Introduction

In Mediterranean vineyards, increasing water deficits and high temperatures are threatening yield, quality, and long-term sustainability, while water scarcity and the need to reduce nitrogen and pesticide inputs are creating additional challenges. Addressing these issues requires both the selection of adapted genotypes and the implementation of optimised cultural practices.

Rootstock and scion varieties show diverse responses to water and nitrogen availability, which remain insufficiently explored (Bota et al., 2001). Disease-tolerant varieties also help reduce phytosanitary inputs. For a given scion/rootstock combination, water and nitrogen availability can be modulated through multiple levers. At the vineyard level, these include optimised irrigation, fertilisation, and soil management, such as cover cropping (Celette et al., 2013). At the plant level, pruning and canopy management modify the leaf-to-fruit ratio and plant architecture, which in turn influence water and nitrogen use (Downtown et al., 1992). These practices also regulate carbohydrate production and allocation (Zufferey, 2000; Abad et al., 2019), requiring a careful balance between reducing canopy size to limit resource demand and maintaining sufficient photosynthetic activity to sustain yield.

At the leaf scale, the impact of water status on photosynthesis is well-documented: carbon assimilation decreases under water stress due to limitations in stomatal conductance (gs) (Medrano et al., 2002) and non-stomatal factors, such as the decrease in mesophyll conductance and thus in CO₂ diffusion to the chloroplasts, limiting dark reactions of photosynthesis and causing biochemical limitations (Lawlor & Tezara, 2009; Villalobos-González et al., 2022). The sensitivity of gs to water deficit varies with genotype (Bota et al., 2001; Wilhelm de Almeida et al., 2024), with stomatal closure typically occurring under moderate water stress (Lebon et al., 2006; Charrier et al., 2018). Leaf nitrogen (LN) content is another key determinant of photosynthetic activity (Evans et al., 2019; Prieto et al., 2012), as high N concentrations are associated with greater chlorophyll and RUBISCO levels, leading to enhanced biomass accumulation (Vrignon-Brenas et al., 2024). Changing the source-sink ratio by modifying total leaf area or the number of clusters also influences developmental processes (Pallas et al., 2008) and photosynthesis (Poupard et al., 2024; Zufferey et al., 2009). In fruit trees, the regulation of photosynthetic activity at the single-leaf level in response to sink demand has been widely observed, with increased activity under high sink demand and down-regulation when demand is reduced (Wünsche et al., 2005; Pallas et al., 2018). The feedback effect of sink demand on photosynthesis arises from the absence of photosynthesis inhibition, when sink demand is sufficiently high to facilitate efficient sugar export from the leaves (Goldschmidt & Huber, 1992).

Addressing the combined effects of water, nitrogen, and source-sink ratio on photosynthesis is crucial but challenging, since these factors interact in complex ways. For example, reducing the source-sink ratio by decreasing canopy size can improve water and nitrogen status, but at the expense of carbohydrate production. Similarly, irrigation can enhance plant water status and promote vegetative growth, potentially at the expense of reproductive development (Carbonneau et al., 2007), thereby increasing the source-sink ratio. Leaf carbohydrate content also plays a key role in regulating photosynthetic activity, either directly or through stomatal conductance, as shown in Arabidopsis thaliana (Westgeest et al., 2023). However, this effect remains unclear in grapevine. Recent studies (Ngao et al., 2021) have investigated the impact of crop load and the light environment on carbon and nitrogen metabolism in apple trees, but they did not consider water deficit, which is a major limiting factor in many vineyards, especially in Mediterranean regions.

Analysing the complex interactions between plant state and functional variables is challenging, as conventional correlation analyses are often insufficient. Given that most variables covary, isolating direct effects is nearly impossible. To address this, many studies have employed path analysis methods (Shipley, 2016), which enable the simultaneous study of multiple direct and indirect relationships. Furthermore, large datasets are needed to capture a wide range of water and nitrogen availability in combination with contrasting source-sink ratios. This necessitates complex experimental designs and extensive measurements of plant water, nitrogen, and carbon status, as well as photosynthesis—all of which are time-consuming when using conventional methods (e.g., pressure chamber, leaf gas analyser, or biochemical assays). New methodologies combining high-throughput measurements and appropriate statistical approaches are being developed. For instance, Losciale et al. (2015) and Coupel-Ledru et al. (2019) proposed using leaf fluorescence, leaf temperature, and stomatal conductance as proxies for photosynthetic activity. Near InfraRed Spectrometry (NIRS) is widely used to estimate plant tissue composition (such as nitrogen and carbohydrate content) (DeBei et al., 2017; Prananto et al., 2021; Van Wyngaard et al., 2021). Advanced chemometric techniques, such as Partial Least Squares Regression (PLSR) (Wold et al., 2001), are commonly applied to link spectral absorbance or reflectance with tissue composition.

