Introduction

In many regions, climate change leads to an increase in air temperature combined with a reduction of rainfall, intensifying climatic demand and water deficits (WD) (Cardell et al., 2019; IPCC, 2022), which in turn may negatively impact grapevine development, yield and grape composition (Santos et al., 2020). In addition, climate change modulates disease pressure, potentially leading to yield and quality losses, besides increasing costs due to more pesticide spraying (Salinari et al., 2007; Santos et al., 2020) and reducing viticulture acceptability by consumers (Guichard et al., 2017). Adopting new fungus disease-tolerant varieties appears as a promising long-term solution to implement smarter viticulture, but unfortunately, little is known regarding the adaptation of these new genotypes to climate fluctuations, such as WD. Indeed, most grapevine breeding programs do not consider drought performance as a primary attribute for selection (Wilhelm et al., 2021). This is related to the difficulty in identifying relevant grapevine ideotypes building a drought-performant behaviour of a perennial fruit crop in the short and long terms.

The first strategy adopted by grapevines, under WD, is the reduction of vegetative growth as a way to reduce transpiring surfaces (Simonneau et al., 2017), reducing total leaf area by firstly limiting secondary ramification, with later effects on the main shoot (Lebon et al., 2006; Pellegrino et al., 2005), in addition to reducing leaf expansion (Doupis et al., 2016) and ultimately annual vegetative biomass (Zufferey et al., 2020).

As the water deficit increases, the vine regulates leaf water losses by closing stomata and, thus, reducing the transpiration rate per leaf area, reducing CO2 assimilation (Flexas et al., 2002). Genotypic variability under drought has been widely explored in photosynthesis reduction as well as in the increase in the intrinsic water use efficiency (WUEi), where both are frequently correlated to regulations in stomatal conductance (Bota et al., 2016; Gutiérrez-Gamboa et al., 2019; Tortosa et al., 2019). Stomata closure sensitivity to WD among varieties led to their classification in either iso or anisohydric behaviour (Chaves et al., 2010). However, such classification has been debated due to controversial results for the same cultivar and the fact that such behaviour was highly dependent on environmental conditions (Hochberg et al., 2018; Martínez-Vilalta and Garcia-Forner, 2017).

Reproductive organs are also importantly affected by seasonal WD, mostly during the same season, mostly leading to reductions in berry weight, with greater impact if WD occurs during the pre-véraison stage (Dayer et al., 2013; Levin et al., 2020; Mirás-Avalos and Intrigliolo, 2017; Ojeda et al., 2001). After véraison, WD can negatively affect reserve replenishment if the photosynthetic activity is not enough to sustain berry sugar demand (Rossouw et al., 2017).

In addition to these seasonal processes, the interseasonal effects of WD include a reduced number of clusters per shoot, number of berries per cluster and reserve replenishment (Dayer et al., 2013; Guilpart et al., 2014; Holzapfel et al., 2010; Levin et al., 2020). Reserve replenishment in grapevine perennial organs (roots and trunks) is an important process as they play a major role in sustaining spring growth and determining yield potential (Holzapfel et al., 2010). Low reserve status has been linked to slower shoot growth, impaired flower formation and poor fruitset (Bennett et al., 2005; Vasconcelos et al., 2009). In fact, C reserves are determinant in ensuring vineyard longevity, a factor of great importance to be considered for a perennial fruit crop.

Despite many studies addressing WD effects on grapevine development, and grape yield and composition, there is a lack of knowledge regarding the interconnected processes involved in the interannual responses to drought, especially for new fungus disease-tolerant genotypes. In the present study, an original methodological approach was proposed to reduce the experimental noise usually linked to open-field experiments. First, the phenotyping was downscaled to the individual vine level, meaning that each vine was considered a single biological replicate. This was performed to avoid averaging plants displaying different water statuses because of other sources of variations (soil heterogeneity, vegetative volume…) than the imposed water regime. Secondly, the stage of ripe berries was determined as the arrest of phloem unloading into berries when both sugar and water content per fruit are maximum (Alem et al., 2021; Bigard et al., 2019; Shahood et al., 2020). Phenotyping the yield at this precise physiological development point and not at a specific Brix level allowed an accurate estimation of the maximum fruit fresh weight per plant, avoiding the uncontrolled effects of berry shrivelling during technical ripening. Thus, the objectives of the present work were to quantitatively evaluate the effects of WD over four consecutive years on leaf gas exchanges, vegetative development and yield components in a set of six new fungus disease-tolerant genotypes issued from the INRAE breeding program.

Materials and methods

Results

1. Growing conditions

1.1. Climatic conditions

Over the 4 years of study, the minimum and maximum monthly averages temperatures for 2019, 2020, 2021 and 2022 growing seasons (April to October) were 9.2 °C/31.4 °C, 9.0 °C/29.6 °C, 9.6 °C/20.3 °C and 11.1 °C/32.7 °C, respectively (Figure S1). Extreme temperatures (Daily Tmax above 35 °C) were mostly recorded in the 2022 season, reaching 6 days, while 2021 and 2019 recorded 3 and 2 days, respectively, and in 2020, none. The 2021 and 2022 growing seasons were characterised as the driest, showing the lowest rainfall, 190 mm and 253 mm, respectively (Figure S1). Climatic water balance (∑Rainfall - ∑ET0) ranged from –716 mm and –755 mm from April to October for those two years.

1.2. Phenology

In general, the 2022 season showed the earliest budburst (~ 5 days) and véraison (~ 10 days) dates, while 2020 and 2021 showed the latest harvest and flowering dates, respectively. The 2021 season showed the longest growing season (131 days) (budburst to véraison) but the shorter sugar loading duration (31 days from véraison to Vmax), around 15 days difference from the 2020 and 2022 averages.

