Marc Plantevin

Mark Gowdy
email: mark.gowdy@inrae.fr

Agnès Destrac-Irvine

Elisa Marguerit

Gregory Gambetta

Cornelis Van Leeuwen
email: vanleeuwen@agro-bordeaux.fr

Sébastien Julliard

Marina Frouin

Xavier Poitou

Agnès Destrac

José S Câmara

Meleksen Akin

Pieter Verboven

Philippe Pieri

Bruno Suter

Using δ 13 C and hydroscapes for discriminating cultivar specific drought responses

Keywords: carbon isotope discrimination, Vitis vinifera, d r o u g h ts t bulk boundary layer conductance, net radiation, transpiration, vineyard water-use models, vine water stress, vapor pressure deficit

Climate change has the potential to affect wine producing regions around the world. As the availability of water to vineyards potentially decreases, whether by decreased precipitation or irrigation water supplies, and/or their use of water increases due to increasing temperatures and atmospheric demand, the drought stress they experience will become greater. Grapevine (Vitis vinifera) varieties can modify their physiology in various ways in response to drought stress, with some better able to maintain their water status in response to decreasing soil water availability, and/or to increasing atmospheric demand than others.

The severity and timing of drought stress can affect the quality potential and yield of grapes produced by a vineyard and is manageable to some degree by growers in meeting their objectives. Understanding the drought stress response of different varieties will help growers anticipate and react accordingly.

The objectives of the studies presented in this thesis were to characterize drought stress response in terms of how grapevines modify their water use, doing so on different time scales. This was achieved using different approaches including: i) measurement of carbon isotope discrimination (𝛿 13 C) in berry juice to characterize water use efficiency and drought stress responses during the berry ripening period; and ii) high frequency determination of vine canopy conductance over a season using heat balance sap flow sensors and other measurements to characterize how transpiration is regulated diurnally and seasonally in response to changes in vine water status and atmospheric variables. In both cases, leaf water potential was also measured to corroborate and further inform findings.

Measurements of these indicators were then used to develop empirical characterizations and classifications of drought stress response across several varieties. Being performed in the VitAdapt common-garden vineyard, with randomly distributed replicate plantings, the effects of variable soil conditions, even within the same vineyard, can be properly accounted for. Also, being performed in the vineyard, these studies produced findings more likely to be relevant to the design and management of working vineyards compared to those obtained in the greenhouse. vi

and Catherine Chabirand. And of course, Nathalie Ollat for her excellent management of a diverse group of researchers, particularly during the trying times of the COVID pandemic.

I also want to thank the numerous interns that I worked with over the five years of both my masters and doctoral studies at ISVV, in particular Martina Haines (2017), Wenxin Li (2018), Stephan Lamy (2019), Guillermo Gutiérrez, Eylul Kadaifci, and Alfonso Domínguez Zamudio (2020), and Marc Plantevin (2021). They all contributed so much to my studies and it was a real pleasure working with all of them. I also want to acknowledge the many work colleagues that contributed to my professional development over the years. In particular Chris Foe and Les Grober from the California State Water Resources Control Board who mentored me in the ways of good scientific method and writing and presentation skills.

I also very much appreciate the support of my family and friends. In particular my mother, Betty Gowdy Stice and my late father, Hank Gowdy for instilling in me the importance of academics, and to my step-father James Stice for his initial encouragement and commiseration throughout my PhD.

And last, but not least… my wife Lynn for all her love and support through the many difficult times during my studies. I look forward to our next chapter together.

iii "It is a capital mistake to theorize before one has data."

-Sherlock Holmes "All models are wrong, but some are useful." -Jay Lund (UC Davis)

"Perfection is the enemy of progress." -Winston Churchill

Introduction

Le changement climatique est susceptible d'affecter les régions viticoles du monde entier, avec des changements incertains et différents selon les régions en matière de précipitations et des températures généralement plus élevées. Comme la disponibilité de l'eau pour les vignobles diminue potentiellement et que leur utilisation de l'eau augmente en raison de l'augmentation des températures et de la demande atmosphérique, le déficit hydrique qu'ils subissent deviendra plus important. Les variétés de vigne (Vitis vinifera) peuvent modifier leur physiologie de différentes manières en réponse à la sécheresse, certaines étant plus aptes que d'autres à maintenir leur statut hydrique en réponse à la diminution de la disponibilité en eau du sol et/ou à l'augmentation de la demande atmosphérique.

La sévérité et le moment de la contrainte hydrique peuvent affecter le potentiel qualitatif et le rendement des raisins produits par un vignoble. L'état hydrique peut affecter la croissance végétative de la vigne, qui à son tour peut affecter le microclimat des grappes de raisin, la dynamique de maturation et les rendements. Il peut également augmenter l'accumulation de composés flavonoïdes tels que les anthocyanines et les flavanols dans les baies, ce qui peut affecter la qualité de la production de vin rouge en particulier. L'état hydrique de la vigne peut être géré dans une certaine mesure par les producteurs pour atteindre leurs objectifs. Comprendre comment l'état hydrique de la vigne répond aux changements de la disponibilité en eau du sol et des températures aidera les producteurs à anticiper et à mettre en place des adaptations nécessaires.

Les objectifs des études présentées dans cette thèse étaient de caractériser la réponse à la sécheresse en termes de modification de l'utilisation de l'eau par la vigne, et ce à différentes échelles de temps. Pour ce faire, différentes approches ont été utilisées, notamment : i) mesure de la discrimination isotopique du carbone (𝛿 13 C) dans le jus de raisin à maturité pour caractériser l'efficience de l'utilisation de l'eau et les réponses à la contrainte hydrique pendant la période de maturation des baies; et ii) détermination de la conductance de la

Résumé en français (suite)

viii canopée de la vigne sur des intervalles de 15 minutes pendant une saison, en utilisant des capteurs de flux de sève à bilan thermique pour caractériser comment elle répond aux changements de l'état hydrique de la vigne et des variables atmosphériques. Dans les deux cas, le potentiel hydrique des feuilles a également été mesuré pour corroborer les résultats.

Les mesures de tous ces indicateurs ont ensuite été utilisées pour élaborer des caractérisations et des classifications empiriques de la réponse à la contrainte hydrique pour plusieurs variétés. Réalisées au vignoble, avec des vignes soumises à des conditions proches de celles de la production, ces études devraient produire des résultats plus pertinents pour la conception et la gestion des vignobles en activité que des études réalisées en serre.

Réponse à la contrainte hydrique pendant la maturation des baies

La mesure de la discrimination isotopique du carbone dans le jus de raisin à maturité (𝛿 13 C) et les mesures du potentiel hydrique pendant la période de maturation des baies sur quatre saisons, ont confirmé les résultats d'études antérieures qui suggèrent que le 𝛿 13 C fournit une évaluation intégrative de l'état hydrique de la vigne pendant la période de maturation des baies. Cela peut être un outil utile pour les producteurs, les acheteurs de raisins et les coopératives pour estimer rétrospectivement l'état hydrique de la vigne qui existait dans le vignoble au cours de la saison de production.

Diverses mesures de 𝛿 13 C et de la phénologie de la vigne réalisées sur 48 variétés différentes, dans des conditions de sol et de climat similaires, pendant sept années, ont ensuite été utilisées pour créer une classification de la tolérance à la sécheresse qui correspondait bien aux connaissances empiriques de la tolérance relative à la sécheresse des variétés étudiées. La classification a été réalisée à l'aide d'une analyse hiérarchique en grappes (HCA) basée sur les traits observés suivants pour chaque variété : le jour de l'année (DOY) de la véraison ; le 𝛿 13 C de l'année la plus humide ; et la fourchette des valeurs du 𝛿 13 C entre celle de l'années la plus sèche et la plus humide. Pour chaque variété, ces mesures tiennent compte respectivement de l'effet du moment de la maturation des baies sur les conditions de sécheresse subies, de l'efficacité innée de l'utilisation de l'eau et de la sensibilité à la contrainte hydrique sur plusieurs saisons. La classification HCA obtenue de cette manière sera utile pour la sélection du matériel végétal basée sur les réponses potentielles au stress dans les futures conditions climatiques.

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Discrimination isotopique du carbone dans l'eau-de-vie en comparaison avec celle obtenue dans le vin

Il a été démontré que le δ 13 C du sucre du moût de raisin est fortement corrélé au δ 13 C de l'éthanol qui résulte de sa fermentation au cours de la transformation du moût de raisin en vin. Cela fournit alors un moyen d'estimer à partir d'un échantillon de vin l'état hydrique de la vigne qui existait pendant la période de maturation des baies qui ont produit ce vin, et peut être utilisé pour établir des relations entre l'état hydrique de la vigne et des attributs sensoriels spécifiques du vin. au cours de cette thèse nous avons examiné comment le δ 13 C dans l'eau-de-vie de vin produite par un processus de double distillation, tel que celui utilisé dans la région de Cognac en France, est potentiellement affecté lorsqu'il est comparé

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x au vin source et au moût de raisin correspondant. Si le signal δ 13 C dans le moût de raisin, le vin et l'eau de vie est conservé, alors le δ 13 C dans l'eau de vie peut être utilisé pour estimer l'état hydrique de la vigne qui existait pendant la période de maturation des baies.

À son tour, cela pourrait être utile pour explorer comment les attributs sensoriels de l'eau de vie sont liés au statut hydrique de la vigne, comme cela a été fait précédemment pour le vin.

Les mesures de δ 13 C dans l'eau de vie produite à partir d'un processus de double distillation ont été comparées au δ 13 C du vin source et du moût de raisin correspondant. Une forte relation linéaire a été trouvée entre le δ 13 C du moût de raisin, du vin et de l'eau de vie, suggérant que ce dernier pourrait être utilisé pour estimer l'état hydrique de la vigne qui existait pendant la période de maturation des baies correspondante. Cela permet une estimation rétrospective de l'état hydrique de la vigne qui existait pendant la maturation des raisins utilisés pour produire le vin et l'eau de vie.

La conductance stomatique de la canopée de la vigne

En réponse aux changements de son statut hydrique, la vigne régule la transpiration en utilisant divers mécanismes physiologiques qui modifient la conductance de l'eau à travers le continuum sol-plante-atmosphère. Une méthode simplifiée est présentée dans cette thèse pour déterminer la conductance de la canopée de la vigne dans un vignoble, basée sur un modèle établi de flux d'énergie de la canopée de la culture, utilisant des mesures du flux de sève de la vigne, de la température et de l'humidité dans la canopée de la vigne, et des estimations du rayonnement net absorbé par la canopée de la vigne. Cette méthode respecte la dynamique particulière des flux d'énergie des vignobles à couvert ouvert, tout en évitant les mesures complexes du flux de chaleur du sol et de la conductance de la couche limite nécessaires à d'autres méthodes, qui peuvent interférer avec les opérations courantes de la conduite du vignoble. Cette méthode fournit un moyen plus simple et plus robuste de calculer la conductance de la canopée de la vigne, nécessaire à l'étude de sa réponse au changement climatique.

Une analyse de sensibilité utilisant une analyse de régression non paramétrique a révélé que le flux de transpiration et le déficit de pression de vapeur sont les variables d'entrée les plus importantes pour le calcul de la conductance stomatique de la canopée, ce qui justifie

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xi l'attention portée à leur mesure sur le terrain. D'autre part, le rayonnement net absorbé et la résistance de la couche limite globale se sont avérés beaucoup moins importants, ce qui suggère des possibilités de simplification de la méthode qui n'auraient pas d'impact significatif sur la qualité des résultats. Il a été observé que les estimations du rayonnement net à grandes longueurs d'onde absorbé par la canopée de la vigne étaient très faibles par rapport au rayonnement net à longueurs d'onde courte absorbé, ce qui suggère qu'il pourrait être éliminé de cette méthodologie sans impact significatif sur le calcul de la conductance stomatique de la canopée. L'avantage de simplifier la méthode et de la rendre plus facile à mettre en oeuvre peut l'emporter sur cette perte de précision, bien que la vérification de cette constatation reste nécessaire dans des vignobles dont la taille du couvert, l'espacement des rangs et l'orientation sont différents. Une méthode simplifiée sur le terrain augmente la possibilité de recueillir davantage de données sur différentes variétés, climats et conditions de conduite.

Réponse de la conductance de la canopée de la vigne à un environnement changeant

La capacité de caractériser la régulation spécifique à la variété de la conductance de la canopée de la vigne, et donc de la transpiration, en réponse aux changements du déficit en eau du sol et de la demande atmosphérique, aiderait à améliorer la modélisation de la composante de la transpiration de la canopée de la vigne dans les modèles de bilan hydrique du vignoble. À cette fin, la méthodologie simplifiée ci-dessus a été utilisée pour estimer la conductance stomatique de la canopée à des intervalles de 15 minutes sur plusieurs vignes de Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc et Sémillon dans un vignoble non irrigué, et sa réponse a été comparée à un certain nombre de paramètres biotiques et abiotiques. Après un processus d'itérations pour prendre en considération la colinéarité et la non-linéarité des données pour les différentes variables, une analyse de régression linéaire multiple a été utilisée pour développer un modèle facilement interprétable de la conductance stomatique de la canopée en fonction de l'état hydrique de la vigne, du déficit de pression de vapeur et du rayonnement net, les coefficients de régression pour les différentes variables prédictives étant physiquement significatifs et comparables entre eux et entre les différentes variétés.

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xii Pour toutes les variétés, la variabilité du déficit de pression de vapeur dans le couvert végétal de la vigne au cours de la journée et le potentiel hydrique de base au cours de la saison expliquent une grande partie de la variabilité de la conductance stomatique de la canopée, la variabilité du rayonnement net absorbé par le couvert végétal expliquant beaucoup moins. L'effet de l'état hydrique de la vigne sur la conductance était plus différencié entre les cinq variétés que l'effet du déficit de pression de vapeur. Et bien qu'il y ait une différence dans l'effet du rayonnement net entre les variétés, son effet global était beaucoup plus faible que celui de l'état hydrique de la vigne et du déficit de pression de vapeur. On a également observé que la réponse de la conductance modélisée, calculée à l'aide des coefficients de régression pour les différentes variables, était de plus grande ampleur (qu'elle soit positive ou négative), aux changements des variables prédicteurs lorsque la conductance dans des conditions non limitantes était plus grande. Il a également été observé que Tempranillo met davantage l'accent sur la réduction de sa conductance en réponse à la diminution de l'état hydrique de la vigne qu'en réponse à l'augmentation des déficits de pression de vapeur, par rapport aux autres variétés.

Les simulations de transpiration basées sur les équations de régression utilisant les données d'entrée de la même saison ont révélé des différences similaires entre les variétés en termes de transpiration quotidienne et saisonnière. Ces simulations se sont également bien comparées à celles d'un modèle reconnu de bilan hydrique du vignoble, bien qu'il semble y avoir des différences entre les deux approches en ce qui concerne le taux de réduction de la conductance, et donc de la transpiration, en fonction de la diminution de la teneur en eau du sol.

Bien que les modèles actuels de bilan hydrique constituent un outil précieux pour aider les viticulteurs à concevoir et à gérer leurs vignobles, ils sont génériques et ne permettent pas de prendre en compte les différences de comportement entre variétés. Bien que davantage de données collectées à l'aide de l'approche décrite soient probablement nécessaires pour caractériser de manière plus définitive le comportement des différentes variétés, elles pourraient permettre de développer un paramétrage spécifique à la variété de la composante transpiration de la vigne dans les futurs modèles de bilan hydrique. En caractérisant mieux la réponse de la conductance stomatique de la canopée aux conditions environnementales, la dynamique de la transpiration de la vigne peut être mieux

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xiii paramétrée dans la modélisation de l'utilisation de l'eau dans les vignobles pour des scénarios climatiques actuels et futurs.

Un autre défi majeur dans la modélisation du bilan hydrique des vignobles est la nécessité de développer une estimation pertinente de l'eau totale transpirable du sol (TTSW). Après la réalisation de l'étude ci-dessus, une relation universelle entre le potentiel matriciel du sol et les déficits en eau du sol a été trouvée, qui pourrait fournir une base à l'avenir pour une méthode d'estimation du TTSW à partir des mesures du potentiel hydrique de base sur la saison. Les estimations réalisées par cette approche pourraient valider les hypothèses de TTSW faites dans cette étude et fournir un moyen raisonnable pour l'estimation de TTSW dans des études futures, bien que l'estimation correcte de la profondeur d'enracinement dans un contexte de vignoble reste un défi majeur.

xiv

The studies presented in this thesis were carried out with financial support from Jas. The final version of this document includes clarification regarding the overall thesis objectives and thoughts regarding potential future studies, which came up during the jury discussions.

Table of Contents xv 1 Introduction

Climate change has the potential to affect wine producing regions around the world with uncertain and regionally differing changes in precipitation, all of which have the potential to significantly alter soil and plant water relations [START_REF] Schultz | Global Climate Change, Sustainability, and Some Challenges for Grape and Wine Production[END_REF]. Higher temperatures will increase atmospheric demand for both plant transpiration and soil evaporation, with a tendency towards lower relative humidity in near-surface air already observed over the last 20 years in the mid-latitudes and Mediterranean [START_REF] Willett | HadISDH land surface multi-variable humidity and temperature record for climate monitoring[END_REF]. Even changes in global radiation, which provide the energy needed for evapotranspiration, can be affected by changes in cloud cover associated with climate change [START_REF] Mendoza | Thermodynamics of climate change between cloud cover, atmospheric temperature and humidity[END_REF] and by human-induced pollution and its interaction with the climate [START_REF] Wild | Global dimming and brightening: A review[END_REF].

As the availability of water to plants decreases (e.g., increasing soil water deficits, or drought), and/or the use of water increases (e.g., increased atmospheric demand), the water status of the plant will decrease. As the plant water status and associated water potential decreases, drought stress becomes greater [START_REF] Turner | Techniques and experimental approaches for the measurement of plant water status[END_REF] and plants can adapt their physiology differently whether in the short-term or long-term (Tardieu et al., 2018). This is true also for the different varieties of Vitis vinifera, which can modify their physiology in various ways in response to drought stress (Lovisolo et al., 2010), with some better able to maintain water status in response to decreasing soil water availability, and/or to increasing atmospheric demand than others (Chaves et al., 2010;Costa et al., 2012;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF][START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF][START_REF] Levin | A continuum of stomatal responses to water deficits among 17 wine grape cultivars (Vitis vinifera)[END_REF].

The severity and timing of drought stress can affect the quality potential and yield of grapes produced by a vineyard, with the optimal levels depending on grower objectives.

Water status can affect the vegetative growth of the vine, which in turn can affect grape cluster microclimate, ripening dynamics, and yields [START_REF] Smart | Sunlight Into Wine[END_REF]. Water status during the berry ripening period can also increase the accumulation of flavonoid compounds such as anthocyanins and flavanols in the berries [START_REF] Castellarin | Water deficits accelerate ripening and induce changes in gene expression regulating flavonoid biosynthesis in grape berries[END_REF], which can affect the quality of red wine production in particular (van Leeuwen et al., 2009). Vine water status is manageable to some degree by growers and understanding how

PhD thesis -Mark Gowdy

2 vine water status responds to changes in soil water availability and temperatures will help growers anticipate and react accordingly [START_REF] Pellegrino | Relationships between plant and soil water status in vine (Vitis vinifera L.)[END_REF].

Because vine physiology is altered by vine water status, measurements of physiological indicators such as transpiration, leaf water potential, changes in stem diameter, leaf and air temperature differences, carbon isotope discrimination, and growth parameters, can be used to assess vine water status [START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF]. For the studies presented in this thesis it was chosen to characterize water status in terms of changes in grapevine water use on different time scales, as achieved by: i) measurement of d 13 C in berry juice as an indication of water use efficiency during the berry ripening period; and ii) high-frequency measurements of changes in transpiration by heat balance sap flow sensors over a growing season in order to estimate vine canopy conductance. In both cases, leaf water potential is also measured to corroborate and further inform findings. These indicators are then used to develop empirical characterizations and classifications of drought stress response across several varieties. And performed in the vineyard, with the vines experiencing realistic growing conditions, these studies should produce findings more relevant to the design and management of working vineyards than those obtained from greenhouse studies.

Much has been published on how plants regulate their water use efficiency and water status in response to drought using measurements of carbon isotope discrimination (d 13 C) in berry juice (Chaves et al., 2010;Costa et al., 2012;Gaudillère et al., 2002;[START_REF] Tomás | Water-use efficiency in grapevine cultivars grown under controlled conditions: effects of water stress at the leaf and whole-plant level[END_REF]Bchir et al., 2016;[START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF]. The study presented in Chapter 3 of this thesis expands on that work and evaluates a range of 48 Mediterranean varieties planted in replicate in the same vineyard, with the intent of classifying their drought response based on varying drought conditions experienced over seven growing seasons. Measurements of d 13 C were also compared against hydroscapes created from measurements of midday and predawn leaf water potential to look for additional relationships. The resulting variety classification should help growers select plant material well adapted for their objectives under future climate scenarios.

The δ 13 C of the sugar in grape must has been shown to be strongly correlated to the δ 13 C of the ethanol that results when it is fermented to wine (Guyon et al., 2015). This then provides a means of estimating from a sample of wine the vine water status that existed during the corresponding berry ripening period, and can be used to establish relations between vine water status and specific sensory attributes of wine quality (Picard et al., 2017). The study presented in Chapter 4 of this thesis examines how the δ 13 C in wine spirit (eau de vie) produced by a double distillation process, such as that used in the Cognac region of France, is potentially affected when compared to the source wine and parent grape must. If the δ 13 C signal in grape must and wine and eau de vie is conserved, then the δ 13 C in eau de vie can be used to estimate the vine water status that existed during the berry ripening period. In turn, this could be useful for exploring how sensory attributes of eau de vie are linked to vine water status, such as has been done previously for wine.

In response to changes in vine water status, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plantatmosphere continuum (McElrone et al., 2013). A simplified method is presented in Chapter 5 of this thesis for determining vine canopy conductance in a vineyard based on an established crop canopy energy flux model (Shuttleworth and Wallace, 1985)

PhD thesis -Mark Gowdy

parameterized to distinguish differences in conductance regulation between varieties, which might otherwise be useful for modeling adaptations to future climate change.

A general review of literature in presented in Chapter 2, followed by the four chapters described above, each centered around a journal article, including additional background and discussion. Chapter 7 concludes with a summary of study results and their implications.

Literature Review

The following is a review of literature relevant to concepts that apply generally across all chapters of this thesis. In each subsequent chapter, further topic-specific literature is presented in the background and discussion sections and in the associated journal articles.

Stomatal regulation

A key physiological reaction of grapevines, as in most plants, in response to drought is to modulate their stomatal aperture and thereby regulate transpiration and the associated hydraulic gradients in its vasculature. The resistance to water vapor diffusion through open stomata is much greater than the resistance of water through the vasculature, with that through the stomata becoming the controlling factor in overall transpiration [START_REF] Sack | Leaf Hydraulics[END_REF][START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF].

Stomatal openings are created between two so-called guard cells that are regulated through a process of osmotic (turgor pressure) adjustment, which changes the size of the aperture between them. Stomata regulate both the diffusion of CO2 into the intercellular space as needed for photosynthesis and the diffusion of water vapor out of the intercellular space (Roelfsema and Hedrich, 2005;Meinzer et al., 2016). As the plant experiences water deficits, the plant adjusts its stomata to conserve water (Roelfsema and Hedrich 2005).

Stomata also act to control minimum leaf water potential [START_REF] Meinzer | Dynamics of leaf water relations components in co-occurring iso-and anisohydric conifer species[END_REF] and protect plant vasculature [START_REF] Sperry | Water deficits and hydraulic limits to leaf water supply[END_REF]. Stomatal opening can also respond to other environmental factors such as light levels, rooting and rootstock, fruit load, temperature, and nutrient status (Keller, 2015). The closure of stomata in response to water stress is influenced by: i) decreases in water potential and hydraulic conductivity in response to decreasing soil water potential; ii) decreases in leaf water potential in response to increasing vapor pressure deficit; and/or iii) abscisic acid hormones (ABA) generated in roots in response to drying soil, which is then transported to leaves via xylem [START_REF] Franks | Anisohydric but isohydrodynamic: seasonally constant plant water potential gradient explained by a stomatal control mechanism incorporating variable plant hydraulic conductance[END_REF]Lovisolo et al., 2010;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF].

Water Potential / Conductivity -Stomata regulation is affected by changes in water potential, although the mechanisms linking the two are not clearly understood (Oren et al., 1999;Roelfsema and Hedrich, 2005). Changes in water potential, and hence stomatal

PhD thesis -Mark Gowdy

regulation, can be associated with decreasing hydraulic conductivity in the plant vasculature by: i) formation of xylem embolisms [START_REF] Sperry | Xylem hydraulics and the soil-plantatmosphere continuum[END_REF][START_REF] Chouzouri | Hydraulic Anatomy, Cavitation Susceptibility and Gas-Exchange of Several Grapevine Cultivars of Different Geographic Origin[END_REF]; ii) changes in xylem characteristics [START_REF] Lovisolo | Effects of water stress on vessel size and xylem hydraulic conductivity in Vitis vinifera L[END_REF]; iii) changes to functioning of aquaporins (Lovisolo et al. 2010); and iv) hormonal (ABA)

signalling [START_REF] Stoll | Hormonal changes induced by partial rootzone drying of irrigated grapevine[END_REF].