This study was carried out over a two-year period on three grapevine varieties tolerant to powdery and downy mildews, which were grown under various agrosystems in the same experimental vineyard combining different soil (cover crops, irrigation, and fertilisation) and plant (winter and spring pruning) management practices. This design allowed contrasting ranges of nitrogen and water to be applied to plants with varying source-sink ratios. High-throughput phenotyping methods based on chlorophyll fluorescence, porometry, and Near InfraRed Spectrometry (NIRS) were implemented. Fluorescence and NIRS variables were used to estimate photosynthetic activity, nitrogen status (leaf nitrogen content), and carbohydrate status (leaf starch and soluble sugar contents). Using this dataset, we examined the variability in photosynthetic activity among the three cultivars in relation to water and nitrogen status, as well as source-sink ratio. We then assessed the relative influence of these three factors on photosynthesis to identify the ranges within which photosynthetic activity remained high. Path analyses were used to investigate the interactions between these variables and the end products of photosynthesis, specifically non-structural carbohydrates. Finally, we aimed to identify if the three different varieties responded differently to the contrasted water, nitrogen and carbon status imposed by the different management practices.

Materials and methods

1. Description of experimental site and design

The study was conducted in 2023–2024 in an experimental vineyard of INRAE (Institut national de recherche pour l’agriculture, l’alimentation et l’environnement) Pech Rouge (Gruissan, France; 43.14° N, 3.14° W) in a hot, semi-arid Mediterranean climate. Three mildew-tolerant grapevine varieties planted in three different plots, no more than 385 m apart, were monitored. The varieties were: ARTABAN (a Vitis hybrid from the Resdur-1 programme), and 3159-B and G5 (3197-81-B), both bred by Alain Bouquet (two Vitis hybrids from a cross between Vitis vinifera and Muscadinia rotundifolia) (Salmon et al., 2018). In addition to its fungus-tolerance, G5 is characterised by reduced hexose accumulation in berries at harvest, leading to low-ethanol wines (Ojeda et al., 2017). The vines were grafted on drought-adapted rootstocks (110R for G5 and ARTABAN; 140RU for 3159-B) and planted in 2012 (3159-B) and 2015 (ARTABAN, G5) at a density of 4400 plants ha⁻¹ (2.5 × 0.9 m), with a southwest-northwest row orientation.

Eight treatments were applied at both plant and plot scale to modulate water, nitrogen, and carbon availability (Table S1). Plots composed of six plants were selected based on homogeneity criteria, including plant health, trunk circumference, vegetative growth, shoot weight, and bud number.

At the plot scale, irrigation and inter-row management differed among treatments. Guard vines separated the different treatments. One treatment involved complete inter-row vegetation removal, organic fertilisation at budburst (40 kg N ha⁻¹ in 2024 only, in order to enhance the difference in soil nitrogen content between the treatments), and weekly irrigation with total water applications of 87, 55, and 98 mm in 2023 and 72, 44, and 77 mm in 2024 for G5, 3159-B, and ARTABAN, respectively (BARE-I₂). These amounts were calculated by dividing the total water supplied per treatment by the soil surface area specific to the treatment. Another treatment comprised a cover crop sown in September and removed in mid-April, with irrigation applied every two weeks (68, 17, and 36 mm in 2023; 34, 16, and 50 mm in 2024 for G5, 3159-B, and ARTABAN, respectively) and no fertilisation to induce moderate water and nitrogen constraints (SOWN-I₁). The third treatment was applied only to G5 and ARTABAN, as the 3159-B plot was not big enough for all treatments with a sufficient number of replicates; it comprised spontaneous inter-row vegetation being maintained until June, without irrigation or fertilisation, resulting in high water and nitrogen constraints (SPO-I₀).

Canopy management strategies comprised vertical shoot positioning (VSP) with standard (75–105 cm) (VSPH) or reduced canopy height (30–60 % reduction, applied in late June) (VSPLw). For 3159-B, minimal pruning was also tested. These treatments aimed to alter the carbon source-sink balance at the plant scale. Three guard vines also separated the different canopy treatments when positioned alongside the same row.

2. Climatic conditions

The year 2023 was particularly dry, with 271 mm of rainfall, of which 116.5 mm between April and August. In 2024, precipitation nearly doubled (491 mm), but it remained similar to 2023 during the growing season (152 mm). Cumulated thermal time (base temperature 10 °C, Lebon et al. (2004)) was higher in 2023 (2555 °Cd vs. 2246 °Cd over the year), but the values over the growing seasons of both years were similar (1689 °Cd in 2023, 1595 °Cd in 2024).

3. Soil characteristics

Soil characteristics were studied in three parcels, with two to three soil pits per parcel, whose locations were chosen based on soil management treatments and surface heterogeneity. According to the World Reference Base for Soil Resources (WRB), the soil in the G5 plot was classified as Calcisol (leptic) in the upper two-thirds and as Cambisol (coluvic) in the lower part. Soils in the 3159-B and ARTABAN plots were classified as Calcisol (arenic). The 3159-B plot had deeper soil than G5, but its sandier texture resulted in lower retention capacities. The soil water holding capacity (AWC) was similar for both G5 (97 mm) and 3159-B (116 mm), while the ARTABAN plot had the lowest depth and an AWC of 70 mm.

