Impact of Water Deficit during Fruit Development on Quality and Yield of Young Table Grape Cultivars

Abstract

Water limitation has a major effect on agricultural crop production, influencing yield as well as external and internal quality parameters of table grapes. Due to their high yield potential, table grapes have a particularly high risk for yield and quality losses when water is limited, but grapevines are known for high heterogeneity within cultivars. Therefore, we investigated the effect of prolonged water deficits (control, moderate, and severe deficit) during fruit development on yield and quality parameters of four different table grape cultivars (Vitis L.). Furthermore, we ranked their suitability for cultivation in areas suffering from water limitation. Up to 31% of irrigation water could be saved in comparison to the control, without significant negative effects on plant yield, berry size, or internal quality parameters, such as total soluble solids and total phenolic content. However, single bunch yield was highest at a moderate deficit and number of seeds in berries increased with the severity of deficit. Cultivar selection had the greatest influence on water consumption and mainly defined yield and quality parameters. The cultivar ‘Fanny’ produced the highest yields (195.17 g per plant), most bunches per plant (2.04), and biggest berries while cv. ‘Nero’ had the highest total soluble solids content (26.33 °Brix) and the highest total phenolic content (67.53 mg gallic acid equivalents per 100 g fresh weight). Overall, ‘Fanny’ was the most promising cultivar for cultivation under water-limited conditions during fruit development, without significant effects on yield and quality parameters.

Full text

horticulturae
Article
Impact of Water Deficit during Fruit Development on
Quality and Yield of Young Table Grape Cultivars
Carolin Susanne Weiler 1,* , Nikolaus Merkt 2and Simone Graeff-Hönninger 1
1Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, 70599 Stuttgart, Germany;
simone.graeff@uni-hohenheim.de
2Institute of Crop Science, Quality of Plant Products, University of Hohenheim, Emil-Wolff-Str. 25,
70599 Stuttgart, Germany; nikolaus.merkt@uni-hohenheim.de
*Correspondence: Carolin.weiler@uni-hohenheim.de; Tel.: +49-711-4592-2380
Received: 28 September 2018; Accepted: 19 November 2018; Published: 23 November 2018


Abstract:
Water limitation has a major effect on agricultural crop production, influencing yield as
well as external and internal quality parameters of table grapes. Due to their high yield potential,
table grapes have a particularly high risk for yield and quality losses when water is limited,
but grapevines are known for high heterogeneity within cultivars. Therefore, we investigated the
effect of prolonged water deficits (control, moderate, and severe deficit) during fruit development on
yield and quality parameters of four different table grape cultivars (Vitis L.). Furthermore, we ranked
their suitability for cultivation in areas suffering from water limitation. Up to 31% of irrigation
water could be saved in comparison to the control, without significant negative effects on plant
yield, berry size, or internal quality parameters, such as total soluble solids and total phenolic
content. However, single bunch yield was highest at a moderate deficit and number of seeds in
berries increased with the severity of deficit. Cultivar selection had the greatest influence on water
consumption and mainly defined yield and quality parameters. The cultivar ‘Fanny’ produced
the highest yields (195.17 g per plant), most bunches per plant (2.04), and biggest berries while cv.
‘Nero’ had the highest total soluble solids content (26.33
◦
Brix) and the highest total phenolic content
(67.53 mg gallic acid equivalents per 100 g fresh weight). Overall, ‘Fanny’ was the most promising
cultivar for cultivation under water-limited conditions during fruit development, without significant
effects on yield and quality parameters.
Keywords: firmness; water use; marketable yield; berry size
1. Introduction
Climate change and its effects have a major influence on agriculture. According to the Food and
Agriculture Organization (FAO) [
1
], water shortage is currently the most significant factor limiting crop
production in the world. Climate change scenarios predict that Central Europe, including Germany,
will be affected by water limitations, changing precipitation patterns especially in Southern Europe,
and will suffer from summer droughts [2].
Grapevines are considered to be drought tolerant plants [
3
], but most current cultivation areas
are located in regions with decreasing water availability and a potential risk of high drought stress
during the growing season [
4
]. In general, table grapes achieve high annual yields and indicate a
high productivity for the water used [
5
–
8
]. However, they are exposed to high risks of quality and
yield losses, when water is limited during growing seasons [
9
]. Timing, intensity, and duration of
water limitation during the growing period are known to affect growth, yield, and quality of grapes
differently [
10
]. Early season water limitation will reduce vegetative growth, while withholding water
after bud break results in lower yields and reduced fruit quality [
11
]. During fruit development,
Horticulturae 2018,4, 45; doi:10.3390/horticulturae4040045 www.mdpi.com/journal/horticulturae

Horticulturae 2018,4, 45 2 of 15
early stage water deficits after flowering and before veraison result in smaller berries and reduced
bunch yields [
12
–
14
]. Furthermore, deterioration of quality in terms of visual appearance of the bunch,
berry color, and uniformity have been reported [
15
]. Contrasting behavior was determined for late
season limitations, as the sensitivity of grapevines to water limitations after veraison is low [16].
In addition, quality parameters such as firmness, crisp texture, total soluble solids (TSS),
phenolic content, and acidity content are essential, as they determine consumer acceptance and
preference
[15,17]
. Several studies investigating the effects of water deficits on table grapes observed
the highest yields and berry weights with the treatment receiving the highest irrigation volume [
11
],
while quality parameters such as firmness decreased, titratable acidity (TA) remained constant and TSS
increased [
18
], ultimately affecting overall consumer acceptance. Besides environmental factors such as
soil water availability, cultivar selection has a major influence on yield and quality [
19
,
20
], and cultivars
differ in their response to water limitation. Comparative studies of the grape cultivars ‘Moscatel’
(syn. Muscat of Alexandria) and ‘Castelão’ determined differences in yield and quality when subjected
to a moderate deficit irrigation during the growing season [
21
]. Furthermore, cultivar dependent
responses to post-veraison moderate deficit irrigation in ‘Autumn Royal’ and ‘Crimson Seedless’ were
observed as they differed in grape production capacity, berry size, and sweetness [
22
]. Therefore,
comparative studies with several cultivars are necessary to screen a wide range of table grape cultivars
concerning the response of fruit quality and yield to water deficit, as they are important for optimal
cultivar selection with regard to a changing climate. However, to our knowledge, no research has
been done in the field on water deficits and plant responses of the most popular table grape cultivars
in Germany.
Therefore, this work aimed to evaluate the effect of water deficits during fruit development
on fruit yield and quality parameters of four common German table grape cultivars. Furthermore,
the suitability of their cultivation under water limitations as a consequence of climate change will
be considered.
2. Materials and Methods
2.1. Experimental Setup
A greenhouse experiment was conducted in 2016 at the University of Hohenheim, Germany, with
four 3-year-old, own-rooted table grape cultivars, namely ‘Nero’, ’Fanny’, ’Palatina’, and ’Muscat
Bleu’. The objective was to evaluate the impact of water deficits between fruit set and harvest on yield
and quality parameters of these four cultivars. Plant material of the four cultivars originated from
Rebveredlung Kühner, Germany.
Twenty-four plants per cultivar were grown in 7-L pots, filled with 40% loam, 50% sand, and 10%
peat by volume, having a maximum water holding capacity of 37.8%. Since the first year, all table
grapes were maintained with one shoot, which was attached to a bamboo stick and cut to an annual
shoot length of 140 cm. Within the first two years, plants were defruited and water was limited
during vegetative growth (2014: 6 weeks; 2015: 10 weeks). Table grapes were kept well hydrated and
supplemented with 50 mL liquefied mineral fertilizers (1 g Hakapos
®
Blue (N 15% + P 10% + K 15% +
Mg 2%) (CAMPO EXPERT, Münster, Germany) and 0.1 g micronutrient fertilizer Fetrilon
®1
Combi
(BASF, Ludwigshafen, Germany)) biweekly. Eight plants per cultivar were randomly assigned to a
control, moderate, or severe water deficit treatment. Each day, control plants were irrigated to the
calculated weight of 75% available water content (AWC), moderate deficit treatments to 50% AWC,
and severe deficit treatments to 25% AWC. Before starting the treatment, AWC was determined for each
pot/plant individually by flooding the pots after sunset to avoid transpiration losses. The excess water
was able to drain overnight. Before sunrise, pots were weighed to get the maximum pot weight/field
capacity. Wilting point was considered as the minimum weight of the pots where all pots were dried
out until a constant weight was reached and plants started wilting. Plants were re-watered and

