Clonal Selection of Autochthonous Grape Varieties in Badacsony, Hungary

Abstract

As the sensitivity of perennial crops to climate change becomes more pronounced, clonal selection, which is already very time-consuming for grapevine, may take even longer, while its importance is increasing. In the case of indigenous grapevine varieties, the purpose of clonal selection is twofold: to mitigate problems of cultivation and at the same time, to preserve the varietal character. The cultivation technique issue of ‘Kéknyelű’ is the low fertility (functionally female-flowered variety), and as for ‘Juhfark’ it is the significant susceptibility to grey rot. Based on daily meteorological data of 11 years, the years were classified into 3 groups and harvest data were analyzed within each group. Significant difference in yield was found between clone B.2. and the base ‘Kéknyelű’. Both clones of ‘Kéknyelű’ matured with significantly lower pH compared to the base variety. Given the acidic character of ‘Kéknyelű’ wine and the predicted rise in must °Brix and pH as a result of climate change, these differences may be useful in the future. Botrytis infection only showed statistically significant differences between year groups for ‘Juhfark’. It is intriguing that in most years, the rate of grey rot infection was lower in both clones compared to the base variety, especially in year groups 1 and 3 when the overall rate of Botrytis infection was quite low.

1. Introduction

Since cultivated grapevine (Vitis vinifera L.) is a climate-sensitive crop, climate change has a major impact on the cultivation and the cultivability of individual varieties. The main adverse effects of climate change are global warming and the increasing frequency and severity of extreme weather events, including drought, which is affecting the growing season more frequently and more severely, and is having a negative impact on the quality of white grapes in particular, together with increasing mean temperature of the growing season, due to acidity loss [1,2,3,4,5].

In the context of climate change, in Hungary, white wine grapes are likely to lose market share to red wine grapes in the coming decades. Additionally, the importance of late- and very-late-ripening grape varieties will increase. There will be an increase in the frequency of extremely high summer temperatures, while the risk of frost injury during the reproductive cycle will decrease [6].

Even the most pessimistic models predict that Hungary will remain among the regions with favorable conditions for growing grapevines. However, the anticipated frequency and severity of extreme climatic events are ominous warning signs. Continental climatic conditions in the Carpathian basin can produce stress effects that can have negative economic repercussions due to inadequate quality and quantity [7].

This is particularly problematic for autochthonous varieties, where the span of environmental tolerance is even narrower, which is why these varieties are grown only in a smaller geographical area—where they can be economically grown in most years. However, it is a great advantage of indigenous varieties that they are unique and special, and for cultural reasons, they possess significant marketing value [8,9,10,11].

The ‘Kéknyelű’ (syn. ‘Blaustängler’) variety is an old, autochthonous, functionally female-flowered variety in the Badacsony Wine Region. It was traditionally grown in horn-trained, large stock plantations, the ‘Budai’ variety was most often used as its pollinator. Recent research shows that this variety grows better with umbrella cultivation and the ‘Rózsakő’ as a pollinator [10,12,13].

The reputation of ‘Kéknyelű’ has been much damaged by the possibility that it may be identical to the ‘Picolit’ variety grown in the Friuli-Venezia Giulia region in Italy. This assumption was based on Goethe’s (1887) claim that the two varieties were identical [14]. Later, Németh (1967) also claimed that based on the descriptions of the two varieties, ‘Kéknyelű’ and ‘Picolit’ are morphologically very similar, but in his opinion the sameness or difference could only be confirmed by a careful morphological comparison of the two varieties planted side by side [15]. Later molecular markers were used to prove, that the two varieties are different [16,17].

Similarly, many producers have confused ‘Juhfark’ and ‘Csomorika’, another ancient Hungarian grape variety, although the difference is morphologically visible, as the two varieties belong to different geo-ecological groups—‘Juhfark’ to V. vinifera L. proles orientalis Negr., ‘Csomorika’ to V. vinifera L. proles pontica Negr. [15,18]. The morphological characteristics and production values of the two varieties were studied by Varga et al. [19,20]. At the level of molecular markers, the two varieties are well distinguishable from each other [21,22,23,24,25].

‘Juhfark’—similarly to ‘Kéknyelű’—was traditionally grown in horn-trained, high vine density plantations. This technology increased the production problem of the variety by making it more susceptible to Botrytis infection due to the low row- and wine spacing and the high canopy density of the vine [19].

Climate change and the reduced environmental tolerance of autochthonous varieties increase their sensitivity to year-to-year variations, resulting in prolonged clonal selection. Their low environmental tolerance usually manifests in some specific problem, in our case the poor fertility of ‘Kéknyelű’ (a functionally female-flowered variety — Figure 1A) and the high susceptibility of ‘Juhfark’ to Botrytis cinerea (Figure 1B) [10,13].

In Hungary, clonotype selection (different from polyclonal selection) and clonal selection (3 or 4-step) were the most commonly used intra-variety selection methods. Clonotype selection requires an already selected base population (at least from a phytosanitary point of view) and entails categorizing intra-varietal variability by one or more significant features, such as flower type. This method was said to be more efficient than mass selection (polyclonal selection) for some varieties, and was developed by Kozma in 1948 on the basis of the floral biology of ‘Furmint’ and ‘Kadarka’ varieties [26,27]. The method of clone selection was adapted by the Hungarians from Germany. Márton Németh developed a four-step individual method, later Ottokár Luntz reduced the number of steps to three [28,29,30].

Both methods have several advantages and disadvantages. While clonotype selection preserves much of the genetic variability in the starting material, allowing for better adaptability, the starting population evolves much more slowly and as such, selection takes longer. However, for genetically degraded varieties existing only in very small populations, this method can be used to produce the amount of propagation material needed to “bring the variety back into production” in a relatively shorter time. Clonal selection, although it is faster and allows earlier clone release, reduces the somatic genetic variability of the variety, which in turn reduces the environmental adaptability. From a virus-elimination point of view, clonal selection is preferable as it only requires the virus elimination of one genotype, whereas virus elimination of a clonotype is impossible or at least very costly [27,30,31].