4. Leaf water status, leaf gas exchanges and near infrared spectroscopy measurements

For the following measurements, we selected mature and healthy leaves, located halfway up the canopy and fully exposed to sunlight (south-east side of the row). Predawn leaf water potential (Ψ_pd) is an indicator of plant and soil water status and is less sensitive to climatic conditions than midday stem potential. Here, it was measured during each leaf gas exchange measurement session using a Scholander chamber on one leaf from six plants per treatment between 1:00 and 5:00. From mid-June to mid-August, leaf gas exchange measurements were conducted every three weeks on sunny days, between 9:00 and 12:30, when Photosynthetically Active Radiation (PAR) was over 1500 µmol m^-2 s^-1 on six plants per treatment. The order in which the plots and the treatments inside each plot were measured was randomly changed between each measurement session. Chlorophyll fluorescence and stomatal conductance were measured using a porometer-fluorimeter (LI-600; LI-COR Biosciences) on one or two leaves per plant, depending on the weather conditions during the session. Simultaneously, maximum CO₂ assimilation rate (A_max) was assessed under ambient CO₂ (400 μmol mol^-1) and saturated light (1500 μmol m² s^-1) using an infrared gas analyser (LI-6800; LI-COR Biosciences) on three leaves per treatment, with optimal chamber conditions (27.5 °C, VPD 1.5–2 kPa, saturated light).

On each leaf measured for ecophysiological parameters, a leaf disc was sampled using a punch, then dried. For three leaves per treatment, a second disc was sampled per leaf and frozen for subsequent biochemical analysis of starch, glucose, fructose, sucrose, and nitrogen content (hereafter referred to as LS, LG, LF, LSc and LN, respectively). NIRS measurements were performed on dried samples using a laboratory NIR spectrometer (ASD LabSpec4) with fibre optics to estimate nitrogen and non-structural carbohydrate contents for samples not analysed by biochemical assays.

5. Estimation of plant leaf areas and source-sink ratios

The carbon balance of aerial organs per plant was estimated using the Leaf-to-Fruit ratio (L:F, cm² berry^-1), defined as the ratio total leaf area (TLA) to total number of berries. L:F values were calculated at harvest. Measurements taken on plants from the VPSLw treatment before late June were excluded, as leaf area values prior to trimming at the end of June would have overestimated the true leaf area after pruning.

To estimate TLA for each of the six plants per treatment, 15 leaves from primary and secondary shoots were sampled, and their surface area was measured with a planimeter (LI-3100C; LI-COR Biosciences). Mean leaf areas for primary (LA_I) and secondary (LA_II) leaves were determined. The lengths of 15 primary shoots were measured for each treatment ($\stackrel{-}{{SLg}_I}$). The number of primary and secondary leaves (NL_I and NL_II, respectively) was also recorded to calculate the average number of leaves per centimetre of primary shoot (L_I and L_II, respectively). The number of shoots per plant (NS_I) was counted on each of the six plants per treatment. The total leaf area per plant was then estimated using the following equation:

$Leaf Total Area= {NS}_I\times \stackrel{-}{{SLg}_I}\times (\stackrel{-}{L_I}\times {LA}_I+\stackrel{-}{L_{II}}\times {LA}_{II})$

where $\stackrel{-}{{\mathrm{L}}_{\mathrm{I}}}$ and $\stackrel{-}{{\mathrm{L}}_{\mathrm{I}\mathrm{I}}}$ are the mean values of L_I and L_II in each treatment.

6. Estimation of nitrogen, carbon related variables and photosynthesis

Partial Least Squares (PLS) regression models were developed to predict leaf nitrogen and carbon-related variables using NIRS spectra or pre-processed NIRS data (e.g., smoothed spectra, standard normal variate spectra, first and second derivatives). The models were built using the ‘pls’ package in R, based on 270 samples on which LS, LG, LF, LSc, and LN were measured by biochemical assays. Model performance was assessed on the basis of R², normalised root mean square error (nRMSE, i.e., RMSE divided by the range of observed values), and relative bias (i.e., bias relative to the mean of observed values). The best models showed R² values of 0.76 and nRMSE of 7.1 % for LN but with a tendency to underestimate the observed values (relative bias = –7.7 %) (Table 1). For the other variables, the bias was close to 0 and R² = 0.78 for LS with nRMSE of 9.6 %, and 0.82 for LG with nRMSE of 8.2 %. For LF and LSc, the R² values were lower (0.58 and 0.55, respectively) with higher nRMSE (12.2 % and 15.3 %, respectively). LG, LF, and LSc concentrations were summed to estimate total non-soluble sugar content (LSS).

For photosynthesis (A_max), a calibration model was built using 322 A_max measurements and variables from LI-600 (e.g., Electron Transport Rate, Photosystem II activity, stomatal conductance, leaf temperature, and VPD). It was verified that the variations in leaf temperature, resulting from light intensity increase during the morning, were equally distributed for each treatment in the calibration set (Figure S2). The A_max model had an R² of 0.71 and nRMSE of 12.5 %, with a tendency to overestimate values.

7. Statistical analyses

All statistical analyses and graphics were performed using R software. The analyses related to the genotypic effect were performed by assessing the differences between the genotypes for a given set of combined values of Ψ_pd, LN, and L:F, which reflect the water, nitrogen and carbon status of the plants. This approach was used because other interactive factors not considered in the present study (e.g., scion × rootstock interactions, nutritional status, pedo-climatic variables, and microclimatic variability) could potentially influence genotype effects.

A multiple linear regression model was built to examine the effects of Ψ_pd, LN, and L:F on A_max. The significance of each explanatory variable was assessed with a Type II ANOVA. The proportion of variance of A_max explained by each variable was computed as the ratio of its sum of squares to the total sum of squares. To meet ANOVA assumptions (normality and homoscedasticity of residuals), A_max was log-transformed.

