Contribution of grape and oak wood barrels to pyrrole content in wines – Influence of several cooperage parameters

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.

Global climate change affects regional climates and hold implications for wine growing regions worldwide. Although winegrowers are constantly adapting to internal and external factors, it seems relevant to develop tools, which will allow them to better define actual and future agro-climatic potentials. Within this context, we develop a modelling approach, able to simulate the impact of environmental conditions and constraints on vine behaviour and to highlight potential adaptation strategies according to different climate change scenarios. Our modeling approach, named SEVE (Simulating Environmental impacts on Viticultural Ecosystems), provides a generic modeling framework for simulating grapevine growth and berry ripening under different conditions and constraints (slope, aspect, soil type, climate variability…) as well as production strategies and adaptation rules according to climate change scenarios. Each activity is represented by an autonomous agent able to react and adapt its reaction to the variability of environmental constraints. Using this model, we have recently analyzed the evolution of vineyards’ exposure to climatic risks (frost, pathogen risk, heat wave) and the adaptation strategies potentially implemented by the winegrowers. This approach, implemented for two climate change scenarios, has been initiated in France on traditional (Loire Valley) and emerging (Brittany) vineyards. The objective is to identify the time horizons of adaptations and new opportunities in these two regions. Carried out in collaboration with wine growers, this approach aims to better understand the variability of climate change impacts at local scale in the medium and long term.

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.

In response to changes in their environment, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plant-atmosphere continuum. Expressed as bulk stomatal conductance at the canopy scale, it varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and hydraulic conductivity of both soil and plant. It is necessary to characterize the response of conductance to these variables to better model how vine transpiration also responds to these variables. Furthermore, to be relevant for vineyard-scale modeling, conductance is best characterized using data collected in a vineyard setting. Applying a crop canopy energy flux model developed by Shuttleworth and Wallace, bulk stomatal conductance was estimated using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. These measurements were taken on several vines in a non-irrigated vineyard in Bordeaux France, using equipment that did not interfere with ongoing vineyard operations. An inverted Penman-Monteith equation was then used to calculate bulk stomatal conductance on 15-minute intervals from July to mid-September 2020. Time-series plots show significant diurnal variation and seasonal decreases in conductance, with overall values similar to those in the literature. Global sensitivity analysis using non-parametric regression found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, with absorbed net radiation and bulk boundary layer conductance being much less important. Conversely, bulk stomatal conductance was one of the most important inputs when calculating vine transpiration, further emphasizing the need for characterizing its response to environmental changes for use in vineyard water use modeling.

Irrigation is an important practice to influence vine quality, especially in Mediterranean regions, characterized by hot summers and severe droughts during the growing season. This study focused on deficit irrigation regime influence on amino acids composition of Monastrell grapevines under semiarid conditions (Albacete, Southeastern of Spain). In 2019, two treatments were applied: non-irrigation (NI) and regulated deficit irrigation (RDI), watered at 30% of the estimated crop evapotranspiration from fruit set to onset of veraison. Grape amino acids content was analyzed by HPLC. Berries from non-irrigated vines showed higher concentration of several amino acids, such as tryptophan (73%), arginine (70%), lysine (36%), isoleucine (27%), and leucine (21%), compared to RDI grapes. Arginine is, together with ammonium ion, the principal nitrogen source for yeasts during the alcoholic fermentation; while isoleucine, tryptophan, and leucine are precursors of fermentative volatile compounds, key compounds for wine quality. Moreover, NI treatment increased in a 14% the total amino acids content in grapes compared to RDI treatment. The reported effects might be because yield was 70% higher in RDI vines than in the NI ones and, therefore, the sink demand was increased in the irrigated vines. In addition, NI vines suffered more severe water stress and it is known that the amino acids synthesis and accumulation can be influenced by the plant response to stress. According to the results, the irrigation regime showed effect on amino acids concentration in Monastrell grapes under semiarid conditions. Grapes from non-irrigated vines showed a higher content of several amino acids relevant to the fermentative process and to the wine aroma compounds formation. It is demonstrated that the final content of nitrogen-related components in grapes is influenced by the irrigation regime. The convenience of the irrigation strategy to suggest will depend on the desired wine style and the target yield levels.