Cultivar-Specific Expression of the Vintage Effect in Furmint Grapes from the Tokaj Wine Region Part I: Berry Growth, Sugar Accumulation and Dry Matter Formation

Abstract

Interannual variability in climatic conditions represents a major source of uncertainty in cool-climate viticulture, highlighting the need for cultivar-specific assessments of climate–quality relationships. A multi-year on-farm experiment with six monitoring sites has been conducted in vineyards representative of the Tokaj wine region to monitor and assess vintage effect. This study, as the first part of a broader research project evaluating must components, quantifies relationships between climatic indices and key yield- and sugar-related traits (berry weight, total soluble solids, and total dry extract) in Vitis vinifera L. cv. Furmint grown in the Tokaj wine region over three contrasting vintages. Thermal, radiative, and water-availability variables were calculated for discrete phenological phases and statistically analyzed to identify climatic predictors of berry growth and must composition. Berry weight exhibited pronounced vintage sensitivity, showing consistent associations with precipitation-related variables during early developmental stages. In contrast, total soluble solids and total dry extract displayed weaker and less consistent responses to interannual climatic variability. Several widely used heat-accumulation indices showed limited explanatory power, indicating a moderate climatic sensitivity of sugar-related traits in this cultivar. Overall, the results suggest that early-season climatic conditions exert a stronger influence on berry growth than late-season thermal extremes, while compositional parameters related to sugar accumulation remain comparatively stable. These findings highlight the need to incorporate cultivar-specific response functions into statistical models that assess projected climate-change effects on grape quality.

1. Introduction

1.1. The Cultivar—Furmint

1.1.1. Use, Significance and Synonyms

Furmint is a historically important white wine grape (Vitis vinifera L.) cultivar best known for its central role in Tokaji wine production in Hungary. Due to its susceptibility to noble rot (Botrytis cinerea), it is especially suited to the production of high-quality botrytized dessert wines [1]. In recent decades, Furmint has gained renewed interest as a dry-wine cultivar because of its high acidity, strong mineral expression, and significant aging potential [2].

The cultivar is known under several names across Central and Southeastern Europe. It retains its original name in Hungary, appears as Tokajská in Slovakia, Mosler in Austria, and Sipelj, Moslavac, or Šipon/Sipon in Slovenia, Croatia, and Serbia. In Romania, it may be labeled as Furmint de Tokaj. Historical synonyms include Zapfner and Posipel, while older wine literature lists Sauvignon Vert, Tokay (France), and Tokaisky (former USSR) as additional related names [3,4].

1.1.2. Origin (Ancestry) and Distribution

Furmint is believed to have originated in the Carpathian Basin and is genetically related to Gouais Blanc [5], placing it among ancient Central European cultivars [3]. It belongs to the Vitis vinifera species, classified under the Proles pontica group and the sub-proles Balkanica subgroup [6].

Hungary remains the primary production area (≈3800 ha, 2016), with 97% of its plantings concentrated in Tokaj. The cultivar is also present in Slovenia (~500 ha), Croatia (~300 ha), Slovakia (~350 ha), and smaller areas (less than 10 ha) of Austria and Serbia. Limited experimental plantings in South Africa (Swartland, Stellenbosch) and the United States (Sonoma Valley) reflect increasing international interest in its acidity retention and longevity [3,7,8].

1.1.3. Wine Sensory Characteristics

Furmint is characterized by naturally high acidity, making it suitable for sparkling wines and wines intended for long-term aging. Its high susceptibility to noble rot is essential for the production of Tokaji Aszú [9], while dry wines typically display citrus, green apple, and mineral-driven aromas that evolve into honeyed and nutty notes with age [10]. Owing to its strong terroir expressiveness, the cultivar exhibits marked sensory variation across regions. Climate change has intensified research interest in its heat resilience and ripening behavior [11].

1.1.4. Environmental Needs

Furmint is an early-budding, late-ripening cultivar adapted to temperate continental climates. Although precise heat-sum requirements are not firmly established, a long-term average of 1600–1800 growing degree days (GDD) in Tokaj appears optimal [12]. It performs well under moderate diurnal temperature variation, which supports balanced ripening and acidity retention.

Water availability strongly influences vine development: adequate early-season water is required, whereas moderate late-season water deficit may enhance flavor concentration through reduced yield and smaller berry size [13]. The cultivar forms loose clusters with medium-sized, thick-skinned berries, is prone to frost damage, moderately susceptible to mildews, and highly sensitive to gray mold in humid conditions [14], playing a crucial role in the production of noble rot wines like Tokaji Aszú. Projected temperature increases may narrow the optimal ripening window and affect acidity preservation [15].

1.2. The Terroir—Tokaj

The Tokaj wine region, listed as a UNESCO World Heritage site, is one of the world’s oldest wine-producing areas and covers approximately 5500 hectares [16]. The geomorphology is characterized by volcanic hills and valleys that create a highly diverse terrain developed on calc-alkaline andesite, dacite, and rhyolite bedrock. Soils consist of volcanic tuffs, loess, and clay, forming Andisoils, Alfisoils and Lithosols (USDA Soil Taxonomy), which contribute to the characteristic minerality of Tokaj wines [17]. Vineyards are located on steep slopes with varying aspects, predominantly facing southwest to southeast, which maximizes solar exposure [18]. Elevation ranges from 100 to 400 m a.s.l., with optimal vineyards located between 120 and 300 m a.s.l. [19,20]. The mesoclimate is temperate continental, characterized by warm summers and long, dry autumns that are crucial for grape ripening and noble rot development [21]. Annual precipitation ranges between 500 and 700 mm, accompanied by 2000–2100 sunshine hours (1991–2020; HungaroMet data [22]).