Considering all 3 years, budburst occurred the earliest for Artaban and the latest for 3176N and 3159N (p-value < 0.01, Table 1). Despite the 15 days difference in budburst timing between those genotypes, small differences in the time of flowering were observed (5 days maximum, p-value < 0.05, Table 1). However, bigger differences were seen in the dates of véraison (p-value < 0.001) and of the physiological ripe stage (p-value < 0.01) among the genotypes (Table 1). Véraison was first recorded in Artaban, 3176N, 3159B and Floreal, and 12 days later in G5 and G14. The first genotype to reach Vmax and to be harvested was Floreal, and the latest was G14 (+16 days) (Table 1). The time for grapes to reach the physiological ripe stage after veraison ranged from 30 days for G5 to 52 days for Artaban. Overall, the seasons (budburst to harvest) were the longest for G14 and Artaban (179 days) and the shortest for 3159B (153 days) (Table 1).

1.3. Plant water status: accumulated 𝚿p per day

The 𝚿p in all genotypes and years decreased during the season, to a greater extent, in non-irrigated vines when compared to irrigated vines (data not shown). In general, the higher Acc-𝚿p values were seen in the 2019, 2020 and 2022 seasons (–0.36, –0.33 and –0.42 MPa, respectively), while 2021 had the lowest value (–0.67 MPa) for all genotypes, agreeing with the climatic data previous presented (Table S1, Figure 1). The differences in Acc-𝚿p between NI and I plants were the highest in 2021, reaching, on average, for all genotypes –52 % MPa (Table 1). Interestingly, in the 2022 season, where no irrigation was performed, both Floreal and G14 showed an inverse behaviour between I and NI vines, where lower values of Acc-𝚿p were seen in previously irrigated vines, although no differences between I and NI vines were observed for other varieties.

Figure 1. Proportion of variance explained (η²) by each factor and its interactions (p-value < 0.05), on slopes (blue colours) and intercepts (orange colours), in each vegetative, reproductive and physiological variable.

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^a`Ln` indicates linearised variables with natural logarithm. `*` indicates variables fitted in function of the accumulated 𝚿p over 2 seasons period (Year_n+ Year _n-1).

Table 1. Budburst (Bud), flowering (Flo), véraison (Ver) and harvest (Vmax) mean dates (day of the year), growing cycle (budburst to veraison), sugar loading (véraison to Vmax) and whole cycle (budburst to Vmax) duration in days, per genotype and year for 6 fungus disease-tolerant genotypes and Syrah, from 2020 to 2022, Gruissan - France, 2020-2022.

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	Phenology				Growing cycle	Sugar loading	Whole cycle
Genotype	Bud	Flo	Ver	Vmax
Syrah	85 ab	151 ab	207 ab	246 ab	122	39	161
3176N	88 b	151 ab	202 a	252 ab	114	50	164
3159B	88 b	152 b	205 a	241 ab	117	36	153
Artaban	73 a	147 a	200 a	252 ab	127	52	179
Floreal	82 ab	150 ab	203 a	239 a	121	36	157
G14	76 ab	151 ab	216 c	255 b	140	39	179
G5	85 ab	152 b	214 bc	244 ab	129	30	159
Year
2020	86 b	148 a	206 b	252 b	120	46	166
2021	82 ab	157 b	213 c	244 a	131	31	162
2022	79 a	146 a	200 a	245 a	121	45	166

*Different letters indicate statistical differences, at 0.05 significance level, among genotypes or year (Bonferroni adjustment) and ‘ns’ indicates no significance.

From 2020 to 2022 and for the two water treatments, the sum of Acc-𝚿p for all genotypes reached the lowest value for Floreal (–1.85 MPa for I to –2.18 MPa for NI) while it was the highest in 3176N and Artaban (–1.03 MPa for I to –1.23 Mpa for NI) (Table 2). Correspondingly, the factor Year explained 46 % of the variation of Acc-𝚿p while Genotype and irrigation explained, respectively, 24 % and 5 % of the total variation (Table S1). In general, NI vines showed a decrease of 25 % MPa when compared to I vines. The strongest decrease between NI and I vines was seen for G5 (–38 %) while the least for Floreal (–18 %) (Table 2).

Table 2. Mean and standard deviations (SD) for the accumulated values of pre-dawn water potential (Acc-𝚿p), number of berries, yield (kg), number of shoots and pruning weight (kg) per plant, and relative difference (%) between NI and I treatments, for 6 fungus disease-tolerant genotypes and Syrah under irrigated (I) and non-irrigated treatments (NI), from 2020 to 2022, Gruissan - France.

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	Acc-𝚿p (MPa)				Relative Diff. (%)				Nb of berries per plant (x 1000)					Relative Diff. (%)				Yield (kg)				Relative Diff. (%)
	I		NI			G	I	G:I	I		NI				G	I	G:I	I		NI			G	I	G:I
	Mean	SD	Mean	SD		***	***	ns	Mean	SD	Mean		SD		***	***	ns	Mean	SD	Mean	SD		***	***	ns
Syrah	-1.55	0.08	-1.83	0.12	-18	b			6.6	1.5	5.2	1.5		-21	e			10.1	2.5	7.2	2.5	-29	cd
3176N	-0.96	0.06	-1.25	0.06	-31	d			4.6	0.9	4.8	0.9		6	cd			10.4	2.2	10.0	2.4	-4	d
3159B	-1.06	0.08	-1.43	0.08	-34	c			4.6	1.2	3.9	0.8		-15	bc			7.8	2.3	6.3	1.3	-19	bc
Artaban	-1.03	0.09	-1.23	0.15	-20	d			4.5	0.9	4.2	0.8		-7	bcd			7.0	1.7	5.8	1.2	-18	b
Floreal	-1.85	0.12	-2.18	0.21	-18	a			3.7	0.7	2.7	0.9		-28	a			5.1	1.0	3.4	1.4	-33	a
G14	-1.42	0.23	-1.76	0.23	-24	b			5.4	1.6	5.1	1.4		-4	de			8.5	3.0	7.3	2.8	-14	bc
G5	-0.98	0.09	-1.35	0.19	-38	cd			3.8	2.1	3.2	0.8		-17	ab			7.1	1.6	5.9	1.6	-18	b
Mean	-1.26	0.34	-1.58	0.37	-25				4.7	1.6	4.2	1.4		-11				8.0	2.7	6.5	2.7	-19
	Nb of shoots per plant				Relative Diff. (%)				Pruning weight (kg)					Relative Diff. (%)
	I		NI			G	I	G:I	I		NI				G	I	G:I
	Mean	SD	Mean	SD		***	ns	*	Mean	SD	Mean	SD			***	***	*
Syrah	52	10	44	10	-14	d		*	2.5	1.0	2.0	0.5		-20	bc		*
3176N	46	8	48	8	5	d		ns	2.2	0.6	1.9	0.7		-16	b		*
3159B	39	7	38	5	-3	c		ns	2.9	0.6	2.3	0.4		-21	c		*
Artaban	28	3	29	4	3	a		ns	1.4	0.3	1.3	0.3		-5	a		ns
Floreal	39	3	35	8	-11	bc		ns	1.5	0.3	1.0	0.4		-30	a		*
G14	37	3	32	5	-14	bc		*	1.2	0.5	1.2	0.4		0	a		ns
G5	32	4	34	5	9	ab		ns	1.0	0.5	1.0	0.3		-2	a		ns
Mean	39	9	37	9	-5				1.8	0.8	1.50	0.6		-17

