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Background. Combined use of various data transformation methods and a multivariate statistical analysis that takes into account several variables would increase the efficiency of selecting promising strawberry genotypes according to a set of traits for industrial and small-scale production.Materials and methods. In 2020–2022, 17 short-day garden strawberry cultivars were studied. The analysis was carried out for productivity (the number of berries, the weight of berries of the 1st order, and the average berry weight), marketable quality of berries (berry pulp density, berry height, and berry diameter), and total weight of berries per plant. Mathematical data processing employed a two-factor analysis of variance, the principal component method, cluster analysis by Ward’s algorithm, and Wilcoxon test.Results. The statistical significance of the cultivar and year factors, and their interaction was measured. The cultivar’s genotype had the greatest effect on the variability of characters. Greater part of the total variance in the set of characters was determined by the first five principal components. The cluster analysis identified two groups of cultivars. The initial data were transformed according to the least significant difference (LSD05) to obtain normalized indices. Taking into account the Wilcoxon test, the cultivars were ranked by the indices. When comparing the groups built in line with mean and total values of the normalized indices with the cluster analysis results, 6 best strawberry cultivars were identified for the studied set of characters.Conclusion. The combined use of multivariate methods and normalized indices made it possible to identify the most promising strawberry cultivars according to their yield and berry quality: ‘Olympia’, ‘Nelli’, ‘Florence’, ‘Kemia’, ‘Jive’, and ‘Alba’.

For strawberry (Fragaria × ananassa Duch., 2n = 8x = 56), which is the leading berry crop in the world, research into the genotype × environment interaction is important. A complicated genomic composition, the diversity of genetic control systems, and a strong modifying effect of growing conditions on the implementation of quantitative traits make it necessary to improve methods for analysis of the genotypic variability of economically valuable traits with the aim of identifying genotypes that are characterized by stability and adaptive qualities in a wide ecological range of growing conditions. In 2016–2018, twenty-seven strawberry varieties were studied in the collections of North Caucasian Federal Scientific Center of Horticulture, Viticulture and Krymsk Experiment Breeding Station, VIR Branch. Field experiments and data counts were set and carried out according to a single scheme. The following characteristics were studied: the number of inflorescences (units per plant), the number of berries (units per plant), the average weight of berry and berry of the first order (g), total and marketable yield (g per plant), firmness of fruit (g), sugar content in berries on Degrees Brix (°Bx), sugar-acid index. The purpose of this work was the development of a methodical approach to assessing the contribution of the genotype– environment interaction to the variability of the traits of productivity and fruit quality and the determination of strawberry varieties with a stable genotype. To this end, the mathematical models of two- and three-factor analysis of variance and cluster analysis using Ward’s method were employed. According to the results of this work, strawberry varieties grown in different climatic conditions show differences in the structure of the variability of the traits of productivity and fruit quality. For the conditions of the city of Krymsk, the influence of the genotype of the variety was predominant, and for the conditions of the city of Krasnodar, in addition to the influence of the genotype of the variety, the environmental component in the form of the genotype–environment interaction is also significant. A statistically significant influence of the growing zone has been established for the traits of productivity and fruit quality, with the exception of the average weight of fruit. At the same time, differences in the mean values of the traits of varieties can be both significant and partially or completely absent. To identify varieties with promise for cultivation in the areas studied, it is recommended to use cluster analysis on the informative complex of traits with the calculation of the Euclidean distances for varieties that were grown under different conditions. The magnitude of the Euclidean distance will be the measure of the influence of a particular environment on the genotype of plants. The smaller the value of the Euclidean distance in a variety, according to the complex of the traits studied, the more stable this variety is.