Horticulturae 2018,4, 45 3 of 15
adjusted to the plant-pot specific weight on a daily basis. The following formula was used to calculate
the individual pot weight for every plant in each treatment:
Weight =PotMin +(PotMax −PotMin)
100 ∗Treatment (1)
In order to determine the total amount of applied water, daily water loss (evaporation and
transpiration) was measured gravimetrically in the morning from fruit set until harvest using a
platform scale (FKB 36K0.1, KERN, KERN & SOHN GmbH, Balingen, Germany) with a maximum
range of 36 kg and 0.1 g accuracy. During the experiment, the pot weight was modified to account
for the additional bunch weight at veraison. Pots were not covered and therefore evaporation was
not prevented.
Temperature and relative humidity in the greenhouse were measured on a daily basis at
five-minute intervals and stored using a datalogger (TGP-4500, Gemini Data Loggers, Chichester, UK).
During the experiment mean temperature was 21.8
◦
C and relative humidity was 63.9% (Appendix A:
Figure A1).
Volumetric water content (VWC) was measured using a soil moisture device (FieldScout TDR 100,
MMM Tech, Germany). VWC was determined two weeks before the start of water deficit treatment at
soil saturation. Soil moisture was measured every 28 days until final harvest of berries.
2.2. Yield and Quality Measurements
Harvest started on 12 September, 90 days after the induction of drought stress at the beginning
of fruit set, and each vine was picked individually. Number of bunches, weight per bunch and total
amount of fruit yield per plant were determined for each individual vine. Per bunch, berries were
sorted into marketable and non-marketable (shot berries, shriveled berries), as well as counted and
weighed. A sample of ten berries per bunch was selected randomly and their fresh weight was
measured to determine the mean berry weight. Diameter and height of each berry were measured
with a Vernier caliper and berry firmness was determined non-destructively using a firmness tester
(Exacta-Härteprüfer HP-DRS, Bareiss). Twenty randomly-selected berries per vine were manually
divided into skin, pulp and seeds, weighed, frozen with liquid nitrogen and stored at
−
20
◦
C,
freeze-dried and weighed again. All the remaining berries per vine were crushed and must total soluble
solids (
◦
Brix) was measured using a digital refractometer (DR101-60, Krüss, Germany). Titratable
acidity was measured by automatic titration (TitroLine easy, SCHOTT, Mainz, Germany) with 0.33 M
NaOH to a pH 7.0 endpoint and was expressed as gram of tartaric acid L
−1
.Total phenolic content
was determined using freeze-dried skin samples, ground to a final firmness of <300
µ
m (Knife Mill,
GRINDOMIX GM 200, Retsch, Germany) using the Folin–Ciocalteu reaction [
23
]. Ground skin samples
were analyzed as described by Sahamishirazi et al. [
24
] with minor modifications to duration of
centrifugation (20 min), volume of skin extract (0.3
µ
L), and to the standard curve consisting of six
standard solutions (0.06 g
·
L
−1
to 2.1 g
·
L
−1
). Total phenols were measured two times per sample
at 760 nm by spectrophotometry (Ultraspec 3100 pro, Amersham Biosciences, Piscataway, NJ, USA)
and calculated based on the calibration curve. The results are expressed as milligram of gallic acid
equivalents per 100 g of fresh weight (mg GAE 100 g
−1
FW). For determination of plant and leaf water
content, fresh weight (FW) of aboveground biomass and of leaves was determined at harvest, dried at
60
◦
C until reaching constant weight and weighed again for dry weight (DW). Plant and leaf water
content were calculated using following formula:
Plant/Le a f water co ntent =100 −DW
FW ∗100 (2)

Horticulturae 2018,4, 45 4 of 15
2.3. Statistical Analysis
Yield and quality of the plant, bunch and berry level of four table grape cultivars (c= 4) on three
water deficit levels (t= 3) were analyzed using PROC MIXED (SAS version 9.2., SAS Institute Inc.,
Cary, NC, USA) with the following model:
yijkl =µ+tk+bkl +τi+ϕj+(τ ϕ )ij +eijkl (3)
where
µ
is the general effect,
tk
and
bkl
are random block effects for the k
th
table and the l
th
block on
the k
th
table, respectively.
τi
,
ϕj
and
(τ ϕ)ij
corresponds to fixed main effects of the i
th
cultivar and j
th
water deficit treatment and its interaction effects, respectively.
eijkl
is the error effect of observation
yijkl
. To analyze average berry height, diameter, and firmness, seeds per berry, and single berry weight,
data was weighted by number of berries measured per bunch. Total plant yield, yield per bunch,
marketable and non-marketable yield per bunch needed to be weighted by the inverse of number
of bunches per plant. Furthermore, the model was extended to allow for separate variances of table
effects for each number of grapes if this decreased the model fit criteria AIC [
25
]. The counted number
of berries and marketable berries were analyzed with PROC GLIMMIX using (3) as linear predictor
with a log link for poisson-distributed data. Residuals were checked graphically for normality and
homogeneity of variances. Data of skin weight, TSS, marketable yield, and weight per berry were
square-root transformed prior to analysis. Data of TA, number and yield of non-marketable berries,
and total phenolic content were logarithm transformed. Percentage values of volumetric water content,
and plant and leaf water content, were transformed by logit transformation. For the evaluation of the
parameter number of non-marketable berries per bunch, zero values had to be changed to 0.01 prior
to logarithmic transformation. In case of a significant F-test, multiple comparisons for levels of the
corresponding factor were performed based on Fisher’s Least Significant Difference (LSD) (P< 0.05).
A letter display was created by using the SAS macro [26].
3. Results
3.1. Yield and Quality of Table Grapes
3.1.1. Plant-Level
Significant differences among cultivars occurred for the number of bunches per plant, bunch yield
per plant, TSS, total phenolic content, and TA (Table 1). Furthermore, TA was significantly influenced
by water deficit. ‘Fanny’ was the cultivar with the greatest number of bunches per plant (2.04),
highest yield (195.17 g per plant), and lowest TSS (16.41
◦
Brix), and TA values (5.3 g
·
L
−1
). ‘Palatina’
had the fewest number of bunches per plant, producing an average of 0.85 bunches over all treatments
in the first year of fruit production, as only 13 out of 24 vines produced bunches. The lowest amounts
of average yield of the fruit producing vines were determined for ‘Muscat Bleu’ (44.22 g) and ‘Palatina’
(54.05 g) per plant. The highest content of TSS was measured for ‘Nero’ (26.33
◦
Brix), followed by
‘Palatina’ (24.78
◦
Brix), and ‘Muscat Bleu’ (22.56
◦
Brix). The highest TA was determined for ‘Nero’ with
an average of 7.31 g
·
L
−1
and the control treatment (6.51 g
·
L
−1
). Within the treatments, the severe water
deficit treatment (5.67 g
·
L
−1
) differed significantly from the control and the moderate water deficit
treatment (6.26 g
·
L
−1
and 6.51 g
·
L
−1
, respectively). For total phenolic content, no differences among
the treatments were observed. Within the cultivars, ‘Nero’ had the highest total phenolic content
(67.53 mg GAE 100 g
−1
FM), followed by ‘Muscat Bleu’ (46.36 mg GAE 100 g
−1
FM) and ‘Palatina’
(13.22 mg GAE 100 g−1FM) and lastly ‘Fanny’ with the lowest amount (7.39 mg GAE 100 g−1FM).

