Représentation holistique d’une dynamique pluridisciplinaire suite à la cartographie des sols en Beaujolais

Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.

Scientific information on grapevine response to predicted levels of climate parameters is scarce and not sufficient to properly position the Wine Industry for the future. It is critical that the combined effects of increased temperature and CO2 on grapevines should be examined, without omitting the important link to soil water conditions. The purpose of this study is to quantify the effects of envisioned changes in climatic parameters on the functioning and growth of young grafted grapevines under controlled conditions, simulating expected future climate changes. Scientific knowledge of precisely how the newly-planted grapevine will react morphologically, anatomically and physiologically (at leaf, root and whole plant level) to the expected changes in important climatic parameters will enable producers to make better-informed decisions regarding terroir, cultivar and rootstock choices as well as the adaptation of current cultivation practices.

Context and purpose of the study – In Valpolicella area (Verona – Italy) Vitis vinifera cv. Corvina is the main wine variety to obtain, after grape withering, Amarone wine: this study was carried out in order to compare two different grape dehydration conditions with the aim of verifying the final composition of Corvina dried grapes and the organoleptic profile of corresponding Amarone wine.

This study aims to investigate the effects of different conditions of grape processing or fermentation on the aroma profile of New Zealand white wines.

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.