In practice, clonal selection is often aided by the correlation between certain morphological traits and cultivation traits [26,32]. For example, despite the demand for ‘Vignoles’ wine is high, its production is limited by the susceptibility of the grape bunches to grey rot, which is linked to the compact bunch structure. The selection objective here was to select a clone of the variety with loose clusters [33,34]. Similar considerations have been made for the selection of loose clustered types for ‘Juhfark’. For the ‘Kéknyelű’ variety, we also looked at cluster compactness, but here we were looking for types with more fertile, and therefore slightly tighter clusters [35].

While selection methods were aided mainly by the knowledge of the correlation of morphological traits in the past [26], lately molecular techniques can also aid clonal discrimination [36] and selection, although their application is limited compared to cross-breeding. For example, ATR-MIR spectroscopy combined with partial least squares discriminant analysis (PLS-DA) has been used to discriminate the origin and vintage year of ‘Tempranillo’ grape clones, and partial least squares (PLS) regression to predict soluble solids (SS), pH and titratable acidity (TA) [37] or SSR and inter-SSR markers were used to identify ’White Riesling’ clones [38]. To aid clonal selection of Croatian indigenous varieties, AFLP and S-SAP markers were used to assess intra-varietal genetic variability [39]. Several studies have been conducted to assess intra-varietal genetic polymorphism in grapevine [38,40,41], but there is a general lack of functional connection between genetic factors and important traits for clonal selection [40,41].

The dynamic genome of the grape is characterized by single mutations that occur only once in a clone. The frequent occurrence of mutations in different clones suggests that the mutations may be locus specific. To investigate this phenomenon, 86 ‘Riesling’ clones were analyzed using ten AFLP primer combinations [42]. 38.5% of the polymorphic marker bands showed single mutations and 17% showed locus-specific mutations, confirming the observation that the grapevine genome is rather dynamic (at least in the case of the ‘Riesling’ variety). This somatic variability helps selection breeding, especially clonal selection, but can cause problems in cultivation due to the instability of the clones.

In Badacsony, clonal selection of ‘Kéknyelű’ started in 2003, based on the previous clonotype selection, while clonal selection of ‘Juhfark’ started in 2005. Since 2011, 2 ‘Kéknyelű’ and 2 ‘Juhfark’ clones have been tested in small plot experiments. In the present study we are presenting the results of the clonal selection work so far, based on harvest results and observations.

2. Materials and Methods

2.1. Experimental Site, Vineyard and Growing Conditions

In Badacsony, the results of small plots (20 vines) of selected clones of ‘Kéknyelű’ and ‘Juhfark’ of the Hungarian University of Agricultural and Life Sciences, Institute for Viticulture and Oenology, Badacsony Research Station and the results of 0.3 ha plantations of both base varieties in the same area were examined. All the vineyards studied were cultivated with the same 2 × 1 m vine spacing on ‘Teleki 5C’ (E20) rootstock and applying the same training system (mid-wire cordon for ‘Juhfark’; umbrella for ‘Kéknyelű’) for the clonal plots and the base variety. Bud load was 7 buds/m² = 14 buds/vine for both of the varieties (‘Juhfark’—2 spurs with 3 buds + 4 spurs with 2 buds pro vine; ‘Kéknyelű’—one cane with 12 buds + one spur with 2 buds pro vine).

2.2. Experimental Harvest, Measures

The date of the key phenological stages: bud burst (BBCH 09, EL 5), beginning of flowering (BBCH 61, EL 19), end of flowering (BBCH 69, EL 26), veraison (BBCH81, EL 35) and harvest (BBCH 89, EL 38) were visually determined and recorded for both varieties [43]. For both varieties in the vineyard of the base variety sampling was carried out every two days from the BBCH phenophase 83 (berries developing colour) on. Each sample consisted of 20 randomly selected berries from different sides (shaded—not shaded) of 5 different vines. Sugar content was determined by refractometer. Although the ripening kinetics of individual berries may vary [44,45], this sample size can give a good estimate of approximate ripening according to research practice. The data were used to estimate when the variety would reach 20 °Bx, at which timepoint the experimental harvest was carried out [46]. After defining the time of experimental harvest based on the berry sugar contents, the harvest was carried out separately for the two varieties, where the base variety and its clones were harvested at the same time.

During the experimental harvest, the following parameters were determined: yield (kg/m²), sugar content of the juice (°Bx), titratable acidity of must (g tartaric acid/l), pH, and visual estimation of the degree of grey rot (Botrytis cinerea) infection (%).

12 years (2011–2022) of meteorological data (daily minimum, maximum and mean temperature; daily precipitation) were recorded by an automatic agrometeorological station (Lufft HP-100 Meß- und Regeltechnik GmbH-1997, Fellbach, Germany) installed in the vineyard of the experimental plots (Badacsony, Hungary).

2.3. Data Analysis

To determine the vintage effect, the following climatic indices were calculated for each variety at different phenological stages of the vine: growing degree days (GDD) [47], Huglin index (HI) [48], hydrothermal coefficient (HTC) [49], and the cumulative rainfall (mm) during flowering for ‘Kéknyelű’ and during maturation of the berries (from veraison to full maturity) for ‘Juhfark’.

The growing degree-days for a given time period or a phenophase is calculated as follows:

$\mathrm{GDD} (°\mathrm{C} \mathrm{day})={\displaystyle \sum }\max \left(0, \frac{{\mathrm{T}}_{\min }+{\mathrm{T}}_{\max }}{2}-10\right)$

where T_min is the daily minimum temperature (°C), T_max is the daily maximum temperature (°C).

The Huglin index [48] for a given time period or a phenophase is calculated as follows:

$\mathrm{HI}=\mathrm{K}·{\displaystyle \sum }\left(\frac{{\mathrm{T}}_{\mathrm{mean}}+{\mathrm{T}}_{\max }}{2}-10\right)$

where T_mean is the daily mean temperature (°C) T_max is the daily maximum temperature (°C).