Vapor pressure deficit -Water potential in the leaves can also decrease if there is an increase in vapor pressure deficit (i.e., atmospheric demand). Vapor pressure deficit (VPD)

is the difference in the partial pressure of water measured in the ambient air versus that of saturated air at the same temperature [START_REF] Kramer | Water relations of plants and soils[END_REF]. At high levels of VPD, the stomata will begin to close to protect the vasculature of the leaf and the rest of the plant [START_REF] Sperry | Water deficits and hydraulic limits to leaf water supply[END_REF]. For example, a study of five Vitis vinifera varieties found that stomatal conductance remained at maximum levels, depending on variety, up to a threshold level of VPD ranging between 1.36 kPa to 1.91 kPa. However, once VPD increased beyond this threshold, stomatal aperture and gas exchange both decreased, with varieties demonstrating differences in how much their gas exchange rates changed per unit increase in VPD (Prieto et al., 2010). Similarly at the whole vine level, in a combined modeling and field study with the variety Sultana, vine canopy conductance, as measured by sap flow, decreased exponentially as VPD increased (Lu et al., 2003). The VPD experienced by vine itself can also be affected by stomatal regulation, as the effects of evaporative cooling and temperature around the leaf changes, thereby changing VPD (Tardieu and Simonneau, 1998). As described further below, plant water potential and ABA triggers can further affect stomatal closure sensitivity to changes in VPD.

Abscisic acid (ABA) -Plants modify their stomatal response to changes in vapor pressure deficits as soil water deficits change [START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]. ABA is a stress hormone produced in the roots, which is transported in sap via the xylem to the leaves where it increases the sensitivity of stomata to changes in VPD (Lovisolo et al. 2010). Stomatal closure is triggered by ABA as part of a chemical reaction that reduces the osmotic/turgor pressure of the guard cells (Roelfsema and Hedrich 2005). While the molecular mechanisms of how ABA affects guard cells are fairly well known, the mechanisms by which water potential and VPD directly, or indirectly affect guard cells are less well known (Roelfsema and Hedrich 2005). The production of ABA in drying leaves has also been identified as a driver of stomatal response to VPD (Lovisolo et al. 2010). It has been hypothesized that osmotic regulation of guard cells may be connected by a feedback mechanism involving the water status of cells near the stomata [START_REF] Buckley | The control of stomata by water balance[END_REF].

Light levels & photosynthesis -Light conditions, and hence photosynthesis and stomatal conductance can vary greatly within the vine canopy [START_REF] Smart | Photosynthesis by Grapevine Canopies[END_REF]. As photosynthesis rate changes in association with changing light conditions or darkness, the plant will adjust its stomata to help conserve water (Roelfsema and Hedrich 2005). The presence of photosynthetic-active radiation triggers light perception pathways in the guard cells that

cause them to open. The guard cells also sense a decrease in CO2 concentration in the intercellular leaf space as it is depleted by photosynthesis [START_REF] Shimazaki | Light Regulation of Stomatal Movement[END_REF]. The size of stomatal openings are affected by the variable intensity of PAR during the day or as a result of cloudiness [START_REF] Shimazaki | Light Regulation of Stomatal Movement[END_REF]. To the extent it affects stomatal opening, the rate at which photosynthesis responses to changes in light, and the maximum rate of this photosynthesis, can also be affected by nitrogen availability [START_REF] Meinzer | Stomatal kinetics and photosynthetic gas exchange along a continuum of isohydric to anisohydric regulation of plant water status[END_REF].

Temperature -Increased temperature was found to both increase stomatal opening and stimulate photosynthesis in Shiraz, with the associated evaporative cooling helping leaves adapt [START_REF] Montoro | Elevated temperature increased stomata size and leaf photosynthesis in shiraz grapevine[END_REF]. Leaves can also adapt their orientation to avoid heat stress [START_REF] Gamon | Leaf movement, stress avoidance and photosynthesis in Vitis californica[END_REF]. However, as leaf temperatures approach 35 o C, photosynthesis becomes inhibited, at the same time increased vapor pressure deficit tends to increase transpiration, which can lead to excessive water loss from the vine canopy (Keller 2015). Eventually, increasing heat and inhibition of photosynthesis will lead to stomatal closure, which will reduce transpiration and evaporative cooling, leading to heat damage in the leaves (Keller 2015).

Rooting and rootstock -When vine roots sense low soil water status, a series of biochemical and genetic responses are triggered that favor selective root growth over leaf and shoot growth in an effort to locate water and nutrients for continued growth (Lovisolo et al. 2010). The type of rootstock can also affect the ability of the plant to find water, [START_REF] Lavoie-Lamoureux | Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis[END_REF], with this ability also affected by the duration and intensity of soil water deficit conditions [START_REF] Marguerit | Rootstock control of scion transpiration and its acclimation to water deficit are controlled by different genes[END_REF]. Roots may also

PhD thesis -Mark Gowdy

preferentially access water from areas of higher water content within the root zone [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF].

Fruit load -Numerous studies have found an increase in gas exchange (i.e., transpiration) and carbon assimilation rates (i.e., photosynthesis) in association with increased fruit load.

This was observed in a study of Sauvignon blanc [START_REF] Naor | Crop load affects assimilation rate, stomatal conductance, stem water potential and water relations of field-grown Sauvignon blanc grapevines[END_REF] and a study of Pinot noir found that increased fruit load relative to leaf area also increased the rate of photosynthesis as the leaves tried to satisfy demand for photosynthates in ripening berries [START_REF] Petrie | Growth and dry matter partitioning of Pinot Noir (Vitis vinifera L.) in relation to leaf area and crop load[END_REF]. Conversely, cluster thinning can reduce demand from ripening berries, leading to a surplus of carbohydrates from photosynthesis, leading to inhibition of photosynthesis in a feedback loop (Keller 2015).

Carbon isotope discrimination (d 13 C)

The source of carbon for the photosynthesis of carbohydrates in the chloroplasts of the mesophyll of leaves comes from the CO2 that diffuses into the intercellular space of the mesophyll through the stomata. During this process there is a discrimination in favor of CO2 containing 12 C isotopes due to its higher reactivity with the Rubisco enzyme in the photosynthesis reaction. In addition, the rate of diffusion of 13 CO2 through the stomata and leaf boundary layer is also less than for 12 CO2. Both of these processes contribute to lower 13 C/ 12 C isotope ratios in carbohydrates generated by photo-synthesis when compared to atmospheric CO2 [START_REF] Farquhar | Carbon Isotope Discrimination And Photosynthesis[END_REF]Santesteban et al., 2015).

These 13 C/ 12 C isotope ratios, however, are modified when stomata close and cut off diffusion of CO2 into the intercellular space of the leaves, causing the relative concentration of 13 CO2 to 12 CO2 to rise in the intercellular space (due to 12 C discrimination). In the range of moderate water deficit, photosynthesis has been shown to decrease at more negative water potentials than that which induces the onset of stomatal closure. In the range of moderate water stress, photosynthesis continues as stomata begin to close, thereby increasing water use efficiency (Chaves et al. 2010). At the leaf level, water use efficiency (WUE) is the mass of carbon assimilated by photosynthesis per mass of water transpired. As it is also related to the relative rates of carbon assimilation and stomatal aperture, d 13 C has been demonstrated to be a useful proxy for WUE, with less negative d 13 C corresponding to less stomatal opening and higher WUE (Bchir et al. 2016).

As stomata close, the sugars formed by the continuing photosynthesis process will contain a modified ratio of carbon isotopes (Bchir et al., 2016). Of particular interest in viticulture are the relative isotope concentrations of the sugars accumulated in the berries, as this provides an integrative measure of extent of water stress (i.e., stomata closure) that existed during the berry ripening period when those sugars were synthesized and accumulated (Gaudillère et al., 2002;[START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF]Santesteban et al., 2015). Water deficit during the time of berry-ripening is also important from a canopy management perspective, as it helps control vine vigor and improves berry cluster microclimate [START_REF] Smart | Sunlight Into Wine[END_REF]. This makes d 13 C of berry juice sugar a particularly useful measure of vine water status (Gaudillère et al., 2002). A comparative study of d 13 C measurements taken on different plant tissues and at different times of the growing season also concluded berry juice sugar measurements to be most appropriate for characterizing drought stress and WUE (Bchir et al. 2016).

As stomata close in response to water deficit, d 13 C becomes less negative usually in a linear relationship with plant water potential, with some varieties showing a significantly different response. A linear relationship between stem water potential and d 13 C was found, with small differences in response observved between Cabernet-Sauvignon, Merlot, and Cabernet franc [START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF]. For Chardonnay, the minimum predawn water potential measured during berry ripening also demonstrated a linear relationship with d 13 C, with a slope of about 1‰ d 13 C per change of -0.2 MPa in water potential. This was similar to observations for Cabernet-Sauvignon, Merlot, Cabernet franc, and Shiraz in two other studies, although the intercepts of the linear relationships were significantly different, suggesting genetic differences between varieties [START_REF] Brillante | Water status and must composition in grapevine cv. Chardonnay with different soils and topography and a mini meta-analysis of the δ 13 C/water potentials correlation[END_REF]. A study of Moscatel and Castelão also found a linear relationship between d 13 C and predawn water potential measurements integrated over the growing season, however, Moscatel was found

to keep stomata open over a larger range of water potentials, while Castelão was more sensitive (Souza et al., 2005). A study of six Vitis vinifera varieties also found a strong positive correlation between d 13 C and leaf-level WUE [START_REF] Tomás | Water-use efficiency in grapevine cultivars grown under controlled conditions: effects of water stress at the leaf and whole-plant level[END_REF].

PhD thesis -Mark Gowdy

With d 13 C being related to WUE, studies of WUE in response to water stress are also useful in understanding stomatal regulation, and have been performed on numerous varieties [START_REF] Tomás | Water-use efficiency in grapevine cultivars grown under controlled conditions: effects of water stress at the leaf and whole-plant level[END_REF], with an extensive review of leaf level WUE studies also provided by [START_REF] Medrano | Improving water use efficiency of vineyards in semi-arid regions. A review[END_REF]. As an example, a potted-plant study of 20 different Spanish varieties, plus Cabernet-Sauvignon, and Chardonnay for comparison, found strong differences between WUE in well-watered treatments. In water stressed treatments, however, stomatal conductance and WUE, and to a lesser degree chlorophyll fluorescence, were affected differently across varieties, with a change in the type of response in some cases when compared to well-watered conditions [START_REF] Bota | Genetic variability of photosynthesis and water use in Balearic grapevine cultivars[END_REF].

Another study of both WUE and d 13 C characteristics of 23 Spanish and other varieties in field conditions over three years found differences between varieties in WUE under wellwater conditions, but with differences not being as great as observed in the potted-plant studies performed by [START_REF] Bota | Genetic variability of photosynthesis and water use in Balearic grapevine cultivars[END_REF], thereby highlighting the importance of performing such studies in the vineyard [START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF]. The WUE differences between varieties were observed to be driven more by differences in water vapor gas exchange than carbon assimilation, perhaps due to differences in mesophyll conductance or the efficiency of certain photosynthesis reactions [START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF]. Under water stress conditions, there were differences in stomata response to water stress observed between varieties, but also changes in the nature of the response for some varieties when compared to well water conditions [START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF].

Water potential and hydroscapes

Water potential is the potential energy per unit volume of water relative to that of pure water at the same temperature and pressure and is defined with units of pressure such as MPa (megapascals) or bars, with 1 MPa = 10 bars [START_REF] Koide | Plant water status, hydraulic resistance and capacitance -Ch. 9[END_REF]. The components of water potential most important to plants are those related to pressure (i.e. turgor) potential, osmotic potential, and gravity potential, although the latter tends to be negligible except in tall trees (Taiz and Zeiger, 2002, Ch. 3), while the turgor and osmotic pressure components tend to be most physiologically relevant [START_REF] Hsiao | Water stress, growth and osmotic adjustment[END_REF][START_REF] Turner | Techniques and experimental approaches for the measurement of plant water status[END_REF] The pressure chamber method is the most common technique for measuring water potential in plants, with many references existing on the underlying theory and its application [START_REF] Koide | Plant water status, hydraulic resistance and capacitance -Ch. 9[END_REF]. The technique involves cutting individual leaves from the plant and inserting them into the chamber with the petiole visible through a hole with an adjustable gasket. Gas is applied to the chamber with increasing pressure until sap just begins to exude from the petiole [START_REF] Ritchie | The Pressure Chamber as an Instrument for Ecological Research[END_REF]. Generally there are three types of water potential measurements: i) mid-day leaf (yl); ii) mid-day stem (ys); and iii) predawn leaf (ypd) (Williams and Araujo, 2002).

Midday leaf water potential (yl) is measured by standard pressure chamber procedure (Scholander et al., 1965) using an sun-exposed mature leaf from a primary shoot. The leaf water potential is a measure of the immediate negative water potential in the leaf in response to factors such as vapor pressure deficit, stomatal closure, soil water status, and plant hydraulic conductivity (Choné et al. 2001). Midday stem water potential is measured by enclosing the leaf while still on the vine in a plastic bag wrapped in aluminum foil at least 45 minutes prior to measurement of its water potential by standard pressure chamber procedure. With the leaf enclosed on the plant, the stomata will close and the leaf will reach equilibrium with the water potential of the adjacent shoot xylem, providing an indication of the whole plant water status based on its transpiration and current soil and root conductivity conditions (Choné et al. 2001). These measurements should be performed on fully developed leaves from primary shoots, and if being compared, should be taken from the same shoot if possible (Choné et al. 2001;Williams and Araujo 2002).

While leaf water potential measurements have been used extensively for studies of grapevine physiology and water relations (Williams & Araujo 2002), various other studies have concluded that stem water potential may be a more reliable indicator of plant water status, as it relates to changes in transpiration and soil water status [START_REF] Mccutchan | Stem-water Potential as a Sensitive Indicator of Water Stress in Prune Trees (Prunus domestica L. cv. French)[END_REF][START_REF] Naor | Stem Water Potential and Apple Size[END_REF][START_REF] Naor | Crop load affects assimilation rate, stomatal conductance, stem water potential and water relations of field-grown Sauvignon blanc grapevines[END_REF]Choné et al., 2001), particularly when the plant is experiencing moderate to severe soil water deficit (Choné et al. 2001). Because the leaf is bagged and the stomata are closed at the time of measurement, there is less variance in the stem water potential measurement when compared to the variable light conditions experienced by the un-bagged leaf water potential measurements [START_REF] Mccutchan | Stem-water Potential as a Sensitive Indicator of Water Stress in Prune Trees (Prunus domestica L. cv. French)[END_REF]Choné et al. 2001).

PhD thesis -Mark Gowdy

Predawn leaf measurements give water potential of the plant when, in theory, the stomata are closed, there is no transpiration, and the plant is in equilibrium with the root zone water potential (Choné et al. 2001). Although this method is widely accepted for evaluation of water potential across the soil and roots, some studies suggest the equilibrium may be with the wettest portion of the root zone [START_REF] Pellegrino | Relationships between plant and soil water status in vine (Vitis vinifera L.)[END_REF][START_REF] Williams | Relationships among Vine-and Soil-Based Measures of Water Status in a Thompson Seedless Vineyard in Response to High-Frequency Drip Irrigation[END_REF].

Nighttime transpiration can also potentially affect these measurements (Rogiers et al., 2009), although such transpiration decreases as plant water stress increases [START_REF] Escalona | Responses of leaf night transpiration to drought stress in Vitis vinifera L[END_REF]. It also has been observed that predawn measurements can equilibrate with even a small portion of the root zone that contains moisture (Améglio et al., 1999), therefore consideration might need to be given to recent rainfall, or access by a portion of the roots to a water source, as can be the case in irrigated vineyard with variable soil water content.

Alternative frameworks for characterizing stomatal response to water stress have been proposed based on metrics derived from the relationships between midday leaf water potential (yl) and predawn leaf water potential (ypd), or Dy (i.e. ylypd) (Martínez-Vilalta et al. 2014;Meinzer et al. 2016). One such metric is called the "hydroscape", which is developed from the area covered by a plot of yl versus ypd data. The larger the area covered by the hydroscape (MPa 2 ), the less stomata regulate leaf water potential, indicating a more anisohydric behavior. Conversely a small hydroscape indicates more stringent stomatal regulation, and hence isohydric behavior (Meinzer et al. 2016). Another metric is the slope of the regression line through a plot of log(Dy) vs. ypd, which shows the relationship between diurnal fluctuation of plant water potential (i.e. degree of stomatal regulation) as a function of predawn measurements (Meinzer et al. 2016). In addition, pressure-volume curves, as developed from water potential measurements at the turgor loss point (yTLP) and cell osmotic potential at full turgor (yΠ 100), have been found to correlate well with the two methods described above, and other stomatal and photosynthetic responses. Such curves may provide a simpler proxy for characterizing drought tolerance of different species and varieties [START_REF] Bartlett | The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global metaanalysis[END_REF][START_REF] Meinzer | Stomatal kinetics and photosynthetic gas exchange along a continuum of isohydric to anisohydric regulation of plant water status[END_REF].

Transpiration and conductivity/conductance

Around 95% of the water taken up by the plant is not actually consumed, but rather passes through to satisfy the evaporative demand from transpiration. But plants also use water as a solvent to transport nutrients from the soil up into the plant and later to distribute the products of photosynthesis back for development of various plant tissues and organs (Keller 2015). Water is also used to maintaining plant cell turgor pressure as needed to

give the plant structure, and to perform other physiological functions such as opening/closing of stomata. Evaporative cooling from the surface of the leaves is also important in maintaining an acceptable temperature for the leaves (Keller 2015).

At the leaf level, transpiration is measured as the stomatal conductance (gs) of water vapor from the leaf surface (mmol m -2 sec -1 ) and is a function of diffusion rates through the stomatal opening, stomatal density, and the size and degree of opening of the stomata at any particular time (Keller 2015). The water vapor diffusion rate through the stomatal opening is affected by: i) solar radiation, which adds energy to the diffusion; ii) vapor pressure deficit, which is the driving force for diffusion; and iii) wind, which affects the boundary layer resistance to diffusion at the leaf surface (Keller 2015). Due to variable conditions within the canopy, the stomatal conductance of leaves in the canopy can be highly variable [START_REF] Smart | Sunlight Into Wine[END_REF].

Two of the main ways to measure stomatal conductance at the leaf level are by: i) porometer; and ii) infra-red gas analyzer, with a recent review of multiple studies finding the former giving readings about 2.1 times higher than the latter, and the use of infra-red gas analyzers gradually replacing use of porometers due to better precision and accuracy [START_REF] Lavoie-Lamoureux | Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis[END_REF]. At the whole plant, or canopy level a bulk stomatal conductance can be measured using sap flow measurements such as described in Chapter 5 of this thesis. Bulk stomatal conductance is effectively the parallel combination of the leaflevel stomatal conductance of every leaf in the crop canopy (Kelliher et al., 1995).

Across the soil-plant-atmosphere continuum, as depicted in Figure 1, transpiration, as driven by atmospheric demand in the gas phase, must create a more negative water potential than in the liquid phase, including that in the water supply in the soil and the resistances to water transport through the plant (Choné et al., 2001;[START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]McElrone et al., 2013). The continuity of this negative pressure from the leaves to the roots is maintained by the cohesion between bi-polar water molecules, and adhesion between these molecules and the xylem walls by capillary action. Referred to as "cohesion-tension theory" this explanation of water movement through plants is generally accepted in the literature [START_REF] Kirkham | Principles of Soil and Plant Water Relations[END_REF].

The relationship between a change in transpiration and the associated water potential gradient across either the whole plant, or a portion of the plant in the soil-plant-atmosphere continuum is governed by the associated hydraulic conductance [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]. Using an analogy with Ohms law for electricity, while neglecting osmotic and elevation water potential and capacitance, this relationship is represented by Equation 1:

E = Kp x Dy (Eqn. 1)
where E is the average transpiration flux per unit leaf area (mmol m -2 s -1 ), Dy is the change in water potential (MPa) across the whole plant, and Kp is the hydraulic conductance of the whole plant (mmol m -2 s -1 MPa -1 ), which when expressed in terms of flux is referred to as leaf-specific hydraulic conductance [START_REF] Tyree | The hydraulic architecture of trees and other woody plants[END_REF][START_REF] Domec | Decoupling the influence of leaf and root hydraulic conductances on stomatal conductance and its sensitivity to vapour pressure deficit as soil dries in a drained loblolly pine plantation[END_REF][START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF].

Regarding terminology, conductance refers to the relationship between flow through the plant, as driven by transpiration, and the water potential gradients along the entire pathway being considered, for example from the roots to the leaves. This is different from conductivity, which describes the flow and water potential relationships across a unit length of flow, for example along a segment of stem, or specific conductivity, which applies on a cross-sectional area basis for structures like xylem (Keller 2015). The Ohm's law analogy assumes that flow in the soil-plant-atmosphere continuum is steady state, meaning there is no flow to or from storage elsewhere in the plant (i.e. capacitance). In reality, however, there can be some capacitance. For example, over the growing season plant tissues or organs such as leaves or berries can be a sink for water as associated cells are expanding [START_REF] Koide | Plant water status, hydraulic resistance and capacitance -Ch. 9[END_REF]. On a diurnal basis water can be released from storage in the trunks, cordons, stems, and fruits in the morning when transpiration begins and stored back again in the afternoon [START_REF] Steppe | An experimental system for analysis of the dynamic sap-flow characteristics in young trees: results of a beech tree[END_REF].

While not addressed in the above equation, or in discussions below, capacitance may need to be considered in short duration studies of plant hydraulics (Martínez-Vilalta et al. 2014).

When considered across the individual elements along the flow pathway, including the roots, stems, leaves, stomata, and boundary layer, the associated individual conductance of each element is summed in series to give the whole plant conductance [START_REF] Tyree | The hydraulic architecture of trees and other woody plants[END_REF]Choné et al. 2001;[START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]. Changes in the conductance of such elements in response to drought tend to happen in the short-term, while in the longterm plant water use is more affected by changes in phenology, yield loss, root architecture, and photosynthetic and water use efficiency (Tardieu et al., 2018). In the

PhD thesis -Mark Gowdy

short-term, changes in vapor pressure deficit elicit a decrease in conductance by the regulation of stomata within minutes to hours over the course of the day [START_REF] Buckley | Stomatal responses to humidity: has the "black box" finally been opened?[END_REF]Tardieu et al., 2018). Plants can also respond to rapid changes in water potential by adjusting the hydraulic conductance of water in roots and shoots as facilitated by aquaporins. Plant can also modify hydraulic conductivity by adjusting cell osmotic water potential and turgor (Lovisolo et al., 2010;Tardieu et al., 2018). Together, these mechanisms inhibit shoot growth and control transpiration by modifying water use over the season (Tardieu et al., 2018), with these responses potentially varying depending on rootstock (Lovisolo et al., 2010). Hydraulic conductance can also be affected by the ratio of the sapwood cross-sectional area to leaf area being supplied. If there is greater sapwood cross-sectional area supplying a given leaf area, there will be less resistance to flow and less decrease in water potential across the associated vasculature (Zhang et al. 2012).

Resistance to flow in leaves represents an important part of the overall resistance to flow in the whole plant and can vary depending on the light conditions of the shoots and leaves [START_REF] Schultz | Xylem development and hydraulic conductance in sun and shade shoots of grapevine (Vitis vinifera L.): evidence that low light uncouples water transport capacity from leaf area[END_REF]. As long as embolisms do not occur, the vascular structure, and hence conductivity of mature leaves is considered to be fairly constant, and therefore, the difference between ys and yl, as measured simultaneously on two leaves from the same shoot, is considered to be a good indicator of transpiration across a range of soil water and vapor pressure deficits, with an increase in the difference indicating an increase in transpiration (Choné et al. 2001).

The hydraulic conductance through the plant tends to be controlled by the resistance to flow through plant tissues in the roots and leaves rather than through the xylem. The xylem generally has higher conductance, except in the case of embolisms (Tardieu et al., 2018).

