Adapting the Priestly-Taylor Index as a Physiological Stress Indicator in Vineyard Agrosystems

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Snyder, Lauren E. Marigliano, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2223673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Seasonal management of plant water status and the accompanying physiological responses are critical aspects of viticultural production. Presently, grapevine ( Vitis vinifera , L.) water status is measured via in-season measurements of stem water potential or post-season analysis of must carbon isotope ratios, with the former limited by reliance on laborious measurements and the latter providing information post-season. Therefore, there is a gap in reliable, real-time measurements of plant water status. Technological advances in surface renewal measurement in vineyards have provided an economical and reliable method for measuring actual evapotranspiration of a vineyard. This experiment utilized surface renewal calculations to derive a novel index of grapevine water stress, the Priestly-Taylor index ( β -index), and related it to measurements of stem water potential, leaf-gas exchange, and must carbon isotopes from three vineyards with differing irrigation strategies over two growing seasons. The sensible heat flux, latent heat flux and net radiation varied across these vineyards and affected the actual vineyard evapotranspiration measured. Likewise, the β -index was different across these vineyards and ranged from 1.7 to 2.1 in the Sacramento Valley of California to 0.5 to 1.2 in the Napa Valley of California. The β -index was related to stem water potential, net carbon assimilation and stomatal conductance (r 2 = 0.42, r 2 = 0.45, r 2 = 0.33, respectively). Results indicated that the β -index was an indicator of real-time vineyard water status and a proxy for physiological responses in vineyards. The coupling of atmospheric controls on evapotranspiration with plant physiological responses makes β a powerful tool for irrigation management in large scale agrosytems. drought evapotranspiration energy balance latent heat sensible heat flux surface renewal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Plant water stress is a key indicator of both the crop yield and fruit composition in agriculture. Water deficits are especially important for viticulture, where moderate water deficits were shown to improve berry and wine composition (Chaves et al., 2010 ) without adversely affecting yield. Furthermore, the global distribution of viticultural regions trends predominately towards temperate climates, which are at risk of experiencing increased temperatures and episodic drought as the climate warms (Gambetta and Kurtural, 2021 ; Hoegh-Guldberg et al., 2018 ). The percentage of grapevines grown with irrigation is expected to increase, relative to dry farmed vineyards, in response to global warming (Costa et al., 2016 ; Resco et al., 2016 ), and advances in irrigation management will be critical to ensuring sustainable production of one of the world’s most economically important fruit crops (Alston and Sambucci, 2019 ). Traditionally, grapevine water status has been measured via midday stem water potentials ( Ψ s). Although Ψ s has proven to be a reliable indicator of plant water status (Chone et al., 2001 ), its scalability in irrigation management is limited by the high number of time-consuming measurements necessary to accurately characterize water status within a commercial vineyard. Additionally, measurements of Ψ s are subject to variability in both soil water availability and meteorological conditions on the day of measurement; thereby convoluting temporal comparisons of Ψ s (Suter et al., 2019 ). With the likelihood of increased climate-induced stress on grapevines, the viticultural community is seeking new and more effective ways by which to define and measure plant water stress (Gambetta et al., 2020 ). Unfortunately, the close coupling of physiological and biochemical responses to water stress ( e.g. , hydraulic conductivity (K), stomatal conductance (g s ), and abscisic acid concentration (ABA)), as well as genetic variability, interferes with the interpretation of plant-based responses to hydric stress. Our current understanding of plant water relations is grounded within the soil-plant-atmosphere-continuum (SPAC) (Philip, 1966 ); wherein the cohesion-tension theory dictates that water flows from the soil reservoir through the plant xylem network under a negative pressure ( i.e. , tension) exerted by the atmosphere on the plant (Tyree, 1997 ). The physical forces pulling water through the SPAC are described by the process of evapotranspiration (Thornthwaite, 1948 ). In their seminal works, Penman and Monteith partitioned plant and soil evaporation into components representing energy conservation, mass transfer (Penman, 1948 ), and electrical resistance (Monteith, 1965 ); resulting in the widely used Penman-Monteith equation for instantaneous evapotranspiration. While the Penman-Monteith equation provided a more theoretically sound method for estimating instantaneous evapotranspiration, the methodology to estimate canopy and aerodynamic resistance was not well-known. Attempts were made to use empirical wind functions to estimate the aerodynamic contribution to evapotranspiration (Doorembos et al. 1977 ; Doorenbos and Pruitt 1977 ), but it was not until the publication of UN-FAO 56 (Allen et al. 1998 ) that the Penman-Monteith equation was widely adopted for use in estimating monthly, daily, and hourly reference evapotranspiration (ET o ) of a 0.12-m tall grass. Until that time, Monteith discouraged the use of the Penman-Monteith equation for estimating evapotranspiration since it was originally designed to study canopy resistance using inputs of evapotranspiration from a lysimeter. It was proposed that the equation be used to estimate ETo using a fixed estimate of canopy resistance and an inverse function of the wind speed to estimate aerodynamic resistance for a daily or hourly modification of the equation. Recall that the original Penman-Monteith equation give instantaneous evapotranspiration rather than hourly or daily evapotranspiration. The hourly Penman-Monteith equation was later modified by the American Society of Civil Engineers – Environmental Water Resources Institute and renamed to the Standardized Reference Evapotranspiration equation for short canopies (Allen et al. 2005 ). The final version of the Standardized ETo equation for short canopies (0.12-m) and the ETr equation for tall canopies (0.5-m) was published in Allen et al. ( 2006 ). Priestly and Taylor (1972) modified the equations of Penman-Monteith, simplifying the inputs necessary to calculate evapotranspiration and deriving an equation that represents wet surface evaporation over large areas. The Priestly-Taylor equation permits quantification of advection-aridity when relating potential to actual evapotranspiration (Brutsaert and Stricker, 1979 ; Granger, 1989 ; Granger and Gray, 1989 ; Parlange and Katul, 1992 ; Venturini et al., 2008 ). In addition, the Priestly-Taylor equation has been used to couple vegetative and atmospheric controls on evapotranspiration (Jarvis and McNaughton, 1986 ). Yet, only in the last decade has affordable technology with the theory governing the evapotranspiration of water; thereby, increasing both the ubiquity and accuracy of measurements of the Priestly-Taylor index. Surface renewal offers an inexpensive and accurate alternative to conventional micrometeorological estimations of actual evapotranspiration (ET a ) (Xue et al., 2020 ). Sensible heat flux, derived from surface renewal calculations can be coupled with estimations of net radiation as well as readily available temperature and windspeed data to calculate a daily equilibrium evapotranspiration (ET eq ). Commercially available surface renewal stations ( e.g. , Tule Technologies) provide accurate estimations of ET a (Fulton et al., 2017 ; Montazar et al., 2018 ; Rieger, 2017 ; Zaccaria et al., 2017 ), which enable real-time comparisons of ET eq to ET a . Priestly and Taylor (1972) postulated that the β coefficient, which directly compared reference to actual evapotranspiration, may one day serve as an index of land-surface aridity. Nearly fifty-years later, Marino et al. ( 2021 ), identified the same β coefficient, as a potential index by which agricultural managers may evaluate physiological stress. The carbon isotope ratio of must sugars (∂ 13 C) has emerged as reliable tool for the assessment of season-long water deficits in vineyards (Brillante et al., 2018 ; Chone et al., 2001 ; Gaudillère et al., 2002 ; Leeuwen et al., 2010 ; Yu et al., 2021a ). This method takes advantage of 13 C discrimination in C 3 plants: under water deficits, stomatal closure limits the intake of carbon, causing the heavier 13 C to be incorporated into the photosynthetic pathway, thereby increasing the ratio of 13 C- 12 C (Farquhar et al., 1989 , 1982 ). Sucrose from the leaves is then translocated to berries (Dai et al., 2013 ) and the berry must sugars can be used as a proxy for plant water status and stomatal conductance (Bchir et al., 2016 ; Farquhar et al., 1989 ). While ∂ 13 C has proven to be an effective method for the assessment of water deficits between veraison and ripening, it is only applicable post- harvest, leaving a dearth in real-time measurements of vineyard water deficits. The objective of the work was to determine if β would serve as a real-time indicator of physiological stress derived from seasonal water deficits. As the ratio between ET a and ET eq , β relates the actual amount of water evaporated by soil and transpired by grapevines to a predicted amount of water calculated from daily measurements of the energy available for evapotranspiration. Thus, we evaluated β as a novel index by which to assess water deficits within commercial plantings of wine grape vineyards. 2. Materials And Methods 2.1. Experimental sites: The experiments were conducted during 2020 and 2021 at three vineyards. Two of the vineyards were located at the UC Davis Oakville Station in Oakville, California, USA (WGS84 coordinates: 38.429°, -122.41°), and will be referred to hereafter as Old Federal Vineyard 3 and 8 (OFV3 and OFV8). The OFV3 is a 0.5 ha vineyard comprised of Cabernet Sauvignon/3309C ( V. riparia x V. rupestris ), with rows planted in a NW-SE orientation and spaced 1.5 m × 2 m (vine x row) trained to a bi-lateral cordons 1.8 m above the ground. The OFV8 is a 1.0 ha vineyard planted to Merlot/3309C ( V. riparia x V. rupestris ) in a NE-SW orientation at a density of 2 m × 3 m (vine × row) with vines trained as quadrilateral cordons at a trunk height of 1.5 m. Both OFV3 and OFV8 were irrigated via two, 2 L/h drip emitters, with irrigation applied from fruit set through harvest and intended to replace 50 and 70% of potential crop evapotranspiration (ETc), respectively. The third experimental site was located on the UC Davis Campus, Davis, California, USA (WGS84 coordinates: 38.538°, -121.762°) and was planted with Petite Sirah grafted onto 1616C and trained to bilateral cordons spaced 1.8 m × 2.8 m (vine × row). The UC Campus vineyard was irrigated to 90% of ET c from fruit set through harvest. The ET c was calculated by multiplying reference evapotranspiration (ET o ) by Williams and Ayars ( 2005 ) crop coefficient (K c ) at all locations as reported by Torres et al. ( 2021 b). Each experimental vineyard was equipped with a Tule Actual ET sensor (Tule Technologies, Davis, CA, USA) installed above the canopy. The Tule sensors provided daily measurements of actual evapotranspiration (ET a ), as well as the energy balance components used in the surface renewal calculation of ET a : net radiation (Rn, MJ m − 2 day − 1 ) and the sensible heat flux (H, MJ m − 2 day − 1 ), Eq.’s 1 & 2: 1 where LE is the latent heat flux (MJ m − 2 day − 1 ) and LE is the latent heat of vaporization (MJ kg − 1 ), or the latent heat required to vaporize 1 L of liquid water. Following the energy balance residual method, LE was calculated as the residual from net radiation, ground heat flux (G, MJ m − 2 day − 1 ), and the sensible heat flux, Eq. 2: LE = Rn − G − H. (2) Notably, for measurements collected daily over the course of the growing season it is safe to assume that the daily total ground heat flux is close to zero (G = 0) (Allen et al., 2005 , 1998 ; Marino et al., 2021 ). Daily weather measurements from the experimental sites were obtained from the California Irrigation Management Information System (CIMIS) station’s #6 and #77 for Davis and Oakville, respectively. Weather data from the 2020 and 2021 growing seasons are presented in Table 1 . Table 1 Average daily values and standard deviations of the main weather variables (Rn: net radiation, MJ m − 2 day − 1 ; Ta: mean air temperature, °C; ū: mean wind speed, m s − 1 ; RH: relative humidity, %) over the 2020 and 2021 growing seasons. Data were obtained from the CIMIS station No. 77 (Oakville, CA) and Tule Actual ET sensor (Tule Technologies) installed above the canopy Year Month Rn (MJ m -2 day -1 ) Ta (°C) ū (m s -2 ) RH (%) 2020 May 14.3 ± 3 18.5 ± 2.6 1.9 ± 0.2 53.8 ± 17.9 June 17 ± 1 20.8 ± 2.3 1.9 ± 0.2 53.3 ± 11.3 July 16.7 ± 0.7 18.9 ±1.2 1.9 ± 0.2 64 ± 6.5 August 13.8 ± 2.1 22 ± 3.6 1.9 ± 0.3 62.5 ± 10.7 September 10.6 ± 1.3 19.8 ± 3.4 1.5 ± 0.3 63.2 ± 12.5 2021 May 17.4 ± 0.5 17.6 ±1 1.5 ± 0.3 60 ± 5.5 June 16.8 ± 0.6 18.6 ± 2.6 1.6 ± 0.4 54.8 ± 10.4 July 16.3 ± 0.5 20 ± 2.4 1.9 ± 0.2 62.9 ± 6.9 August 14.4 ± 1.6 18.5 ± 1.6 1.7 ± 0.1 66.6 ± 6.8 September 11 ± 1.6 18.1 ± 2.6 1.5 ± 0.1 61.5 ± 13.6 2.2. Experimental design: 2.2.1. Derivation of Beta Data from CIMIS and Tule stations were used to calculate a daily equilibrium evapotranspiration rate (ETeq, mm day − 1 ). The ETeq is the component of evapotranspiration driven by the diabatic effect of net radiation on the latent heat flux (Slatyer and McIlroy, 1961 ). In simpler terms, ET eq approximates the contribution of Rn to LE which is a key determinant in ET a (see Eq.’s 2–3). The calculation for ET eq followed Denmead & McIlroy’s ( 1970 ) modification of the Penman Equation (Penman, 1948 ) and is shown in Eq. 3 : $${\text{E}\text{T}}_{\text{e}\text{q}}= \left(\frac{\varDelta }{\varDelta + {\gamma }}\right)\frac{\text{R}\text{n}}{ }$$ 3 where λ was treated as a constant ( λ = 2.45 MJ m − 2 day − 1 ) to convert the Rn units into mm day − 1 ; although λ is a function the wet-bulb temperature (Tw), the two are only weakly related when Tw > 5°C, therefore the effect of Tw on ET eq was ignored over the growing season (Fritschen and Gay, 1979 ). The Δ (kPa K − 1 ) is the slope of the saturation vapor pressure curve at mean daily air temperature (Tm, °C) and γ is the psychrometric constant ( γ ≈ 0.066 kPa K − 1 ). The Δ term is estimated in Eq. 4 Tetens ( 1930 ): $$\text{D} = \frac{4098 \bullet {\text{e}}_{\text{s}}}{{({\text{T}}_{\text{m}}+ 237.3)}^{2}}$$ 4 where es (kPa) is the saturation vapor pressure at Tm, estimated according to Tetens’ ( 1930 ) formula in Eq. 5 : $${\text{e}}_{\text{s}}=0.6108\text{e}\text{x}\text{p}\left(\frac{17.27 \bullet {\text{T}}_{\text{m}} }{{\text{T}}_{\text{m}} + 237.3}\right) .$$ 5 ET eq was then used in Eq. 6 to calculate the Priestly-Taylor coefficient ( β ) following Marino et al.’s ( 2021 ) amendment of Priestly and Taylor’s (1972) equation: $${\beta }= \frac{{\text{E}\text{T}}_{\text{a}}}{{\text{E}\text{T}}_{\text{e}\text{q}}}= \frac{{\text{E}\text{T}}_{\text{a}}}{\left(\frac{\varDelta }{\varDelta + {\gamma }}\right)\frac{\text{R}\text{n}}{ }}$$ 6 2.2.2. Measurement of Plant Water Status and Leaf Gas Exchange In OFV3 and OFV8 midday stem water potentials ( Ψ s) were measured biweekly over the course of the growing season in each year. Three shaded leaves were chosen from the main shoot axes on the grapevines and leaves were then bagged in pinch-sealed Mylar® bags up to two hours prior to measurement. The Ψ s readings were taken with a pressure chamber (Model 615D, PMS Instrument Company, Albany, OR, USA) (Williams and Araujo, 2002 ). Simultaneously, leaf gas exchange measurements were taken with a portable infrared gas analyzer CIRAS-3 (PP Systems, Amesbury, MA, USA). Three sun-exposed leaves were selected from the main shoot axes, and three measurements were taken per leaf. The standard environmental condition in the infrared gas analyzer was set with a relative humidity at 40% and a reference CO 2 concentration at 400 µmol CO 2 mol − 1 . Net carbon assimilation rate ( A N , µmol CO 2 m − 2 s − 1 ) and stomatal conductance (g s , mmol H 2 O m − 2 s − 1 ) were measured directly by the infra-red gas analyzer. Intrinsic water use efficiency (WUE i , µmol CO 2 mmol − 1 H 2 O) was then calculated post hoc as the ratio between A N and g s (WUEi = A N /g s ). 