Decline of new vineyards in Southern Spain

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.

homogeneous zones by intersecting several partial zonings of major factors that influence vineyard growth. Each of them follows specific process from their corresponding disciplines. Soil zoning specifically refers to a Soil Resource Inventory map that has traditionally been generated by conventional soil mapping methods. These methods have shortcomings in reaching fine cartographic and categorical details and involve significant expenses, which undermines their applicability. A new framework named Digital Soil Mapping has introduced quantitative models by statistical techniques to establish soil-landscape relationships and is able to provide intensive scale cartography.

In the present study, a microzoning at 1:10.000 scale is generated from an initial zoning, where the conventional soil map with polytaxic map units is replaced by a new one from digital techniques that disaggregates them. The comparison between the zonings considers a quantitative evaluation of capability for each Homogeneous Terroir Unit by means of the Viticultural Quality Index and its categorization based on its distribution by map. The spatial intersection of both maps gives rise to a confusion matrix in which the flows of class variations after the substitution are assessed.

The results show a five-fold increase in the number of Homogeneous Terroir Units identified and a larger differentiation among them, evidenced by a wider range in the capability index distribution. Both elements are accompanied by an increase in the detection of areas of higher potential within previously undervalued uniform zones.These features are a direct effect of the improvements brought by Digital Soil Mapping techniques and would verify the advantages of their implementation in the Integrated Terroir zoning. Eventually, such new highly detailed terroir units would benefit precision viticulture and sustainable management practices.

Newer sequencing technologies have allowed for the addition of microbes to the story of terroir. The same environmental factors that influence the phenotypic expression of a crop also shape the composition of the microbial communities found on that crop. For fermented goods, such as wine, that microbial community ultimately influences the organoleptic properties of the final product that is delivered to customers. Recent studies have begun to study the biogeography of wine-associated microbes within different growing regions, finding that communities are distinct across landscapes. Despite this new knowledge, there are still many questions about what factors drive these differences. Our goal was to quantify differences in yeast communities due to management style between seven pairs of conventional and biodynamic vineyards (14 in total) throughout Oregon, USA. We wanted to answer the following questions: 1) are yeast communities distinct between biodynamic vineyards and conventional vineyards? 2) are these differences consistent across a large geographic region? 3) can differences in yeast communities be tied to differences in metabolite profiles of the bottled wine? To collect our data we took soil, bark, leaf, and grape samples from within each vineyard from five different vines of pinot noir. We also collected must and a 10º brix sample from each winery. Using these samples, we performed 18S amplicon sequencing to identify the yeast present. We then used metabolomics to characterize the organoleptic compounds present in the bottled wine from the blocks the year that we sampled. We are actively in the process of analysing our data from this study.

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.

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.