1.3. Physiological Overview of the Effects of Climate on Quantity and Quality

Annual variability in wine quality and yield, known as the vintage effect, is a common feature of wine production. Because climate is the primary environmental factor that varies among years, correlations can be expected between berry and must parameters and climatic conditions.

1.3.1. Berry Weight/Must Ratio

As a primary yield component, cluster weight is proportional to must yield per unit vineyard area; however, it is also influenced by viticultural practices, such as cluster thinning [23] and cluster selection. Cluster number and cluster weight are negatively correlated, whereas growing degree days (temperature) generally exert a negative effect, and water supply (irrigation and evapotranspiration balance) a limited positive effect on cluster and berry weight [24].

1.3.2. Total Soluble Solids (°Brix) and Dry Extract

These parameters directly or indirectly represent the total content of sugars in the grape juice. Sugar accumulates throughout the growing season as a product of photosynthesis, which is enhanced by temperature and solar radiation. These relationships are curvilinear, and sugar accumulation may plateau or decline beyond the optimum range [25,26]. Temperatures above the optimum accelerate cellular respiration and glycolysis, increasing losses of berry sugar concentration [27,28,29,30]. However, high temperature also leads to increased evapotranspiration rates, resulting in higher sugar concentration in the berry due to dehydration [31,32]. Under optimal water supply, sugar accumulation is not directly limited; however, increased berry growth may dilute sugar concentration, leading to lower measured sugar levels [33].

1.4. Main Goals

This study addresses a key gap in understanding the response of Furmint to climatic variability by analyzing the relationships among must quality parameters, berry weight, and climate conditions across three consecutive vintages in the cultivar’s primary growing region. The objectives were to quantify vintage effects by linking berry and must traits to thermal and precipitation patterns, and to identify robust climatic predictors from a broad set of temperature- and precipitation-based indices calculated across relevant temporal windows. The results provide insights for vineyard management under warming conditions by clarifying how specific climatic drivers influence the performance of this high-acidity cultivar. As part of a broader research program evaluating additional must components—including total acidity, pH, potassium, and tannin content—together with the interactions between topoclimate and soil elemental composition, this work contributes to an integrated understanding of the environmental controls on Furmint grape quality. Such knowledge is essential for sustaining wine quality and typicity under ongoing climate change.

2. Materials and Methods

2.1. Vineyard Characteristics

The multi-year on-farm trial was conducted in the Tokaj wine region (east of Mád), across four representative vineyards—Szent Tamás, Betsek, Kővágó, and Szilvás—covering the principal topoclimatic conditions of the area. The landscape forms a basin enclosed by slopes composed predominantly of rhyolite, zeolite, and their tuffs, overlain by mineral-rich, clayey soils [21]. Slope aspects vary among the vineyards: Szent Tamás and Kővágó face east–southeast, Betsek faces south, while Szilvás lies at the base of the depression and is nearly flat, with rows oriented north–south.

The predominant cultivars in the region are Furmint, Hárslevelű, and Yellow Muscat; however, only Furmint was included in this study. The vineyards are mainly trained to single cordon at 2.2–2.8 m row distance, with vines typically 20–30 years old, except in Kővágó, which was replanted in 2017.

2.2. Meteorological Data

The experimental design incorporates all major topoclimatic zones of the region. Six monitoring stations were installed across elevation gradients within the four vineyards. Betsek U, Kővágó, and Szent Tamás U represent elevated sites (216–300 m a.s.l.) on or near upper slopes, while Betsek-L, Szent Tamás L, and Szilvás are positioned on lower slopes or in the valley (150–186 m a.s.l.). At each station, climatic data and vine sap flow were monitored, and phenological stages were recorded during the 2022–2024 growing seasons (Figure 1).

Air temperature, solar irradiation, and precipitation were continuously measured using ClimaVue 50 multi-sensors connected to CR1000 data loggers (Campbell Scientific, Logan, UT, USA) at each site. Factory-calibrated sensors were installed above the vine cordons at the WMO standard height of 2 m. Measurements were made at 10 s frequency, logged at 10 min intervals, and aggregated to daily values for climatic index calculations. Data accuracy for the relevant parameters: ±0.6 °C (air temperature), ±5% (solar radiation), ±5% (precipitation); the rate of missing data did not exceed 1%.

Vine physiological activity was monitored using sap-flow sensors (EXO-Skin SGEX-25, Dynamax Inc., Houston, TX, USA) installed on three vines per station to determine phenological timing and activity periods. In the absence of continuous visual monitoring, the date of bud burst was obtained by an empirical algorithm based on the following conditions: growing degree days exceeded 40 °C AND at least two sap flow sensors returned minimum 2% of their multi-year summer maximum. Leaf fall was stated when readings from at least two sensors fell below 2% of their multi-year summer maximum.