A_max was classified into three groups: i) high (> 14 µmol m^-2 s^-1, 30 % of data), ii) low (< 8 µmol m^-2 s^-1, 10 % of data), and iii) intermediate (8–14 µmol m^-2 s^-1). A concentration ellipsoid was fitted to the 3D point cloud of Ψ_pd, LN, and L:F to enclose the maximal number of high-A_max values while minimising low-A_max values, using the ellipse3d function from the rgl package. The ellipsoid’s centre and its minimum and maximum Ψ_pd, LN, and L:F values were computed. The range of each variable within the ellipsoid was expressed as a fraction of its total observed range in the dataset.

Pearson correlation coefficients were computed among A_max, LN, L:F, Ψ_pd, LS, and LSS, both across and within genotypes, and their significance was assessed.

Structural equation modeling (path analysis, Shipley (2016)) was used to analyse the relationships between water, source-sink, and nitrogen-related variables (Ψ_pd, L:F, LN), maximal photosynthetic activity (A_max), and Non Structural Carbohydrate (NSC) contents (LSS and LS). The same model was applied across genotypes and measurement periods (before or after veraison) to assess differences in variable relationships based on standardised Lavaan coefficients. This approach accounts for all covariations among variables, estimating the independent effect of each one. The model was designed to integrate physiological knowledge about variable interactions. It assumed causal relationships: (1) between L:F, Ψ_pd, LN, and A_max, (2) between L:F, Ψ_pd, LN, and NSC content (LSS, LS), and (3) between A_max and LSS/LS, with a free correlation between LSS and LS. Potential causal links among water, source-sink, and nitrogen-related variables were also considered, including relationships between L:F and LN, Ψ_pd and LN, and a free correlation between L:F and Ψ_pd. The final model retained only significant relationships, leading to the removal of some pre-established links, such as the causal effects of Ψ_pd on LS and LSS.

Results

1. Variability of leaf water and nitrogen status, source-sink ratio and photosynthesis between genotypes

The experimental design generated a broad range of predawn leaf water potential (Ψ_pd), leaf nitrogen surface concentration (LN), and leaf-to-fruit ratio (L:F) over the two years (Figure 1). Ψ_pd tended to decrease after veraison. Before veraison, its values were similar across genotypes, but after veraison, G5 reached the lowest values (-1.5 MPa); meanwhile, ARTABAN never dropped below -1 MPa. Additionally, 3159-B and G5 exhibited greater Ψ_pd variability than ARTABAN after veraison (Figure 1A).

Seasonal LN variations were less pronounced than those of Ψ_pd, with a slight decline after veraison for G5 and 3159-B (median values dropping from 0.13 and 0.17 to 0.12 and 0.14 mg cm^-2, respectively). G5 consistently exhibited lower LN than ARTABAN and 3159-B, particularly after veraison (Figure 1B). Before veraison, ARTABAN displayed the lowest LN variability, but after veraison, all genotypes showed similar levels of variation.

L:F varied widely among genotypes, with G5 showing the highest values (median of 53 cm² berry^-1), followed by 3159-B (30 cm² berry^-1) and ARTABAN (12 cm² berry^-1). ARTABAN had the smallest within-genotype variability, with a range of 4.2–33.3 cm² berry^-1 (Figure 1C).

ARTABAN exhibited higher maximal photosynthesis (A_max), with a median value of 14.3 μmol m^-2 s^-1, compared to 11.4 and 12.3 μmol m^-2 s^-1 for G5 and 3159-B, respectively (Figure 1D). Median A_max values declined after veraison for all genotypes. Variability remained comparable for G5 and 3159-B before and after veraison, whereas ARTABAN showed more variability after veraison, with a larger number of very high values (A_max > 25 μmol m^-2 s^-1).

Boxplot representation of the variables used to determine water and nitrogen leaf status, balance between sink and source organs for carbon, and carbon assimilation rate in G5, 3159-B and ARTABAN in 2023 and 2024: a) predawn leaf potential Ψ_pd for water status, b) nitrogen estimated surface leaf concentration LN for nitrogen status, c) Leaf-to-fruit ratio L:F for balance between sink and source organs for carbon, and d) photosynthetic maximal net activity (A_max) for carbon assimilation rate. Each point represents the value for one leaf at a given measurement period (n = 950).

2. Leaf maximum photosynthesis response to the combined effects of water, nitrogen leaf status and source-sink ratio induced by management practices

The variation of A_max as a function of Ψ_pd, LN, and log(L:F) was analysed considering the whole dataset (Figure 2) and separately by measurement period (Table 2). An ellipsoid was fitted to capture the highest number of high A_max values (> 14 µmol m^-2 s^-1) while minimising low A_max values (< 8 µmol m^-2 s^-1), thus grouping 72.2 % of high A_max and only 6.4 % of low A_max (Table 2). The ellipsoid's coordinates ranged from -0.12 to -0.75 MPa for Ψ_pd, 0.09 to 0.21 mg cm^-2 for LN, and 1.66 to 4.59 cm² berry^-1 for log(L:F), encompassing 45 %, 59 %, and 80 % of total dataset variation in these variables, respectively. This suggests a stronger influence of Ψ_pd on A_max than the other variables. The ellipsoid centre (Ψ_pd = -0.44 MPa; LN = 0.15 mg cm²; log(L:F) = 3.12 cm² berry^-1) indicates that high A_max values are associated with higher Ψ_pd and LN but lower L:F ratios.