As water potential becomes more negative in response to increasing water deficit, this can result is embolism formation in the xylem, whereby air enters at certain vulnerable points in the xylem structure and breaks the adhesion of the water column leading to a drastic decrease in conductance [START_REF] Sperry | Xylem hydraulics and the soil-plantatmosphere continuum[END_REF][START_REF] Chouzouri | Hydraulic Anatomy, Cavitation Susceptibility and Gas-Exchange of Several Grapevine Cultivars of Different Geographic Origin[END_REF]. Stomata have been shown to fully close several days before embolisms appear in the leaves, with basal leaves being more vulnerable to embolism than apical leaves. Once embolisms form, these basal leaves drop, which helps protect the stem and younger leaves and prevents further water loss. (Hochberg et al., 2017b). Also, when vine roots sense low soil water status, biochemical and genetic responses are triggered that lead to reduction of xylem vessel size, which decreases hydraulic conductivity helping moderate transpiration and prevent embolism formation in stems and leaves [START_REF] Lovisolo | Effects of water stress on vessel size and xylem hydraulic conductivity in Vitis vinifera L[END_REF].

As vines acclimate to water deficits over the course of the season, there also appear to be physiological changes in the hydraulics of their vasculature and leaf area that allows for increased gas exchange in later drought events when compared to well water vines (Hochberg et al., 2017a). It was also demonstrated that grapevines can increasingly maintain stomatal conductance and adjust their turgor loss in the face of increasingly negative stem water potential over the season, suggesting it is important to account for such seasonal changes when characterizing drought response (Herrera et al., 2021).

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. As the soil dries, more negative pressure is needed by the plant to overcome increasingly negative soil matric potential, particularly once the fraction of transpirable soil water drops below about 0.4, depending on the texture of the soil. This leads to a corresponding reduction in the hydraulic conductivity within the soil matrix supplying water to the roots [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]. Studies suggest the greatest resistance to flow between the soil and the plant occurs at the soil/root interface rather than within the soil matrix, particularly at higher soil water contents. At very low soil water content the resistances from the interface and the soil matrix become similar [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]. Studies suggest, however, the resistance to flow at the soil/root interface is due more to a decrease in soil water conductivity as soil dries rather than the associated decrease in soil water potential. It has been hypothesized this is due mostly to root shrinkage, which reduces the contact between the root and the surrounding soil [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]. The extent of the rooting system for a given plant can also affect overall plant conductance, with a more extensive root system better able to locate water. Grapevines also adjust relative root to shoot growth, reducing root growth less than aboveground growth in response to increasing soil water deficit [START_REF] Krounbi | Soil Hydraulic Properties Affecting Root Water Uptake[END_REF]Lovisolo et al. 2010).

Varietal differences in stress response

Plants tend to modify their stomatal response to changes in vapor pressure deficits as soil water deficits increase, with different plant species, as well as Vitis vinifera varieties, regulating their stomata differently in response to changes in these variables (Chaves et al., 2010;Costa et al., 2012;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF][START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF][START_REF] Levin | A continuum of stomatal responses to water deficits among 17 wine grape cultivars (Vitis vinifera)[END_REF].

Plant species that readily close their stomata to maintain constant leaf water potential when faced with increasing VPD, and/or decreasing soil water status are classified as isohydric.

Plant species that keep their stomata open (continuing with gas exchange and transpiration)

and allow leaf water potential to decrease when faced with increasing VPD and/or decreasing soil water status are classified as anisohydric (Tardieu and Simonneau 1998).

Stomatal regulation, however, does not always fit neatly into these categories (Martínez-Vilalta et al. 2014), with some species demonstrating responses that vary with water stress, or a hybrid response such as isohydrodynamic, whereby stomata are regulated to maintain a roughly constant difference between leaf and soil water potential [START_REF] Franks | Anisohydric but isohydrodynamic: seasonally constant plant water potential gradient explained by a stomatal control mechanism incorporating variable plant hydraulic conductance[END_REF]Zhang et al. 2012;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF].

Within the species Vitis vinifera, different varieties also demonstrate a range of response dynamics, which are difficult to categorize, or they display inconsistent dynamics across numerous different studies (Chaves et al. 2010;[START_REF] Lavoie-Lamoureux | Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis[END_REF]. For example, Syrah has often been found in studies to demonstrate anisohydric response (Schultz, 2003;Prieto et al., 2010), but in others has demonstrated near-isohydric response [START_REF] Pou | Anisohydric behaviour in grapevines results in better performance under moderate water stress and recovery than isohydric behaviour[END_REF]. And whether isohydric or anisohydric varieties are more "drought tolerant" is subject to interpretation and circumstances. For example, in non-irrigated vineyards an anisohydric variety under conditions of water stress could be more at risk during a drought [START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]Lovisolo et al. 2010). On the other hand, under moderate water deficit, as in an irrigated vineyard, an anisohydric variety might maintain stomata open longer and assimilate more carbon and have potentially higher yields [START_REF] Pou | Anisohydric behaviour in grapevines results in better performance under moderate water stress and recovery than isohydric behaviour[END_REF].

Regardless of how they are characterized, the differing varietal responses to drought stress appear to result from the varying interplay of the different mechanisms controlling stomata. For example, studies on typically anisohydric varieties Merlot (Zhang et al., 2012) and Semillon [START_REF] Rogiers | Stomatal response of an anisohydric grapevine cultivar to evaporative demand, available soil moisture and abscisic acid[END_REF] observed anisohydric response at higher soil water status, but more isohydric response for both varieties at lower soil water status. For both varieties, the isohydric response tended towards greater changes in leaf/canopy gas exchange rates in response to changes in vapor pressure deficit, with this behavior being well correlated with reference gas exchange rates as expected from empirical and theoretical modeling (Oren et al., 1999;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]. The reference gas exchange rate is the gas exchange rate (mmol m -2 sec -1 ) at under conditions of no water stress and full solar radiation. In the Merlot study, it was suggested the transition from anisohydric to isohydric response was due to a decrease in whole-plant, leaf-specific conductance associated with decreased soil water status (Zhang et al. 2012). The Semillon study suggested the shift to isohydric behavior was attributable to increased ABA generated at lower soil water status, which further increases stomatal sensitivity to vapor pressure deficits [START_REF] Rogiers | Stomatal response of an anisohydric grapevine cultivar to evaporative demand, available soil moisture and abscisic acid[END_REF][START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]. Similarly, the typically anisohydric variety Shiraz demonstrated a more isohydric response when subjected to partial root zone drying than when receiving the same level of water over the whole root zone. This was attributed to higher ABA levels being generated in the dried portion of the root during the treatment [START_REF] Collins | Partial rootzone drying and deficit irrigation increase stomatal sensitivity to vapour pressure deficit in anisohydric grapevines[END_REF].

Studies in other species may also provide insights on stomatal response. In a study on Pinus taeda (loblolly pine) by [START_REF] Domec | Decoupling the influence of leaf and root hydraulic conductances on stomatal conductance and its sensitivity to vapour pressure deficit as soil dries in a drained loblolly pine plantation[END_REF] it was found as soil dried, the canopy reference gas exchange (at VPD = 0) decreased as expected based on the findings of Oren et al. (1999), but there was also a corresponding significant decrease in whole plant hydraulic conductance as calculated from sap flow and water potential measurements, suggesting changes in plant hydraulics had a role in stomatal regulation [START_REF] Domec | Decoupling the influence of leaf and root hydraulic conductances on stomatal conductance and its sensitivity to vapour pressure deficit as soil dries in a drained loblolly pine plantation[END_REF]). This study further observed in field measurements that whole plant conductivity was controlled by leaf conductance at higher soil moisture levels and root conductance at lower soil moisture [START_REF] Domec | Decoupling the influence of leaf and root hydraulic conductances on stomatal conductance and its sensitivity to vapour pressure deficit as soil dries in a drained loblolly pine plantation[END_REF].

PhD thesis -Mark Gowdy 203 Varietal drought response classification

Background

Plants regulate their water use efficiency in response to changes in water status brought about in response to drought conditions. This water use efficiency, and hence level of drought stress during the berry ripening period can be assessed using measurement of carbon isotope discrimination (d 13 C) in berry juice sugars collected at maturity (Chaves et al., 2010;Costa et al., 2012;Gaudillère et al., 2002;[START_REF] Tomás | Water-use efficiency in grapevine cultivars grown under controlled conditions: effects of water stress at the leaf and whole-plant level[END_REF]Bchir et al., 2016;[START_REF] Bota | Differences among grapevine cultivars in their stomatal behavior and water use efficiency under progressive water stress[END_REF]. By collecting d 13 C measurements across multiple years with different drought conditions, the drought stress response of a given variety may be assessed. If vines of different varieties are studied under the same conditions, then the relative drought stress response of the varieties can be compared. In the journal article presented in this chapter, entitled "Using δ 13 C and hydroscapes for discriminating cultivar specific drought responses" (Plantevin et al., 2022), the drought stress responses of 48 different varieties are compared based on data collected over seven years from vines planted in four replicate plots in the VitAdapt experimental vineyard in Bordeaux. As depicted in Figure 2, with the location of Ugni blanc vines circled in red as an example, the common-garden replicate layout of the VitAdapt vineyards allows for the variability of soil conditions, which can exist even within the same vineyard, to be properly accounted for (Destrac-Irvine and van Leeuwen, 2017). With multiple years of data, the innate water use efficiency can be evaluated in years of less stressed conditions and the range of responses can be assessed across varying drought stress conditions. With corresponding measurement of the phenology in the vineyard, the effects of the timing of the post-véraison period in relationship to the progression of drought conditions over the season can also be accounted for. This data is then used to create a drought tolerance classification of these varieties using a hierarchical cluster analysis. The classification will help inform grower selection of plant material well adapted for their yield or berry composition objectives in current, or future growing conditions at a given location.

Midday and predawn leaf water potential (𝛹L and 𝛹PD) are two other means of assessing vine water status, and if made during the berry ripening period can corroborate d 13 C measurements. They can also be used to create hydroscapes, which provide an indication of how strictly a variety manages its water status across a range of drought conditions (Martínez-Vilalta et al., 2014;Meinzer et al., 2016). In this study certain metrics from such hydroscapes were compared against d 13 C measurements to identify relationships between the different metrics.

INTRODUCTION

Vines are cultivated in a wide range of climates, from very dry (such as Cyprus or Aragon, Spain) to extremely wet (such as the Hunter Valley, Australia). The reason for the commercial success of vineyards across such a large range of environments can be explained by the winegrowers' ability to adapt through viticultural practices or by choosing plant material adapted to the local climatic conditions. These adaptations are increasingly important as local environmental conditions are changing under the effect of climate change (IPCC, 2021). Rainfall patterns are changing, with precipitation increasing, or decreasing depending on the region. An increase in temperature and thus evaporative demand will induce drier conditions in most winegrowing regions, even in those with unchanged precipitation regimes. As a result, adaptations are needed to maintain commercially viable viticulture in current winegrowing areas [START_REF] Fraga | An overview of climate change impacts on European viticulture[END_REF]Ollat et al., 2016;[START_REF] Van Leeuwen | The Impact of Climate Change on Viticulture and Wine Quality[END_REF]. [START_REF] Winkel | Stomatal conductance of some Agricultural and Forest Meteorology[END_REF]Williams and Araujo, 2002), which reduces carbon assimilation [START_REF] Flexas | Down-regulation leaves[END_REF]. Despite this improved (better light penetration in the canopy due to lower vigour); more carbohydrates are available for berry ripening due to reduced competition with shoot growth; and berries become smaller with increased levels of anthocyanins in the produced wines (Roby et al., 2004;[START_REF] Keller | The Science of Grapevines-1st Edition[END_REF][START_REF] Carbonneau | Traité de la vigne: Physiologie, terroir, culture[END_REF][START_REF] Triolo | Impact of vine water status on berry mass and berry tissue development of Cabernet franc (Vitis vinifera L.), assessed at berry level[END_REF]. The positive impact of mild in grapes and wines [START_REF] Van Leeuwen | Recent advancements in understanding the terroir effect on with the XIII th International Terroir Congress November 17-18 2020[END_REF] as well as on the sensory properties of aged red Bordeaux wines [START_REF] Le Menn | Effect of Vine Water and Nitrogen Status, as Well as Temperature, on Some Aroma Compounds of Aged Red Bordeaux Wines[END_REF].

may negatively impact commercial viability (van Leeuwen et al impacted and grape ripening (e.g., sugar accumulation) can slow down or even stop in extreme conditions [START_REF] Van Leeuwen | The Impact of Climate Change on Viticulture and Wine Quality[END_REF]. Although vines can survive under severe droughts and are unlikely to die [START_REF] Charrier | Drought will not leave your glass empty: Low risk of hydraulic failure revealed by long-term drought observations in world's top wine regions[END_REF],

Many options for adapting viticulture to a warmer and drier future are available, such as changes in canopy management practices, vineyard planting densities, or plant material selection (e.g., clones, rootstocks, and/or varieties) [START_REF] Fraga | An overview of climate change impacts on European viticulture[END_REF]van Leeuwen et al., 2019a;[START_REF] Van Leeuwen | An update on the impact of climate change in viticulture and potential adaptations[END_REF]. To inform future plant material selection, the drought responses of different varieties need to be better characterized.

Assessing the regulation of vine water status is fundamental in making this characterization. Measurements of vine water potentials and the use of carbon isotope discrimination are two methods that have proven particularly useful in assessing water status, each with its own caveats (Choné et al., 2001;Gaudillère et al., 2002;[START_REF] Cifre | Physiological tools for irrigation scheduling in grapevine (Vitis vinifera Agriculture[END_REF]van Leeuwen et al., 2009;[START_REF] Santesteban | Carbon isotope ratio of whole berries as an estimator of plant water status in grapevine (Vitis vinifera L.) cv. 'Tempranillo[END_REF].

13 C provides an integrative measure of the water status of the plant over a given period of time and can be assessed in leaves and/or berry juice at the end of the season (Gaudillère et al., 2002;[START_REF] Santesteban | Carbon isotope ratio of whole berries as an estimator of plant water status in grapevine (Vitis vinifera L.) cv. 'Tempranillo[END_REF]. During the process of photosynthesis there is a discrimination against assimilation of CO 2 containing 13 C isotopes due to the higher reactivity of 12 C isotopes with the Rubisco enzyme in the photosynthesis reaction. In addition, the rate of diffusion of 13 CO 2 through the stomata and leaf boundary layer is less than for 12 CO 2 . This results in lower 13 C/ 12 C isotope ratios in carbohydrates photosynthesized at that time when compared to atmospheric CO 2 [START_REF] Farquhar | Carbon Isotope Discrimination And Photosynthesis[END_REF][START_REF] Santesteban | Carbon isotope ratio of whole berries as an estimator of plant water status in grapevine (Vitis vinifera L.) cv. 'Tempranillo[END_REF]. As stomata close in response to drought stress, the 13 C/ 12 C ratio rises in the intercellular space and sugars formed and incorporated ratio of carbon isotopes (Bchir et al., 2016).

13

C is a useful time sugars in the berry juice accumulate (van Leeuwen et al., 13 C effectively provides a measure of the ratio between the rate of photosynthesis and the stomatal opening over a period of time [START_REF] Farquhar | Carbon Isotope Discrimination And Photosynthesis[END_REF]. This is similar to the ratio level is the amount of carbon assimilated by photosynthesis (A n ) per amount of water transpired (g s ), with a less negative 13 C corresponding to less stomatal opening and higher WUE (Medrano et al., 2010, Souza et al., 2005, Bchir et al., 2016).

A study of eight own-rooted Vitis vinifera varieties found a 13

C and leaf-level WUE [START_REF] Tomás | Variability of mesophyll conductance in grapevine cultivars under water stress[END_REF].

Vine water status can also be assessed by different measures L ) is a measure of the immediate negative water potential (i.e., suction) in the leaf in response to many factors, such status, and plant hydraulic conductivity (Choné et al., 2001). PD ) is experienced at night when the vines have equilibrated with the soil water potential (Williams and Araujo, 2002) and can be considered as a proxy for soil water availability [START_REF] Gaudin | Linking the transpirable soil water content of a vineyard to predawn leaf water potential measurements[END_REF], or more precisely of the water availability in the most humid soil layer (Améglio et al., 1999).

The amount of stomatal regulation in response to changing L varies among genotypes, a concept which has been referred to as (an)isohydristicity (Tardieu and Simonneau, 1998;Hochberg et al., 2018). An anisohydric species allows L while an isohydric species will regulate stomata at less negative L , decreasing transpiration and eventually photosynthesis (Tardieu and Simonneau, 1998). Plant species do not adhere a continuum from anisohydric to isohydric (Chaves et al., 2010;[START_REF] Klein | The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours[END_REF] and this is also true across Vitis vinifera varieties, for which differences across genotypes mainly et al., 2019). In addition, growth conditions (e.g., greenhouse vs grown) and the climatic conditions which impact the range of behaviour even within the same genotype (Chaves et al., 2010;[START_REF] Charrier | Drought will not leave your glass empty: Low risk of hydraulic failure revealed by long-term drought observations in world's top wine regions[END_REF], Hochberg et al., 2018).

Recently the "hydroscape" approach was proposed to visualize how plants regulate their water potential by means of stomatal regulation and to classify genotypes according to their behaviour under drought stress (Martínez-Vilalta et al., 2014 L PD, with the larger the range, the less strict (i.e., more anisohydric) the genotype is at controlling its stomatal conductance under et al., 2016). Conversely, a small hydroscape indicates a genotype with more strict stomatal control (i.e., more isohydric). This methodology has shown to be effective in classifying drought-responses of different species (Li et al., 2019, Meinzer et al., 2016[START_REF] Álvarez-Maldini | behaviour in almond cultivars[END_REF]. Hydroscapes, however, are time-consuming to PD L

This study evaluates the drought stress responses of several 13 C measurements in berry juice sugars and by hydroscapes. Some apparent relationships between the results obtained by both methods are also discussed.

MATERIALS AND METHODS

VitAdapt vineyard

Data for this study were obtained in the VitAdapt experimental vineyard located at the INRAE (Institut National de Recherche pour l'Agriculture et l'Alimentation) research center of Bordeaux, in France (44°47'23.8''N, 0°34'39.3"W) (Destrac-Irvine and van Leeuwen, 2017). The soil is composed mainly of gravel and sand, representative of the Pessac-Leognan AOP (Bordeaux, France). The vineyard was planted in 2009 with 52 different varieties, including 4 hybrids and 48 Vitis vinifera L. cultivars on a 0.7 ha parcel with 46 rows of 75 vines each on a density of 5,555 vines/ha (row spacing of 1.8 m and inter vine spacing of 1.0 m). The trunk height is 0.5 m and grapevines are pruned to a double guyot and topped during the growing season at 1.5 m. Each variety was planted on Selection Oppenheim 4 (SO4) rootstock, clone 761. Each variety was tested for major virus diseases and only clean material was planted. There is a mowed cover crop in between each vine row with mechanical tillage under the vine row. The vineyard is dry-farmed and fertilisation is adjusted based on petiole analyses carried out at mid-veraison. To account for soil variability, the VitAdapt experimental design was laid out in randomized blocks with each block containing two rows sampled.

Carbon isotope discrimination ( 13 C)

13 C were taken from berry juice sugar collected each year at maturity from 10 vines of each variety in each of the four blocks. Measurements of grape sugars and acids were carried out weekly starting at mid-véraison and maturity was considered when sugars started to plateau-off after initial planting in 2009, and continued each year through 2020. Berries were harvested from the 10 vines in each block and juice was extracted and centrifuged at 13,500 rpm for 10 minutes (Sigma 13 6K15, SIGMA Laborzentrifugen GmbH, Osterode am Harz, Germany). The juice was then analyzed on a Vario Micro Cube elemental analyzer spectrometer (IsoPrime, Elementar). Glutamic acid USGS40 and Caffeine IAEA-600 were used as international standards during the analyses. All results are expressed in delta notation 13 C) and reported relative to the Vienna Pee Dee Belemnite (VPDB) international reference. Over the seven years a total 13 C measurements were obtained for each variety.

13

C was also measured on eight individual vines of six varieties (Cabernet-Sauvignon, Merlot, Grenache, Tempranillo, Semillon, Ugni blanc), two of each from the four blocks, upon which water potential measurements were also taken periodically over the season. Of these individual vines, some started to lose too many leaves due to disease pressure or multiple water potential measurements, in which case water potential was taken on an 13 C was also measured with the values being averaged.

Water Potential

Water potential measurements were taken periodically over the seasons 2017 through 2020 on two vines each in the four blocks of Cabernet-Sauvignon, Grenache, Merlot, Semillon, Tempranillo and Ugni blanc. These varieties are some of the most widely planted varieties worldwide [START_REF] Anderson | Which winegrape varieties are grown where? A global empirical picture[END_REF]. Merlot, Tempranillo and Semillon (Rogiers et al., 2009) are known for being sensitive to drought stress, while Cabernet-Sauvignon and Grenache are renown for being less sensitive [START_REF] Santesteban | Effect of decline of excised leaves of four grapevine (Vitis vinifera L.) cultivars[END_REF]. No data were obtained on Grenache in 2018 because of downy mildew damage and measurements on Ugni blanc did not start until 2018. Measurements were collected at seven timepoints in 2017 and 2018 and six timepoints in 2019 and 2020.

From each vine on each sampling date, three types of water L ) S ) measurements beginning at PD ) started around 3 a.m. the morning after, with all measurements S measurements the leaves were sealed in foil covered bags while on the plant one hour prior (Choné et al., 2001). Measurements were taken by the method of Scholander et al. (1965) using a pressure chamber with digital manometer (DG MECA, 33175 Gradignan, France).

Hydroscapes

The method for preparing hydroscapes was adapted from the concepts presented by Meinzer et al. (2016), [START_REF] Charrier | Drought will not leave your glass empty: Low risk of hydraulic failure revealed by long-term drought observations in world's top wine regions[END_REF]Hochberg et al. (2018). First, following the procedure of Meinzer et al., (2016), a scatter plot is 1). In theory, the greater the area enclosed by the hydroscape, the more anisohydric the variety (Meinzer et al., 2016).

Filtering, graphing, and linear regression analysis was performed in the R software environment using several functions from the dplyr package (Wickham et al., 2021a) and the package [START_REF] Wickham | grammar of graphics[END_REF] with hierarchical clustering analyses and dendrograms being done using the cluster package.

RESULTS AND DISCUSSION

1. Climate conditions followed by 2018. Some years showed particular climatic conditions. In 2015 (globally a dry year), August was very rainy (109.5 mm). In 2017, evaporative demand was low in May and rainfall was high in September (82 mm), which July and August. 2020 was one of the earliest vintages ever in Bordeaux, with high rainfall in May and June, while July was very dry (2.5 mm) and rainfall in August was close to average (46.5 mm). More information regarding vine water status during these years is presented in Figures S1 andS2.

Carbon isotope discrimination ( 13 C)

2.1 Relationship of berry juice 13 C to leaf water status Figure 2 presents plots of 13 C on an individual vine basis for six varieties from 2017 through 2020 versus the PD measured during berry ripening (i.e., from 20 days before mid-veraison until 60 days after veraison). The 13 C and average PD were well correlated for Cabernet-Sauvignon, Grenache, Semillon and Tempranillo, while not as well correlated for Merlot and Ugni blanc (Note, only three years of data were collected for Ugni blanc and Grenache). Plots of 13 C versus S (not shown) showed similar relationships. These results are consistent with previous studies showing 13 C as being well correlated to PD (Gaudillère et al., 2002) and to midday S (Santesteban et al 2.2 Varietal differences in 13 C Figure 3 presents the range of 13 C values for each of the 48 varieties across the seven years of measurement, with the thickness of the violin plot indicative of the distribution of values and the black dot representing the mean of all measurements. As discussed in the next section, the average 13 C measured in the year with the most negative values is an important metric of drought responses for a given variety. The red dots on the violin plots represent this minimum mean value for each variety and are the values upon which the varieties are ordered along the vertical axis in Figure 3. The difference in these mean 13 C values across the 48 varieties is up to 2.8 ‰, representing a 44 % variation across the full range of varieties relative to Tinto Cão. Some varieties (e.g., Liliorila and Ugni blanc) appear to operate in a narrower range of 13 C values across the years, while others (e.g., Touriga nacional and Saperavi) operate across a wider range of 13 C. As discussed in the next section, the range of 13 C provides an indication of the plasticity of a variety to adapt its WUE to dry versus wet conditions. An effect is also observed across the years with the lowest and highest mean 13 C values for each variety. For years with the most negative 13 C values, as associated with wetter conditions and lower WUE during ripening, a difference of up to 3.1 ‰, or a 50 % variation was observed across all varieties relative to the variety with the most negative corresponding 13 C value (Saperavi). For years with the least negative 13 C values, as associated with drier conditions and higher WUE during ripening, a difference of up to 4.0 ‰, or a 63 % variation was observed across all varieties relative to the variety with the least negative corresponding 13 C values (Xynomavro) 13 C may also be impacted by the different phenology of each variety, with later ripening varieties being more likely to than earlier varieties. Thus, in attempting to characterize 13 C, differences in the phenology of the berry ripening period in relationship to the driest times of the season must also be considered.

