Characterization of genetic diversity in germplasm collections requires an efficient set of molecular markers. We assessed the efficiency of 36 new SCoT markers, 10 new ISSR markers, and 5 microsatellites for the characterization of genetic diversity in chrysanthemum core collection of 95 accessions (Russian and foreign cultivars). Seven new SCoT (SCoT12, 20, 21, 23, 29, 31, 34) and six new ISSR markers ((GA)8T, (CT)8G, (CTTCA)3, (GGAGA)3, (TC)8C, (CT)8TG) were efficient for the genetic diversity analysis in Chrysanthemum × morifolium collection. After STRUCTURE analysis, most Russian cultivars showed 20–50% of genetic admixtures of the foreign cultivars. Neighbor joining analysis based on the combination of SSR, ISSR, and SCoT data showed the best accordance with phenotype and origin compared to the separate analysis by each marker type. The position of the accessions within the phylogenetic tree corresponded with the origin and with some important traits, namely, plant height, stem and peduncle thickness, inflorescence type, composite flower and floret types, flower color, and disc color. In addition, several SCoT markers were suitable to separate the groups distinctly by the phenotypical traits such as plant height (SCoT29, SCoT34), thickness of the stem and peduncle (SCoT31, SCoT34), and leaf size and the floret type (SCoT31). These results provide new findings for the selection of markers associated with important traits in Chrysanthemum for trait-oriented breeding and germplasm characterization.

This chapter concerns the assessment of environmental and varietal affects on photosynthetic pigment groups. It is revealed that the dynamics of pigment accumulation in subtropical plants within the conditions of Russia’s damp subtropics is a complex process and depends on species plants. With this in mind, hazelnut maximum green pigments were achieved in May, and then in August, there was a decline associated with the onset of short periods of drought and increased temperatures. In Actinidia and tea plants, the highest content of green pigments is achieved in August, which is associated with the biology of these crops. However, regardless of species, with the onset of the dry period (July–August), plants increase their accumulation of carotenoids. The high content of carotenoids that accounts for stress during a period of water availability is evidence of their participation in the formation mechanism of subtropical plants’ resistance to adverse conditions.

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The article presents an application example of statistical methods for data analysis on diagnosis of the adaptive capacity of subtropical plants varieties. We depicted selection indicators and basic physiological parameters that were defined as diagnostic. We used evaluation on a set of parameters of water regime, there are: determination of water deficit of the leaves, determining the fractional composition of water and detection parameters of the concentration of cell sap (CCS) (for tea culture flushes). These settings are characterized by high liability and high responsiveness to the effects of many abiotic factors that determined the particular care in the selection of plant material for analysis and consideration of the impact on sustainability. On the basis of the experimental data calculated the coefficients of pair correlation between climatic factors and used physiological indicators. The result was a selection of physiological and biochemical indicators proposed to assess the adaptability and included in the basis of methodical recommendations on diagnostics of the functional state of the studied cultures. Analysis of complex studies involving a large number of indicators is quite difficult, especially does not allow to quickly identify the similarity of new varieties for their adaptive responses to adverse factors, and, therefore, to set general requirements to conditions of cultivation. Use of cluster analysis suggests that in the analysis of only quantitative data; define a set of variables used to assess varieties (and the more sampling, the more accurate the clustering will happen), be sure to ascertain the measure of similarity (or difference) between objects. It is shown that the identification of diagnostic features, which are subjected to statistical processing, impact the accuracy of the varieties classification. Selection in result of the mono-clusters analysis (variety tea Kolhida; hazelnut Lombardsky red; variety kiwi Monty and Hydrangea forma rosea) shown as a helpful tool to detect drastically different varieties.