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OENO One | By the International Viticulture and Enology Society 2024 | volume 58–3 | 1
*correspondence:
michele.faralli@unitn.it
Associate editor:
Daniel Molitor
Received:
11 April 2024
Accepted:
6 August 2024
Published:
16 September 2024
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Four decades in the vineyard:
the impact of climate change on
grapevine phenology and wine
quality in northern Italy
Michele Faralli1,2*, Stefano Mallucci1, Alessandro Bignardi1, Mauro Varner3 and
Massimo Bertamini1,2
1 Center Agriculture Food Environment (C3A), University of Trento, Via Mach 1, San Michele
all’Adige, 38010 Trento, Italy
2 Research and Innovation Centre, Fondazione Edmund Mach, Via Mach 1, San Michele all’Adige,
38010 Trento, Italy
3 Mezzacorona s.c.a., via del Teroldego 1, 38016 Mezzocorona, TN, Italy
ABSTRACT
The wine sector, among the most protable agricultural segments, has been markedly affected
by the ongoing climate change impacts, such as warmer climate conditions with higher
frequency of extreme temperatures and a trend of decreasing precipitation. All this results in
higher evaporative demand and therefore higher occurrence of water stress events leading
to advancement of temperature-sensitive phenological stages (e.g., budburst and ripening).
Such negative effects eventually affect berry development and quality, especially in historically
valuable viticultural areas, forcing winegrowers to work within a compressed harvest period
to maintain wine typicity. In this work we examined the relationship between environmental
variables (air and soil temperature, relative humidity, precipitation, and solar radiation),
phenology, berry, and wine quality for the two varieties (Chardonnay and Teroldego) in Trentino
Alto-Adige/South Tyrol (Italy) over 36 years. Huglin Index (a bioclimatic heat index), growing
degree days (measure of heat accumulation), and overall mean temperature showed linear
increase (p < 0.001) in the last years, while no variations were recorded for precipitations.
Despite no major effects being observed for phenological interval lengths, the onset of most
of the phenological stages for both varieties had signicantly (p < 0.001) advanced. However,
i) early budburst pushed the budburst-owering interphase by -1.2 days every two years toward
putative colder periods with increased late frost probability and potential slower phenological
progression towards owering, and ii) early veraison shifted the veraison-ripening interphase by
0.25 day per year into warmer periods that oppositely impose faster phenological advancement.
Hence, a substantial equilibrium in the seasonal growing length over years was maintained.
Potential carry-over effects from the previous season were observed, particularly associated
with heat requirements to unlock early phenological events, raising additional concerns on
the additive effects of climate change to viticulture. Generally, white wine quality increased
(p < 0.05) over the years, while red and sparkling wines remained unaffected. This was
putatively related to accurate harvest date decision-making dictated by berry quality parameters:
sugar-to-acidity ratio for Chardonnay and bunch sanitary status for Teroldego. Overall, this
work provides evidence of the dynamics involved in climate change, and, to our knowledge,
its overlooked effects on viticulture, thus providing new insights that can contribute to further
developing adaptive strategies.
KEYWORDS: Climate change, grapevine, phenology, viticulture, growing degree days, late frost,
wine quality
ORIGINAL RESEARCH ARTICLE
OPEN ACCESS
DOI: https://doi.org/10.20870/oeno-one.2024.58.3.8083
OENO One | By the International Viticulture and Enology Society2 | volume 57–3 | 2024
INTRODUCTION
Historically, viticulture has thrived in agricultural contexts
in which wine typicity is dened by the interplay of
the cultivar with the environment, pedological features
and rootstock (Reynolds, 2021; Stefanis et al., 2023;
van Leeuwen and Seguin, 2006). In most viticultural areas,
this contact point has been the basis for the establishment
of a prosperous and economically important industry
(Alston and Sambucci, 2019; Meloni and Swinnen,
2018; OIV, 2022). As with any farming system, this
virtuous loop has always been subjected to unpredictable
meteorological dynamics (Bucur and Babes, 2016), which
can diminish or increase berry quality and productivity
(Baciocco et al., 2014; Salinger et al., 2015) via a series
of erratic environmental conditions, such as low or
high temperature (Buttrose, 1974; Downey et al., 2006;
Eltom et al., 2017; Hendrickson et al., 2004; Keller, 2010;
Kliewer, 1977; Mori et al., 2007; Petrie and Clingeleffer, 2005;
Sweetman et al., 2014), hailstorm (Fernández-Mena et al.,
2023; Petoumenou et al., 2019; Rana et al., 2022), and low
or high rainfall (Gambetta et al., 2020; Grimes and Williams,
1990; Keller et al., 2008; Keller et al., 2016;
Mirás-Avalos and Intrigliolo, 2017; Williams et al., 2010).
All these can act either directly (e.g., bunch and/or canopy
damages) (Gambetta et al., 2021; Petoumenou et al., 2019)
or indirectly (e.g., higher incidence of pathogens infection)
(Bois et al., 2017; Reineke and Thiéry, 2016; Seem et al.,
2000) on the vineyard, making their prediction complex and
sometimes spurious (Beauchet et al., 2020; Fraga et al., 2016;
González-Fernández et al., 2020; Molitor et al., 2020). In recent
years, specic and long-term environmental trends have been
observed in many viticultural areas, with a general increase
in thermal accumulation (Droulia and Charalampopoulos,
2022; Duchêne and Schneider, 2005; IPCC, 2022;
Schultze et al., 2016b; Venios et al., 2020), evaporative
demand (Duchêne and Schneider, 2005; van Leeuwen et al.,
2019) and phenological advancement (Alikadic et al.,
2019; Bock et al., 2011; Cameron et al., 2021, 2022;
De Cortázar-Atauri et al., 2017; Dinu et al., 2021;
Tomasi et al., 2011; Xyras et al., 2022), potentially
enhancing the risk of water limitation (Santos et al., 2020;
van Leeuwen et al., 2019) multifactorial stress occurrence
(Santos et al., 2020; van Leeuwen et al., 2019) and a general
advancement in berry ripening that occurs over periods
during which higher temperatures and lower precipitation are
expected (Cameron et al., 2020; De Cortázar-Atauri et al.,
2017; Kurtural and Gambetta, 2021).