Categorization based on key traits

Several combinations of key 13 C and other physiological traits were used in a hierarchical clustering analysis (HCA) to identify groupings of varieties with similar characteristics. The most consistent groupings were obtained when HCA "minimum 13 C" (i.e., 13 C in non-limiting conditions) and "range of 13 C" (i.e., the difference between 13 C in a wet vintage compared to a dry vintage). The HCA in Figure 4 suggests a range and groupings of varieties based on the above non-drought-tolerant (green arrows) were separated between the two distinct groups. Moreover, varieties generally grown in dry climates were separated from varieties generally grown in more humid climates. While it is very complex to possibly be reached by considering the yield and quality data the wine industry. consistent results with the minimum value of 13 C (i.e., nonlimiting conditions) compared to the maximum value of 13 C (i.e., limiting conditions). Varieties with a more negative 13 C in non-limiting conditions may have a greater stomatal conductance and thus greater transpiration and water use early in the season which could more quickly deplete available soil water (Lebon et al., 2003) and increase the likelihood PD data (Figure 2) where Tempranillo and Semillon, varieties with very relatively negative 13 C in non-limiting conditions are the two varieties that reached PD . The other metric of interest in this value of 13 C (i.e., the 13 C under non-limiting conditions) and the maximum value of 13 C (i.e., the 13 C under limiting conditions). This trait would characterize the plasticity of the variety to adapt to the drought conditions of the vintage.

One confounding factor in assessing the 13 C characteristics of different varieties is the timing of phenological stages. Of particular concern with 13 C in berry juice sugars is the vine water status during the period of sugar loading in the berries, roughly starting one week to ten days before FIGURE 3. Violin plot of the 13 C data for 48 varieties from 2014 to 2020 in Villenave d'Ornon, France. The black dots represent the average value of 13 C and the red dots the average value of 13 C in the wettest year (classified from the less negative to the most negative). The full names of the varieties are presented in Supplementary Materials, Table S1. Lcrit is a very important metric related to the mortality thresholds of various plant species under drought [START_REF] Choat | Triggers of tree mortality under drought[END_REF]. Cultivars with high stomatal conductance in well-watered conditions will deplete soil water earlier in the season. Depending on the degree to which stomatal conductance is adjusted under decreasing PD , these cultivars could For example in this study, Tempranillo had one of the largest hydroscapes and most negative L . It was also observed that among the 48 varieties, Tempranillo was one of the earliest varieties to show leaf wilting and leaf abscission.

PD at which a variety normally operates, representing its ability to extract water at low soil water potential. As discussed above, this can be linked to less stringent stomatal control (e.g., Grenache PD because of a very strict stomatal control). Some varieties appear able to extract water PD which may be due to the root architecture associated with the cultivar x rootstock combination. It is impossible, however, to assess the rooting zone of the vines in the VitAdapt parcel due to soil heterogeneity. It is also to explore the soil more or less profoundly (Tandonnet et al., 2010). Vines having access to a greater volume of soil water due to a greater rooting depth are likely to experience less PD . The approach presented offers a possible way of classifying varieties according to their level of (an)isohydricity, an important criterion in drought tolerance (i.e., the larger the hydroscape, the more drought sensitive). Results are in line with the literature regarding stomatal behaviour, in particular for the well documented varieties Grenache and Semillon (Schultz, 2003;Rogiers et al., 2009). Results were obtained of cultivars. It would also be interesting to assess whether, for a given variety, the hydroscapes have a different shape when the data are collected on different soil types, or with different rootstocks. One of the limitations of the hydroscape approach collected, ideally over multiple years.

Comparison of 13 C and hydroscapes

Both 13 C and hydroscapes are mainly driven by the stomatal behaviour of plants [START_REF] Farquhar | Carbon Isotope Discrimination And Photosynthesis[END_REF]Meinzer et al., 2016). Hydroscapes were constructed as described above for six varieties for which there were also measurements of 13 C. Several metrics were extracted from the analysis performed using these two tools and are listed in Table 1 along with the associated values for each variety.

A correlation analysis of the metrics presented in Table 1 across the different varieties is presented in Figure 6. It is observed that the size of the hydroscapes is strongly

CONCLUSION

The measured 13 C in berry juice sugars provides an indication of vine water status during the period of sugar accumulation comparison against corresponding measurements of predawn water potential on six varieties over four seasons. Distinct differences in 13 C measured in berry juice sugar collected in a vineyard setting over seven years of differing climatic

The drought responses of these varieties were then ranked 13 C in years of relatively low water stress; ii) the range of 13 C for a variety as measured in wet versus dry years; and iii) the day of year of mid-veraison. This ranking corresponded well with generally understood drought tolerance characteristics of the different varieties. Of particular importance were the highly variable mid-veraison dates for the different varieties hence the corresponding measured 13 C in a given year, independent of the genotype response.

The predawn measurements on the six varieties over four years, along with corresponding midday leaf water potential measurements were used to construct hydroscapes, which allowed insight into the stomatal behaviour of these varieties. Among several hydroscape metrics, minimum critical leaf Lcrit ), and the closely related total area of the hydroscape were considered to provide a good indication Lcrit and correspondingly smaller hydroscape areas suggest an isohydric variety, which regulates its stomata more strictly Lcrit and larger hydroscapes are suggestive of a more anisohydric variety, which regulates its stomata less strictly, allowing for more negative water potentials.

Comparison of 13 C and hydroscape metrics found minimum 13 C (i.e., from years with less limiting conditions) to be correlated negatively with the area of the hydroscapes and positively correlated with Lcrit , suggesting the anisohydric behaviour of a larger hydroscape is somehow associated with increased water use in non-limiting conditions as suggested by more negative L more stress later in the season and hence less negative 13 C. This also suggests that this minimum 13 C could provide a relatively easy way to classify grapevine varieties according to their drought stress responses.

Discussion

Measurement of carbon isotope discrimination in berry juice at maturity (𝛿 13 C) and corresponding water potential measurements during the berry ripening period over four seasons confirmed results from prior studies that suggested 𝛿 13 C provides an integrated assessment of vine water status during the period of berry ripening (Gaudillère et al., 2002;[START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF]Bchir et al., 2016). But in order for such measurements to be useful for growers as an indication of the relative level of drought stress during berry ripening, 𝛿 13 C data over several seasons would be required to establish the range of drought response of a given variety into the level of stress in a given year can be judged.

Various 𝛿 13 C metrics and vine phenology measured on 48 different varieties in the same growing conditions over seven years were also used to create a classification of drought tolerance that matched well with prior understandings of the relative drought tolerance of different varieties, whether gained empirically based on grower observations in dry conditions, or in published articles in peer reviewed journals. The selected classification was performed using a hierarchical cluster analysis (HCA) based on the following observed traits for each variety: i) the day of year (DOY) of véraison; ii) the 𝛿 13 C in the wettest year; and iii) the range of 𝛿 13 C measurements between those in the driest and wettest years. As varieties with earlier véraison dates will tend to ripen in less stressed conditions that later varieties, this is important to account for this in any classification based on measurements of stress. 𝛿 13 C in the wettest year provides an idea of the water use efficiency in less water limited conditions, with less water efficient varieties potentially consuming available water more quickly early in the season and then experiencing greater stress levels later in the season. The range of 𝛿 13 C between wet and dry years indicates the reactivity, or sensitivity of the stress response of the varieties across extremes.

The HCA classification depicted in Figure 4 of Plantevin et al. (2022) is reproduced in Hydroscapes were also constructed from measurements of midday and predawn leaf water potential (𝛹L and 𝛹PD) measurements using a method that was modified from previous methods developed by Meinzer et al. (2016) and [START_REF] Charrier | Drought will not leave your glass empty: Low risk of hydraulic failure revealed by long-term drought observations in world's top wine regions[END_REF]. The sizes of the hydroscapes are indicative of range of 𝛹L over which the vine will operate with smaller hydroscapes associated with more isohydric varieties that more readily close their stomata in order to regulate their leaf water potential, compared to those with larger hydroscapes, indicative of anisohydric varieties that allow their leaf water potential to operate at more negative levels. It is observed that the size of the hydroscape for a variety is strongly dependent on two hydroscape metrics, the lowest level of 𝛹L under non-limiting conditions (i.e., intercept at 𝛹PD = 0) and the lowest level of 𝛹L at high levels of stress (i.e., 𝛹Lcrit at very negative 𝛹PD). These key hydroscape metrics were then found to be well correlated with the 𝛿 13 C obtained in years of relatively non-limiting conditions. This suggests the anisohydric behavior of a larger hydroscape is associated with a lower level of water use efficiency in well-watered conditions. Increased water use in non-limiting conditions, as suggested by a larger hydroscape, may lead to accelerated soil water depletion and greater water deficits later in the season and hence increasing 𝛿 13 C. The relationship between these hydroscape and 𝛿 13 C metrics lends some interpretation and validation to the significance of the minimum year 𝛿 13 C metric, with the latter being much easier to obtain in the vineyard over several seasons and a wide range of varieties.

4 Carbon isotope discrimination in must, wine, and spirit

Background

The pending journal article presented in this chapter, entitled "Carbon isotope discrimination as an indicator of vine water status is comparable in grape must, wine, and distilled wine spirit" investigates how δ 13 C in wine spirit (eau de vie) is potentially affected by a double distillation process, such as that used in the production of Cognac. If the δ 13 C signal in eau de vie is conserved when compared to that in the source wine and parent grape must, then the δ 13 C in eau de vie could be used to estimate the vine water status that existed during the corresponding berry ripening period. In turn, this could be useful for exploring retrospectively how sensory attributes of eau de vie are linked to vine water status, such as has been done previously for wine (Picard et al., 2017).

INTRODUCTION

As part of the Calvin cycle in the photosynthetic process of C 3 plants such as grapevines, the Rubisco enzyme preferentially binds to the more reactive 12 C isotopes contained in atmospheric CO 2 , causing the 13 C/ 12 C ratio of the carbon fixed by the plant to be less that of the atmosphere [START_REF] Farquhar | Carbon Isotope Discrimination And Photosynthesis[END_REF]. Carbon isotope discrimination (δ 13 C), expressed in parts per thousand (‰), is a function of the 13 C/ 12 C ratio measured in the carbon fixed by the plant relative to the 13 C/ 12 C ratio measured in a standard representative of the atmosphere (Santesteban et al., 2015). Discrimination in favor of 12 C, however, is reduced as stomata close in response to drought stress, and the amount of available 12 C in the intercellular space of the leaves gradually decreases (Santesteban et al., 2015). This makes measurement of δ 13 C in berry juice sugars a good integrative indicator of vine water status during the corresponding berry ripening period (Gaudillère et al., 2002;Guyon et al., 2015;Bchir et al., 2016).

Being able to characterize wine water status, particularly during the berry ripening period, is useful because it has an important effect on canopy vigor, berry size and yield, and associated grape composition and development of phenolic and aroma compounds in finished wine (van Leeuwen et al., 2009). Measurement of δ 13 C over multiple seasons under different levels of water stress can also provide an indication of varietal drought tolerance (Plantevin et al., 2022). The δ 13 Cofthesugaringrapemusthasbeenfoundtobelinearly related to the δ 13 Coftheethanolthatresultsfromfermentationtowine, although shifted upwards in the range of 1.7‰ (Roßmann et al., 1996;Guyon et al., 2015). This connection between the δ 13 Cingrapemust and wine provides a means of estimating from a sample of wine the vine water status that existed during the corresponding berry ripening period. This can be used then to establish relations between vine water status and specifics e n s o r ya t t r i b u t e so fw i n e ( Picard et al., 2017).

Cognac is a type of wine brandy produced in France, whereby wine fermented primarily from Vitis vinfera L. cv. Ugni blanc, Folle blanche, and Colombard is double distilled in copper pot stills to produce a wine spirit (eau de vie) that is then aged in oak barrels. The aim of this research is to characterize how δ 13 C in the eau de vie produced by this process might be altered when compared to the source wine and parent grape must. If the δ 13 C in the eau de vie is conserved in a quantifiable manner, this will allow for future studies to retrospectively estimate the vine water status that existed during the ripening of the grapes used to produce the eau de vie. This can then be used to study the relationships between vine water status and specific sensory attributes of eau de vie, as has been done before for wine.

MATERIALS AND METHODS

Carbon isotope discrimination (δ 13 C) was measured in grape must, fermented wine, and distilled spirit from varieties of interest in the Cognac region, including Ugni blanc, Ugni blanc lacinié, Monbadon, and Vidal 36 in both 2019 and 2020 and also on Colombard, Montils, Semillon, Folle blanche, and Folignan in 2020. The vines were planted as part of the ampelographic collection of the Conservatoire du Vignoble Charentais in Cherves-Richemont, France, just north of the city of Cognac (45 °43′16.1″N0 °21′45.6″W) with an average age of about 20 years. The vineyard was planted on clay-limestone soil at a density of 5,000 vines/ha, with mowed grass cover crop and identical cultural practices. The vines are trained on a vertical shoot positioning trellis system with double Guyot pruning.

Each year for each variety, 10 kg of grapes were harvested and pressed in two stages at 3 bars of pressure, from which a sample was collected of the resulting grape must. A micro-vinification was then performed in the Charentaise style, without addition of sulphites or sugar, using a Cognac-specific LSA FC 9 yeast fermentation controlled at 21 °C, no malolactic fermentation, and resulting in wine at about 10%-12% alcohol by volume. A sample was collected from the resulting wine, which was then stored at 4 °C prior to distillation. A traditional double distillation process was then performed on the wine (without lees) whereby the first cycle (chauffe de vin) had 0.5% of the heads removed with the heart taken over 8 hours, followed by a second cycle (bonne chauffe) that had 1.0% of the heads removed with the heart taken over five to 6 hours and no recycling of the heads or tails. These distillations were performed in a 1-L still made of the same material and proportions as a traditional 25 hL capacity Charentaise style still, including a traditional tête de Maure still head (Lavergne, 20201 ).

Each grape must sample was mixed with an agitator-type vortex to ensure uniformity and centrifuged at 12,500 rpm for 10 min (Sigma 13 6K15, SIGMA Laborzentrifugen GmbH, Osterode am Harz, Germany). Afterwards, 5 μLw e r ep i p e t t e di n t oat i nc a p s u l e and oven dried at 60 °C. Random control samples were prepared from beet root juice with known δ 13 Cl e v e l s .W i n ea n de a ud ev i e samples were analyzed directly without centrifuging or drying. All samples were analyzed at an external laboratory (UMR CNRS/ Plateforme GISMO UMR 6282 BIOGEOSCIENCES Université de Bourgogne, 21000 Dijon, France) where the carbon isotope content was measured using an Elementary Vario Analyzer (Elementar, Hanau) coupled for continuous flow to an isotopic mass spectrometer (Isoprime, Manchester). An analytical standard of Vienna Pee Dee Belemnite (VPDB) was used for reference, with δ 13 Ce x p r e s s e di np a r t sp e rt h o u s a n d( ‰).

Linear regression was used to characterize the relationship between measurements of δ 13 C in eau de vie as the dependent variable versus δ 13 C measured in grape must as the independent variable. The same was done for δ 13 C in eau de vie versus δ 13 C in source wine. The ordinary least square assumptions for each regression were then evaluated with: 1) quantile-quantile plots of standardized residuals to check for normal distribution and zero mean; 2) plots of standardized residuals versus fitted values to confirm absence of heteroscedasticity; and 3) plots of standardized residuals versus the independent variables to confirm the absence of endogeneity. Multiple linear regression analysis was performed in the R software environment using the lm function (R Core Team, 2021) and graphing was performed using the ggplot2 package (Wickham et al., 2021).

RESULTS AND DISCUSSION

Based on linear regression analysis, a strong linear relationship was observed between δ 13 C measured in eau de vie samples versus those measured in the corresponding wine samples (blue line in Figure 1) with a slope of 0.95, r 2 = 0.98 and p-value = 3.6 × 10 -11 . Likewise, a strong linear relationship was observed between δ 13 C measured in eau de vie samples versus those measured in the corresponding grape must (red line in Figure 1) with a slope of 0.92, r 2 = 0.99 and p-value = 5.0 × 10 -12 . The residual plots for both regressions (not shown) demonstrated good normal distribution of residuals with zero mean and no heteroscedasticity, or endogeneity.

As shown in Table 1, for the range of measured values across all varieties and years, the average difference in δ 13 C between wine and eau de vie samples was -0.16‰. This difference is not great despite the potential for the distillation process to separate compounds with lighter isotopes more easily than those with heavy isotopes. A possible explanation for this is the first fractions from the distillation (i.e., heads) enriched in light isotopes and the last fractions (i.e., tails) enriched in heavy isotopes are both discarded, and the remaining middle fraction (i.e., heart), which is conserved as eau de vie, has an average δ 13 C close to that of the corresponding wine used in the distillation.

The average difference in δ 13 Cb e t w e e ng r a p em u s ta n dw i n e measurements was 1.63‰ with a standard deviation of 0.20‰.This difference is close to the range of 1.7‰ observed by Roßmann et al. (1996) across multiple samples of Italian and German grape musts and wines over two different years. Then finally, the average difference between δ 13 Cingrapemustandeaudeviesampleswas1.47‰ with a standard deviation of 0.15‰.Thisvalueisveryclosetothesumofthe must-to-wine and wine-to-eau de vie average differences.

CONCLUSION

The strong linear relationship observed between the δ 13 Co fg r a p e must and eau de vie suggests the possibility of using the latter as a means of estimating the vine water status that existed during the corresponding berry ripening period. Based on this understanding, future studies can explore the relationships between sensory attributes of eau de vie and vine water status during ripening of the source grapes, as has been done before for wine. Also, to the extent that eau de vie samples are segregated by vineyard location or region, the δ 13 Cofthosesamplescouldbeusedtoexploreassociated differences in the vine water status across those locations. These findings could be further validated by measuring δ 13 Cingrapemust, wine and eau de vie under conditions of full-scale production and with different amounts of heads and hearts being taken during the first and second distillation cycles as might be experienced during such production. These findings were based on eau de vie produced by a double distillation process as traditionally used in the Cognac region of France, and could be different for spirits produced by other distillation methods, such as with the continuous column still method used in the Armagnac region of France.

Discussion

Measurements of δ 13 C in eau de vie produced from a double distillation process, such as used in the Cognac AOC, was compared to δ 13 C of the source wine and parent grape must.

As confirmation, a strong linear relationship between δ 13 C in grape must and associated wine was very similar to that found previously in the literature (Roßmann et al., 1996).

A nearly 1:1 linear relationship (r 2 = 0.98) was then found between the δ 13 C of eau de vie and the associated source wine, suggesting the δ 13 C of eau de vie could be used to estimate the vine water status that existed during the corresponding berry ripening period. This

difference is negligible despite the potential for the distillation process to separate compounds with lighter isotopes more easily than those with heavy isotopes. A possible explanation for this is the first fractions from the distillation (i.e., heads) enriched in light isotopes and the last fractions (i.e., tails) enriched in heavy isotopes are both discarded, and the remaining middle fraction (i.e., heart), which is conserved as eau de vie, has an average δ 13 C close to that of the corresponding wine used in the distillation.

An understanding of the effect of this process on δ 13 C allows a retrospective estimation of the vine water status that existed during the ripening of the grapes used to produce the wine and subsequent eau de vie. As has been done for wine, this could then be used in future studies to establish relations between vine water status and specific sensory attributes of eau de vie quality. This could also be used to explore associated differences in the vine water status across different source vineyard locations.

Determining Bulk Stomatal Conductance (gbs)

Background

In response to changes in vine water status, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plantatmosphere continuum (McElrone et al., 2013). Expressed as bulk stomatal conductance

(gbs) at the canopy scale it is effectively the parallel combination of the stomatal conductance (gs) of every leaf in the crop canopy (Kelliher et al., 1995) and varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and the hydraulic conductivity of both the plant and the soil.

The journal article presented in this chapter, entitled "Estimating Bulk Stomatal

Conductance in Grapevine Canopies" (Gowdy et al., 2022a) presents a simplified method for determination of gbs in a vineyard setting. This approach is based on an established crop canopy energy flux model (Shuttleworth and Wallace, 1985) In modeling vineyards with open canopies (i.e., vines cultivated in rows and surrounded by exposed ground), however, the assumption of uniform distribution of sensible and latent heat flux from the canopy required for the big leaf approach is not met and can lead to anomalous determinations of gc (Monteith and Unsworth 2013). In such cases, a modified two-source approach as developed by Shuttleworth and Wallace (1985) can be used to evaluate heat and water vapor flux from the crop canopy separately from the exposed ground surrounding the canopy.

As depicted schematically in Figure 5

Long wave radiation

Long wave (heat) radiation from the atmosphere, surrounding ground, and adjacent vine canopy rows are also a source of radiation for the vine canopies (Pieri, 2010) and it is recommended in FAO Irrigation and drainage paper No. 56 that it be accounted for in modeling of crop evapotranspiration (Allen et al., 1998). The proportion of radiation flux from the sky, ground, and adjacent vine rows incident on the vine canopy were calculated in this study using radiation view factors between these sources and the faces of the vine canopy adjusted to account for canopy porosity as described by Pieri ( 2010) and as depicted in Figure 6 through 8 below, including the equations for each. The canopies themselves were also assumed to radiate heat energy as a function of their temperature. PhD thesis -Mark Gowdy

Sap flow sensors

Key to the implementation of this method is the measurement of vine transpiration. For this purpose, we used heat balance sap flow sensors (Model SGEX, Dynamax Inc., Houston, TX, USA) that were installed on vine canes at a location where the flow of at least 30% of the whole vine would be measured (based on the relative number of shoots downstream of the sensor). Sap flow (g s -1 ) was calculated from sensor signals collected every 15 minutes 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 (Lascano et al., 1992). 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 (g s -1 m -2 ).

Heat balance sap-flow sensors contain a series of thermocouples, a thermopile, and a heat source in a collar that is wrapped around a stem or trunk. The placement of thermocouples in the sensor collar are designed to measure radial and axial temperature gradients, which when combined with assumptions regarding thermal conductivity yield the flux of energy along the stem and radially outward. The flux of heat energy in the sap, which is related to the quantity of sap flow (mass/time) is then calculated by the difference between the axial and radial heat flux and the input of heat to the stem [START_REF] Lascano | The Stem Heat Balance Method to Measure Transpiration: Evaluation of a New Sensor[END_REF]. While other methods of measuring sap flow have been developed [START_REF] Löf | Estimating sap flow from stem heat balances in Quercus robur L. seedlings in relation to light intensity: A comparison of two methods during the establishment phase[END_REF], the Dynamax collar design was preferred as it is relatively non-invasive, reducing the risk of long-term damage to the vines.

Journal article

Estimating Bulk Stomatal Conductance in Grapevine Canopies

Mark Gowdy1 , Philippe Pieri 1 , Bruno Suter 1 , Elisa Marguerit 1 , Agnès Destrac-Irvine 1 , Gregory Gambetta 1 , Cornelis van Leeuwen 1

In response to changes in their environments, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soilplant-atmosphere continuum. Expressed as bulk stomatal conductance at the canopy scale, it varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and hydraulic conductivity of both the soil and plant. To help with future characterization of this dynamic response, a simplified method is presented for determining bulk stomatal conductance based on the crop canopy energy flux model by Shuttleworth and Wallace using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. The methodology presented respects the energy flux dynamics of vineyards with open canopies, while avoiding problematic measurements of soil heat flux and boundary layer conductance needed by other methods, which might otherwise interfere with ongoing vineyard management practices.

Based on this method and measurements taken on several vines in a non-irrigated vineyard in Bordeaux France, bulk stomatal conductance was estimated on 15-minute intervals from July to mid-September 2020 producing values similar to those presented for vineyards in the literature. Time-series plots of this conductance show significant diurnal variation and seasonal decreases in conductance associated with increased vine water stress as measured by predawn leaf water potential. Global sensitivity analysis using non-parametric regression found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, with absorbed net radiation and bulk boundary layer conductance being much less important. Conversely, bulk stomatal conductance was one of the most important inputs when calculating vine transpiration, emphasizing the usefulness of characterizing its dynamic response for the purpose of estimating vine canopy transpiration in water use models.

INTRODUCTION

Grapevines regulate their water use (i.e., transpiration) in response to changing atmospheric demand and drought stress by regulating the conductance of water through the plant from the soil to the atmosphere (Oren et al., 1999;McElrone et al., 2013, Keller, 2015). This conductance is regulated by various physiologic mechanisms such as control of stomatal aperture and hydraulic conductivity of the vasculature (Lovisolo et al., 2010), with differences in response observed between varieties (Prieto et al., 2010;Costa et al., 2012). This stomata regulation is also affected by changes in plant water status through various physiological mechanisms (Oren et al., 1999;Roelfsema and Hedrich, 2005). Moreover, conductance varies diurnally (Lu et al., 2003;Zhang et al., 2012;Bai et al., 2015) and across the season (Herrera et al., 2021). Vineyard water use modeling is often conducted using the FAO 56 approach of applying seasonally variable crop coefficients to estimates of evapotranspiration from a hypothetical reference crop (ET o ). Calculation of ET o is based on an application of the Penman Monteith (PM) equation, including the assumption of a fixed conductance for the reference crop (Allen et al., 1998). As a result, this approach does not account for changes in conductance in response to changes in atmospheric demand or drought stress. One vineyard water use model adapted from the FAO approach applies a generic adjustment to transpiration as a function of diminishing soil water content (Lebon et al., 2003), but none include a dynamic representation of conductance response to changes in key environmental variables such as net radiation, vapor pressure deficit, or soil water availability. As a first step in developing such representations, a technically robust and implementable methodology for calculating vine canopy conductance in a vineyard setting is needed.