*Values correspond to the accumulated data per plant and treatment 2020, 2021 and 2022 for all genotypes. No irrigation treatment was performed in 2022. ‘Relative Diff (%)’ indicates the difference between NI and I treatments (NI-I) / I * 100. ‘G’, ‘I’ and ‘G:I’ indicate the effect of genotype, irrigation and their interaction, respectively. ‘***’, ‘**’, ‘*’ indicates 0.001, 0.01 and 0.05 significance levels and ‘ns’ indicates no significance. Different letters indicate statistical differences among genotypes (Bonferroni adjustment).

3. Genotype, year and 𝚿p contributions in plant responses to water deficit

All plants variables were then fitted as a function of Acc-𝚿p, considering each plant, per genotype and year, as a biological replication to quantify the general effects of each parameter of the study (𝚿p or Acc-𝚿p, Genotype and Year) on plant responses (Figure 1).

3.1. Overall Genotype and Year effects on plants responses to water deficit

The variations of 𝚿p and their interactions with the ‘Genotype’ and ‘Year’ could explain 52 % to 90 % of variations found for vegetative and reproductive variables (Figure 1). The lowest η² values were reported in physiological variables, reaching 17 % and 18 % for WUEi and WUEinst, respectively. Variations of water status (𝚿p or Acc-𝚿p) explained up to 59 % of variations in berry weight, 40 % in shoot weight and 50 to 36 % of gas exchanges (gs, A and E).

The variations of the slopes due to the Genotype (𝚿p:Genotype) explained from 0.5 % in berry weight to a maximum of 4.3 % in WUEi. Interestingly, for those variables depending on the Acc-𝚿p from Year_n and Year_n-1, the effect of genotype on slopes was not significant (variables marked with an ‘*’, Figure 1). The genotype effect on slopes (𝚿p:Genotype) is illustrated by the fitted curves of leaf photosynthesis (Figure 2), individual berry weight (Figure 3) and plant pruning weight (Figure 4).

In contrast, the Genotype highly impacted the maximum values of the variables (intercept, 𝚿p or Acc-𝚿p = 0, ‘Genotype’), accounting for 3.7 % to 39 % of the variations in photosynthesis (dashed lines, Figure 2) and Nb of berries per cluster, respectively. In general, Year and the interaction Genotype:Year (which are illustrated by the dashed lines in Figures 3 and 4) showed lower contributions in intercept variations than Genotype, except for Yield, which showed the highest η² for Year (21 %) (Figure 1).

Figure 2. Photosynthesis response to 𝚿p for Syrah and 6 fungus disease-tolerant genotypes, Gruissan -France.

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^aEach point represents one plant measured in that specific year (gray, blue, red and brown colours for 2018, 2019, 2020 and 2021, respectively). Point shapes ‘●’ and ‘+’ indicate irrigated and non-irrigated plants, respectively. Lines are linear regressions (Ln A = 𝚿p + Genotype + 𝚿p:Genotype). Dashed lines indicate the intercepts for each genotype.

Figure 3. Individual berry weight (g) response to accumulated-𝚿p (Acc-𝚿p) for Syrah and 6 fungus disease-tolerant genotypes, Gruissan - France.

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^aEach point represents one plant measured in that specific year (blue, red, brown and dark grey colours for 2019, 2020, 2021 and 2022, respectively). Point shapes ‘●’ and ‘+’ indicate irrigated and non-irrigated plants, respectively. Lines are linear regressions (Berry weight = 𝚿p + Genotype*Year + 𝚿p:Genotype). Dashed lines indicate the intercepts for each year within genotype.

Figure 4. Pruning weight (g) response to accumulated-𝚿p (Acc-𝚿p) for Syrah and 6 fungus disease-tolerant genotypes, Gruissan - France.

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^aEach point represents one plant measured in that specific year (blue, red, brown and dark grey colours for 2019, 2020, 2021 and 2022, respectively). Point shapes ‘●’ and ‘+’ indicate irrigated and non-irrigated plants, respectively. Lines are linear regressions (Ln pruning weight = 𝚿p + Genotype*Year + 𝚿p:Genotype). Dashed lines indicate the intercepts for each year within genotype.