Phenological onset in grapevine xes the occurrence of
certain physiological processes associated with productivity
and quality. Advancing or delaying any phenological onset
pushes the subsequent phenological stage towards periods
with a higher probability of warm and cold conditions
respectively, thus imposing possible additive effects
(Cameron et al., 2022; Keller, 2023; Lorenz et al., 1995;
Mosedale et al., 2016). This trend has been shown to
be curvilinear when large thermal variation is included,
suggesting that the rate of decrease in specic interval
length will slow until a plateau is reached, potentially due
to trade-offs with temperature conditions (Cameron et al.,
2022). However, to date, a large amount of information
corroborates a linear progression in grapevine phenological
onset, and hence harvest date, in a series of viticultural areas
in several countries (e.g., Australia, California, Greece,
France, Luxembourg, and Italy), indicating that generally
changing climate still has a direct effect on phenological
dynamics (Cameron et al., 2020, 2021; Cuccia et al., 2014;
De Cortázar-Atauri et al., 2017; Jarvis et al., 2019; Koufos et al.,
2018; Koufos et al., 2020; Koufos et al., 2022; Molitor et al.,
2020;Molitor and Junk, 2019; Morales-Castilla et al., 2020;
Tomasi et al., 2011; Xyras et al., 2022).
It is known that wine grape varieties display a plethora of
optimum temperature ranges within which they can produce
high-quality wines, hence implying the possibility of using
different varieties for future climatic contexts (Duchêne et al.,
2012; Fortes and Gallusci, 2017; Keller, 2023; van Leeuwen
et al., 2019). However, switching variety is often complex
for the wine industry because of the losses in varietal
connotation, wine typicity, and oenological knowledge
associated with such a choice. Therefore alternative paths
are adopted by viticulturists to avoid the abovementioned
negative effects of global warming, such as elevational shifts
of new vineyards and/or expensive agronomic practices
with the aim of synchronise ripening dynamics and/or
protecting bunches from excessive radiation and temperature
(Alikadic et al., 2019; Arias et al., 2022; Bertamini and Faralli,
2023; Faralli et al., 2022; Centinari et al., 2018;
Gambetta et al., 2021; Poni et al., 2022).
Most studies published to date agree on the major
role of temperature in vine physiology and phenology
(Cameron et al., 2021; Parker et al., 2020; Schultze et al.,
2016a; Venios et al., 2020), focusing mainly on the timing
of different phenological stages and on the direct effect of
temperature on single variables. However, there is a lack of
area-specic studies which enable detailed analysis of the
mutual relationships among a wide range of environmental
and phenological data.
This work aims at unravelling the effects of climate change
on the grapevine growth cycle and wine quality by analysing
the time series of environmental and phenological data
collected in the period 1986-2022 in two vineyards located in
northern Italy (Trentino Alto-Adige). Indeed, understanding
the historical trend in terms of functional processes (i.e.,
phenology and ripening) alongside specic meteorological
events may be a milestone in further dening novel adaptation
strategies via genetic improvement or vineyard management.
MATERIALS AND METHODS
1. Study sites and vineyards
This study is based on a multi-decennial data collection
(from 1986 to 2022) from two vineyards located in Piana
Rotaliana in the Autonomous Province of Trento, Trentino
Alto-Adige/South Tyrol region, Italy (see Figure 1).
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 3
The two vineyards were planted with Chardonnay (SMA130
clone) and Teroldego (SMA138 clone) grape varieties in
1980. The Chardonnay vineyard was located in Carost (or
Chiarost), between Mezzocorona and Roverè della Luna.
Scions were grafted onto SO4 rootstock and trained as
“pergola doppia” with a plant density of 5.5 x 0.625 m.
The Teroldego vineyard was located in Novai, between
Mezzocorona and Mezzolombardo. Scions were grafted onto
101/14 rootstock and trained as “pergola doppia” with a
plant density of 6 x 0.5 m. Both vineyards have recently been
replanted (Chardonnay in 2015 and Teroldego in 2018) and
the monitoring site has been consequently replaced respecting
the same area, training system and pedoclimatic conditions
(i.e., the adjacent vineyards from 2016 in Chardonnay and
2018 in Teroldego).
2. Meteorological data and viticultural
agroclimatic indices
The available dataset consists of a long time-series, from 1986
to 2022, of meteorological and phenological data, collected
in the two vineyards described above. The environmental
historical series, recorded by meteorological stations located
one kilometre from the vineyards (Figure 1) and available on
a daily and/or hourly scale, includes: daily mean, maximum
and minimum air temperature ( °C); mean soil temperature
( °C); mean relative humidity ( %); precipitation (mm); solar
radiation (MJ/m2); and reference evapotranspiration (ET0).
The meteorological data were validated after a comparison
with those recorded by three nearby public meteorological
stations (Figure 1): namely Mezzolombardo Convento
recording from 1921 to 2006; Mezzolombardo Maso delle
Part recording from 2012; and San Michele all’Adige
recording from 1926 to 2005. This same check was also
carried out with the data recorded by the Mezzocorona
Novali meteorological station active since mid-1999 and
much closer to the survey site (Figure 1). The comparison
showed a strong similarity in the overlapping periods, and
therefore the Mezzocorona Novali data were used to ll
small gaps in the original series, allowing long time series of
continuous and homogeneous data to be obtained. Gap lling
was not performed by interpolations to avoid introducing
inhomogeneities and errors in the historical series.
3. Calculation of agroclimatic indices
Growing degree days (GDD) were calculated annually
(between 1 January and 31 December of each given year)
using a base temperature (Tbase) of 10 °C, 7.2 °C and 6 °C,
as follows:
where Tmax is the daily maximum temperature and Tmin
is the daily minimum temperature (Jones et al., 2010;
McMaster and Wilhelm, 1997). For all the base temperatures,
we assigned a daily value of 0 when the mean temperature
was below Tbase. GDD are extremely helpful for predicting
phenological onsets (Camargo-Alvarez et al., 2020;
Piña-rey et al., 2021; Zapata et al., 2015) and they offer a hint
of the potential ripening of varieties and of the wine styles
that can be produced when classied according to the Winkler
region (Anderson et al., 2012; Charalampopoulos et al., 2024).
FIGURE1. Insert: the location of the study area in the Trentino-Alto Adige/South Tyrol region in Italy. Main image:
Map of the Piana Rotaliana winegrowing area (Val d’Adige). Pushpins indicate the locations of Chardonnay and
Teroldego vineyards, paddles indicate the locations of the weather stations. Names, coordinates and altitudes of the
locations are reported in the legend.
