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Application of the method of analysis of main components aiming at recognition of phenotypic and genotypie divergence of several spring wheat varieties is presented in the paper. The analysis of main components was performed on three -year means for varieties and their GCA effects. Six traits / GCA effects/ or seven traits /phenotypic means/ concerning the grain yield per plant and components of the former were taken into consideration. Three first main components obtained for phenotypic means explain 86% of general variance of traits. In case of GCA effects two first main components explain over 87% of the general variance. The first main components for varietal means and GCA effects of the varieties tested discriminate them mainly in accordance with the two interrelated traits: number of stalks and number of ears per plant* The second main components determine discrimination in relation to the ear length. The Mephisto variety /FRG/ shows the greatest phenotypic and genotypie distance in relation to the remaining varieties. On the whole, a conformability between the results for varietal means and GCA effects has been found. In the light of the investigations as presented above the method of main components can be regarded as useful for investigations of divergence between genotypes.
Most common approach to interpretation of genotype-environment interaction (GxE interaction) for breeding and variety recommendation purposes is stability and adaptation analysis of genotypes in a target region of cultivation. The aim of this paper was to review very rich both scientific and practical achievements in statistical methodology of stability and adaptation analysis of genotypes. The methods used could be divided into three groups: univariate parametric methods, univariate nonparametric methods and multivariate methods. Most of the methods are based on both fixed and mixed linear and multiplicative models. Stability measures defined in many models are useful to evaluating similarity of a genotype trait response to environmental conditions in a target region to a norm (concept) of dynamic (agronomic) stability which has been introduced by Becker and Leon (1988). Joint regression models belong to those most of ten used in considered studies. Recently, multivariate models and methods have become a standard statistical tool in interpreting GxE interaction for various purposes. They are extensions of the conventional joint regression models, both fixed and mixed ones. Among them, AMMI models and related methods have been most effective and, then of ten used in field experimentation. The AMMI models incorporate both additive and different kinds of multiplicative components. Some parametric and nonparametric criteria, incorporating both yield-means and yield-stability measures can be effective to selecting such genotypes which productivity in a target region indicates their wide adaptation.
This review is based on both classical and newest original research publications found in prominent journals on plant genetic resources. It exemplifies, illustrates and summarizes statistical methodology for analyzing multivariate data from trials and surveys applied to evaluate phenotypic diversity in germplasm collections. Specific methodological knowledge necessary to select appropriate techniques, carry out data analysis and make proper interpretations of analytical results is discussed. Those uni- and multivariate statistical methods proved effective for evaluating phenotypic diversity in plant genetic resources for quantitative traits are shortly characterized and discussed with respect to their use with the SAS package. Univariate methods are based on random ANOVA model for data originating from a trial carried out in a randomized complete block design and also in a multi-environment trials carried out in the same block design. Pattern analysis is mainly recommended, among multivariate techniques, as an effective procedure to evaluate quantitative phenotypic diversity in the germplasm collections. It is a combined approach including cluster analysis as a classification procedure and principal component analysis or canonical variant analysis as the ordination procedures to show graphically similarities and dissimilarities among accessions and their homogenous groups.
The method of univeriate analysis of variance for an incomplete factorial mating /North Caroline II/ design with reciprocal crossings is described in the paper. The mating design provides testing offspring in an experimental design of random blocks or complete random design with the constant number or replications. The linear model joining in itself properties of the Griffing's model /1956/ with the model for classical cross classification has been assumed in the analysis. Formulae for estimation of the model parameters and their standard errors as well as formulae for sums of deviation squares and degrees of freedom in the table of analysis of variance have been derived. Some properties and theorems of vector spaces have been made use of.
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Metody statystyczne analizy skladowych plonu

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Paper reviewed the methods of yield component analysis. Mostly the methods taking into account a form of relationship between yield and ifs components were considered; the elaboration presented interpretation possibilities of the methods and their statistical appropriateness (without considering complex mathematical formulas). Some of presented methods are just of historical significance, while the others being quite new and not well known. Moreover, sequential and non-sequential cases of the components development were treated independently. Besides the discussion on the methods, some recommendations regarding the choice of appropriate method to yield component analysis were given. Finally it was concluded that there are no methods hoth convenient in interpretation and appropriate from the statistical point of view.
The analysis of genetics structure of n parent lines presented in this paper, is based on the Hayman-Jinks' and Mather's model, in which it was assumed that there was no episthasis. The analysis alows to determine the effects of additivity and dominance and provides the information about the distribution of genes in parental forms. The final section is an example for 6 lines of winter wheat.
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