When the dataset by phenological stage was split before veraison, the ellipsoid captured 85.5 % of high A_max values, with only 3.6 % of low A_max. After veraison, 72 % of high A_max values were captured, but low A_max values increased to 10.6 %. The impact of each variable (Ψ_pd, LN and L:F) on A_max variations within the ellipsoid also shifted over time. Before veraison, Ψ_pd had the strongest impact, the ellipsoid covering only 45.9 % of its total variation, compared to 87.7 % for LN and 88.4 % for L:F. After veraison, the contributions of Ψ_pd and LN to discriminating A_max values were similar (around 50 % of their total variations), while L:F discriminated A_max values to a lower extent (78 % of its total variation).

Figure 2. 3D representation of photosynthetic maximal net activity (A_max) depending on predawn leaf potential Ψ_pd, nitrogen estimated surface leaf concentration LN and Leaf-to-fruit ratio log(L:F), respectively.

The cultural practices and genotypes associated with high A_max values within the ellipsoids were investigated next. ARTABAN was the most represented genotype (46 % of high A_max values), followed by 3159-B (31 %) and G5 (23%) (Figure 3).

The distribution of high A_max between the treatments varied among genotypes. For G5, high A_max were mainly observed in low VSP with BARE-I₂ (38 %) and SOWN-I₁ (28 %), followed by high VSP with the same soil managements (24 and 10 %, respectively). No data from VSP-SPO-I₀ were within the ‘optimal’ ellipsoids for this genotype. In 3159-B, where spontaneous crop cover was not tested - and nor was VSPLw with SOWN-I₁ - over 60 % of high A_max values came from high VSP with BARE-I₂ and SOWN-I₁ (30 % each), followed by VSPLw-BARE-I₂ (24 %), while only 12 % were observed in MP-BARE-I₂. For ARTABAN, all tested treatments were associated with high A_max, with more than 50 % of high A_max values found in BARE-I₂ , both in high (27 %) and low VSP (26 %). The remaining values were almost equally distributed between the SOWN-I₁ and SPO-I₀ treatments.

Figure 3. Distribution of genotypes (3A) and cultural management treatments (3B, 3C, 3D) associated with high A_max values within the ellipsoid fitted to the whole dataset.

3. Analysis of the relationships between leaf water and nitrogen status, sink-source ratio variables, photosynthesis and leaf non-structural carbohydrate content

A multi-linear model was used to assess the effects of water, nitrogen, and source-sink variables on A_max, and it was applied to the entire dataset, as well as separately per genotype and phenological stage (before vs. after veraison) (Table 3). Ψ_pd was identified as the most impactful variable and the part of A_max variance explained by Ψ_pd declined from 22 % before veraison to 12 % after veraison. L:F had a significant negative effect on A_max throughout the season, with the part of explained variance of A_max increasing from 2.9 % before to 6 % after veraison. Conversely, LN had no significant impact. Considering genotypes separately, Ψ_pd explained a similar proportion of the variance to the whole dataset before veraison, but after veraison its influence varied, ranging from 5 % of explained variance in G5 to 21 % in 3159-B. The L:F significant effect observed before veraison lost significance when distinguishing genotypes, but remained significant and negative for 3159-B and ARTABAN after veraison.

Significant correlations were observed among Ψ_pd, LN, and L:F before and after veraison (Figure 4A and 4B). Ψ_pd and LN were positively correlated after veraison considering all genotypes together (R² = 0.35) and across genotypes, whereas before veraison their relationship varied, with a positive correlation for G5 but a negative correlation for ARTABAN and 3159-B. Meanwhile, Ψ_pd and L:F showed weak but significant correlations (R² = 0.03 and 0.06), shifting from positive before veraison for ARTABAN and 3159-B (R² = 0.26 and 0.10) to negative after veraison for G5 (R² = 0.12). LN and L:F showed a negative correlation throughout the season (overall R² = 0.1 and 0.15 before and after veraison), which was significant for each genotype after veraison and only for G5 and 3159-B to a lesser extent before veraison (R² = 0.27 and 0.04, respectively).

The correlation analyses were carried out on LS, LSS and A_max. Correlations between A_max and LS were consistently positive, whereas A_max and LSS correlated weakly – albeit significantly – after veraison only (overall R² = 0.01). A_max and LS correlations were weaker for G5 throughout the season, while A_max and LSS correlations were significant only for G5 before veraison and for 3159-B after veraison.

Ψ_pd, LN and L:F also impacted the concentration of non structural carbohydrates. Before veraison, Ψ_pd was the only variable that correlated with LS, whereas after veraison LS correlated positively with Ψ_pd and LN, but negatively with L:F. Before veraison, LSS correlated negatively with LN (R² = 0.05) and, to a lesser extent, positively with L:F and Ψpd (R² = 0.24 and 0.01, respectively). After veraison, only the negative correlation with LN and the positive one with L:F remained significant (R² = 0.1 and 0.15). Variations were observed in the correlations between genotypes: LS and Ψ_pd correlations were weaker for G5 before veraison, while LS and L:F correlations were not significant for 3159-B. After veraison, LS correlated strongly with Ψ_pd and LN for each genotype, while its negative correlation with L:F remained significant only for G5. LSS only correlated positively with Ψ_pd for ARTABAN before veraison (R² = 0.06) and for 3159-B and G5 after veraison. The negative correlation between LSS and LN remained significant for G5 and ARTABAN throughout the season, while the positive LSS and L:F correlation - significant overall - was only weakly significant for 3159-B and ARTABAN before veraison. Finally, LS and LSS were correlated after veraison: negatively for G5 and positively for 3159-B (R² = 0.08 and 0.04, respectively).

