AMMI VERSUS NONPARAMETRIC ANALYSIS FOR INVESTIGATION OF GE INTERACTION OF PLANT DISEASE EVALUATION

Abstract

In breeding for plant disease resistance programs, a large number of new improvedgenotypes are tested over a range of test pathogens or environments and theunderlying statistics used to model this system may be rather complicated. Due toordinal nature of most measured traits of disease responses, some nonparametricmethods used for analyzing genotype × environment (GE) interaction in twodatasets for disease severity of gray leaf spot of maize (with ten genotypes plantedin 10 and 11 environments). Usually, the presence of the GE interaction effectcomplicates the selection of the most favorable genotypes and there are severalstatistical procedures available to analyze these dataset including a range ofunivariate, nonparametric and multivariate procedures. Present analysis separatednonparametric methods based on dynamic concept from those which are based onthe static type indicated that RS statistic following to S6, NP2, NP3 and RSstatistics were found to be useful in detecting the non-complicated phenotypicstability in disease severity dataset. In complicated GE interaction, the ability ofAMMI stability parameters especially SPC1, SPCF, D1, DF, EV1, EVF and ASVstatistics were high in the detection of stability in complicated GE interaction. Ingeneral, nonparametric methods are useful alternatives to parametric methods andallow drawing valid conclusions with considerably better chances of detecting theGE interaction in experiments of plant pathology. Also, in some cases the GEinteraction structure is too complex to be summarized by only one parameter andso, it is essential to use multivariate statistical methods like AMMI.