2.2.3. Must Carbon Isotope Ratio Analysis At harvest, one-hundred random berries were collected from each experimental unit. The berries were crushed by hand and 1.5 mL of the resulting must was pipetted into 2 mL conical tubes. The tubes were centrifuged at 4,000 RPM for 15 min and 10 µL of the supernatant was transferred into tin capsules (Thermo Fisher Scientific, Waltham, MA, USA) and placed on a microplate. The microplates were baked overnight at 80°C. Tin capsules containing completely dehydrated samples were then folded into cubits and wrapped with another tin capsule to ensure a tight seal. Each sample’s isotope ratio was analyzed on a Vario MicroCube elemental analyzer coupled in a continuous flow mode to an isotope ratio mass spectrometer (IsoPrime, Elementar, Ronkonkoma, NY, USA). The δ13C values are reported in parts per thousand (‰) relative to the Vienna Peedeebelemnite- CO 2 (VPDB-CO 2 ) international reference. 2.3. Statistical Analysis A combination of the Python programming language (Python Software Foundation, version 3.7) and StataIC statistical software (StataCorp, version 15) were used to analyze the data. Data management and visualization were processed in Python. Stata was used to perform ANOVA and regression analyses. Statistically significant differences between samples were determined when p -values returned by an ANOVA were 0.05 or less. Coefficients of determination between variables were calculated in regression analyses, p -values and mean squared errors (MSE) were used to determine the significance of fit. The coefficient β was related to measurements of Ψ s, g s , A N , and δ 13 C. Since β was a daily value calculated over an entire vineyard, it was necessary to relate β to average daily values of Ψ s, g s , A N . The mean was selected over the median because Ψ s, g s , A N , all exhibited normal Gaussian distributions. 3. Results 3.1. Weather at Experimental Site Analysis of key weather variables (Rn: net radiation, MJ m − 2 day − 1 ; Ta: mean air temperature at 1.5-m height, °C; ū: mean wind speed at 2-m height, m s − 1 ; RH: relative humidity at 1.5-m height, %) revealed similar weather patterns at Oakville over both the 2020 and 2021 seasons (Table 1 ). Notably, net radiation (Rn) and relative humidity (RH) presented an inverse covariation in both years. Net radiation reached its peak in June around 17 MJ m − 2 day − 1 and then decreased steadily throughout the season. Relative humidity, on the other hand, increased from an average of around 54% in June and reached its maximum in September. The average daily air temperature (Ta) ranged from 15.9 to 22°C with the lowest temperatures occurring in September and the highest in July and August. Wind speed (ū) remained relatively constant across the entire experiment, ranging from 1.5 to 1.9 m s − 2 . The same key weather variables, i.e. Rn, Ta, ū, and RH, from the 2020–2021 growing seasons at Davis, California are presented in Table 2 . In both years, Rn decreased from a peak of around 16 MJ m − 2 day − 1 to a minimum of about 10 MJ m − 2 day − 1 in September. While Rn was lower at Davis than Oakville, the Ta was higher, fluctuating between 21.7 and 24°C over the course of the season. Wind speed at Davis was also higher than Oakville, varying from 1.9 to 2.5 m s − 1 over the two growing seasons. Finally, Davis also had a lower RH than Oakville, reaching a maximum RH of 55.6% in July of 2020. Interestingly, the seasonal patterns of RH seem to be reversed between Davis and Oakville, with Davis achieving its highest values of RH in the beginning of the season, and Oakville tending to reach its maximum RH values near the end of the growing season. Table 2 Average daily values and standard deviations of the main weather variables (Rn: net radiation, MJ m-2 day-1; Ta: mean air temperature, °C; ū: mean wind speed, m s-1; RH: relative humidity, %) over the 2020 and 2021 growing seasons. Data were obtained from the CIMIS station No. 6 (Davis, CA) and Tule Actual ET sensor (Tule Technologies) installed above the canopy. Year Month Rn (MJ m -2 day -1 ) Ta (°C) ū (m s -2 ) RH (%) 2020 July 15.7 ± 0.4 22.2 ± 0.8 2.4 ± 0.2 55.6 ± 4.1 August 12.9 ± 1.6 23.3 ± 2.8 1.9 ± 0.5 51.9 ± 9.5 September 10.6 ± 1.7 24 ± 3.1 1.9 ± 1.1 49.2 ± 11 2021 May 16.5 ± 1.2 21.7 ± 2.4 2.3 ± 0.4 47.6 ± 8.3 June 15.9 ± 0.9 22.2 ± 4 2.3 ± 0.4 47.4 ± 8.4 July 15.1 ± 1 23.2 ± 2.1 2.3 ± 0.4 51.7 ± 6.6 August 12.9 ± 0.9 22.1 ± 1.6 2 ± 0.4 52.6 ± 7.3 September 10.4 ± 1.2 21.8 ± 3 2.2 ± 0.8 46.7 ± 12.6 3.2. Seasonal Dynamics of Evapotranspiration The seasonal evolution of ET a and ET eq at the UC Davis and Oakville Station vineyards for both the 2020 and 2021 growing seasons are presented in Fig. 1 . During both growing seasons, ET a was consistently higher than ET eq , across all three vineyards. Furthermore, ET a and ET eq reached their maximum values within the first 75 days of the growing season, after which point, they declined steadily over the remainder of the season. In 2020, a precipitous drop in ETa was observed around DOY 250. This sharp decrease is an artifact of radiation reducing smoke associated with the wildfires burning in the region. In general, radiation decreases throughout the growing season (Fig. 2 ), and the magnitude of ET trends towards higher variability later in the growing season, due to this study’s focus on growing season dynamics, we limited the range of data from May through September. ET a exhibited a much wider range of variability when compared to ET eq . The principal energy balance components (H, LE, Rn) calculated over the 2020 and 2021 growing seasons from both Oakville and UC Davis are depicted in Fig. 2 . Rn progressively decreased by 30% across the growing season, with the highest Rn measured in June and May for the 2020 and 2021 growing seasons, respectively. The Rn measured was similar between both Oakville and UC Davis, with Oakville receiving about 1 MJ m − 2 day − 1 more radiation than Davis. Sensible heat flux (H) varied between a maximum of 8.1 MJ m day − 1 in July to that of less than 1 MJ m day − 1 in September. Overall, OFV3 and OFV8, had nearly identical values of H over both growing seasons. Conversely the vineyard at UC Davis had significantly lower values of H. Since LE was the difference between the H and the Rn, it followed that the vineyard at UC Davis had the highest values of LE. All three vineyards had similar values of Rn, but Davis had considerably lower values of H. In both growing seasons values of LE decreased from a maximum in either May or June and reached their minimum value in September. This trend can be observed across all three vineyards. The OFV3 and OFV8 presented similar values of LE, with the OFV3 exhibiting slightly lower values of LE towards the end of the season. The Priestly-Taylor beta-coefficient ( β ), was calculated by dividing the ET a by the ET eq . The theoretical framework by which to interpret β can be inferred from Fig. 1 . The greater the distance between the blue and orange lines (Fig. 1 ), the greater β will be at a given vineyard. Interestingly, ET eq varied little between the three locations, with OFV3, OFV8, and UC Davis reporting mean values of 2.02, 2.04, and 1.76 mm day − 1 , respectively. ET a , on the other hand, varied considerably between the vineyards. The OFV3 had the lowest ET a , with an average of 2.73 mm day − 1 , whereas the ET a at OFV8 averaged a value of 4.93 mm day − 1, and UC Davis had the highest mean ET a with a value of 7.85 mm day − 1 . The differences between ET a and ET eq were then evident in Fig. 3 , where the average monthly values of β are depicted across each location for both the 2020 and 2021 growing seasons. In comparison, the β at OFV3 was the lowest, averaging 0.60 over the 2020 and 2021 growing seasons. OFV8 had a higher value of β , with a mean value of 1.2 over the two growing seasons. Finally, the vineyard at UC Davis had the greatest value of β , averaging 1.7 over the two growing seasons. Seasonal comparisons of 2020 and 2021 were nearly identical in OFV3 and were similar in OFV8. 3.3. Grapevine Water Status The average of the monthly Ψ s (MPa) of the three vineyards over the 2020 and 2021 growing seasons are presented in Fig. 4 a. The highest level of plant water stress was measured at OFV3, where minimums of -1.40 and − 1.52 MPa Ψ s were measured in August of 2020 and 2021, respectively. Furthermore, OFV3 began the season with the most negative water potentials, reaching a mean Ψ s of about − 1.05 MPa in June during both seasons. OFV8 also recorded its least negative Ψ s in June of 2020, with the average of the vineyard being − 0.9 MPa. In OFV8, the most negative water potentials were recorded in August of 2020 and June of 2021, with the lowest Ψ s, -1.19 MPa, occurring in June 2021. Overall, the vineyard at UC Davis was the least stressed of the vineyards studied, varying between a Ψ s of -0.60 and − 0.87 MPa. The lowest Ψ s at Davis was measured in August of 2021. Graphically, the variability in water potentials between the three vineyards can be distinctly observed (Fig. 4 a). In both years, OFV3 began the season with Ψ s > -1.0 MPa and progressively decreased its Ψ s until it reached the greatest plant water stress in August. In OFV8 stem water potentials were maintained near a value of -1.0 MPa for most of both growing seasons. The vineyard at Davis, which had the lowest water deficit of the three vineyards varied between a mean daily Ψ s of -0.5 to -1.0 MPa. 3.4. Stomatal Conductance Figure 4 b displays monthly average values of stomatal conductance (g s , mmol H 2 O m − 2 s − 1 ) measured at OFV3 and OFV8 over both growing seasons. Overall, OFV8 had significantly higher values of g s than OFV3, with averages of 176 and 111 mmol H 2 O m − 2 s − 1 , respectively, over both growing seasons. The maximum g s in OFV3 was only 164 mmol H 2 O m − 2 s − 1 compared to 208 mmol H 2 O m − 2 s − 1 in OFV8. The lowest values of stomatal conductance were reached in June 2020 in OFV3 and in August 2021 in OFV8. Similar to Ψ s, values of g s can also be visually differentiated between OFV3 and OFV8 (Fig. 4 b). The evolution of g s followed a similar course in both vineyards. In 2020, both OFV3 and OFV8 decreased from their maximum values of g s in May, with OFV3 reaching its minimum stomatal conductance in June, and OFV8 arriving at its minimum stomatal conductance in August. In 2021, g s increased in both vineyards form June to August. OFV3 maintained consistently lower values of g s over the course of both seasons. 3.5. Relationship between Ψs andβ The β was related to Ψ s with a linear relationship (Fig. 5 ). The range of β across the three experimental vineyards was between 0.5 and 2. The more water stressed vineyards (OFV3 and OFV8), ranged between values of 0.5 and 1.4. On the other hand, the less water stressed vineyard at UC Davis displayed a range between about 1.7 and 2.2. β was more closely related to Ψ s when the mean daily values of Ψ s were used (Fig. 5 a), as opposed to all the daily values (Fig. 5 b), R 2 = 0.42 and 0.32, respectively. The relationship between β and Ψ s was highly significant (p < 0.001) for the daily values of Ψ s as well as the for the means. 3.6. Relationship between Leaf Gas Exchange andβ The leaf-gas exchange variables of g s and A N were regressed against β at both an inter and intra -vineyard level. There was a significant and linear relationship between g s , A N , and β at the inter-vineyard level (Fig. 6 ). Regressions between β and the mean daily values of g s and A N were significant (p < 0.001 and 0.001, respectively), with β accounting for 45% of the variability in the mean values of g s and 33% of the variability in mean values of A N (Fig. 6 a and 6 c). While the regressions between β and daily values of g s and A N were also significant (p < 0.001), β only explained 25% of the variance in daily values of g s and 21% of the variance in daily values of A N (Fig. 6 b and 6 d). The intra-vineyard relationships between β and leaf-gas exchange variables are shown in Fig. 7 . Within OFV3, there was a statistically significant (p < 0.01) quadratic relationship between β and the mean daily value of g s (Fig. 7 a), with β accounting for 33% of the variability in measurements of g s . The quadratic relationship between β and g s , in OFV3, reached its maximum at a β value of about 0.65 and a g s ~135 mmol H 2 O m − 2 s − 1 . There was also a quadratic relationship between β and the mean daily value of A N in OFV3 (Fig. 7 c). The quadratic relationship reached its maximum at a β of roughly 0.65 and an A N of 11 µmol CO 2 m − 2 s − 1 . The regression between β and A N in OFV3 was significant (p < 0.01) and β explained 41% of the variance within mean values of A N . The relationships between β and leaf-gas exchange variables were less clear in OFV8. There was a statistically significant relationship between β and mean g s over the 2020 and 2021 growing seasons (Fig. 7 b) where β explained 46% of the variance within mean values of g s . Additionally, while a quadratic regression between β and A N was of interest, it was not statistically insignificant (Fig. 7 d). 3.7. Relationship between Integrals of Ψs and ∂ 13 C The relationship between seasonal integrals of Ψ s and must carbon isotopes (∂ 13 C) is shown in each vineyard annually (Fig. 8 ). There was a direct relationship between Ψ s and ∂ 13 C at OFV3 in both the 2020 and 2021 growing seasons (Fig. 8 a and 8 b), with ∂ 13 C explaining 94 and 83% of the variability in integrals of Ψ s, respectively (p < 0.001). There was a statistically significant (p < 0.05) but relatively weak (R 2 = 0.18) linear relationship between ∂ 13 C and integrals of Ψ s from the 2021 season at OFV8 (Fig. 8 c). The linear regression between integrals of Ψ s and ∂ 13 C at Davis in 2021 was statistically significant (p < 0.01), and linear (R 2 = 0.76) (Fig. 8 d). The range of ∂ 13 C was highest in OFV3, spanning from − 26.8 to 22.5 % in 2020 and from − 27.3 to 23.4 % in 2021. UC Davis presented a much narrower range, with ∂13C values between − 27.3 an -25 %. 4. Discussion 4.1. The evaluation of β -index The three vineyards utilized in this study all fall under a temperate, dry climate classification (Peel et al., 2007 ), but both the 2020 and 2021 seasons took place during hyper arid conditions (N. Torres et al., 2021 ). All three vineyards received essentially no measurable precipitation over the course of the growing seasons, limiting the only water additions to those delivered via irrigation. Furthermore, the intentional hierarchy of irrigation management, replacing 90% of ETc at Davis, 70% of ETc in OFV8, and 50% of ETc in Oakville, provided the opportunity to evaluate the applicability of the Priestly-Taylor β -index in the elucidation of vineyard water status under seasonal water deficit conditions. The hypothesis follows that if a vineyard was experiencing water stress, grapevines would their close stomata to regulate water status, therefore reducing the ETa of the vineyard and leading to a reduction of the β -index (Eq. 7). The Priestly-Taylor β -coefficient was conceived to measure the aridity of a land surface when compared to a water saturated surface (Priestley and Taylor, 1972 ). In the context of wet-surface evapotranspiration, the coefficient converges on a theoretical minimum value of 1.26 (Eichinger et al., 1996 ). Across much of the literature regarding the β -index, it deviates minimally from the theoretical value of 1.26. Herein, we report values of β that range from 0.5 to 2.2 (Fig. 3 ). The β index ranged widely because the boundary layer of a non-water saturated surface was rougher than that of a saturated one (Campbell and Norman, 1998 ), furthermore, the boundary layer of a tall woody crop is both rougher and more variable than that of a wet turf grass (Marino et al., 2021 ). Finally, the ability of grapevine to close their stomata and arrest transpiration is a phenomenon that occurs outside of theoretical considerations of wet-surface evaporation. In the context of this study, there was ample energy to drive evapotranspiration from bare soils and grapevines (Fig. 1 ), and the limitations on evapotranspiration were those imposed by the vineyard water management regime. The energy balance components play a critical role in driving ETa. The daily latent heat flux (LE) is the product of “E”, i.e. the water mass flux density (kg m − 2 d − 1 ) and “λ”, which is the amount of energy needed to vaporize 1.0 kg of water (λ = 2.45 MJ kg − 1 ) (Shapland et al., 2014 ) and, therefore, E = LE/λ and LE ≈ Rn - H on a daily basis. Since 1.0 kg of water covers 1.0 m − 2 to a depth of 1.0 mm, 1.0 kg m − 2 d − 1 = 1.0 mm d − 1 . This process is critical to the transfer of water vapor from leaf stomata (Keller, 2020 ) and from the soil and other wet surfaces. Since Rn is typically large compared to H, the LE is determined principally by the value of Rn. Observed Rn values were similar between the three vineyards, so the differences in the LE of each vineyard were determined mainly by the value of H, which is the aerodynamic component of evapotranspiration as previously reported in other cropping systems. Sensible heat flux is the transfer of kinetic energy per unit area per unit time from one location to another. For vertical