2.3. Must Analysis

Timing of the sample collection was synchronized to the local producers’ practice instead of strict maturity criteria, for best possible conformity with the actual quality level of the vintage. At harvest, standardized berry samples (200–250 berries from 20 clusters of 5 vines within a 10 m radius of each monitoring site per year) were collected for determination of berry weight and must composition using laboratory analytical procedures. Average berry weight was measured using a TP2202 balance (VWR International, Lutterworth, UK). Berry juice was obtained by centrifugation at 4500 rpm for 10 min using a MegaStar 1.6R centrifuge (VWR International, Lutterworth, UK).

Total soluble solids and total dry extract were quantified using Fourier-transform infrared spectroscopy (WineScan, Foss Electric, Hillerød, Denmark), a validated method for rapid must composition analysis.

2.4. Data Processing and Statistical Analysis

Statistical analyses of berry, must, and climatic variables were performed using SPSS 28.0 (IBM Inc., Armonk, NY, USA) and climatic indices were computed in Excel^® for Microsoft 365 MSO (version 2502, build 16.0.18526.20168; Microsoft Corp., Redmond, WA, USA) prior to statistical evaluation.

2.5. Climatic Parameter Matrix and Selection

2.5.1. Generation of Climatic Parameters

To identify optimal predictors of vintage effects on berry weight and must composition, a wide range of climatic parameters was computed. In addition to standard viticultural indices and time-specific variables, numerous combinations of climatic factors and time intervals were generated (

Table A1. Matrix of the time-specific climatic parameters and indices used.

	Sap Flow Related		Phenology Related Periods			Calendar Months								Custom Periods for Specific Indices
Climatic Parameters/Indices	SapFlow Bud Burst—Berry Sampling	SapFlow Bud Burst—Leaf Fall	Blooming Period (15 May–15 June)	Ripening Period (1 June– 30 Sep)	Harvest Time (15 Aug–15 Oct)	April	May	June	July	August	September	October	November	March–May	April–September	April–October	June–September	June–August	September–November
Huglin’s Heliothermic Index
Winkler index
Cool Night Index
No. of Tropical Nights
No. of Summer Days
No. of Hot Days
No. of Extremely Hot Days
GS Average Temperature
GS Minimum Temperature
GS Maximum Temperature
GS Thermal Amplitude
Harvesttime Max. Temperature
Harvesttime Min. Temperature
Harvesttime Thermal Amplitude
Mean July monthly Avg. Temp.
July Diurnal Range
Ripening Avg Temperature
Number of Spring Frost Days
Sum of Spring Freezing Temps
Number of Fall Frost Days
Sum of Fall Freezing Temps
Growing Season Rainfall
Growing Season Rainy Days
Summer Rainfall
Blooming Period Rainfall
Ripening Period Rainfall
Ribéreau–Gayon–Peynaud Index
Dunkel’s Radiothermal Index

Relevant time windows are specified for each climatic parameter/index. Gray colored blocks mark the period X parameter combinations retained for analysis.

and

Table A2. Matrix of the not-time-specific climatic parameters used.

	Actual Phenological Stages									Sap Flow Related		Phenology Related Periods			Calendar Months
Climatic Parameters/Indices	Bud Burst—Flowering	Flowering—Berries Peas Size	Berries Pea-Size—Véraison	Véraison—Post Véraison	Post-Véraison—Berry Sampling	Berry Sampling—Leaf Fall	Bud Burst—Leaf Fall	Bud Burst—Berry Sampling	Bud Burst—End of Sap Flow	SapFlow Bud Burst—Berry Sampling	SapFlow Bud Burst—Leaf Fall	Blooming Period (15 May–15 June)	Ripening Period (1 June–30 Sep)	Harvest Time (15 Aug–15 Oct)	April	May	June	July	August	September	October	November
Growing Degree Days
Biologically Effective Degree Days
Minimum Temperature (Minimum)
Minimum Temperature (Average)
Minimum Temperature (Maximum)
Mean Temperature (Minimum)
Mean Temperature (Average)
Mean Temperature (Maximum)
Maximum Temperature (Minimum)
Maximum Temperature (Average)
Maximum Temperature (Maximum)
Minimum Temperature > 20 °C
Maximum Temperature ≥ 25 °C
Maximum Temperature ≥ 30 °C
Maximum Temperature ≥ 35 °C
Diurnal Range of Temperature
Minimum Temp. < 0 °C (Frost Days)
Freezing Degree Days
Heavy Rainfall Days (R ≥ 10 mm)
Extreme Rainfall Days (R ≥ 20 mm)
Sum of Global Irradiation
Average Daily Irradiation
High Global Irr. Days (SR > 2.5 kJ)
Highest Daily Irradiation

Relevant time windows are specified for each climatic parameter/index. Grey colored blocks mark the period X parameter combinations retained for analysis.

in Appendix A).

2.5.2. Elimination and Selection

Data distribution was assessed (Kolmogorov–Smirnov and Shapiro–Wilk tests) prior to correlation analysis (Pearson’s and Spearman’s correlation), and the strongest climate–trait relationships were selected based on statistical significance and correlation strength.

Vintage effects and parameter relevance were evaluated using appropriate parametric (one-way ANOVA) or non-parametric (Kruskal–Wallis) tests, followed by the reduction in climatic predictors to statistically meaningful variables.

Final predictor selection incorporated physiological relevance by prioritizing phenology-aligned climatic intervals over calendar-based periods.

