Relations between soil characteristics and must and wine composition in different terroirs of Emilia Romagna (Italy)

This work is a first step to make a map of vineyard soils. The characterization of the soils of the Protected Designation of Origin (D.P.O.) Valdepeñas will allow to group the studied profiles according to their physico-chemical characteristics and the concentrations of most relevant chemical elements. 90 soil profiles were analysed throughout the territory and the soils were sampled and described according to FAO (2006) and classified according to and Soil Taxonomy (2014). All samples were air dried, sieved and some physico-chemical parameters were determined following standard protocols. Also, major and trace elements were analysed by X-ray fluorescence. The statistically study was made using the SPSS program. Trend maps were made using the ArcGIS program. The studied soils have the following average properties: pH, 8.3; electrical conductivity, 0,20 dS/m (low); clay, 18.8% (medium) and CaCO3, 17.1% (high). In the study for the major elements. The major elements of these soils are Si, followed by Ca and Al, with an average content of 203.7 g/kg, 105.5 g/kg and 74.0 g/kg respectively. On the other hand, 27 trace elements have been studied. Of all of them, it can be highlighted the average values of Ba (361.8 mg/kg), Sr (129.3 mg/kg), Rb (83.4 mg/kg), V (74.2 mg/kg) and Ce (70.6 mg/kg). Ba, V and Ce values are higher and the values of Sr and Rb are lower to those found in the literature. The discriminant analysis shows a percentage of grouping of 91%. The content of chemical elements together with the physico-chemical characteristics allows grouping the soils in 4 group according to their order in the classification to Soil Taxonomy; due to the importance of the Calcisols in Castilla-La Mancha, it has been decided to establish them as their own group even if they do not appear in Soil Taxonomy classification.

The work shows the results of a year of experimentation (2020) in a Syrah variety vineyard in La Roda (Castilla-La Mancha, Spain). The trial approach was on a randomized block design with two factors: Irrigation (I) and Pruning (P).
Irrigation schedules were adjusted to apply amounts close to 1,500 m3/ha. With this provision, 2 different irrigation treatments were proposed: I1) Start of irrigation from pea-sized grape to post-harvest (providing at least 20 % of the total amount of irrigation water to be provided post-harvest); I2) Start of irrigation from pea-sized grape to harvest (usual irrigation practice in the study area). Pruning was proposed with two treatments, one at the end of January (P1), which is pruning on a conventional date; and P2) pruning carried out at the beginning of budding. In total, 4 repetitions were designed with 4 elementary plots, each one of them representing one of the proposed treatments (I1P1; I1P2; I2P1; I2P2). In total, 16 plots were worked on and each elementary plot consisted of 30 strains, distributed in 3 lines.
The productive response was evaluated with the yield results of the harvest harvested at 23 ºBrix. The qualitative response was measured in the musts through the indices of technological (acidity, pH and potassium) and phenolic maturity and aromatic compounds in free and glycosylated fractions. The treatments tested had, in general, an effect on the different variables analyzed.

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.

As elsewhere, southwestern France vineyards face more recurrent summer heat waves these last years. Among the possibilities of adaptation to this climate changing parameter, the use of net shading is a technique that allow for limiting canopy exposure to radiations. In this trial, we tested net shading installed on one face of the canopy, on a north-south row-oriented plot of cv. Gros Manseng trained on VSP system in the PDO Pacherenc-du-Vic-Bilh. The purpose was to characterize the effects on the ambient canopy temperatures and radiations during the season and to observe the consequences on the composition of grapes and wines. Two sorts of net were used with two levels of obstruction (50% and 75%) of the photosynthesis active radiation (PAR). They have been installed on the west side of the canopy and compared to a netless control. Temperature and PAR sensors registered hourly data during the season. On specific summer day (hot and sunny) manual measurements took also place on bunches (temperature) and in different spots of the canopy (PAR). The results showed that, on clear days, the radiation is lowered by the shade nets respecting the supplier criteria. The effects on the ambient canopy temperature were inconstant on this plot when we observed the data from the global period of shading between fruit set and harvest. However, during hot days (>30°C), the temperature in the canopy was reduced during afternoon and the temperature of the bunch surface was reduced as well comparing to the control. A decrease of the maturity parameters of the berries, sugar and acidity, was also observed. Concerning the wine aromatic potential, no differences clearly appeared.

Agriculture, natural resource management and the production and sale of products such as wine are increasingly data-driven activities. Thus, the use of remote and proximal crop and soil sensors to aid management decisions is becoming commonplace and ‘Agtech’ is proliferating commercially; mapping, underpinned by geographical information systems and complex methods of spatial analysis, is widely used. Likewise, the chemical and sensory analysis of wines draws on multivariate statistics; the efficient winery intake of grapes, subsequent production of wines and their delivery to markets relies on logistics; whilst the sales and marketing of wines is increasingly driven by artificial intelligence linked to the recorded purchasing behaviour of consumers. In brief, there is data everywhere!

Opinions will vary on whether these developments are a good thing. Those concerned with the ‘mystique’ of wine, or the historical aspects of terroir and its preservation, may find them confronting. In contrast, they offer an opportunity to those interested in the biophysical elements of terroir, and efforts aimed at better understanding how these impact on vineyard performance and the sensory attributes of resultant wines. At the previous Terroir Congress, we demonstrated the potential of analytical methods used at the within-vineyard scale in the development of Precision Viticulture, in contributing to a quantitative understanding of regional terroir. For this conference, we take this approach forward with examples from contrasting locations in both the northern and southern hemispheres. We show how, by focussing on the vineyards within winegrowing regions, as opposed to all of the land within those regions, we might move towards a more robust terroir zoning than one derived from a mixture of history, thematic mapping, heuristics and the whims of marketers. Aside from providing improved understanding by underpinning terroir with data, such methods should also promote improved management of the entire wine value chain.