3.2. Intercepts: the genotype potential in the absence of water deficit (𝚿p = 0)

Based on the intercept values (𝚿p or Acc-𝚿p = 0), genotypes could be divided into different groups regarding their maximum photosynthetic activity (Amax) and WUEi (WUEi max). The first group, including 3159B and 3176N, displayed the lowest Amax (Ln Amax 2.8 μmol CO₂ m^-2 s^-1, Amax 16 μmol CO₂ m^-2 s^-1) and WUEi max (59 μmol CO₂ mol H₂O^-1), similarly to Syrah. The second group, formed by G14 and G5, showed higher values of Amax (Ln Amax 3.0 μmol CO₂ m^-2 s^-1, Amax 20 μmol CO₂ m^-2 s^-1) and WUEi max (71 μmol CO₂ mol H₂O^-1) (Figure 2, Figure 5A, B). Lastly, compared to Syrah, Artaban presented a similar Amax but higher WUEi max, while Floreal showed a higher Amax but similar WUEi max (Figure 5A, B).

Figure 5. Intercept (Ψp = 0) per genotype for photosynthesis (A) and intrinsic water use efficiency, WUEi (B).

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^aDifferent lowercase letters indicate statistical differences among genotypes (Bonferroni adjustment, p-value < 0.05). The variable of photosynthesis was linearised with natural logarithm, the left and right y-axis display the linearised and real values, respectively.

The reproductive and vegetative variables’ maximum values per genotype and year are shown in Figure 6. In general, compared to Syrah, maximum values of berry weight (BWmax) were higher for G5 and 3176N (~ 2.9 g), lower for Floreal and 3159B (~ 2.0 g) or similar for Artaban and G14 (~ 2.3 g) (Figure 3 and 6). The number of clusters per shoot and yield were either similar to or lower than those values seen in Syrah. Artaban and G5 showed a similar number of clusters (1.4), and 3176N and G14 had a similar yield (4.7 kg) as Syrah. Ultimately, 3176N, 3159B, Floreal and G14 displayed a lower number of clusters, and 3159B, Artaban, Floreal and G5 showed lower yields than Syrah (Figure 6).

Figure 6. Intercept (𝚿p = 0) per genotype and year for reproductive variables, berry weight (‘BW (g)’), number of clusters per shoot (‘Nb of clusters’), yield (‘Yield (kg)’), and vegetative variables, Ln shoot weight (‘Ln SW (g)’), Ln pruning weight (‘Ln PW (g)’).

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^aDifferent lowercase and uppercase letters indicate statistical differences among years within genotypes and among genotypes, respectively (Bonferroni adjustment, p-value < 0.05). The variables of shoot weight and pruning weight were linearised with natural logarithm.

With regards to vegetative variables, the only genotype to show lower vigour (shoot weight) than Syrah (Ln SW 4.4 g, SW 82g) was G5 (Ln SW 3.95g, SW 52g) (Figure 6). The maximum pruning weight was either similar (G14 and 3159B, Ln PW 7.25 kg, PW 1.4 kg) or lower (G5, Artaban, Floreal and 3176N, Ln PW 6.56 kg, PW 0.7 kg) than Syrah (Figure 4 and Figure 6).

Besides the genotype, the intercepts of reproductive and vegetative variables also varied among the years. Variation among years within each genotype was rather low for individual berry weight (Figure 3 and Figure 6) and plant pruning weight (except for G14) (Figure 4 and Figure 6), but variations were greater for individual shoot weight (Ln SW), number of clusters per shoot and yield (Figure 6). In general, the seasons of 2020 and 2022 showed higher intercept values, while 2021 was the lowest (Figure 3, Figure 4 and Figure 6).

3.3. Slopes: the genotype sensibility to variations in 𝚿p

To compare genotype sensibility to water deficit, we extracted the slopes from a set of plant functioning variables (Table 3). All variables decreased as 𝚿p dropped, with the exception of WUEi. The most and least sensitive variables were yield and number of clusters per shoot, showing a general reduction of –0.34 kg and –0.1 x 10^-4 per 0.1 MPa decrease, respectively, for all genotypes (Table 3).

Table 3. Slopes (reductions in absolute values per 0.1 MPa decrease) per genotype (𝚿p: Genotype) and general slope (𝚿p) for the reproductive (berry weight, number of clusters per shoot and yield), vegetative (shoot weight and pruning weight) and physiological (linearised photosynthesis (A) and, intrinsic water use efficiency (WUEi) variables for 6 fungus disease-tolerant genotypes and Syrah.

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Genotype	Berry weight (g)	Number of cluster per shoot	Yield (kg)	Ln Shoot weight (g)	Ln Pruning weight (g)	Ln A (µmol CO2 m -² s -1)	WUEi (µmol CO2 mol H2O -1)
Syrah	-0.16 a	0.07	-0.60	-0.10 ab	-0.19 ab	-0.14 a	3.29 b
3176N	-0.20 ab	0.06	-0.28	-0.23 ab	-0.23 b	-0.15 ab	2.51 bc
3159B	-0.15 a	-0.05	-0.41	-0.22 b	-0.24 b	-0.19 bc	0.81 bcd
Artaban	-0.13 a	0.12	-0.04	-0.15 ab	-0.19 ab	-0.21 c	-2.42 d
Floreal	-0.14 a	-0.06	-0.27	-0.13 ab	-0.17 ab	-0.18 abc	7.63 a
G14	-0.18 ab	-0.02	-0.51	-0.20 b	-0.20 b	-0.21 c	1.77 ns
G5	-0.23 b	-0.01	-0.12	-0.08 a	-0.07 a	-0.28 d	-0.81 cd
General slope (𝚿p)	-0.17	1,00E-04	-0.34	-0.16	-0.18	-0.14	6.00
𝚿p: Genotype	***	ns	ns	**	**	***	***
𝚿p	***	***	***	***	***	***	***

* Values indicate reductions (berry weight, Number of clusters per shoot, yield, shoot weight, pruning weight and photosynthesis) and gain (WUEi) per 0.1 MPa decrease. The variables of shoot weight, pruning weight and photosynthesis were linearised with natural logarithm. ‘***’, ‘**’, ‘*’ indicates 0.001, 0.01 and 0.05 significance levels and ‘ns’ indicates no significance. Different letters indicate statistical differences among genotypes (Bonferroni adjustment).