2.5.3. Climatic Parameters

The calculation methods of the climatic and viticultural indices and parameters are listed in

Table 1. Formulas and sources of the indices and climatic parameters used.

Parameter	Abbr.	Formula	References
Growing Degree Days 1–11,18–20 [°C]	GDD	$\sum max\left[\left(T_{AVG}-10\right);0\right]$	[34]
Absolute Minimum Temp. 1–11 [°C]	MIN TMIN	$min\left(T_{MIN}\right)$	-
Average Minimum Temp. 1–11 [°C]	AVG TMIN	$avg\left(T_{MIN}\right)$	-
Maximal Minimum Temp. 1–11 [°C]	MAX TMIN	$max\left(T_{MIN}\right)$	-
Minimum of Mean Temp. 1–11 [°C]	MIN TAVG	$min\left(T_{AVG}\right)$	-
Average of Mean Temp. 1–11 [°C]	AVG TAVG	$avg\left(T_{AVG}\right)$	-
Maximum Mean Temp. 1–11 [°C]	MAX TAVG	$max\left(T_{AVG}\right)$	-
Minimal Maximum Temp. 1–11 [°C]	MIN TMAX	$min\left(T_{MAX}\right)$	-
Average Maximum Temp. 1–11 [°C]	AVG TMAX	$avg\left(T_{MAX}\right)$	-
Absolute Maximum Temp. 1–11 [°C]	MAX TMAX	$max\left(T_{MAX}\right)$	-
Number of Tropical Nights 1–11,14 [day]	NTN	$count\left(T_{MIN\left(\mathrm{day}\right)}\ge 20°\mathrm{C}\right)$	-
No. of Summer Days 1–11,14 [day]	NSD	$count\left(T_{MAX\left(\mathrm{day}\right)}\ge 25°\mathrm{C}\right)$	-
No. of Hot Days 1–11,14 [day]	NHD	$count\left(T_{MAX\left(\mathrm{day}\right)}\ge 30°\mathrm{C}\right)$	-
No. of Extremely Hot Days 1–11,14 [day]	NEHD	$count\left(T_{MAX\left(\mathrm{day}\right)}\ge 35°\mathrm{C}\right)$	-
Diurnal Range of Temp. 1–11 [°C]	DRT	$avg\left(T_{MAX\left(\mathrm{day}\right)}{-T}_{MIN\left(\mathrm{day}\right)}\right)$	-
Number of Frost Days 1–11 [day]	NFD	$count\left(T_{MIN\left(\mathrm{day}\right)}<0°\mathrm{C}\right)$	-
Freezing Degree Days 1–11 [°C]	FDD	$\sum min\left(T_{MIN};0\right)$	[35]
Rainfall Days 1–11 [day]	RD	$count\left({Rainfall}_{\left(\mathrm{day}\right)}>0\mathrm{mm}\right)$	-
Heavy Rainfall Days 1–11 [day]	HRD	$count\left({Rainfall}_{\left(\mathrm{day}\right)}\ge 10\mathrm{mm}\right)$	-
Extreme Rainfall Days 1–11 [day]	ERD	$count\left({Rainfall}_{\left(\mathrm{day}\right)}\ge 20\mathrm{mm}\right)$	-
Sum of Global Irradiation 1–11 [kJ m−2]	IR	$\sum GlobalIrradiation$	-
Average Daily Irradiation 1–11 [kJ m−2]	IRDAVG	$avg\left({GlobalIrradiation}_{\left(\mathrm{day}\right)}\right)$	-
High Global Irrad. Days 1–11 [day]	HIR	$count\left({GlobalIrradiation}_{\left(\mathrm{day}\right)}\ge 2.5\mathrm{kJ}·{\mathrm{m}}^{-2}\right)$	-
Max. Daily Irradiation 1–11 [kJ m−2]	IRMAX	$max\left({GlobalIrradiation}_{\left(period\right)}\right)$	-
Biologically Effective. Degree Days 1–11,14,18–20 [°C]	BEDD	${\displaystyle \sum _{Apr1}^{Oct31}}max\left[min\left(T_{AVG};19\right)-10;0\right]$	[36,37]
Huglin’s Heliothermic Index 13 [°C]	HI	${\displaystyle \sum _{Apr1}^{Sep30}}{\displaystyle \frac{\left[max\right(0;\left(T_{MAX}-10\right));0+max\left(0;\left(T_{AVG}-10\right)\right)]}{2}}·K$	[38]
Winkler Index 14 [°C]	WI	${\displaystyle \sum _{Apr1}^{Oct31}}\left[max\left(T_{AVG}-10\right)\right]$	[39,40]
Cool Night Index 9 [°C]	CNI	$\underset{\mathit{Sep}1–30}{\mathit{avg}}\left(T_{MIN}\right)$	[41]
Growing Season Avg. Temp. 2,3,14 [°C]	GSAT	$\underset{\mathit{Apr}1–\mathit{Oct}31}{\mathit{avg}}\left(T_{AVG}\right)$	[42]
G.S. Average Min. Temp. 2,3,14 [°C]	GSATN	$\underset{\mathit{Apr}1–\mathit{Oct}31}{\mathit{avg}}\left(T_{MIN}\right)$	[42]
G.S. Avg. Maximum Temp. 2,3,14 [°C]	GSATX	$\underset{\mathit{Apr}1–\mathit{Oct}31}{\mathit{avg}}\left(T_{MAX}\right)$	[42]
G.S. Diurnal Range 2,3,14 [°C]	GSDR	$\underset{\mathit{Apr}1–\mathit{Oct}31}{\mathit{avg}}\left(T_{MAX}-T_{MIN}\right)$	-
Ripening Period Max. Temp. 15,20 [°C]	RMX	$\underset{\mathit{Jun}1–\mathit{Sep}30}{\mathit{avg}}\left(T_{MAX}\right)$	[43]
Ripening P. Minimum Temp. 15,20 [°C]	RMN	$\underset{\mathit{Jun}1–\mathit{Sep}30}{\mathit{avg}}\left(T_{MIN}\right)$	-
Ripening P. Diurnal Range 15,20 [°C]	RDR	$\underset{\mathit{Jun}1–\mathit{Sep}30}{\mathit{avg}}\left(T_{MAX}-T_{MIN}\right)$	[44]
Mean July Temperature 7 [°C]	MJT	$\underset{\mathit{Jul}1–31}{\mathit{avg}}\left(T_{AVG}\right)$	[44]
July Diurnal Range 7 [°C]	JDR	$\underset{\mathit{Jul}1–31}{\mathit{avg}}\left(T_{MAX}-T_{MIN}\right)$	[45]
Ripening Average Temp. 20 [°C]	RAT	$\underset{\mathit{Aug}15–\mathit{Oct}15}{\mathit{avg}}\left(T_{AVG}\right)$	[42]
No. of Spring Frost Days 12 [day]	NSFD	$\underset{\mathit{Mar}1–\mathit{May}31}{\mathit{count}}\left(T_{MIN\left(\mathrm{day}\right)}<0°\mathrm{C}\right)$	-
Sum of Spring Freezing T. 12 [°C]	SSFT	${\displaystyle \sum _{Mar1–May31}}min\left(T_{MIN};0\right)$	-
Number of Fall Frost Days 17 [day]	NFFD	$\underset{\mathit{Sep}1–\mathit{Nov}30}{\mathit{count}}\left(T_{MIN\left(\mathrm{day}\right)}<0°\mathrm{C}\right)$	-
Sum of Fall Freezing Temps. 17 [°C]	SFFT	${\displaystyle \sum _{Sep1–Nov30}}min\left(T_{MIN};0\right)$	-
Growing Season Rainfall 2,3,14 [mm]	GSR	${\displaystyle \sum _{Apr1–Oct31}}Rainfall$	[46,47]
Growing Season Rainy Days 2,3,14 [day]	GSRD	$\underset{\mathit{Apr}1–\mathit{Oct}31}{\mathit{count}}\left({Rainfall}_{\left(\mathrm{day}\right)}>0\mathrm{mm}\right)$	-
Summer Rainfall 16 [mm]	SR	${\displaystyle \sum _{Jun1–Aug31}}Rainfall$	[48]
Bloom Period Rainfall 18 [mm]	BR	${\displaystyle \sum _{May15–Jun15}}Rainfall$	-
Ripening Period Rainfall 20 [mm]	RR	${\displaystyle \sum _{Aug15–Oct15}}Rainfall$	[49]
Ribéreau-Gayon-Peynaud Ind. 2,3,14 [–]	RGPI	${\displaystyle \sum _{Apr1–Oct31}}max\left[\left(T_{AVG\left(\mathrm{day}\right)}-10\right);0\right]-R{ainfall}_{\left(\mathrm{day}\right)}$	[50]
Dunkel’s Radiothermal Index 2,3,14 [–]	DRI	$\left({\displaystyle \frac{A·G}{n}}\right)·{10}^{-2}$	[51]

