^{ 1 }EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, F-33882 Villenave d'Ornon, France*corresponding author: vanleeuwen@agro-bordeaux.fr

^{ 1 }

^{ 1 }

^{ 1 }

^{ 1 }

^{ 1 }

^{ * }

^{ 1 }

In wine growing regions around the world, climate change has the potential to affect vine transpiration and overall vineyard water use due to related changes in daily atmospheric conditions and soil water deficits. Grapevines control their transpiration in response to such changes by regulating conductance of water through the soil-plant-atmosphere continuum. The response of bulk stomatal conductance, the vine canopy equivalent of stomatal conductance, to such changes were studied on Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon vines in a non-irrigated vineyard in Bordeaux France. Whole-vine sap flow, temperature and humidity in the vine canopy, and net radiation absorbed by the vine canopy were measured on 15-minute intervals from early July through mid-September 2020, together with periodic measurements of leaf area, canopy porosity, and predawn leaf water potential. From these data, bulk stomatal conductance was calculated on 15-minute intervals, and multiple linear regression analysis was performed to identify key variables and their relative effect on conductance. For the regression analysis, attention was focused on addressing non-linearity and collinearity in the explanatory variables and developing a model that was readily interpretable.

Variability of vapour pressure deficit in the vine canopy over the day and predawn water potential over the season explained much of the variability in bulk stomatal conductance overall, with relative differences between varieties appearing to be driven in large part by differences in conductance response to predawn water potential between the varieties. Transpiration simulations based on the regression equations found similar differences between varieties in terms of daily and seasonal transpiration. These simulations also compared well with those from an accepted vineyard water balance model, although there appeared to be differences between the two approaches in the rate at which conductance, and hence transpiration is reduced as a function of decreasing soil water content (i.e., increasing water deficit stress). By better characterizing the response of bulk stomatal conductance, the dynamics of vine transpiration can be better parameterized in vineyard water use modeling of current and future climate scenarios.

Keywords: grapevine, climate change, drought stress, vineyard water use models,

Grapevines regulate their water use in response to changing atmospheric demand and drought stress by regulating the conductance of water through the soil-plant-atmosphere continuum (

Plant species that readily close their stomata to reduce transpiration and maintain constant leaf water potential when faced with increasing vapour pressure deficit, and/or decreasing soil water status are classified as

The ability of the soil to deliver water through its pore spaces to the roots is also a key component in the soil-plant-atmosphere continuum. Depending on the texture of the soil, water potential decreases as the soil water content decreases, particularly once the fraction of transpirable soil water drops below about 0.4 (

Crop evapotranspiration (ET_{c}) is often modeled using the FAO 56 approach of applying seasonally variable crop coefficients (K_{c}) to estimates of reference crop evapotranspiration (ET_{o}), which are calculated using the Penman Monteith (PM) equation based on an assumption of well-watered conditions and an associated fixed crop canopy conductance (_{s}) to ET_{c }to calculate adjusted crop evapotranspiration (ET_{c adj}). K_{s} is assumed to be 1.0 (i.e.

Being able to characterize variety-specific changes in conductance in response to changing atmospheric conditions and drought would help improve modeling of the vine canopy transpiration component in vineyard water balance models, particularly for evaluating different adaptation strategies under future climate change scenarios. Therefore, the main goal of this study is to quantify and differentiate the response of bulk stomatal conductance (_{bs}
_{bs}
_{bs}

The diffusion of water vapour through stomata is affected by: i) solar radiation, which provides energy for evaporation and diffusion; ii) vapour pressure deficit, which is the driving force for diffusion; and iii) the effects of boundary layer resistance to diffusion at the leaf surface (_{c}
_{c}
_{c}

Additionally, vine water deficit stress can have an important effect on vine transpiration and conductance. Physiological vine responses, such as changes in photosynthesis, stomatal closure, and shoot growth characteristics in response to water deficit stress, are often correlated well with water potential (_{PD}) is an accepted plant-based measurement of plant water status (

In this paper, a multiple linear regression approach was used in an iterative fashion to develop an explanatory statistical model of calculated _{bs}

The measurements for this study were taken on 10 individual grapevines in a vineyard, two each of