sensible heat flux (H) is positive, the warm air flux is upwards and it reduces the energy is available for vaporization of water at the surface. When H is negative, the warm air flux is downward and it increases energy available for vaporization at the surface. High ET rates occur when the Rn is high and H is either close to zero or negative. High positive H values are indicative of low vaporization and high heat transfer from the surface. Therefore, it follows that more positive values of H indicate lower values of β (Marino et al., 2021 ). Our results indicate that higher values of H do indeed lead to lower values of β that corroborate the work by Marino et al. ( 2021 ). The OFV3 and OFV8 had significantly higher values of H than Davis, and they also had significantly lower values of β . Yet, while OFV3 and OFV8 had nearly identical values of H, the β value of the vineyards was significantly different. The difference in the β value of the two Oakville Vineyards likely resulted from different irrigation regimes tailored to the style of wine grapes produced from each vineyard. The OFV8 received 20% more applied water than OFV3. Therefore, OFV8 maintained a higher stomatal conductance, relative to OFV3, over the course of the season, allowing it to transpire more water. The idea of β relating to water status is in line with Marino et al., 2021 , who posited that orchards experiencing transpiration reducing water stress may also display lower values of β due to partial stomatal closure that increases canopy resistance and reduces transpiration. While the physical laws governing β are complex, it serves to remember that β is simply the ratio of ETa to ETeq. Clearly, Eta = ETeq when β = 1.00 and β < 1.00 whenever ETa has fallen below ETeq, which represents a well-watered equilibrium evapotranspiration (Eichinger et al., 1996 ; Marino et al., 2021 ). Values greater than β = 1.00, imply that there is sufficient energy available and resistances are low enough to drive ETa as high or higher than ETeq. β approaches 1.00 in situations where the total resistance (aerodynamic + canopy) from the canopy to the overlying air column is sufficiently high to decrease ETa to near ETeq. Yet, in the arid setting of northern California’s irrigated vineyards, it seems that there is often enough energy and low enough resistance to energy flux to keep ETa above ETeq. In fact, when values of β fell below 1.00 it was indicative that the vines were incurring transpiration reducing stress, and the subsequent stomatal closure was restricting stomatal conductance and transpiration, which lowers ETa to values near that of a well-watered ETeq. OFV3 provided the best example of a vineyard experiencing transpiration reducing stress, as the season progressed values of ETa fell below ETeq, indicating that the water stressed vineyard had dropped beneath equilibrium values of evapotranspiration. The water deficits at OFV8 drove a similar behavior, where Eta decreased towards ETeq as the season progressed, yet because the water deficits in OFV8 were milder, values of β approach closer to 1.00. Finally, the vineyard at UC Davis incurred only moderate water deficits, and the relative availability of soil water allowed for sustained transpiration and maintained values of Eta much greater than those of ETeq. 4.2. β as an Index of Grapevine Water Status In the three vineyards studied, three tiers of water deficit emerged. The vineyard at UC Davis exhibited minimal water deficits in both growing seasons ( Ψ s > -1.0 Mpa); OFV8 experienced low to moderate levels of water deficit in both seasons; and OFV3 reached moderate to severe tiers of water deficit ( Ψ s < -1.4 MPa) in both seasons (van Leeuwen et al., 2010 ). The three levels of water deficit carried over to the regression of β against mean daily values of Ψ s (Fig. 5 a). Lower values of β correspond to more negative values of Ψ s. The relationship between β and Ψ s presented itself as linear, which implied that there were two end-member behaviors with a maximum Ψ s occurring at minimum values of beta. OFV3 represented the stressed endmember and maintained a strong linear correlation between Ψ s and β.Within the low-stress endmember, UC Davis, a linear regression was less effective in characterizing the relationship between Ψ s and β. The breakdown of a linear relationship at Davis corroborates our hypothesis that β serves as an index of physiological stress. According to Fig. 5 , β is best employed as a proxy of grapevine physiological response to stress when observing water-stressed vineyards (e.g., OFV3 and OFV8). An inter-vineyard relationship, however, still exists, with higher values of β clearly indicating lower values of water stress (i.e., UC Davis). One possible explanation for the breakdown of the β coefficient in the non-stressed vineyard at UC Davis is that the vines at Davis reached their maximum photosynthetic efficiency. Whereby, the large leaf area index of the Petit Sirah grapevines likely induced higher levels of transpiration (Campos et al., 2010 ; Williams and Ayars, 2005 ). And so much water was moving through the grapevines at UC Davis that it is plausible that grapevines may have exceeded their maximum photosynthetic efficiency, running into feedback inhibitions to photosynthesis (Bota et al., 2004 ; Paul and Pellny, 2003 ). The presence of photosynthetic feedback inhibitions explains the breakdown in linear relationship between β and Ψ s. On one end, stomatal closure in response to water deficits drove down values of β until they reached a threshold of 0.5. On the other end, ample free energy for evapotranspiration coupled with adequate water availability and a large enough canopy drove values of transpiration so high that there was not a sink available for the products of photosynthesis; feedback inhibitions lead to stomatal closure and conserved Ψ s, even at relatively high values of β . An alternative hypothesis was that the timing of measurements was not adequate to capture changes in transpiration that occur within a single day. In such a scenario, the high energy available for evapotranspiration induced maximum values of transpiration in the morning, which were curtailed in the afternoon as grapevines closed stomata to regulate their water status in response to increasingly negative soil water potentials (Personne et al., 2003 ). Measurements would have then been carried out after grapevines had arrived after their most negative values of Ψ s for the day, at which point transpiration would have been reduced in an effort to conserve water (Nazareth Torres et al., 2021 ). Disentanglement of feedback inhibitions to photosynthesis and limitations of soil water potential is complex and would require detailed knowledge of the rate limiting variable, either soil water availability or sink availability for the products of photosynthesis. Regardless, an up sampling of the β index would have been useful in tracking the dynamic relationship between photosynthesis and transpiration. Even though β and Ψ s were correlated between vineyards, the relationship did not carry over to the individual vineyards. The lack of a relationship within individual vineyards was not surprising, as it would require the correlation of a biometeorogical index derived to assess the aridity of large wet surfaces (Priestley and Taylor, 1972 ) with individual grapevines from a single vineyard block. A more realistic application of β would be the differentiation of water status within vineyard blocks spread across a large production setting. In our experiment, β was able to parse differences in the mean daily Ψ s of OFV3 and OFV8, which were about 200 m apart, as well as at UC Davis, approximately 60 km away. 4.3. Sensitivity of β index to Leaf Gas Exchange The relationship between β and leaf-gas exchange variables differed from Ψ s, in that gs and A N were both linearly correlated with β when measured between vineyards. The linear relationship found allowed us to reject the null hypothesis of the experiment, wherein experimental vineyards would not have lower values of gs an d A N due to the season-long water restrictions. The OFV8 maintained significantly higher transpiration (gs) and assimilation rates ( A N ) over the course of both growing seasons. While both the mean daily gs and A N were significantly correlated to β , the strength of the relationship was relatively weak, with β explaining less than 20% of the variance in leaf-gas exchange measurements between vineyards. The weak correlation can be explained by the native complexity involved in leaf-level regulation of transpiration (Buckley, 2019 ), which was further compounded by differences of environment and genotype within the experiment. Focusing instead, on the intra-vineyard relationship between β and leaf-gas exchange parameters yielded much more interesting results. In OFV3, where water deficits were higher, a quadratic relationship emerged between β and both gs and A N . As β approached 0.5, gs fell below 100 mmol H2O m 2 s − 1 and A N dropped to less than 10 µmol CO2 m 2 s − 1 indicating that stomatal closure is closely coupled to the ratio of ET a :ET eq (Cifre et al., 2005 ; Levin et al., 2019 ; Lovisolo et al., 2010 ). At the other end of the spectrum, when β approached 0.8, values of both gs and A N also approached their minimum. Stomatal conductance reaching a minimum at the highest values of β was a result of either: 1) the aforementioned photosynthetic feedback inhibitions; 2) a very close coupling of ETa and ETeq in water stressed vineyards; 3) timing of measurements. The water deficits in OFV3 reduced transpiration within the vineyard and caused β to be more responsive to environmental conditions than applied water amounts, relative to OFV8 and UC Davis. It appeared that the closer ET a was to ET eq , the more effect ET eq had on determining the value of β . In the case of reduced stomatal conductance at higher values of β , we postulate that large increases in the mean daily temperature ( i.e. , heat waves) induced stomatal closure while also decreasing the value of ETeq and thereby increasing the value of β even as values of ET a were driven down. The ability of β to capture stomatal closure associated with water deficits as β approached its minimum, and stomatal closure resultant of excessive air temperature as β approached its maximum, is exemplary of the insights provided by coupling plant physiology and environmental parameters within a single index. Once again, an alternative hypothesis was that the timing of measurements was inadequate to reveal diurnal patterns of transpiration in response to available soil water content. The scenario unfolds as mentioned above, where maximum free energy for evapotranspiration drives a large flux of transpiration in the morning, at which point stomata close to conserve soil water. Measurements carried out after stomatal closure would read minimum values of g s on days where β was at a maximum. The moderate water deficits in OFV8 resulted in a less defined relationship between β and leaf-gas exchange. Average daily values of stomatal conductance never fell below 125 mmol H 2 O m 2 s − 1 in either season and the general absence of transpiration reducing stress was not sensed by the β coefficient at an intra-vineyard level. Once again, however, the ability of β to differentiate values of g s between OFV3 and OFV8 along a linear regression line pointed to the applicability of the β -index in larger scale commercial vineyards. 4.4. β as an Index of Water Stress Linear regressions between seasonal integrals of Ψ s and ∂ 13 C confirmed the consensus that ∂13C was a reliable indicator by which to assess seasonal water deficits in grapevines (Bchir et al., 2016 ; Chone et al., 2001 ; Gaudillère et al., 2002 ; van Leeuwen et al., 2010 ; Yu et al., 2021b ). Additionally, regressions between ∂ 13 C and gas exchange measurements, g s and A N , showed strong linear correlations, reaffirming the findings of Yu et al. (2021) and providing further evidence for the applicability of ∂ 13 C as a proxy for leaf photosynthesis in arid climates. Measurements of ∂ 13 C added another layer of validation to the hypothesis that OFV3 would reduce its transpiration in response to severe water deficits. On the other hand, UC Davis vineyard was again shown not to have incurred as severe water stress, maintaining ∂ 13 C values well above the threshold of -23% (van Leeuwen et al., 2010 , Brillante et al., 2020). Unexpectedly, there was no clear relationship between ∂ 13 C and seasonal integrals of Ψ s in OFV8. We posited that the lack of canopy cover over the bare soil, a function of the young vines (< 3 years), has mitigated this otherwise fundamental relationship (Nazareth Torres et al., 2021 ). Our results indicated daily evaluation of the β -index is a means by which to assess vine-water status in lieu of repeated pressure chamber measurements or infra-red gas exchange measurements. Furthermore, the β -index may be especially useful in tracking leaf-gas exchange measurements in water-stressed vineyards and as a proxy for stem water potential in commercial production settings. 5. Conclusions The two-year comparison of measurements of β to ground-truth measurement of grapevine water status and leaf gas exchange provided an opportunity to evaluate β as a novel index of grapevine water stress. We found that given access to the equipment necessary to measure surface renewal and a local weather station, the β can be measured with relative ease, allowing for rapid, real-time quantification of vineyard water status at scale. Furthermore, β was well related to Ψ s, g s , and A N if enough water stress existed within the vineyard. The ability to relate real-time measures of β to grapevine physiological responses to water deficits may allow β to serve as a powerful tool for vineyard operators. The β can be used as a replacement for real time assessment of Ψ s and can provide indication of when to switch on irrigation. Declarations FUNDING This project did not receive any funding. CONTRIBUTIONS SKK and DZ conceived and designed the trial. SK, RY, JT and LEM curated the data. SK curated, analyzed the data and wrote the first version of the manuscript. RS critically reviewed the manuscript and curated the data. All authors read and made contributions to the manuscript and approved the final version. CONFLICT of INTEREST The authors declare no conflicts of interest or competing interests. Availability of data, code and materials Contact the corresponding author to request data, code and material. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome 300, D05109. 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Thermocouple frequency response compensation leads to convergence of the surface renewal alpha calibration. Agric. For. Meteorol. 189, 36–47. https://doi.org/https://doi.org/10.1016/j.agrformet.2014.01.008 Slatyer, R.O., McIlroy, I.C., 1961. Practical Micrometeorology, 311 pp. Suter, B., Triolo, R., Pernet, D., Dai, Z., Van Leeuwen, C., 2019. Modeling stem water potential by separating the effects of soil water availability and climatic conditions on water status in grapevine (Vitis vinifera L.). Front. Plant Sci. 10, 1485. https://doi.org/https://doi.org/10.3389/fpls.2019.01485 Tetens, O., 1930. Uber einige meteorologische Begriffe. Z. Geophys 6, 297–309. Thornthwaite, C.W., 1948. An approach toward a rational classification of climate. Geogr. Rev. 38, 55–94. https://doi.org/https://doi.org/10.2307/210739 Torres, N., Yu, R., Martinez-Lüscher, J., Kostaki, E., Kurtural, S.K., 2021. Application of Fractions of Crop Evapotranspiration Affects Carbon Partitioning of Grapevine Differentially in a Hot Climate. Front. Plant Sci. 22, 75. https://doi.org/10.3389/fpls.2021.633600 Torres, Nazareth, Yu, R., Martínez-Lüscher, J., Kostaki, E., Kurtural, S.K., 2021. Effects of Irrigation at Different Fractions of Crop Evapotranspiration on Water Productivity and Flavonoid Composition of Cabernet Sauvignon Grapevine. Front. Plant Sci. 12, 1858. https://doi.org/10.3389/fpls.2021.712622 Tyree, M.T., 1997. The cohesion-tension theory of sap ascent: current controversies. J. Exp. Bot. 48, 1753–1765. https://doi.org/https://doi.org/10.1093/jxb/48.10.1753 Venturini, V., Islam, S., Rodriguez, L., 2008. Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model. Remote Sens. Environ. 112, 132–141. https://doi.org/https://doi.org/10.1016/j.rse.2007.04.014 Williams, L.E., Araujo, F.J., 2002. Correlations among predawn leaf, midday leaf, and midday stem water potential and their correlations with other measures of soil and plant water status in Vitis vinifera. J. Am. Soc. Hortic. Sci. 127, 448–454. https://doi.org/https://doi.org/10.21273/JASHS.127.3.448 Williams, L.E., Ayars, J.E., 2005. Grapevine water use and the crop coefficient are linear functions of the shaded area measured beneath the canopy. Agric. For. Meteorol. 132, 201–211. https://doi.org/https://doi.org/10.1016/j.agrformet.2005.07.010 Xue, J., Bali, K.M., Light, S., Hessels, T., Kisekka, I., 2020. Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize. Agric. Water Manag. 238, 106228. https://doi.org/https://doi.org/10.1016/j.agwat.2020.106228 Yu, R., Brillante, L., Torres, N., Kurtural, S.K., 2021a. Proximal Sensing of Vineyard Soil and Canopy Vegetation Provide Direction in Identifying Vineyard Spatial Variability in Plant Physiology and Berry Chemistry. Oeno One. https://doi.org/https://doi.org/10.20870/oeno-one.2021.55.2.4598 Yu, R., Zaccaria, D., Kisekka, I., Kurtural, S.K., 2021b. Soil apparent electrical conductivity and must carbon isotope ratio provide indication of plant water status in wine grape vineyards. Precis. Agric. https://doi.org/10.1007/s11119-021-09787-x Zaccaria, D., Carrillo-Cobo, M.T., Montazar, A., Putnam, D.H., Bali, K., 2017. Assessing the viability of sub-surface drip irrigation for resource-efficient alfalfa production in central and southern California. Water 9, 837. https://doi.org/https://doi.org/10.3390/w9110837 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2223673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":148640555,"identity":"7b61b57b-7762-4597-adeb-ce263399b8f7","order_by":0,"name":"Sean Kacur","email":"","orcid":"","institution":"University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"Kacur","suffix":""},{"id":148640556,"identity":"4ae42dda-45f9-4aa5-bbe5-e755b83f5888","order_by":1,"name":"Runze Yu","email":"","orcid":"","institution":"University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Runze","middleName":"","lastName":"Yu","suffix":""},{"id":148640557,"identity":"2d202237-a83a-40d1-83e5-e68e9f1a8741","order_by":2,"name":"Daniele Zaccaria","email":"","orcid":"","institution":"University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Zaccaria","suffix":""},{"id":148640558,"identity":"43489298-21c0-4a38-8a8e-3ab059b8116d","order_by":3,"name":"Richard L. Snyder","email":"","orcid":"","institution":"University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Richard","middleName":"L.","lastName":"Snyder","suffix":""},{"id":148640559,"identity":"65282c03-2b6a-4f03-ae03-821043f3b297","order_by":4,"name":"Lauren E. Marigliano","email":"","orcid":"","institution":"University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"E.","lastName":"Marigliano","suffix":""},{"id":148640560,"identity":"0c312f92-cb63-4c3d-9dce-33da39cf729e","order_by":5,"name":"Gregory A. Gambetta","email":"","orcid":"","institution":"EGFV, Bordeaux Sciences Agro, INRAE, Université de Bordeaux, ISVV","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gregory","middleName":"A.","lastName":"Gambetta","suffix":""},{"id":148640561,"identity":"0ccd5183-6c2a-4fbe-80f9-e47f2038c0a9","order_by":6,"name":"Khaled M. Bali","email":"","orcid":"","institution":"University of California Kearney Agricultural Research and Extension Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"M.","lastName":"Bali","suffix":""},{"id":148640562,"identity":"b9e45e71-6815-4631-bc57-9e74d1e89d82","order_by":7,"name":"Sahap Kurtural","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACHjYog535AAMDGxgBRQlqSQBiZrYEkrXwGEDVE9Ai33MsTbryx+F8/maebxI/yuwS+/gPMD5424Zbi8HZtmOSZxIOW844zLtNsudccmKbRAKz4Vx8WvjZ2yQbEg4bMAC1SDO2MRuzSTCwSfPi0SLfD9Uif5jnGVBLvTEb/wH23/i0MIAcBtJicJiHDajlsBwbQwIbMz4tBmeOJVs2pKUbGB5mM7bsOXdcjk0isVlyzjk8DutJM7zZYGNtIHe8+eGNH2XVPPL9hw9+eFOGx2FYAGMDaepHwSgYBaNgFGAAAFtgR3CzPorlAAAAAElFTkSuQmCC","orcid":"","institution":"University of California Davis","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sahap","middleName":"","lastName":"Kurtural","suffix":""}],"badges":[],"createdAt":"2022-11-01 00:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2223673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2223673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28668401,"identity":"f22e56d9-1504-49db-9873-0ba44dd489a4","added_by":"auto","created_at":"2022-11-04 14:15:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":571469,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e over the 2020-2021 growing seasons at OFV3 and OFV8 (Oakville) and UC Davis; the blue line represents average ET\u003csub\u003ea\u003c/sub\u003e and the orange line is the average ET\u003csub\u003eeq\u003c/sub\u003e. Data from 2020 is on the left-hand-side of the figure and 2021 is plotted on the right-hand-side. Both ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e peak before day of year (DOY) 200, after which they trend negatively for the remainder of the growing season. The temporal-changes in both ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e remain relatively consistent across both growing season and location.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/e5162d95ab012945146d9e1e.png"},{"id":28668406,"identity":"b68295c0-62ae-4ea4-b073-7a5d38860a86","added_by":"auto","created_at":"2022-11-04 14:15:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":447737,"visible":true,"origin":"","legend":"\u003cp\u003eAverage monthly values of a) sensible heat flux, H; b) latent heat flux, LE; and c) net radiation, Rn, (all in units of MJ m-2 day-1) for the OFV3 (blue), OFV8 (orange), and UC Davis (green) vineyards. Data from 2020 is on the left-hand-side of the figure and 2021 is plotted on the right-hand-side. The standard error of the monthly mean is displayed with the error bars.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/417477689138a6d430542cbe.png"},{"id":28669063,"identity":"69473426-1b62-4854-af16-3983221fd0d3","added_by":"auto","created_at":"2022-11-04 14:23:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153262,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal evolution of beta index (b) across vineyards, OFV3 (blue), OFV8 (orange), UC Davis (Green), and growing seasons 2020 (solid line) and 2021 (dashed line). Values of b are shown as monthly averages and the error–bars are the standard error from the mean. At UC Davis, values of b were only calculated from July onwards in 2020.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/2ce3082bf7c2ddeda4fb8a1e.png"},{"id":28668399,"identity":"8c343a48-e9b9-481d-818b-cdefb261ff08","added_by":"auto","created_at":"2022-11-04 14:15:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":268730,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal evolution of stem water potentials (Ys) and stomatal conductance (g\u003csub\u003es\u003c/sub\u003e); a) average monthly values of Ys from the vineyards OFV3 (blue), OFV8 (orange), and UC Davis (green) from both 2020 (solid line) and 2021 (dashed line); b) average monthly values of gs (mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e) from OFV3 (blue) and OFV8 (orange) from both 2020 (solid line) and 2021 (dashed line). Error bars represent the standard error of the mean.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/26a96b65a933038154d0c054.png"},{"id":28669533,"identity":"8d3a6254-7e4b-4367-bd3b-2283b0682dc5","added_by":"auto","created_at":"2022-11-04 14:31:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224173,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between stem water potential (Ys) and beta index (b) a) Linear regression between the daily b value and the average daily Ys; b) linear regression between the daily b value and Ys. Points are color-coded by location: OFV3 (blue), OFV8 (orange), Davis (green); and by year: 2020 (closed circles), 2021 (x-shaped). Regression lines are colored black and shaded with a light gray 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/e643bbcbecc3abc42d420405.png"},{"id":28669064,"identity":"e51f975c-91ae-4fac-a118-267bcd0332b9","added_by":"auto","created_at":"2022-11-04 14:23:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":318716,"visible":true,"origin":"","legend":"\u003cp\u003eInter-vineyard relationships between beta (b) and leaf gas exchange variables (g\u003csub\u003es\u003c/sub\u003e and A\u003csub\u003eN\u003c/sub\u003e) from OFV3 and OFV8 over the 2020 and 2021 growing seasons. a) mean daily values of g\u003csub\u003es\u003c/sub\u003e; b) daily values of g\u003csub\u003es\u003c/sub\u003e; c) mean daily values of A\u003csub\u003eN\u003c/sub\u003e; d) daily values of A\u003csub\u003eN\u003c/sub\u003e. Data from OFV3 are plotted blue, OFV8 are shown in orange, the year 2020 is indicated by closed circles, and 2021 is marked by x-shapes. The regression line is plotted black and shaded with a 95 % confidence interval shown in gray.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/86869726b500884a518d251f.png"},{"id":28669532,"identity":"91fd641f-af29-46c1-8541-1da826d1ed19","added_by":"auto","created_at":"2022-11-04 14:31:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":225612,"visible":true,"origin":"","legend":"\u003cp\u003eIntra-vineyard relationships between beta (b) and the daily mean leaf-gas exchange variables (g\u003csub\u003es\u003c/sub\u003e and A\u003csub\u003eN\u003c/sub\u003e) from OFV3 and OFV8. a) g\u003csub\u003es\u003c/sub\u003e from OFV3; b) g\u003csub\u003es\u003c/sub\u003e from OFV8; c) A\u003csub\u003eN\u003c/sub\u003e from OFV3; d) A\u003csub\u003eN\u003c/sub\u003e from OFV8. Data from OFV3 are on the left-hand side of the figure, plotted in blue; data from OFV8 are plotted orange on the right-hand side of the figure. The year 2020 is plotted as closed circles, 2021 as x-shapes. Regression lines are drawn in black and enveloped in a light gray confidence interval (95%).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/0005e6858645e87b16117725.png"},{"id":28668405,"identity":"9fcbb614-63b3-4f34-954f-d88e1d4ede97","added_by":"auto","created_at":"2022-11-04 14:15:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":354043,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between carbon isotope ratio analysis (∂13C) and stem water potential integrals; a) OFV3 2020, b) OFV3 2021; c) OFV8 2021; d) UC Davis 2021. Regression lines are shown in black with a 95 % confidence envelope plotted in gray.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/2aeb209471382baf4c2f5c09.png"},{"id":30512763,"identity":"df453457-58b9-4e70-a82e-de3081d321fd","added_by":"auto","created_at":"2022-12-19 12:15:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2906995,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2223673/v1/17ab693a-1cc4-4e07-a070-7e626e33ce18.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adapting the Priestly-Taylor Index as a Physiological Stress Indicator in Vineyard Agrosystems","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlant water stress is a key indicator of both the crop yield and fruit composition in agriculture. Water deficits are especially important for viticulture, where moderate water deficits were shown to improve berry and wine composition (Chaves et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) without adversely affecting yield. Furthermore, the global distribution of viticultural regions trends predominately towards temperate climates, which are at risk of experiencing increased temperatures and episodic drought as the climate warms (Gambetta and Kurtural, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoegh-Guldberg et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The percentage of grapevines grown with irrigation is expected to increase, relative to dry farmed vineyards, in response to global warming (Costa et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Resco et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and advances in irrigation management will be critical to ensuring sustainable production of one of the world\u0026rsquo;s most economically important fruit crops (Alston and Sambucci, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, grapevine water status has been measured via midday stem water potentials (\u003cem\u003eΨ\u003c/em\u003es). Although \u003cem\u003eΨ\u003c/em\u003es has proven to be a reliable indicator of plant water status (Chone et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), its scalability in irrigation management is limited by the high number of time-consuming measurements necessary to accurately characterize water status within a commercial vineyard. Additionally, measurements of \u003cem\u003eΨ\u003c/em\u003es are subject to variability in both soil water availability and meteorological conditions on the day of measurement; thereby convoluting temporal comparisons of \u003cem\u003eΨ\u003c/em\u003es (Suter et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With the likelihood of increased climate-induced stress on grapevines, the viticultural community is seeking new and more effective ways by which to define and measure plant water stress (Gambetta et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Unfortunately, the close coupling of physiological and biochemical responses to water stress (\u003cem\u003ee.g.\u003c/em\u003e, hydraulic conductivity (K), stomatal conductance (g\u003csub\u003es\u003c/sub\u003e), and abscisic acid concentration (ABA)), as well as genetic variability, interferes with the interpretation of plant-based responses to hydric stress.\u003c/p\u003e \u003cp\u003eOur current understanding of plant water relations is grounded within the soil-plant-atmosphere-continuum (SPAC) (Philip, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1966\u003c/span\u003e); wherein the cohesion-tension theory dictates that water flows from the soil reservoir through the plant xylem network under a negative pressure (\u003cem\u003ei.e.\u003c/em\u003e, tension) exerted by the atmosphere on the plant (Tyree, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The physical forces pulling water through the SPAC are described by the process of evapotranspiration (Thornthwaite, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1948\u003c/span\u003e). In their seminal works, Penman and Monteith partitioned plant and soil evaporation into components representing energy conservation, mass transfer (Penman, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1948\u003c/span\u003e), and electrical resistance (Monteith, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1965\u003c/span\u003e); resulting in the widely used Penman-Monteith equation for instantaneous evapotranspiration.\u003c/p\u003e \u003cp\u003eWhile the Penman-Monteith equation provided a more theoretically sound method for estimating instantaneous evapotranspiration, the methodology to estimate canopy and aerodynamic resistance was not well-known. Attempts were made to use empirical wind functions to estimate the aerodynamic contribution to evapotranspiration (Doorembos et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Doorenbos and Pruitt \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), but it was not until the publication of UN-FAO 56 (Allen et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) that the Penman-Monteith equation was widely adopted for use in estimating monthly, daily, and hourly reference evapotranspiration (ET\u003csub\u003eo\u003c/sub\u003e) of a 0.12-m tall grass. Until that time, Monteith discouraged the use of the Penman-Monteith equation for estimating evapotranspiration since it was originally designed to study canopy resistance using inputs of evapotranspiration from a lysimeter. It was proposed that the equation be used to estimate ETo using a fixed estimate of canopy resistance and an inverse function of the wind speed to estimate aerodynamic resistance for a daily or hourly modification of the equation. Recall that the original Penman-Monteith equation give instantaneous evapotranspiration rather than hourly or daily evapotranspiration. The hourly Penman-Monteith equation was later modified by the American Society of Civil Engineers \u0026ndash; Environmental Water Resources Institute and renamed to the Standardized Reference Evapotranspiration equation for short canopies (Allen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The final version of the Standardized ETo equation for short canopies (0.12-m) and the ETr equation for tall canopies (0.5-m) was published in Allen et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePriestly and Taylor (1972) modified the equations of Penman-Monteith, simplifying the inputs necessary to calculate evapotranspiration and deriving an equation that represents wet surface evaporation over large areas. The Priestly-Taylor equation permits quantification of advection-aridity when relating potential to actual evapotranspiration (Brutsaert and Stricker, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Granger, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Granger and Gray, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Parlange and Katul, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Venturini et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition, the Priestly-Taylor equation has been used to couple vegetative and atmospheric controls on evapotranspiration (Jarvis and McNaughton, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Yet, only in the last decade has affordable technology with the theory governing the evapotranspiration of water; thereby, increasing both the ubiquity and accuracy of measurements of the Priestly-Taylor index.