Figure 4. Representation of the correlations between the variables before (4A) and after (4B) veraison.

4. Path analysis to establish causal relationships between source-sink ratio, nitrogen, and carbon metabolism-related variables

The correlation analyses revealed numerous associations between variables, but the high number of correlations prevented us from drawing clear conclusions about causal relationships. To disentangle the specific contributions of each variable, we applied structural equation modelling to the dataset. The final model retained only significant relationships between the variables, leading to the removal of pre-established causal relationships, such as those between Ψ_pd, LS, and LSS. This analysis revealed that A_max was directly influenced by Ψ_pd across both periods and all genotypes (Figure 5A and 5B). Before veraison, L:F was positively associated with Ψ_pd for the whole dataset, as well as for 3159-B and ARTABAN. However, after veraison, this relationship became negative for the whole dataset and G5. Additionally, the analysis showed that the positive correlation between A_max and LN observed after veraison (Figure 4) mainly resulted from an indirect effect via the negative relationship between Ψ_pd and LN, as no clear effect of LN on A_max was observed, except for a slight positive effect before veraison.

The path analysis further showed that A_max was positively associated with LS, and that L:F also directly influenced LS independently of A_max. This direct effect was positive before veraison for the whole dataset, G5, and ARTABAN, but became negative after veraison for the whole dataset and G5 while remaining positive for ARTABAN. Although Ψ_pd and LS were positively correlated for the whole dataset and by genotype, the path analysis showed that this relationship was mainly an indirect effect via A_max, as no clear direct relationship between Ψ_pd and LS was indicated, except in G5 after veraison, where Ψ_pd had a small direct negative effect on LSS.

Furthermore, the positive correlation between L:F and LSS for the whole dataset was mainly attributed to a direct effect of L:F, with no indirect influence through A_max. Finally, after veraison, LS and LSS showed a small but significant relationship, whose nature (positive or negative) varied depending on the genotype.

Figure 5. Results of the path analysis performed on photosynthetic maximal net activity A_max (with logarithm transformation), leaf water, nitrogen, carbon status, and balance between sink and source organs for carbon, assessed on the basis of predawn leaf potential Ψ_pd, estimated leaf surface nitrogen concentration (LN), starch, estimated leaf soluble sugars concentrations (LS and LSS), and Leaf-to-fruit ratio (L:F), respectively for all the genotypes or considering G5, 3159-B and ARTABAN, separately before (5A) and after (5B) veraison.

Discussion

1. The high throughput phenotyping methods and experimental design enabled identification of optimal conditions for maximising photosynthetic activity.

One originality of this work is the broad range of contrasting conditions combining water and nitrogen status and sink-source ratio, which was achieved through the implementation of contrasting cultural practices at both plot and plant level. Variations in plant water and nitrogen status were induced via soil management practices, including the use of cover crops (Celette et al., 2013) and differential fertilisation and irrigation treatments. Furthermore, winter and summer pruning techniques, comprising minimal pruning and varying VSP canopy heights, created diverse source-sink ratio conditions while influencing water and nitrogen consumption at the plant level (Downtown et al., 1992; Poni et al., 2023). Ψ_pd and LN exhibited a wide range of values, depending on the cultivar, management practices and period during the growing season. This range of variation encompassed both non-limiting conditions and severe deficits in soil water availability (Williams et al., 2007) and nitrogen content (Verdenal, 2021). The narrower variation in Ψ_pd at higher values observed for ARTABAN compared to G5 and 3159-B could have been explained by a genotypic effect. Indeed, one would typically expect lower Ψ_pd given the lower soil water-holding capacity of the ARTABAN plot. A higher intrinsic water-use efficiency (WUEi) under non-limiting water conditions has previously been reported for ARTABAN by Wilhelm de Almeida et al. (2023), potentially contributing to limiting water loss as water deficit increased. Nevertheless, other factors inherent to the plot could have interfered with the genotype effect, making it difficult to draw reliable conclusions about the genotype effect. The L:F indicator was estimated using the number of berries per plant instead of berry weight, as proposed by Keller et al. (2010), in order to minimise potential biases arising from the direct effects of water, nitrogen status and carbon trophic competition on berry growth (Pallas et al., 2008). The L:F values, expressed in cm² g^-1 of fruit, ranged from approximately 3 cm² g^-1 to 120 cm² g^-1, covering the optimal values required to reach fruit maturity (7 to 14 cm² g^-1) (Howell, 2001) but also both underload and overload cropping conditions. The observed range of A_max values was consistent with those previously reported for grapevine (Wilhelm de Almeida et al., 2023; Prieto et al., 2012; Zufferey et al., 2009). A_max varied roughly between 5 and 25 µmol m^-2 s^-1, which is similar to values observed in other studies on grapevine subjected to well-watered and water deficit conditions (Naor et al., 1997; Poupard et al., 2024). ARTABAN showed a tendency for higher A_max values for a given value of Ψ_pd. Higher crop loads measured for ARTABAN may have stimulated photosynthesis, as observed in previous studies.