avg: average, min: minimum, max: maximum, K (HI): constant for Huglin Index (K = 1.05 for the latitude of Hungary), A (DRI): Growing Degree Days for growing season (°C), G (DRI): global irradiation during the growing season (Jcm⁻²), n (DRI): the length of the growing season (day). Units are given in square brackets for each climatic variables, all formulas correspond to daily aggregated data. Time windows used for the parameters/indices: ¹ actual phenological stages, ² sap-flow related growing season (bud burst to berry sampling) ³ sap-flow related growing season (bud burst to leaf fall), ⁴ April, ⁵ May, ⁶ June, ⁷ July, ⁸ August, ⁹ September, ¹⁰ October, ¹¹ November, ¹² March-May, ¹³ April-September, ¹⁴ April–October, ¹⁵ June–September, ¹⁶ June–August, ¹⁷ September–November, ¹⁸ blooming period (15 May–15 June), ¹⁹ ripening period (1 June–30 September), ²⁰ harvest time (15 August–15 October).

.

3. Results

3.1. Climatic Characteristics of the Experimental Years

The three vintages showed distinct thermal regimes, with 2022 characterized by the highest temperature extremes, while mean seasonal temperatures differed only slightly among years, especially prior to the pea-size (BPS) stage (Figure 2).

Heat accumulation indices indicated that 2024 was the warmest vintage, showing the highest GDD and a clear positive anomaly in the Winkler Index (1757 °C, 1767 °C, 1894 °C and 1650 °C, in 2022, 2023, 2024 and 2002–2024, respectively) and Huglin Index (2263 °C, 2197 °C, 2424 °C and 2200 °C) relative to both 2022–2023 and the long-term mean recorded at the Tarcal climate station (124 m a.s.l., Hungaromet). Elevated solar irradiation during the flowering to pea-size (FW–BPS) and post-véraison to berry sampling (PVR–BS) periods further supported the exceptional thermal load in 2024. Outside these intervals, the highest irradiation sums occurred in 2022.

