Tree vegetative and generative properties and their inter-correlations for prospective apple cultivars under two training systems for young trees

Little information is available on vegetative and generative performance of apple cultivars during the early growth of trees in orchards. The aim of this five-year study was to evaluate 2 vegetative (trunk cross sectional area (TCSA) and tree height (TH)) and 7 generative parameters (tree yield (TY), fruit number per tree (FNT), crop load (CL), fruit diameter (FD), shape index (SI), fruit surface color (FSC) and fruit color intensity (FCI)) and their inter-correlations (Pearson correlation, regression analyses and PCA) for young apple trees, on 9 apple cultivars (‘Jugala’, ‘Galaval’, ‘Gala Venus Fengal’, ‘Gala Decarli-Fendeca’, ‘Gala Schnitzer (S) Schniga’, ‘Fuji September Wonder’, ‘Crimson Crisp (Co-op 39)’, ‘Jeromine’, and ‘Red Idared’) in a slender spindle training system (2597 trees ha−1) and on 4 apple cultivars (‘Wilton’s Red Jonaprince’, ‘Red Cap Valtod (S)’, ‘Early Red One’, and ‘Red Topaz’) in a super spindle training system (5194 trees ha−1) in Eastern Hungary. The strongest vegetative growth was observed in ‘Red Idared’, while the weakest was in ‘Early Red One’. Most ‘Gala’ mutants showed high yields in all years, except for ‘Galaval’. On the 6 year-old trees, the lowest tree yield was found in ‘Fuji September Wonder’ (8.2 kg tree−1), while the highest was found in ‘Gala Venus Fengal’ (35.8 kg tree−1). The lowest fruit number per tree (15 fruit tree−1) was found in ‘Jeromine’, while the highest (222 fruit tree−1) was in ‘Gala Venus Fengal’. The highest crop load was found in ‘Gala Venus Fengal’ (12.72 fruit per cm2 TCSA), while the lowest was in ‘Jeromine’ (2.13 fruit per cm2 TCSA). The smallest fruit diameter (66.3 mm) was recorded in ‘Gala Schnitzer (S) Schniga’, while highest (93.6 mm) was in ‘Red Idared’. The lowest shape index (0.73) was found in ‘Red Topaz’, while the highest (0.92) was in ‘Red Idared’. The majority of the cultivars reached very good fruit surface color (80–100%). The lowest fruit surface color (40%) was observed in ‘Gala Schnitzer (S) Schniga’, while the highest (100%) was in ‘Jeromine’ and ‘Early Red One’. The highest fruit color intensity was observed in most cultivars with the exception of ‘Jeromine’, ‘Gala Schnitzer (S) Schniga’ and’Fuji September Wonder’. In addition, correlation and regression analyses revealed strong and significant (p = 0.05) relationships between TH vs TCSA, TY vs TCSA, TH vs TY, TY vs FNT, and FCI vs FSC. PCA explained 87% of the total variance and PC1, PC2, PC3, and PC4 accounted for 33, 21, 20, and 13% of the variance, respectively, and correlated with TSCA, TH, TY and FNT; with FNT, CL and FS; with FSC and FCI; and with TH and SI, respectively. In conclusion, our study provides useful tree property data on prospective mutants/cultivars for growers/advisors in order to select the most suitable cultivars for establishing new orchards under climate conditions similar to central Europe.