The hydrothermal coefficient [49] is calculated using the following formula:

$\mathrm{HTC}=\frac{\mathrm{P}}{{{\displaystyle \sum }}_{\mathrm{T}>10}\frac{\mathrm{T}}{10}}$

where P is the sum of precipitation amounts (mm) and T is the sum of temperatures (°C) for the months with mean temperatures >10 °C [50].

Years were grouped for both varieties separately based on the calculated biometeorological indices. After the standardization of the variables, k-means clustering was performed [51,52] resulting in 3 groups. Principal component analysis [53] was also performed using these 3 groups.

Harvest data were also analyzed by the 3 groups of years and by variety separately, comparing a particular clone always to the corresponding base variety. In this way, it was also possible to compare the vintage sensitivity of each clone.

The homogeneity of variances and the distribution of the harvest results data (normality test) were checked by the Levene test and the Shapiro-Wilk test respectively, and then, as these did not meet the basic conditions for analysis of variance, the data were evaluated by Analysis of Variance of Aligned Rank Transformed Data (ART-ANOVA). The ART-ANOVA results indicated that the expected values differed at a significance level of at least 90%, the expected values were compared pairwise using “Aligned Ranked Transform Contrasts” test [54,55].

All data evaluation and analysis were carried out using the R software package [56].

3. Results

3.1. Evaluation of the Meteorological Data and Indices

The recorded meteorological data are detailed in Supplementary Table S1. The dates of the phenological stages of ‘Kéknyelű’ and ‘Juhfark’ are shown in Table 1 and Table 2 respectively. The values of the calculated indices are summarized in Table 3.

3.1.1. K-Means Clustering and Principal Component Analyses (PCA) of the Years

Based on the k-means clustering of years, 3 groups (clusters) were formed for both varieties. In the case of ‘Kéknyelű’, Group 1 contains the years 2018, 2020 and 2022; group 2 the years 2011, 2014, 2019 and 2021; as group 3 the years 2012, 2013, 2015, 2016 and 2017. In the case of ‘Juhfark’ the clusters are the following: group 1: 2012, 2015, 2016, 2021 and 2022; group 2: 2014, 2019 and group 3: 2011, 2013, 2017, 2018 and 2020 (Figure 2).

The graphical representation of the result of PCA of the 3 groups of years is shown in Figure 2. The PC1 and PC2 principal components explain 37.2% and 26.4% of the variance between years for ‘Kéknyelű’ and 29.4% and 25.4% for ‘Juhfark’, respectively. For both of the varieties the years can be clearly distinguished using these two components.

3.1.2. Evaluation of PCA Results of Agrometeorological Indices for ‘Kéknyelű’ Phenophases

In the PCA results of the years for ‘Kéknyelű’ (Figure 2A), the first principal component has large positive associations with:

• growing degree-days from budburst to the beginning of flowering (GDD1)

• growing degree-days from the end of flowering to veraison (GDD3),

• Huglin index from budburst to the beginning of flowering (HI1) and

• Huglin index from the end of flowering to veraison (HI3) defying the year group 3. This may mean, that the temperature in these periods has fundamental importance in the development of ‘Kéknyelű’ in years of group 3.

The following indices have small positive loadings on PC1 and large negative loadings on PC2:

• hydrothermal coefficient during flowering (HTC2) and

• cumulative rainfall (precipitation) during flowering (P2) while the next indices have negative associations with both PC1 and PC2:

• hydrothermal coefficient from budburst to the beginning of flowering (HTC1),

• hydrothermal coefficient from the end of flowering to veraison (HTC3),

• Huglin index from veraison to maturity (HI4) and

• growing degree-days from veraison to maturity (GDD4).

The last 6 indices mainly defy the year group 1. In the years of this group, the rainfall during flowering and the temperatures during berry maturation may have fundamental effect on the productivity of ‘Kéknyelű’. Being a functionally female flowered variety, the importance of the weather during flowering is not surprising.

Positive association can be found with both the 1st and 2nd principal component of

• Huglin index during flowering (HI2),

• growing degree-days during flowering (GDD2) and

• hydrothermal coefficient during berry maturity (HTC4) defying the year group 2. In this group of years, the importance of the temperature during flowering could be emphasized.

It is worth noting that, unsurprisingly during flowering, the importance of rainfall (represented by P2 and HTC2) and temperature (represented by HI2 and GDD2) have almost opposite effects (shown with arrows pointing in opposite directions in Figure 2A).

3.1.3. Evaluation of PCA Results of Agrometeorological Indices for ‘Juhfark’

In the PCA results of the years for ‘Juhfark’ (Figure 2B), principal component 1 has positive associations with:

• growing degree-days from the end of flowering to veraison (GDD3) and

• Huglin index from the end of flowering to veraison (HI3)

  defying the year group 1 for ‘Juhfark’, probably meaning that the temperature in this phenological stage (berry development) has fundamental impact on the productivity of this variety in the years of group 1.

• growing degree-days from budburst to the beginning of flowering (GDD1),

• Huglin index from budburst to the beginning of flowering (HI1) and

• hydrothermal coefficient during flowering (HTC2) are in negative association with principal component 2 while

• Huglin index during berry maturity (HI4) and

• growing degree-days during berry maturity (GDD4) are in negative association with both principal components 1 or 2 of the years for ‘Juhfark’ defying group 3. This may mean, that in these years the most defying meteorological parameter from budburst to the beginning of flowering and during berry maturity was temperature, while rainfall also played an important role during flowering.

Most indices were in negative associations with PC1 and positive association with PC2:

• Huglin index during flowering (HI2),

• growing degree-days during flowering (GDD2),

• hydrothermal coefficient from budburst to the beginning of flowering (HTC1),

• hydrothermal coefficient during berry maturity (HTC4) and

• cumulative rainfall (precipitation) during berry maturity (P4).

These indices defined the year group 2 for ‘Juhfark’. In this case the temperature during flowering and the rainfall during berry maturity seem to play a crucial role.

The hydrothermal coefficient from the end of flowering to veraison (HTC3) has negative effect on principal component 1, but seems not to affect the clustering results.