The study was performed in a 0.6-hectare common garden experimental vineyard in Bordeaux, France (44° 47’ 0” N, 0° 34’ 39” W) with 52 varieties planted in a randomized block design, in which the varieties were planted in 5 replicate blocks of 10 vines each. All measured vines were located in different blocks of the vineyard, with the exception of the two Merlot vines, which were in two different rows within the same block. The vines are trained on a vertical shoot positioning trellis system with double Guyot pruning. The top and bottom of the vine canopy are 1.5 m and 0.5 m above the ground respectively and 0.4 m wide, with canopy dimensions maintained by hedging twice during the growing season. Vine rows are orientated north-south with 1.8 m row spacing and 1.0 m vine spacing. There is a mowed cover crop in between each vine row with mechanical tillage under the vine row. The vines were planted on SO4 rootstock and the soils are sandy-clay-gravel typical for the Pessac-Léognan wine appellation (^{-2} and average maximum daily temperature from May through September of 25.5 C°.

Leaf area was measured three times during the season in the first halves of July, August and September respectively. Leaf area was determined on each vine by first measuring the length and width of all individual leaves on one primary shoot and all its secondary shoots, which averaged just over 100 leaves on each vine on each measurement date. Those dimensions were well correlated with individual leaf area as measured by a leaf area meter (Model LI-3100 LICOR Inc., Lincoln, NE, USA) before field measurements began. An average size of leaves on primary and secondary shoots were then calculated and applied to a count of all the leaves on the remaining primary and secondary shoots on each vine. Leaf area index (^{2} m^{-2}) is calculated as the total leaf area (m^{2}) for a vine divided by the area of vineyard ground attributable to each vine (i.e., row spacing x vine spacing). The porosity of each vine canopy was measured in the vineyard using a camera phone application (CANAPEO, Oklahoma State University Department of Plant and Soil Sciences, Stillwater, OK, USA) on the same dates as leaf area measurement with any missing measurements being filled in using a regression between measured leaf area and porosity.

Transpiration flux (_{c}
^{-1}) was calculated from sensor signals collected by a datalogger (Model SapIP, Dynamax Inc., Houston, TX, USA) and then scaled up for the whole vine based on the ratio of leaf area of the whole vine over the leaf area of shoots downstream the sap flow sensor. Sap flow (g s^{-1}) was then divided by the area of vineyard ground attributable to each vine (i.e., row spacing x vine spacing) to give canopy transpiration flux, _{c}
^{-1} m^{-2}).

Vapour pressure deficit (_{c}
_{sc}
_{c}
_{c}
_{sc}

Global (shortwave) radiation flux was measured at a weather station next to the vineyard using a horizontally mounted pyranometer (Model No. CMP6 by Kipp & Zonen, Delft - The Netherlands) on one-hour intervals, and then linearly interpolated to 15-minute intervals.

Measurements of 𝛹_{PD} were taken on each vine in the study at six times during the season, roughly 10-14 days apart depending on weather, from early July through early September 2020. Sampling and measurement were done early enough to ensure all measurements were completed no later than 30 minutes prior to sunrise. Measurements were taken on one leaf per vine by the method of

A multiple linear regression analysis was performed in an iterative fashion by: i) applying data filters and calculating bulk stomatal conductance; ii) making necessary data transformations; iii) selecting predictor variables to include in the final regression model; and iv) verifying that ordinary least squared assumptions are properly met in the final regression model. The intent of the above is to obtain a readily interpretable model with the best possible fit. While non-linear, or non-parametric regression analysis might provide a better fit with lower residuals, the coefficients from such models become less readily interpretable (

A database of _{c}
_{c}
_{PD} and _{PD} taken just 4 days before and 7 days afterwards. During the remainder of the study period, no other daily rainfall total exceeded more than a few millimeters, which is well below an amount that would affect predawn water potential.

The equation for calculating _{bs}
_{c}
_{c}
_{c}

The selected filters were applied separately to the data from individual vines when _{c}
^{-2 }or _{c}
_{c}
^{th} percentile of all values for a given vine. The latter filter was applied on a percentile basis due to the differing ranges of _{c}
_{c}
_{c}
_{c}

After the data from each vine was filtered, they were all combined into one database for regression analysis. Summary statistics for the combined database are presented in Supplementary Table S1. It is noted that due to sporadic instrument outages and differing start times, the number of 15-minute data points collected on the vines of each variety were different. Also, units for _{c}
_{c}
^{2} and kPa respectively to avoid problematically small coefficients that resulted when using the units specified for Equations 1 and 2.