The length of the growing season at the six monitoring stations significantly exceeded the 2002–2024 long-term mean of Tarcal (188 days), with observed ranges of 182–201 days (avg. 189) in 2022, 188–196 days (avg. 195) in 2023, and 211–225 days (avg. 220) in 2024. This prolongation is primarily driven by delayed leaf fall, which outpaces the observed shift in bud burst. Deviations from long-term averages may, however, reflect microclimatic variations between monitoring sites. Water availability differed strongly among vintages, with 2022 representing a dry year, 2023 an intermediate year, and 2024 a markedly wet year during pre-véraison development. From bud burst (BB) to berry sampling (BS), average precipitation across the six study sites was 95 mm in 2022, 209 mm in 2023, and 437 mm in 2024, compared with the 2002–2024 mean of 268 mm at Tarcal. The 2022 vintage was not only the driest but also the shortest, with the fewest rainfall days across most phenological stages. Seasonal totals were 28, 42, and 51 days in 2022, 2023, and 2024, respectively, only one of which exceeded the long-term average of 46 days (Figure 2).

These climatic contrasts set the basis for interpreting the berry/must responses described below.

3.2. Vintage Effect on the Berry and Must Parameters

The study years represented three distinct vintages in terms of thermal conditions and water supply. Their effects on berry and must characteristics were assessed using a Kruskal–Wallis H test (

Table 2. Kruskal–Wallis test for the vintage effect on berry/must parameters (2022–2024).

(Mean ± Std. Error)		2022	2023	2024
Berry weight (BW) **	g	1.90 ± 0.27 b	2.89 ± 0.30 a	2.94 ± 0.47 a
Total soluble solids (TSS)	°Bx	23.1 ± 2.2 a	20.1 ± 1.5 a	22.0 ± 1.6 a
Total dry extract (TDE)	g l−1	248 ± 26 a	218 ± 17 a	246 ± 19 a

Sample size: 200–250 berries from 5 different vines per vineyard from 6 vineyard monitoring sites per year (n = 30). Level of significance: α = 0.01 **. Different letters (a, b) represent a significant difference in a given parameter by vintage. (For graphical boxplot analyses, see Figure A1 in Appendix B).

).

Among the measured traits, only berry weight showed a significant vintage effect at the 0.01 level, whereas total soluble solids and total dry extract did not vary significantly across years (Table 2). The 3°Bx absolute range, however, represents a noticeable difference in terms of winemaking. The lowest sugar content and dry matter accumulation of the study period were recorded in 2023, whereas higher sugar yields were observed in both 2022 and 2024. The absence of an inverse relationship between berry weight and sugar-related traits in 2024 suggests that seasonal water dynamics modulated the typical dilution effect.

3.3. Climatic Predictors for Berry/Must Parameters

The pool of potential climatic predictors was refined based on the presence of a significant vintage effect (α = 0.05), their relevance, and their correlation with each berry/must parameter. Abbreviations of climatic variables are listed in Table 1 in Section 2, while the associated periods can be found in the notation (

Table 3. Statistical evaluation of key climatic predictors for berry weight (2022–2024).

Parameter	Period	ANOVA	Tests of Normality		Correlation Coefficients
		Kruskal–Wallis H Test	Kolmogorov–Smirnov Test	Shapiro–Wilk Test	Spearman	Pearson	R2
ERD	FW-BPS	0.0024	0.0161	0.0037	0.84		0.70
BEDD	FW-BPS	0.0017	0.0000	0.0000	0.85		0.72
SR	SUM	0.0033	0.0002	0.0003	0.82		0.68
NEHD	FW-BPS	0.0003	0.0000	0.0000	−0.81		0.66
MAX TMIN	FW-BPS	0.0029	0.2000	0.0951		−0.80	0.64
HIR	BB-BS	0.0008	0.0003	0.0011	−0.79		0.62
Rainfall	BLP	0.0005	0.0019	0.0018	0.78		0.61
HRD	MAY	0.0092	0.0014	0.0029	0.78		0.61
MAX TAVG	BB-BS	0.0009	0.1477	0.0805		−0.78	0.60
IR	JUN	0.0026	0.0000	0.0002	−0.77		0.59
AVG TMIN	FW-BPS	0.0033	0.2000	0.0928		−0.77	0.59
MIN TAVG	JUL	0.0005	0.2000	0.0510		0.76	0.57
IRMAX	JUN	0.0005	0.0087	0.0155	−0.75		0.57
BEDD	VRS-PVR	0.0004	0.0003	0.0004	0.75		0.56
GDD	PVR-BS	0.0005	0.0000	0.0000	0.75		0.56
BEDD	PVR-BS	0.0005	0.0000	0.0001	0.75		0.56
BEDD	BB-BS	0.0011	0.0001	0.0001	0.74		0.55
AVG TAVG	APR	0.0005	0.0000	0.0001	0.72		0.52
NEHD	BB-BS	0.0006	0.0126	0.0009	−0.71		0.50
NEHD	SFBB-BS	0.0006	0.0126	0.0009	−0.71		0.50

FW-BPS: flowering to berries pea-size; VRS-PVR: véraison to post-véraison; PVR-BS: post-véraison to berry sampling; BB-BS: bud burst to berry sampling; SFBB-BS: bud burst (based on sap flow activity) to berry sampling; BLP: blooming period (15 May–15 June); APR, MAY, JUN, JUL: calendar months; SUM: summer period (1 June–31 Aug.); Vintage effect was considered significant at α = 0.05. Number of observations used in correlations: n = 90. R² values were obtained from either Pearson’s (normal distribution) or Spearman’s (non-normal distribution) correlation coefficients.