\u003c/p\u003e \u003cp\u003eSurface renewal offers an inexpensive and accurate alternative to conventional micrometeorological estimations of actual evapotranspiration (ET\u003csub\u003ea\u003c/sub\u003e) (Xue et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sensible heat flux, derived from surface renewal calculations can be coupled with estimations of net radiation as well as readily available temperature and windspeed data to calculate a daily equilibrium evapotranspiration (ET\u003csub\u003eeq\u003c/sub\u003e). Commercially available surface renewal stations (\u003cem\u003ee.g.\u003c/em\u003e, Tule Technologies) provide accurate estimations of ET\u003csub\u003ea\u003c/sub\u003e (Fulton et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Montazar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rieger, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zaccaria et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which enable real-time comparisons of ET\u003csub\u003eeq\u003c/sub\u003e to ET\u003csub\u003ea\u003c/sub\u003e. Priestly and Taylor (1972) postulated that the \u003cem\u003eβ \u003c/em\u003ecoefficient, which directly compared reference to actual evapotranspiration, may one day serve as an index of land-surface aridity. Nearly fifty-years later, Marino et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), identified the same \u003cem\u003eβ\u003c/em\u003e coefficient, as a potential index by which agricultural managers may evaluate physiological stress.\u003c/p\u003e \u003cp\u003eThe carbon isotope ratio of must sugars (\u0026part;\u003csup\u003e13\u003c/sup\u003eC) has emerged as reliable tool for the assessment of season-long water deficits in vineyards (Brillante et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chone et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gaudill\u0026egrave;re et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Leeuwen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). This method takes advantage of \u003csup\u003e13\u003c/sup\u003eC discrimination in C\u003csub\u003e3\u003c/sub\u003e plants: under water deficits, stomatal closure limits the intake of carbon, causing the heavier \u003csup\u003e13\u003c/sup\u003eC to be incorporated into the photosynthetic pathway, thereby increasing the ratio of \u003csup\u003e13\u003c/sup\u003eC-\u003csup\u003e12\u003c/sup\u003eC (Farquhar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Sucrose from the leaves is then translocated to berries (Dai et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the berry must sugars can be used as a proxy for plant water status and stomatal conductance (Bchir et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Farquhar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). While \u0026part;\u003csup\u003e13\u003c/sup\u003eC has proven to be an effective method for the assessment of water deficits between veraison and ripening, it is only applicable post- harvest, leaving a dearth in real-time measurements of vineyard water deficits.\u003c/p\u003e \u003cp\u003eThe objective of the work was to determine if \u003cem\u003eβ\u003c/em\u003e would serve as a real-time indicator of physiological stress derived from seasonal water deficits. As the ratio between ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e, \u003cem\u003eβ\u003c/em\u003e relates the actual amount of water evaporated by soil and transpired by grapevines to a predicted amount of water calculated from daily measurements of the energy available for evapotranspiration. Thus, we evaluated \u003cem\u003eβ\u003c/em\u003e as a novel index by which to assess water deficits within commercial plantings of wine grape vineyards.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1. Experimental sites:\u003c/h2\u003e\n \u003cp\u003eThe experiments were conducted during 2020 and 2021 at three vineyards. Two of the vineyards were located at the UC Davis Oakville Station in Oakville, California, USA (WGS84 coordinates: 38.429\u0026deg;, -122.41\u0026deg;), and will be referred to hereafter as Old Federal Vineyard 3 and 8 (OFV3 and OFV8). The OFV3 is a 0.5 ha vineyard comprised of Cabernet Sauvignon/3309C (\u003cem\u003eV. riparia\u003c/em\u003e x \u003cem\u003eV. rupestris\u003c/em\u003e), with rows planted in a NW-SE orientation and spaced 1.5 m \u0026times; 2 m (vine x row) trained to a bi-lateral cordons 1.8 m above the ground. The OFV8 is a 1.0 ha vineyard planted to Merlot/3309C (\u003cem\u003eV. riparia\u003c/em\u003e x \u003cem\u003eV. rupestris\u003c/em\u003e) in a NE-SW orientation at a density of 2 m \u0026times; 3 m (vine \u0026times; row) with vines trained as quadrilateral cordons at a trunk height of 1.5 m. Both OFV3 and OFV8 were irrigated via two, 2 L/h drip emitters, with irrigation applied from fruit set through harvest and intended to replace 50 and 70% of potential crop evapotranspiration (ETc), respectively. The third experimental site was located on the UC Davis Campus, Davis, California, USA (WGS84 coordinates: 38.538\u0026deg;, -121.762\u0026deg;) and was planted with Petite Sirah grafted onto 1616C and trained to bilateral cordons spaced 1.8 m \u0026times; 2.8 m (vine \u0026times; row). The UC Campus vineyard was irrigated to 90% of ET\u003csub\u003ec\u003c/sub\u003e from fruit set through harvest. The ET\u003csub\u003ec\u003c/sub\u003e was calculated by multiplying reference evapotranspiration (ET\u003csub\u003eo\u003c/sub\u003e) by Williams and Ayars (\u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) crop coefficient (K\u003csub\u003ec\u003c/sub\u003e) at all locations as reported by Torres et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003eb).\u003c/p\u003e\n \u003cp\u003eEach experimental vineyard was equipped with a Tule Actual ET sensor (Tule Technologies, Davis, CA, USA) installed above the canopy. The Tule sensors provided daily measurements of actual evapotranspiration (ET\u003csub\u003ea\u003c/sub\u003e), as well as the energy balance components used in the surface renewal calculation of ET\u003csub\u003ea\u003c/sub\u003e: net radiation (Rn, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the sensible heat flux (H, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Eq.\u0026rsquo;s 1 \u0026amp; 2:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere LE is the latent heat flux (MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and \u003cem\u003eLE\u003c/em\u003e is the latent heat of vaporization (MJ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), or the latent heat required to vaporize 1 L of liquid water. Following the energy balance residual method, LE was calculated as the residual from net radiation, ground heat flux (G, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and the sensible heat flux, Eq.\u0026nbsp;2:\u003c/p\u003e\n \u003cp\u003eLE\u0026thinsp;=\u0026thinsp;Rn\u0026thinsp;\u0026minus;\u0026thinsp;G\u0026thinsp;\u0026minus;\u0026thinsp;H. (2)\u003c/p\u003e\n \u003cp\u003eNotably, for measurements collected daily over the course of the growing season it is safe to assume that the daily total ground heat flux is close to zero (G\u0026thinsp;=\u0026thinsp;0) (Allen et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Marino et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDaily weather measurements from the experimental sites were obtained from the California Irrigation Management Information System (CIMIS) station\u0026rsquo;s #6 and #77 for Davis and Oakville, respectively. Weather data from the 2020 and 2021 growing seasons are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverage daily values and standard deviations of the main weather variables (Rn: net radiation, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Ta: mean air temperature, \u0026deg;C; ū: mean wind speed, m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; RH: relative humidity, %) over the 2020 and 2021 growing seasons. Data were obtained from the CIMIS station No. 77 (Oakville, CA) and Tule Actual ET sensor (Tule Technologies) installed above the canopy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMonth\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRn (MJ m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTa (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eū (m s\u003c/em\u003e\u003csup\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRH (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.3 \u0026plusmn; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.5 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.8 \u0026plusmn; 17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 \u0026plusmn; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.3 \u0026plusmn; 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.9 \u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64 \u0026plusmn; 6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.5 \u0026plusmn; 10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.8 \u0026plusmn; 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.2 \u0026plusmn; 12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.6 \u0026plusmn;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60 \u0026plusmn; 5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.8 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.8 \u0026plusmn; 10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.9 \u0026plusmn; 6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.4 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.5 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.6 \u0026plusmn; 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.1 \u0026plusmn; 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.5 \u0026plusmn; 13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2. Experimental design:\u003c/h2\u003e\n \u003cdiv class=\"Section3\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.2.1. Derivation of Beta\u003c/h2\u003e\n \u003cp\u003eData from CIMIS and Tule stations were used to calculate a daily equilibrium evapotranspiration rate (ETeq, mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The ETeq is the component of evapotranspiration driven by the diabatic effect of net radiation on the latent heat flux (Slatyer and McIlroy, \u003cspan class=\"CitationRef\"\u003e1961\u003c/span\u003e). In simpler terms, ET\u003csub\u003eeq\u003c/sub\u003e approximates the contribution of Rn to LE which is a key determinant in ET\u003csub\u003ea\u003c/sub\u003e (see Eq.\u0026rsquo;s 2\u0026ndash;3). The calculation for ET\u003csub\u003eeq\u003c/sub\u003e followed Denmead \u0026amp; McIlroy\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e1970\u003c/span\u003e) modification of the Penman Equation (Penman, \u003cspan class=\"CitationRef\"\u003e1948\u003c/span\u003e) and is shown in Eq. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${\\text{E}\\text{T}}_{\\text{e}\\text{q}}= \\left(\\frac{\\varDelta }{\\varDelta + {\\gamma }}\\right)\\frac{\\text{R}\\text{n}}{ }$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003e\u0026lambda;\u003c/em\u003e was treated as a constant (\u003cem\u003e\u0026lambda;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.45 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to convert the Rn units into mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; although \u003cem\u003e\u0026lambda;\u003c/em\u003e is a function the wet-bulb temperature (Tw), the two are only weakly related when Tw\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026deg;C, therefore the effect of Tw on ET\u003csub\u003eeq\u003c/sub\u003e was ignored over the growing season (Fritschen and Gay, \u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e). The \u003cem\u003e\u0026Delta;\u003c/em\u003e (kPa K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the slope of the saturation vapor pressure curve at mean daily air temperature (Tm, \u0026deg;C) and \u003cem\u003e\u0026gamma;\u003c/em\u003e is the psychrometric constant (\u003cem\u003e\u0026gamma; \u0026asymp;\u003c/em\u003e 0.066 kPa K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The \u003cem\u003e\u0026Delta;\u003c/em\u003e term is estimated in Eq. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e Tetens (\u003cspan class=\"CitationRef\"\u003e1930\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\text{D} = \\frac{4098 \\bullet {\\text{e}}_{\\text{s}}}{{({\\text{T}}_{\\text{m}}+ 237.3)}^{2}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere es (kPa) is the saturation vapor pressure at Tm, estimated according to Tetens\u0026rsquo; (\u003cspan class=\"CitationRef\"\u003e1930\u003c/span\u003e) formula in Eq. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ4\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$${\\text{e}}_{\\text{s}}=0.6108\\text{e}\\text{x}\\text{p}\\left(\\frac{17.27 \\bullet {\\text{T}}_{\\text{m}} }{{\\text{T}}_{\\text{m}} + 237.3}\\right) .$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eET\u003csub\u003eeq\u003c/sub\u003e was then used in Eq. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e to calculate the Priestly-Taylor coefficient (\u003cem\u003e\u0026beta;\u003c/em\u003e) following Marino et al.\u0026rsquo;s (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) amendment of Priestly and Taylor\u0026rsquo;s (1972) equation:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ5\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$${\\beta }= \\frac{{\\text{E}\\text{T}}_{\\text{a}}}{{\\text{E}\\text{T}}_{\\text{e}\\text{q}}}= \\frac{{\\text{E}\\text{T}}_{\\text{a}}}{\\left(\\frac{\\varDelta }{\\varDelta + {\\gamma }}\\right)\\frac{\\text{R}\\text{n}}{ }}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.2.2. Measurement of Plant Water Status and Leaf Gas Exchange\u003c/h2\u003e\n \u003cp\u003eIn OFV3 and OFV8 midday stem water potentials (\u003cem\u003e\u0026Psi;\u003c/em\u003es) were measured biweekly over the course of the growing season in each year. Three shaded leaves were chosen from the main shoot axes on the grapevines and leaves were then bagged in pinch-sealed Mylar\u0026reg; bags up to two hours prior to measurement. The \u003cem\u003e\u0026Psi;\u003c/em\u003es readings were taken with a pressure chamber (Model 615D, PMS Instrument Company, Albany, OR, USA) (Williams and Araujo, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSimultaneously, leaf gas exchange measurements were taken with a portable infrared gas analyzer CIRAS-3 (PP Systems, Amesbury, MA, USA). Three sun-exposed leaves were selected from the main shoot axes, and three measurements were taken per leaf. The standard environmental condition in the infrared gas analyzer was set with a relative humidity at 40% and a reference CO\u003csub\u003e2\u003c/sub\u003e concentration at 400 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Net carbon assimilation rate (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and stomatal conductance (g\u003csub\u003es\u003c/sub\u003e, mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were measured directly by the infra-red gas analyzer. Intrinsic water use efficiency (WUE\u003csub\u003ei\u003c/sub\u003e, \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e mmol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e H\u003csub\u003e2\u003c/sub\u003eO) was then calculated post hoc as the ratio between \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e and g\u003csub\u003es\u003c/sub\u003e (WUEi\u0026thinsp;=\u0026thinsp;\u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e/g\u003csub\u003es\u003c/sub\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.2.3. Must Carbon Isotope Ratio Analysis\u003c/h2\u003e\n \u003cp\u003eAt harvest, one-hundred random berries were collected from each experimental unit. The berries were crushed by hand and 1.5 mL of the resulting must was pipetted into 2 mL conical tubes. The tubes were centrifuged at 4,000 RPM for 15 min and 10 \u0026micro;L of the supernatant was transferred into tin capsules (Thermo Fisher Scientific, Waltham, MA, USA) and placed on a microplate. The microplates were baked overnight at 80\u0026deg;C. Tin capsules containing completely dehydrated samples were then folded into cubits and wrapped with another tin capsule to ensure a tight seal. Each sample\u0026rsquo;s isotope ratio was analyzed on a Vario MicroCube elemental analyzer coupled in a continuous flow mode to an isotope ratio mass spectrometer (IsoPrime, Elementar, Ronkonkoma, NY, USA). The \u0026delta;13C values are reported in parts per thousand (\u0026permil;) relative to the Vienna Peedeebelemnite- CO\u003csub\u003e2\u003c/sub\u003e (VPDB-CO\u003csub\u003e2\u003c/sub\u003e) international reference.