Selection of strawberry cultivars according to their productivity and berry quality using normalized indices

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 (LSD 05 ) 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’.