Using of multidimensional statistics methods for winter wheat selection

The article presents the using of multidimensional statistics methods for assessing the morphological and biological quality of winter wheat varieties in experiments conducted under the conditions of the forest-steppe of the Central Chernozem region in 2005-2016. Agrotechnics is generally accepted for the region. Mathematical processing was carried out by the package Statistica 6.1. There were tested different methods of grouping the samples, such as cluster analysis (successive dichotomy, k-means, hierarchical classification), neural network data processing (Kohen network). Only three characteristics had a significant effect on the clustering of varieties: the sprouting period, the height of the plant and the mass of 1000 grains; it was established by dispersion method and discriminant analysis. The parent components were selected on the basis of their belonging to different clusters in hybridization crosses. Crosses were between varieties with contrasting signs. The using of the k-averages method made it possible to obtain valuable winter wheat hybrids which possessed polymorphism, favorable transgressions, high productivity, winter hardiness, and lodging resistance. This selection principle was used in practice during the creating of a new variety of soft winter wheat Alexia, which was presented to the State Variety Test in 2016. The approved method of k-averages simplifies the selection of parent components for crosses and allows the creation of hybrids of winter wheat which is characterized by high productivity, winter hardiness and resistance to unfavorable environmental factors.

озимая пшеница, селекционные образцы, морфобиологические показатели, кластерный, дисперсионный и дискриминантный анализы, гибриды, подбор родительских компонентов для скрещиваний, winter wheat, selection samples, morphological and biological indicators, cluster, dispersion and discriminant analyzes, hybrids, selection of parent components for crosses

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