OENO One | By the International Viticulture and Enology Society4 | volume 57–3 | 2024
However, different Tbase were needed to better evaluate
the potential effect of Tbase on estimating BBCH07 (green
tip), especially due to cultivar-specicity of this parameter
(de Cortázar-Atauri et al., 2009; Laurent et al., 2020,
Faralli et al., 2024).
The De Martonne index (DM), an aridity parameter used
worldwide to identify dry/humid climate conditions
(García-Martín et al., 2022; Szűgyi-Reiczigel et al., 2022),
is calculated as follow:
where DM varies with values less than 10 for arid conditions
to values above 55 for extremely wet conditions.
The Huglin Heliothermic Index (HI) [ °C], a bioclimatic heat
index calculated as the temperature sum over a temperature
threshold of 10 °C, summed for all days from beginning of
April to end of September, is calculated as follows:
where Tmean is daily mean temperature, Tmax is daily
maximum temperature, and k (the daylength coefcient)
is equal to 1.05 according to the latitude of the vineyards
(Jones et al., 2010). A daily value of 0 was assigned when the
temperatures (Tmean and/or Tmax) were below 10 °C.
In addition, the number of days exceeding the temperature
thresholds of 30 °C and 35 °C were calculated for each
year, while seasonal and annual variables were assessed
by aggregating the original variables by means (e.g., for
temperature and humidity) or by sums (e.g., for precipitation,
solar radiation, evapotranspiration, GDD and HI). In the
years with data gaps temporal aggregations were avoided.
For the scope of this work, seasons were conventionally
divided as follows: winter from January to March, spring
from April to June, summer from July to September, and
autumn from October to December. Temporal aggregations
were also carried out for the period 1 April to 30 September,
considered representative of the grapevine developmental
cycle, and for the period 15 May to 15 June, critical for the
productivity of the vineyard in the following year due to bud
differentiation (Petrie and Clingeleffer, 2005).
4. Phenological data, berry quality and wine
rating
Phenological data comprise a historical series of day of the
year (DOY) corresponding to the onset of key phenological
phases in Chardonnay and Teroldego (e.g., BBCH07
“Beginning of bud burst: green shoot tips just visible”;
BBCH15 “Five leaves unfolded”; BBCH18 “Eight leaves
unfolded”; BBCH61 “Beginning of owering: 10 % of
ower- hoods fallen”; BBCH65 “Full owering: 50 % of
owerhoods fallen”; BBCH75 “Berries pea-sized, bunches
hang”; BBCH81 “Beginning of ripening: berries begin to
brighten in colour”; BBCH85 “Softening of berries”), which
were assessed by the same operator via visual observation
of both vineyards over the 1986-2022 period and following
the Lorenz et al., (1994) scale. Harvest date, a technical
parameter depending on the oenological aim rather than a
proper phenological phase, was included. Wine quality data
were available as well, covering white and sparkling wine
quality for Chardonnay and red wine quality for Teroldego.
The evaluation was carried out by the wine producer,
Mezzacorona Sca, with an index ranging from 1 to 5 for
increasing quality, as a routine practice. Other evaluations
were not available. Sugar concentration measurements of
Babo grade (°Ba) and total acidity (g/L) of Chardonnay and
Teroldego berries were recorded. Sampling took place on
different dates each year, in the weeks preceding harvest.
5. Data analysis
The analyses were performed using the software Excel
(Microsoft 2022) and R (R Core Team 2022). Correlation
analysis (Pearson correlation) was performed to assess the
relationships between changes in phenological variables and
possible concurrent drivers. Additionally, trend analysis was
applied to the couples of variables that showed the strongest
statistically signicant (p < 0.05) relationships. The data
set was rst displayed graphically (i.e., via scatterplots and
boxplots), and the main statistics of all the available variables
(i.e., averages, maximums, minimums and quantiles) were
calculated to unravel the potential presence of outliers and
to check data quality. Hence, the data were validated after
the removal of outliers veried on a case-by-case basis
(in total, three outliers were detected overall on environmental
data). Trends were then calculated by applying the
Mann-Kendall test with a signicance level of alpha = 0.05
(Supplementary Table 2), and linear models were tted by
least-squares through R statistical software package “stats”
and the function “lm” for tting linear models and computing
the p-value. Only statistically signicant trends are reported
in the results of this study (see gures). Pearson’s correlation
was calculated to estimate the level of correlation between
all the couples of variables. The statistical signicance of
the correlations was evaluated with a signicance level of
alpha = 0.05. In addition, the available data included several
measurements of berry quality parameters sampled on
different days during the ripening period (every year since
1986 to 2022). With the aim of investigating the effect of
climate change on harvest decision, the DOY of the measures
was transformed into days to harvest (see the abscissa of
Figure 11F, given the days of harvest of Chardonnay and
Teroldego for each year). In addition, we linearly interpolated
the intra-annual measures when the time elapsed between
two consecutive samples was less than two weeks, assuming
the error made with this approximation could be neglected
compared to the uncertainty of sampling. Such an approach
allowed us to have continuous Babo grade and acidity data
every year, from the rst sampling day (i.e., about 40 days
before harvest) to the day of harvest itself every year. Finally,
xing the day to harvest for each year, we calculated the
annual trends of Babo grade and acidity (with signicance
level alpha = 0.05). The correlation analysis (Pearson) was
performed by means of the R statistical software package
“stats”. The correlations between all the couples of variables
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 5
were illustrated by means of a correlation matrix, in which
the colour of each cell of the matrix represents the value
of the correlation coefcient between the variables of the
corresponding row and column. The R package used for this
plotting method is “corrplot”, which also allowed the non-
signicant correlations to be hidden.
RESULTS
1. Environmental and agroclimatic indices
trends over years
From 1986 to 2022 and for the area considered (i.e., Piana
Rotaliana in Trentino-Alto Adige/South Tyrol), a signicant
(p < 0.001, R2 = 0.43) increase in annual mean temperature was
FIGURE2. Trends between 1986 and 2022. A) Annual mean air temperature; B) annual mean soil temperature; C)
cumulative annual precipitations; D) cumulative annual radiation; E), F), G) and H) average winter, spring, summer,
and autumn air temperature respectively; I), J), K) and L) average winter, spring, summer, and autumn soil temperature
respectively; M), N), O) and P) cumulative winter, spring, summer, and autumn precipitations respectively. Only
signicant relationships (p<0.05) are shown in the graphs. Linear model equations referring to relationships between
the studied parameters, along with the corresponding R2 are shown in the graphs for signicant models only.