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eA combination of the Python programming language (Python Software Foundation, version 3.7) and StataIC statistical software (StataCorp, version 15) were used to analyze the data. Data management and visualization were processed in Python. Stata was used to perform ANOVA and regression analyses. Statistically significant differences between samples were determined when \u003cem\u003ep\u003c/em\u003e-values returned by an ANOVA were 0.05 or less. Coefficients of determination between variables were calculated in regression analyses, \u003cem\u003ep\u003c/em\u003e-values and mean squared errors (MSE) were used to determine the significance of fit.\u003c/p\u003e\n \u003cp\u003eThe coefficient \u003cem\u003e\u0026beta;\u003c/em\u003e was related to measurements of \u003cem\u003e\u0026Psi;\u003c/em\u003es, g\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u0026delta;\u003csup\u003e13\u003c/sup\u003eC. Since \u003cem\u003e\u0026beta;\u003c/em\u003e was a daily value calculated over an entire vineyard, it was necessary to relate \u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e to average daily values of \u003cem\u003e\u0026Psi;\u003c/em\u003es, g\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e. The mean was selected over the median because \u003cem\u003e\u0026Psi;\u003c/em\u003es, g\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, all exhibited normal Gaussian distributions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Weather at Experimental Site\u003c/h2\u003e \u003cp\u003eAnalysis of key weather variables (Rn: net radiation, MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Ta: mean air temperature at 1.5-m height, \u0026deg;C; ū: mean wind speed at 2-m height, m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; RH: relative humidity at 1.5-m height, %) revealed similar weather patterns at Oakville over both the 2020 and 2021 seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, net radiation (Rn) and relative humidity (RH) presented an inverse covariation in both years. Net radiation reached its peak in June around 17 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and then decreased steadily throughout the season. Relative humidity, on the other hand, increased from an average of around 54% in June and reached its maximum in September. The average daily air temperature (Ta) ranged from 15.9 to 22\u0026deg;C with the lowest temperatures occurring in September and the highest in July and August. Wind speed (ū) remained relatively constant across the entire experiment, ranging from 1.5 to 1.9 m s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe same key weather variables, i.e. Rn, Ta, ū, and RH, from the 2020\u0026ndash;2021 growing seasons at Davis, California are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In both years, Rn decreased from a peak of around 16 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to a minimum of about 10 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in September. While Rn was lower at Davis than Oakville, the Ta was higher, fluctuating between 21.7 and 24\u0026deg;C over the course of the season. Wind speed at Davis was also higher than Oakville, varying from 1.9 to 2.5 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over the two growing seasons. Finally, Davis also had a lower RH than Oakville, reaching a maximum RH of 55.6% in July of 2020. Interestingly, the seasonal patterns of RH seem to be reversed between Davis and Oakville, with Davis achieving its highest values of RH in the beginning of the season, and Oakville tending to reach its maximum RH values near the end of the growing season.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage daily values and standard deviations of the main weather variables (Rn: net radiation, MJ m-2 day-1; Ta: mean air temperature, \u0026deg;C; ū: mean wind speed, m s-1; RH: relative humidity, %) over the 2020 and 2021 growing seasons. Data were obtained from the CIMIS station No. 6 (Davis, CA) and Tule Actual ET sensor (Tule Technologies) installed above the canopy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRn (MJ m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTa (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eū (m s\u003c/em\u003e\u003csup\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRH (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.7 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.2 \u0026plusmn; 0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.4 \u0026plusmn; 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e55.6 \u0026plusmn; 4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.9 \u0026plusmn; 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.3 \u0026plusmn; 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.9 \u0026plusmn; 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e51.9 \u0026plusmn; 9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.6 \u0026plusmn; 1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24 \u0026plusmn; 3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.9 \u0026plusmn; 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e49.2 \u0026plusmn; 11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.5 \u0026plusmn; 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e21.7 \u0026plusmn; 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.3 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e47.6 \u0026plusmn; 8.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.9 \u0026plusmn; 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.2 \u0026plusmn; 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.3 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e47.4 \u0026plusmn; 8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.1 \u0026plusmn; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.2 \u0026plusmn; 2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.3 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e51.7 \u0026plusmn; 6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.9 \u0026plusmn; 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.1 \u0026plusmn; 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2 \u0026plusmn; 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e52.6 \u0026plusmn; 7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.4 \u0026plusmn; 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e21.8 \u0026plusmn; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.2 \u0026plusmn; 0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e46.7 \u0026plusmn; 12.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Seasonal Dynamics of Evapotranspiration\u003c/h2\u003e \u003cp\u003eThe seasonal evolution of ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e at the UC Davis and Oakville Station vineyards for both the 2020 and 2021 growing seasons are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During both growing seasons, ET\u003csub\u003ea\u003c/sub\u003e was consistently higher than ET\u003csub\u003eeq\u003c/sub\u003e, across all three vineyards. Furthermore, ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e reached their maximum values within the first 75 days of the growing season, after which point, they declined steadily over the remainder of the season. In 2020, a precipitous drop in ETa was observed around DOY 250. This sharp decrease is an artifact of radiation reducing smoke associated with the wildfires burning in the region. In general, radiation decreases throughout the growing season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the magnitude of ET trends towards higher variability later in the growing season, due to this study\u0026rsquo;s focus on growing season dynamics, we limited the range of data from May through September. ET\u003csub\u003ea\u003c/sub\u003e exhibited a much wider range of variability when compared to ET\u003csub\u003eeq\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe principal energy balance components (H, LE, Rn) calculated over the 2020 and 2021 growing seasons from both Oakville and UC Davis are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Rn progressively decreased by 30% across the growing season, with the highest Rn measured in June and May for the 2020 and 2021 growing seasons, respectively. The Rn measured was similar between both Oakville and UC Davis, with Oakville receiving about 1 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e more radiation than Davis.\u003c/p\u003e \u003cp\u003eSensible heat flux (H) varied between a maximum of 8.1 MJ m day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in July to that of less than 1 MJ m day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in September. Overall, OFV3 and OFV8, had nearly identical values of H over both growing seasons. Conversely the vineyard at UC Davis had significantly lower values of H. Since LE was the difference between the H and the Rn, it followed that the vineyard at UC Davis had the highest values of LE. All three vineyards had similar values of Rn, but Davis had considerably lower values of H. In both growing seasons values of LE decreased from a maximum in either May or June and reached their minimum value in September. This trend can be observed across all three vineyards. The OFV3 and OFV8 presented similar values of LE, with the OFV3 exhibiting slightly lower values of LE towards the end of the season.\u003c/p\u003e \u003cp\u003eThe Priestly-Taylor beta-coefficient (\u003cem\u003eβ\u003c/em\u003e), was calculated by dividing the ET\u003csub\u003ea\u003c/sub\u003e by the ET\u003csub\u003eeq\u003c/sub\u003e. The theoretical framework by which to interpret \u003cem\u003eβ\u003c/em\u003e can be inferred from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The greater the distance between the blue and orange lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the greater \u003cem\u003eβ\u003c/em\u003e will be at a given vineyard. Interestingly, ET\u003csub\u003eeq\u003c/sub\u003e varied little between the three locations, with OFV3, OFV8, and UC Davis reporting mean values of 2.02, 2.04, and 1.76 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. ET\u003csub\u003ea\u003c/sub\u003e, on the other hand, varied considerably between the vineyards. The OFV3 had the lowest ET\u003csub\u003ea\u003c/sub\u003e, with an average of 2.73 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas the ET\u003csub\u003ea\u003c/sub\u003e at OFV8 averaged a value of 4.93 mm day\u003csup\u003e\u0026minus;\u003c/sup\u003e 1, and UC Davis had the highest mean ET\u003csub\u003ea\u003c/sub\u003e with a value of 7.85 mm day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The differences between ET\u003csub\u003ea\u003c/sub\u003e and ET\u003csub\u003eeq\u003c/sub\u003e were then evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where the average monthly values of \u003cem\u003eβ\u003c/em\u003e are depicted across each location for both the 2020 and 2021 growing seasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn comparison, the \u003cem\u003eβ\u003c/em\u003e at OFV3 was the lowest, averaging 0.60 over the 2020 and 2021 growing seasons. OFV8 had a higher value of \u003cem\u003eβ\u003c/em\u003e, with a mean value of 1.2 over the two growing seasons. Finally, the vineyard at UC Davis had the greatest value of \u003cem\u003eβ\u003c/em\u003e, averaging 1.7 over the two growing seasons. Seasonal comparisons of 2020 and 2021 were nearly identical in OFV3 and were similar in OFV8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Grapevine Water Status\u003c/h2\u003e \u003cp\u003eThe average of the monthly \u003cem\u003eΨ\u003c/em\u003es (MPa) of the three vineyards over the 2020 and 2021 growing seasons are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. The highest level of plant water stress was measured at OFV3, where minimums of -1.40 and \u0026minus;\u0026thinsp;1.52 MPa \u003cem\u003eΨ\u003c/em\u003es were measured in August of 2020 and 2021, respectively. Furthermore, OFV3 began the season with the most negative water potentials, reaching a mean \u003cem\u003eΨ\u003c/em\u003es of about \u0026minus;\u0026thinsp;1.05 MPa in June during both seasons. OFV8 also recorded its least negative \u003cem\u003eΨ\u003c/em\u003es in June of 2020, with the average of the vineyard being \u0026minus;\u0026thinsp;0.9 MPa. In OFV8, the most negative water potentials were recorded in August of 2020 and June of 2021, with the lowest \u003cem\u003eΨ\u003c/em\u003es, -1.19 MPa, occurring in June 2021. Overall, the vineyard at UC Davis was the least stressed of the vineyards studied, varying between a \u003cem\u003eΨ\u003c/em\u003es of -0.60 and \u0026minus;\u0026thinsp;0.87 MPa. The lowest \u003cem\u003eΨ\u003c/em\u003es at Davis was measured in August of 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraphically, the variability in water potentials between the three vineyards can be distinctly observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In both years, OFV3 began the season with \u003cem\u003eΨ\u003c/em\u003es \u0026gt; -1.0 MPa and progressively decreased its \u003cem\u003eΨ\u003c/em\u003es until it reached the greatest plant water stress in August. In OFV8 stem water potentials were maintained near a value of -1.0 MPa for most of both growing seasons. The vineyard at Davis, which had the lowest water deficit of the three vineyards varied between a mean daily \u003cem\u003eΨ\u003c/em\u003es of -0.5 to -1.0 MPa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Stomatal Conductance\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb displays monthly average values of stomatal conductance (g\u003csub\u003es\u003c/sub\u003e, mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) measured at OFV3 and OFV8 over both growing seasons. Overall, OFV8 had significantly higher values of g\u003csub\u003es\u003c/sub\u003e than OFV3, with averages of 176 and 111 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, over both growing seasons. The maximum g\u003csub\u003es\u003c/sub\u003e in OFV3 was only 164 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to 208 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in OFV8. The lowest values of stomatal conductance were reached in June 2020 in OFV3 and in August 2021 in OFV8.\u003c/p\u003e \u003cp\u003eSimilar to \u003cem\u003eΨ\u003c/em\u003es, values of g\u003csub\u003es\u003c/sub\u003e can also be visually differentiated between OFV3 and OFV8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The evolution of g\u003csub\u003es\u003c/sub\u003e followed a similar course in both vineyards. In 2020, both OFV3 and OFV8 decreased from their maximum values of g\u003csub\u003es\u003c/sub\u003e in May, with OFV3 reaching its minimum stomatal conductance in June, and OFV8 arriving at its minimum stomatal conductance in August. In 2021, g\u003csub\u003es\u003c/sub\u003e increased in both vineyards form June to August. OFV3 maintained consistently lower values of g\u003csub\u003es\u003c/sub\u003e over the course of both seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Relationship between Ψs andβ\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eβ\u003c/em\u003e was related to \u003cem\u003eΨ\u003c/em\u003es with a linear relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The range of \u003cem\u003eβ\u003c/em\u003e across the three experimental vineyards was between 0.5 and 2. The more water stressed vineyards (OFV3 and OFV8), ranged between values of 0.5 and 1.4. On the other hand, the less water stressed vineyard at UC Davis displayed a range between about 1.7 and 2.2. \u003cem\u003eβ\u003c/em\u003e was more closely related to \u003cem\u003eΨ\u003c/em\u003es when the mean daily values of \u003cem\u003eΨ\u003c/em\u003es were used (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), as opposed to all the daily values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.42 and 0.32, respectively. The relationship between \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eΨ\u003c/em\u003es was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for the daily values of \u003cem\u003eΨ\u003c/em\u003es as well as the for the means.