NONPARAMETRIC STABILITY ANALYSIS IN MULTI-ENVIRONMENT TRIAL OF CANOLA

Abstract

Rapeseed is the world’s second most important source of vegetable oils. Development of genotypes having high seed yield with stable performance is of paramount importance. In the present investigation seventeen genotypes were grown at seven locations during two growing seasons in semi-cold regions of Iran. Data recorded on seed yield were subjected to different nonparametric measures which do not require distributional assumptions. Results of nonparametric tests of G, E and GE interaction and a combined ANOVA across environments showed there were both cross over and non-cross over interactions for G and E and only non-cross over type for GE interaction. In this study, high values of Top (proportion of environments in which a genotype ranked in the top third) and mean of rank were associated with high mean yield. However, Rank-sum measure was successful to detect genotypes showing simultaneous high yield and yield stability. Cluster analysis and principal component (PC) analysis help to group genotypes and measures and they revealed association among different statistics. Finally, among nonparametric measures, Top, Si (1) and Rank[1]sum statistics of nonparametric procedures were found to be useful in detecting the stability of the genotypes. According to these parameters Geronimo was found as stable and high yield canola genotype