Data compilation, filtering, and graphing were performed in the

As presented by _{bs}

Equation 1:

(W m^{-2})

This equation is first rearranged to give the bulk stomatal resistance (_{bs}
_{bs}

Equation 2:

(s m^{-1})

and bulk stomatal conductance is given by inversion:

Equation 3:_{bs} = r_{bs}
^{-1}
^{-1})

where:

_{c}
^{-2} s^{-1})

^{-1})

_{c}

_{c}
^{-2})

_{bs}
^{-1})

_{bh}
^{-1})

𝛾 = psychrometric constant at 1 atm and 20°C = 65.8 (Pa C°^{-1})

^{-1})

𝜌^{-1})

The output units for Equation 2 (s m^{-1}) and Equation 3 (m s^{-1}) result when the above input units are used, with all conductance/resistance and fluxes expressed in terms of unit area of vineyard ground attributable to each vine (i.e., row spacing x vine spacing).

The method of _{c}
^{-2}) of shortwave radiation absorbed by the vine canopy expressed in terms of the unit area of vineyard ground attributable to each vine (i.e

The canopy bulk boundary layer resistance to heat flux (_{bh}

Equation 4:

(s m^{-1})

where:

_{bl}
^{-1}) = 25 s m^{-1} per

Using input data from the compiled and filtered database, _{bs}
_{bh}

Biological and environmental data often demonstrate non-linear relationships and collinearity between variables (

Figure 1 presents bulk stomatal conductance (_{bs}
_{c}
_{c}
_{PD}
_{bs}
_{c}
_{bs}
_{PD} data, while the relationship between _{bs}
_{c}

To address the non-linearity in the relationship between _{bs}
_{c}
_{PD}, a log_{10} transformation of _{bs}
_{10} transformation of _{bs}
_{c}
_{c}
_{PD} data demonstrating roughly linear relationships for each relationship. The effect of the data filters can also be seen in the plot panels for _{c}
_{c}

The process of selecting variables to consider for inclusion in the regression analysis began with 𝛹_{PD} and the key input variables to Equations 2 and 3 for calculation of _{bs}
_{c}
_{c}
_{bh}

Vine water status, as measured by 𝛹_{PD}, is well understood to have an effect on conductance, and was found to have a statistically significant and strong effect on _{bs}
_{c}
_{c}
_{PD} will miss some of the range and response of the varying water status experienced by the vines. This is unavoidable due to the nature of measuring leaf water potential. The final regression analysis also shows _{c}
_{c}
_{bs}

Regression model iterations including _{bh}
_{bs}
_{bh}
_{bs}
_{bh}
_{PD}, which contributed to very high variance inflation factors for the two variables and their interactions. This collinearity is associated with the leaf loss that coincides with the gradual increase in water stress over the season and the resulting more negative 𝛹_{PD}. As a result, _{bh}
_{c}
_{bs}
_{c}

In addition to the main effects of the predictor variables described above, interaction terms between these variables were also considered in a regression analysis based on standardized data (Supplementary Table S2). While nearly all interaction terms from this regression had coefficients that were significant (_{c}
_{c}
_{PD}, with no interaction terms included.

The formula used for the regression analysis of _{10}(g_{bs})_{c}
_{c}
_{PD} as continuous predictor variables and

Equation 5:_{10}(g_{bs}) ~ _{c}
_{c}
_{PD}) *

Expressed in equation form, the multiple linear regression model resulting for each variety using the predictors selected above is given by Equation 6:

Equation 6:_{bs} = 𝛽_{0} + 𝛽_{Rc}*R_{c} + 𝛽_{Dc}*D_{c} + 𝛽_{ 𝛹}*𝛹_{PD}

where:

_{0} =

_{Rc} = R_{c}

_{ Dc} = D_{c}

_{ 𝛹} =_{PD}

The effect of each predictor is characterized by the associated coefficient (

Due to sporadic instrument outages and differing start times, the number of 15-minute data points collected on the vines of each variety were different. Also, even though it was minimized by the filtering and transformations, there was still a low level of collinearity between input variables as observed in the variance inflation factors (described further below). Such factors may complicate an ad hoc comparison of regression coefficients that might be obtained from regressions performed using the data from each variety separately.