Developed conceptually at the leaf scale, the PM equation calculates latent heat (water vapor) flux from a leaf surface as a function of net radiation absorbed by the leaf, vapor pressure deficit gradients from within the leaf to the atmosphere, and boundary layer and stomatal resistances to this flux (Monteith and Unsworth, 2013). At the field scale, transpiration from a crop canopy can be estimated by conceptually applying the PM equation as if the canopy were a big leaf that is horizontally uniform and entirely covers the soil below (Monteith and Unsworth, 2013). At the leaf scale, stomatal conductance (g s ) is the ratio of water vapor, or carbon flux divided by the concentration gradient driving flux across the boundary layer at the leaf surface (Monteith and Unsworth, 2013) and is often measured by means of a porometer or gas exchange meter on individual leaves. Applying the big leaf approach at the crop canopy scale, canopy conductance (g c ) can be calculated by rearranging the PM equation and inputting measured canopy transpiration, atmospheric vapor pressure deficits, net radiation absorbed by the canopy, and the within-canopy (bulk) equivalent of boundary layer conductance, with all fluxes and resistances expressed in terms of unit ground area below the canopy (Granier and Loustau, 1994;Granier et al., 2000;Lu et al., 2003). The g c resulting from this approach is effectively the parallel combination of the g s of every leaf in the crop canopy (Kelliher et al., 1995).

The big leaf approach using the inverted PM equation has been applied to determining conductance of forest canopies based on sap flow measurement of transpiration from trees [START_REF] Kelliher | Transpiration and canopy conductance in a pristine broad-leaved forest of Nothofagus: an analysis of xylem sap flow and eddy correlation measurements[END_REF]Granier et al., 2000;[START_REF] Ewers | Effects of stand age and tree species on canopy transpiration and average stomatal conductance of boreal forests[END_REF][START_REF] Ghimire | Transpiration and canopy conductance of two contrasting forest types in the Lesser Himalaya of Central Nepal[END_REF][START_REF] Wang | Optimization of canopy conductance models from concurrent measurements of sap flow and stem water potential on Drooping Sheoak in South Australia[END_REF][START_REF] Kučera | Direct Penman-Monteith parameterization for estimating stomatal conductance and modeling sap flow[END_REF], and has also been applied to determining g c of vineyard canopies (Lu et al., 2003;Zhang et al., 2012;Bai et al., 2015). When applying the inverted PM equation in this manner, however, the determination of net radiation absorbed by the canopy must account for heat flux between the canopy and the ground below (Monteith and Unsworth, 2013). Estimating soil heat flux usually involves burying soil heat flux plates, determining soil thermal properties, measurement of temperature and moisture content in the soil profile, and will need to be implemented in multiple locations to account for spatial heterogeneity [START_REF] Gao | A novel approach to evaluate soil heat flux calculation: an analytical review of nine methods[END_REF]. Such instrumentation can be complicated and time consuming to implement properly, particularly in stony soils. The inverted PM equation approach to canopies also requires accounting for aerodynamic boundary layer conductance above the vineyard, involving measurement of wind speed and temperature profiles at multiple heights above the vineyard canopy (Monteith and Unsworth, 2013). Such measurement of soil heat flux and boundary layer conductance can be cumbersome and also interfere with ongoing management practices in a working vineyard. Alternatively, using the big leaf approach and an assumption of strong coupling between the crop canopy and the atmosphere allows for use of a simplified form of the inverted PM equation that does not require input of soil heat flux or aerodynamic boundary layer conductance [START_REF] Phillips | A comparison of daily representations of canopy conductance based on two conditional time-averaging methods and the dependence of daily conductance on environmental factors[END_REF][START_REF] Ewers | Analyses of assumptions and errors in the calculation of stomatal conductance from sap flux measurements[END_REF] and has been applied to vineyards (Bai et al., 2015). The assumption of strong coupling between the vineyard canopy and the atmosphere, however, is based on the assumption that temperatures within the canopy (i.e., big leaf) and bulk air temperature above the canopy are similar [START_REF] Ewers | Analyses of assumptions and errors in the calculation of stomatal conductance from sap flux measurements[END_REF]Monteith and Unsworth, 2013), which may not be appropriate depending on meteorological conditions within and above the vineyard canopy.

In vineyards with open canopies, such as with vines cultivated in rows and surrounded by exposed ground, soil evaporation can account for over half of evapotranspiration (Lascano et al., 1992;[START_REF] Heilman | Soil and canopy energy balances in a west Texas vineyard[END_REF]. The big leaf model, however, is based on the assumption of uniform spatial distribution of sensible and latent heat flux from the canopy, and if applied to crops with open canopies may lead to anomalous determinations of g c (Monteith and Unsworth, 2013). In such cases, a modified two-source approach as developed by Shuttleworth and Wallace (1985) can be used to evaluate heat and water vapor flux from the crop canopy separately from the exposed ground surrounding the canopy. This leads to a calculation of bulk stomatal conductance (g bs ) that does not require the measurement of soil heat flux or boundary layer conductance above the vineyard, greatly simplifying its determination. This g bs is similar to g c in that it represents the net effect of the g s of all leaves in the portion of the vine canopy being considered. This two-source approach has been used to estimate evapotranspiration vineyards that [START_REF] Ortega-Farias | Latent heat flux over cabernet sauvignon vineyard using the shuttleworth and wallace model[END_REF][START_REF] Ortega-Farias | Parameterization of a two-layer model for estimating vineyard evapotranspiration using meteorological measurements[END_REF][START_REF] Ding | Scaling up stomatal conductance from leaf to canopy using a dual-leaf model for estimating crop evapotranspiration[END_REF].

Based on this two-source approach, a methodology is presented here for determination of g bs for vineyards with open canopies using data from instrumentation having minimal interference with the operations of a working vineyard and avoiding problematic methodologies for measurement of parameters such as soil heat flux and atmospheric boundary layer.

METHODS

The heat and mass transfer theory behind the PM equation and the two-source energy flux model is presented in the literature using resistance to fluxes rather than conductance in order to facilitate the use of Ohms Law analogies (Monteith and Unsworth, 2013). The following presentation also uses resistances (s m 1 ), with results being converted to conductance (m s 1 ) by inversion, with the latter often used in plant physiology literature.

Heat Flux Model

For crops with open canopies, Shuttleworth and Wallace (1985) developed a one-dimensional model of sensible and latent heat fluxes separately from the crop canopy and the ground surrounding the canopy as part of a network of temperature and vapor pressure gradients and corresponding flux resistances. Figure 1 presents a simplified schematic of this two-source representation applied to a vineyard with an open canopy.

An important assumption in this method is the mean canopy height (z c ), or the height at which constituents such as temperature and humidity are considered to be well mixed and uniformly distributed horizontally through the canopy (Shuttleworth and Wallace, 1985). It is the effective height from within the canopy where heat and vapor fluxes can be calculated using a PM type equation (Lhomme et al., 2012). This assumption relies on the open canopies being horizontally consistent across the field and with good aerodynamic mixing within the canopy (Shuttleworth and Wallace, 1985). While z c is not needed in the calculation of g bs , it was needed to determine the height at which the temperature and humidity sensors in the canopy were placed.

The mean canopy height is determined as the sum of the canopy zero plane displacement height (d, m) and the roughness length (z o , m) (Shuttleworth and Wallace, 1985). Both are related to the apparent drag between the crop canopy and the wind moving over the canopy, with d being the height above the ground of the lower asymptote of the wind speed profile above the canopy, and z o being the height above d where the wind speed theoretically goes to zero (Monteith and Unsworth, 2013;[START_REF] Alfieri | Influence of wind direction on the surface roughness of vineyards[END_REF]. Studies have found values of d and z o to be affected by canopy characteristics and differed as a function of wind direction (Chahine et al., 2014), but that flux estimates from a two-source energy balance model were relatively insensitive to such differences [START_REF] Alfieri | Influence of wind direction on the surface roughness of vineyards[END_REF]. Based on a study in a similarly configured vineyard, values of d ranged from 0.62 to 0.75h and z o ranged from 0.08 to 0.14h, where h is the total height of the canopy above the ground (Chahine et al., 2014). With a vine canopy height at h = 1.5 m above the ground, d was estimated to be 1m above the ground and z o at 0.15 m above that, with mean canopy height (z c ) being a total of 1.15 m above the ground.

Bulk Stomatal Conductance

Based on the two-source schematic as shown in Figure 1 an adaptation of the PM equation is used to calculate latent heat flux from the vine canopy (λE c ) (Lhomme et al., 2012):

λE c = 4R c + ρCp (D c ) /r bh 4+γ ⇣ n + r bs r bh ⌘ (Wm 2 ) (1)
where: Equation 1 can then be rearranged to give bulk stomatal resistance:

E c =
r bs = 4R c r bh + ρCp(D c ) λE c γ r bh ✓ 4 γ n ◆ (sm 1 ) (2)
and bulk stomatal conductance is given by inversion:

g bs = r bs 1 (ms 1 ) (3)
The output units for the equations above result when input units shown in the text 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). Multiplying g bs by the molar volume of air (41.04 mol/m 3 at 1 atm and 20 C) and converting units gives g bs in terms of mmol m 2 s 1 , as often used in plant physiology literature.

Conceptually, r bs is the parallel sum of the leaf level stomatal resistances (r s ,sm 1 ) of all individual leaves in the canopy (Kelliher et al., 1995), and calculated as above provides an integrated measure of individual leaf resistances across the range of micro-meteorological conditions experienced by all the leaves in the canopy. A relationship between bulk and leaf-level stomatal conductance is given by the following equation based on leaf area index (LAI) and considering whether leaves have stomata on one or both sides (Lhomme et al., 2012):

r bs = n ⇤ r s 2 ⇤ LAI (sm 1 ) (4)
where n = 2 for grapevine leaves with stomata on one side only. If the vine canopy covers more of the ground, however, such as with pergola style trellising or sprawling canopies, use of the big leaf model approach may be more appropriate with measurement of soil heat fluxes from beneath the canopy becoming more important.

Sensitivity Analysis

A global sensitivity analysis determines the relative importance of the input variables (predictors) to a mathematical model in determining the variation in the output of the model (response), across the range of input values [START_REF] Iooss | Introduction to sensitivity analysis[END_REF]. Such analysis can also be used to characterize the effects of input variable interactions [START_REF] Razavi | What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models[END_REF][START_REF] Iooss | Introduction to sensitivity analysis[END_REF]. One approach to understanding the relative importance of input variables involves using the output of the model as the response variable in a regression analysis with the inputs to the model as the predictor variables [START_REF] Saltelli | Global Sensitivity Analysis: The Primer[END_REF][START_REF] Razavi | What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models[END_REF].

Such a regression approach is the basis of a global sensitivity analysis of the PM equation using the input data collected for this study. The purpose is to understand the relative importance of the predictor variables in order to prioritize efforts in their determination. For example, the field measurement or estimation of a predictor variable with low importance might be simplified without significantly affecting model results.

A database of E c ,D c ,R c , and r bh data was first compiled from all vines in the study together with associated g bs calculated using Eqs 2 and 3. This database was then used for the following two regression analyses:

• g bs as the response variable with E c ,D c ,R c , and r bh as predictors, for the purpose of assessing the relative importance of the predictors in a regression model with the same form as the inverted PM equation used to calculate g bs (Eqs 2 and 3). • E c as the response with g bs ,D c ,R c , and r bh as predictors, for the purpose of assessing the relative importance of the predictors in a regression model with the same form as the regular PM equation used to calculate transpiration (Eq. 1).

Of particular interest is the relative importance of g bs in the determination of E c .

Preliminary review of the data suggested significant multicollinearity in the predictor variables (see correlation matrix in Supplementary Figure S1). Such multicollinearity is not surprising as transpiration (as regulated by changing conductance) is naturally related to vapor pressure deficit and net radiation [START_REF] Jackson | Canopy temperature as a crop water stress indicator[END_REF], and solar radiation is related to temperature [START_REF] Hargreaves | History and evaluation of hargreaves evapotranspiration equation[END_REF], and hence vapor pressure deficit.

This multicollinearity, however, can complicate global sensitivity analysis based on classical one-at-a-time, or linear regression methods [START_REF] Razavi | What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models[END_REF]. As an alternative, the random forest non-parametric regression method has been demonstrated as an effective way to perform global sensitivity analysis that is capable of handling interaction between predictors (Grömping, 2009;Antoniadis et al., 2021). A random forest methodology was therefore chosen for the two regression analyses described above.

The random forest methodology processes random selections of predictor variables through a large number of decision trees to find variable relationships that minimize mean square error in the response estimate when compared across many randomly generated test data sets [START_REF] Breiman | Random forests[END_REF]. Unlike parametric (i.e., linear or non-linear) regression methods, non-parametric methods such as random forest do not generate regression

Gowdy et al. Grapevine Bulk Stomatal Conductance

coefficients that might otherwise be used to assess the relative importance of predictors. For this purpose, a minimal depth approach is used, which quantifies how quickly a predictor variable contributes to determining the response estimate across all the decision trees in the random forest model. The lower the depth number for a predictor, the more important the predictor, with the greatest possible importance having a depth of zero [START_REF] Ishwaran | Random survival forests for high-dimensional data[END_REF].

The interactions between predictor variables were also evaluated from the random forest models using a second order maximal subtree approach by which normalized relative minimal depths of the different predictors are evaluated in a pairwise manner against each other [START_REF] Ishwaran | Random survival forests for high-dimensional data[END_REF]. The lower the difference in normalized minimal depths between predictors the more closely associated they are, with the interaction effect between variables of high importance having a greater effect on the response variable. For each pair of predictors, this approach generates a normalized index with 0 representing strong interaction and 1 representing none [START_REF] Ishwaran | Random survival forests for high-dimensional data[END_REF].

Data analysis was performed using the R software environment (R Project for Statistical Computing, RRID:SCR_001905). Both random forest models were run using the rfsrc function of the randomForestSRC package for R [START_REF] Ishwaran | randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)[END_REF]. The hyper parameters for both random forest models were first optimized using the ranger function of the ranger package for R [START_REF] Wright | ranger: A Fast Implementation of Random Forests[END_REF]. The relative importance of the predictor variables in the determining the response variables in both random forest models are evaluated using the max.subtree function of the randomForestSRC package for R [START_REF] Ishwaran | randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)[END_REF]. The interactions between the predictor variables in both random forest models are evaluated using the find.interactions function of the randomForestSRC package for R [START_REF] Ishwaran | randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)[END_REF].

MATERIALS AND EQUIPMENT

The measurements for this study were taken on 10 individual grapevines in a vineyard, two each of Vitis vinifera L., cv. Cabernet-Sauvignon, Merlot, Tempranillo, Semillon, and Ugni blanc. Measurements of sap flow, temperature and humidity, and solar radiation were taken or interpolated to 15-minute intervals from June 30 to September 15, 2020 and canopy characteristics were measured periodically through the season.

Vineyard and Canopy Characteristics

The study was performed in a 0.6-hectare common garden experimental vineyard in Bordeaux, France (44 47 0 0 00 N, 0 34 0 39 00 W) with 52 varieties planted in a randomized block design. 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 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 northsouth with 1.8 m row spacing and 1.0 m vine spacing. The vines were planted on SO4 rootstock and the soils are clay-gravel typical for the Pessac-Léognan wine appellation (Destrac-Irvine and van Leeuwen, 2016). From 1991 through 2020 average annual total rainfall and reference evapotranspiration were 902 mm and 929 mm, respectively, with annual total solar radiation of 4,790 MJ m 2 and average maximum daily temperature from May through June of 25.5 C.

Leaf area was measured at three separate times during the season, in the first halves of July, August and September, respectively. Leaf area was determined by measuring the length and width of all the individual leaves on a subset (approximately 25%) of primary and secondary shoots of each vine. Leaf length and width dimensions were well correlated with individual leaf area as measured by a leaf area meter (Model LI-3100 LICOR Inc., Lincoln, NE, United States) before field measurements began. An average leaf size was calculated from all these individual leaf area measurements segregated based on primary or secondary shoots. These average leaf areas were then applied to a count of all leaves on the remaining primary and secondary shoots on each vine. Leaf area index (LAI,m 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 ⇥ 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, United States) and interpolated linearly between measurements dates.

Transpiration Flux (E c )

Heat balance sap flow sensors (Model SGEX, Dynamax Inc., Houston, TX, United States) were installed on one of the two canes of each vine, which were trained to the bottom trellis wire in a double Guyot manner at 50 cm above the ground. Based on manufacturer recommendations, the location of the sap flow sensor on a cane needed to be such that the flow of at least 30% of the vine's total shoots would be captured by the sensor. It was also found that the quality of the sensor readings benefited from emphasizing installation on straight and smooth cane internodes and by protecting sensors well against the rain.

Sap flow (g s 1 ) was calculated from sensor signals collected by a datalogger (Model SapIP, Dynamax Inc., Houston, TX, United States) and then scaled up for the whole vine based on the ratio of leaf area of the whole vine to 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 evaporation (transpiration) flux, E c (g s 1 m 2 ).

Vapor Pressure Deficit (D c ) Saturation vapor pressure, e sc (Pa) at mean canopy height (z c = 1.15 m above the ground) was calculated by Teten's equation using measured temperature T c ( C) with the partial vapor pressure, e c (Pa) calculated from e sc using measured relative humidity. Temperature and humidity were measured using TinyTag Plus 2 probe/data loggers (Model TGP-4505 by Gemini Data Loggers, Chichester, West Sussex, England) with the temperature/relative humidity probes installed inside solar radiation shields (Model RS3 by Prosensor, Amanvillers,

Gowdy et al. Grapevine Bulk Stomatal Conductance

France) and hung from a trellis wire in the vine canopy at the mean canopy height.

Net Radiation Flux Absorbed by the Vine Canopy (R c )

The radiation flux (energy/time/unit area) incident on a crop canopy comes from sources of both shortwave solar radiation and long wave heat radiation in the environment. The net (intercepted minus reflected) radiation absorbed by the canopy is the sum of net shortwave radiation and net long wave radiation (Allen et al., 1998).

Shortwave Radiation

The method of Riou et al. (1989) was used for estimating the amount of such radiation absorbed by grapevine canopy. This radiation model was also used in the water balance model developed by Lebon et al. (2003). This method models the net shortwave radiation absorbed by a vine canopy as the sum of shortwave radiation from: (i) direct radiation from the sun; (ii) diffuse radiation scattered by the atmosphere or clouds; and (iii) both beam and diffuse radiation reflected from the soil in the space between vine rows minus that reflected again by the leaves of the canopy. The model outputs radiation flux (W m -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., row spacing × vine spacing) (Riou et al., 1989).

The method relies on inputs of canopy dimensions, row spacing, and vine spacing and accounts for an assumed, or measured porosity of the canopy. For the direct component of global radiation absorbed by the canopy, solar angles, such as the hour and height angles, were calculated for input to the model based on the latitude and longitude of the study site [START_REF] Kalogirou | Solar Energy Engineering[END_REF][START_REF] Widén | Solar Radiation Theory[END_REF]. Based on measurements of global (shortwave) radiation incident on the vineyard, and applying the approach described in [START_REF] Liu | The interrelationship and characteristic distribution of direct, diffuse and total solar radiation[END_REF], the relative amounts of direct and diffuse shortwave radiation were calculated on 15-minute intervals over the season for input to the model. 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 -Netherlands) on one-hour intervals, and then linearly interpolated to 15-minute intervals for the above calculations.

Long Wave Radiation

Long wave (heat) radiation from the atmosphere, surrounding ground, and adjacent vine canopy rows are also a source of radiation for the vine canopies (Pieri, 2010). The proportion of radiation flux from the sky, ground, and adjacent vine rows incident on the vine canopy were calculated using radiation view factors between these sources and the faces of the vine canopy. The amount of radiation flux intercepted by the canopy was then adjusted to account for canopy porosity. The canopies themselves were also assumed to radiate heat energy as a function of their temperature. The net long wave radiation absorbed by the vine canopy was then the sum of the amount absorbed from all sources, minus the amount radiated from the canopy, together expressed as long wave radiation flux (W m -2 ) in terms of unit area of vineyard ground attributable to each vine (i.e., row spacing x vine spacing).

Long wave radiation flux from the sky (W m -2 ) was measured on 15-minute intervals with an upward facing pyrgeometer (Model No. SL-510-SS, Apogee Instruments, Logan, UT, United States) on a mast 1.5 m above the vine canopy. Long wave radiation flux from the surrounding ground was determined by measurement of ground temperature using a rectangular field of view infrared radiometer (Model No. SI-1H1 IR, Apogee Instruments, Logan, UT, United States) and converted to long wave radiation flux (W m -2 ) using the Stefan-Boltzmann equation with an assumed emissivity for dry grass of 0.98 [START_REF] Rubio | Emissivity measurements of several soils and vegetation types in the 8-14, µm Wave band: analysis of two field methods[END_REF]. The long wave energy radiated by the vine canopy and intercepted from adjacent vine canopies were calculated from the temperatures measured in the canopy and application of the Stefan-Boltzmann equation with an assumed emissivity for vine foliage of 0.98 [START_REF] Jones | Radiation measurement for plant ecophysiology[END_REF]Pieri, 2010).

Bulk Boundary Layer Resistances to Heat and Vapor Flux (r bh and r bv ) Development of Eq. 1 in the literature uses the bulk boundary layer conductance to heat flux (r bh , sm -1 ) as opposed to bulk boundary layer resistance to vapor flux (r bv , sm -1 ) based on the relationships in Eqs 5 and 6 below, in which n = 2 for grapevine leaves with stomata on one side only. Similar to bulk stomatal resistance, the bulk boundary layer resistances to heat and water vapor flux are canopy-level summations of the leaflevel boundary layer resistances across all leaves in the canopy stated in terms of unit ground area (Lhomme et al., 2012):

r bh = r bl 2 * LAI (sm -1 ) (5) r bv = n * r bh = 2 * r bl 2 * LAI (sm -1 ) (6)
where: r bl = is the one-sided leaf-level boundary layer resistance (s m -1 ). LAI = leaf area index. One method of estimating r bl requires the determination of wind speed at the top of the crop canopy [START_REF] Choudhury | A four-layer model for the heat budget of homogeneous land surfaces[END_REF]Lhomme et al., 2012). This can be inferred from wind speed profiles measured at multiple heights above the crop canopy in the atmospheric boundary layer (Monteith and Unsworth, 2013). As an alternative, a basic assumption of r bl = 25 s m -1 was proposed by Shuttleworth and Wallace (1985) and demonstrated to be adequate based on the relatively low importance of r bh in the PM equation. The low importance of r bh in the calculation of g bs is also confirmed in the sensitivity analysis presented in the next section. This assumption of r bl = 25 s m -1 has the additional benefit of avoiding the need for wind speed measurements as needed to determine the wind speed profile above the canopy. In climates with different prevailing temperature, vapor pressure deficit, or wind conditions, however, it may be found that r bh is more important than presented here, in which case measurement of wind speeds may be beneficial.

Predawn Leaf Water Potential

Predawn leaf water potential (9 PD ) measurements provide the water potential of the plant at night when the stomata are closed and the plant is in equilibrium with the root zone water potential (Choné et al., 2001) and is an accepted plant-based measurement of plant water stress [START_REF] Sperry | New evidence for large negative xylem pressures and their measurement by the pressure chamber method[END_REF]. Measurements of 9 PD were taken on each vine in the study at six times, roughly 10-14 days apart depending on weather, from early July through early September 2020. Leaf sampling and measurement were done early enough to ensure all was completed no later than 30 minutes prior to sunrise. Measurements were taken by the pressure chamber method of Scholander et al. (1965) using a pressure chamber with digital manometer (DG MECA, 33175 Gradignan, France).

RESULTS AND DISCUSSION

Bulk Stomatal Conductance

The time series of bulk stomatal conductance (g bs , mm s 1 ) calculated on 15-minute intervals is presented as an example in Figure 2 for vine C6-2 between 27 July and 24 August 2020. Gaps in this 15-minute interval time series are caused by filtering of data when net radiation absorbed by vine canopy (R c ) is less than 50 W m 2 during the early morning and evening, or at night. The calculation of g bs using Eqs 2 and 3 is prone to inaccuracy at low levels of R c , and corresponding low levels of D c and E c . This approach of filtering low solar radiation data was also used in previous studies of conductance in vineyards (Lu et al., 2003;Zhang et al., 2012). The diffusion rate of water vapor through stomata (i.e., stomatal conductance) is affected by: (i) solar radiation, which adds energy to the diffusion; (ii) vapor pressure deficit, which is the driving force for diffusion; and (iii) the effects of boundary layer resistance to diffusion at the leaf surface (Keller, 2015). At the canopy scale, diurnal fluctuations in R c and D c are likely responsible for the strong diurnal variation in 15-minute g bs also observed in Figure 2, particularly on days with higher overall levels of conductance. The effect of boundary layer resistance, as accounted for in r bh , however, will be largely a function of changes in leaf area over the course of the season.

