Year 2016, Volume 1, Issue 1

Year : 2016
Volume : 1
Issue : 1
Authors : Naser SABAGHNIA
Abstract : In breeding for plant disease resistance programs, a large number of new improved genotypes are tested over a range of test pathogens or environments and the underlying statistics used to model this system may be rather complicated. Due to ordinal nature of most measured traits of disease responses, some nonparametric methods used for analyzing genotype × environment (GE) interaction in two datasets for disease severity of gray leaf spot of maize (with ten genotypes planted in 10 and 11 environments). Usually, the presence of the GE interaction effect complicates the selection of the most favorable genotypes and there are several statistical procedures available to analyze these dataset including a range of univariate, nonparametric and multivariate procedures. Present analysis separated nonparametric methods based on dynamic concept from those which are based on the static type indicated that RS statistic following to S6, NP2, NP3 and RS statistics were found to be useful in detecting the non-complicated phenotypic stability in disease severity dataset. In complicated GE interaction, the ability of AMMI stability parameters especially SPC1, SPCF, D1, DF, EV1, EVF and ASV statistics were high in the detection of stability in complicated GE interaction. In general, nonparametric methods are useful alternatives to parametric methods and allow drawing valid conclusions with considerably better chances of detecting the GE interaction in experiments of plant pathology. Also, in some cases the GE interaction structure is too complex to be summarized by only one parameter and so, it is essential to use multivariate statistical methods like AMMI.
For citation : Sabaghnia, N. (2016). AMMI versus nonparametric analysis for investigation of GE interaction of plant disease evaluation. AGROFOR International Journal, Volume 1. Issue No. 1. pp. 157-166. DOI:10.7251/AGRENG1601157S
Keywords : stability analysis, disease severity, ranked based dataset, principal components analysis
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