Horticulturae 2018,4, 45 5 of 15
Table 1.
Table grape parameters at the plant level: Number of bunches per plant, yield per plant, TSS, TA, and total phenolic content of four three-year-old table grape
cultivars under three water deficit treatments in 2016.
Parameter Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
Grapes per plant
Control 1.13 2.13 1.38 0.63
Moderate
1.13 B z2.00 A 1.25 B 0.88 B
Severe 1.63 2.00 1.25 1.13
Yield per plant (g)
Control
44.17
189.76
96.44 36.20
Moderate 46.17
B 216.10 A
57.49
B
73.72
B
Severe
42.31
179.65
54.54 52.23
TSS (◦Brix)
Control
21.26
16.80
26.39 24.77
Moderate 24.13
C 16.15 D
26.49
A
25.16
B
Severe
22.32
16.29
26.10 24.41
TA (g·L−1)
Control 7.04 a 5.43 a 7.90 a 6.86 a
Moderate
6.27 a B 5.55 a C 6.73 a A 5.81 a BC
Severe 5.55 b 4.91 b 7.30 b n.a.
Total phenolic content
(mg GAE 100 g−1FW)
Control
42.22
7.64
73.90 19.91
Moderate 53.80
B 7.90 D
64.87
A
11.23
C
Severe
43.86
6.68
64.25 10.33
ANOVA Grapes per plant Yield per plant TSS TTA Total phenolic content
Cultivar (C)
<0.0098
**
<0.0001
*** <0.0001 ***
<0.0001
***
<0.0001
***
Treatment (T)
0.6363
n.s.
0.4115
n.s. 0.413 n.s.
0.0215
*
0.0837
n.s.
C*T
0.9445
n.s.
0.4016
n.s. 0.525 n.s.
0.3225
n.s.
0.2279
n.s.
The data represents mean values (yield per plant) and mean values of back transformed data (bunches per plant, TSS, TA, and total phenolic content). Treatments included: Control:
daily irrigation to 75% of available water capacity; Moderate: daily irrigation to 50% of available water capacity; and Severe: daily irrigation to 25% of available water capacity. Total
soluble solids (TSS); titratable acidity (TA); Gallic acid equivalents (GAE); fresh weight (FW); gram (g); liter (L); milligram (mg); not available (n.a.).
z
Different letters indicate significant
differences for cultivar (capital letters) and treatment (lower case) for P< 0.05; No letters indicate no differences for cultivar and/or treatment. ANOVA: ***: P< 0.001; **: P< 0.01;
*: P< 0.05; not significant (n.s.).

Horticulturae 2018,4, 45 6 of 15
3.1.2. Bunch-Level
When comparing table grape parameters at the bunch level, significant differences between
the cultivars occurred for yield per bunch, marketable and non-marketable bunch yield, and the
number of non-marketable berries (Table 2). Yield per bunch of all cultivars was also affected by
the treatment. Number of berries and number of marketable berries per bunch showed significant
interactions between treatments and cultivars. Over all cultivars, ‘Fanny’ was the cultivar with the
highest bunch yields (103.23 g). The smallest yields were found with ‘Muscat Bleu’ (35.25 g) and
‘Palatina’ (39.95 g). Within the treatments, the moderate water deficit resulted in the highest average
bunch yields (65.83 g), followed by the control (56.70 g) and lowest yields with the severe water deficit
(51.88 g). For marketable yield, maximum values were observed for ‘Fanny’ (98.34 g), while ‘Muscat
Bleu’ had the lowest yields (29.86 g). The highest values for non-marketable yields were found for
‘Nero’ with approximately 8.83 g. The lowest values for yield losses were measured for ’Muscat Bleu’
(0.57 g), and ‘Palatina’ (0.90 g). Regarding numbers of berries, we found similar results for total number
of berries and number of marketable berries per bunch. In the control irrigation treatment, ‘Nero’
was the cultivar with highest number of berries (42.32) and marketable berries (32.70) along with
‘Fanny (33.89). When moderately stressed, ‘Palatina’ had the highest total number of berries (55.33)
and marketable berries (39.05). ‘Fanny’ formed the most berries in total when severely stressed (32.97).
‘Muscat Bleu’ was the cultivar with the lowest number of berries and marketable berries within all
treatments. The number of non-marketable berries was highest in ‘Palatina’ (6.66), followed by ‘Nero’
(1.83). ‘Fanny’ and ‘Muscat Bleu’ were the cultivars with the lowest amount of non-marketable berries
per bunch (0.06 and 0.08, respectively).

Horticulturae 2018,4, 45 7 of 15
Table 2.
Table grape parameters on the bunch level: Yield, marketable and non-marketable yield, number of berries, and number of marketable and non-marketable
berries of four three-year-old table grape cultivars under three water deficit treatments in 2016.
Parameter Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
Yield (g per bunch)
Control
33.51
Abz
C
96.15 ab
A
63.00 ab
B
34.13 ab
C
Moderate
38.19
a 117.76 a 55.43 a 51.91 a
Severe
34.05
b 95.77 b 43.88 b 33.81 b
Marketable yield
(g per bunch)
Control
28.82
90.80 43.83 30.70
Moderate
32.10
C 110.74 A 39.96 B 44.64 BC
Severe
28.73
94.03 34.13 29.39
Non-marketable yield
(g per bunch)
Control 0.26 1.26 11.43 0.64
Moderate 0.00 B 3.89 B 8.90 A 1.24 B
Severe 0.84 0.33 6.77 0.87
Number of berries
(per bunch)
Control
21.08
a C 33.54 a B 42.32 a A 40.92 b AB
Moderate
16.78
a C 33.74 a B 31.18 b B 55.33 a A
Severe
16.16
a C 34.58 a A 22.08 c B 35.84 b A
Number of marketable
berries (per bunch)
Control
18.58
a B 33.89 a A 32.70 a A 24.82 b B
Moderate
17.17
a C 34.90 a A 22.71 b B 39.05 a A
Severe
15.94
a B 32.97 a A 19.27 b B 26.74 b A
Number of
non-marketable
berries (per bunch)
Control 0.80 0.06 2.86 3.12
Moderate 0.01 B 0.11 B 2.76 A 12.03 A
Severe 0.07 0.03 0.78 7.85
ANOVA
Yield
Marketable yield
Non-marketable
yield Number of berries Number of
marketable berries
Number of
non-marketable berries
Cultivar (C)
<0.0001
***
<0.0001
***
<0.0001
*** <0.0001 *** <0.0001 *** <0.0001 ***
Treatment (T)
0.0204
*
0.0628
n.s.
0.1421
n.s. <0.0001 *** 0.0196 * 0.4825 n.s.
C*T
0.2721
n.s.
0.6606
n.s.
0.2635
n.s. <0.0001 *** 0.0019 ** 0.2167 n.s.
The data represents mean values (yield per bunch, total number of berries per bunch, and the number of marketable berries per bunch) and mean values of back transformed data
(marketable and non-marketable yield and number of non-marketable berries). Treatments included: Control: daily irrigation to 75% of available water capacity; Moderate: daily irrigation
to 50% of available water capacity; and Severe: daily irrigation to 25% of available water capacity. Gram (g). zDifferent letters indicate significant differences for cultivar (capital letters)
and treatment (lower case) for P< 0.05; No letters indicate no differences for cultivar and/or treatment. ANOVA: ***: P< 0.001; **: P< 0.01; *: P< 0.05; not significant (n.s.).