Stomata regulation is also affected by changes in plant water status (Oren et al., 1999;Roelfsema and Hedrich, 2005), with predawn leaf water potential (9 PD ) providing a useful measure [START_REF] Sperry | New evidence for large negative xylem pressures and their measurement by the pressure chamber method[END_REF]Choné et al., 2001). The effect of 9 PD on g bs is observed in Figure 3, which presents the daily maximum hourly running average of 15-minute estimates of g bs from a selected vine (C5-2) between 30 June and 15 September 2020, with shading of the line representing the corresponding measured 9 PD , varying from 0.0 MPa (light gray) to 1.0 MPa (black). The lowest levels of g bs are observed when 9 PD is more negative. The shading associated with corresponding 9 PD measurements is also included in the plots of daily maximum hourly running average of 15-minute estimates of g bs for all ten vines in Supplementary Figure S2.

Under heterogeneous moisture conditions, predawn water potential measurements have been found to equilibrate at less negative values with portions of the root zone having higher moisture content (Améglio et al., 1999), as may happen in a dry soil after a rainfall. Both Figures 2,3 show a noticeable increase in overall levels of conductance for a few days after the only significant rainfall of the season (36 mm between 11-13 August). This may be the result of less negative 9 PD resulting from infiltration of rainfall into the upper part of the root zone. A similar increase in conductance after this rainfall was observed in the daily maximum hourly running average of g bs for most of the ten vines as presented in Supplementary Figure S2.

The maximum observed g bs over the season for each of the 10 vines in this study ranged from around 5 to 10 mm s 1 with occasional peaks from around 10 to 15 mm s 1 . This compares well with canopy conductance up to 7.0 mm s 1 determined on cv. Sultana (Lu et al., 2003), values up to 8.5 mm s 1 determined on cv. Merlot (Zhang et al., 2012), and upward of 16 mm s 1 in morning hours and 12 mm s 1 in the afternoon hours determined on cv. Thompson Seedless (Bai et al., 2015). The differences in maximum observed g bs between individual vines at a given time may be due to a combination of factors, such as differences in individual vine root access to water and hence vine water status, and genetic differences between varieties in stomatal response to changes in R c and D c .

A multi variable analysis of the above factors and others is beyond the scope of this paper, although the methodology presented here provides a useful means of determining g bs for such future analysis.

Sensitivity Analysis and Variable Interactions

Two optimized random forest regression models, one model with g bs as the response variable and the other with E c as the response variable were created using the combined data from all 10 vines in the study. The minimum depths and pairwise variable interactions for the predictors from both of these random forest models are presented in Table 1.

Based on the minimum depth methodology, a predictor with a minimum depth of 0 is the most important in determining the response of the model, with the other predictors being relatively less important as their corresponding minimum depths increase [START_REF] Ishwaran | Random survival forests for high-dimensional data[END_REF]. For the model with g bs as the response variable, Table 1(A) indicates that E c is the most important predictor. Due to this importance, proper implementation of sap flow measurements to determine transpiration, and hence E c should be emphasized. Conversely, in the model with E c as the response variable, Table 1(B) indicates g bs was the most important predictor, confirming the importance of properly characterizing g bs when modeling the vine canopy component of transpiration in vineyard water balance models.

After E c and g bs ,D c is a significant predictor in both regressions, suggesting the quality of temperature and humidity measurements in the vine canopy and proper placement of instruments at the mean canopy height (z c ) should be emphasized. R c was of relatively low importance in both regressions, and therefore a simplification in its method of estimation is proposed in the next section. The least important predictor in both regressions was r bh , further justifying use of the simplified assumption of r bl = 25 s m 1 as described earlier according to Shuttleworth and Wallace (1985) in Eqs 5 and 6, and eliminating the need for measurement of wind speed above the canopy.

The normalized pairwise relative minimum depths between predictor variables are also presented in Tables 1(A,B) for both regression models. The normalized depths (with 0 indicating strong interaction and 1 indicating no interaction) are shown for a given predictor variable in each row, with the strength of the interaction being greater for predictor variables of higher importance [START_REF] Ishwaran | randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)[END_REF]. The interactions between E c and D c , the most important variables in the regression model for g bs , are very strong at 0.05 in both pairings. Likewise, the interactions between g bs and D c , the most important variables in the regression model for E c , are also strong at 0.05 and 0.07 for the two pairings. Interactions are observed between other variables, but they are less significant because of the lower importance of those variables. It should be noted, however, the results of the sensitivity analysis could vary with different ranges of D c , R c , wind speed, or LAI as might be experienced in different years, locations, or vineyards with different designs or subject to different management practices.

The finding of significant interactions between important variables confirms the preliminary observations in the correlation matrix in Supplementary Figure S1 and supports the use of the random forest regression approach for the sensitivity analysis, which is capable of handling such variable interactions. Such strong interaction may also affect any additional regression analysis aimed at characterizing the relationship between g bs and other environmental variables.

Net Radiation (R c )

Figure 4 presents modeled net shortwave and net long wave radiation flux absorbed by the vine canopy and the measured global radiation flux incident on the whole vineyard, expressed in terms of the amount of energy absorbed by the vine canopy per day per unit vineyard ground area (MJ day 1 m 2 ). Daily fluxes were tallied with values of 15-minute R c greater than 50 W m 2 to match the filtering used in the calculation of g bs .

The shortwave radiation absorbed by the canopy follows closely with global radiation incident on the vineyard and represents the majority of the total radiation absorbed by the vine canopy. Net long wave radiation averaged close to zero on most days, although it could represent up to 30% of total net radiation when shortwave radiation is low. This is associated with cloudy conditions when transpiration is already low and not contributing much to vineyard water use.

Gowdy et al. Grapevine Bulk Stomatal Conductance

For that reason, and because of the relatively low importance of R c in the regressions with either g bs or E c , net long wave radiation absorbed by the vine canopy was disregarded in the estimation of R c used in the final calculations of g bs . This has the benefit in the future of eliminating instrumentation in the vineyard for measurement of long wave radiation from the sky and surrounding ground. Similarly, other studies of conductance in vineyards did not account for long wave radiation in determining net radiation (Lu et al., 2003;Zhang et al., 2012;Bai et al., 2015). However, if R c is found to be a more important variable in the determination of g bs or E c , then measurement of long wave radiation may be beneficial, perhaps as in cloudier climates.

CONCLUSION

Based on the two-source energy flux approach of Shuttleworth and Wallace (1985), a methodology is presented here for determination of g bs for vineyards with open canopies, using measurements of: (i) vine transpiration measured by sap flow as needed to calculate transpiration flux from the canopy; (ii) measurements of temperature and relative humidity in the vine canopy as needed to calculate vapor pressure deficit; (iii) solar radiation measures as needed to estimate the net radiation absorbed by the vine canopy; and (iv) vine canopy dimensions, porosity, and leaf area as needed for the above. Diurnal variations in 15-minute estimates of g bs were observed, with maximum daily levels comparing well with previously published values for grapevines the literature. Decreases in g bs over the season were also observed in association with more negative 9 PD . This methodology respects the energy flux dynamics of vineyards with open canopies, while avoiding involved and errorprone measurements that can also interfere with the operations of a working vineyard. It does not require measurement of soil heat flux, boundary layer conductance above the vineyard, nor long wave radiation, although consideration may need to be given to such measurements in other climates or vineyard configurations. It provides a practical means of quantifying conductance for further study of conductance response to changes in atmospheric demand and drought stress.

A global sensitivity analysis using data from the study found E c and D c are the most important variables in the determination of g bs , therefore warranting attention in their field measurement. Conversely, g bs was found to be the most important predictor of E c, emphasizing the importance of having better representations of conductance response in vineyard water use models.

Discussion

Based on the two-source energy flux approach of Shuttleworth and Wallace (1985) and as described further by Lhomme (2012), a method is presented for a relatively straightforward determination of gbs for vineyards with open canopies, using measurements of: i) vine transpiration measured by sap flow as needed to calculate transpiration flux from the canopy; ii) measurements of temperature and relative humidity in the vine canopy as needed to calculate vapor pressure deficit; iii) solar radiation measures as needed to estimate the net radiation absorbed by the vine canopy; and iv) vine canopy dimensions, porosity, and leaf area. The method produced results that compared well with other estimates of vine canopy conductance in the literature and demonstrated the expected diurnal and seasonal trends. This method considers separately the heat and vapor flux from the vine canopy and the ground surrounding the vine canopy in the interrow space, providing a more realistic representation of the actual energy fluxes from an open canopy than the big leaf application of the Penman Monteith (PM) equation, which is more appropriate for closed crop canopies (Monteith and Unsworth, 2013). It also does not involve time consuming and error-prone measurements of heat flux from the ground below the crop, nor the boundary layer conductance above the vineyard, which can also otherwise interfere with the operations of an operational vineyard. A more simplified field method increases the likelihood that more data can be collected across different varieties, climates, and growing conditions.

A global sensitivity analysis was performed using a random forest non-parametric regression analysis (Grömping, 2009;Antoniadis et al., 2021), which found Ec and Dc to be the most important variables in the determination of gbs, therefore warranting attention in their field measurement. Conversely, gbs was found to be the most important predictor of Ec, emphasizing the importance of having better representations of conductance response in vineyard water use models.

Also, based on measurements of long wave radiation from both the ground and the sky, and temperature measurements within the canopy, the net long wave radiation absorbed by the vine canopy was also calculated using a view factor method as described by Pieri (2010). From this, it was observed that estimates of net long wave radiation absorbed by the vine canopy were very low when compared to net absorbed shortwave radiation.

Combined with the finding from the sensitivity analysis (i.e., that total net radiation absorbed by the vine canopy is of lesser significance), it is proposed that the measurement of long wave radiation can be eliminated from this methodology without a significant impact on the calculation of bulk stomatal conductance. The benefit of simplifying the method and making it easier to implement may outweigh this loss, although verification of this finding may be warranted in vineyards with different canopy sizes, row spacings, and orientations.

Important in applying this method is the determination of the mean canopy height for the placement of temperature and humidity sensors as needed to calculating Dc, which based on the sensitivity analysis is one of the more important variables in the calculation of gbs.

The mean canopy height is the height at which constituents such as temperature and humidity are considered to be well mixed and uniformly distributed within and above the canopy (Shuttleworth and Wallace, 1985). While methods for estimating mean canopy height in vineyards have been published (Chahine et al., 2014), it might be useful in future studies, and not that difficult, to perform temperature and humidity measurement both inside the vine canopy and in the interrow space at difference elevations above the ground to perhaps confirm the estimated mean canopy height.

Varietal conductance response to changing environment

Background

Plant transpiration can be affected by vine water status [START_REF] Hsiao | Plant Responses to Water Stress[END_REF], vapor pressure deficits (Oren et al., 1999) and net radiation (Monteith and Unsworth, 2013). Transpiration is moderated as the plant regulates the conductance of water across the soil-plantatmosphere continuum (McElrone et al., 2013) in response to such factors. This regulation of conductance is achieved by various physiological mechanisms such as control of stomatal aperture and hydraulic conductivity of the vasculature (Lovisolo et al., 2010).

Determining a relationship between conductance, and hence transpiration with these factors could be useful for modeling transpiration under current or future climate conditions. And while other factors can affect vine conductance, these factors are of interest due to the potential for them to be affected by climate change.

The purpose of the journal article presented in this chapter, entitled "Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress." [START_REF] Gowdy | Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress[END_REF] was to simultaneously measure bulk stomatal conductance (gbs) as the response variable, and vine water status, vapor pressure deficit, and net radiation as the predictor variables to develop relationships between these variables for five different varieties using a readily interpretable multiple linear regression analysis. The regression coefficients for the predictors from this type of analysis will be comparable between themselves for a given variety and across the different varieties. Also, using these regression coefficients, simulations of transpiration can be generated for each variety and compared against the vine transpiration component of an existing vineyard balance model.

Current water balance models use an approach based on estimates of reference crop evapotranspiration to which a stress coefficient is applied to account for drought stress related decreases in transpiration (Allen et al., 1998;Lebon et al., 2003;[START_REF] Pieri | Feuille de calcul excel du modèle de bilan hydrique de la vigne[END_REF]. Such modeling approaches, however, are not parameterized to distinguish differences in conductance/transpiration regulation between different varieties. If this study discernable and significant differences in the conductance, and hence transpiration were observed in response to changes in soil water deficits and atmospheric demand for the different varieties.

Prior studies have evaluated the canopy conductance of individual grapevine varieties at the vineyard scale (Lu et al., 2003;Rogiers et al., 2009;Zhang et al., 2012;Bai et al., 2015), however, this study provides a direct comparison between five varieties in the same growing conditions and using the same, relatively simple and robust methodology, producing readily interpretable coefficients from a multiple linear regression analysis. This could be useful for future development of variety specific vineyard water balance models, which could then be useful for evaluating adaptation strategies under future climate change scenarios.

Multiple linear regression analysis

The selection of an appropriate regression analysis was an important consideration in developing the relationships between vine canopy conductance as the independent variable, and the various predictor variables that were considered. A multiple linear regression analysis was chosen in order to obtain a readily interpretable model. While nonlinear, or non-parametric regression analysis might provide a better fit with lower model residuals than multiple linear regression, the coefficients from such models become less readily interpretable [START_REF] Shmueli | To Explain or To Predict?[END_REF]. The predictor variable coefficients from multiple linear regression are readily interpretable as the mean change in the response variable for one standard deviation change in the associated predictor when based on standardized data, with their absolute values being directly comparable [START_REF] Frost | Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models[END_REF] for a given variety or across the different varieties.

The input data needed for this type of study naturally contains non-linearity and collinearity, so careful data filtering, transformation, and selection of variables was needed to create readily interpretable multiple linear regression model that satisfied ordinary least squares assumptions. For example, the equation for calculating gbs was observed to give unrealistically high and erratic results when input values of radiation, vapor pressure deficit, and transpiration were low, particularly in the early morning and evening. Previous studies of vine conductance in vineyards made similar observations and took an approach of filtering low net radiation data to address such issues (Lu et al., 2003;Zhang et al., 2012). Also, biological and environmental data often demonstrate non-linear relationships and collinearity between variables [START_REF] Dormann | Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[END_REF], so consideration of variable transformation were also required. In addition, interactions between the predictor variables can significantly inflate the variance of the residuals [START_REF] Frost | Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models[END_REF], and consideration may need to be given not using certain variables that create problems. All of these considerations will involve a trial-and-error process involving inspection of the various residual plots after each iteration.

Vineyard water balance modeling

Simulations of vine transpiration using the coefficients generated by the regressions were compared against those from an existing vineyard water balance model. In concept, crop water balance models add all the inputs of water to the crop root zone (e.g. precipitation, irrigation) and subtract all the extractions (e.g. transpiration, soil evaporation), with the difference either adding or subtracting to the root zone water balance (i.e., FTSW) within upper and lower limits. These models provide a daily estimate of soil water content and are useful alternatives to direct measures that may be too intensive or impractical due to site conditions [START_REF] Pellegrino | A model-based diagnosis tool to evaluate the water stress experienced by grapevine in field sites[END_REF][START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF].

The calculations of the [START_REF] Pieri | Feuille de calcul excel du modèle de bilan hydrique de la vigne[END_REF] vineyard water balance model used for this study, which are based in large part on those outlined in Lebon et al. (2003), are broken into three components consisting of: i) a soil water reservoir with a volume equal to the total transpirable soil water (TTSW) from which inputs and extractions to the vine root zone are tabulated; ii) a geometric representation of the vine canopy used to partition incident solar radiation between separate calculations of vine transpiration and soil evaporation; and iii) a daily representation of atmospheric conditions based on local meteorological data for the purpose of calculating reference evapotranspiration and rainfall. The model outputs daily estimates of vine transpiration, soil evaporation, and fraction of total available transpirable soil water (FTSW, mm 3 /mm 3 ) in the root zone.

The evapotranspiration component of both these models is divided into two parts: i) soil evaporation; and ii) vine canopy transpiration. The vine canopy transpiration component is calculated in two steps. First the potential vine transpiration (TVp), or the vine transpiration assumed under well-watered, non-stressed conditions, is calculated as the portion of reference evapotranspiration for the entire vineyard, calculated using the Penman formula [START_REF] Penman | Natural evaporation from open water, bare soil and grass[END_REF], that is equal to the proportion of total solar radiation incident on the vineyard absorbed by the vine canopy, determined by the method of Riou et al. (1989). As the soil water content in the modeled soil water reservoir decreases to a threshold value, these models begin factoring in reductions to TVp to account for the reduction in transpiration/conductance associated with drought stress. Lebon et al. (2003) uses an empirically based reduction function that assumes no effect on vine transpiration until the fraction of transpirable soil water (FTSW) reached 0.4 with a linear reduction in transpiration as FTSW continues below that to zero. The method of [START_REF] Pieri | Feuille de calcul excel du modèle de bilan hydrique de la vigne[END_REF], however, uses a non-linear reduction function mechanistically based on the relationship between soil matric potential and FTSW as presented in Pieri and Gaudillère (2005). Neither of these approaches, however, account for the potential differences that may exist between varieties in how they regulate conductance, and hence transpiration, in response to changes in vine water status, atmospheric demand, and net radiation levels. water vapour diffusion through open stomata is much greater than the resistance of water through the vasculature, with that through the stomata becoming the controlling factor in overall transpiration [START_REF] Sack | Leaf Hydraulics[END_REF]Krounbi and varies diurnally (Bai et al., 2015;Lu et al., 2003;Zhang et al. et al. differences in response to changing environmental variables observed between varieties (Costa et al., 2012;Prieto et al., Plant species that readily close their stomata to reduce transpiration and maintain constant leaf water potential isohydric, while those that keep their stomata open and allow for more negative anisohydric et al. that vary with water stress (Levin et al. response such as isohydrodynamic, whereby stomata are regulated to maintain a roughly constant difference between leaf and soil water potential [START_REF] Franks | Anisohydric but isohydrodynamic: seasonally constant plant water potential gradient explained by a stomatal control mechanism incorporating variable plant hydraulic conductance[END_REF][START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]Zhang et al., 2012;Charrier et al., Vitis vinifera, different LAI can be affected by vine vigour, which can depend on the variety, the rootstock and the available resources, such as water and nutrients (Smart and Robinson, to increasingly intense drought conditions as a way of controlling their transpiration (Zufferey et al.

vine responses, such as changes in photosynthesis, stomatal closure, and shoot growth characteristics in response to water (Lebon et al., 2003;Pellegrino et al. of predawn leaf water potential ( PD plant-based measurement of plant water status (Sperry et al., of the night when the stomata are closed and the plant is in equilibrium with the root zone water potential (Choné et al., new vineyards can be decreased, or irrigation can be used (van Leeuwen et al., 2019a;van Leeuwen et al. In this paper, a multiple linear regression approach was used in an iterative fashion to develop an explanatory statistical model of calculated g bs net potential on developing a model that is readily interpretable for the Measurements of 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 Sampling and measurement were done early enough to ensure all measurements were completed no

MATERIALS AND EQUIPMENT

Measurements were taken on one leaf per vine by the method of Scholander et al.

using a pressure chamber with digital manometer

METHODS

A multiple linear regression analysis was performed in an linear, or non-parametric regression analysis might provide

Data compilation and filtering

A database of R c and D c was compiled from measurements and estimates on 15-minute intervals from 30 June through interpolation if there were no more than four missing 15-minute measurements between available data points, of PD and LAI were also included, but as they were obtained only periodically during the season, those data were interpolated to a daily time step using a local weighted 36 mm on 11 through 13 August 2020, with PD taken just 4 days before and 7 days afterwards the study period, no other daily rainfall total exceeded more than a few millimeters, which is well below an amount that g bs (as described in the next erratic results when input values of R c , D c , or E c were low, values also introduced irregularities in the residual plots for Previous studies of conductance in vineyards took a similar issues (Lu et al., 2003;Zhang et al. from individual vines when R c -2 or D c < 900 Pa or if E c was less than the 30 th percentile of all values for a basis due to the differing ranges of E c observed across the might disproportionally remove more data from vines with generally lower E c E c data in general was strongly skewed towards lower values, so application of E c was very low, resulting in only a 9 to 14 percent reduction, depending reduction ranged slightly higher from 9 to 18 percent for transpiration during times when drought stress, as measured higher transpiration rates when the majority of vine water Summary statistics for the combined database are presented due to sporadic instrument outages and differing start times, the number of 15-minute data points collected on the vines of each units for R c and D c 2 and kPa respectively to Equation 2:

in
(s m -1 and bulk stomatal conductance is given by inversion:

Equation 3:

where: where:

E c (g m -2 s -1 = latent heat
r bl = leaf-level boundary layer resistance (s m -1 25 s m -1 per LAI g bs was calculated using Equations 2 and 3 and r bh was calculated using Equation 4, with the results of both appended radiation absorbed by the canopy (R c predawn leaf water potential ( PD Vitis vinifera varieties, nonlinear relationships between g bs and D c have been observed (Prieto et al., 2010;Lu et al. relationship also existed between the g bs and PD data, while the relationship between g bs and R c did not demonstrate any g bs and both D c and PD , a log 10 transformation of g bs was used

For the data from all 10 vines combined, Figure 2 presents the log 10 transformation of g bs plotted against 15-minute D c , R c , and interpolated PD data demonstrating roughly linear R c and D c in both

Predictor variable selection

in the regression analysis began with PD and the key input variables to Equations 2 and 3 for calculation of g bs , including R c , D c , and r bh Vine water status, as measured by PD , is well understood to have an effect on conductance, and was found to have a g bs measurements of R c and D c , the periodic measurements of PD will miss some of the range and response of the varying R c and D c as having g bs and were

Regression model iterations including r bh as a predictor p that variable, but they explained only a small portion of variation in g bs et al. found r bh to be of much less importance than the other input variables in the determination of g bs strong collinearity between r bh and PD , which contributed to (Riou et al., 1989;Pieri, D c by vine transpiration, it was more strongly driven by ambient transpiration simulations, R c and D c data from the vine with the most complete time series over the season was used in the input of PD PD was developed using the output of FTSW from the corresponding water balance model, which was converted to PD by the FTSW to PD relationships published in Figure 3 Again, with a mean value of R c -2 , the effect of more negative PD PD also be observed in Figure 6, with an overall decrease in the levels of bs at the more negative PD varieties, however, remains fairly similar between the two levels of PD , particularly at lower levels of D c bs between varieties were observed in the plot of bs versus R c , particularly at the lower 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 they would need to be part of a model that tracks soil water PD over the course of the season in a transpiration rates for each variety, the resulting time series of FTSW, and hence PD over the season, and its subsequent

DISCUSSION

In this study we developed estimates of whole-vine bulk stomatal conductance (g bs vine canopy (D c predawn leaf water potential ( PD of changes in g bs , with net radiation absorbed by the vine canopy (R c

Although environmental modeled g bs ( bs ) response, and hence simulated water use over the growing season were observed between varieties, however, further study may be needed to better distinguish For both TTSW assumptions presented in Figures 8, Merlot regard to leaf-level stomatal conductance in response to predawn water potential (Medrano et al., 2003;Yuste et al., nor compare against any of the of the other varieties included between bs versus R c , D c or PD is observed in Figures 5,6, and 7 respectively to be generally greater, whether positive or negative, for those varieties with higher bs at non-limited g bs under less limited conditions will transpire more, deplete soil water reserves more quickly, and experience greater levels season than those with overall lower g bs PD becomes more negative, then varieties with a greater will begin to depth and resulting TTSW for individual vines or varieties are also an important consideration in the onset of soil water may result from varying interplay between the different studies on typically anisohydric varieties Merlot (Zhang et al. et al. anisohydric response at higher soil water status, but more In both cases, the isohydric response followed a general tendency towards greater stomatal closure in response to the differences in g bs response across varieties compared to A comparison of transpiration simulated using the above growing season transpiration between varieties that appeared to be driven in large part by the difference across varieties in the effect of PD on g bs towards greater modeled g bs ( bs or negative, to changes in the predictor variables when bs g bs in response to decreasing PD than in response to increasing D c simulations were also compared against those from an accepted vineyard water balance model and found similar results, 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 contains non-linearity and collinearity, but careful selection a method for quantifying the response of vine conductance to different environmental variables, the described approach Knowledge of such differences in g bs response could help with selection of varieties that are better suited for future changes in certain growing conditions in a region, such as the FIGURE S1. Residual plots for final multiple linear regression between log10(gbs) as the response variable and Rc, Dc, and PD as the predictors without interactions and based on raw data, including a) quantile-quantile plot, b) histogram of standardized residuals, c) standardized residuals versus fitted value plot, and d-f) standardized residuals versus predictor variable plots for Rc, Dc, and PD respectively.