OENO One | By the International Viticulture and Enology Society6 | volume 57–3 | 2024
observed (Figure 2A). Between the coldest year of the dataset
(1989) and the hottest (2022), the mean temperature delta
was 2.51 °C, with an increase of 0.108 °C every three years
(i.e., 0.036 °C/year), estimated via linear regression.
By contrast, there were no signicant trends (p > 0.05) for
annual mean soil temperature and cumulative precipitation
(Figure 2B and C). When radiation was plotted as yearly
cumulative, a signicant (p < 0.001, R2 = 0.65) and linear
trend was observed, with a general increase of 28 MJ/ m2
per year and a delta of 978 MJ/m2 between 1989 and 2022
(Figure 2D). When the annual values were divided according
to seasons, signicant increases in temperature values
(p < 0.001) were only observed for spring (0.13 °C every
three years), winter (0.12 °C every three years) and autumn
(0.11 °C every three years) (Figure 2E-H). However, the
largest differences between 1989 and 2022 were detected
for autumn mean temperature, in which a differential of
3.35 °C was observed (1.39 and 2.85 °C for winter and
spring respectively). Winter and autumn seasonal mean soil
temperatures showed signicant trends (p < 0.001) between
years as well (Figure 2I-L). No signicant differences
(p > 0.05) were observed for seasonal dynamics of
precipitations (Figure 2M-P).
The environmental data associated with the growing season
(i.e., from March to August) of grapevine conrmed the
annual/seasonal trends (Figure 3). Overall, a signicant
(p < 0.001) increase in average and minimum air temperature
of up to 0.093 °C was observed from 1986 to 2022 every
three years. Similarly, a signicant (p < 0.001) and linear
increase in reference evapotranspiration was noted, with a
FIGURE 3. Environmental and agroclimatic indices trends between 1986 and 2022.
A) Mean air temperature for the period April to September; B) maximum air temperature for the period April to
September; C) minimum air temperature for the period April to September; D) evapotranspiration; E), F), G) and H)
Huglin index, GDD10, GDD7.2, and GDD6 respectively; I) Number of days (NOD) with temperature above 30°C; J)
Number of days (NOD) with temperature above 35°C; K) and L) Number of days (NOD) with precipitation above
30 and 50mm respectively. Only signicant relationships (p<0.05) are shown in the graphs. Linear model equations
referring to relationships between studied parameters, along with corresponding R2 are shown in the graphs for
signicant models only.
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 7
difference in evaporative demand of 230mm between the
periods 1986-1990 and 2018-2022. No signicant trend
was detected for maximum air temperature. All these
trends were conrmed by a series of agroclimatic indices,
with a signicant (p < 0.001) increase in Huglin Index and
GDD10, GDD7.2 and GDD6. Notably, GDD10 increased from
an average of ~1600 between 1986-1990 up to ~1850 on
average for 1998-2022. Temperature and rainfall anomalies
were also calculated, revealing minimal and non-signicant
trends over the years. There was a minimal (R2 = 0.07) trend
for yearly number of days above 30 °C, which, however, was
not signicant (p = 0.12).
FIGURE4. A) Number of days with temperatures below 0°C over the years; B) day of the year in which the last
frost event occurred, and C) the day of the year in which budburst occurs for Chardonnay subtracted from the day in
which the last frost event occurs. In C) negative values (purple circles) represent years in which frost events occurred
after budburst (i.e., BBCH07). Data were analysed via liner regression, and only signicant relationships (p<0.05)
are shown in the graphs. The linear model equations referring to the relationships between the studied parameters,
along with the corresponding R2 are shown in the graphs and for signicant models only.
FIGURE5. Major phenological events onset between 1986 and 2022 for Chardonnay and Teroldego as occurrence
on day of the year (DOY). BBCH07 corresponds to budburst (A); BBCH61 corresponds to 10 % owering (B);
BBCH81 corresponds to 10 % veraison (C); and (D) represents harvest date. Other BBCH scale points are not
pictured since priority was given to pivotal phenological stages. Yellow squares represent cv. Chardonnay while
purple circles represent Teroldego. Data were collected by the same operator and on the same vineyards throughout
the years. Data were analysed via linear regression and only signicant relationships (p<0.05) are shown in the
graphs. Linear model equations referring to relationships between studied parameters, along with corresponding R2
are shown in the graphs and for signicant models only.
OENO One | By the International Viticulture and Enology Society8 | volume 57–3 | 2024
When frost events were included as days with temperatures
below 0 °C, a signicant and linear reduction (p < 0.001,
R2 = 0.27) was observed (0.8 day/year; Figure 4A).
However, while the last frost event DOY shows signicant
interannual variability, it is not signicant over the years
(Figure 4B). When Chardonnay budburst DOY was
subtracted from the last frost event DOY, no trends were
observed. However, some historical late frost events were
conrmed (i.e., negative values for, for example, 1997, 2016
and 2017), and a general tendency for near-zero to negative
values can be observed (Figure 4C).
2. Phenological dynamics associated with
climate change
Even though Chardonnay and Teroldego displayed similar
budburst dates, they differed for veraison and harvest dates,
with a generally earlier onset for Chardonnay (Figure 5).
When the occurrence of specic phenological phases
(dened as day of the year (DOY) on which they occured) are
FIGURE6. A) Length of phenological interphases (in days) calculated from phenological data and plotted against
year of occurrence. BBCH07 represents budburst, BBCH 61 represents 10% owering, and BBCH81 represents
veraison. Data were analysed via liner regression and only signicant relationships (p<0.05) are shown in the
graphs. The linear model equations referring to the relationships between the studied parameters, along with the
corresponding R2 are shown in the graphs and for signicant models only. B) Length of phenological interphases (in
days) calculated from phenological data and plotted against the occurrence of specic phenological stages. Data
were analysed via liner regression, and only signicant relationships (p<0.05) are shown in the graphs. The linear
model equations referring to the relationships between the studied parameters, along with the corresponding R2 are
shown in the graphs and for signicant models only.
