Vino e paesaggio: materiali per il governo del territorio vitivinicolo. Il piano regolatore delle città del vino

Soils in vineyards are recognized as complex agrosystems whose characteristics reflect complex interactions between natural factors (lithology, climate, slope, biodiversity) and human activities. To date, most of the unknown lies in an incomplete understanding of soil ecosystems, and specifically in the microbial biodiversity even though soil microbiota is involved in many key functions, such as nutrient cycling and carbon sequestration. Soil biological properties are indicative of soil quality. Therefore, understanding how soil communities are related to soil ecosystem functioning is becoming an essential issue for soil strategy conservation. Here, we propose to assess the importance of land-cover history on the present-day microbiological and physico-chemical properties. The studied area was selected in the Burgundian vineyards (Pernand-Vergelesses, Burgundy, France) where land occupation has been reconstructed over the last 40 years. Soil samples were collected in five areas reflecting various land cover history (forest, vineyards, shifting from forest to vineyards). For each area, physico-chemical parameters (pH, C, N, P, grain size) were measured and DNA was extracted to characterize the abundance and diversity of microbial communities. The obtained results show significant differences in the five areas suggesting that present-day microbial molecular biomass and bacterial taxonomic is partly inherited from past land occupation. Over longer period of time, such study of land-uses legacies may help to better assess ecosystem recovery and the impact of management practices for a better soil quality and vineyards sustainability.

Viticulture is entangled with weather and climate. Therefore, areas currently suitable for grape production can be challenged by climate change. Winegrowers in Italy already experiences the effect of climate change, especially in the form of warmer growing season, more frequent drought periods, and increased frequency of weather extremes.
The aim of this study is to investigate the impact of climate variability and change on grape yield in Italy to provide winegrowers the information needed to make their business more sustainable and resilient to climate change. We computed a specific range of bioclimatic indices, selected by the International Organisation of Vine and Wine (OIV), and correlated them to grape yield data. We have worked in collaboration with some wine consortiums in northern and central Italy, which provided grape yield data for our analysis.
Using climate variables from the E-OBS dataset we investigate how the bioclimatic indices changed in the past, and the impact of this change on grape productivity in the study areas. The climate impact on productivity is also investigated by using high-resolution convection-permitting models (CPMs – 2.2 horizontal resolution), with the purpose of estimating productivity in future emission scenarios. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of small-scale processes and features, explicitly resolve deep convection, and show an improved representation of extremes. In our study, we also compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to assess the added value of high-resolution models for impact studies. Further development of our study will lead to assessing the future suitability for vine cultivation and could lead to the construction of a statistical model for future projection of grape yield.

Water status impact in viticulture has been widely explored, as it strongly affects grapevine physiology and grape chemical composition. It is considered as a key component of vitivinicultural terroir. Most of the studies concerning grapevine water status have focused on either physiological traits, or berry compounds, or traits involved in wine quality. Here, the response of grapevine to water availability during the ripening period is assessed through non-targeted metabolomics analysis of grape berries by ultra-high resolution mass spectrometry. The grapevine water status has been assessed during 2 consecutive years (2019 & 2020), through carbon isotope discrimination on juices from berries collected at maturity (21.5 brix approx.) for 2 Vitis vinifera cv. Pinot noir (PN) and Chardonnay (CH). A total of 220 grape juices were collected from 5 countries worldwide (Italy; Argentina; France; Germany; Portugal). Measured δ13C (‰) varied from -28.73 to -22.6 for PN, and from -28.79 to -21.67 for CH. These results also clearly revealed higher water stress for the 2020 vintage. The same grape juices have been analysed by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) and Liquid Chromatography coupled to Mass Spectrometry (LC-qTOF-MS), leading to the detection of up to 4500 CHONS containing elemental compositions, and thus likely tens of thousands of individual compounds, which include fatty acids, organic acids, peptides, phenolics, also with high levels of glycosylation. Multivariate statistical analysis revealed that up to 160 elemental compositions, covering the whole range of detected masses (100 –1000 m/z), were significantly correlated to the observed gradients of water status. Examples of chemical markers, which are representative of these complex fingerprints, include various derivatives of the known abscisic acid (ABA), such as phaesic acid or abscisic acid glucose ester, which are significantly correlated with higher water stress, regardless of the variety. Cultivar-specific behaviours could also be identified from these fingerprints. Our results provide an unprecedented representation of the metabolic diversity, which is involved in the water status regulation at the grape level, and which could contribute to a better knowledge of the grapevine mitigation strategy in a climate change context.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).

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.