,

Table 4. Statistical evaluation of key climatic predictors for total soluble solids (2022–2024).

Parameter	Period	ANOVA	Tests of Normality		Correlation Coefficients
		Kruskal–Wallis H Test	Kolmogorov–Smirnov Test	Shapiro–Wilk Test	Spearman	Pearson	R2
MIN TMIN	FW-BPS	0.0319	0.2000	0.9894		0.73	0.53
HRD	JUL	0.0082	0.0035	0.0026	0.68		0.46
IR	FW-BPS	0.0057	0.2000	0.3801		−0.60	0.36
MAX TMIN	MAY	0.0222	0.1651	0.0887		−0.56	0.31
MIN TMIN	MAY	0.0183	0.0412	0.0096	0.55		0.31
AVG TMIN	PVR-BS	0.0091	0.2000	0.5005		0.54	0.29
AVG TMIN	JUN	0.0028	0.1304	0.4178		0.53	0.28
MIN TMIN	VRS-PVR	0.0009	0.2000	0.6001		0.53	0.28
NTN	PVR-BS	0.0005	0.0005	0.0020	0.52		0.27
MIN TMAX	PVR-BS	0.0015	0.0228	0.0047	−0.52		0.27
MAX TMAX	VRS-PVR	0.0005	0.2000	0.2891		−0.49	0.24
MAX TAVG	VRS-PVR	0.0006	0.2000	0.3003		−0.46	0.21

FW-BPS: flowering to berries pea-size; VRS-PVR: véraison to post-véraison; PVR-BS: post-véraison to berry sampling; MAY, JUN, JUL: calendar months. Vintage effect was considered significant at α = 0.05. Number of observations used in correlations: n = 90. R² values were obtained from either Pearson’s (normal distribution) or Spearman’s (non-normal distribution) correlation coefficients.

and

Table 5. Statistical evaluation of key climatic predictors for total dry extract (2022–2024).

Parameter	Period	ANOVA	Tests of Normality		Correlation Coefficients
		Kruskal–Wallis H Test	Kolmogorov–Smirnov Test	Shapiro–Wilk Test	Spearman	Pearson	R2
MIN TMIN	FW-BPS	0.0319	0.2000	0.9894		0.76	0.57
HRD	JUL	0.0082	0.0035	0.0026	0.72		0.51
MIN TMIN	MAY	0.0183	0.0412	0.0096	0.61		0.38
MAX TAVG	AUG	0.0011	0.2000	0.4037		−0.61	0.37
AVG TMIN	JUN	0.0028	0.1304	0.4178		0.60	0.36
MINTMIN	VRS-PVR	0.0009	0.2000	0.6001		0.59	0.35
MAX TMIN	MAY	0.0222	0.1651	0.0887		−0.59	0.35
MIN TMAX	PVR-BS	0.0015	0.0228	0.0047	−0.57		0.33
NTN	PVR-BS	0.0005	0.0005	0.0020	0.57		0.33
NEHD	VRS-PVR	0.0017	0.0000	0.0000	−0.56		0.32
MAX TMAX	VRS-PVR	0.0005	0.2000	0.2891		−0.55	0.31

FW-BPS: flowering to berries pea-size; VRS-PVR: véraison to post-véraison; PVR-BS: post-véraison to berry sampling; MAY, JUN, JUL, AUG: calendar months. Vintage effect was considered significant at α = 0.05. Number of observations used in correlations: n = 90. R² values were obtained from either Pearson’s (normal distribution) or Spearman’s (non-normal distribution) correlation coefficients.

).

Berry weight was primarily controlled by early-season water availability and thermal conditions, confirming the dominant role of pre-véraison climate in yield formation (Table 3). With respect to irradiation, sensitivity extended into the BS stage. The direction of correlation was positive for all precipitation-related parameters, including heavy and extreme rainfall. Regarding thermal conditions, heat accumulation showed a positive correlation with Furmint berry growth, whereas high extremes in minimum and maximum temperature exhibited an inverse relationship. Solar radiation is not a limiting factor for berry development in the Tokaj region; consequently, higher irradiance values throughout the growing season correlate with decreased berry weight.

Sugar-related traits exhibited weak climate sensitivity, indicating relative stability of sugar accumulation across the examined climatic variability (Table 4). During the pre-véraison stages, total soluble solids correlated moderately positively with heavy rainfall days and negatively with global irradiation. Conversely, thermal parameters exhibited no consistent correlation direction and lacked explanatory power within the observed thermal range throughout the Furmint growing season.

Total dry extract showed limited and inconsistent climatic control, with only moderate associations to minimum temperature and rainfall-related variables (Table 5). Regarding thermal variables, minimum temperature extremes and extreme heat days correlated positively and negatively with TDE, respectively, consistent with physiological regularities. Heavy rainfall during peak water demand (July) showed a moderate positive relationship with dry extract accumulation, confirming precipitation as a limiting factor for Furmint.

