Sweet cherry is in demand in many countries of the world. The share of sweet cherry fertile plantations in the world is more than 3.1%, which makes about 12.5 thousand hectares. The majority of these plantations – 42.6% are concentrated in the Middle East and European countries (Durau et al., 2012). Ukraine ranks tenth in the world in terms of sweet cherry fruit production. In 2018, the gross output of the fruit amounted to 84.6 thousand tons, which is about 3.3% share of the world's production (Zheplinska et al., 2019). According to FAO data, the world market for sweet cherries is undersupplied. According to scientifically based standards, the annual consumption of sweet cherry fruit per capita should be 2 kg (Winkler and Knoche, 2019). The global production of sweet cherries should reach 14 million tons to ensure such volumes. Now it is 4 million tons.

The fruit of the sweet cherry cultivar, being the first to open the season, is a source of easily digestible monosaccharides and contains a powerful immunostimulatory complex of several valuable nutrients. This is the reason for their popularity not only in Ukraine but also in Hungary, Bulgaria, Turkey, etc. (Blagov et al., 2009). Studies by Canadian and Russian scientists have shown that each variety is characterized by a certain genetically determined biochemical composition (de Carvalho et al., 2019). High testing assessment of sweet cherries is due to favorable soluble solids content. Most dry matter is represented by soluble solids content (SSC), which includes carbohydrates, acids, nitrogen compounds, enzymes, soluble forms of pectin and vitamins, phenolic compounds, mineral salts, etc. (Ivanovych, 2018).

The ratios of soluble solids and insoluble solids according to Ukrainian and foreign researchers (Ivanova, Herasko and Dolhova, 2019) range within the scope of 14.61 – 18.98% and 2.15 – 2.65%, respectively. The results of many scientific studies proved that the level of soluble solids content affected the intensity and direction of oxidationreduction processes during the storage of fruit and determined the choice of process specifications (Basanta et al., 2014). Provided there is a low soluble solids content and excess moisture in the fruit, transpiration processes are intensified (Mikhailik et al., 2014).

Several scientific research traditionally used regression and correlation analyses to assess the degree of influence of parameters on the experimental indicators. However, in conditions when the number of factors for analysis significantly exceeds the number of experimental research, it is not possible to apply a traditional least squares method to build a regression model. Therefore, it has been proposed to build a model using the method of factor analysis – principal component analysis (Chigozie, 2012).

Evaluation of climatic conditions during cultivation and selection of rational parameters for obtaining juice from cherry berries will increase the efficiency of the processing process and improve the method of determining the content of soluble solids in various fruit and berry raw materials, which in turn will intensify the process.

The level of soluble solids accumulation can change under the influence of soil and weather conditions, yield, and the degree of fruit ripeness. In the south of Ukraine, the fruit of the best cultivars of sweet cherry contain 12.1 – 19.9% of soluble solids, whereas in the central part of the country this indication is only 11.3 – 12.8% (Slavin and Lloyd, 2012; Bublyk et al., 2014; Ivanova et al., 2019). The foreign scientists established the change in the biochemical composition of sweet cherry cultivars of different ripening terms depending on the region of cultivation (Cittadini et al., 2008; Basanta et al., 2014; Chen et al., 2018). The dominant influence of weather factors on the accumulation of SSC in comparison with the cultivar characteristics was established in our previous research (Serdiuk et al., 2020b). With that said, the soluble solids content in sweet cherries depends on many factors. However, the decisive influence of weather factors is noted (Sansavini and Lugli, 2008). Given this, the issue of predicting the soluble solids content of sweet cherries depending on the share of weather factors is relevant for the sake of further improvement of transportation and processing technology (Zheplinska et al., 2021).

Year, treatment, and their interaction had significant effects on fruit size, thickness, and weight. Fruit height, the height: width ratio and SSC were not influenced by year and treatment, though the interaction of these factors significantly affected SSC (Bobos et al., 2019).

The difference between daytime and night-time temperatures positively influenced the accumulation of dry solids and sugar content in sweet cherry fruits, whereas the amount of precipitation had a negative impact, forming fissures (Caprio and Quamme, 2006). No direct influence of the amount of rainfall and mean daily temperature on SSC was observed (Sheiko et al., 2019).

Low temperatures during the blooming cause reduction of the yield capacity, but winter frosts and relatively low temperatures during the pollination might also negatively affect yields (Smetanska et al., 2021; Hedhly et al., 2004).

The SSC (consisting mostly of sugars) varies between 11 and 25 °Brix in sweet cherry (Serrano et al., 2005). In general, American consumers preferred sweet cherries with SSC >16% (Crisosto et al., 2003).

The results of twelve years of research suggest that the average content of CPP in cherries grown in the analyzed region was at the level of 16.7%. The average content of CPP in the fruits of cherries of the early maturing varieties was at the level of 15.3% (Table 1), ie it was 8.4% lower compared to the average varietal value.

In the fruits of cherries of groups of varieties of medium and late maturity, the average content of SSC exceeded the average varietal value by 1.8 and 7.2%, respectively (Tables 2 and Table 3). Thus, among the studied varieties, the maximum content of SSC was characterized by cherries of the late-ripening group.

The variety of early maturity, which according to the results of twelve years of research was characterized by the largest mass fraction of SSC was Rubinov early, and the smallest – Merchant (Table 1).

Among the varieties of the group of medium maturity, the maximum average content of CPP was recorded in the fruits of the varieties Talisman and Dachnytsia, and the groups of the late group - in the fruits of the variety Udivitelna.

These research results indicate significant variability in the content of SSC over the years of research in the group of varieties of early maturity. The greatest influence of abiotic factors on the content of CPP in the fruits of this group was found for varieties Kazka and Valeriy Chkalov with coefficients of variation of 22.8 and 22.7%, respectively. The most stable in terms of SSC are varieties Zabuta, Merchant, Bigaro Burlat, as evidenced by the corresponding coefficients of variation of 18.4, 19.3, and 19.6%. The variability of these varieties under the influence of weather factors is considered average.

The variability of the SSC content over the years of research in the fruits of cherries of the groups of varieties of medium and late maturity was average (V_p = 19.6 and 17.7%, respectively). Among the group of varieties of medium maturity, the most stable dry matter content was in the fruits of the variety Dachnytsia (V_p = 14%), and the most variable - in the variety Space (V_p = 29%). In the group of late-maturing varieties, the greatest variability in the content of SSC was recorded in the fruits of the varieties Kolkhoznytsia (V_p = 21.5%) and Anons (V_p = 21.6%), the smallest - in the varieties Krupnoplidna (V_p = 13.4%) and Prazdichna ( V_p = 13.7%).

Thus, in terms of dry matter content and variability of their formation under the influence of weather factors in the analyzed region, the most promising from a technological point of view were varieties Dachnytsia, medium ripening, and large-ripening varieties Krupnoplidna. These varieties differed in the high content of SSC, and their low variability over the years of research. Among the group of varieties of early maturity of the most promising variety is not identified. All studied varieties with a high content of CPP were characterized by high variability. Varieties Zabuta, Merchant, Bigaro Burlat, in which the variability of SSC content over the years of research was average, were characterized by their low content.

The average cultivar content of SSC in the sweet cherry of 33 researched cultivars grown in the South Steppe subzone of Ukraine was at the level of 16.7%. In the group of cultivars of early ripening, the maximum average SSC is recorded in the fruit of Rubinova Rannya sweet cherries. It was at the level of 16.8%.

Among the cultivars of the other two groups, the fruit of Talisman and Dachnytsia, Krupnoplidna, and Udivitelna cultivars were characterized by the highest average SSC. They accumulated 18.5 – 18.6% of SSC. The fruits of cultivars Prostir and Anons, collected in 2015, were characterized by the lowest quantity of dry solutes. The quantity of the dry solutes was lower than the average cultivar value, by 59.8 and 61.7% accordingly. The highest mass fraction of the dry solutes was recorded in fruits of the Dachnytsia and Karina cultivars, collected in 2012 (Serdiuk et al., 2020a).

To establish the share of the influence of the weather parameters and cultivar characteristics on the formation of SSC of sweet cherry fruit, a two-factor analysis of variance was performed. Its results showed that for all groups of cultivars, regardless of ripeness, the weather parameters of the year of research has the dominant influence on the formation of SSC fund with the share of influence for the cultivars of early ripening being 74.5%, for those of medium ripening – 61.9% and for the groups of lateripening – 69.4%.

The influence of the characteristics of the cultivar was less significant. The results of the study are presented in more detail in the research of Serdiuk et al. (2020b).

Thus, the results obtained earlier proved the feasibility of predicting the SSC in sweet cherries following the average values for the certain group of cultivars, and not separately for each pomological cultivar. Medium dependence was found between the fruit weight and the fruit SSC (correlation coefficient 0.53 – 0.59) (Fatih et al, 2014; Kishchak, 2014; Polat et al., 2009). Subsequently, a search was made for the presence of correlations between the index of accumulation of soluble solids in the sweet cherries of early (Y₁), middle (Y₂), late (Y₃) ripening, and the complex of hydrothermal parameters (factors) – X_і. There were selected 25 indicators of weather factors (X_і) that within the specified growing period can significantly affect the accumulation of SSC in the fruit of sweet cherry of early (Y₁), medium (Y₂), late (Y₃) ripening (Table 1). These thermal indicators of air (°C) shall be the average temperature in May (Х₁), June (Х₂); absolute minimum temperature in May (Х₃), the absolute maximum temperature in May (Х₄) June (Х₅), the average maximum temperature of April (Х₆), May (Х₇), June (Х₈); average minimum temperature in May (Х₉), June (Х₁₀); the annual sum of effective temperatures over 15 °C (Х₁₁); the difference between the average maximum and minimum temperatures of April (Х₁₂); the sum of active temperatures during the vegetation period (Х₁₃); the average temperature at blossom (Х₁₄); the average minimum temperature at ** The influence of the factor is insignificant, Δ_i ≺ 0.5% blossom (Х₁₅); the average maximum temperature at blossom (Х₁₆); hydrothermal coefficient at blossom (Х₁₇); the sum of effective temperatures at blossom (Х₁₈). The indicators of humidity (%) shall be: average relative humidity in May (Х₁₉), June (Х₂₀); absolute minimum relative humidity in May (Х₂₁), June (Х₂₂), at blossom (Х₂₃); average minimum relative humidity at blossom (Х₂₄), the average relative humidity at blossom (Х₂₅).

For these factors the presence of the weak, medium, and strong linear correlations, as evidenced by the values of matching correlation coefficients between values of each factor and the resulting rate r Y 1 X i , r Y 2 X i , r Y 3 X i . According to the hypothesis testing of significance of correlation coefficients by Student's test at the significance level of 0.05 significant matching correlation coefficients are within the limit of r Y j X i ≥0.55, i=1...25, j=1...3 .

Thus medium ripening cultivars, we identified 17 and, for early and the late-ripening cultivars, there were 16 weather parameters, for which a strong or medium linear correlation dependence with the analyzed indicator (SSC) was established. Further research was carried out according to the pattern given above.

Y ^ 1 = 11.9533 + 0.30626 x 1 ˜ + 0.1346 x 3 ˜ + 0.3707 x 4 ˜ + + 0.0446 x 5 ˜ + 0.3397 x 7 ˜ + 0.34266 x 8 ˜ + 0.1195 x 10 ˜ − − 0.0172 x 12 ˜ + 0.0980 x 14 ˜ − 0.3792 x 15 ˜ − 0.0345 x 16 ˜ + + 0.1120 x 18 ˜ + 0.003184 x 19 ˜ − 0.0329 x 21 ˜ − 0.090 x 22 ˜ − − 0.3481 x 15 ˜ − 0.2086 x 24 ˜ − 0.2238 x 25 ˜ Y ^ 2 = 12.8530 + 0.2431 x 1 ˜ + 0.1800 x 3 ˜ + 0.0242 x 4 ˜ + + 0.2355 x 5 ˜ + 0.1486 x 7 ˜ + 0.2646 x 8 ˜ + 0.2562 x 10 ˜ + + 0.0838 x 12 ˜ − 0.0211 x 14 ˜ + 0.03209 x 15 ˜ − 0.0676 x 16 ˜ − − 0.6099 x 18 ˜ 0.03234 x 19 ˜ − 0.0148 x 21 ˜ − 0.3914 x 22 ˜ + + 0.09913 x 15 ˜ − 0.2207 x 24 ˜ − 0.2531 x 25 ˜ Y ^ 3 = 13.1095 + 0.0580 x 1 ˜ + 0.3055 x 2 ˜ + 0.083 x 3 ˜ + + 0.1839 x 6 ˜ − 0.0526 x 7 ˜ + 0.0891 x 8 ˜ + 0.3236 x 9 ˜ + + 0.0844 x 10 ˜ − 0.2534 x 11 ˜ + 0.1379 x 13 ˜ + 0.1245 x 17 ˜ − − 0.0332 x 18 ˜ − 0.2331 x 20 ˜ − 0.2869 x 23 ˜ − 0.3914 x 22 ˜ + + 0.09913 x 15 ˜ − 0.2207 x 24 ˜ − 0.2531 x 25 ˜

Based on the constructed models, the coefficients Δ_i, (i = 1…25) are calculated for each factor according to formula (3). The coefficients Δ_i determine the share of each factor in the total variance of the SSC indicator. The results of the calculation are shown in Table 1.

For the cultivars of early and medium ripening terms Δ_i varies within 0.12 – 16.06% (Table 1), for the late-ripening group this index was 0.84 – 13.98%. For the further analysis of the research results, the factors depending on the values of coefficients Δ_i, (i = 1…25) were divided into three groups.

The first group includes the factors that have a strong influence on the accumulation of SSC with the value of Δ_i ≥ 9.50%. For the cultivars of early ripening sweet cherries, these include the average monthly, absolute maximum, average maximum, and minimum air temperature in May and absolute minimum relative humidity in May. For the fruit of the medium ripening group, these are the average monthly and average minimum air temperatures in May, the average maximum temperature in April, the sum of annual effective temperatures above 15 °C, and the absolute minimum relative humidity in May. For the fruit of late-ripening, the average monthly, average maximum, and average minimum temperature on June, the absolute minimum, and the average relative humidity in the period of blossom. The second group includes the factors that have an average influence on the accumulation of SSC on the sweet cherry fruit with the meaning of Δ_i from 2.00 – 9.48% (Table 4). The third group includes other factors that have a weak influence on the accumulation of SSC with the meaning of Δ_i ≤ 2.00% (Table 4). The total percentage of the influence of the factors of this group on the cultivars of early ripening is 5.3%, for the cultivars of medium ripening, it is 4.62%, for the late-ripening group it is 4.18%. It was found out that SSC extremely correlated with the time of the blooming, proving the key role of the spring temperatures in the monitoring of the time of flowering (Caprio and Quamme, 2006; Guo et al., 2013; Habib et al., 2017; Menzel et al., 2006; Miller-Rushing et al., 2007).

As a result, the weather conditions of the last month of fruit formation have the greatest influence on the accumulation of SSC in sweet cherry fruit, regardless of the ripening term. For the sweet cherry cultivars of early and medium ripening, these are the weather conditions of May, for the late-ripening cultivars it is June.