The most sensitive genotype regarding reproductive and physiological variables was G5, which showed the highest reductions in berry weight, photosynthesis (Ln A) and WUEi (–0.23 g, –0.28 μmol CO₂ m^-2 s^-1 and –0.81 μmol CO₂ mol H₂O^-1, respectively). In contrast, Syrah, 3159B and Floreal showed lower reductions of both berry weight and photosynthesis and higher increase in WUEi (Figure 2, Figure 3 and Table 3).

The highest reductions in vegetative development (Ln shoot weight and Ln pruning weight) were seen in G14, 3159B and 3176N, while the lowest was in G5 (Figure 4 and Table 3). Artaban consistently showed intermediate values of slopes, except for WUEi, where it showed the highest reductions (–2.42 µmol CO₂ mol H₂O^-1 per 0.1 MPa decrease) (Table 3).

Despite differences observed among genotypes in all variables, genotypes showed similar responses to Syrah (except for G5 with higher berry weight reduction). However, regarding photosynthesis response, genotypes were either similar (3176N and Floreal) or more sensitive (3159B, Artaban, G14 and G5) than Syrah (Figure 2 and Table 3). Yet, as regards to WUEi, genotypes showed higher (Floreal), lower (Artaban and G5) or similar (3176N and 3159B) increases under WD as Syrah (Table 3).

3.3. PCA of plant responses to water deficit

Principal components analysis with the slopes and intercepts extracted from the linear fitted models (Table 3, Figure 5 and Figure 6) are shown in Figure 7. The PCA with genotype sensitivity to water deficit (slopes) explained 66 % of the variation, where the first and second principal components (Dim1 and Dim2) accounted for 42.9 % and 23 % (Figure 7A). Dim1 was mainly related to the slopes of photosynthesis (S_A) and pruning weight (S_PW), and Dim2 was related to the slopes of the number of clusters per shoot (S_F) and WUEi (S_WUEi). Genotypes displayed different acclimations to water deficit. The genotype G5 showed a higher sensitivity of berry weight and photosynthesis to water deficit while fewer regulations of canopy development. Floreal and G14 (oppositely from Artaban) showed greater regulations in yield and number of clusters per shoot. Floreal also correlated with WUEi, thus, indicating an increase in this variable as the water deficit increased. Finally, 3159B and 3176N were characterised by a greater sensitivity to water deficit of vegetative development (pruning weight and shoot weight) and lower sensitivity to water deficit of WUEi for 3176N. The genotype G14 was poorly represented on this PCA (Figure 7A).

Figure 7. PCA of plant responses, slopes (A), intercepts at 𝚿p = 0 MPa (B) and at 𝚿p = -0.5 MPa (C), for berry weight (BW), yield, number of clusters per shoot (F), pruning weight (PW), shoot weight (SW), photosynthesis (A) and intrinsic water use efficiency (WUEi) per genotype and year, Gruissan - France.

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^a ‘S’, ‘Ii’ and ‘I’ stand for slope, intercept per year and intercept for each variable.

The PCA with genotype and year effects on the maximum values of plant variables, i.e., under unlimited water supply (intercepts) and under high water deficit (𝚿p = –0.5 MPa), explained 60 % and 58 % of the variation, respectively. Where Dim1 and Dim2 accounted for 38.7 % and 21.5 % in the former (Figure 7B) and 34.2 % and 24.3 % in the latter (Figure 7C).

Under unlimited water supply, photosynthesis (I_A) was opposite to the pruning weight (Ii_PW) and shoot weight (Ii_SW), and all three correlated to Dim1, while Yield (Ii_Yield) and the number of clusters per shoot (Ii_F) mostly contributed to Dim2. Genotypes were mainly spread on Dim1, where Syrah and 3159B were opposite to G5, characterised, respectively, by higher vegetative biomass and photosynthetic capacity. G14, Artaban and Floreal were poorly represented by this PCA (central position). Years were mainly related to Dim2, clearly separating 2021 (correlated to lower Yield potential) from the 2019, 2020 and 2022 seasons. Indeed, the 2021 corresponded to the driest season (Figure 7B).

Under high water deficit (Acc 𝚿p of –0.5 MPa), pruning weight was correlated to shoot weight and yield on Dim1, while WUEi was correlated to A, and both were inversely correlated to the number of clusters on Dim2. Genotypes were mainly distributed on Dim2, while Years were spread on Dim1. Floreal and 3176N were mostly correlated to higher WUEi and A and opposite to G5. Yet, Syrah and 3159B continued showing the highest vegetative development and yield. Artaban was poorly represented by this PCA. As previously remarked, 2021, the driest season, was opposite to most of the vegetative and reproductive variables (Figure 7C).

Discussion

The classical approach of studying a population of plants submitted to the same treatment (I and NI) is a challenging task when working in field conditions where many times, exterior factors are uncontrolled. Such factors as weather annual fluctuations and soil heterogeneity, for example, can contribute to a large part of the effects and cause great variation, making it hard to evaluate the real effect of the treatment applied. To reduce the usual experimental noises linked to open field experiments (weather, soil heterogeneity, plant age, rootstock, root development, etc.), we downscaled the phenotyping unit to the individual vine level (i.e. each vine was considered as a single biological replicate), taking into account the real perception of WD by the plant (𝚿p). Plus, to quantify the yield per plant in terms of fresh weight, it is necessary to determine the correct sampling time at the physiological ripe stage, especially when quantifying the effect of WD on fruits. Thus, by harvesting at phloem unloading arrest, we got a better estimate of the plant's maximum water accumulation, avoiding the bias linked to water losses due to berry shrivelling (Alem et al., 2021; Bigard et al., 2019; Shahood et al., 2020).

