Modeling Stem Water Potential by Separating the Effects of Soil Water Availability and Climatic Conditions on Water Status in Grapevine (Vitis vinifera L.)

Abstract

Measuring seasonal plant water status is critical in choosing appropriate management strategies to ensure yields and quality of agricultural products, particularly in a context of climate change. Water status of grapevines is known to be a key factor for yield, grape composition, and wine quality. Predawn leaf water potential (PLWP) and stem water potential (SWP) proved to be simple and precise indicators for assessing grapevine water status and subsequent same-day spatial comparisons. A drawback of SWP is that it does not allow for temporal comparisons, because the measured value is impacted both by soil water availability and climatic conditions on the day of measurement. The objectives of this study are i) to provide a model that separates the effect of soil water content from the effect of climatic conditions on the SWP value and ii) to standardize the SWP value to a value under predefined reference climatic conditions in order to compare SWP values collected under different climatic conditions. SWP and PLWP were temporally assessed on three soil types in Saint-Émilion (Bordeaux, France) in 2015 and on five soil types in Margaux (Bordeaux, France) in 2018 using a pressure chamber. SWP measurements on two consecutive days with contrasting climatic conditions allowed to assess the impact of these conditions on SWP values. A large portion of the variability in SWP values was explained by PLWP. Model selection further showed that the addition of maximum air temperature and seasonality explained a significant amount of the remaining variability in SWP values. SWP values could be successfully standardized to a theoretical value under reference climatic conditions, which allows for temporal comparisons of SWP values. A plant-based measurement, such as the water potential, can be considered as the most straightforward indicator of plant water status as it integrates the effects of soil, plant, and atmospheric conditions. More precise interpretation of SWP values provides winegrowers with a tool to more adequately implement short- and long-term management strategies to adapt to drought in order to ensure yield and grape quality.

Keywords: grapevine; maximum air temperature; modeling; predawn leaf water potential; stem water potential; water status.

Figure 1

Seasonal pattern of stem water potential and predawn leaf water potential on the primary axis, and precipitation and T_max on the secondary axis in (A) Saint-Émilion in 2015 and (B) Margaux in 2018. T_max at the days of sampling are specifically represented by the gray circles. Vertical arrows represent dates of mid-veraison for Merlot (30/07) and Cabernet franc (08/08) in 2015, and Merlot and Cabernet Sauvignon (08/08) in 2018. Values are averages over plots as described in Table 1 . Error bars indicate standard deviations.

Figure 2

Relationship between stem water potential and predawn leaf water potential (PLWP) along a temperature gradient [T_max, from 23.8°C (blue) to 38.9°C (red)] collected during the 2015 and 2018 seasons for each experimental plot (n=1,061). The letters correspond to the plots as specified in Table 1 . The vertical dotted line is drawn for reference at −0.3 MPa PLWP.

Figure 3

Stem water potential over the course of the 2015 and 2018 growing season along a temperature gradient [T_max, from 23.8°C (blue) to 38.9°C (red)], where the size of the points corresponds to predawn leaf water potential (PLWP) categories in steps of 0.1 MPa. Data was averaged for each plot within each PLWP category. DOY, day of the year.

Figure 4

Comparison of (A) the observed stem water potential (SWP) on day 1 versus the observed SWP on day 2 and (B) the observed SWP on day 1 versus the observed SWP on day 2 standardized to T_max on day 1 as per Eqn. 1. The solid line represents the 1:1 line and the dotted line represents the linear regression (n = 512).

Figure 5

Comparison of (A) the observed stem water potential (SWP) on DOY_n versus the observed SWP on DOY_n>x, where x is at least greater than 2 days and has a predawn leaf water potential almost equal to the observed SWP on DOY_n and (B) the observed SWP on DOY_n versus the observed SWP at that later date standardized according to Eqn. 2. The solid line represents the 1:1 line and the dotted line represents the linear regression (n = 385).