Water scarcity is a key limiting factor in agriculture. Grapevines react at the physiological, biochemical and genetic level to tolerate water constraints. Even though grapevines are considered relatively tolerant to water deficits, grapevine growth and yield can be seriously reduced under water deficit. Drought‐tolerant rootstocks are expected to enable the scion to grow and yield when water supply is limited. Genetic machinery allows rootstocks to control water extraction capacity and scion transpiration. Numerous works have demonstrated the positive role of drought‐tolerant rootstocks on the control of cultivar's leaf stomatal conductance and therefore on canopy transpiration. The mechanisms, in terms of signalisation and gene functioning, need further study. Furthermore, there is no standardised methodology to rank rootstocks in terms of their tolerance to drought. A potential effect of rootstocks on stomatal development is also discussed. This review will critically discuss the current knowledge of the mechanisms of drought tolerance afforded by rootstocks, taking into account the scion/rootstock interaction, and will present some of the challenges for future investigations.

Abstract:The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF) and extreme gradient boosting (XGBoost), for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA) and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176), RF produced a test accuracy of 83.3% (KHAT (kappa analysis) = 0.67), and XGBoost a test accuracy of 80.0% (KHAT = 0.6). Using the subset of wavebands (p = 18) produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%). The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

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Background and Aims:  Carotenoids and chlorophylls perform a number of essential roles in plants making their accurate quantification important to a variety of studies. We aimed to develop an extraction protocol to accurately determine the photosynthetic pigments in grapevine leaf and berry tissue, specifically focusing on limiting the degradation of these pigments. Methods and Results:  An extraction protocol for grapevine leaf and berry tissue was systematically optimised by identifying a number of critical parameters. Extracted pigments were analysed using Reversed Phase‐High Performance Liquid Chromatography (RP‐HPLC). Specific parameters that were optimised included avoiding freeze‐drying the material; the volume of acetone and the time required to extract all the pigments from the tissue; the addition of 0.1% (v/v) N‐ethyldiisopropylamine to berry extracts to minimise pigment degradation during the extraction procedure; and avoiding concentration of the extracts that otherwise resulted in differential degradation of pigments. Additionally, the method of extraction and normalisation with an internal standard was adapted and improved for accuracy. The optimised protocol was validated using authentic standards and its utility shown by analysing the pigment content of berries and leaves at different growth stages. Conclusions:  A method has been developed that is able to extract and accurately quantify, by means of HPLC profiling, the levels of photosynthetic pigments from grape berries and leaves. The method avoided any degradation of the pigments during the extraction and was applicable to both berries and leaves in different stages of growth and development, indicating its general usefulness to vegetative and reproductive organs, even if their metabolic states are very different. Significance of Study:  The divergence of methods used for photosynthetic pigment analysis in plants, each with specific advantages and disadvantages were considered and used to optimise a number of parameters in a single method that proved to be applicable to plant organs in different developmental stages. The method is fast, applicable to vegetative and reproductive grapevine tissues, avoids degradation of pigments and ensures maximum accuracy when quantifying these important pigments.