Overall, climatic control was the strongest for berry weight and substantially weaker for sugar-related traits, highlighting Furmint’s trait-specific sensitivity to thermal and water-supply conditions, particularly during early phenological stages (FW–BPS), and to a lesser extent during mid-late to late stages (VRS–BS).

4. Discussion

4.1. Berry Weight

It is generally established that water availability is the principal driver of BW, aside from agrotechnical factors such as genotype (including rootstock-scion combination), canopy management, crop load regulation, and fertilization. Quantitative yield variations largely result from fluctuations in rainfall, which may be further influenced by micro- and mesoclimatic variables (temperature, humidity, solar radiation, wind) and viticultural practices that determine the actual water balance by modulating the difference between rainfall and evapotranspiration [52,53,54,55,56].

The results are consistent with previous research and confirm a strong vintage dependence of Furmint berry weight. Across three characteristic years, summer precipitation, the frequency of extreme rainfall events, and the biologically effective growing degree days during early phenological stages emerged as the strongest predictors. The positive correlation with extreme precipitation indicates that the exceptional water-retention capacity of the soil and zeolite bedrock of the experimental site compensates for high runoff, enhancing water use efficiency. The positive response to extreme precipitation suggests a potential for “luxury consumption” of water, where the plant maintains high turgor without proportional increases in vegetative vigor. This trait may be linked to Furmint’s ability to maintain metabolic activity under the fluctuating precipitation patterns typical of the Tokaj region. Literature indicates that the role of the zeolite bedrock extends beyond simple water retention; its cation-exchange capacity and thermal inertia are likely to stabilize the rhizosphere microclimate [57,58]. This pedological buffer may mitigate the impact of short-term drought stress, ensuring continuous nutrient transport and hydraulic conductance even during periods of high evaporative demand.

Relief energy (and the deriving topoclimatic effects), alongside unconsidered variables such as viticultural management, rootstock, and clone-terroir interactions, likely modulate actual water availability. During post-véraison stages, a dilution effect may, as well, weaken the influence of precipitation-related factors on berry weight.

4.2. Total Soluble Solids and Total Dry Extract

In viticultural science, it is generally understood that temperature influences sugar accumulation (TSS and TDE) provided that water or nutrient limitations do not constrain the process. The underlying physiological mechanisms are inherently non-linear, as interactions among respiration, glycolysis, evapotranspiration, and dilution impart complexity to sugar accumulation [30,32,53,59,60,61].

The absence of a clear vintage effect and the weak climate correlations indicate limited climatic control over sugar-related traits in Furmint, suggesting cultivar-specific buffering of temperature-driven respiration effects. Although minimum temperatures were the best thermal predictors for sugar concentration, their positive correlations contradict expectations based on the Arrhenius equation, which associates higher temperatures with accelerated respiration. This suggests that physiological responses may not be solely temperature-driven but influenced by cultivar-specific metabolic or phenological dynamics [62]. Inverse relationships with extreme heat suggest that excessive temperature may constrain dry matter accumulation (TDE), consistent with stress-related metabolic limitations and cultivar-specific thermal sensitivity [63]. Dry matter accumulation in Furmint exhibited the highest sensitivity to water supply during July. This observation aligns with findings that, under non-limiting thermal and radiation conditions, peak evaporative demand between flowering and véraison is a primary driver of sugar accumulation dynamics [64]. Beyond soil moisture, the vapor pressure deficit significantly modulates stomatal conductance and carbon assimilation in Furmint. High atmospheric demand, even with adequate soil water, may induce physiological stress, potentially explaining the observed non-linear responses of dry matter accumulation to thermal extremes.

The prevalence of inconsistent climatic effects underscores Furmint’s broad thermal tolerance. Within this tolerance range, the cultivar exhibits limited sensitivity; furthermore, the buffering effect of variables such as soil heterogeneity and viticultural practices may further attenuate the direct climatic influences in field-scale settings.

5. Conclusions

This study quantified interannual variability in Furmint grape berry weight and must composition and evaluated their dependence on climatic drivers underlying the vintage effect in the Tokaj wine region. Berry weight showed clear vintage sensitivity, whereas sugar-related traits exhibited limited and inconsistent climatic responses, and several identified predictors differed from previously reported relationships, particularly for dry matter–related parameters. These discrepancies highlight the need for extended investigations, both in terms of study duration and the range of parameters evaluated.

Future analyses should incorporate atmospheric humidity and evapotranspiration to improve characterization of vineyard water balance and plant physiological stress responses to atmospheric drought.

Ongoing research integrates acidity, pH, potassium concentration, ammonia and tannin content, and analysis of soil properties and mineral composition, with particular attention to potassium. These integrated datasets are expected to clarify interactions among dry matter accumulation, acidity, potassium dynamics, and climatic variability, and how Furmint winemaking is affected by the dynamic climatic environment. Ultimately, establishing long-term, high-resolution monitoring is essential to decouple the complex interactions between topoclimate, management practices, and cultivar-specific physiological responses. Such integrated approaches will provide the necessary precision for climate change adaptation strategies, ensuring the stability of must composition and the preservation of the unique Tokaj terroir characteristics under shifting environmental conditions.