3.2. Evaluation of the Harvest Results of ‘Kéknyelű’

The harvest results of the ‘Kéknyelű’ variety have been evaluated on the basis of eleven years of data between 2011 and 2022. In 2016, the bird damage in the plantation was such high, that no significant amount of fruit could be harvested. The harvest results are summarized in Table 4.

Harvest data show that the ’Kéknyelű’ has yielded an average of 1.32 kg/m² over eleven years, which allows the variety to be grown economically [12,57].

There was a significant difference in yield between the clone B.2. and the base variety, which is probably due to the better fertilization of the flowers. Since the selection for this variety was aimed to achieve better fertilization, this is an important positive result indicating the success of clonal selection.

By comparing the year groups, years belonging to group 1 of ’Kéknyelű’ yielded more than in another years (Figure 3). These differences are significant at 90% confidence level. Based on Figure 3, ‘Kéknyelű’ gives better yield especially in years (group 1) where the HTC is low from bud break up to veraison and the temperature is lower during ripening (Figure 2A).

The average sugar content (Figure 4) of the ‘Kéknyelű’ must in the examined years was 18.36 °Bx. Examining the sugar content results, we found no significant differences between the single clones and the base variety, but the ‘Kéknyelű’ clone B.1 had higher °Bx in years of group 2 than in group 1 in 99% confidence level.

The average titratable acid content of the grape juice was 8.24 g/L over the examined years, no significant differences were detected between clones or year clusters.

The pH of the grape juice was pH 3.5. Both clones of ‘Kéknyelű’ matured with significantly lower pH at the 99% and 95% level (Figure 5). Only clone B.1. showed a significant difference between year types (95% level) indicating the vintage sensitivity of the clone regarding the pH. Clone B.1. shows higher pH in year group 3 (high temperature indices during shoot development and berry development) (Figure 5A). Regarding pH, clone B.2. gives better results in year of group 2 (defined mainly by temperature indices during flowering) (Figure 5B).

The average proportion of botrytis incidence in ‘Kéknyelű’ is 4.03, which is very low, but characteristic for the variety, because of its loose clusters. There were no significant differences between the clones and the base variety, meaning that the better fertilization and higher yield for ‘Kéknyelű’ clone B. 2. did not result in dense clusters that would have increased the sensitivity to grey rot.

There were significant differences (95% significance) in Botrytis infection among the different clusters of years, which means that the weather had a greater influence on the rate of rotting than the clone.

3.3. Evaluation of the Results of the Harvest of the Variety ‘Juhfark’

Harvest results are summarized in Table 5.

The harvest performance of the ‘Juhfark’ variety was evaluated based on eleven years of data between 2011 and 2022. In 2016 the plantation suffered bird damage to such an extent that we were unable to harvest a qualifiable crop (in 2019 only the base variety and in 2022 only the B.1. clone and grapes in the base vineyard could be harvested).

‘Juhfark’ was harvested with a mean yield of 1.23 kg/m² over ten years, which is a relatively good result for an autochtonous variety and is above the economic threshold. There were no statistically significant differences in terms of yield between clones or year groups.

The average sugar content of the ‘Juhfark’ grape juice was 18.5 °Bx in the examined years. In practice, due to the susceptibility of the variety to rot, it is often necessary to carry out a forced early harvest before full ripening is reached, which often results in lower sugar levels. Given the effects of global warming, lower sugar levels may even be beneficial in the future. No significant differences were detected between clones or year groups.

Titratable acid content of the grape juice was 10.73 g/L, which is quite high, but this is typical for this variety and can be considered a positive varietal characteristic. No statistically significant differences were found in titratable acidity between clones and year groups.

The average pH value of the ‘Juhfark’ must was pH 3.31, which is relatively low. As the pH scale is logarithmic and inversely related to the activity of hydrogen ions in solution, a lower pH indicates a more acidic character, but may also indicate a different ratio of the two main organic acids, tartaric and malic in the grape juice. The lower pH of ‘Juhfark’ musts and wines is also typical. The differences between the values were not significant here either.

The rate of botrytis infection was 19.67%, which is high, also characteristic for this variety. Significant differences were only detected between year groups in botrytis infection. In most years, the rate of rotting was lower in selected clones compared to the base variety, especially in the year groups, when the overall rotting was quite low (year cluster 1 and 3). The average rate of infection was lower in clone B.1. compared to the base variety in every year group (Figure 6), however this difference was not statistically significant.

4. Discussion

The genetic adaptation of crops to abiotic stress is a topic of growing significance in light of climate change. This is particularly relevant for perennial crops with substantial economic value, such as grapevine. Considering the wide range of habitats in which this botanical species thrives, along with its substantial diversity at both the intra- and interspecific levels, it is reasonable to infer that grapevine genomes carry a wide range of alleles that hold potential for exploiting in order to safeguard the long-term viability of viticulture [58].The incorporation of traditional indigenous varieties into breeding initiatives can yield benefits in terms of enhancing resilience to diverse climatic circumstances. This is mostly attributed to their inherent resistance to adverse weather phenomena such as low temperatures, frost, and drought [59].

Although the old local varieties lag behind the world varieties in terms of their production value (e.g., yield, climate resilience), market demands for quality and uniqueness clearly require them to be addressed, making clonal selection work particularly valuable, as improvement must be achieved while maintaining the original dominant varietal characteristics [60,61,62].

The idea of using the native Crimean grape varieties in breeding to produce more adaptable grape varieties that can compete in the viticulture and winemaking markets was studied as part of the research of the biology of these local grape varieties [63]. The grape plant, during a span of more than two millennia of cultivation, has demonstrated a remarkable ability to adapt to various stressors. However, through the process of introgression, where genes conferring resistance to drought, low temperatures, and diseases are incorporated, we are able to effectively manipulate the genetic diversity of the crop, resulting in the development of a diverse array of novel grape varieties. Vitis vinifera L. proles pontica (Negr.) and proles orientalis (Negr.) can play an important role in mitigating the effects of climate change. They can also provide important breeding material in terms of drought tolerance and frost tolerance. For example, ‘Tinto Velasco’ the autochthonous grape variety of Andalusia (Spain) or the Bulgarian indigenous variety ‘Wide Melnik Vine’, belonging to proles pontica (Negr.) group, show good drought tolerance [64,65]. The analyses of winter hardiness and frost resistance of the largest genebank of Russia (Anapa ampelographic collection) proles pontica (Negr.) and proles orientalis (Negr.) varieties gave better results, than the proles occidentalis (Negr.) varieties [66]. These enhance the importance of the clonal selection Hungarian autochthonous varieties, which mostly belong to these two ecogeographical groups.

