric method of Fox et al. (1990). Considering mean yield versusnonparametric stability values via their plotting in a plot, indicated four differentsections as A, B, C and D. The genotype fall in the section D were the mostfavorable genotypes due to high mean yield as well as high stability performance.Plot of the most nonparametric methods showed that genotypes G1 (1.21 t ha-1), G2(1.34 t ha-1) and G5 (1.38 t ha-1) were the most favorable genotypes and so thesegenotypes considered both yield and stability simultaneously. Although, most ofthe nonparametric methods have static (biological) concept of stability and measurethe real concept of stability but plotting them versus mean yield and selecting thegenotypes of section D, could identify relatively the high mean yield genotypes asthe most stable ones.