As an alternative, the filtered and transformed data from all vines was pooled together and a factor variable representing the different varieties was added, which is then included as part of an interaction term with each of the predictors. From this regression, the coefficients for each predictor are calculated as _{10}(g_{bs})

After each iteration, variance inflation factors (VIFs) were evaluated for each predictor variable and its interactions, if included. VIFs identify the presence and strength of interactions between model terms. The goal is for the VIFs for each predictor variable and any interaction term to be below a generally accepted value of 5.0, with 1.0 representing complete independence (_{c}
_{c}
_{PD} in the final regression model based on combined data, whether standardized, or raw were 1.2, 1.2, and 1.1 respectively, which are well below a standard threshold of 5.0. The low VIFs suggest the effects of interactions were either successfully avoided by variable selection, or removed by filtering and transformation. The coefficient of determination (r^{2}), or the fraction of _{10}(g_{bs}) ^{2} and predicted r^{2} were also 0.701 in both cases, suggesting the model is not overfitted.

For multiple linear regression it is also important to check whether the underlying ordinary least square assumptions are satisfied. In Supplementary Figures S1 a) and b) the standardized model residuals are normally distributed with a mean of zero. Supplementary Figure S1 c) shows relatively constant variance in the standardized residuals plotted versus fitted values (i.e., no heteroscedasticity) and in Supplementary Figures S1 d) through f) there is relatively constant variance in the standardized residuals plotted versus the three predictor variable plots (i.e., no endogeneity). The residual plots for regressions based on standardized and raw data were identical, except for being on different scales.

Multiple linear regression analysis was performed in the

The variety-specific predictor variable coefficients from the regression analysis based on raw data were used to simulate and compare vine canopy transpiration across varieties and to compare against simulations from an existing vineyard water balance model.

A time series of daily vine canopy transpiration and associated soil water depletions were estimated using the vineyard water balance model developed by

The vine canopy transpiration component of evapotranspiration in this model is determined by first calculating the fraction of total incident radiation to the vineyard that is captured by the canopy (minus that which is reflected) using the method of

As the fraction of remaining total available transpirable soil water (_{max}

Using the final variety-specific regression equations, transpiration was simulated by first calculating a time series of _{10}(g_{bs})_{bs}
_{10}(g_{bs}
_{c}
_{c}
_{bh}
^{-1}). All data, including that which was filtered before the regression analysis, was included in the transpiration simulations.

For these simulations, a time series of 15-minute measurements of _{c}
_{c}
_{c}
_{c}
_{c}
_{c}
_{c}
_{c}

The regression model-based simulations also require an input of 𝛹_{PD}. For this purpose, a daily time series of 𝛹_{PD} was developed using the output of _{PD} by the _{PD} relationships published in Figure 3 of

Figure 3 presents the 15-minute time series of bulk stomatal conductance (_{bs}
^{-1}) calculated with Equations 2 and 3 using, as an example, data between 3 August through 13 September 2020 from one vine of Cabernet-Sauvignon. Gaps in this time series are due to filtering of data early in the morning and late in the evening that otherwise caused erratic determinations of _{bs}
_{PD}. Studies using somewhat similar approaches to estimate vine canopy conductance based on whole-plant transpiration measurements in vineyards found similar overall conductance levels and responses to changes in micrometeorological variables for c.v. Merlot (

A noticeable increase in overall levels of conductance is observed after 36 mm of precipitation on 11 through 13 August, the only significant rainfall of the study period. Predawn water potential measurements have been found to equilibrate with portions of the root zone having the highest water content (_{bs}
_{PD} (i.e., water deficit stress) and/or perhaps developmental changes over the season. It also appears that the _{bs}
_{c}

The time series of _{bs}

The multiple linear regression of _{10}(g_{bs})_{c}
_{c}
_{PD} as continuous predictor variables was performed using _{c}
_{c}
_{PD} respectively. As these coefficients were developed based on standardized data, the magnitudes of their absolute values are directly comparable across varieties.

There appears to be more of a differentiation between varieties in the 𝛹_{PD} coefficients in Table 1c when compared to the predictors. And while the _{c}

titre du tableau
Variety
Low CL
Upper CL
Pairwise
a) Standardized
_{c}
Cabernet-Sauvignon
0.161
0.145
0.177
~ 0
a
Semillon
0.225
0.202
0.247
~ 0
b
Tempranillo
0.255
0.235
0.275
~ 0
b
Ugni blanc
0.256
0.241
0.272
~ 0
b
Merlot
0.303
0.285
0.320
~ 0
c
b) Standardized
_{c}
Ugni blanc
-0.625
-0.641
-0.609
~ 0
a
Semillon
-0.582
-0.601
-0.563
~ 0
b
Cabernet-Sauvignon
-0.560
-0.577
-0.543
~ 0
b
Merlot
-0.558
-0.575
-0.541
~ 0
b
Tempranillo
-0.433
-0.452
-0.414
~ 0
c
c) Standardized
_{PD}
Merlot
0.423
0.400
0.445
~ 0
a
Semillon
0.458
0.441
0.474
~ 0
ab
Ugni blanc
0.485
0.458
0.512
~ 0
bc
Cabernet-Sauvignon
0.512
0.499
0.524
~ 0
c
Tempranillo
0.773
0.752
0.794
~ 0
d