In Hungary, only eight grapevine varieties, all of which are white wine varieties, managed to withstand the devastating phylloxera epidemic and are now recognised as authentic ”Hungarica”. ‘Juhfark’ and ‘Kéknyelű’ are two of these varieties [67]. In Hungary, it is customary for bottled wines to prominently display the name of the grape variety as the primary label information, rather than prioritizing the vineyard location name. The primary factor contributing to this phenomenon is the historical disregard for clone selection over a span of four decades in the socialist era. Instead, there has been a global preference for widely distributed commercial varieties (CVs), resulting in the simultaneous neglect of traditional Hungarian varieties. However, it is worth noting that international specialists have been pleasantly surprised by the excellent taste and smell of several Hungarian varieties, such as ‘Kéknyelű’ and ‘Juhfark’ [68].

In our work, meteorological data measured over the years were evaluated as values of different biometeorological indices (GDD, Huglin index, etc.) and average or sum of meteorological parameters calculated at key phenophases of the two autochthonous grapevine varieties examined.

‘Kéknyelű has functionally female flowers, which causes low yield, what could be traced back to pollen sterility caused by the semitectate structure of pollen exine, which could be responsible for the sterility of its pollen [69]. As in the clonal selection of female flowered varieties the weather conditions (especially precipitation) during flowering have a crucial role [70,71] the inclusion of sum of precipitation during flowering as meteorological parameter was decided for ‘Kéknyelű’.

The agronomic problem of ‘Juhfark’ is its high susceptibility to rot. There is a limited active defense against necrotrophic fungi such as Botrytis cinerea. However, stilbenes play crucial role in the defence against this pathogen. During the green berry stage of grape plants, the expression of VvPAL and VvSTS genes is activated. These genes are involved in the immunological pathway of phenylpropanoid biosynthesis, resulting in the production of stilbenes (such as resveratrol and its derivatives) and lignin [72]. Like most varietiesI sensitive to grey rot, ‘Juhfark’ is expected to express symptoms of noble rot, as is ‘Furmint’. Similar to grey rot, the noble rot process alters the regulation of STS isoforms. Similar to berries affected by typical bunch rot disease, stilbene synthase (STS) isoforms encoded in two massive clusters on chromosomes 10 and 16 were find uniformly upregulated in noble rot samples [73,74].

On the other hand, traits of the grape cluster (thickness of the berry skin, bunch compactness) can passively contribute to lower sensitivity to botrytis in grapevine [75]. Both the production of diverse antimicrobial compounds (stilbenes, PR proteins, etc.) and bunch morphology are typical multigenic quantitative traits and as such, bunch rot caused by Botrytis cinerea is largely affected by meteorological conditions during grape ripening [76,77]. As ‘Juhfark’ is very sensitive to the infection of this epidemic, the inclusion of sum of precipitation from veraison to harvest as meteorological parameter was decided for this variety.

In viticultural research, but especially when comparing clones, it is crucial to determine the date of ripening as accurately as possible [44,45]. Our results have shown that for the ‘Kéknyelű’ and ‘Juhfark’ varieties, although ripening is strongly influenced by weather conditions and vintage, the protocol used (see Section 2.2) allows the experimental harvest to be planned properly [46].

As both varieties are local, their cultivation is limited to a small geographical area in the Lake Balaton Highlands. Since autochthonous varieties are characterized by their year-round sensitivity, the selected clones need to be evaluated over many years. Our results show that, for most harvest parameters, differences between clones are not evident even after 11 years of testing. If several years are considered in an open-field experiment, it is very challenging to dissect the effect of the vintage and the effect of the clones, while investigating fruiting cuttings of different clones in controlled greenhouse environment, considerable clonal differences can be detected [78]. Investigating phenolic compounds of ‘Merlot’ and ‘Cabernet franc’ clones in just one year, differences could be detected between the investigated clones [79]. If two years were considered, the classification of clones based on maturity and phenolic data enabled a 75% correct classification of the clones [80]. Comparing ‘Riesling’ clones for just two years, it was possible to show the effect of both the clone, the year and their interactions [81]. Similarly, the metabolomic changes after ESCA infection proved to be both clone and year dependent [82,83]. When comparing water use efficiency of ‘Tempranillo’ clones in a two years open-field experiment it was possible to link biomass production to physiological parameters of the clones but the results proved to be also vintage dependent [83]. On the other hand, Regner et al. were able to identify small agronomically important differences between five ‘Pinot blanc’ clones in a five-years trial [84].

To come around the problem of vintage effects, we applied a strategy, where the years were clustered according to four climatic indicators dissected to phenological stages, which were determined specifically for all year and both varieties. This way we were able to investigate the varieties and their clones dissected in three types of years, providing better insight into the reactions of the variety and of the clones to the ecological conditions of the year. This is especially important, because the environmental interactions can be highly clone-specific [85].

In the case of ‘Kéknyelű’ our results showed that both clones gave higher yields than the base variety, the difference being significant only for clone B.2. This is important because low yields due to poor fertility are the main problem of ‘Kéknyelű’ [10,13,16]. Among the quality parameters, in the context of global warming, the significantly lower pH of both clones should be highlighted. Strong relationships between total acidity, titratable acidity and pH were shown several times [86,87]. A lower pH is associated with higher acidity. ‘Kéknyelű’ is famous for the special varietal character of its wine, which is said to have pronounced acidity. Given the acidic character of ‘Kéknyelű’ wine and the increasing must degrees and increasing pH values expected due to climate change, these differences could be positive in the long term [7,88,89,90,91]. This again underlines the importance of clonal selection in the adaptation of viticulture for climate change [61,62,78,92].