Another novelty of this work is the extensive number of water-, nitrogen-, carbon status- and photosynthesis-related variables that were analysed. This was made possible through the application of high throughput phenotyping methods, coupling NIRS and fluo-porometry (Table 1). In line with previous findings (Van Wyngaard et al., 2021), NIRS significantly reduced data collection time for NSC and leaf nitrogen contents. The prediction accuracy for LN was acceptable, with R²calibration (R²cal) of 0.76 (Table 1). In comparison, Prananto et al. (2021) reported an R²cal of 0.94 for dried cotton leaves, while Cuq et al. (2019) obtained R²cal of 0.96 for grapevine leaves, using a MicroNIR OnSite Spectrometer. LS prediction accuracy was also acceptable (R²cal = 0.78). LSS predictions were less accurate as reported by De Bei et al. (2017), with lower R²cal for LF (0.58) and LSc (0.55), except for LG (R²cal = 0.82), likely due to a broader LS range in the calibration dataset compared to the LSS range. We applied a methodology previously proposed by Coindre et al. (2025) to estimate plant net photosynthesis. However, in our case, we estimated maximal photosynthesis (i.e., photosynthesis at saturating light) at a fixed temperature using poro-fluorescence measurements conducted under fluctuating light and temperature conditions. This refinement aimed to avoid the effects of light and temperature variation on photosynthesis measurements, during and between measurements dates (Galat Giorgi et al., 2019). Ultimately, high-throughput methods enabled the collection of approximately 950 measurements of NSC, nitrogen content, and photosynthesis. To our knowledge, this is the first time such a vast amount of leaf nutrient status and gas exchange observations has been collected in real field conditions. These phenotyping techniques hold great potential for integration into high-throughput field phenotyping platforms (Martins et al., 2023).

2. Water status, and to a lesser extent, the source-sink ratio, are the factors explaining variations in photosynthesis.

The distribution of A_max as a function of Ψ_pd, LN and log(L:F) was analysed over the whole season for the three genotypes (Figure 2). The fitted ellipsoid captured a major part of high A_max values (> 14 µmol m^-2 s^-1), while limiting the inclusion of low values (< 8 µmol m^-2 s^-1). The ‘optimal’ ellipsoid centre for high A_max values corresponded to intermediate Ψ_pd (-0.44 MPa) and LN (0.15 mg cm²), with L:F at around 3.12 cm² berry^-1, (i.e., around 15 cm² g of fruit), slightly above the threshold known to reach ‘optimal’ maturity (Howell, 2001). Beyond this centre, the ellipsoid contains high A_max values which were associated with quite a wide range of Ψ_pd values, including very low values (-0.75 MPa) associated with severe stress (Deloire et al., 2004). The ranges of LN and L:F within the ellipsoid were even higher than those of Ψ_pd, indicating the limited contribution of these two factors to reaching high A_max.

Unsurprisingly, except for ARTABAN, which displayed high A_max for non-irrigated treatment (SPO-I₀), the ellipsoid mostly includes the irrigated treatments, primarily the most irrigated one (BARE-I₂), and then the intermediate one (SOWN-I₁) (Figure 3). Interestingly, high A_max were mainly observed for VSP with a low canopy in both I₂ and I₁ for G5, and to a lesser extent for I₁ in ARTABAN (no low VSP was tested for 3159-B, I₁). However, high A_max was achieved for these treatments despite the low Ψ_pd values after veraison (< -0.5 MPa) (data not shown). For these treatments (i.e., VSPLw-I₂, VSPLw-I₁), we hypothesized that photosynthesis was stimulated by the low source-sink ratio to achieve berry maturation, as mentioned previously (Poni et al., 2006; Zufferey, 2000). On the other hand, in some situations, reduced total leaf area may have mitigated the daily decline in water potential by reducing transpiration at the plant level, which can in turn limit the drop in photosynthesis, as previously observed under high evaporative demand or water deficit conditions (Abad et al., 2019; Pascual et al., 2015; Mirás-Avalos et al., 2017). Nevertheless, depending on the genotypic characteristics, the trimming practices that are used to reduce leaf area development can induce a compensation of leaf area reduction by the development of lateral axes, thus limiting the expected effects of such practices (Poni et al., 2023). Ultimately, as well as the three variables that were measured to explain A_max variations (Ψ_pd, LN, L:F), additional factors influencing overall plant functioning (e.g., transpiration dynamics driven by microclimatic fluctuations or differences in aerodynamic conductance at the leaf level) may have also affected the photosynthesis of the targeted leaf (Villalobos-González et al., 2019; Albasha et al., 2019).

3. This study reveals “hidden” causal relationships between water and nitrogen status, source-sink ratio and photosynthesis

A path analysis was conducted to explore the causal relationships in a highly interdependent system in which most factors influencing photosynthesis and carbohydrate concentration are strongly correlated. A_max was directly influenced by Ψ_pd, reflecting the plant’s rapid adaptation to water constraint not only through stomatal control (Medrano et al., 2002), but also through non-stomatal limitations, affecting light harvesting efficiency and biochemical processes or mesophyll conductance under severe water deficit (Lawlor & Tezara, 2009; Villalobos-González et al., 2022); these limitations are known to be coordinated (Flexas et al., 2016; Hochberg et al., 2013).