PhD thesis -Mark Gowdy

Discussion

Regression analysis results

The methodology presented in the previous chapter was used to estimate bulk stomatal conductance on 15-minute intervals from July to mid-September 2020 on several vines of Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon, with its response being compared against measurements of vine water status, vapor pressure deficits, net radiation, leaf area index, and other parameters. After a process of trial-and-error to address non-linearity and collinearity in the data for the different variables, a multiple linear regression analysis found the variability of vapor pressure deficit in the vine canopy, and to a lesser extent the net radiation absorbed by the vine canopy over the course of the day, and predawn water potential over the season explained much of the variability in bulk stomatal conductance.

The intent of using a multiple linear analysis approach was to develop a model that was readily interpretable, with resulting regression coefficients for the different variables being physically meaningful and comparable between themselves and across the different varieties. This type of regression analysis, however, is not well suited for addressing nonlinearity and collinearity in the explanatory variables, as is required to satisfy the underlying statistical assumptions. Non-linear, or non-parametric regression modeling approaches, such as used by [START_REF] Brillante | Ecophysiological Modeling of Grapevine Water Stress in Burgundy Terroirs by a Machine-Learning Approach[END_REF], are less affected by collinearity in the input variables and may allow for a better modeling fit. But such models are less interpretable, in terms of characterizing and comparing the effect of different variables, and are more site-specific and less broadly applicable to vineyards with different soil, climate, and other conditions.

During the study there was an observed decrease in vine leaf area, and hence LAI for all vines after mid-August, which corresponded with a general decrease in vine water status (𝛹PD) over the same period. As bulk boundary layer resistance (rbh) is calculated based on LAI, this created relatively strong collinearity between rbh and 𝛹PD, which resulted in high variable inflation factors and problems with the residual plots from the corresponding linear regressions. Based on this issue with collinearity, and the prior sensitivity analysis, which found rbh to be of relatively low significance in the determination of gbs (Gowdy et al., 2022a), it was decided to eliminate rbh from the final regression analysis. This can further simplify future application of this methodology by eliminating time consuming measurement of leaf area index needed for calculation of rbh.

Depending on the variety, vapor pressure deficit (Dc) and vine water status (𝛹PD) each had a generally greater effect on gbs than Rc. Of these two predictors, the 𝛹PD regression coefficient was more differentiated across the five varieties than was the Dc coefficient.

And while there was a significant range in Rc coefficients across varieties, their overall lower values mean they had less of an influence on the differences in gbs response across varieties compared to the other predictors. Then using these coefficients along with a common input dataset, simulations of gbs (ĝbs) showed a greater response to changes in each of the predictor variables when ĝbs was overall greater under non-limiting conditions.

With respect to Dc, this was as expected from empirical and theoretical modeling (Oren et al., 1999;[START_REF] Domec | Does homeostasis or disturbance of homeostasis in minimum leaf water potential explain the isohydric versus anisohydric behavior of Vitis vinifera L. cultivars?[END_REF]. From these ĝbs simulations (using common input data sets), it was also observed that Tempranillo appears to put greater emphasis on reducing its gbs in response to decreasing 𝛹PD than in response to increasing Dc when compared to the other varieties.

Comparison with water balance model

Transpiration simulations based on the regression equations using input data from the 2020 season 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). These differences in the transpiration reduction functions (see Figure 9 of [START_REF] Gowdy | Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress[END_REF] begin to provide a mechanistic explanation of the observed differences in simulated While current water balance modeling is an invaluable tool to help growers design and manage their vineyards, they are generic, without the ability to distinguish differences between varieties. While more data collected using the described approach is likely needed to make more definitive characterization of the behavior of the different varieties, it could provide the means of developing variety specific parameterization of the vine transpiration component of future water balance models. 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. Physiological vine responses, such as changes in photosynthesis, stomatal closure, and shoot growth PhD thesis -Mark Gowdy 100 characteristics in response to water deficits, are often correlated well with water potential [START_REF] Pellegrino | A model-based diagnosis tool to evaluate the water stress experienced by grapevine in field sites[END_REF]Lebon et al. 2003) and could potentially be evaluated using the output from water balance models that output estimates of soil water content.

And while all vines measured in this study were subjected to the same climate conditions, the gbs response would be better characterized if using data from different years with different relative amounts of Rc, Dc and 𝛹PD. The results could also likely be different if performed in an overall different climate (e.g., southern Europe, or northern France). Even being performed in the same vineyard, the soil conditions around the root zone of individual vines and its effect on rooting volume, access to water, etc., however, can still be quite heterogeneous [START_REF] Smart | Physiological Changes in Plant Hydraulics Induced by Partial Root Removal of Irrigated Grapevine (Vitis vinifera cv. Syrah)[END_REF]. Therefore, with only two vines per variety included in this study, the results could potentially be biased by the specific growing conditions experienced by those vines. Rootstocks and canopy management could also be influencing factor on vine conductance [START_REF] Marguerit | Rootstock control of scion transpiration and its acclimation to water deficit are controlled by different genes[END_REF][START_REF] Picón-Toro | Effects of canopy size and water stress over the crop coefficient of a "Tempranillo" vineyard in south-western Spain[END_REF] and should be taken into consideration.

TTSW assumptions for water balance modeling

As part of developing the water balance modeling needed to create the time series of 𝛹PD for the regression-based transpiration simulations, and for comparison of the water balance model against those simulations, assumptions regarding the total transpirable soil water (TTSW) were required. It provides the beginning water balance for the water balance model, from which additions (from rainfall, or irrigation) or subtractions (from vine transpiration or soil evaporation) are made, and the daily fraction of transpirable soil water (FTSW) is calculated. It is effectively the amount of water available in the soil for the vines between field capacity and the wilting point, and is influenced by the soil texture, percent of course elements, and root depth and density [START_REF] Pellegrino | Relationships between plant and soil water status in vine (Vitis vinifera L.)[END_REF]. Its determination is important, as results of water balance models are very sensitive to this assumption [START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF].

The minimum soil water level can be determined through field and laboratory measurement of FTSW, for example at a minimum soil water potential of -1.5 MPa [START_REF] Pellegrino | Relationships between plant and soil water status in vine (Vitis vinifera L.)[END_REF]). Sometimes, however, direct field measurement of soil water content is not practical and TTSW can be estimating by applying FTSW to predawn water potential relationships from other similar vineyards using a trial-and-error curve fitting technique [START_REF] Pellegrino | A model-based diagnosis tool to evaluate the water stress experienced by grapevine in field sites[END_REF]. It is also possible that theoretical relationships between soil matric potential and FTSW [START_REF] Saxton | Estimating Generalized Soil-water Characteristics from Texture[END_REF][START_REF] Pieri | Vines water stress derived from a soil water balance model -sensitivity to soil and training system parameters[END_REF] or soil matric potential and soil water content [START_REF] Van Genuchten | A closed-form equation for predicting the hydraulic conductivity of unsaturated soils[END_REF]) could be used to aid in such a curve fitting exercise, although the issue of estimating rooting depth may be difficult to resolve.

Measurements, or estimates of TTSW for the VitAdapt vineyard have not been made, and furthermore, could be highly variable on a vine-by-vine basis. Such measurements in the field can also be problematic in stony soils [START_REF] Van Leeuwen | Comparison of Three Operational Tools for the Assessment of Vine Water Status: Stem Water Potential, Carbon Isotope Discrimination Measured on Grape Sugar and Water Balance -Ch. 7[END_REF], such as those experienced in the vineyard used for this study. For this study it was decided, therefore to select two TTSW assumptions that generated a range of daily transpiration rates that were comparable to those measured using the sap flow sensors across all the vines.

Conclusions

The purpose of this thesis was to gain insight into how different grapevine varieties respond to drought stress by measuring changes in vine water use on different time scales.

As the amount of soil water available to the plant decreases and/or its water loss increases due to increasing atmospheric demand, the water status of the vines will decrease and drought stress will increase. Different varieties are better able than others to maintain their water status and minimize drought stress in response.

Grapevines can have many different responses to drought stress, which may also differ depending on the timing of that stress during the growing season (Tardieu et al., 2018).

Such stress responses generally include turgor loss, leaf loss, yield reduction, changes in berry ripening dynamics and composition, etc. The studies performed as part of this thesis were focused on how different grapevine varieties modify their water use as evaluated on different time scales, specifically: i) how water use efficiency is affected by drought stress during berry ripening period; and ii) how vine canopy conductance, and hence transpiration is affected over the season as a function of drought stress. In both cases, these responses are driven by how reactive the different varieties are in regulating stomatal closure, and hence water use in response to drought stress.

The study of drought stress response during the berry ripening period was based primarily on measurements of carbon isotope discrimination in berry juice sugar (δ 13 C) and predawn and leaf water potential, allowing for a classification of differences in drought response across 48 varieties. Being performed in the VitAdapt common-garden vineyard, with randomly distributed replicate plantings, the effects of variable soil conditions, even within the same vineyard, could be properly accounted for. Then using an innovative method of measuring vine canopy conductance in a vineyard setting, the response of this conductance, and hence vine transpiration, were quantified against changes in soil water deficits and atmospheric conditions, highlighting different responses across five varieties.

The following is a summary of specific key conclusions from these studies and their implications. These studies were all performed in a working vineyard setting, with the intent of producing results more relevant to vineyard managers and producers of wine and wine-based spirits when compared to those coming from greenhouse studies.

Drought stress response during berry ripening

Measurement of carbon isotope discrimination in berry juice at maturity (𝛿 13 C) and corresponding water potential measurements during the berry ripening period over four seasons confirmed results from prior studies that suggest 𝛿 13 C provides an integrated assessment of vine water status during the period of berry ripening. Being able to characterize wine water status during this critical period is useful because it has an important effect on the quality potential of grapes and the resulting wine quality. This may be a useful tool for growers, purchasers of grapes (e.g., cooperative), or winemakers to retrospectively estimate the vine water status that existed in the vineyard for a particular growing season. For this purpose, however, the collection of 𝛿 13 C data over several seasons would be required to establish the range of drought response of a given variety against which the measured level of stress in a given year can be assessed.

To extend this use, measurements of δ 13 C in eau de vie produced from a double distillation process, such as used in the Cognac AOC, was compared to the δ 13 C of the source wine and parent grape must. A strong linear relationship was found between the δ 13 C of grape must, wine and eau de vie, suggesting the latter could be used to estimate the vine water status that existed during the corresponding berry ripening period. This allows a retrospective estimation of the vine water status that existed during the ripening of the grapes used to produce the wine and subsequent eau de vie. As has been done for wine, this could then be used in future studies to establish relations between vine water status and specific sensory attributes of eau de vie quality.

Various 𝛿 13 C metrics and vine phenology measured on 48 different varieties in the same growing conditions over seven years were also used to create a classification of drought tolerance that matched well with prior understandings of the relative drought tolerance of different varieties. The selected classification was performed using a hierarchical cluster analysis (HCA) based on the following observed traits for each variety: the day of year (DOY) of véraison; the 𝛿 13 C in the wettest year; and the range of 𝛿 13 C measurements between those in the driest and wettest years. For each variety, these respectively account for the effect of the timing of berry ripening on experienced drought conditions, the innate water use efficiency, and the drought stress sensitivity across multiple seasons. The HCA classification obtained in this way can be useful for grower selection of plant material based on the potential stress responses under future anticipated growing conditions.

Using measurements of predawn and midday leaf water potential (𝛹PD and 𝛹L respectively) on a subset of six varieties, hydroscapes were created and a list of metrics indicative of the sensitivity of stomatal regulation of vine water status to water stress was produced. Such hydroscapes are indicative of range of 𝛹L over which the vine will operate, with smaller hydroscapes associated with more isohydric varieties and larger hydroscapes indicative of anisohydric varieties. It was observed that the size of the hydroscape for a variety is strongly dependent on the lowest level of 𝛹L under non-limiting conditions and the lowest level of 𝛹L at high levels of stress. These key hydroscape metrics were then found to be well correlated with the 𝛿 13 C obtained in years of relatively non-limiting conditions. This suggests the anisohydric behavior of a larger hydroscape is associated with a lower level of water use efficiency in well-watered conditions as indicated by more negative 𝛿 13 C. Increased water use in non-limiting conditions, as suggested by a larger hydroscape, may lead to greater water deficits later in the season and hence increasing 𝛿 13 C. The relationship between these hydroscape and 𝛿 13 C metrics lends some validation to the significance of the minimum year 𝛿 13 C metric, with the latter being much easier to obtain in the vineyard over several seasons and a wide range of varieties.

Vine canopy conductance response to drought stress

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. In response to such changes, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plant-atmosphere continuum. As part of this thesis a simplified method for the determination of bulk stomatal conductance in a vineyard setting was developed and then used to study how that conductance was regulated by five different varieties in response to changes in vine water status, vapor pressure deficit, and net radiation.

The simplified method used in this study to determine bulk stomatal conductance is based a two-source crop canopy energy flux model, which considers separately the heat and vapor flux from the vine canopy and the ground surrounding the vine canopy in the interrow space, using relatively straight-forward measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. In addition to providing a more realistic model of the actual energy fluxes in open vineyard canopies, this approach allows the determination of bulk stomatal conductance without the measurement of the flux of heat from the ground surrounding the vine canopy, nor the determination of the boundary layer resistance above the vineyard.

The method produced results that compared well with other estimates of vine canopy conductance in the literature and demonstrated the expected diurnal and seasonal trends.

Sensitivity analysis using non-parametric regression analysis found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, therefore warranting attention in their field measurement. On the other hand, absorbed net radiation and bulk boundary layer resistance were found to be much less important suggesting some opportunities for method simplification that would not significantly impact quality of the results. For example, it was observed that estimates of net long wave radiation absorbed by the vine canopy were very low when compared to net absorbed shortwave radiation, suggesting it could be eliminated from this methodology without a significant impact on the calculation of bulk stomatal conductance. The benefit of simplifying the method and making it easier to implement may outweigh this loss, although verification of this finding may be warranted in vineyards with different canopy sizes, row spacings, and orientations. A simplified field method increases the likelihood that more data can be collected across different varieties, climates, and growing conditions.

The above methodology was then used to estimate bulk stomatal conductance at 15-minute intervals on several vines of Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon in a non-irrigated vineyard, with its response being compared against a number of biotic and abiotic parameters. After a process of trial-and-error to address collinearity and non-linearity in the data for the different variables, a multiple linear regression analysis was used to develop a readily interpretable model of bulk stomatal conductance as a function of vine water status, vapor pressure deficit, and net radiation, with regression coefficients for the different predictor variables being physically meaningful and comparable between themselves and across the different varieties. While other non-linear or non-parametric regression models can provide a better fit and handle some degree of PhD thesis -Mark Gowdy 106 collinearity in the variables, they are less interpretable and more site-specific that the multiple linear regression approach used in this study.

Based on comparing the standardized coefficients generated by the regression modeling, the variability of vapor pressure deficit in the vine canopy over the day and predawn water potential over the season was observed to explain much of the variability in bulk stomatal conductance, with the variability of net radiation absorbed by the vine canopy explaining much less. The effect of vine water status on conductance was more differentiated across the five varieties than was the effect of vapor pressure deficit. And while there was a difference in the effect of net radiation across the varieties, its overall effect was much less when compared to vine water status and vapor pressure deficit.

It was also observed that the response of modeled conductance, as calculated using the regression model coefficients and input data from the same season, was of greater magnitude (whether positive or negative), to changes in the predictor variables when conductance at non-limiting conditions was also greater. Tempranillo was observed to put greater emphasis on reducing its conductance in response to decreasing vine water status than in response to increasing vapor pressure deficits when compared to the other varieties.

Transpiration simulations, also based on the regression model coefficients and input data from the same season, 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).

The multiple linear regression approach used in this study, however, was not able to distinguish the effects of collinear variables, such as reduced leaf area index, which were strongly related to vine water status. As a result, bulk boundary layer resistance, which is based on leaf area index, and which was also found in the sensitivity analysis to be of less significance in the calculation of conductance, was eliminated from the final regression analysis. One benefit of this, however, it that it further simplifies this methodology by eliminating the time-consuming measurement of leaf area index, while not having a significant negative effect on the explanatory power of the model. While current water balance modeling is an invaluable tool to help growers design and manage their vineyards, they are generic, without the ability to distinguish differences between varieties. While more data collected using the described approach is likely needed to make more definitive characterization of the behavior of the different varieties, it could provide the means of developing variety specific parameterization of the vine transpiration component of future water balance models. By better characterizing the response of bulk stomatal conductance, the dynamics of vine transpiration could be better parameterized in vineyard water use modeling of current and future climate scenarios.

Another major challenge in water balance modeling of vineyards is the need to develop a realistic estimate of the total transpirable soil water (TTSW), which can have an important effect on results. TTSW can be highly variable on a vine-by-vine basis and problematic to measure, particularly in stony soils. For this study, measurements of TTSW were not available, and two TTSW assumptions were made such that the range of daily transpiration rates estimated by the water balance model were comparable to those measured using the sap flow sensors. Future studies could possibly use a curve fitting technique comparing measurements of predawn water potential to estimates, or measurements of soil water content to develop estimates of TTSW, although such approaches require some understanding of the nature of those relationship, which are challenging to obtain.

The topic of varietal responses to water deficits is complex, with many physiological responses and time frames to consider, and numerous means of measurement available.

The studies presented in this thesis were able to characterize the drought stress response of several different varieties both in terms of changes in water use efficiency over the berry ripening period and changes in vine conductance both diurnally and seasonally, while using and comparing a number of different approaches to determining these responses. In the future, it may also be possible with data collected over multiple seasons to compare drought response characterizations produced by these two methods. For example, the response of canopy conductance to drought stress during the berry ripening period could be compared to measurements of 𝛿 13 C.

  Par ailleurs, en utilisant des mesures du potentiel hydrique foliaire de base et de potentiel hydrique foliaire de mi-journée(𝛹PD et 𝛹L respectivement) sur un sous-ensemble de six variétés, des hydroscapes ont été créés et une série de paramètres indiquant la sensibilité de la régulation stomatique du statut hydrique de la vigne au stress hydrique a été produite. De tels hydroscapes sont indicatifs de la gamme de 𝛹L sur laquelle la vigne peut fonctionner, avec des hydroscapes plus petits associés à des variétés plus isohydriques et des hydroscapes plus grands indiquant des variétés anisohydriques. Il a été observé que la taille de l'hydroscape d'une variété dépend fortement du niveau le plus bas de 𝛹L dans des conditions non limitantes et du niveau le plus bas de 𝛹L à des niveaux élevés de stress hydrique. On a ensuite constaté que ces paramètres clés de l'hydroscape étaient bien corrélés avec le 𝛿 13 C obtenu au cours des années où les conditions hydriques étaient peu limitantes. Cela suggère que le comportement anisohydrique d'un hydroscape plus grand est associé à un niveau inférieur d'efficience d'utilisation de l'eau dans des conditions bien arrosées, comme l'indique un 𝛿 13 C plus négatif. Une utilisation accrue de l'eau dans des conditions non limitantes, comme le suggère un hydroscape plus grand, peut entraîner des déficits hydriques plus importants plus tard dans la saison et donc une augmentation du 𝛿13C (vers des valeurs plus faiblement négatifs). La relation entre ces paramètres d'hydroscape et de 𝛿 13 C permet d'interpréter et de valider l'importance du paramètre 𝛿 13 C obtenu au cours d'une année avec peu de contrainte hydrique, ce dernier étant relativement facile à obtenir dans le vignoble sur plusieurs saisons et sur une large gamme de variétés.

  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). The vineyard is maintained by the UE 1442 Vigne & Vin Bordeaux Grande Ferrade.

  using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. This methodology respects the particular energy flux dynamics of vineyards with open canopies, while avoiding problematic measurements of soil heat flux and boundary layer conductance needed by other methods, which can otherwise interfere with ongoing vineyard management practices. This method provides a more straightforward and robust means of calculating vine canopy conductance as needed to study its response to climate change. Being able to characterize variety-specific regulation of vine canopy conductance, and hence transpiration, in response to changes in soil water deficits and atmospheric conditions would help improve modeling of the vine canopy transpiration component in vineyard water balance models. The purpose of the study presented in Chapter 6 of this thesis is to quantify and differentiate the response of bulk stomatal conductance (gbs) of different grapevine varieties to changes in vine water status, vapor pressure deficit in the vine canopy, and net radiation absorbed by the vine canopy. Using coefficients from a readily interpretable multiple linear regression model, simulations of transpiration are then compared to those from an existing vineyard water balance model to understand how well variety specific behavior might be distinguished. Current water balance models are not

Figure 1 .

 1 Figure 1. Schematic depiction of the soil-plant-atmosphere continuum, showing gradients of water potential across different elements of the supply, transport and atmospheric demand for water, and the boundary between gas and liquid phases.

Figure 2 .

 2 Figure 2. Schematic plan of the VitAdapt experimental vineyard, showing as an example, the location of four groups of ten vines each of Ugni blanc (red circles).

Marc

  Plantevin et al. OENO One | By the International Viticulture and Enology Society 2022 | volume 56-2 | 3

  13C is variety dependent, with Tinto Cão showing the most negative mean values across all years (black dots) and Colombard showing the least negative mean values.

  OENO One | By the International Viticulture and Enology Society 6 | volume 56-2 | 2022

  OENO One | By the International Viticulture and Enology Society 8 | volume 56-2 | 2022 The variation observed for Tempranillo and Semillon could be due to a variety of factors, including differences in vapour measurement artifacts (e.g., severe levels of stress causing petiole embolisms, hydraulically disconnecting the measured PD Lcrit is reached. For example, Merlot reaches Lcrit PD Lcrit at PD = -0.7 MPa.

FIGURE 5 .

 5 FIGURE 5. Modified hydroscape representation for field data, with their corresponding areas in MPa 2 . Varieties are classified from the smallest to the largest hydroscape. The study was carried out in Villenave d'Ornon in 2017 (except for Ugni blanc), 2018 (except for Grenache), 2019 and 2020.

  SUPPLEMENTARY DATAPlantevin, M.,Gowdy, M., Destrac-Irvine, A., Marguerit, E., Gambetta, G. A., & van Leeuwen, C. (2022). Using δ 13 C and hydroscapes for discriminating cultivar specific drought responses: This article is published in cooperation with Terclim 2022 (XIVth International Terroir Congress and 2nd ClimWine Symposium), 3-8 July 2022, Bordeaux, France. OENO One, 56(2). https://doi.org/10.20870/oeno-one.2022.56.2.5434 Supplemental Materials SUPPLEMENTARY FIGURE S1. Monthly evaporative demand (Rainfall -Evapotranspiration) for 2014 to 2020. SUPPLEMENTARY FIGURE S2. Monthly fraction of transpirable soil water (FTSW) during the growing season for the years 2014 to 2020.

Figure 3 .

 3 Figure 3. Laboratory-scale Charentaise style still used for production of eau de vie (photo courtesy of Conservatoire du Vignoble Charentais).

4. 2

 2 Journal article Carbon isotope discrimination as an indicator of vine water status is comparable in grape must, wine, and distilled wine spirit Mark Gowdy 1 , Sébastien Juillard 2 , Marina Frouin 2 , Xavier Poitou 3 , Agnès Destrac-Irvine 1 and Cornelis van Leeuwen 1*

FIGURE 1 |

 1 FIGURE 1 | Measured δ 13 C(‰) in eau-de-vie samples versus those measured in grape must samples (red line) and wine samples (blue line).

  using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. The methodology presented respects the particular energy flux dynamics of vineyards with open canopies, while avoiding problematic measurements of soil heat flux and boundary layer conductance needed by other methods, which can otherwise interfere with ongoing vineyard management practices.This method provides a more straightforward and robust means of calculating vine canopy conductance used in the next chapter of this thesis to evaluate the response of gbs to changing atmospheric conditions and drought.5.1.1 Big leaf vs. two-source energy flux modelsAt the leaf scale, stomatal conductance (gs) is the ratio of water vapor, or carbon flux divided by the concentration gradient driving flux across the boundary layer at the leaf surface and can be estimated by using an inverted form of the Penman Monteith (PM) equation(Monteith and Unsworth 2013). As depicted schematically in Figure4below, at the field scale, transpiration from a crop canopy can be estimated by conceptually applying the PM equation as if the canopy were a big leaf that is horizontally uniform and entirely covers the soil below(Monteith and Unsworth 2013). This equation is based on an energy balance assumption that has the sum of sensible heat flux (C) and latent heat flux (λE) being equal, but opposite to the net radiation absorbed by the canopy (Rg).Applying the big leaf approach at the crop canopy scale, canopy conductance (gc) can be calculated by rearranging the PM equation and inputting measured canopy transpiration (E), atmospheric vapor pressure deficits (VPD), net radiation absorbed by the canopy (Rg), and the bulk equivalent of boundary layer conductance (gbl), with all fluxes and resistances expressed in terms of unit ground area below the canopy(Granier and Loustau, 1994; Granier et al., 2000; Lu et al., 2003). When applying the inverted PM equation in this manner, however, the big leaf approach requires the determination of net radiation absorbed by the canopy must account for heat flux from the ground below the canopy (G) and the aerodynamic boundary layer conductance (gbl) above the vineyard(Monteith and Unsworth 2013). Such measurements can be cumbersome, error prone, and interfere with ongoing management practices in a production setting.