. Market acceptance of a cultivar is highly influenced by the fruit skin color. Among cultivar coloration, red skin color is most preferred, and a better fruit coloration increases the market value of a cultivar (e.g. Anton and Willen 2014). Therefore, breeding programs all over the world were aimed to improve the fruit skin color of the bicolor cultivars (e.g. Guerra and Sansavini 2012). Some mutants have already reached 100% fruit surface color, such as for 'Gala' sport; therefore, the total coloration combined with larger fruit diameter and earlier ripening time become the next priorities in breeding .
Fruit coloration is dependent on the genetic properties of the cultivars, but environmental factors also highly influence anthocyanin production (Naumann 1964;Proctor 1974). One of the essential factors for red color development is available light, as more light results in a higher amount of anthocyanin and red color in the fruit peel (e.g. Ubi et al. 2006). Fruit coloration is also dependent on other factors such as day and night differences in temperature (e.g. Blankenship 1987), altitude (Douglas 1983), training system and planting density (e.g. Wertheim et al. 1986;Weber 2001) and production technology elements including fruit thinning (e.g. Gonzalez et al. 2020), irrigation (e.g. Musacchi and Serra 2018), nutrition supply (e.g. Awad and Jager 2002) and ground cover (e.g. Schuhknecht et al. 2018). Various ecological circumstances and production technologies change not only fruit coloration of newly bred cultivars but can result in diverse vegetative and generative features under different climate conditions and production sites (e.g. Anton and Willen 2014).
Little information is available on vegetative and generative performance of apple cultivars during the early growth of trees in orchards. Therefore, the aim of this five-year study was to evaluate two vegetative (trunk cross sectional area (TCSA) and tree height (TH)) and seven generative parameters (tree yield (TY), fruit number per tree (FNT), crop load (CL), fruit diameter (FD), shape index (SI), fruit surface color (FSC) and fruit color intensity (FCI)) of thirteen prospective apple cultivars (twelve mutants of popular cultivars and one disease-resistant 'crips' cultivar) in two high-density training systems (slender spindle or super spindle) in a young (2-6 years old) apple orchard in Eastern Hungary. Inter-correlations (correlation, regression and principal component analyses) were also performed among the nine vegetative and generative parameters in order to explore the best relationships of the correlated parameters for the investigated cultivars/mutants and training systems.

Location, weather, plant material and orchard management
A five-year study (2013)(2014)(2015)(2016)(2017) was carried out in a commercial apple orchard in Eastern Hungary. The orchard was located in the city of Nyírbátor (47°50′54′′N, 22°03′09′′E). Orchard soil type was sandy soil with a humus content of 0.6-0.7%. The main meteorological parameters were recorded by a Metos Agrometeorological Station located in the orchard. Monthly mean temperature ranged from − 6.6 to 22.5 °C in the period of 2013-2017 (Table 1). In 2016, repeated late spring frosts in April caused a 100% yield loss; therefore, it was not possible to evaluate generative parameters in this year (Table 1). The annual mean precipitation ranged from 335 to 758 mm among the years (Table 1). Table 1 The mean and minimum temperatures and precipitation (Nyírbátor, Hungary, 2013-2017 Months Mean temperature ( ----------500  335  495  758  555 Thirteen prospective apple cultivars, including twelve mutants of popular cultivars and one disease-resistant 'crips' cultivar, were evaluated in the orchard (Table 2). All cultivars were grafted on M.9 rootstock and were planted in the autumn of 2010 or in the spring of 2011 (Table 2). Two training systems were used in the experimental orchard: slender spindle (3.85 × 1.0 m = 2597 trees ha −1 ) for nine cultivars and super spindle (3.85 × 0.5 m = 5194 trees ha −1 ) for four cultivars ( Table 2). The cultivars 'Red Jonaprince', 'Red Cap', 'Early Red One' and 'Red Topaz' were planted in the more intensive training system (super spindle) according to the breeder recommendations.
Orchard management followed the European Integrated Fruit Production (IFP) guidelines. Fungicides and pesticides approved in IFP were used in the orchards with annual spray schedules from 13 to 21 pesticide applications during the growing seasons of 2013-2017. Winter pruning was performed each year during the dormant periods. Mechanical fruit thinning and summer pruning were performed in 2014, 2015 and 2017 for all cultivars after petal fall and in June, respectively. The orchard was irrigated each year with a drop irrigation system. For nutrient supply, an NPK fertilizer (Péti Kevert NPK, Nitrogénművek, GmbH, Pétfürdő, Hungary) was applied at mid-March each year at a dosage of 130 kg ha −1 active ingredient with a 10:15:15 N-P-K ratio. Grass between rows was cut three times in each year (mid-June, mid-July, and mid-August) with an orchard flail mower.

Assessment of vegetative parameters
Two vegetative parameters were assessed: trunk cross sectional area (TCSA) and tree height (TH). For each cultivar, 5 trees, replicated four times, were selected for assessments of both parameters. For trunk cross sectional area, diameter of the trunk was measured with a Vernier caliper at the trunk halfway between the graft point and the first branches, and then this data was used to calculate trunk cross sectional area in cm 2 . Tree height in cm was recorded as the distance between the ground and the top of the tree not including the one year old shoots at the tree top. Both parameters were assessed each year after leaf fall in November.

Assessment of generative parameters
Seven generative parameters were assessed: tree yield (TY), fruit number per tree (FNT), crop load (CL), fruit diameter (FD), fruit shape index (FSI), fruit surface color (FSC) and fruit color intensity (FCI). For each cultivar, 5 trees, replicated four times, were selected for assessments of all parameters. All generative parameters were assessed at harvest in 2013-2015 and 2017. Harvest was done at the biological maturity stage of the cultivars determined by the starchiodine test in 2013-2015 and 2017 (Table 3).
Fruit yield as kg tree −1 and fruit number per tree were recorded for each selected tree. After this, crop load was determined as fruit number per tree divided by trunk cross sectional area (fruit number per cm 2 ). Fruit diameter was determined as fruit diameter (mm) measured with a Vernier caliper. Fruit shape index (as 0-1) was determined as the ratio of fruit diameter and fruit height (mm). Fruit surface color was determined as 1-100% using an image analyses color scale. Fruit color intensity was used to express the intensity of red skin color using a scale ranging from 1 to 5.

ANOVA
Data of each parameter were averaged to obtain a single value for each cultivar, training system and year. Data were subjected to analyses of variance (ANOVA) in order to determine the effect of cultivars and years; and their interactions were assessed on the nine parameters, separately for slender spindle and super spindle training systems. Then, within the two training systems, significant F tests (p = 0.05) were followed by a least significant difference (LSD) test for each year and cultivar in order to compare the means of all vegetative and generative parameters using LSD 0.05 values. For the analyses, Genstat Release 9.1 (Lawes Agricultural Trust, IACR, Rothamsted, UK) was used.

Correlation and linear regression analyses
Relationships among all vegetative and generative parameters were analysed by Pearson correlation coefficients (r) and their associated significance levels (p = 0.05) were separately determined for slender spindle and super spindle training systems. Then the strongest significantly correlated pairs were determined for both training systems. Then the Pearson correlation coefficient (r) of these significant pair variables was also determined for the thirteen cultivars, separately. In addition, the strongest significantly correlated pairs were plotted against each other and linear regression functions were then fitted for slender spindle and super spindle training systems. Then a t test was used to determine whether the regression slopes were significantly (p = 0.05) different from slender spindle to super spindle training systems.

Principal component analyses
A standardized Principal Component Analysis (PCA) was prepared using the correlation matrix with the variables of trunk cross sectional area, tree height, tree yield, fruit number per tree, crop load, fruit diameter, fruit shape index, fruit surface color and fruit color intensity. The values of all parameters were transformed to z-scores in order to standardize the variables. During performing PCA, model fits were also tested using the value of Root Mean Square Residual (RMSR) (Basto and Pereira 2012). Principal Components (PCs) were shown in biplot diagrams. R 4.04 (R Core Team 2021) with the psych (Revelle 2015), FactoMiner (Lê et al. 2008) and factoextra (Kassambara and Mundt 2019) packages were used to perform PCA.

ANOVA for vegetative and generative parameters
Analysis of variance (ANOVA) on the parameters of trunk cross sectional area, tree height, tree yield, fruit number per tree, crop load, fruit diameter, fruit shape index, fruit surface color and fruit color intensity showed that there were significant (p < 0.05) effects for cultivars and years in the two training systems (Table 4). Interactions for the treatment effects were non-significant (Table 4). According to ANOVA, the data of all vegetative and generative parameters were presented separately for cultivars and years in the two training systems.

Vegetative parameters of apple cultivars
The trunk cross sectional area values ranged from 4.8 to 41.4 cm 2 in the five-year period for the thirteen cultivars and two training systems (Table 5). The yearly trunk cross sectional area ranges were between 4.8 and 14.3, 5.9 and 16.  (Table 5). The trunk cross sectional area values continuously increased for all cultivars with years from the tree age of 2 to 6 years old.

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Wonder', while the highest was in 'Crimson Crisp', both in the slender spindle training system (Table 5). Most cultivars reached the optimal tree height (3.0-3.5 m) after 4-5 years, except for 'Fuji SW', 'Jeromine', 'Red Idared','Red Cap Valtod (S)', 'Early Red One' and 'Red Topaz'. Cultivars'Red Cap Valtod (S)', 'Early Red One' and 'Red Topaz' in the super spindle canopy were not able to reach the optimal height even at 6 years old (Table 5).

Generative parameters of apple cultivars
In the assessed period, the tree yield ranged between 2.4 and 35.8 kg tree −1 corresponding to 6.3 and 93.1 t ha −1 (  (Table 6). The fruit number per tree ranged between 15 and 222 fruit tree −1 in the assessed period ( Table 6). The lowest fruit number (15 fruit tree −1 ) was found in 'Jeromine' in 2013 in the slender spindle training system while the highest (222 fruit tree −1 ) was in 'Gala Venus Fengal' in 2017, also in the slender spindle training system (Table 6). By the year 2017, more than 100 fruit tree −1 was observed in all 'Gala' mutants ('Jugala', 'Galaval', 'Gala Venus Fengal', 'Gala Decarli-Fendeca', and 'Gala Schnitzer (S) Schniga') and in 'Fuji September Wonder' in the slender spindle training system (Table 6).
During the assessed period, the highest crop load was found in 'Gala Venus Fengal' in 2013 on the 3-year-old trees (12.72 fruit per cm 2 TCSA), while the lowest was in 'Jeromine' (2.13 fruit per cm 2 TCSA) in 2017 on the 6-year-old trees. Generally, cultivars with lower fruit number per tree (e.g. 'Fuji September Wonder', 'Early Red One', and'Red Cap Valtod (S)') showed medium or high crop loads (Table 6).
Most cultivars presented the smallest fruit diameter in 2013 (Table 7), when fruit thinning was not applied. During the assessed period, the smallest fruit diameter (66.3 mm) was found in 'Gala Schnitzer (S) Schniga' in 2013, while highest (93.6 mm) was in 'Red Idared' in 2014 (Table 7). Fruit diameter of all cultivars reached the market required minimum of 70 mm by 2017.
During the assessed period, the lowest shape index (0.73) was found in 'Red Topaz' in 2013 and 2014, while the highest (0.92) was in'Gala Schnitzer (S) Schniga' in 2017 Table 6 Tree yield (TY (kg tree −1 )), fruit number per tree (FNT) and crop load (CL (fruit number per cm 2 TCSA)) of thirteen apple cultivars grown in slender spindle and super spindle training systems (Nyírbátor, Hungary, 2013-2017 Values followed by the same letter are not significantly different according to LSD test (p = 0.05). LSD 0.05 = least significant differences at   (Table 7). Most cultivars showed a globular shape (shape index between 0.85 and 0.90), with the exception of 'Red Topaz', which displayed flat fruit with a shape index of 0.73-0.75 (Table 7). Fruit shape of 'Red Idared' considerably varied among the years as the shape index ranged between 0.75 and 0.86 (Table 7). During the assessed period, the lowest fruit surface color (40%) was found in 'Gala Schnitzer (S) Schniga' in 2014, while the highest (100%) was in 'Jeromine' in 2014 and 2017 (Table 7). Fruit surface color was below 80% for all years in the case of 'Gala Schnitzer (S) Schniga' and 'Fuji September Wonder'. During the assessed period, the best colored cultivars were 'Jeromine' and 'Early Red One'.
During the assessed period, the lowest fruit color intensity (2.9) was found in 'Fuji September Wonder' in 2017, while the highest (5) was found in almost all cultivars at least in one year, with the exception of 'Jugala', 'Gala Schnitzer (S) Schniga' and 'Fuji September Wonder' (Table 7).

Correlation and regression analyses among vegetative and generative parameters
Pearson correlation coefficient (r) was the highest (r = 0.81) between FNT and TY in the slender spindle training system (Fig. 1AB). The six correlation pairs were significant (p = 0.05) for both training systems; 5 parameter pairs correlated positively (TH vs TCSA, TY vs TCSA, TH vs TY, TY vs FNT, and FSI vs FSC), and one negatively (CL vs FS) (Fig. 1AB). These six correlation pairs also showed strong and significant (p = 0.05) relationships for most of the thirteen cultivars with the best overall correlation values in 'Jugala', 'Fendeca', 'Red Cap', 'Early Red One' and 'Red Topaz' (Table 8).
The linear regression analysis showed significant relationships for all 6 pair-variables with r = 0.732-0.916, p = 0.04-0.001 and with r = 0.711-0.899, p = 0.02-0.001 for the slender spindle and super spindle training systems, respectively. The slopes of the 6 pair-variables for the slender spindle training systems were not significantly (P = 0.05) different from the slopes for the super spindle training systems. In the case of TH vs TCSA relationships, a relatively high tree height was observed for almost all TCSA values ( Fig. 2A). In the case of TY vs TCSA relationships, the majority of TCSA values were directly proportional to the increase of tree yield (Fig. 2B). In the case of TY vs TH relationships, the speed of the yield increase was not directly proportional to the increase of tree height (Fig. 2C). In the case of FNT vs TY relationships, the increase of fruit number was directly proportional to the increase of tree yield (Fig. 2D). In the case of FCI vs FSC relationships, the two parameters were directly proportional to each other (Fig. 2E). In the case of FS vs CL relationships, increasing Table 7 Fruit diameter (FD (mm)), shape index (SI), fruit surface color (FSC (%)) and fruit color intensity (FCI) (1-5) of thirteen apple cultivars grown in slender spindle and super spindle training systems (Nyírbátor, Hungary, 2013-2017 Values followed by the same letter are not significantly different according to LSD test (p = 0.05). LSD 0.05 = least significant differences at  crop load values resulted in slight decreases of fruit diameter for both training systems (Fig. 2E).

Principal component analyses among vegetative and generative parameters
PCA explained 87% of the total variance and the PCs had been justified. The RMSR was 0.05 indicating good fit. PC1 accounted for 33% of the variance and correlated with TSCA, TH, TY, and FNT (Table 9). PC2 accounted for 21% of the variance and correlated with FNT, CL and FS. PC3 accounted for 20% of the variance and correlated with FSC and FCI. PC4 accounted for 13% of the variance and correlated with TH and SI (Table 9). According to the biplot figure for the training systems, PC1 and PC2 axes played considerable roles for both training systems (slender and super spindle) (Fig. 3A). However, the biplot figure showed that the PC2 axis was more dominant for the super spindle training system while both PC1 and PC2 axes were dominant for the slender spindle training system (Fig. 3A).
According to the biplot figure for cultivars, the PC1 and PC2 axes played various roles for the thirteen cultivars (Fig. 3BC). According to the biplot figure for the nine cultivars in the slender spindle training sytsem, the PC1 axis was more dominant for 'Fuji September Wonder' and 'Gala Schnitzer (S) Schniga', and the PC2 axis for 'Jeromine' (Fig. 3B); while both PC1 and PC2 axes were dominant for the remining cultivars ('Crimson Crisp-Co-op 39', 'Gala Decarli-Fendeca', 'Gala Venus Fengal', 'Galaval', 'Jugala', and 'Red Idared') (Fig. 3B). According to the biplot figure for the four cultivars in the super spindle training system, both PC1 and PC2 axes were dominant for 'Red Cap Valtod (S)' and 'Red Topaz' while the PC2 axis was more dominant for 'Early Red One' and 'Wilton's Red Jonaprince' (Fig. 3C).
Long arrows' length and proximity of the variables of TSCA, TH, FS, TY, FNT, CL, FSC, and FCI indicated a strong decisive role of both PC groups. All these results indicated strong associations among tree vegetative and generative properties (Fig. 3ABC).