An intriguing result is that the two clones of ‘Kéknyelű’ responded differently to distinct weather conditions (in different year groups). In the case of this variety,’ our results demonstrated significant differences in yield between year groups for clone B.2. However, the °Bx and pH values of clone B.1 varied between year clusters. Both ‘Kéknyelű’ clones had a significantly higher incidence of Botrytis infection in group 2 years than in group 3 years.

For the ‘Kéknyelű’ clone B.1., in years belonging to the group 1 the pH value was significantly lower, and the rate of rotting was significantly higher than in years belonging to group 3. The pH value depends to a large extent on the acid composition of the grape juice, with a lower pH usually associated with a higher malic acid content. In this case, the relationship between the lower pH and higher grey rot incidence is essential. It could be explained by the fact that the Botrytis cinerea fungus uses sugars as a food source originating from the conversion of organic acids, mainly malic acid. It has been reported, that the conversion of acids into sugars during grape ripening promotes the development of Botrytis cinerea [93,94]. The sugar, malic acid, potassium, and sodium content of berry secretions promote mycelial growth of the pathogen [95].

No statistically verifiable difference in harvest parameters was observed for ‘Juhfark’, suggesting that further studies will be needed due to climate change and the high vintage sensitivity of the variety [61,62,92].

In the case of ‘Juhfark’, the vintage sensitivity is related to the problem of the variety, the sensitivity to bunch rot. Both of the clones showed significantly different sensitivity to botrytis infection in different year clusters. In group 2 years, they gave higher values (worse results) than in other vintage group years.

5. Conclusions

Genetic variability of the starting population is an essential basis of clonal selection. In our study, we aimed to answer the question whether clonal selection breeding of autochthonous grapevine varieties ‘Juhfark’ and ‘Kéknyelű’ is a suitable method to adapt to climate change. In the clonal selection breeding, demonstrated in this study, we sought after to increase productivity via increased fertilization in the case of ‘Kéknyelű’ and decrease Botrytis cinerea susceptibility in the case of ‘Juhfark’. The long-term experiment enabled us—at least partially—to dissect the effects of different environmental conditions and to identify clone specific reactions to different types of vintages (year groups).

In the case of ‘Kéknyelű’ 11 years of data allowed us to statistically prove the yield increase of clone B.2. The reduction in Botrytis susceptibility was not statistically confirmed for the ‘Juhfark’ variety. The performance of clones of the two genotypes was evaluated within each year cluster. Our findings revealed that both varieties are vintage-sensitive.

This study demonstrates that clonal selection is not a complete solution to climate change mitigation, especially in the case of ‘Juhfark’, but it can be used to improve certain parameters for certain varieties like ‘Kéknyelű’. The long study span made it possible to confirm the stability of the patented clones.

6. Patents

The clones of the ‘Kéknyelű’ and ‘Juhfark’ varieties tested and described in this manuscript have been submitted for state registration in Hungary.

Author Contributions

Conceptualization, methodology, formal analysis, visualization, project administration writing—original draft preparation, review and editing G.J.; validation, formal analysis, data curation: E.A.F., G.K.S. and C.N. writing—review and editing: E.A.F., T.D., R.O. and K.O.; supervision and funding acquisition: B.S. and D.Á.N.S. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study is contained within the article or are available in Supplementary Table S1.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. (A) The functionally female flowers of ‘Kéknyelű’ causes fertilization problems. (B) The dense clusters of ‘Juhfark’ make the bunches very susceptible to Botrytis.

Figure 2. Result of PCA (Principal Component Analyses) of the years for ‘Kéknyelű’ (A) and ‘Juhfark’ (B). The 3 groups of years are shown in red, green and blue respectively.

Figure 2. Result of PCA (Principal Component Analyses) of the years for ‘Kéknyelű’ (A) and ‘Juhfark’ (B). The 3 groups of years are shown in red, green and blue respectively.

Figure 3. Yield results of the ‘Kéknyelű’ clone B.2. and base variety (Badacsony, 2011–2022; data in kg/m2). Group 1, 2 and 3 represent the year groups of k-means clustering.

Figure 4. Sugar content of the juice of the ‘Kéknyelű’ clone B.1. and the base variety in different year clusters (Badacsony, 2011–2022; data in Brix). Group 1, 2 and 3 represent the year groups of k-means clustering.

Figure 5. pH values in different year clusters compared to the ‘Kéknyelű’ base variety by clones B.1. (A) and B.2. (B) respectively. Group 1, 2 and 3 represent the year groups of k-means clustering.

Figure 6. Rate of Botrytis infection (%) in different year clusters compared to the ‘Juhfark’ base variety by B.1. and B.2. clones.

Table 1

Dates of the key phenological stages of the ‘Kéknyelű’ variety (Badacsony, 2011–2022).

Year/Phenophase-BBCH Code	Budburst 9		Beginning of Flowering—61		End of Flowering—69		Veraison 81		Maturity/ Harvest—89
	Date	DOY	Date	DOY	Date	DOY	Date	DOY	Date	DOY
2011	13 April	102	30 May	149	13 June	163	30 July	210	21 September	263
2012	11 April	101	29 May	149	7 June	158	27 July	208	6 September	249
2013	23 April	112	7 June	157	17 June	167	8 July	219	1 October	273
2014	7 April	96	4 June	154	15 June	165	5 August	216	22 September	264
2015	20 April	109	4 June	154	13 June	163	4 August	215	12 September	334
2017	6 April	95	8 June	158	19 June	169	1 August	212	20 September	262
2018	16 April	105	21 May	140	28 May	147	16 July	196	20 September	262
2019	12 April	101	6 June	156	20 June	170	29 July	209	2 October	274
2020	9 April	99	4 June	155	12 June	163	4 August	216	22 September	265
2021	23 April	112	13 June	163	23 June	173	6 August	217	30 September	272
2022	14 April	103	2 June	152	9 June	159	29 July	209	22 September	264

Table 2

Dates of the key phenological stages of the ‘Juhfark’ variety (Badacsony, 2011–2022).

