Influence of berry maturity, maceration time and wine maturation on the polyphenols and sensory characteristics of pinot noir and Cabernet-Sauvignon

There is currently a trend towards the production of wines with low alcohol content. To achieve this, grapes with low sugar content must be used. There are techniques at the vineyard level that can delay ripening and avoid excessive sugar accumulation without, a priori, affecting the final polyphenol content. Postveraison shoot trimming (PVST) is experimentally evaluated for these purposes, but its impact under Uruguayan climatic conditions with high interannual variability is not known. The aim of this work is to assess the PVST in Tannat and Merlot cultivars and their impact on yield components, plant balance and berry primary composition. In this study, two commercial vineyards of 10 years old Tannat and Merlot (grafted on SO4) at Canelones Department were selected. During the 2020-201 growing season, grapevines were submitted to PVST when grapes reached 15º Brix. In a randomized block, trimmed (T) and control (C) plants were evaluated with three repetitions each cultivar. Evaluation of the evolution of primary berry composition during ripening, measurement of yield components and plant balance were performed. For both cultivars, PVST did not affect yield components. Merlot reached 5.4 kg per plant and Tannat 7.1 kg, with not statistical significance between treatments. However, statistical differences were observed in terms of plant balance. In Merlot Ravaz Index reached a difference of 5.3 (12.0 in T and 6.7 in C) meanwhile Tannat reached 3.5 of statistical difference (13.7 in T and 10.2 in C). The tendency to imbalance for the treated plants had an impact on the final grape composition. Merlot grapes showed statistical difference in final total acidity (0.3 g of difference between treatments) while treatments impact final sugar content on Tannat grapes (10.0 g of difference between treatments). Further studies are needed to assess the impact of different canopy management techniques in our conditions.

In viticulture, a warming climate can have a very significant impact on grapevine development and therefore on the quality and characteristics of wines across different spatial scales, ranging from global to local. In order to adapt wine-growing to climate change, global climate models can be used to define future scenarios, but only at the scale of major wine regions. Despite the huge progress made over the last ten years in terms of the spatial resolution of climate models (now downscaled to a few square kilometres), they are not yet sufficiently precise to account for the local climate variability associated with such parameters as local topography, in spite of these parameters being decisive for vine and wine characteristics. This study describes a method to downscale future climate scenarios to vineyard scale. Networks of data loggers have been used to collect air temperature at canopy level in the Waipara winegrowing region (New Zealand) over five growing seasons. These measurements allow the creation of fine-scale geostatistical models and maps of temperature (at 100 m resolution) for the growing season. In order to model climate change at pilot site scale, these geostatistical models have been combined with regional climate change predictions for the periods 2031-2050 and 2081-2100 based on the RCP8.5 climate change scenario. The integration of local climate variability with regionalized climate change simulations allows assessment of the impacts of climate change at the vineyard scale. The improved knowledge gained using this methodology results from the increased horizontal resolution that better addresses the concerns of winegrowers. The results provide the local winegrowers with information necessary to understand current processes, as well as historical and future viticulture trends at the scale of their site, thereby facilitating decisions about future response strategies.

The development of new generations of multispectral satellites such as Sentinel-2 opens possibilities as to vine water status assessment (Cohen et al., 2019). Based on a three years field campaign, a model of Stem Water Potential (SWP) estimation on vine using four satellite bands in Red, Red-Edge, NIR and SWIR domains was developed (Laroche-Pinel et al., 2021). The model relies on SWP field measures done using a pressure chamber (Scholander et al., 1965), which is a common, robust and precise method to assess vine water status (Acevedo-Opazo et al., 2008). The model was mainly developed from from SWP measures on Syrah N (Laroche Pinel E., 2021).

A large scale monitoring was organized in different vineyards in the Mediterranean region in 2021. 10 varieties amongst the most represented in this area were monitored (Cabernet sauvignon N, Chardonnay B, Cinsault N, Grenache N, Merlot N, Mourvèdre N, Sauvignon B, Syrah N, Vermentino B, Viognier B). The model was used to produce water status maps from Sentinel-2 images, starting from the beginning of June (fruit set) up to September (harvest). The average estimated SWP for each vine was compared to actual field SWP measures done by wine growers or technicians during usual monitoring of irrigation programs. The correlations between mean estimated SWP and mean measured SWP were at the same level than expected by the model. (Laroche Pinel, 2021) The general SWP kinetics were comparable. The estimated SWP would have led to same irrigation decisions concerning the date of first irrigation in comparison with measured SWP.

Acevedo-Opazo, C., Tisseyre, B., Ojeda, H., Ortega-Farias, S., Guillaume, S. (2008). Is it possible to assess the spatial variability of vine water status? OENO One, 42(4), 203.
Cohen, Y., Gogumalla, P., Bahat, I., Netzer, Y., Ben-Gal, A., Lenski, I., … Helman, D. (2019). Can time series of multispectral satellite images be used to estimate stem water potential in vineyards? In Precision agriculture ’19, The Netherlands: Wageningen Academic Publishers, pp. 445–451.
Laroche-Pinel, E., Duthoit, S., Albughdadi, M., Costard, A. D., Rousseau, J., Chéret, V., & Clenet, H. (2021). Towards vine water status monitoring on a large scale using sentinel-2 images. remote sensing, 13(9), 1837.
Laroche-Pinel,E. (2021). Suivi du statut hydrique de la vigne par télédétection hyper et multispectrale. Thèse INP Toulouse, France.
Scholander, P.F., Bradstreet, E.D., Hemmingsen, E.A., & Hammel, H.T. (1965). Sap pressure in vascular plants: Negative hydrostatic pressure can be measured in plants. Science, 148(3668), 339–346.

Root system architecture (RSA) is important for soil exploration and edaphic resources acquisition by the plant, and thus contributes largely to its productivity and adaptation to environmental stresses, particularly soil water deficit. In grafted grapevine, while the degree of drought tolerance induced by the rootstock has been well documented in the vineyard, information about the underlying physiological processes, particularly at the root level, is scarce, due to the inherent difficulties in observing large root systems in situ. The objectives of this study were to determine genetic differences in the root architectural traits and their relationships to water uptake in two Vitis rootstocks genotypes (RGM, 140Ru) differing in their adaptation to drought. Young rootstocks grafted upon the Riesling variety were transplanted into cylindrical tubes and in 2D rhizotrons under two conditions, well watered and moderate water stress. Root traits were analyzed by digital imaging and the amount of transpired water was measured gravimetrically twice a week. Root phenotyping after 30 days reveal substantial variation in RSA traits between genotypes despite similar total root mass; the drought-tolerant 140Ru showed higher root length density in the deep layer, while the drought-sensitive RGM was characterised by shallow-angled root system development with more basal roots and a larger proportion of fine roots in the upper half of the tube. Water deficit affected canopy size and shoot mass to a greater extent than root development and architectural-related traits for both 140Ru and RGM, suggesting vertical distribution of roots was controlled by genotype rather than plasticity to soil water regime. The deeper root system of 140Ru as compared to RGM correlated with greater daily water uptake and sustained stomata opening under water-limited conditions but had little effect on above-ground growth. Our results highlight that grapevine rootstocks have constitutively distinct RSA phenotypes and that, in the context of climate change, those that develop an extensive root network at depth may provide a desirable advantage to the plant in coping with reduced water resources.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.