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Relationship between Leaf Gas Exchange andβ\u003c/h2\u003e \u003cp\u003eThe leaf-gas exchange variables of g\u003csub\u003es\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e were regressed against \u003cem\u003eβ\u003c/em\u003e at both an inter and intra -vineyard level. There was a significant and linear relationship between g\u003csub\u003es\u003c/sub\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, and \u003cem\u003eβ\u003c/em\u003e at the inter-vineyard level (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Regressions between \u003cem\u003eβ\u003c/em\u003e and the mean daily values of g\u003csub\u003es\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.001, respectively), with \u003cem\u003eβ\u003c/em\u003e accounting for 45% of the variability in the mean values of g\u003csub\u003es\u003c/sub\u003e and 33% of the variability in mean values of \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). While the regressions between \u003cem\u003eβ\u003c/em\u003e and daily values of g\u003csub\u003es\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e were also significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), \u003cem\u003eβ\u003c/em\u003e only explained 25% of the variance in daily values of g\u003csub\u003es\u003c/sub\u003e and 21% of the variance in daily values of \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe intra-vineyard relationships between \u003cem\u003eβ\u003c/em\u003e and leaf-gas exchange variables are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Within OFV3, there was a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) quadratic relationship between \u003cem\u003eβ\u003c/em\u003e and the mean daily value of g\u003csub\u003es\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), with \u003cem\u003eβ\u003c/em\u003e accounting for 33% of the variability in measurements of g\u003csub\u003es\u003c/sub\u003e. The quadratic relationship between \u003cem\u003eβ\u003c/em\u003e and g\u003csub\u003es\u003c/sub\u003e, in OFV3, reached its maximum at a \u003cem\u003eβ\u003c/em\u003e value of about 0.65 and a g\u003csub\u003es\u003c/sub\u003e ~135 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. There was also a quadratic relationship between \u003cem\u003eβ\u003c/em\u003e and the mean daily value of \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e in OFV3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). The quadratic relationship reached its maximum at a \u003cem\u003eβ\u003c/em\u003e of roughly 0.65 and an \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e of 11 \u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The regression between \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e in OFV3 was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eβ\u003c/em\u003e explained 41% of the variance within mean values of \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e. The relationships between \u003cem\u003eβ\u003c/em\u003e and leaf-gas exchange variables were less clear in OFV8. There was a statistically significant relationship between \u003cem\u003eβ\u003c/em\u003e and mean g\u003csub\u003es\u003c/sub\u003e over the 2020 and 2021 growing seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb) where \u003cem\u003eβ\u003c/em\u003e explained 46% of the variance within mean values of g\u003csub\u003es\u003c/sub\u003e. Additionally, while a quadratic regression between \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e was of interest, it was not statistically insignificant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Relationship between Integrals of Ψs and \u0026part;\u003csup\u003e13\u003c/sup\u003eC\u003c/h2\u003e \u003cp\u003eThe relationship between seasonal integrals of \u003cem\u003eΨ\u003c/em\u003es and must carbon isotopes (\u0026part;\u003csup\u003e13\u003c/sup\u003eC) is shown in each vineyard annually (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). There was a direct relationship between \u003cem\u003eΨ\u003c/em\u003es and \u0026part;\u003csup\u003e13\u003c/sup\u003eC at OFV3 in both the 2020 and 2021 growing seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), with \u0026part;\u003csup\u003e13\u003c/sup\u003eC explaining 94 and 83% of the variability in integrals of \u003cem\u003eΨ\u003c/em\u003es, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but relatively weak (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18) linear relationship between \u0026part;\u003csup\u003e13\u003c/sup\u003eC and integrals of \u003cem\u003eΨ\u003c/em\u003es from the 2021 season at OFV8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). The linear regression between integrals of \u003cem\u003eΨ\u003c/em\u003es and \u0026part;\u003csup\u003e13\u003c/sup\u003eC at Davis in 2021 was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and linear (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). The range of \u0026part;\u003csup\u003e13\u003c/sup\u003eC was highest in OFV3, spanning from \u0026minus;\u0026thinsp;26.8 to 22.5 % in 2020 and from \u0026minus;\u0026thinsp;27.3 to 23.4 % in 2021. UC Davis presented a much narrower range, with \u0026part;13C values between \u0026minus;\u0026thinsp;27.3 an -25 %.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The evaluation of \u003cem\u003eβ\u003c/em\u003e-index\u003c/h2\u003e \u003cp\u003eThe three vineyards utilized in this study all fall under a temperate, dry climate classification (Peel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), but both the 2020 and 2021 seasons took place during hyper arid conditions (N. Torres et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All three vineyards received essentially no measurable precipitation over the course of the growing seasons, limiting the only water additions to those delivered via irrigation. Furthermore, the intentional hierarchy of irrigation management, replacing 90% of ETc at Davis, 70% of ETc in OFV8, and 50% of ETc in Oakville, provided the opportunity to evaluate the applicability of the Priestly-Taylor \u003cem\u003eβ\u003c/em\u003e-index in the elucidation of vineyard water status under seasonal water deficit conditions. The hypothesis follows that if a vineyard was experiencing water stress, grapevines would their close stomata to regulate water status, therefore reducing the ETa of the vineyard and leading to a reduction of the \u003cem\u003eβ\u003c/em\u003e-index (Eq.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eThe Priestly-Taylor \u003cem\u003eβ\u003c/em\u003e-coefficient was conceived to measure the aridity of a land surface when compared to a water saturated surface (Priestley and Taylor, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). In the context of wet-surface evapotranspiration, the coefficient converges on a theoretical minimum value of 1.26 (Eichinger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Across much of the literature regarding the \u003cem\u003eβ\u003c/em\u003e-index, it deviates minimally from the theoretical value of 1.26. Herein, we report values of \u003cem\u003eβ\u003c/em\u003e that range from 0.5 to 2.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The \u003cem\u003eβ\u003c/em\u003e index ranged widely because the boundary layer of a non-water saturated surface was rougher than that of a saturated one (Campbell and Norman, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), furthermore, the boundary layer of a tall woody crop is both rougher and more variable than that of a wet turf grass (Marino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, the ability of grapevine to close their stomata and arrest transpiration is a phenomenon that occurs outside of theoretical considerations of wet-surface evaporation. In the context of this study, there was ample energy to drive evapotranspiration from bare soils and grapevines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the limitations on evapotranspiration were those imposed by the vineyard water management regime.\u003c/p\u003e \u003cp\u003eThe energy balance components play a critical role in driving ETa. The daily latent heat flux (LE) is the product of \u0026ldquo;E\u0026rdquo;, i.e. the water mass flux density (kg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and \u0026ldquo;λ\u0026rdquo;, which is the amount of energy needed to vaporize 1.0 kg of water (λ\u0026thinsp;=\u0026thinsp;2.45 MJ kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Shapland et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and, therefore, E\u0026thinsp;=\u0026thinsp;LE/λ and LE \u0026asymp; Rn - H on a daily basis. Since 1.0 kg of water covers 1.0 m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e to a depth of 1.0 mm, 1.0 kg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e = 1.0 mm d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This process is critical to the transfer of water vapor from leaf stomata (Keller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and from the soil and other wet surfaces. Since Rn is typically large compared to H, the LE is determined principally by the value of Rn. Observed Rn values were similar between the three vineyards, so the differences in the LE of each vineyard were determined mainly by the value of H, which is the aerodynamic component of evapotranspiration as previously reported in other cropping systems. Sensible heat flux is the transfer of kinetic energy per unit area per unit time from one location to another. For vertical sensible heat flux (H) is positive, the warm air flux is upwards and it reduces the energy is available for vaporization of water at the surface. When H is negative, the warm air flux is downward and it increases energy available for vaporization at the surface. High ET rates occur when the Rn is high and H is either close to zero or negative. High positive H values are indicative of low vaporization and high heat transfer from the surface. Therefore, it follows that more positive values of H indicate lower values of \u003cem\u003eβ\u003c/em\u003e (Marino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our results indicate that higher values of H do indeed lead to lower values of \u003cem\u003eβ \u003c/em\u003ethat corroborate the work by Marino et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The OFV3 and OFV8 had significantly higher values of H than Davis, and they also had significantly lower values of \u003cem\u003eβ\u003c/em\u003e. Yet, while OFV3 and OFV8 had nearly identical values of H, the \u003cem\u003eβ\u003c/em\u003e value of the vineyards was significantly different. The difference in the \u003cem\u003eβ\u003c/em\u003e value of the two Oakville Vineyards likely resulted from different irrigation regimes tailored to the style of wine grapes produced from each vineyard. The OFV8 received 20% more applied water than OFV3. Therefore, OFV8 maintained a higher stomatal conductance, relative to OFV3, over the course of the season, allowing it to transpire more water. The idea of \u003cem\u003eβ\u003c/em\u003e relating to water status is in line with Marino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, who posited that orchards experiencing transpiration reducing water stress may also display lower values of \u003cem\u003eβ\u003c/em\u003e due to partial stomatal closure that increases canopy resistance and reduces transpiration.\u003c/p\u003e \u003cp\u003eWhile the physical laws governing \u003cem\u003eβ\u003c/em\u003e are complex, it serves to remember that \u003cem\u003eβ\u003c/em\u003e is simply the ratio of ETa to ETeq.\u0026nbsp;Clearly, Eta\u0026thinsp;=\u0026thinsp;ETeq when \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00 and \u003cem\u003eβ \u003c/em\u003e\u0026lt; 1.00 whenever ETa has fallen below ETeq, which represents a well-watered equilibrium evapotranspiration (Eichinger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Marino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Values greater than \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00, imply that there is sufficient energy available and resistances are low enough to drive ETa as high or higher than ETeq.\u0026nbsp;\u003cem\u003eβ\u003c/em\u003e approaches 1.00 in situations where the total resistance (aerodynamic\u0026thinsp;+\u0026thinsp;canopy) from the canopy to the overlying air column is sufficiently high to decrease ETa to near ETeq.\u0026nbsp;Yet, in the arid setting of northern California\u0026rsquo;s irrigated vineyards, it seems that there is often enough energy and low enough resistance to energy flux to keep ETa above ETeq.\u0026nbsp;In fact, when values of \u003cem\u003eβ\u003c/em\u003e fell below 1.00 it was indicative that the vines were incurring transpiration reducing stress, and the subsequent stomatal closure was restricting stomatal conductance and transpiration, which lowers ETa to values near that of a well-watered ETeq.\u003c/p\u003e \u003cp\u003eOFV3 provided the best example of a vineyard experiencing transpiration reducing stress, as the season progressed values of ETa fell below ETeq, indicating that the water stressed vineyard had dropped beneath equilibrium values of evapotranspiration. The water deficits at OFV8 drove a similar behavior, where Eta decreased towards ETeq as the season progressed, yet because the water deficits in OFV8 were milder, values of \u003cem\u003eβ\u003c/em\u003e approach closer to 1.00. Finally, the vineyard at UC Davis incurred only moderate water deficits, and the relative availability of soil water allowed for sustained transpiration and maintained values of Eta much greater than those of ETeq.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. \u003cem\u003eβ\u003c/em\u003e as an Index of Grapevine Water Status\u003c/h2\u003e \u003cp\u003eIn the three vineyards studied, three tiers of water deficit emerged. The vineyard at UC Davis exhibited minimal water deficits in both growing seasons (\u003cem\u003eΨ\u003c/em\u003es \u0026gt; -1.0 Mpa); OFV8 experienced low to moderate levels of water deficit in both seasons; and OFV3 reached moderate to severe tiers of water deficit (\u003cem\u003eΨ\u003c/em\u003es \u0026lt; -1.4 MPa) in both seasons (van Leeuwen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The three levels of water deficit carried over to the regression of \u003cem\u003eβ\u003c/em\u003e against mean daily values of \u003cem\u003eΨ\u003c/em\u003es (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Lower values of \u003cem\u003eβ\u003c/em\u003e correspond to more negative values of \u003cem\u003eΨ\u003c/em\u003es. The relationship between \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eΨ\u003c/em\u003es presented itself as linear, which implied that there were two end-member behaviors with a maximum \u003cem\u003eΨ\u003c/em\u003es occurring at minimum values of beta. OFV3 represented the stressed endmember and maintained a strong linear correlation between \u003cem\u003eΨ\u003c/em\u003es and β.Within the low-stress endmember, UC Davis, a linear regression was less effective in characterizing the relationship between \u003cem\u003eΨ\u003c/em\u003es and β. The breakdown of a linear relationship at Davis corroborates our hypothesis that β serves as an index of physiological stress. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, β is best employed as a proxy of grapevine physiological response to stress when observing water-stressed vineyards (e.g., OFV3 and OFV8). An inter-vineyard relationship, however, still exists, with higher values of β clearly indicating lower values of water stress (i.e., UC Davis).\u003c/p\u003e \u003cp\u003eOne possible explanation for the breakdown of the β coefficient in the non-stressed vineyard at UC Davis is that the vines at Davis reached their maximum photosynthetic efficiency. Whereby, the large leaf area index of the Petit Sirah grapevines likely induced higher levels of transpiration (Campos et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Williams and Ayars, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). And so much water was moving through the grapevines at UC Davis that it is plausible that grapevines may have exceeded their maximum photosynthetic efficiency, running into feedback inhibitions to photosynthesis (Bota et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paul and Pellny, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The presence of photosynthetic feedback inhibitions explains the breakdown in linear relationship between \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eΨ\u003c/em\u003es. On one end, stomatal closure in response to water deficits drove down values of \u003cem\u003eβ\u003c/em\u003e until they reached a threshold of 0.5. On the other end, ample free energy for evapotranspiration coupled with adequate water availability and a large enough canopy drove values of transpiration so high that there was not a sink available for the products of photosynthesis; feedback inhibitions lead to stomatal closure and conserved \u003cem\u003eΨ\u003c/em\u003es, even at relatively high values of \u003cem\u003eβ\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAn alternative hypothesis was that the timing of measurements was not adequate to capture changes in transpiration that occur within a single day. In such a scenario, the high energy available for evapotranspiration induced maximum values of transpiration in the morning, which were curtailed in the afternoon as grapevines closed stomata to regulate their water status in response to increasingly negative soil water potentials (Personne et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Measurements would have then been carried out after grapevines had arrived after their most negative values of \u003cem\u003eΨ\u003c/em\u003es for the day, at which point transpiration would have been reduced in an effort to conserve water (Nazareth Torres et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Disentanglement of feedback inhibitions to photosynthesis and limitations of soil water potential is complex and would require detailed knowledge of the rate limiting variable, either soil water availability or sink availability for the products of photosynthesis. Regardless, an up sampling of the \u003cem\u003eβ\u003c/em\u003e index would have been useful in tracking the dynamic relationship between photosynthesis and transpiration.