Conclusion

In the present study, we focused on general physiological, vegetative and yield responses to WD in a rather integrative approach, considering the general effects of seasonal, interannual and multi-annual WD. All variables were negatively affected by decreases in Acc-𝚿p or 𝚿p, with the exception of WUEi and total non-structural carbohydrates. Among these variables, we identified those that were mostly explained by Acc-𝚿p or 𝚿p, such as berry weight, gs, A, E, shoot weight and pruning weight, from those related to season variations, such as yield, cluster weight and the number of clusters per shoot. Physiological and vegetative variables showed great variability regarding genotype sensitivities to WD, while from reproductive variables, only berry weight responses were found to be genotype-dependent.

This report provides data to rank genotypes regarding their sensitivities to water deficit and better understand their strategies to cope with it. It was possible to distinguish those genotypes that show a similar physiological behaviour under WD to Syrah (3176N and Floreal) from those showing a contrasting response (G5, G14, 3159B and Artaban). However, even if two contrasting groups were formed regarding their physiological response to WD when considering integrative variables, such division became blurred where some genotypes, such as Artaban and G5, reduced all functioning variables while others maintained functioning at higher values, such as 3176N and Syrah. However, others displayed combined responses, such as reducing reproductive development more than vegetative (G14) and vice-versa (3159B) or reducing both vegetative and reproductive functioning but keeping gas exchanges at higher values, such as in Floreal. This highlights the complexity when integrating whole plant seasonal and interannual regulations and multi-annual acclimation to WD.

In the same study, we also studied responses to WD of the same genotypic panel at the fruit level. For this, during the 4 years of experiments, we have analysed, at a single plant level, the grape composition in main primary (sugars, organic acids, nitrogen compounds), secondary metabolites (anthocyanins, aromatic glycosylated precursors), major cations and the variations of carbon isotopic incorporation in sugars (δC13). This will provide additional information about the effect of WD on fruit physiology and the accumulation of compounds of interest by this panel of new fungus disease-tolerant genotypes.

Acknowledgements

The authors thank Emmanuelle García-Adrados for her essential technical support and management of the experimental fields and the students Emilie Barnett, Martina Baliani and Bruno Izquierdo for their support in data acquisition. The study was funded by the Occitanie Region, the Conseil Interprofessionnel des Vins du Languedoc—CIVL, the International Organisation of Vine and Wine—OIV, INRAE and Institut Agro Montpellier (France).

