Bioprotective non-Saccharomyces yeast as an alternative to sulfites for the winemaking process

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

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.

Wine terroir describes the collectively recognized relation between a geographical area and the distinctive organoleptic characteristics of the wines produced in it. The overriding objective in terroir studies is therefore to provide scientific proof relating the properties of terroir components to wine quality and typicity. In scientific circles, the role of climate (macro-, meso- and micro-) on grape and wine characteristics is well documented and accepted as the most critical. Moreover, there has been increasing interest in recent years about new elements with possible importance in shaping wine terroir like berry/leaf/soil microbiology or even aromatic plants in proximity to the vineyard conferring flavors to the grapes. However, the actual effect of these factors is also dependent on complex interactions with plant material (variety/clone, rootstock, vine age) and with human factors.
The contribution of soil, although a fundamental component of terroir and extremely popular among wine enthusiasts, remains a much-debated issue among researchers. The role of geology is probably the one mostly associated by consumers with the notion of terroir with different parent rocks considered to give birth to different wine styles. However, the relationship between wine properties and the underlying parent material raises a lot of controversy especially regarding the actual existence of rock-derived flavors in the wine (e.g. minerality). As far as the actual soil properties are concerned, the effect of soil physical properties is generally regarded as the most significant (e.g sandy soils being associated with lighter wines while those on clay with colored and tannic ones) mostly through control of water availability which ultimately modifies berry ripening conditions either directly by triggering biosynthetic pathways, or indirectly by altering vigor and yield components. The role of soil chemistry seems to be weakly associated to wine sensory characteristic, although N, K, S and Ca, but also soil pH, are often considered important in the overall soil effect.
Recently, in the light of evidence provided by precision agriculture studies reporting a high variability of vineyard soils, the spatial scale should also be taken into consideration in the evaluation of the soil effects on wines. While it is accepted that soil effects become more significant than climate on a local level, it is not clear whether these micro-variations of vineyard soils are determining in the terroir effect. Moreover, as terroir is not a set of only natural factors, the magnitude of the contribution of human-related factors (irrigation, fertilization, soil management) to the soil effect still remains ambiguous. Lastly, a major shortcoming of the majority of works about soil effects on wine characteristics is the absence of connection with actual vine physiological processes since all soil effects on grape and wine chemistry and sensorial properties are ultimately mediated through vine responses.
This article attempts to breakdown the main soil attributes involved in the terroir effect to suggest an improved understanding about soil’s true contribution to wine sensory characteristics. It is proposed that soil parameters per se are not as significant determining factors in the terroir effect but rather their mutual interactions as well as with other natural and human factors included in the terroir concept. Consequently, similarly to bioclimatic indices, composite soil indices (i.e. soil depth, water holding capacity, fertility, temperature etc), incorporating multiple soil parameters, might provide a more accurate and quantifiable means to assess the relative weight of the soil component in the terroir effect.

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.