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 9
FIGURE7. Growing degree days on base 10°C to unlock specic phenological stages from 1986 to 2022. In A)
GDD10 to unlock early budburst (BBCH07) in Chardonnay and Teroldego; in B) GDD10 to unlock early (10%) owering
(BBCH61) in Chardonnay and Teroldego; in C) GDD10 to unlock early (10%) veraison (BBCH81) in Chardonnay
and Teroldego; in D) GDD10 to reach harvest in Chardonnay and Teroldego. Data were analysed via liner regression,
and only signicant relationships (p< 0.05) are shown in the graphs. The linear model equations referring to the
relationships between the studied parameters, along with the corresponding R2 are shown in the graphs and for
signicant models only.
plotted against years (see Figure 5), signicant (p < 0.001)
negative linear trends can be observed. In general, budburst
occurred around mid-late April for both varieties between
1986 and 1990. However, a signicant (p < 0.001) earlier
occurrence of budburst between mid-March and early April
was discernible in the most recent years of the dataset (i.e.,
from 2000 onwards). Similar advancements in owering and
veraison were observed with a general signicant (p < 0.001)
tendency for early phenological onset. Harvest date has also
been affected over the last forty years: in the mid-1980s,
Chardonnay was harvested around mid-September, while in
2022 it was harvested on 20 August. Similarly, Teroldego’s
harvest took place on average in early October between 1986
and 1900, while in 2022 it started on 8 September. Overall,
all the phenological occurrences showed an advancement
over time of between 0.3 and 0.5 day/year, resulting in an
overall advancement of between 20 and 30 days from 1986
to 2022.
Phenological intervals (i.e., the number of days required to
unlock a subsequent phenological event) were calculated
from the initial phenological data (Figure 6A). Overall,
two major interphases showed signicant compression
(p < 0.001) in the last 36 years: those of veraison to harvest
date and owering to harvest date. The interval length
between veraison and harvest date showed a signicant
reduction for Chardonnay only (p < 0.001, R2 = 0.15), while
for Teroldego the trend was not statistically signicant.
Conversely, the interval length between owering and harvest
showed a signicant reduction (p < 0.001) in number of days
for both varieties, decreasing from an average duration of
115 and 105 days (Teroldego and Chardonnay respectively)
in 1986-1990 to an average of 110 and 95 days (Teroldego
and Chardonnay respectively) in 2018-2022. No signicant
reductions in length were observed for other phenological
interphases. Plotting the average phenological events
occurrence in DOY (Figure 6B) for the two varieties to dene
phenological intervals highlighted two specic trends: late
budburst, generally resulting in lower and near-proportional
(1.2 days every two years) budburst-to-owering interval
length (p < 0.001, R2 = 0.36); and delayed veraison, yielding
a signicant increase (0.25 day/year) in the veraison-to-
harvest interval length (p < 0.001, R2 = 0.20). No signicant
trends were observed for the other associations (Figure 6B).
3. New insights into environment-phenology-
quality interactions
Signicant negative relationships were observed between
the growing degree days necessary to unlock specic
phenological stages and the years. A signicant reduction
(p < 0.05) in GDD10 was noted for reaching BBCH07 over
the years (Figure 7A). Similar trends were found between
Teroldego and Chardonnay, and a reduction in GDD10 was
also found for the unlocking of owering time (p < 0.05,
Figure 7B). No trends were observed for heat requirements
for unlocking BBCH81 and harvest (Figure 7C and D).
The correlation matrix shown in Figure 8 includes the
statistically signicant correlations between phenological,
environmental and quality variables of the same year.
The signicant correlations between the onset of subsequent
phenological phases and for both varieties conrmed that any
delay or anticipation of a given phenological phase results
in a similar behaviour of the following phenological phases.
As expected, there was an inverse correlation between
phenological onset DOY and the thermal accumulation
variables. Regarding inter-cultivar variation, in Chardonnay,
precipitation was inversely associated with the owering-
veraison interphase and annual evapotranspiration was
inversely proportional to the time between veraison and
harvest. Conversely, for Teroldego, precipitation was
positively correlated with the length of the budburst-veraison
and owering-veraison interphases, while it was inversely
proportional to the time between veraison and harvest.
OENO One | By the International Viticulture and Enology Society10 | volume 57–3 | 2024
FIGURE8. Correlation matrix showing the signicance of linear correlations between phenological, environmental
and quality variables of the same year. Colours are Pearson product–moment correlation coefcients and white square
represent non-signicant correlations. The descriptions of the acronyms are reported in Supplementary Table1.
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 11
FIGURE 9. Correlation matrix showing the signicance of linear correlations between phenological and quality
variables with the environmental data of the previous year. Colours are Pearson product–moment correlation
coefcients and white square represent non-signicant correlations. The descriptions of the acronyms are reported in
Supplementary Table 1.
OENO One | By the International Viticulture and Enology Society12 | volume 57–3 | 2024
FIGURE 10. Correlation matrix showing the statistically signicant correlations between heat requirements to
unlock a phenological stage with the environmental data of the previous year. Colours are Pearson product–moment
correlation coefcients and white square represent non-signicant correlations. The descriptions of the acronyms are
reported in Supplementary Table1.
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 13
The correlation matrix shown in Figure 9 includes the
statistically signicant correlations between phenological and
quality variables and the environmental data of the previous
year. There was no correlation between a phenological
phase DOY and environmental variables of the summer
period for the previous year (1 April to 30 September or
15 May to 15 June; Figure 9). However, an association was
observed for a current year in which all the summer thermal
variables showed a strong effect on advancing phenological
phases (Figure 8). Inverse correlations were observed for
soil temperatures of the previous cold season (October to
March) and for air temperatures (mainly between April
and June of the same growth season) with phenological
onsets. Regarding interannual relationships, an increase in
De Martonne index corresponded to an advancement of the
phenological phases of the following year (Figure 9), but
not of the current year (Figure 8). Budburst (BBCH07) was
the only phenological phase showing an inverse correlation
with the sum of GDD10 of the previous year, while all the
other phases showed this relationship only with respect to the
current year environmental dynamics.
As expected, GDD requirements are all positively correlated
with the thermal environmental variables of the same year,
especially with mean air temperature and Huglin Index,
although GDD also showed some correlations with thermal
variables of the previous year (see Figure 10). In addition,
the sum of GDD10 to reach BBCH07 and BBCH61, the
De Martonne index and the mean air temperature of autumn
are inversely proportional to total annual evapotranspiration
of the previous year for both Teroldego and Chardonnay.