References

• Alem, H., Ojeda, H., Rigou, P., Schneider, R., & Torregrosa, L. (2021). The reduction of plant sink/source does not systematically improve the metabolic composition of Vitis vinifera white fruit. Food Chemistry, 345, 128825. https://doi.org/10.1016/j.foodchem.2020.128825
• Bchir, A., Escalona, J. M., Gallé, A., Hernández-Montes, E., Tortosa, I., Braham, M., & Medrano, H. (2016). Carbon isotope discrimination (δ13C) as an indicator of vine water status and water use efficiency (WUE): Looking for the most representative sample and sampling time. Agricultural Water Management, 167, 11–20. https://doi.org/10.1016/j.agwat.2015.12.018
• Bennett, J., Jarvis, P., Creasy, G. L., & Trought, M. C. T. (2005). Influence of Defoliation on Overwintering Carbohydrate Reserves, Return Bloom, and Yield of Mature Chardonnay Grapevines. American Journal of Enology and Viticulture, 56(4), 386–393. https://doi.org/10.5344/ajev.2005.56.4.386
• Bigard, A., Romieu, C., Ojeda, H., & Torregrosa, L. J.-M. (2022). The sugarless grape trait characterised by single berry phenotyping. OENO One, 56(3), Article 3. https://doi.org/10.20870/oeno-one.2022.56.3.5495
• Bigard, A., Romieu, C., Sire, Y., Veyret, M., Ojéda, H., & Torregrosa, L. (2019). The kinetics of grape ripening revisited through berry density sorting. OENO One, 53(4), Article 4. https://doi.org/10.20870/oeno-one.2019.53.4.2224
• Bota, J., Tomás, M., Flexas, J., Medrano, H., & Escalona, J. M. (2016). Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress. Agricultural Water Management, 164, 91–99. https://doi.org/doi.org/10.1016/j.agwat.2015.07.016
• Cardell, M. F., Amengual, A., & Romero, R. (2019). Future effects of climate change on the suitability of wine grape production across Europe. Regional Environmental Change, 19(8), 2299–2310. https://doi.org/10.1007/s10113-019-01502-x
• Chaves, M. M., Santos, T. P., Souza, C. R., Ortuño, M., Rodrigues, M. I., Lopes, C. M., Maroco, J. P., & Pereira, J. S. (2007). Deficit irrigation in grapevine improves water-use efficiency while controlling vigour and production quality. Annals of Applied Biology, 150(2), 237–252. https://doi.org/10.1111/j.1744-7348.2006.00123.x
• Chaves, M. M., Zarrouk, O., Francisco, R., Costa, J. M., Santos, T., Regalado, A. P., Rodrigues, M. L., & Lopes, C. M. (2010). Grapevine under deficit irrigation: Hints from physiological and molecular data. Annals of Botany, 105(5), 661–676. https://doi.org/10.1093/aob/mcq030
• Dayer, S., Prieto, J. A., Galat, E., & Peña, J. P. (2013). Carbohydrate reserve status of Malbec grapevines after several years of regulated deficit irrigation and crop load regulation. Australian Journal of Grape and Wine Research, 19(3), 422–430. https://doi.org/10.1111/ajgw.12044
• Deloire, A., Carbonneau, A., Wang, Z., & Ojeda, H. (2004). Vine and water: A short review. OENO One, 38(1), 1. https://doi.org/10.20870/oeno-one.2004.38.1.932
• Doupis, G., Bosabalidis, A. M., & Patakas, A. (2016). Comparative effects of water deficit and enhanced UV-B radiation on photosynthetic capacity and leaf anatomy traits of two grapevine (Vitis vinifera L.) cultivars. Theoretical and Experimental Plant Physiology, 28(1), 131–141. https://doi.org/10.1007/s40626-016-0055-9
• Escudier, J.-L., Bigard, A., Ojeda, H., Samson, A., Caillé, S., & Romieu, C. (2017). De la vigne au vin: Des créations variétales adaptées au changement climatique et résistant aux maladies cryptogamiques. Revue des Oenologues, 165, 6.
• Flexas, J., Bota, J., Escalona, J. M., Sampol, B., & Medrano, H. (2002). Effects of drought on photosynthesis in grapevines under field conditions: An evaluation of stomatal and mesophyll limitations. Functional Plant Biology, 29(4), 461. https://doi.org/10.1071/PP01119
• Gomez, L., Bancel, D., Rubio, E., & Vercambre, G. (2007). The microplate reader: An efficient tool for the separate enzymatic analysis of sugars in plant tissues - Validation of a micro-method. Journal of the Science of Food and Agriculture, 87, 1893–1905. https://doi.org/10.1002/jsfa.2924
• Guichard, L., Dedieu, F., Jeuffroy, M.-H., Meynard, J.-M., Reau, R., & Savini, I. (2017). Le plan Ecophyto de réduction d’usage des pesticides en France: Décryptage d’un échec et raisons d’espérer. Cahiers Agricultures, 26(1), Article 1. https://doi.org/10.1051/cagri/2017004
• Guilpart, N., Metay, A., & Gary, C. (2014). Grapevine bud fertility and number of berries per bunch are determined by water and nitrogen stress around flowering in the previous year. European Journal of Agronomy, 54, 9–20. https://doi.org/10.1016/j.eja.2013.11.002
• Gutiérrez-Gamboa, G., Pérez-Donoso, A. G., Pou-Mir, A., Acevedo-Opazo, C., & Valdés-Gómez, H. (2019). Hydric behaviour and gas exchange in different grapevine varieties (Vitis vinifera L.) from the Maule Valley (Chile). South African Journal of Enology and Viticulture, 40(2). https://doi.org/10.21548/40-2-3224
• Hochberg, U., Rockwell, F. E., Holbrook, N. M., & Cochard, H. (2018). Iso/Anisohydry: A Plant–Environment Interaction Rather Than a Simple Hydraulic Trait. Trends in Plant Science, 23(2), 112–120. https://doi.org/10.1016/j.tplants.2017.11.002
• Holzapfel, B. P., Smith, J. P., Field, S. K., & Hardie, W. J. (2010). Dynamics of Carbohydrate Reserves in Cultivated Grapevines. In Horticultural Reviews (Jules Janick ed., Vol. 37, pp. 143–211). Wiley-Blackwell.
• IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability (p. 3056). Contribution of working group II to the Sixth Assessment Report of the Intergovernmental Panel on Climae Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambrigne University Press. https://doi.org/10.1017/9781009325844
• Keller, M. (2005). Deficit Irrigation and Vine Mineral Nutrition. American Journal of Enology and Viticulture, 56(3), 267–282. https://doi.org/10.5344/ajev.2005.56.3.267
• Lebon, E., Pellegrino, A., Louarn, G., & Lecoeur, J. (2006). Branch Development Controls Leaf Area Dynamics in Grapevine (Vitis vinifera) Growing in Drying Soil. Annals of Botany, 98(1), 175–185. https://doi.org/10.1093/aob/mcl085
• Levin, A. D., Matthews, M. A., & Williams, L. E. (2020). Effect of Preveraison Water Deficits on the Yield Components of 15 Winegrape Cultivars. American Journal of Enology and Viticulture, 71(3), 208–221. https://doi.org/10.5344/ajev.2020.19073
• Lopes, C., & Cadima, J. (2021). Grapevine bunch weight estimation using image-based features: Comparing the predictive performance of number of visible berries and bunch area. OENO One, 55(4), 209–226. https://doi.org/10.20870/oeno-one.2021.55.4.4741