Figure 4 .

 4 Figure 4. Schematic representation of the big leaf model based on the Penman Monteith equation (see text for variable descriptions.)

  below, this method only requires the determination of net radiation absorbed by the vine canopy (Rn), vapor pressure deficit in the vine canopy (Dc), transpiration flux (E), and an estimate of bulk boundary layer resistance to vapor flux (rbv) and leads to a calculation of bulk stomatal conductance (gbs), which is the inverse of bulk stomatal resistance (rbs). Aside from being based on a more realistic representation of energy flux patterns of an open canopy, this approach does not require the measurement of soil heat flux or boundary layer conductance above the vineyard, greatly simplifying its determination.

Figure 5 .

 5 Figure 5. Schematic representation of the two-source energy fluxes of an open vineyard canopy.

Figure 6 .

 6 Figure 6. Calculation of long wave radiation absorbed from the sky by the vine canopy.

Figure 7 .

 7 Figure 7. Calculation of long wave radiation reflected from the ground and absorbed by the vine canopy.

Figure 8 .

 8 Figure 8. Calculation of long wave radiation absorbed by the vine canopy from adjacent vine canopies

Figure 9 Figure 10 .

 910 Figure 9 shows: a) SGEX sap flow sensor collar; and b) schematic of thermocouples and stem heater in the collar. For this study, sap flow signals were logged every 15 minutes, which produced sap flow signals such as those in Figure 10.

  evaporative flux from canopy per unit ground area (g m 2 s 1 ) λ = latent heat of vaporization for water = 2257 (J g 1 ) D c = vapor pressure deficit at mean canopy height (Pa) R c = net radiation absorbed by the vine canopy per unit ground area (W m 2 ) r bs = bulk stomatal resistance (s m 1 ) r bh = bulk boundary layer resistance to heat flux (s m 1 ) n = 2 for grapevine leaves with stomata on one side only γ = psychrometric constant at 1 atm and 20 C = 65.8 (Pa C 1 ) 1 = rate of change in saturation vapor pressure versus temperature = 145 (Pa C 1 ) ρC p = heat content per unit volume of air at 20 C = 1212 (Pa C 1 )

FIGURE 1 |

 1 FIGURE 1 | Schematic of convective and radiative heat fluxes to and from the vine canopy and surrounding ground based onShuttleworth and Wallace, 1985; Lhomme et al., 2012.

FIGURE 2 |

 2 FIGURE 2 | Example timeseries of bulk stomatal conductance (g bs , mm s -1 ) calculated on 15-minute intervals from a selected vine (C6-2) between 27 July and 24 August, 2020. Shading indicates days over which there was 36 mm of precipitation.

FIGURE 3 |

 3 FIGURE 3 | Daily maximum hourly running average of 15-min estimates of the bulk stomatal conductance, g bs (mm s -1 ) from a selected vine (C5-2) between 30 June and 15 September, 2020 with shading of line representative of corresponding measured predawn leaf water potential varying from 0.0 MPa (light gray) to -1.0 MPa (black).

FIGURE 4 |

 4 FIGURE 4 | Daily shortwave and long wave radiation flux (MJ day -1 m -2 ) absorbed by the vine canopy (dashed lines) and solar radiation flux (MJ day -1 m -2 ) incident on the vineyard (solid line).

INTRODUCTION

  atmospheric demand and drought stress by regulating the conductance of water through the soil-plant-atmosphere continuum (McElrone et al. regulated by various physiological mechanisms such as control of stomatal aperture and hydraulic conductivity of the vasculature (Lovisolo et al.

  changes in light levels(Shimazaki et al. et al. By analogy at the vine canopy scale, the amount of net radiation absorbed by the vine canopy (R c changes in cloud cover associated with climate change (Mendoza et al. associated with human-induced pollution and its interaction also an important factor in determining R c (Riou et al., adaptation to climate change (van Leeuwen et al. D c be affected by climate change related impacts on air and canopy temperatures, and the tendency towards lower relative humidity in near-surface air over land as observed in Mark Gowdy et al. OENO One | By the International Viticulture and Enology Society 2022 | volume 56-2 | 3 the last 20 years in the mid-latitudes and Mediterranean et al. the leaf area index (LAI et al.

  grapevines in a vineyard, two each of Vitis vinifera humidity, and solar radiation were taken or interpolated to 15-minute intervals from 30 June through 15 September 2020 and canopy characteristics were measured periodically1. Vineyard and canopy characteristicsexperimental vineyard in Bordeaux, France (44° 47' 0" N, 0° design, in which the varieties were planted in 5 replicate different blocks of the vineyard, with the exception of the two Merlot vines, which were in two different rows within dimensions maintained by hedging twice during the growing in between each vine row with mechanical tillage under the soils are sandy-clay-gravel typical for the Pessac-Léognan From 1991 through 2020 average annual total rainfall and reference evapotranspiration were 902 mm and 929 mm respectively with annual solar radiation of 4790 MJ m -2 and average maximum daily temperature from May through Leaf area was measured three times during the season in the length and width of all individual leaves on one primary shoot and all its secondary shoots, which averaged just leaf area as measured by a leaf area meter (Model LI-3100 shoots were then calculated and applied to a count of all the leaves on the remaining primary and secondary shoots on LAI, m 2 m -2 total leaf area (m 22. Field measurements E cof at least 30 % of the whole vine would be measured (based -1 the ratio of leaf area of the whole vine over the leaf area of -1 then divided by the area of vineyard ground attributable to to give canopy E c (g s -1 m -2 D c in the vine canopy at canopy and ground surrounding the vine rows is well mixed ( et al. relationships for vineyards based primarily on canopy height OENO One | By the International Viticulture and Enology Society 4 | volume 56-2 | 2022 (Chahine et al. e sc temperature T c e c e sc radiation shields (Model RS3 by Prosensor, Amanvillers, station next to the vineyard using a horizontally mounted

  of vaporization for water = 2257 (J g -1 D c R c = net radiation absorbed by the vine canopy per unit -2 r bs = bulk stomatal resistance (s m -1 r bh (s m -1 n = 2 for grapevine leaves with stomata on one side only -1 = rate of change in saturation vapour pressure versus temperature = 145 (Pa C°-1 = heat content per unit volume of air at 20°C amount of such radiation absorbed by grapevine canopy (R cRelying on inputs of canopy dimensions, row spacing, vine spacing, and canopy porosity, along with measured solar radiation and various solar angles, -2 shortwave radiation absorbed by the vine canopy expressed in terms of the unit area of vineyard ground attributable to .(Riou et al., r bh needed as input to Equations 1 and 2 is given by Equation4below and represents the canopy-level summation of the leaf-level boundary layer resistances across all leaves in the canopy stated in terms of unit ground area(Lhomme et al.,

OENO

  One | By the International Viticulture and Enology Society 8 | volume 56-2 | 2022 R c , D c , and PD model based on combined data, whether standardized, or raw of interactions were either successfully avoided by variable 2 log 10 (g bs ) adjusted r 2 and predicted r 2 For multiple linear regression it is also important to check whether the underlying ordinary least square assumptions standardized model residuals are normally distributed relatively constant variance in the standardized residuals constant variance in the standardized residuals plotted versus Multiple linear regression analysis was performed in the R lm the vif function of the car package (Fox et al. calculation of estimated marginal means of linear slopes were done using the emtrends function from the emmeans package (Lenth et al. 6. Transpiration simulations 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 6.1 Water balance model simulations A time series of daily vine canopy transpiration and associated soil water depletions were estimated using the vineyard water balance model developed by Pieri and Bois et al. model-based simulations, actual data from the 2020 growing season was also used in the water balance model, consisting of measured daily maximum and minimum temperature, transpirable soil water (TTSW parameterized to distinguish differences in transpiration response between varieties, so only one generic transpiration calculating the fraction of total incident radiation to the vineyard that is captured by the canopy (minus that which is et al. is then applied to the reference crop evapotranspiration by the Penman equation and adjusted early in the season to account for gradual development of the canopy starting precipitation, the model then tracks the daily balance of water in the assumed volume of TTSW within the vineyard As the fraction of remaining total available transpirable soil water (FTSW in reductions in transpiration to account for the reduced stomatal conductance associated with increasing drought the ratio of modeled vine transpiration to maximum vine transpiration (TV/TV max FTSW as presented in Pieri 6.2 Regression model-based simulations of log 10 (g bs ) for each variety using a common 15-minute time series of predictor input data as measured during A time series of g bs was then calculated by taking the base 10 antilog of log 10 (g bs along with the corresponding R c , D c , and r bh data to give an associated estimate of transpiration summed on a daily basis (mm day -1 the regression analysis, was included in the transpiration For these simulations, a time series of 15-minute measurements of R c , and D c from the 2020 season was R c and D c across all 10 vines measured in the study were very absorbed by the vine canopy (R c canopy dimensions and porosity

  of Lebon et al., TTSW were assumed in the water balance model in order to understand the effect of the differing levels of associated FTSW on the regression Mark Gowdy et al. OENO One | By the International Viticulture and Enology Society 10 | volume 56-2 | 2022 are now comparable across the three predictors for a It is observed the absolute value of the D c and PD R c greater importance in explaining the variation of g bs observed response to R c may be due to the relative consistent range of R c season, R c does not have the larger extremes seen in D c and PD modeled bulk stomatal conductance ( bs -1 as needed for

0

  same across all varieties due to the regression model If the regression is performed between log 10 (g bs ) and R c , D c , and PD without including the variety factor variable, the intercept of the regression is zero, as expected when using3. Sensitivity analysisbs transformed from modeled log 10 (g bs R c , D c , and PD shows the non-linear relationships with D c and PD of bs is somewhat diminished compared to the plots of raw calculated g bs presented in Figure 1 due to the least squared Plots of bs across a range of hypothetical PD levels of D c D c value of R c -2 bs , and a greater spread in bs between varieties at lower D c and more positive PD PD bs quickly when compared to the other varieties as PD becomes D c can also be observed on the overall decrease in the bs response curve with respect to PD In keeping with the lower absolute value of its D c bs the other varieties as D c

FIGURE 4 .

 4 FIGURE 4. Back-transformed bs from modeled log 10 (g bs ) plotted against D c , R c , and PD data used for regressions from all 10 vines combined.

FIGURE 5 .FIGURE 6 .FIGURE 7 .

 567 FIGURE 5. Modeled g bs ( bs, mm s -1 ) by variety plotted against a hypothetical range of PD (MPa) with mean value of R c = 0.263 kW m -2 and: a) D c = 1.5 kPa; and b) D c = 4.5 kPa.

FIGURE 8 .

 8 FIGURE 8. Daily total vine transpiration (mm/day) for five varieties simulated using regression coefficients (coloured lines) and the daily total vine transpiration (mm/day) component from the water balance (black line) in panel a) based on TTSW = 240mm and in panel b) based TTSW = 200mm, with legend including total simulated transpiration from 30 June to 15 September 2020.

  had the highest seasonal total simulated transpiration, R c , D c and PD to the calculation of simulated transpiration are the same for all varieties, these differences are then explained by overall differences in bs ordering of varieties in terms of daily transpiration are the ordering is somewhat different, particularly with regards to bs in response to increasing D c bs in response to decreasing PD when compared greater emphasis on responding to decreasing PD than on increasing D c

FIGURE 9 .

 9 FIGURE 9. Relative transpiration (transpiration / maximum transpiration) as calculated using the regression models versus FTSW for each variety (coloured lines) and the transpiration reduction function used in the water balance model (black line).

  transpiration between the regression-based and water balance model simulations. The lower transpiration reduction early in the season associated with the curve used in the water balance model as based on[START_REF] Pieri | Vines water stress derived from a soil water balance model -sensitivity to soil and training system parameters[END_REF], when compared to those from the regression-based models, allows for more transpiration early in the season and a quicker depletion of soil water, and quicker decrease in transpiration, leading to a relative underestimate of transpiration later in the season.For comparison purposes, in Figure11below, the regression model-based transpiration reduction functions are overlaid on a reproduction of the conductance reduction data presented in Figure4fromLebon et al. (2003), showing similar results.

Figure 11 .

 11 Figure 11. Reproduction of Figure 4 from Lebon et al. (2003) showing ratio of measured leaf level stomatal conductance (gs) over the maximum gs (gsmax) as a function of the fraction of transpirable soil water (FTSW) for Syrah from field measurements (open square) and a pot experiment (closed square), with transpiration (conductance) reduction curves from regression analysis for five varieties.

Table 1

 1 below, including for reference, the corresponding values of véraison date (DOY), 𝛿 13 C in the wettest year, and the range of 𝛿 13 C measurements. The HCA classification

obtained in this way is unique and could be very useful for growers considering what plant material to select based on their potential stress responses to a set of anticipated growing conditions.

Table 1 . Classification of 48 varieties resulting from the hierarchical cluster analysis based on véraison date (DOY), 𝛿 13 C in the wettest year, and the range of 𝛿 13 C measurements.

 1

TABLE 1 |

 1 Measurements of δ 13 C(‰) in grape must (Must), corresponding wine (Wine) and eau de vie (EDV) for nine varieties in 2019 and 2020 with differences between each and overall averages.

	Variety	Year	Must	Wine	EDV	Must -Wine	Wine -EDV	Must -EDV
	Vidal 36	2019	-22.84	-24.70	-24.56	1.86	-0.14	1.72
	Vidal 36	2020	-23.76	-25.38	-25.37	1.62	-0.01	1.62
	Monbadon	2019	-23.42	-25.17	-24.92	1.75	-0.25	1.50
	Monbadon	2020	-24.37	-25.72	-25.65	1.35	-0.07	1.28
	Ugni blanc lacinié	2019	-24.30	-25.89	-25.75	1.59	-0.14	1.45
	Ugni blanc lacinié	2020	-25.15	-26.73	-26.63	1.58	-0.10	1.48
	Ugni blanc	2019	-25.24	-26.92	-26.84	1.68	-0.08	1.59
	Ugni blanc	2020	-25.62	-27.26	-27.18	1.64	-0.08	1.56
	Folignan	2020	-25.44	-27.36	-27.02	1.91	-0.33	1.58
	Folle blanche	2020	-25.71	-27.08	-27.10	1.37	0.02	1.39
	Sémillon	2020	-26.23	-27.97	-27.54	1.74	-0.43	1.31
	Montils	2020	-26.37	-28.17	-27.76	1.80	-0.41	1.39
	Colombard	2020	-26.61	-27.85	-27.83	1.24	-0.02	1.22
					Average	1.63	-0.16	1.47

  Energy fluxes: Cc, sensible heat fluxes from the canopy; Cgr, at the ground; Ctot, total; λEc, latent heat fluxes from the canopy; λEgr, at the ground; λEtot, total; Rc, net radiation absorbed by the canopy; Rgr, by the ground; Dimensions: h, height to top of canopy above the ground; d, zero plane displacement; zo, roughness length; zc, mean canopy height; zr, reference height; Atmospheric parameters: Tc, temperature at mean canopy height; Tgr, at the ground; Tr, at reference height; esc, saturation vapor pressure at Tc; esgr, at Tgr; esr, at Tr; ec, measured vapor pressure at zc; er, at zr; Dc, vapor pressure deficit (escec) at zc; Dr, vapor pressure deficit (esrer) at zr; ur, wind speed at zr; Bulk canopy resistances: r bs , bulk stomatal resistance; r bh , bulk boundary layer resistance to heat flux; r bv , bulk boundary layer resistance to water vapor flux; Water stress: 9PD, pre-dawn leaf water potential.

Abbreviations:

TABLE 1 |

 1 Predictor variable minimum depths and pairwise relative minimum depths between predictor variables for two regression models: (A) with g bs as the response variable; and (B) with Ec as the response variable.

	Gowdy et al.

TABLE 1 .

 1 Predictor variable coefficients ( ) from regressions using standardized log 10 (g bs ) as the response and standardized predictors R c (a), D c (b), and PD (c) as continuous predictor variables, by variety with upper and lower 95 % confidence limits (CL), p-values, and compact letter display of pairwise significance.

		Variety			Low CL	Upper CL	p-value	Pairwise
		Cabernet-Sauvignon	0.161	0.145	0.177	~ 0	a
		Semillon	0.225	0.202	0.247	~ 0	b
	a) Standardized R c coefficient	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
		Ugni blanc	-0.625	-0.641	-0.609	~ 0	a
		Semillon	-0.582	-0.601	-0.563	~ 0	b
	b) Standardized D c coefficient	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
		Merlot		0.423	0.400	0.445	~ 0	a
		Semillon	0.458	0.441	0.474	~ 0	ab
	c) Standardized PD coefficient	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
			Variety	0	Rc		Dc
			Merlot	-0.036	0.303	-0.558	0.423
			Semillon	-0.036	0.225	-0.582	0.458
	a) with standardized data	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
			Merlot	0.788	1.110	-0.146	0.066
			Semillon	0.788	0.824	-0.153	0.072
	b) with raw data		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

Pairwise comparisons with shared letters not significantly different (p < 0.05).

TABLE 2 .

 2 Regression coefficients ( ) from final regression analysis for y-axis intercepts and predictor variables R c , D c , and PD with a) standardized data, and b) raw data.

OENO One | By the International Viticulture and Enology Society 2022 | volume 56-2 | 11

  Changes in nature of the stomatal response for several byLevin et al. sensitive to changes in PD Direct comparison of varietal responses from different studies is confounded by differences in measurement methodology, such as leaf-level measurement of conductance by gas Future comparisons using the same, or similar methods would lead this study were subjected to the same climate conditions, the g bs response would be better characterized if using data from different years with different relative amounts of R c , D c and

	positively correlated with the reference gas exchange rate at non-limiting conditions, as expected from empirical and et al., 1999; Domec and Johnson,
	from anisohydric to isohydric response was due to a decrease in whole-plant conductance associated with decreased soil water status (Zhang et al. suggested the shift to isohydric behavior was attributable
	water status, which further increases stomatal sensitivity to VPD (Rogiers et al. also linked to changes in stomatal sensitivity in Cabernet-Sauvignon (Speirs et al. et al.
	PD an overall different climate (e.g., southern Europe, or northern Even being performed in the same vineyard, the soil conditions around the root zone of individual vines and
	can still be quite heterogeneous (Smart et al.
	Rootstocks
	vine conductance (Marguerit et al.,	et al.,
	CONCLUSIONS
	Based on data collected in a vineyard setting, a multiple linear regression analysis was able to quantify relationships
	bulk stomatal conductance (g bs variables: net radiation absorbed by the vine canopy (R c D c lewaf water potential ( PD . Depending on the variety, D c and PD g bs than R c PD
	varieties than was the D c R c
	OENO One | By the International Viticulture and Enology Society	2022 | volume 56-2 | 15

MarcPlantevin et al.

Fraction of Transpirable Soil Water (fractions)JuneJuly August September October

-26.4 1.0

EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, 33882 Villenave d'Ornon

Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, 16370 Cherves Richemont

Jas. Hennessy & Co., 16100 Cognac Brief research report published in Frontiers in Food Chemistry and Technology, July 2022, Volume 2, Article 936745

Lavergne, J. (2020). Conservatoire du Vignoble Charentais, Cherves-Richemont, France. personal communication.Frontiers in Food Science and Technology | www.frontiersin.org July

| Volume 2 | Article 936745

Frontiers in Food Science and Technology | www.frontiersin.org July 2022 | Volume 2 | Article 936745

EGFV, Bordeaux Sciences Agro, INRAE, Univ. Bordeaux, ISVV, F-33882 Villenave d'Ornon, France Published in Frontiers in Plant Science, March

2022, Volume 13, Article 839378

Frontiers in Plant Science | www.frontiersin.org

March 2022 | Volume 13 | Article 839378

EGFV, Bordeaux Sciences Agro, INRAE, Univ. Bordeaux, ISVV, F-33882 Villenave d'Ornon, France Published in Oeno One, June

2022, Volume 56-2, Article 5435

MarkGowdy et al.

Acknowledgements

I want to start by thanking Kees van Leeuwen for entrusting me with the opportunity to do this PhD, and for his support and guidance throughout. I also want to thank the folks at Hennessy for their financial support and interest in my studies, including Xavier Poitou, Mathilde Boisseau, and Sandrine Weingartner.

The work of this thesis was not possible without the support of everyone at the UMR Ecophysiologie et Génomique Fonctionnelle de la Vigne at ISVV. I'm particularly grateful for the travail d'équipe, both in the vineyard and the laboratory with Agnes Destrac Irvine, the invaluable technical collaboration with Greg Gambetta, Bruno Suter, Philippe Pieri, and Elisa Marguerit, and the ever-important administrative support from Cathy Thioulouse ACKNOWLEDGMENTS This study was carried out with support from Jas. Hennessy & Co. (16100 Cognac, France). The VitAdapt Project is supported by the Conseil Interprofessionnel des Vins de Bordeaux (CIVB), the Conseil Régional d'Aquitaine and the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE). This study 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 authors would like to thank INRAE, UEVB, F-33882, Villenave d'Ornon, France, for its contribution to the maintenance of the VitAdapt experiment.

ACKNOWLEDGMENTS

In collaboration between the Institut des Sciences de la Vigne et du Vin and the Conservatoire du Vignoble Charentais, Institut de Formation de Richemont.

ACKNOWLEDGMENTS

We would like to thank Guillermo Gutiérrez, Eylul Kadaifci, and Alfonso Domínguez Zamudio for their tireless involvement in data collection, and to the INRAE, UEVB, F-33882, Villenave d'Ornon, France, for its management of the VitAdapt vineyard and assistance with implementing instrumentation.

ACKNOWLEDGEMENTS

the VitAdapt vineyard and assistance with implementing Investments for the future Programme, within the Cluster vineyard is supported by the Conseil Interprofessionnel des National de Recherche pour l'Agriculture, l'Alimentation et

FUNDING

This study has been carried out with financial support from Jas Hennessy & Co.

FUNDING

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).

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

SUPPLEMENTARY DATA

Plantevin, M., Gowdy, M., Destrac-Irvine, A., Marguerit, E., Gambetta, G. A., & van Leeuwen, C. (2022). Using δ 13 C and hydroscapes for discriminating cultivar specific drought responses: This article is published in cooperation with Terclim 2022 (XIVth International Terroir Congress and 2nd ClimWine Symposium), 3-8 July 2022, Bordeaux, France. OENO One, 56(2). https://doi.org/10.20870/oeno-one.2022.56.2.5434 SUPPLEMENTARY Table S1. Full names of the varieties studied and their relative abbreviations. S4. The different metrics of the hydroscape.

SUPPLEMENTARY Figure

AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct, and intellectual contribution tot h ew o r ka n da p p r o v e di tf o r publication.

Conflict of Interest: XP was employed by Jas Hennessy & Co., Cognac, France.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022. 839378/full#supplementary-material

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Gowdy, Pieri, Suter, Marguerit, Destrac-Irvine, Gambetta and van Leeuwen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. S1. Summary statistics of raw input data to multiple linear regression analysis with log10(gbs) (gbsTrans) as the response variable, Rc (kRc), Dc (kDc), and PD (PDWP) as predictors, and variety (Cepage) as a factor variable. S2. Summary statistics of multiple linear regression based on standardized data with log10(gbs) (gbsTrans) as the response variable, Rc (kRc), Dc (kDc), and PD (PDWP) as the predictor variables and their interactions, including variety (Cepage) as an interaction factor variable.

OENO One

SUPPLEMENTARY TABLE

SUPPLEMENTARY TABLE

SUPPLEMENTARY DATA Gowdy, M., Suter, B., Pieri, P., Marguerit, E, Destrac Irvine, A., Gambetta, G., & van Leeuwen, C. (2022).

Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress. OENO One, 56(2). Retrieved from https://oeno-one.eu/article/view/5435. S3. Summary statistics of multiple linear regression based on standardized data with log10(gbs) (gbsTrans) as the response variable, Rc (kRc), Dc (kDc), and PD (PDWP) as continuous predictor variables and variety (Cepage) as a factor variable, without interaction terms. S4. Summary statistics of multiple linear regression based on raw data with log10(gbs) (gbsTrans) as the response variable, Rc (kRc), Dc (kDc), and PD (PDWP) as continuous predictor variables and variety (Cepage) as a factor variable, without interaction terms.

SUPPLEMENTARY TABLE

SUPPLEMENTARY TABLE