Year/Phenophase-BBCH Code	Budburst 09		Beginning of Flowering—61		End of Flowering—69		Veraison 81		Maturity/ Harvest-89
	Date	DOY	Date	DOY	Date	DOY	Date	DOY	Date	DOY
2011	11 April	100	27 May	146	8 June	158	25 July	205	15 September	257
2012	4 April	94	24 May	144	6 June	157	23 July	204	10 September	253
2013	18 April	107	4 June	154	14 June	164	27 July	207	27 September	269
2014	4 April	93	27 May	146	9 June	159	30 July	210	16 September	258
2015	18 April	107	2 June	152	6 June	156	31 July	211	9 September	251
2017	4 April	93	6 June	156	15 June	165	25 July	205	13 September	255
2018	12 April	101	18 May	137	25 May	144	9 July	189	5 September	247
2020	30 March	89	29 May	149	9 June	160	24 July	205	10 September	253
2021	17 April	106	11 June	161	18 June	168	29 July	209	9 September	251
2022	9 April	98	25 May	144	2 June	152	19 July	199	1 September	243

Table 3

Climatic indices for ‘Kéknyelű’ (A) and ‘Juhfark’ (B) in Badacsony, Hungary (2011–2022).

(A)
Year/Climate index	Growing Degree Days				Huglin Index				Hydrothermal Coefficient				Precipitation (mm) Rainfall **
Phenophases/BBCH *	9–61	61–69	69–81	81–89	09–61	61–69	69–81	81–89	09–61	61–69	69–81	81–89	61–69
2011	298.53	165.55	543.61	651.80	450.89	217.43	734.16	877.33	0.20	0.28	0.82	0.46	8.60
2012	284.50	82.69	695.46	590.05	441.66	119.49	910.64	782.54	0.85	0.42	0.75	0.04	7.20
2013	297.59	118.33	754.67	436.35	448.66	159.35	995.75	647.92	1.12	0.39	0.42	1.28	8.60
2014	274.50	151.52	604.13	414.16	441.32	198.78	807.10	591.50	1.03	0.00	1.07	3.50	0.00
2015	299.63	125.32	670.72	488.73	449.31	162.34	886.58	665.45	0.98	0.00	0.32	0.81	0.00
2016	304.80	63.50	697.20	471.00	479.17	90.67	916.86	661.61	1.39	1.11	0.94	0.60	14.80
2017	350.57	124.98	592.36	547.79	516.65	169.15	787.37	750.64	0.64	0.11	0.94	0.99	2.60
2018	298.11	80.03	556.93	882.49	419.64	107.75	753.81	1216.37	1.30	0.84	1.26	1.17	12.60
2019	237.70	201.50	513.80	708.70	362.78	259.04	683.71	992.30	2.05	0.25	1.07	0.77	8.50
2020	274.50	71.70	637.00	568.80	463.10	102.80	857.69	783.14	0.74	1.58	1.18	0.62	24.00
2021	275.20	149.80	636.10	526.40	432.23	195.72	829.08	762.04	0.85	0.00	0.69	0.51	0.00
2022	300.50	80.50	688.10	613.50	443.73	106.26	906.52	830.66	1.28	2.34	0.57	0.83	35.20
Average	291.34	117.95	632.51	574.98	445.76	157.4	839.11	796.79	1.04	0.61	0.84	0.97	10.18
(B)
Year / Climate index	Growing degree days				Huglin index				Hydrothermal coefficient				Precipitation (mm) rainfall
phenophases/BBCH	09–61	61–69	69–81	81–89	09–61	61–69	69–81	81–89	09–61	61–69	69–81	81–89	81–89
2011	280.37	135.97	551.99	645.11	427.78	182.48	742.82	863.10	0.19	0.40	0.49	0.72	83.80
2012	256.88	108.36	653.20	693.47	401.71	159.26	854.12	922.30	1.06	0.33	0.72	0.11	12.80
2013	306.02	93.10	582.11	651.14	464.79	129.41	773.54	917.78	1.19	0.50	0.52	0.77	97.60
2014	239.56	113.87	608.82	448.57	387.33	163.94	811.13	625.89	1.26	0.02	1.07	3.12	289.60
2015	273.06	54.75	711.22	531.04	418.80	71.01	937.93	712.71	1.03	0.00	0.31	0.75	69.60
2016	274.60	75.50	632.30	484.10	436.59	107.10	835.80	668.80	1.19	1.56	0.97	0.77	70.20
2017	337.33	97.22	541.70	615.51	498.93	133.05	721.19	830.92	0.66	0.05	0.95	0.63	70.40
2018	298.68	66.44	513.95	799.28	420.36	91.89	690.87	1082.17	1.29	0.92	1.18	1.43	200.50
2019	234.60	187.50	515.90	643.40	356.53	238.93	688.70	870.35	2.22	0.00	1.07	0.87	102.50
2020	256.40	75.50	509.80	587.90	435.12	115.03	698.62	790.76	0.64	1.44	1.06	1.01	107.40
2021	255.70	84.70	624.10	466.80	411.50	112.88	810.18	648.69	1.19	0.32	0.30	0.90	79.90
2022	249.60	58.90	599.40	630.40	377.74	83.58	794.85	819.68	1.17	1.40	0.96	0.60	64.70
Average	271.90	95.98	587.04	599.73	419.77	132.38	779.98	812.76	1.09	0.58	0.80	0.97	104.08

* BBCH phenpohase codes: 9–61: from budburst to the beginning of flowering, 61–69: flowering; 69–81: from the end of flowering to veraison; 81–89: from veraison to harvest (maturity) ** For ‘Kéknyelű’, only precipitation during flowering (BBCH61–69) was considered because fertilization is crucial for this physiological female flowered variety. For ‘Juhfark’, only precipitation during ripening (BBCH81–89) was considered because of the high sensitivity to botrytis of this dense clustered variety.

Table 4

Harvest results of the ‘Kéknyelű’ variety (Badacsony, 2011–2022).