Horticulturae 2018,4, 45 8 of 15
3.1.3. Berry-Level
The quality and yield parameters of berries showed a significant impact of cultivar on all measured
parameters including single berry weight, skin, pulp, and seed weight, the number of seeds as well
as diameter, height and firmness (Table 3). Furthermore, water deficit treatment affected the number
of seeds per berry. Overall, ’Fanny’ was the cultivar with highest weight of a single berry (3.76 g),
skin (0.51 g) and pulp (2.33 g). For seed weight and number of seeds per berry, ‘Muscat Bleu’ was
the cultivar with the highest seed weight of 0.17 g per seed and approximately 2.7 seeds per berry.
Furthermore, the biggest berries were observed for ‘Fanny’ with an average of 17.67 mm in diameter and
a height of 18.38 mm. In contrast, ‘Palatina’ was the cultivar with the lowest single berry weight (1.17 g),
weight of skin (0.31 g), pulp (0.71 g), seed (0.06 g), and lowest number of seeds (1.5). For all cultivars,
number of seeds was affected by the treatment and severe water deficit resulted in higher numbers of
seeds per berry (2.4) in contrast to the control (2.1). ‘Palatina’ was determined as the cultivar with the
smallest berries (diameter: 12.45 mm, height: 13.55 mm), which were 30% smaller than ‘Fanny’. Even if
differences in berry size occurred for ‘Palatina’ and ‘Fanny’, both cultivars had the highest firmness
(13.67 N and 14.73 N, respectively) and ‘Muscat Bleu’ had the softest berries (11.45 N).
Table 3.
Table grape yield and quality parameters on the berry level: Single berry weight, weights of
skin, pulp and seeds, diameter, height, and firmness of four three-year-old table grape cultivars under
three water deficit treatments in 2016.
Parameter Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
Weight (g per berry)
Control 2.01
3.52 1.77 1.08
Moderate 2.79 B
z
3.98
A
1.94
C
1.24
D
Severe 2.36
3.79 2.00 1.20
Skin weight (g FM per berry)
Control 0.37
0.48 0.40 0.20
Moderate 0.33
BC 0.57
A
0.37
B
0.34
C
Severe 0.39
0.49 0.46 0.42
Pulp weight (g FM per berry)
Control 1.13
2.30 0.56 0.84
Moderate 1.24 B
2.37
A
0.79
C
0.84
C
Severe 1.34
2.33 0.84 0.47
Seed weight (g FM per berry)
Control 0.16
0.12 0.09 0.06
Moderate 0.21 A
0.15
B
0.09
C
0.06
D
Severe 0.16
0.16 0.14 0.06
Number of seeds (per berry)
Control 2.51 b
2.30
b
1.98
b
1.47
b
Moderate 2.52 ab A
2.69
ab A
2.11
ab B
1.44
ab C
Severe 3.07 a
2.76
a
2.44
a
1.66
a
Diameter (mm)
Control 13.94
17.35 12.37 12.61
Moderate 14.40 B
17.71
A
12.60
C
12.57
C
Severe 14.80
17.95 13.17 12.17
Height (mm)
Control 15.54
18.09 15.15 13.76
Moderate 16.64 B
18.46
A
15.32
C
13.75
D
Severe 16.84
18.62 15.59 13.12
Firmness (N)
Control 11.26
14.87 10.88 14.58
Moderate 11.75 B
15.11
A
10.74
B
12.82
A
Severe 11.33
14.23 10.74 13.59
ANOVA Weight Skin weight Pulp weight Seed weight
Cultivar (C)
<0.0001
***
0.0002
***
<0.0001
***
<0.0001
***
Treatment (T) 0.0933 n.s.
0.0911
n.s.
0.4900
n.s.
0.0864
n.s.
C*T 0.9237 n.s.
0.2491
n.s.
0.1736
n.s.
0.1061
n.s.
Number of Seeds Diameter Height Firmness
Cultivar (C)
<0.0001
***
<0.0001
***
<0.0001
***
<0.0001
***
Treatment (T) 0.0239 *
0.3277
n.s.
0.4132
n.s.
0.6837
n.s.
C*T 0.9263 n.s.
0.8211
n.s.
0.7352
n.s.
0.8105
n.s.
The data represents mean values (pulp weight, seed weight, number of seeds per berry, diameter, height, and firmness)
and mean values of back transformed data (weight per single berry and skin weight). Treatments included: Control:
daily irrigation to 75% of available water capacity; Moderate: daily irrigation to 50% of available water capacity;
and Severe: daily irrigation to 25% of available water capacity. Fresh mass (FM); gram (g); millimeter (mm); Newton (N).
z
Different letters indicate significant differences for cultivar (capital letters) and treatment (lower case) for P< 0.05;
No letters indicate no differences for cultivar and/or treatment. ANOVA: ***: P< 0.001;
**: P< 0.01
; *: P< 0.05; not
significant (n.s.).

Horticulturae 2018,4, 45 9 of 15
3.2. Water Use and Water Contents of Soil, Plant and Leaves
In 2016, the water deficit treatments were applied daily from fruit set to harvest
(
June to September
). Maximum water volumes were applied for the control treatment (Table 4).
The cultivars differed in the volume of water that was applied, which was based on daily measurements.
The highest values for irrigation volumes were observed for ‘Nero’ (891 mm) and the lowest volumes
of water were applied to ‘Muscat Bleu’ (761 mm). Moderately stressed plants received 0.2 to 82 mm less
water as compared to the control, which saved 0% water in ‘Muscat Bleu’ and 9% in ‘Nero’. The highest
reduction in applied water was achieved in the severe stress treatment with 164 mm to a maximum of
263 mm in ‘Fanny’. This stands for a reduction of 22 to 31% of irrigation water.
Table 4.
Amounts of applied water (mm), differences in amount of applied water (mm) and water
savings (%) from fruit set to harvest under different water deficit treatments in 2016 in table grape
cultivars ‘Muscat Bleu’, ‘Fanny’, ‘Nero’, and ‘Palatina’.
Parameter Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
Irrigation amounts (mm)
fruit set to harvest
Control 761.2 859.7 891.0 832.0
Moderate 760.9 787.1 809.1 784.2
Severe 596.9 597.0 637.4 598.0
Differences of irrigation
amounts (mm)
Control—Moderate
0.2 72.6 81.9 47.8
Moderate—Severe
164.1 190.1 171.7 186.2
Control—Severe
164.3 262.7 253.6 234.0
Saved water (%)
Control—Moderate
0.0 8.4 9.2 5.7
Moderate—Severe
21.6 24.2 21.2 23.7
Control—Severe
21.6 30.6 28.5 28.1
Measurements of soil volumetric water content (VWC) before and during the experiment differed
significantly among cultivars (Table 5). Overall, ‘Palatina’ had the highest VWC in all measurements,
ranging between 34.73% at saturation and 17.74% VWC at harvest. Lowest values of soil moisture
content were measured for ‘Fanny’ at harvest with 12.24% VWC. In June, when pots were saturated,
no differences between the water deficit treatments were observed. Significant differences were
found for the irrigation treatments, having the lowest VWC values for severe water deficit treatment
(July (14.02%), August (13.08%), and September (12.37%)) and highest in the control treatment
(July (20.26%), August (20.66%), and September (20.08%)).
For plant and leaf water content, significant differences among cultivars were found (Table 6).
‘Palatina’ was the cultivar having the highest water content in both plant (63.53%) and leaf (69.34%)
tissues. The cultivar ‘Nero’ had the lowest water content (plant (60.64%) and leaf (66.01%). Within
treatments, water deficit did not affect plant water content, but affected leaf water content. Moderate
(67.75%) and severe water deficits (67.71%) showed no differences, while they differed significantly
from the control (69.11%).

Horticulturae 2018,4, 45 10 of 15
Table 5.
Soil volumetric water content (in %) at soil saturation and during the experiment of four
three-year-old table grape cultivars under three water deficit treatments in 2016.
Volumetric Water Contentz(in %) Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
June
Control
31.41
Cy
32.15
B
33.27
AB
35.49
A
Moderate
30.06 31.95 33.21 34.00
Severe
29.83 33.82 33.89 34.71
July
Control
20.11
a
B
19.69
a
B
18.28
a
B
23.18
a
A
Moderate
14.10
b
15.05
b
15.69
b
18.89
b
Severe
13.72
c
13.00
c
13.59
c
15.93
c
August
Control
20.80
a
B
19.28
a
BC
18.59
a
C
24.27
a
A
Moderate
14.99
b
13.79
b
13.82
b
18.88
b
Severe
13.32
c
12.87
c
11.95
c
14.28
c
September
Control
21.77
a
B
16.00
a
C
20.95
a
AB
22.11
a
A
Moderate
15.80
b
11.56
b
17.20
b
18.51
b
Severe
12.40
c 9.82 c
14.21
c
13.46
c
ANOVA June July August September
Cultivar (C)
<0.0001
*** <0.0001 ***
<0.0001
*** <0.0001 ***
Treatment (T)
0.4600
n.s. <0.0001 ***
<0.0001
*** <0.0001 ***
C*T
0.7557
n.s. 0.3728 n.s.
0.3740
n.s. 0.3591 n.s.
z
June: Volumetric Water Content was measured two weeks before water deficit treatment at soil saturation.
July: Volumetric Water Content was measured two weeks after start of water deficit treatment before irrigation.
September: Volumetric Water Content was measured before harvest. The data represents mean values of back
transformed data of Volumetric Water Content. Treatments included: Control: daily irrigation to 75% of available
water capacity; Moderate: daily irrigation to 50% of available water capacity; and Severe: daily irrigation to 25% of
available water capacity.
y
Different letters indicate significant differences for cultivar (capital letters) and treatment
(lower case) for P< 0.05; No letters indicate no differences for cultivar and/or treatment. ANOVA: ***: P< 0.001;
**: P< 0.01; *: P< 0.05; not significant (n.s.).
Table 6.
Plant and leaf water content (absolute values) of four three-year-old table grape cultivars
under three water deficit treatments at harvest in 2016.
Parameter Cultivar Muscat Bleu Fanny Nero Palatina
Treatment
Plant water content
Control
62.13
BC
z
63.21
AB
60.84
C
63.04
A
Moderate 60.20 63.12 60.14 64.94
Severe
61.76 62.49 60.94 62.59
Leaf water content
Control
69.59
a
A
69.41
a
A
67.50
a
B
69.92
a
A
Moderate 66.20
b
69.36
b
66.24
b
69.12
b
Severe
68.70
b
68.81
b
64.25
b
68.98
b
ANOVA Plant water content Leaf water content
Cultivar (C) 0.0020 *** <0.0001 ***
Treatment (T) 0.8797 n.s. 0.0295 *
C*T 0.4909 n.s. 0.1592 n.s.
The data represents mean values of back transformed data of plant and leaf water content. Treatments included:
Control: daily irrigation to 75% of available water capacity; Moderate: daily irrigation to 50% of available water
capacity; and Severe: daily irrigation to 25% of available water capacity.
z
Different letters indicate significant
differences for cultivar (capital letters) and treatment (lower case) for P< 0.05; No letters indicate no differences for
cultivar and/or treatment. ANOVA: ***: P< 0.001; **: P< 0.01; *: P< 0.05; not significant (n.s.).
4. Discussion
Only a few studies have focused on the comparison of yield and quality of different table grape
cultivars exposed to water deficits [
22
], especially on young vines. Most studies have investigated
the performance of a single cultivar [
9
,
10
,
27
,
28
] most of which to our knowledge are not cultivated in
Germany currently. Therefore, the aim of our experiment was to evaluate the effect of water deficit
treatment on quality and yield of four 3-year old, own rooted and potted table grape cultivars already
grown in Germany and to evaluate their suitability for a potential cultivation under foreseen water
limiting conditions due to climate change.
Overall, the different water deficit levels had only a minor effect on yield and quality of the table
grape cultivars in our study. Vines did not reduce total plant yield or berry size even if AWC was

Horticulturae 2018,4, 45 11 of 15
lowered by 50% in the severe treatment, which resulted in a water saving of up to 31% irrigation water.
However, at the bunch level, cultivars responded to the deficit by decreasing bunch yield and produced
highest yields when vines were moderately stressed. This indicated that a moderate water deficit and
stress treatment may have a beneficial effect on bunch weights in comparison to the control and the
severe water deficit treatments, which achieved lower yields. Most grapevine studies have reported
that drought stress and limited water supply led to decreasing bunch yields as well as berry sizes and
weight [
9
,
15
,
22
]. Differences between the results of these studies and our study can be explained by
variations in plant age, timing, duration, and intensity of water deficits during the growing season.
In general, water limitation in the early season is known to reduce berry size more than late season
deficits [
22
]. Tarricone et al. [
27
] reported a negative effect on bunch weight, berry weight, and berry
size during the early fruit growth stages of ‘Sublima Seedless’, when mild or severe drought stress
was applied at the beginning of berry set. In our study, the total number of berries and number of
marketable berries showed a cultivar specific reaction to the deficit. ‘Nero’ decreased the total number
of berries as well as the number of marketable berries per bunch and a moderate deficit resulted in
the highest number of berries in ‘Palatina’. The remaining cultivars reacted to the water limitation in
the same manner as reported by Conesa et al. [
16
], where the number of berries of ‘Crimson Seedless’
was not influenced by different deficit treatments. Further, the firmness of berries is an important
quality parameter of table grapes, as it determines the acceptance of consumers along with post-harvest
quality [
12
,
29
]. Decreasing fruit firmness was described by several authors, when vines were treated
with increasing water limitation and drought stress levels [
12
,
18
]. However, Zunñiga et al. [
9
,
30
]
did not find decreasing fruit firmness when screening the response of quality and yield parameters
of ‘Flame Seedless’ and ‘Thompson Seedless’ to different irrigation amounts. Overall, firmness is
known to be highly dependent on the maturity stage of the berries, turgor pressure, water content [
31
],
epidermal deterioration, and increasing cell wall elasticity [
32
]. Therefore, our results indicated an
adequate water supply to maintain fruit firmness when water limitation occurred.
TSS, an important quality factor, did not increase or decrease when water deficit increased.
Faci et al. [
22
] observed similar results when irrigation was limited, while the TSS values in a
study of El-Ansary et al. [
18
] increased when drought stress intensified. Increased TSS contents
are most likely caused by a concentration effect, due to smaller berries and/or reduced shoot growth
combined with the reallocation of carbohydrates [
14
]. Therefore, since berry size and weight did not
decrease with the severity of water deficit in our study, no concentrating of sugar within the berries
occurred. Furthermore, total phenolic content was neither positively nor negatively affected when
water deficit became more severe. This indicated that the plants most likely did not activate the
phenolic biosynthesis pathways as a defense mechanism [
16
]. The only internal quality parameter
in our study that decreased when water deficit increased was TA. Williams and Matthews [
33
] and
Dos Santos et al.
[
34
] reported higher TA in the control group or fully irrigated treatments as TA is
known to be influenced by and respond to irrigation [33].
In contrast to the water deficit treatments, cultivar was the primary factor affecting yield and
quality in our study regardless of the deficit treatment implemented. ‘Fanny’ was the cultivar that
produced the highest number of bunches per plant in the first year of fruit production. The other
cultivars differed in the number of fruit-producing plants, where at least four vines produced a
minimum of one bunch per plant. Differences between the number of fruit-yielding plants per cultivar
resulted in major yield differences. As vines were only three years old and in their first year of fruit
production, this fact might have contributed to the large differences between cultivars. Furthermore,
variations in the ability of buds to produce flowering bunches (fruitfulness) in grapevine cultivars were
found by Somkuwar [
35
] in India, where ‘Thompson Seedless’ showed high variation, while ‘Muscat
Hamburg’ and ‘Arkavati’, which are known for consistently high fruitfulness, did not. In a study
by Lisek [
36
], twenty table grape cultivars were compared regarding yield and healthiness. Overall,
differences between the cultivars were found by Lisek [
36
], where ‘Muscat Bleu’ was the cultivar
producing the highest marketable yields within the six-year study, ranging between 0.65–2.52 kg

Horticulturae 2018,4, 45 12 of 15
per vine and year. ‘Fanny’ produced lower yields (0.68–2.51 kg per vine and year) compared to
‘Muscat Bleu’, but had higher bunch and berry weights. In our study, ‘Fanny’ was the cultivar
with the highest yield per vine, bunch yield, and marketable yield. Compared to field studies of
Lisek [
36
],
Zulini et al.
[
37
] and Kadir et al. [
38
] (Table 7), lower yields per plant were determined in
our study. According to Lisek [
36
], differences in the yield of grapevine depend mainly on genetic
factors, plant age, climatic conditions, fruitfulness, and fertilization. At harvest, all screened cultivars
reached maturity (based on OIV maturity standards for table grapes [
39
]) having an equal or higher
TSS degree of 16
◦
Brix or higher and a TSS:TA ratio higher than 20:1. Within the selected table grapes,
high variations in TSS were found. Overall, the highest TSS values and softest berries of ‘Nero’
occurred most likely as a consequence of a delayed harvest time for this cultivar, as Kadir et al. [
38
]
described ‘Nero’, in a comparison of 24 wine and table grape cultivars, as a European cultivar with an
early harvest time. In our study, over-ripening and dehydration of the berries, due to a late harvest
date, could have led to the decrease in quality such as the higher amount of shriveled, non-marketable
berries in ‘Nero’. Lisek [
36
] reported lower TSS values for ‘Fanny’ and ‘Muscat Bleu’ (14.3% and
15.8% respectively). These values are lower than our results as the grapes in the study of Lisek [
36
]
were harvested at individual harvest dates and climatic differences between Poland and Germany
may result in lower TSS values. The highest amounts of bioactive compounds (total phenolic content)
were found in the cultivar ‘Nero’, classified as a blue, black grape [
38
] followed by the blue cultivar
‘Muscat Bleu’. Within the green table grapes, the highest total phenolic contents were measured in the
skin of ‘Palatina’ berries, while ‘Fanny’ had the lowest content of all four cultivars. Kanner et al. [
40
]
and Katalini´c et al. [
41
] also found variations in wine grapes and table grapes ranging from 260 to
930 mg·kg−1and 435 to 3486 mg·kg−1, respectively. Values of most of our cultivars were in the same
range, with total phenolic contents between 73.9 to 675.3 mg
·
kg
−1
FW. In most cases, red/blue grape
skins have a higher total phenolic content than white/green grapes. However, cultivar differences are
reported as main reason for higher or lower phenolic contents in grape berries, not the skin color of
the berries [41–43].
Table 7.
Yield and maturity characteristics of field grown table grape cultivars ‘Muscat Bleu’, ‘Fanny’,
‘Nero’ and ‘Fanny’ based on literature.
Cultivar Muscat Bleu Fanny Nero Palatina
Parameter
Yield (kg·plant−1) 0.65–2.52 1[36]0.68–2.51 1[36] 1.5–6.5 [37,38] 3.2 [37]
Bunch weight (g) 93–181 [36] 239–281 [36,37] 46–138 [38] 152 [37]
Total soluble solids (◦Brix) 15.8–18.4 [36,37] 14.3–15.4 [36,37] 17.3–19.8 [38] 19.0 [37]
Titratable acidity (g·L−1)5.20 [37] 4.49 [37] 6.2–8.7 [37,38] 7.64 [37]
TSS:TA ratio 35.38 2[37]34.3 2[37]21.49 2[37]24.87 2[37]
1
Marketable fruit yield;
2
Calculated based on TSS and TA values given by Zulini et al. [
37
]. Gram (g); kilogram (kg);
liter (L); Total soluble solids (TSS); Titratable acidity (TA).
5. Conclusions
Yield and quality parameters of four young table grape cultivars grown under controlled
conditions in Germany were affected only to a minor extent by water deficits during fruit development.
Severe and prolonged limitation of irrigation water saved up 31% of irrigation water compared to
the control, without having a major influence on important fruit quality parameters. Highest bunch
yields were produced when a moderate water deficit (9% saved water) was implemented. Based on
our results, the tested cultivars could be suitable for cultivation under water deficit due to climate
change. ‘Fanny’, the cultivar having the highest fruitfulness, yields, and biggest berries regardless of
water deficit level, was the most promising cultivar and should be further investigated. Moreover,
since the taste of table grape cultivars is one of the most important quality parameters for consumers,
sensory panels should be conducted to check the acceptance of the most promising cultivars.

Horticulturae 2018,4, 45 13 of 15
Author Contributions:
Conceptualization, C.S.W., N.M. and S.G.H.; Data curation, C.S.W.; Formal analysis,
C.S.W.; Funding acquisition, C.S.W. and S.G.H.; Investigation, C.S.W.; Methodology, C.S.W. and S.G.H.; Project
administration, C.S.W. and S.G.H.; Resources, C.S.W., N.M. and S.G.H.; Supervision, N.M. and S.G.H.; Validation,
C.S.W. and S.G.H.; Visualization, C.S.W.; Writing—original draft, C.S.W.; Writing—review and editing, C.S.W.,
N.M. and S.G.H.
Funding:
This research was funded by the Anton & Petra Ehrmann-Stiftung Research Training Group “Water
People Agriculture”.
Acknowledgments:
This study was conducted within the framework of the Anton & Petra Ehrmann-Stiftung
Research Training Group “Water People Agriculture” at the University of Hohenheim.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Appendix A
Horticulturae 2018, 4, 13 of 15
Funding: This research was funded by the Anton & Petra Ehrmann-Stiftung Research Training Group “Water
People Agriculture”.
Acknowledgments: This study was conducted within the framework of the Anton & Petra Ehrmann-Stiftung
Research Training Group “Water People Agriculture” at the University of Hohenheim.
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Appendix A
Figure A1: Daily mean values of air temperature and air humidity in the open greenhouse during the
experimental period from fruit set until harvest in 2016.
References
1. FAO. The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)—Managing Systems
at Risk; Food and Agriculture Organization of the United Nations: Rome, Italy; Earthscan: London, UK,
2011, doi:978-1-84971-326-9.
2. IPCC. Climate Change 2001: Impacts, Adaptation, and Vulnerability; Cambridge University Press: New York,
NY, USA, 2001.
3. Serra, I.; Strever, A.; Myburgh, P.; Deloire, A. Review: The interaction between rootstocks and cultivars
(Vitis vinifera L.) to enhance drought tolerance in grapevine. Aust. J. Grape Wine Res. 2014, 20, 1–14,
doi:10.1111/ajgw.12054.
4. Costa, J.M.; Ortuño, M.F.; Chaves, M.M. Deficit irrigation as a strategy to save water: Physiology and
potential application to horticulture. J. Integr. Plant Biol. 2007, 49, 1421–1434, doi:10.1111/j.1672-
9072.2007.00556.x.
5. Permanhani, M.; Costa, J.M.; Conceição, M.A.F.; de Souza, R.T.; Vasconcellos, M.A.S.; Chaves, M.M. Deficit
irrigation in table grape: Eco-physiological basis and potential use to save water and improve quality. Theor.
Exp. Plant Physiol. 2016, 28, 85–108, doi:10.1007/s40626-016-0063-9.
6. Teixeira, A.H.d.C.; Bastiaanssen, W.G.M.; Ahmad, M.D.; Bos, M.G. Reviewing SEBAL input parameters for
assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil.
Part B: Application to the regional scale. Agric. Forest Meteorol. 2009, 149, 477–490,
doi:10.1016/j.agrformet.2008.09.014.
7. Molden, D.; Oweis, T.; Steduto, P.; Bindraban, P.; Hanjra, M.A.; Kijne, J. Improving agricultural water
productivity: Between optimism and caution. Agric. Water Manag. 2010, 97, 528–535,
doi:10.1016/j.agwat.2009.03.023.
Figure A1.
Daily mean values of air temperature and air humidity in the open greenhouse during the
experimental period from fruit set until harvest in 2016.
References
1.
FAO. The State of the World’s Land and Water Resources for Food and Agriculture (SOLAW)—Managing Systems at
Risk; Food and Agriculture Organization of the United Nations: Rome, Italy; Earthscan: London, UK, 2011.
2.
IPCC. Climate Change 2001: Impacts, Adaptation, and Vulnerability; Cambridge University Press: New York,
NY, USA, 2001.
3.
Serra, I.; Strever, A.; Myburgh, P.; Deloire, A. Review: The interaction between rootstocks and cultivars
(Vitis vinifera L.) to enhance drought tolerance in grapevine. Aust. J. Grape Wine Res.
2014
,20, 1–14. [CrossRef]
4.
Costa, J.M.; Ortuño, M.F.; Chaves, M.M. Deficit irrigation as a strategy to save water: Physiology and
potential application to horticulture. J. Integr. Plant Biol. 2007,49, 1421–1434. [CrossRef]
5.
Permanhani, M.; Costa, J.M.; Conceição, M.A.F.; de Souza, R.T.; Vasconcellos, M.A.S.; Chaves, M.M. Deficit
irrigation in table grape: Eco-physiological basis and potential use to save water and improve quality.
Theor. Exp. Plant Physiol. 2016,28, 85–108. [CrossRef]
6.
Teixeira, A.H.D.C.; Bastiaanssen, W.G.M.; Ahmad, M.D.; Bos, M.G. Reviewing SEBAL input parameters for
assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil.
Part B: Application to the regional scale. Agric. Forest Meteorol. 2009,149, 477–490. [CrossRef]
7.
Molden, D.; Oweis, T.; Steduto, P.; Bindraban, P.; Hanjra, M.A.; Kijne, J. Improving agricultural water
productivity: Between optimism and caution. Agric. Water Manag. 2010,97, 528–535. [CrossRef]

Horticulturae 2018,4, 45 14 of 15
8.
Pereira, L.S.; Cordery, I.; Iacovides, I. Improved indicators of water use performance and productivity for
sustainable water conservation and saving. Agric. Water Manag. 2012,108, 39–51. [CrossRef]
9.
Zúñiga-Espinoza, C.; Aspillaga, C.; Ferreyra, R.; Selles, G. Response of Table Grape to Irrigation Water in the
Aconcagua Valley, Chile. Agronomy 2015,5, 405–417. [CrossRef]
10.
Blanco, O.; Faci, J.M.; Negueroles, J. Response of table grape cultivar ‘Autumn Royal’ to regulated deficit
irrigation applied in post-veraison period. Spanish J. Agric. Res. 2010,8, 76–85. [CrossRef]
11.
Ezzahouani, A.; Williams, L.E. Effect of irrigation amount and preharvest cutoff date on vine water status
and productivity of Danlas grapevines. Am. J. Enol. Viticul. 2007,58, 333–340.
12.
Conesa, M.R.; de la Rosa, J.M.; Artés-Hernández, F.; Dodd, I.C.; Domingo, R.; Pérez-Pastor, A. Long-term
impact of deficit irrigation on the physical quality of berries in “Crimson Seedless” table grapes. J. Sci.
Food Agric. 2015,95, 2510–2520. [CrossRef] [PubMed]
13.
Matthews, M.A.; Cheng, G.; Weinbaum, S.A. Changes in water potential and dermal extensibility during
grape berry development. J. Am. Soc. Hortic. Sci. 1987,112, 314–319.
14.
Reynolds, A.G.; Naylor, A.P. ‘Pinot noir’ and ‘Riesling’ Grapevines Respond to Water Stress Duration and
Soil Water-holding Capacity. HortScience 1994,29, 1505–1510.
15.
Perniola, R.; Crupi, P.; Genghi, R.; Antonacci, D. Cultivar and rootstock interaction affects the physiology
and fruit quality of table grape with different water management—Preliminary results. Acta Hortic.
2016,1136, 129–136. [CrossRef]
16.
Conesa, M.R.; Falagán, N.; de la Rosa, J.M.; Aguayo, E.; Domingo, R.; Pérez Pastor, A. Post-veraison
deficit irrigation regimes enhance berry coloration and health-promoting bioactive compounds in “Crimson
Seedless” table grapes. Agric. Water Manag. 2016,163, 9–18. [CrossRef]
17.
Jayasena, V.; Cameron, I. Brix/Acid ratio as a predictor of consumer acceptability of Crimson Seedless table
grapes. J. Food Qual. 2008,31, 736–750. [CrossRef]
18. El-Ansary, D.O.; Nakayama, S.; Hirano, K.; Okamoto, G. Response of Muscat of Alexandria table grapes to
post-veraison regulated deficit irrigation in Japan. Vitis J. Grapevine Res. 2005,44, 5–9.
19.
Santesteban, L.G.; Miranda, C.; Royo, J.B. Effect of water deficit and rewatering on leaf gas exchange and
transpiration decline of excised leaves of four grapevine (Vitis vinifera L.) cultivars. Sci. Hortic.
2009
,121,
434–439. [CrossRef]
20.
Kamiloglu, O.; Sivritepe, N.; Önder, S.; Daghan, H. Effects of water stress on plant growth and physiological
characteristics of some grape varieties. Fresenius Environ. Bull. 2014,23, 2155–2163.
21.
Chaves, M.M.; Santos, T.P.; Souza, C.R.; Ortuño, M.F.; Rodrigues, M.L.; Lopes, C.M.; Maroco, J.P.; Pereira, J.S.
Deficit irrigation in grapevine improves water-use efficiency while controlling vigour and production quality.
Ann. Appl. Biol. 2007,150, 237–252. [CrossRef]
22.
Faci, J.M.; Blanco, O.; Medina, E.T.; Martínez-Cob, A. Effect of post veraison regulated deficit irrigation in
production and berry quality of Autumn Royal and Crimson table grape cultivars. Agric. Water Manag.
2014,134, 73–83. [CrossRef]
23.
Singleton, V.L.; Orthofer, R.; Lamuela-Raventos, R.M. Analysis of total phenols and other oxidation substrate
and antioxidants by means of Folin-Ciocalteu reagent. Methods Enzymol. 1999,299, 152–178.
24.
Sahamishirazi, S.; Moehring, J.; Claupein, W.; Graeff-Hoenninger, S. Quality assessment of 178 cultivars
of plum regarding phenolic, anthocyanin and sugar content. Food Chem.
2017
,214, 694–701. [CrossRef]
[PubMed]
25.
Wolfinger, R. Covariance structure selection in general mixed models. Commun. Stat. Simul. Comput.
1993,22, 1079–1106. [CrossRef]
26.
Piepho, H.P. A SAS macro for generating letter displays of pairwise mean comparisons. Commun. Biom.
Crop Sci. 2012,7, 4–13.
27.
Tarricone, L.; Di Gennaro, D.; Amendolagine, A.M.; Notarangelo, L.; Vox, G.; Schettini, E.; De Palma, L.
Effects of water regimes on vine performance and quality of “Sublima” seedless table grape covered with
plastic film to advance grape ripening. Acta Hortic. 2014,1038, 593–6000. [CrossRef]
28.
El-Ansary, D.O.; Okamoto, G. Vine water relations and quality of “Muscat of Alexandria” table grapes
subjected to partial root-zone drying and regulated deficit irrigation. J. Jpn. Soc. Hortic. Sci.
2007
,76, 13–19.
[CrossRef]
29.
Mahajan, B.V.C.; Arora, N.K.; Gil, M.I.S.; Ghuman, B.S. Studies on extending storage life of ‘Flame Seedless’
grapes. J. Hortic. Sci. Ornamental Plants 2010,2, 88–92.

Horticulturae 2018,4, 45 15 of 15
30.
Zuñiga, C.; Aspillaga, C.; Ferreyra, R.; Selles, G. Response of “Flame Seedless” vines to different levels of
irrigation water in the Aconcagua Valley, Chile. Acta Hortic. 2017,1150, 295–302. [CrossRef]
31.
Bernstein, Z.; Lustig, I. A new method of firmness measurement of grape berries and other juicy fruits. Vitis
1981,20, 15–21.
32.
Matthews, M.A.; Thomas, T.R.; Shackel, K.A. Fruit ripening in Vitis vinifera L.: Possible relation of veraison
to turgor and berry softening. Aust. J. Grape Wine Res. 2009,15, 278–283. [CrossRef]
33.
Williams, L.E.; Matthews, M.A. Grapevine. In Irrigation of Agricultural Crops; Stewart, B.A., Nielsen, D.R.,
Eds.; ASA-CSSA-SSSA: Madison, WI, USA, 1990; pp. 1019–1055.
34.
Dos Santos, T.P.; Lopes, C.M.; Rodrigues, M.L.; de Souza, C.R.; Ricardo-da-Silva, J.M.; Maroco, J.P.;
Pereira, J.S.; Chaves, M.M. Effects of deficit irrigation strategies on cluster microclimate for improving
fruit composition of Moscatel field-grown grapevines. Sci. Hortic. 2007,112, 321–330. [CrossRef]
35. Somkuwar, R.G. Fruitfulness in Grapes; National Research Centre for Grapes: Pune, India, 2005.
36.
Lisek, J. Evaluation of yield and healthiness of twenty table grapevine cultivars grown in central poland.
J. Hortic. Res. 2014,22, 101–107. [CrossRef]
37.
Zulini, L.; Vecchione, A.; Antonelli, L.; Stefanini, M. Characteristics of wine and table grapevine hybrids
tested for cultivation in Trentino (northern Italy). IOBS/wprs Bull. 2008,36, 215–219.
38.
Kadir, S.; Ennahli, S.; Griffin, J.; Ryer, R.; Shelton, M. Growth, Yield, Fruit Composition of 24 Wine and Table
Grape Cultivars and Selections. Int. J. Fruit Sci. 2007,7, 17–30. [CrossRef]
39. OIV. OIV Standard on Minimum Maturity Requirements for Table Grapes; OIV: Paris, France, 2008.
40.
Kanner, J.; Frankel, E.; Granit, R.; German, B.; Kinsella, J.E. Natural antioxidants in grapes and wines. J. Agric.
Food Chem. 1994,42, 64–69. [CrossRef]
41.
Katalini´c, V.; Možina, S.S.; Skroza, D.; Generali´c, I.; Abramoviˇc, H.; Miloš, M.; Ljubenkov, I.; Piskernik, S.;
Pezo, I.; Terpinc, P.; et al. Polyphenolic profile, antioxidant properties and antimicrobial activity of grape skin
extracts of 14 Vitis vinifera varieties grown in Dalmatia (Croatia). Food Chem.
2010
,119, 715–723. [CrossRef]
42.
Baiano, A.; Terracone, C. Varietal differences among the phenolic profiles and antioxidant activities of seven
table grape cultivars grown in the south of Italy based on chemometrics. J. Agric. Food Chem.
2011
,59,
9815–9826. [CrossRef] [PubMed]
43.
Yang, J.; Martinson, T.E.; Liu, R.H. Phytochemical profiles and antioxidant activities of wine grapes.
Food Chem. 2009,116, 332–339. [CrossRef]
©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).