Monitoring of ripening and yield of vineyards in Nemea region using UAV

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.

Climate change impacts regional and local climates, which in turn affects the world’s wine regions. In the short term, these modifications rises issues about maintaining quality and style of wine, and in a longer term about the suitability of grape varieties and the sustainability of traditional wine regions. Thus, adaptation to climate change represents a major challenge for viticulture. In this context, island and coastal vineyards could become coveted areas due to their specific climatic conditions. In regions subject to warming, the proximity of the sea can moderate extremes temperatures, which could be an advantage for wine. However, coastal and island areas are particular prized spaces and subject to multiple pressures that make the establishment or extension of viticulture complex.
In this perspective, it seems relevant to assess the potentialities of coastal and island areas for viticulture. This contribution will present a spatial optimization model that tends to characterize most suitable agroclimatic patterns in historical or emerging vineyards according to different scenarios. Thanks to an in-depth bibliography a global inventory of coastal and insular vineyards on a worldwide scale has been realized. Relevant criteria have been identified to describe the specificities of these vineyards. They are used as input data in the optimization process, which will optimize some objectives and spatial aspects. According to a predefined scenario, the objectives are set in three main categories associated with climatic characteristics, vineyards characteristics and management strategies. At the end of this optimization process, a series of maps presents the different spatial configurations that maximize the scenario objectives.

Biodiversity conservation and restoration are essential for guarantee the provision of ecosystem services associated to vineyard agroecosystem such as climate regulation trough carbon sequestration and control of pests and diseases. Most of published research dealing with the complexity of the vineyard agroecosystems emphasizes the necessity of innovative approaches, including the integration of information at different temporal and spatial scales and development of systemic analysis based on modelling. A biodiversity survey was conducted in the Franciacorta wine-growing area (Lombardy, Italy), one of the most important Italian wine-growing regions for sparkling wine production, considering a portion of the territory of 112 ha. The area was divided into several Environmental Units (EUs), defined as a whole vineyard or portion of vineyard homogenous in terms of four agronomic characteristics: planting year, planting density, cultivar, and training system. In each EU a set of compartments was identified and characterised by specific variables. The compartments are meteorology, morphology (altitude, slope, aspect, row orientation, and solar irradiance), ecological infrastructures and management. The landscape surrounding EU was also characterised in terms of land-use in a buffer zone of 500 m. For each component a specific methodology was identified and applied. Different statistical approaches were used to evaluate the method to integrate the information related to different compartments within the EU and related to the buffer zone. These approaches were also preliminarily evaluated for their ability to describe the contribution of biodiversity and landscape components to ecosystem services. This methodological exploration provides useful indication for the development of a fully systemic approach to structural and functional biodiversity in vineyard agroecosystems, contributing to promote a multifunctional perspective for the all wine-growing sector.

Air temperature is the key driver of grapevine phenology and a significant environmental factor impacting yield and quality for a winegrape growing region. In this study the optimal downscaled CMIP5 ensemble for computing thegrowing season average temperature (GST) viticulture climate classification index was determined to spatially compute on a decadal basis predictions of the GST climate index and the grapevine sugar ripeness (GSR) model for Pinot Noir throughout the Willamette Valley (WV) American Viticultural Area (AVA). Forecasts for average temperature and a 220 g/L target sugar concentration level were computed using daily Localized Constructed Analogs (LOCA) downscaled CMIP5 historic and Representative Concentration Pathways (RCP) future climate projections of minimum and maximum daily temperature. We explore spatiotemporal trends of the GST climate classification index and Pinot Noir specific applications of the GSR phenology model for the WV AVA. Spatiotemporal computations of the GST climate index and Pinot Noir specific applications of the GSR model enable the opportunity to explore relationships between their computed values with one intent being to provide updated GST ranges that better align with current temperature-based modeling understanding of Pinot Noir grapevine phenology and the viticultural application of LOCA CMIP5 climate projections for the WV AVA. The Pinot Noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot Noir production is currently at or near its peak value in the upper 80s to lower 90s of this century.

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.