Discussion
In this study, we evaluated tree vegetative and generative properties of thirteen prospective apple cultivars/mutants on 2-to 6-year-old trees trained to slender or super spindle training systems under natural environmental conditions. Data showed clear differences among years and cultivars for the two training systems (Tables 5, 6, 7 and 8). Correlation and regression analyses revealed strong and significant (p = 0.05) relationships between TH vs TCSA, TY vs TCSA, TH vs TY, TY vs FNT, and FCI vs FSC in both training systems (Tables 9; Figs. 1 and 2). In addition, PCA indicated strong associations among tree vegetative and generative properties for the cultivars and training systems (Table 9 and Fig. 3).
In the case of vegetative parameters, most cultivars trained to slender spindle reached higher trunk thickness Table 8 Pearson correlation coefficients (r) of the five best correlated pairs of measured parameters for the thirteen apple cultivars (Nyírbátor, Hungary, 2013-2017 (Table 5) with the exception of trees of 'Wilton's Red Jonaprince' trained to super spindle. This result was in agreement with the study of Ossenbruggen (2012) reporting that this cultivar had strong vigour, especially in the younger age of the trees. This indicates that a more vigorous cultivar planted in denser tree spacing can reach larger trunk thickness than a less vigorous cultivar grown in a larger planting distance, despite the higher root competition in the higher density plantations. In such cases, the space utilization of the cultivar may be more dominant than the tree spaces provided by training systems.
Our study showed that the trunk of the six-year-old trees of 'Early Red One' was relatively thin (14.5 cm 2 ), suggesting a low vigour, while the studies of Dennis et al. (1996) andFarina et al. (2009) ranked this cultivar as medium vigour (similar to the standard 'Red Delicious'). Similar to 'Early Red One', the six-year-old trees of 'Red Cap Valtod (S)' also showed similar weak trunk thickness (14.8 cm 2 ) in our study; however, a previous study of Guerra et al. (2008) reported that this cultivar has medium vigour such as the spur-type Red Delicious strains. The possible reasons for these different results may be due to that the previous studies evaluated the above cultivars under different climatic and ecological conditions as well as training systems compared to this study.
Higher fruit number per tree of the slender spindle system canopy was observed in our study in agreement with the results of the studies of Weber (2001) and Hampson et al. (2002). However, our examinations also showed that the slender spindle canopy with a less vigorous cultivar may also have lower fruit number per tree, which was in agreement with the study of Ezzat et al. (2020). This indicates that fruit number and TCSA together can equalize the differences of cultivars and training systems; i.e. the lower fruit number of the super spindle trained trees with smaller cropping surface can provide similar or higher crop loads compared to the larger cropping surface of slender spindle trained trees with higher fruit number.
In this study, 'Gala Schnitzer (S) Schniga', 'Gala Decarli-Fendeca' and 'Gala Venus Fengal' reached a crop load of 9.7-12.7 fruit per cm 2 TCSA on three-year-old trees. A previous study by Stebbins (1989) showed that a crop load between 7 and 13 fruit per cm 2 TCSA resulted in poor yield for 10 apple cultivars in the following year. In addition, Robinson and Watkins (2003) reported that a crop load above 10 fruit per cm 2 TCSA considerably reduced fruit diameter and fruit coloration. This result was in agreement with our results of the negative relationship between crop load and fruit diameter, which indicated that the increasing crop load results in a decreasing fruit diameter for most cultivars (Table 8) and for both training systems ( Figs. 1 and 2). Robinson and Watkins (2003) also reported that the fruit coloration started to decrease at a crop load of 6 fruit per cm 2 TCSA. Robinson (2008) recommended a crop load of 6 fruit per cm 2 TCSA for 'Gala' (i.e. 30-50 apples for a three-year-old tree of this study). However, our study indicated that crop loads between 9 and 11 fruit per cm 2 TCSA were acceptable for the 'Gala' mutants, as fruit surface coloration did not decrease considerably. The contradicting results may be due to that the above cultivars were grown in different climatic conditions in this study compared to the previous ones.
Our study clearly showed that the fruit diameter was dependent on the cultivar's genetic features, which was in agreement with the results of Dallabetta et al. (2014). For example, 'Gala Schnitzer (S) Schniga' is classified as a small-fruited cultivar with a fruit size of around 70 mm (Yoon et al. 2020), which was similar to the ranges of fruit diameter of this cultivar (66.3 and 73.5 mm) in this study (Table 7). All cultivars reached the minimum market requirement of 70 mm fruit size (Yoon et al. 2020) by the tree age of 4 years old (Table 7). Cultivars 'Wilton's Red Jonaprince' and 'Red Idared' showed a large fruit diameter (around 80 mm), and in 2014, 'Red Idared' exceeded the fruit diameter of 90 mm, which can negatively influence harvest packaging and fruit health during storage (Holb et al. 2012).
This study revealed that tree yield had strong relationships with TCSA, height and fruit number per tree for most cultivars (Table 9) in both training systems (Tables 8 and  Figs. 1, 2 and 3). Yield values of the investigated cultivars showed medium or high levels in this study (Table 6) compared to previous studies (e.g. Robinson 2008;Kviklys et al. 2013;Reig et al. 2019). On the other hand, in this study, the high yield of 'Wilton's Red Jonaprince' in 2015 1 3 (Table 6) was in agreement with the studies of Kviklys et al. (2013) and Wójcik (2020) who reported an outstanding productivity of this cultivar.
In agreement with previous studies, most cultivars showed a globular shape (the shape index was between 0.85 and 0.90), with the exception of, for example 'Red Topaz', which displayed flat fruit (Table 7). In addition, fruit of mutants of 'Red Delicious' ('Jeromine', 'Red Cap Valtod (S)' and 'Early Red One') showed flattened shape in trees of 3-4 years old, but later their fruits became globular or slightly elongated (the shape index was between 0.84 and 0.90). This phenomenon corresponds to the results of Bassi et al. (2010) as the fruit shape of the cultivar group of 'Red Delicious' can be flattened or globular on young trees, but later fruits become more elongated.

Conclusions
This study revealed various vegetative and generative properties of the evaluated cultivars in 2-to 6-year-old trees and indicated strong associations among these properties under high density training systems under natural environmental conditions in Central Europe. Our results were partly contradictory with previous studies, which indicated the importance of cultivar testing under different climatic conditions, as the results of this testing largely influence the cultivar choice of the growers for designing new plantations. In this study, the overall vegetative and generative performances can be considered as best for 'Galaval', 'Gala Decarli-Fendeca', 'Crimson Crisp', 'Wilton's Red Jonaprince' and 'Red Idared' grown under central European climatic conditions. These cultivars are advised to growers/advisors for establishing new apple orchards under climatic conditions similar to central Europe.
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