• Martínez-Vilalta, J., & Garcia-Forner, N. (2017). Water potential regulation, stomatal behaviour and hydraulic transport under drought: Deconstructing the iso/anisohydric concept: Deconstructing the iso/anisohydric concept. Plant, Cell & Environment, 40(6), 962–976. https://doi.org/10.1111/pce.12846
• Mirás-Avalos, J. M., & Intrigliolo, D. S. (2017). Grape Composition under Abiotic Constrains: Water Stress and Salinity. Frontiers in Plant Science, 8, 8. https://doi.org/10.3389/fpls.2017.00851
• Ojeda, H., Deloire, A., & CARBONNEAU, A. (2001). Influence of water deficits on grape berry growth. Vitis, 40, 141–145.
• Pellegrino, A., Clingeleffer, P., Cooley, N., & Walker, R. (2014). Management practices impact vine carbohydrate status to a greater extent than vine productivity. Frontiers in Plant Science, 5. https://doi.org/10.3389/fpls.2014.00283
• Pellegrino, A., Lebon, E., Simonneau, T., & Wery, J. (2005). Towards a simple indicator of water stress in grapevine (Vitis vinifera L.) based on the differential sensitivities of vegetative growth components. Australian Journal of Grape and Wine Research, 11(3), 306–315. https://doi.org/10.1111/j.1755-0238.2005.tb00030.x
• Petrie, P. R., Cooley, N. M., & Clingeleffer, P. R. (2004). The effect of post-veraison water deficit on yield components and maturation of irrigated Shiraz (Vitis vinifera L.) in the current and following season. Australian Journal of Grape and Wine Research, 10(3), 203–215. https://doi.org/10.1111/j.1755-0238.2004.tb00024.x
• Pichon, L., Brunel, G., Payan, J.-C., & Tisseyre, B. (2020). Apex-Vigne: A mobile application to facilitate the monitoring of growth and estimate the water status of the viticulture plots. IVES Technical Reviews, vine and wine. https://doi.org/10.20870/IVES-TR.2020.3558
• Pl@ntGrape (2009). Catalogue des vignes cultivées en France, IFV - INRAE - l’Institut Agro | Montpellier SupAgro. https://plantgrape.plantnet-project.org/es/credits
• Prieto, J. A., Lebon, É., & Ojeda, H. (2010). Stomatal behavior of different grapevine cultivars in response to soil water status and air water vapor pressure deficit. J. Int. Sci. Vigne Vin, 12. https://doi.org/10.20870/oeno-one.2010.44.1.1459
• Rossouw, G. C., Smith, J. P., Barril, C., Deloire, A., & Holzapfel, B. P. (2017). Carbohydrate distribution during berry ripening of potted grapevines: Impact of water availability and leaf-to-fruit ratio. Scientia Horticulturae, 216, 215–225. https://doi.org/10.1016/j.scienta.2017.01.008
• Salinari, F., Giosuè, S., Rossi, V., Tubiello, F. N., Rosenzweig, C., & Gullino, M. L. (2007). Downy mildew outbreaks on grapevine under climate change: Elaboration and application of an empirical-statistical model. EPPO Bulletin, 37(2), 317–326. https://doi.org/10.1111/j.1365-2338.2007.01126.x
• Santos, J. A., Fraga, H., Malheiro, A. C., Moutinho-Pereira, J., Dinis, L.-T., Correia, C., Moriondo, M., Leolini, L., Dibari, C., Costafreda-Aumedes, S., Kartschall, T., Menz, C., Molitor, D., Junk, J., Beyer, M., & Schultz, H. R. (2020). A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture. Applied Sciences, 10(9), 3092. https://doi.org/10.3390/app10093092
• Savi, T., García González, A., Herrera, J. C., & Forneck, A. (2019). Gas exchange, biomass and non-structural carbohydrates dynamics in vines under combined drought and biotic stress. BMC Plant Biology, 19(1), 408. https://doi.org/10.1186/s12870-019-2017-2
• Schneider, C., Onimus, C., Prado, E., Dumas, V., Wiedemann-Merdinoglu, S., Dorne, M. A., Lacombe, M. C., Piron, M. C., Umar-Faruk, A., Duchêne, E., Mestre, P., & Merdinoglu, D. (2019). INRA-ResDur: The French grapevine breeding programme for durable resistance to downy and powdery mildew. Acta Horticulturae, 207–214. https://doi.org/10.17660/ActaHortic.2019.1248.30
• Schultz, H. (2000). Physiological mechanisms of water use efficiency in grapevines under drought conditions. Acta Horticulturae, 526, 115–136. https://doi.org/10.17660/ActaHortic.2000.526.9
• Shahood, R., Torregrosa, L., Savoi, S., & Romieu, C. (2020). First quantitative assessment of growth, sugar accumulation and malate breakdown in a single ripening berry. OENO One, 54(4), 1077–1092. https://doi.org/10.20870/oeno-one.2020.54.4.3787
• Simonneau, T., Lebon, E., Coupel-Ledru, A., Marguerit, E., Rossdeutsch, L., & Ollat, N. (2017). Adapting plant material to face water stress in vineyards: Which physiological targets for an optimal control of plant water status? OENO One, 51(2), 167. https://doi.org/10.20870/oeno-one.2016.0.0.1870
• Tortosa, I., Douthe, C., Pou, A., Balda, P., Hernandez-Montes, E., Toro, G., Escalona, J. M., & Medrano, H. (2019). Variability in Water Use Efficiency of Grapevine Tempranillo Clones and Stability over Years at Field Conditions. Agronomy, 9(11), 701. https://doi.org/10.3390/agronomy9110701
• Vasconcelos, M. C., Greven, M., Winefield, C. S., Trought, M. C. T., & Raw, V. (2009). The Flowering Process of Vitis vinifera: A Review. American Journal of Enology and Viticulture, 60(4), 411–434. https://doi.org/10.5344/ajev.2009.60.4.411
• Wilhelm, L., Pellegrino, A., Ojeda, H., & Torregrosa, L. (2021). La Revue des Œnologues | Caractériser la tolérance à la sécheresse des nouvelles variétés résistantes aux maladies fongiques. La Revue des Œnologues, 181, 4.
• Zapata, C., Deleens, E., Chaillou, S., & Magne, C. (2004). Partitioning and mobilization of starch and N reserves in grapevine (Vitis vinifera L.). Journal of Plant Physiology, 161, 1031–1040. https://doi.org/10.1016/j.jplph.2003.11.009
• Zufferey, V., Murisier, F., Vivin, P., Belcher, S., Lorenzini, F., Spring, J. L., & Viret, O. (2012). Carbohydrate reserves in grapevine (Vitis vinifera L. ’Chasselas’): The influence of the leaf to fruit ratio. VITIS - Journal of Grapevine Research, 51(3), 103–110. https://doi.org/10.5073/VITIS.2012.51.103-110
• Zufferey, V., Spring, J.-L., Verdenal, T., Dienes, A., Belcher, S., Lorenzini, F., Koestel, C., Rösti, J., Gindro, K., Spangenberg, J., & Viret, O. (2017). Influence of water stress on plant hydraulics, gas exchange, berry composition and quality of Pinot noir wines in Switzerland. OENO One, 51(1), 17–27. https://doi.org/10.20870/oeno-one.2017.51.1.1314
• Zufferey, V., Verdenal, T., Dienes, A., Belcher, S., Lorenzini, F., Koestel, C., Blackford, M., Bourdin, G., Gindro, K., Spangenberg, J. E., Rösti, J., Viret, O., Carlen, C., & Spring, J.-L. (2020). The influence of vine water regime on the leaf gas exchange, berry composition and wine quality of Arvine grapes in Switzerland. OENO One, 54(3), 553–568. https://doi.org/10.20870/oeno-one.2020.54.3.3106