Clone *	Year	Yield kg/m2	Sugar Content of the Juice °Bx	Titratable Acid Content of the Juice g/L (in Tartaric Acid)	pH	Botrytis Infection %
B.1.	2011	0.88	20.20	7.25	3.08	10.00
B.2.	2011	1.26	17.40	7.09	3.07	10.00
Base	2011	0.99	18.60	6.59	3.38	10.00
B.1.	2012	0.95	18.30	6.52	3.55	0.00
B.2.	2012	1.10	17.50	6.36	3.67	0.00
Base	2012	1.08	18.50	4.64	3.47	0.00
B.1.	2013	1.39	18.90	11.15	3.26	0.00
B.2.	2013	1.61	17.70	10.36	3.31	0.00
Base	2013	1.21	21.20	9.60	3.56	0.00
B.1.	2014	1.01	18.10	16.60	3.19	30.00
B.2.	2014	0.91	18.00	17.91	3.25	30.00
Base	2014	0.72	19.50	15.57	3.24	30.00
B.1.	2015	1.07	18.40	7.82	3.39	0.00
B.2.	2015	1.20	18.00	6.83	3.40	0.00
Base	2015	0.97	18.20	6.98	3.51	0.00
B.1.	2017	1.15	18.10	6.71	3.25	0.00
B.2.	2017	1.33	17.70	5.72	3.38	0.00
Base	2017	0.92	18.20	6.74	3.36	0.00
B.1.	2018	1.23	17.70	8.26	3.43	3.00
B.2.	2018	1.90	18.20	6.96	3.39	0.00
Base	2018	1.76	17.70	6.85	3.55	5.00
B.1.	2019	1.39	18.70	8.70	3.28	0.00
B.2.	2019	1.46	18.60	3.29	3.80	0.00
Base	2019	1.27	18.70	7.56	3.44	5.00
B.1.	2020	1.26	17.70	8.68	3.14	0.00
B.2.	2020	1.31	18.20	7.60	3.43	0.00
Base	2020	1.03	18.80	8.57	3.55	0.00
B.1.	2021	1.28	18.90	8.62	3.29	0.00
B.2.	2021	1.24	20.20	7.40	3.25	0.00
Base	2021	1.04	20.20	9.38	3.37	0.00
B.1.	2022	2.79	16.50	7.40	3.19	0.00
B.2.	2022	2.88	16.30	6.10	3.41	0.00
Base	2022	2.03	17.00	6.20	3.58	0.00
B.1.	2011–2022	1.31	18.32	8.88	3.28	3.91
B.2.	2011–2022	1.47	17.98	7.78	3.40	3.64
Base	2011–2022	1.18	18.78	8.06	3.46	4.55
Mean		1.32	18.36	8.24	3.50	4.03

* B.1. and B.2. represent the ‘Kéknyelű’ clones B.1. and B.2. respectively, Base represents the base variety of ‘Kéknyelű’.

Table 5

Harvest results of the ‘Juhfark’ variety (Badacsony, 2011–2021).

Clone *	Year	Yield kg/m2	Sugar Content of the Juice °Bx	Titratable Acid Contant of the Juice g/L (in Tartaric Acid)	pH	Botrytis Infection %
B.1.	2011	0.28	20.80	7.47	3.13	50.00
B.2.	2011	0.64	20.20	6.86	2.96	35.00
Base	2011	0.87	20.70	8.55	2.96	30.00
B.1.	2012	1.38	17.40	8.31	3.59	0.00
B.2.	2012	1.34	17.40	7.21	3.42	0.00
Base	2012	1.06	20.10	7.16	3.55	0.00
B.1.	2013	1.35	20.70	12.07	3.37	3.00
B.2.	2013	1.56	19.90	10.82	3.33	2.00
Base	2013	1.08	19.80	11.50	3.21	5.00
B.1.	2014	0.73	17.40	19.84	3.28	60.00
B.2.	2014	0.21	16.50	17.52	3.18	80.00
Base	2014	0.21	15.10	16.41	3.20	85.00
B.1.	2015	1.01	19.40	8.62	3.52	3.00
B.2.	2015	1.21	18.20	8.04	3.47	5.00
Base	2015	1.46	17.80	9.91	3.50	10.00
B.1.	2017	1.31	17.70	7.71	3.36	5.00
B.2.	2017	1.28	18.40	7.45	3.33	5.00
Base	2017	1.57	17.50	9.44	3.28	5.00
B.1.	2018	2.26	18.00	7.29	3.46	7.00
B.2.	2018	1.99	17.40	8.48	3.47	5.00
Base	2018	2.02	17.90	9.94	3.35	10.00
B.1.	2019	nd.	nd.	nd.	nd.	nd.
B.2.	2019	nd.	nd.	nd.	nd.	nd.
Base	2019	1.43	20.80	12.55	3.42	40.00
B.1.	2020	1.17	20.40	11.88	3.41	20.00
B.2.	2020	1.06	19.90	11.08	3.34	25.00
Base	2020	1.55	17.20	12.40	3.29	20.00
B.1.	2021	1.12	19.60	14.20	3.14	20.00
B.2.	2021	1.09	19.00	12.40	3.18	20.00
Base	2021	1.48	15.10	18.80	3.01	20.00
B.1.	2022	0.96	17.70	7.47	3.37	10.00
B.2.	2022	nd.	nd.	nd.	nd.	nd.
Base	2022	2.25	17.10	10.54	3.14	10.00
B.1.	2011–2022	1.16 1	18.91 1	10.49 1	3.36 1	17.80 1	8.50 2
B.2.	2011–2022	1.15 1	18.54 1	9.98 1	3.30 1	19.67 1	5.00 2
Base	2011–2022	1.36 1	18.10 1	11.56 1	3.26 1	21.36 1	10.00 2
Mean	-	1.23 1	18.5	10.73	3.31	19.67

* B.1. and B.2. represent the ‘Juhfark’ clones B.1. and B.2. respectively, Base represents the base variety of ‘Juhfark. ¹ Arithmetic mean of the 2011–2022 years data; ² Median of the botrytis infection data; nd. = no data (Crop haven’t been harvested because of bird damage).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae9090994/s1, Supplementary Table S1: Detailed daily meteorological data.

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