Pairwise comparisons with shared letters not significantly different (

Table 2a presents the same predictor coefficients as in Tables 1a through 1c, instead listed in rows by variety, together with the y-axis intercepts. Based on standardized data, the magnitude of the absolute value of these coefficients are now comparable across the three predictors for a given variety. It is observed the absolute value of the _{c}
_{PD} coefficients are comparable in magnitude, with both being about 1.5 to 3.5 times larger than the _{c}
_{bs}
_{c}
_{c}
_{c}
_{c}
_{PD}, which then elicit greater responses from the vines.

For reference, Table 2b presents the same predictor coefficients based on raw data. When based on raw data, these coefficients can be used to calculate modeled bulk stomatal conductance (_{bs}
^{-1}) as needed for subsequent sensitivity analysis and transpiration simulations.

The intercepts (_{0}
_{10}(g_{bs})_{c}
_{c}
_{PD} without including the variety factor variable, the intercept of the regression is zero, as expected when using standardized data. Including the factor variable for variety, however, introduces a very small intercept term (-0.036) as seen in Table 2a.

titre du tableau
Variety
_{0}
_{ Rc}
_{ Dc}
_{ 𝛹}
a) with standardized data
Merlot
-0.036
0.303
-0.558
0.423
Semillon
-0.036
0.225
-0.582
0.458
Ugni blanc
-0.036
0.256
-0.625
0.485
Cabernet-Sauvignon
-0.036
0.161
-0.560
0.512
Tempranillo
-0.036
0.255
-0.433
0.773
b) with raw data
Merlot
0.788
1.110
-0.146
0.066
Semillon
0.788
0.824
-0.153
0.072
Ugni blanc
0.788
0.940
-0.164
0.076
Cabernet-Sauvignon
0.788
0.589
-0.147
0.080
Tempranillo
0.788
0.934
-0.114
0.121

The modeled bulk stomatal conductance (_{bs}
_{10}
_{bs}
_{c}
_{c}
_{PD} shows the non-linear relationships with _{c}
_{PD} that remains after regression (Figure 4). The range of _{bs}
_{bs}

Plots of _{bs}
_{PD} values at fixed levels of _{c}
_{c}
_{c}
^{-2} (Figure 5) show higher overall _{bs}
_{bs}
_{c}
_{PD} levels. And in keeping with its larger 𝛹_{PD} coefficient in Table 1c, _{bs}
_{PD} becomes more negative. The general effect of increasing _{c}
_{bs}
_{PD}.

In keeping with the lower absolute value of its _{c}
_{bs}
_{c}
_{c}
^{-2}, the effect of more negative 𝛹_{PD} = - 0.5 MPa versus 𝛹_{PD} = - 0.1 MPa can also be observed in Figure 6, with an overall decrease in the levels of _{bs}
_{PD}. The spread between varieties, however, remains fairly similar between the two levels of 𝛹_{PD}, particularly at lower levels of _{c}

The biggest differences in _{bs}
_{bs}
_{c}
_{c}
_{bs}
_{c}
_{c}
_{c}
_{c}
_{bs}
_{c}
_{PD} = _{c}
_{bs}

In general, across all the plots in Figures 5 through 7, Merlot has the highest _{bs}

Similar to the differences in _{bs}
_{PD} regression coefficients by variety in Table 1c. Although there are greater differences between varieties for the _{c}
_{c}
_{c}
_{PD} were substantially smaller and hence have less effect on _{bs}

These simulations were based on common _{c}
_{c}
_{PD} needed for the simulations generated by the water balance model using climate data for the same time period and assuming

Comparison of the regression model-based simulation of vine transpiration (Figures 8a and 8b, coloured lines) against the water balance model simulation of vine transpiration excluding soil evaporation (Figures 8a and 8b, black line) finds generally good agreement. It appears, however, that the water balance model tends towards relatively higher transpiration, and therefore faster reductions in