FIGURE11. A) White, B) sparkling, and C) red vintage quality (internal Mezzacorona Sca rating) over the years.
Average quality rating as a function of annual rainfall (D) and GDD10. Data were analysed via liner regression
and only signicant relationships (p< 0.05) are shown in the graphs. The linear model equations referring to the
relationships between the studied parameters, along with the corresponding R2 are shown in the graphs and for
signicant models only. Statistically signicant annual trends of°Ba/year for Teroldego (F) and Chardonnay (G), and
total acidity (per year, expressed as g/L) for Teroldego (H). Different measurement timepoints are noted in days before
harvest on the x axis. Hence, moving from right to left in each graph, the trends refer to days closer to the harvest. It
should be noted that there are no signicant trends in acidity for Chardonnay (not shown).
OENO One | By the International Viticulture and Enology Society14 | volume 57–3 | 2024
Internal (Mezzacorona Sca) wine rating over years (i.e.,
over the climatic changes provided above) showed a linear
and signicant increase (p < 0.05) in white wine quality
while no trends were observed for sparkling and red wine
(Figures 11A, B and C respectively). The overall quality was
linearly, negatively and signicantly (p < 0.05) associated
with annual rainfall (Figure 11D), while no overall correlation
was observed between quality and GDD10 (Figure 11E).
Babo grade trends showed specic behaviour (increasing for
Teroldego and decreasing for Chardonnay) at every time point
before harvest (Figures 11F and 11G). Acidity trends were
signicant for Teroldego only (Figure 11H) and the Babo
grades were similar for different time points before harvest,
but slightly higher (about -0.02 grams/year) 22 days before
harvest and on the day of harvest itself. Sugar concentrations
behave oppositely in Chardonnay and Teroldego during
the days preceding harvest: for Teroldego the Babo grade
increased from 1986 to 2022 (about +0.05 °Ba/year),
while for Chardonnay it decreased (about -0.1 °Ba/year);
conversely, only the acidity of Teroldego showed signicant
trends, with a decrease of about -0.1 g/year.
DISCUSSION
1. Climate change in the Trentino region
impacted several agroclimatic indicators and
grapevine phenological onset over the last
forty years.
In this work, we used a long-term dataset for phenology
and specic environmental data to evaluate the climatic
tendencies imposed by climate change, as well as
the relationships between phenology and quality for
two grapevine varieties grown under homogeneous growing
conditions and management. An overall and long-term shift
in temperatures and weather patterns is widely documented
in the literature (Alikadic et al., 2019; Bock et al., 2011;
Cameron et al., 2020; Cameron et al., 2021; Cameron et al.,
2022; De Cortázar-Atauri et al., 2017; Dinu et al., 2021;
Jones et al., 2005; Laget et al., 2008; Tomasi et al., 2011;
Xyras et al., 2022). In the Trentino basin, we conrm
a signicant increase in average air temperature, which
is estimated to be ~0.1 °C every three years over the last
thirty-six years, a similar value to several other studies, such
as those of Xyras et al. (2022) (0.06 °C annual increase
for the period 1980-2020 on Santorini Island) and Laget
et al. (2008) (increase of 1.3 °C for the period 1980-2006
(mean annual increase of 0.06 °C) in the South of France).
Jones et al. (2005) found that growing season temperatures
in several wine regions throughout the world had increased
by 1.3 °C on average over the last 50 years, with local peaks
greater than 2.5 °C. Indeed, we found that following this
trend the mean air temperature had increased in the Trentino
region by 2 °C over 40 years. No tendencies were observed
for annual and seasonal precipitation, as well as number of
days with rainfall higher than 30 and 50 mm, in contrast to
several reports in which a trend of decreasing precipitation
has been observed in other viticultural regions (Laget et al.,
2008; Moreno et al., 2017; Ramos et al., 2008; Xyras et al.,
2022). In the present study, the observed warming trend has
signicantly increased evaporative demand, as is the case
in many other regions worldwide, suggesting a potential
negative effect on water availability during the growing
season and overall drought feedback. Similarly, the warming
trend has positioned Trentino (at least the valley areas; i.e.,
Piana Rotaliana) within a higher bioclimatic region of the
Winkler index, with 2022 pushing towards Region IV (1600
to up to 2000 GDD on base 10 °C) or “warm” grapevine
grouping (16.5 to around 18.5 °C on average during the
growing season) (Amerine and Winkler, 1944). These results
reect the repositioning of Santorini Island from Region III to
IV as observed by Xyras et al. (2022), and are in line with the
forecast shift in Greek viticultural areas to GDD conditions
above the ranges of the Winkler Regions in the RCP8.5
emission scenario (Koufos et al., 2018). The associated
exploitation and characterisation of different pedoclimatic
conditions for specic oenological aims was not evaluated
in the present study, as achieving high-elevation viticulture is
still a possibility in the Trentino region, and thus a potential
means of keep varietal specicity within the same territory
(Alikadic et al. 2019). Overall, part of this linear warming
was accompanied by an autumn to spring increase in air
temperature, which was in turn associated with an overall
increase in autumn to winter soil temperature and general
increase in annual cumulative radiation. We speculate that
the increase in radiation may be explained by a reduction in
cloud cover, as well as by changes in aerosol composition
(Isaksen et al. 2009), and that this trend may have been
critical in the phenological advancement shown for both the
assessed varieties, for which, on average, the advancement in
phenological onset is occurring at a rate of between 0.3 and
0.5 days/year. Similarly, in a study of 29 cultivars in Greece,
Koufos et al. (2020) identied a harvest advancement trend
of 0.76 days/year on average, over a period spanning 1980 to
2017. While the earlier owering, veraison and harvest time
can be explained by a general higher thermal accumulation,
higher radiation during the growing season and potential
increase in developing water limitation, the advancement
in budburst date can be linked to the air/soil warming
occurring in winter, thus anticipating ecodormancy release.
Early budburst is an ongoing issue in viticulture (Poni et al.,
2022; Faralli et al., 2024), and northern Italy has historically
suffered from winter vine survival rather than post-budburst
freezing damages. Climate change is signicantly pushing
the budburst day to last freezing day subtraction towards
negative values, thus increasing the possibilities of late frost
occurrence on young and fragile shoots. This observation
agrees with a series of phenological modelling studies, which
predict - albeit with degrees of uncertainty - an increase in
late frost risk in many viticultural areas under future climate
scenarios (Kartschall et al., 2015; Leolini et al., 2018;
Meier et al., 2018; Mosedale et al., 2015; Sgubin et al.,
2018). Our work suggests that at this climatic rate, and
for Chardonnay (a variety that undergoes relatively early
budburst), there will be an increase in the possibility of frost
damages, based on two specic environmental dynamics:
i) higher winter-to-spring thermal accumulation, in any case
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 15
associated with lower number of total days with temperatures
below 0 °C, and ii) the unchanged timing of the last day
on which late frost occurs - between 75 and 120 DOY on
average. Therefore, higher temperatures shift phenology and
the post-veraison period into seasonal cycles that represent a
challenge for the production of quality wine, and thus typicity
maintenance, with potential negative effects on productivity
(late frost damages, potential lowered bud differentiation,
lower must yield): our work indeed shows evidence of these
dynamics. As is the case in many other viticultural areas,
short-term adaptation strategies are also required in the
northern Italian basin.
2. Phenological interphases are only partially
compressed by climate change
It is still a matter of debate whether phenological compression
or shifted vegetative growing season are the main drivers
of the overall changes in phenological timing observed in
the many studies (Cameron et al., 2020; Cameron et al.,
2021; Cameron et al., 2022; De Cortázar-Atauri et al., 2017;
Dinu et al., 2021; Jones et al., 2005; Laget et al., 2008;
Tomasi et al., 2011; Xyras et al., 2022) included in
the present work. Different pedoclimatic conditions,
varieties, rootstocks, crop load, row orientation and general
management practices may explain part of the variation
observed in several studies, with contrasting conclusions
regarding interphase dynamics. However, we observed a
general advancing trend for owering to harvest interval
length in both varieties. The advancement was more
pronounced for Chardonnay (0.18 days/year) than Teroldego,
although this cannot explain the larger advancement in
harvest date. Assuming that harvest cannot be classied
as a phenological growth stage (Menzel et al., 2006a;
Menzel et al., 2006b), our work denes an advancement in the
only interval length for which the end point (i.e., harvest) is
dened by a complex and often indenable feedback interaction
between thermal accumulation, timing of the phenological
onset (in this case, onset of owering) and oenological
aim (Cameron et al., 2022). While this observation will be
examined in the last section of the discussion, it indicates
that we were unable to detect a trend for the compression of
any actual phenological interphase length, in contrast to, for
example, Cameron et al. (2022). However, the association
between BBCH07 DOY and BBCH07 to BBCH61 interval
length suggests that, overall, as a result of early budburst, the
subsequent interphase will experience higher probability of
cooler periods, hence reducing the time to unlock owering.
Indeed, this has already been observed by Cameron et al.
(2022), who detected a curvilinear response between phase
length (budburst to owering) and thermal accumulation
and found that for most of the tested varieties lower average
temperatures produced an extended interphase. Indeed,
Cameron et al. (2022), found the budburst to owering
interphase to have the highest slope with increasing average
air temperature, hence suggesting a signicant interval-
length plasticity to environmental conditions. Another
factor, which is less dependent than temperature on year-to-
year variability, is the photoperiod. A variation in budburst
onset between DOY 90 and 110 equates to a daylight length
differing by almost one hour (at the Northern Italy latitude),
which inevitably impacts daily photosynthetic CO2 uptake by
the growing autotroph shoot. If early budburst means longer
time to reach owering, late veraison - potentially for similar
reasons - means longer interval length between veraison and
harvest. Although inevitably associated with the dynamics
of berry quality, this data corroborates and strengthens those
studies aiming at postponing ripening to colder periods
(Böttcher et al., 2022). In our work, delaying veraison
from late July to mid-August extended the veraison to
harvest interval length by 15 days potentially due to i) lower
thermal accumulation post-veraison, and ii) reduced daily
photoperiod. Taken together, our work provides evidence of
a minimal effect of climate change on phenological interval
length, as early onset of a given growth stage shifts the
subsequent growth period towards environmental conditions
less (budburst) or more (veraison) favourable for vine growth.
3. Dynamic effects of previous seasons on
phenological onset and growing degree days
required to unlock a phenological phase: can
climate change impose an additive effect on
phenology over years?
Inter-annual effects have been shown to be common in
perennial tree crops with long-term responses that include
several structural changes at the organ, tissue, and cellular
levels (De Micco and Aronne, 2012; Kim et al., 2007;
Neumann, 1995; Von Arx et al., 2012; Zait et al., 2019).
For instance, it has been shown that a perennial water-
stress memory response exists in Vitis vinifera, and that this
inuences petiole structure at the beginning of the following
season (Shtein et al., 2021). Regulating water availability
during the period of stem cambial activity intra-annually
could be a means for viticulturists to inuence water status
and determine wine quality by manipulating xylem structure
(Netzer et al., 2019). Recent work has also provided evidence
of carbon availability mechanisms under high temperature
conditions that inuence bud ower differentiation and
hence following year productivity (Tombesi et al., 2022),
as well as an endo-to-ecodormancy transition leading to
earlier budbreak following previous year water limitation
(Shellie et al., 2018). Although in some cases the
mechanisms behind the inter-annual environmental effects
remain elusive, part of these trends were conrmed by our
analysis. Of interest from a viticultural point of view, there
was a trend of reduced GDD10 requirements for unlocking
early phenological events, particularly BBCH07. Along
with the winter-to-spring warming and the non-signicant
trend for the last day of frost, these data are associated with
further issues surrounding late frost. One direct explanation
of this may be attributed to the tight association between cold
hardiness, dormancy depth and de-acclimation rates under
low-to-high temperature transition that may affect time of
budbreak. Recently, North and Kovalesky (2024) suggested
the inclusion of cold hardiness evaluation to better assess
and model budbreak in different species, as cold hardiness
depth was affecting time to budbreak after a longer time
to lose supercooling ability. Since cold hardiness is driven
OENO One | By the International Viticulture and Enology Society16 | volume 57–3 | 2024
by low-temperature exposure, increase in autumn-winter
temperatures observed in our work over the last forty years
may have weakened the cold hardiness state and therefore
speeded up bud de-acclimation; i.e., the removal of the
supercooling state necessary for budbreak. The negative
correlation between BBCH07 and winter temperature
corroborates this hypothesis. Moreover, endodormancy
release (hence cold hardiness build up) is governed by
abscisic acid. Short-day photoperiod regulates the onset
of endodormancy via in situ biosynthesis of abscisic acid
(ABA) in the buds, and the abundance of this phytohormone
is associated with endodormancy depth (Pérez and Rubio,
2022; Rubio et al., 2016; Rubio et al., 2019b; Rubio et al.,
2019a). Further, ABA release happens after the fulllment
of chilling requirement and a consequent upregulation of
ABA catabolism (Dantas et al., 2020; Parada et al., 2016;
Pérez and Rubio, 2022; Rubio et al., 2016, 2019b, 2019a;
Rubio and Pérez, 2019; Vergara et al., 2017). However,
Shellie et al. (2018) highlighted a drought stress-induced
regulatory network that interacts with environmental and
hormonal regulatory signals, mainly leading to an earlier
budbreak. These data are in contradiction to the expected
ABA-induced dormancy effect, as a deep endodormancy
induction should be expected following a severe water stress
event driving ABA biosynthesis and accumulation. However,
the data are in line with our long-term analysis, in which
the lower GDD10 required to reach BBCH07 (accelerating
budburst) was associated with increased average annual
temperature, the GDD10 of the previous year and the
De Martonne index. Our data provide further indication,
supported by the ndings of Shellie et al. (2018), of possible
additive effects of stressful environmental conditions in the
previous year on thermal response and phenological onset,
raising additional concerns on phenological advancements.
The mechanisms behind this trend should be further dissected.
4. Early harvest is a strategy for maintaining
must acidity in white berry varieties, while
harvest time is dictated by the temperature to
rainfall ratio in red berry varieties.
The harvest date for grapevine is rarely dictated by ripening
per se, as the interaction between several technological
parameters denes the optimal harvest time point for the
chosen oenological aim. Evidence of the decisional bias
associated with harvest date compared to other phenological
observations have been provided (van Leeuwen and Darriet,
2016). While in i) northern and south-west France
(e.g., Alsace and Bordeaux respectively) growers take
advantage of warmer conditions to harvest at greater levels
of ripeness, in ii) Mediterranean-like viticulture in France
and in Mediterranean viticulture in Greece, increasing
ripeness levels is not an oenological target. Therefore, in
the second case the advancement in harvest date is much
more prominent than in the rst. However, tipping points for
specic oenological targets have been observed at increasing
ripening progressions or average temperatures, such as for
anthocyanins (Gambetta and Kurtural, 2021), suggesting
potential future harvest advancement with climate change,
even at higher latitudes. In our case, the oenological aims
of the two varieties differed, as expected. Indeed, for
Chardonnay, the harvest date was always chosen by setting
the same acidity target (which in fact shows no variations),
even when it means a reduction in sugar content. This is in
line with the fact that in white/sparkling wine the Brix/acidity
ratio of the juice is a determinant (Jones et al., 2014), as well
as with phenological advancement and shorter owering to
harvest interval length. However, while white wine quality
(according to the internal winery rating) has improved over
years, no effect has been observed for sparkling wine quality,
which shows an inverse correlation with the number of days
exceeding 35 °C. This data provides evidence of potential
further negative effects of climate change, mainly via a
de-coupling of malic acid degradation (faster) and sugar
accumulation (slower at high temperature) (Palliotti et al.,
2014), thus potentially affecting the quality of berries used for
sparkling wine, even in the foreseeable future. In Teroldego,
we observed a reduction in juice acidity and a slight increase
in sugar content over the years, with no apparent negative
impacts on wine quality. However, quality was positively
correlated with total evapotranspiration and maximum
annual temperatures, suggesting a generally positive role
(Jones and Davis, 2000) of warming trends for red berry
varieties in the Trentino basin. In addition, the total rainfall
values were directly proportional to the periods between
bud and veraison and between owering and veraison, but
inversely proportional to the time between veraison and
harvest. Therefore, it is very likely that early harvest in
Teroldego was associated with post-veraison rainfall rather
than with warming (that enhanced some chemical parameters
of the berry). The bunch-zone microclimate, in particular
humidity (Tello and Ibáñez, 2018), is an important factor that
denes berry sanitary status, and high precipitation before
harvest may increase the occurrence of botrytis (cold) or acid
rot (warm), which was possibly minimised in Teroldego as
a result of the early harvest. Our work provides evidence of
the complex interplay between climate change, variety, and
oenological aims, which, according to our data, have been
well managed by viticulturists to avoid loss of wine quality
and typicity. Although speculative at this stage, the expected
further changes in climatic conditions could additionally
affect viticulture to such an extent that management and
harvest date decisions will unlikely be effective, thus
warranting further work on future adaptation strategies.
CONCLUSIONS
We showed that between 1986 and 2022 in the northern
Italian region of Trentino mean air temperature increased
by ~ 0.1 °C every three years. Several agroclimatic indices,
particularly the Growing degree days and Huglin Index,
served as reliable indicators of such dynamics, corroborating
previous studies on this topic carried out for other viticultural
regions worldwide. Consistent with other studies, climate
change resulted in a signicant advancement (by up to 20 to
30 days) in the onset of phenological stages in both white and
red berry varieties during the analysed period; meanwhile,
a substantial equilibrium in the seasonal growing length
Michele Faralli et al.
OENO One | By the International Viticulture and Enology Society 2024 | volume 57–3 | 17
over the years was maintained due to the phenology-heat
accumulation relationship. We found evidence of additive
effects on phenological onset resulting from stressful
environmental conditions in preceding seasons and associated
with thermal units required to unlock budburst therefore
increasing the probability of late frost damages. However,
the quality of white wine improved over the years, while
red and sparkling wines remained unaffected, possibly due
to the precise determination of harvest dates based on berry
quality parameters; specically, the sugar-to-acidity ratio
dictated the harvest date for Chardonnay, while the sanitary
status determined the harvest date for Teroldego. This study
highlights the dynamics involved in climate change and, to
our knowledge, its overlooked effects on viticulture, thus
providing new information that can contribute to further
developing adaptation strategies.
ACKNOWLEDGEMENTS
The work was supported by internal funding from University
of Trento awarded to MF and MB.
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