\u003c/p\u003e \u003cp\u003eEven though \u003cem\u003eβ\u003c/em\u003e and \u003cem\u003eΨ\u003c/em\u003es were correlated between vineyards, the relationship did not carry over to the individual vineyards. The lack of a relationship within individual vineyards was not surprising, as it would require the correlation of a biometeorogical index derived to assess the aridity of large wet surfaces (Priestley and Taylor, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) with individual grapevines from a single vineyard block. A more realistic application of \u003cem\u003eβ\u003c/em\u003e would be the differentiation of water status within vineyard blocks spread across a large production setting. In our experiment, \u003cem\u003eβ\u003c/em\u003e was able to parse differences in the mean daily \u003cem\u003eΨ\u003c/em\u003es of OFV3 and OFV8, which were about 200 m apart, as well as at UC Davis, approximately 60 km away.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. \u003cem\u003eSensitivity of β index\u003c/em\u003e to \u003cem\u003eLeaf Gas Exchange\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe relationship between \u003cem\u003eβ\u003c/em\u003e and leaf-gas exchange variables differed from \u003cem\u003eΨ\u003c/em\u003es, in that gs and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e were both linearly correlated with \u003cem\u003eβ\u003c/em\u003e when measured between vineyards. The linear relationship found allowed us to reject the null hypothesis of the experiment, wherein experimental vineyards would not have lower values of gs \u003csub\u003ean\u003c/sub\u003ed \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e due to the season-long water restrictions. The OFV8 maintained significantly higher transpiration (gs) and assimilation rates (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e) over the course of both growing seasons. While both the mean daily gs and \u003cem\u003eA\u003c/em\u003eN were significantly correlated to \u003cem\u003eβ\u003c/em\u003e, the strength of the relationship was relatively weak, with \u003cem\u003eβ\u003c/em\u003e explaining less than 20% of the variance in leaf-gas exchange measurements between vineyards. The weak correlation can be explained by the native complexity involved in leaf-level regulation of transpiration (Buckley, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was further compounded by differences of environment and genotype within the experiment.\u003c/p\u003e \u003cp\u003eFocusing instead, on the intra-vineyard relationship between \u003cem\u003eβ\u003c/em\u003e and leaf-gas exchange parameters yielded much more interesting results. In OFV3, where water deficits were higher, a quadratic relationship emerged between \u003cem\u003eβ\u003c/em\u003e and both gs and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e. As \u003cem\u003eβ\u003c/em\u003e approached 0.5, gs fell below 100 mmol H2O m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e dropped to less than 10 \u0026micro;mol CO2 m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e indicating that stomatal closure is closely coupled to the ratio of ET\u003csub\u003ea\u003c/sub\u003e:ET\u003csub\u003eeq\u003c/sub\u003e (Cifre et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Levin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lovisolo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). At the other end of the spectrum, when \u003cem\u003eβ\u003c/em\u003e approached 0.8, values of both gs and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e also approached their minimum. Stomatal conductance reaching a minimum at the highest values of \u003cem\u003eβ\u003c/em\u003e was a result of either: 1) the aforementioned photosynthetic feedback inhibitions; 2) a very close coupling of ETa and ETeq in water stressed vineyards; 3) timing of measurements.\u003c/p\u003e \u003cp\u003eThe water deficits in OFV3 reduced transpiration within the vineyard and caused \u003cem\u003eβ\u003c/em\u003e to be more responsive to environmental conditions than applied water amounts, relative to OFV8 and UC Davis. It appeared that the closer ET\u003csub\u003ea\u003c/sub\u003e was to ET\u003csub\u003eeq\u003c/sub\u003e, the more effect ET\u003csub\u003eeq\u003c/sub\u003e had on determining the value of \u003cem\u003eβ\u003c/em\u003e. In the case of reduced stomatal conductance at higher values of \u003cem\u003eβ\u003c/em\u003e, we postulate that large increases in the mean daily temperature (\u003cem\u003ei.e.\u003c/em\u003e, heat waves) induced stomatal closure while also decreasing the value of ETeq and thereby increasing the value of \u003cem\u003eβ\u003c/em\u003e even as values of ET\u003csub\u003ea\u003c/sub\u003e were driven down. The ability of \u003cem\u003eβ\u003c/em\u003e to capture stomatal closure associated with water deficits as \u003cem\u003eβ\u003c/em\u003e approached its minimum, and stomatal closure resultant of excessive air temperature as \u003cem\u003eβ\u003c/em\u003e approached its maximum, is exemplary of the insights provided by coupling plant physiology and environmental parameters within a single index.\u003c/p\u003e \u003cp\u003eOnce again, an alternative hypothesis was that the timing of measurements was inadequate to reveal diurnal patterns of transpiration in response to available soil water content. The scenario unfolds as mentioned above, where maximum free energy for evapotranspiration drives a large flux of transpiration in the morning, at which point stomata close to conserve soil water. Measurements carried out after stomatal closure would read minimum values of g\u003csub\u003es\u003c/sub\u003e on days where \u003cem\u003eβ\u003c/em\u003e was at a maximum.\u003c/p\u003e \u003cp\u003eThe moderate water deficits in OFV8 resulted in a less defined relationship between \u003cem\u003eβ\u003c/em\u003e and leaf-gas exchange. Average daily values of stomatal conductance never fell below 125 mmol H\u003csub\u003e2\u003c/sub\u003eO m\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in either season and the general absence of transpiration reducing stress was not sensed by the \u003cem\u003eβ\u003c/em\u003e coefficient at an intra-vineyard level. Once again, however, the ability of \u003cem\u003eβ\u003c/em\u003e to differentiate values of g\u003csub\u003es\u003c/sub\u003e between OFV3 and OFV8 along a linear regression line pointed to the applicability of the \u003cem\u003eβ\u003c/em\u003e-index in larger scale commercial vineyards.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. \u003cem\u003eβ as an Index of Water Stress\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eLinear regressions between seasonal integrals of \u003cem\u003eΨ\u003c/em\u003es and \u0026part;\u003csup\u003e13\u003c/sup\u003eC confirmed the consensus that \u0026part;13C was a reliable indicator by which to assess seasonal water deficits in grapevines (Bchir et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chone et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gaudill\u0026egrave;re et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; van Leeuwen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Additionally, regressions between \u0026part;\u003csup\u003e13\u003c/sup\u003eC and gas exchange measurements, g\u003csub\u003es\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e, showed strong linear correlations, reaffirming the findings of Yu et al. (2021) and providing further evidence for the applicability of \u0026part;\u003csup\u003e13\u003c/sup\u003eC as a proxy for leaf photosynthesis in arid climates. Measurements of \u0026part;\u003csup\u003e13\u003c/sup\u003eC added another layer of validation to the hypothesis that OFV3 would reduce its transpiration in response to severe water deficits. On the other hand, UC Davis vineyard was again shown not to have incurred as severe water stress, maintaining \u0026part;\u003csup\u003e13\u003c/sup\u003eC values well above the threshold of -23% (van Leeuwen et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Brillante et al., 2020). Unexpectedly, there was no clear relationship between \u0026part;\u003csup\u003e13\u003c/sup\u003eC and seasonal integrals of \u003cem\u003eΨ\u003c/em\u003es in OFV8. We posited that the lack of canopy cover over the bare soil, a function of the young vines (\u0026lt;\u0026thinsp;3 years), has mitigated this otherwise fundamental relationship (Nazareth Torres et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results indicated daily evaluation of the \u003cem\u003eβ\u003c/em\u003e-index is a means by which to assess vine-water status in lieu of repeated pressure chamber measurements or infra-red gas exchange measurements. Furthermore, the \u003cem\u003eβ\u003c/em\u003e-index may be especially useful in tracking leaf-gas exchange measurements in water-stressed vineyards and as a proxy for stem water potential in commercial production settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe two-year comparison of measurements of \u003cem\u003eβ\u003c/em\u003e to ground-truth measurement of grapevine water status and leaf gas exchange provided an opportunity to evaluate \u003cem\u003eβ\u003c/em\u003e as a novel index of grapevine water stress. We found that given access to the equipment necessary to measure surface renewal and a local weather station, the \u003cem\u003eβ\u003c/em\u003e can be measured with relative ease, allowing for rapid, real-time quantification of vineyard water status at scale. Furthermore, \u003cem\u003eβ\u003c/em\u003e was well related to \u003cem\u003eΨ\u003c/em\u003es, g\u003csub\u003es\u003c/sub\u003e, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e if enough water stress existed within the vineyard. The ability to relate real-time measures of \u003cem\u003eβ\u003c/em\u003e to grapevine physiological responses to water deficits may allow \u003cem\u003eβ\u003c/em\u003e to serve as a powerful tool for vineyard operators. The \u003cem\u003eβ\u003c/em\u003e can be used as a replacement for real time assessment of \u003cem\u003eΨ\u003c/em\u003es and can provide indication of when to switch on irrigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSKK and DZ conceived and designed the trial. \u0026nbsp;SK, RY, JT and LEM curated the data. \u0026nbsp;SK curated, analyzed the data and wrote the first version of the manuscript. \u0026nbsp; RS critically reviewed the manuscript and curated the data. All authors read and made contributions to the manuscript and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT of INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest or competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data, code and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContact the corresponding author to request data, code and material.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome 300, D05109.\u003c/li\u003e\n\u003cli\u003eAllen, R.G., Walter, I.A., Elliott, R., Howell, T.A., Itenfisu, D., Jensen, M.E., 2005. The ASCE standardized reference evapotranspiration equation.\u003c/li\u003e\n\u003cli\u003eAllen, R.G., Pruitt, W.O., Wright, J.L., Howell, T.A., Ventura, F., Snyder, R.L., Itenfisu, D., Steduto, P., Berengena, J., Baselga Yrisarry, J., Smith, M., Pereira, L.S., Raes, D., Perrier, A., Alves, I., Walter, I. and Elliott, R. 2006. A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method. Agricultural Water Manual, 81: 1-22. \u003c/li\u003e\n\u003cli\u003eAlston, J.M., Sambucci, O., 2019. Grapes in the world economy, in: The Grape Genome. Springer, pp. 1\u0026ndash;24. https://doi.org/10.1007/978-3-030-18601-2_1\u003c/li\u003e\n\u003cli\u003eBchir, A., Escalona, J.M., Gall\u0026eacute;, A., Hern\u0026aacute;ndez-montes, E., Tortosa, I., Braham, M., Medrano, H., 2016. Carbon isotope discrimination (\u0026delta;13C) as an indicator of vine water status and water use efficiency (WUE): Looking for the most representative sample and sampling time. Agric. Water Manag. 167, 11\u0026ndash;20. https://doi.org/10.1016/j.agwat.2015.12.018\u003c/li\u003e\n\u003cli\u003eBota, J., Stasyk, O., Flexas, J., Medrano, H., 2004. Effect of water stress on partitioning of 14C-labelled photosynthates in Vitis vinifera. Funct. Plant Biol. 31, 697\u0026ndash;708. https://doi.org/https://doi.org/10.1071/FP03262\u003c/li\u003e\n\u003cli\u003eBrillante, L., Mathieu, O., L\u0026eacute;v\u0026ecirc;que, J., van Leeuwen, C., Bois, B., 2018. Water status and must composition in grapevine cv. Chardonnay with different soils and topography and a mini meta-analysis of the \u0026delta;13C/water potentials correlation. J. Sci. Food Agric. 98, 691\u0026ndash;697. https://doi.org/10.1002/jsfa.8516\u003c/li\u003e\n\u003cli\u003eBrutsaert, W., Stricker, H., 1979. 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O. 1977. \u003cem\u003eCrop evapotranspiration.\u003c/em\u003e FAO Irrigation and Drainage Paper No. 24, FAO, Rome, Italy, p 34.\u003c/li\u003e\n\u003cli\u003eDenmead, O.T., McIlroy, I.C., 1970. Measurements of non-potential evaporation from wheat. Agric. Meteorol. 7, 285\u0026ndash;302. https://doi.org/https://doi.org/10.1016/0002-1571(70)90024-5\u003c/li\u003e\n\u003cli\u003eDoorembos, J., Pruitt, W.O., Aboukhaled, A., Damagnez, J., Dastane, N.G., Van Den Berg, C., Rijtema, P.E., Ashford, O.M., Frere, M., 1977. Guidelines for predicting crop water requirements. FAO Irrig. Drain. Pap. 24.\u003c/li\u003e\n\u003cli\u003eEichinger, W.E., Parlange, M.B., Stricker, H., 1996. On the Concept of Equilibrium Evaporation and the Value of the Priestley-Taylor Coefficient. Water Resour. Res. 32, 161\u0026ndash;164. https://doi.org/https://doi.org/10.1029/95WR02920\u003c/li\u003e\n\u003cli\u003eFarquhar, G.D., Ehleringer, J.R., Hubick, K.T., 1989. Carbon isotope discrimination and photosynthesis. 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Water 9, 837. https://doi.org/https://doi.org/10.3390/w9110837\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"drought, evapotranspiration, energy balance, latent heat, sensible heat flux, surface renewal","lastPublishedDoi":"10.21203/rs.3.rs-2223673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2223673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeasonal management of plant water status and the accompanying physiological responses are critical aspects of viticultural production. Presently, grapevine (\u003cem\u003eVitis vinifera\u003c/em\u003e, L.) water status is measured via in-season measurements of stem water potential or post-season analysis of must carbon isotope ratios, with the former limited by reliance on laborious measurements and the latter providing information post-season. Therefore, there is a gap in reliable, real-time measurements of plant water status. Technological advances in surface renewal measurement in vineyards have provided an economical and reliable method for measuring actual evapotranspiration of a vineyard. This experiment utilized surface renewal calculations to derive a novel index of grapevine water stress, the Priestly-Taylor index (\u003cem\u003eβ\u003c/em\u003e-index), and related it to measurements of stem water potential, leaf-gas exchange, and must carbon isotopes from three vineyards with differing irrigation strategies over two growing seasons. The sensible heat flux, latent heat flux and net radiation varied across these vineyards and affected the actual vineyard evapotranspiration measured. Likewise, the \u003cem\u003eβ\u003c/em\u003e-index was different across these vineyards and ranged from 1.7 to 2.1 in the Sacramento Valley of California to 0.5 to 1.2 in the Napa Valley of California. The \u003cem\u003eβ\u003c/em\u003e-index was related to stem water potential, net carbon assimilation and stomatal conductance (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.42, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45, r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.33, respectively). Results indicated that the \u003cem\u003eβ\u003c/em\u003e-index was an indicator of real-time vineyard water status and a proxy for physiological responses in vineyards. The coupling of atmospheric controls on evapotranspiration with plant physiological responses makes \u003cem\u003eβ\u003c/em\u003e a powerful tool for irrigation management in large scale agrosytems.\u003c/p\u003e","manuscriptTitle":"Adapting the Priestly-Taylor Index as a Physiological Stress Indicator in Vineyard Agrosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-04 14:15:04","doi":"10.21203/rs.3.rs-2223673/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b8775eb-fbcf-4c4f-a5ca-2809f473783f","owner":[],"postedDate":"November 4th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-19T12:14:48+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-04 14:15:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2223673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2223673","identity":"rs-2223673","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}