Higher water status promotes vegetative growth, increasing L:F (Lebon et al., 2006). Accordingly, Ψ_pd and L:F were positively correlated before veraison for 3159-B and ARTABAN. After veraison, the negative L:F and Ψ_pd relationship observed for G5 likely means that increased leaf area (source organs) (Figure S1) led to greater transpiration at the plant scale, reducing plant water status (Bravdo et al., 1985; Intrigliolo & Castel, 2011; Santesteban et al., 2011). This corroborates the indirect and negative impact of L:F on A_max through Ψ_pd after veraison for G5. Furthermore, L:F showed a direct negative effect on A_max for 3159-B and ARTABAN, consistent with photosynthesis regulation by sink demand (Naor et al., 1997; Poupard et al., 2024; Zufferey et al., 2009). The direct and independent effects of Ψ_pd and L:F on A_max are consistent with the findings of Poupard et al. (2024) and Dayer et al. (2016).

The positive relationship between Ψ_pd and LN can be explained by the fact that nitrogen root assimilation improves under sufficient soil water availability (Verdenal et al., 2021). While nitrogen availability can directly influence plant growth (Metay et al., 2014) and photosynthesis (Prieto et al., 2012) under well-watered conditions, this direct effect was not observed in our study, likely due to the greater impact of water deficit. Future studies using foliar nitrogen application, would allow leaf nitrogen content to be controlled more precisely without interference from soil water availability.

A positive effect of A_max on both LS and LSS was expected, as carbon assimilation depends on photosynthesis. Higher photosynthetic rates promote carbohydrate accumulation, primarily as starch (Gordon et al., 1986). Before and after veraison, a direct positive impact of L:F on LSS was also observed, as in previous studies (Zufferey et al., 2009). In high L:F conditions, sugar accumulation in leaves was observed due to the low carbon demand for sink growth compared to the amount of carbohydrate production. As such, L:F had a direct positive impact on LS before veraison. After veraison, this effect was less clear: it remained positive for ARTABAN (Faralli et al., 2022) but turned negative for G5, likely due to increased sink demand from grape growth, preventing transient sugar storage in leaves (Dayer et al., 2016).

In many species, a direct feedback effect of sugar accumulation in leaves has been observed (Pallas et al., 2018). However, in our study, no negative relationship between starch and photosynthesis was found. This suggests that the source-sink ratio likely affects photosynthesis through the regulation of stomatal conductance, rather than through sugar accumulation. This is supported by the literature, which highlights two main mechanisms for regulating photosynthesis: stomatal closure and end-product saturation, both influenced by fruit or seed carbon demand (Wünsche et al., 2005; Andrade et al., 2019).

While direct effects of water status on LS and LSS have been reported, albeit with contradictory results, in our study no clear effect was observed. Previous studies have found that water stress increases LSS while decreasing LS, with total LS+LSS remaining constant (Dayer et al., 2016; Pellegrino et al., 2014). This is likely due to the conversion of LS to LSS to maintain turgor pressure via osmotic adjustments and respiratory metabolism under prolonged water deficit (Düring et al., 1984; McDowell, 2011).

Conclusion

An innovative experimental design was implemented to create a broad range of combinations of water, nitrogen status and source-sink ratio, which were applied to fungi-tolerant grapevine varieties, and thus representing a promising strategy for reducing phytosanitary inputs in viticulture. High-throughput phenotyping techniques enabled the analysis of photosynthesis regulation in these contrasting conditions. This study highlights the dominant role of Ψ_pd in regulating photosynthesis and the additional influence of source-sink balance; meanwhile, no feedback regulation of photosynthesis by carbohydrate accumulation was observed. First insights into the best levers for increasing photosynthesis were obtained: with higher photosynthesis resulting from the two implemented levels of irrigation and their combination with canopy height reduction.

Future research should focus on carbon allocation dynamics at the plant scale, particularly how variations in source-sink balance under different water and nitrogen conditions affect carbon partitioning between vegetative and reproductive organs. Understanding these processes could clarify trade-offs between leaf photosynthesis, fruit development, and carbohydrate reserves in perennial structures. Moreover, investigating the combined effects of severe drought, nitrogen limitation, and source-sink ratio variations over a larger number of growing seasons will be essential.

Acknowledgements

The authors thanks Emmanuelle Garcia-Adrados, Marion Amathieu, Raphaël Galtier, Noé Lalouette-Marier-D’unienville, Vivian Zufferey, Guillaume Coulouma, Hélène Sosnowski, and the bachelor and master students Melissa Baiocchi, Laëtitia DeFelix, Caroline Président, Jules Laforgue, Camille Paulauqui, Léandre Bertin, Valentin Sabatelli and Emilien Faure for their support in data acquisition. We would also like to thank Jean-Noël Lacapère and the team in charge of the maintenance of the vineyard: Frédéric Caumette, Fabien Robert, Jérôme Cerutti, Jérôme Degroise, Jean-Jacques Regadera, Bastien Julien.

The study was funded by the Occitanie region and the Agropolis Foundation.