In the water balance model, transpiration is regulated as a function of FTSW using the relative transpiration (_{max}
_{max}
_{max}

The ordering of the _{max}
_{PD} coefficients in Table 1c. The variety most affected by _{PD} coefficient in Table 1c, remembering in all cases, that FTSW and 𝛹_{PD} are assumed to follow the relationship published in Figure 4 of

As the

Based on a constructed time series of input data, the simulation results above are not intended for direct comparisons to measured data, although there is general agreement with the latter falling in a similar range. In order to fully model transpiration using the variety specific regression equations, they would need to be part of a model that tracks soil water deficits or associated 𝛹_{PD} over the course of the season in a feedback loop to the transpiration calculation. With differing transpiration rates for each variety, the resulting time series of _{PD} over the season, and its subsequent effect on transpiration would each be different.

In this study we developed estimates of whole-vine bulk stomatal conductance (_{bs}
_{c}
_{PD}) were the main drivers of changes in _{bs}, _{c}
_{bs }(ĝ_{bs})

For both _{c}
_{c}
_{PD} to the calculation of simulated transpiration are the same for all varieties, these differences are then explained by overall differences in _{bs}
_{bs}
_{c}
_{bs}
_{PD} (Figure 7) when compared to the other four varieties. This suggests Tempranillo puts greater emphasis on responding to decreasing 𝛹_{PD} than on increasing _{c}

Field studies of Tempranillo found similar dynamics with regard to leaf-level stomatal conductance in response to predawn water potential (

The change in the steepness of the slope in the relationship between _{bs}
_{c}
_{c}
_{PD} is observed in Figures 5, 6, and 7 respectively to be generally greater, whether positive or negative, for those varieties with higher _{bs}
_{bs}
_{bs}
_{PD} becomes more negative, then varieties with a greater _{𝛹}

The different ways varieties regulate their conductance may result from varying interplay between the different physiological mechanisms controlling stomata. For example, studies on typically anisohydric varieties Merlot (Zhang _{PD} than Cabernet-Sauvignon.

Direct comparison of varietal responses from different studies is confounded by differences in measurement methodology, such as leaf-level measurement of conductance by gas exchange versus canopy conductance by sap flow. Future comparisons using the same, or similar methods would lead to more comparable results. And while all vines measured in this study were subjected to the same climate conditions, the _{bs}
_{c}
_{c}
_{PD}. The results could also likely be different if performed in an overall different climate (

Based on data collected in a vineyard setting, a multiple linear regression analysis was able to quantify relationships across five different grapevine varieties between vine canopy bulk stomatal conductance (_{bs}
_{c}
_{c}
_{PD})_{c}
_{PD} each had about 1.5 to 3.5 times greater effect on _{bs}
_{c}
_{PD} regression coefficient was more differentiated across the five varieties than was the _{c}
_{c}
_{bs}

A comparison of transpiration simulated using the above regression results found a significant difference in the total growing season transpiration between varieties that appeared to be driven in large part by the difference across varieties in the effect of 𝛹_{PD} on _{bs}
_{bs}
_{bs}
_{bs}
_{bs}
_{PD} than in response to increasing _{c}

The input data needed for this type of study naturally contains non-linearity and collinearity, but careful selection of variables, transformations and filtering made for a readily interpretable multiple linear regression model that satisfied ordinary least squares assumptions. And aside from providing a method for quantifying the response of vine conductance to different environmental variables, the described approach may provide a basis for variety-specific modeling of the vine transpiration component in vineyard water balance models. Knowledge of such differences in _{bs}

The authors would like to thank Guillermo Gutiérrez, Eylul Kadaifci, and Alfonso Domínguez Zamudio for their very hard work in the vineyard, and to the INRAE, UEVB, F-33882, Villenave d'Ornon, France, for its management of the VitAdapt vineyard and assistance with implementing instrumentation.

This study has been carried out with financial support from Jas. Hennessy & Co. (16100 Cognac, France) and the French National Research Agency (ANR) in the frame of the Investments for the future Programme, within the Cluster of Excellence COTE (ANR-10-LABX-45). The VitAdapt vineyard is supported by the Conseil Interprofessionnel des Vins de Bordeaux (CIVB), the Conseil Régional d’Aquitaine, Bordeaux University through LabEx and the Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE).