The grapesim model: a model to better understand the complex interactions between carbon and nitrogen cycles in grapevines

Plants have been shown to redistribute water between root sections and soil layers along a gradient of decreasing water availability. One benefit of this hydraulic redistribution is that water can be transported from roots in wet soil to others in dry soil, delaying the onset of water stress and increasing root longevity in dry environments. Grapevines are thought to redistribute water laterally across the trunk from wet to dry portions of the root system. However, it is unknown whether the phloem contributes to such water redistribution.

Fossil fuel combustion continues to drive increases in atmospheric carbon dioxide, consequently elevating the global annual mean temperature and specifically increasing the growing season temperatures in many of the world’s most important wine growing regions (IPCC 2014; Jones et al 2005). Grapes are sensitive to changes in growing season temperatures, and past models have shown a direct link between warming temperatures and earlier harvest dates (Cook and Wolkovich 2016). Globally, there have been shifts of 1-2 weeks for wine growing regions (Wolkovich et al 2017 and references within). The phenological shifts resulting from growing season temperature increases are documented internationally, and models predicting phenology using temperature are becoming more precise (Parker et al 2011).

The aim of this study is to provide an overview of the terroir of Saint-Romain, Burgundy, based on three main information sources: official data relating to vines (CVI), soil cartography and a survey of winegrowers’ practices.

As environmental issues come more to the fore, vineyards residues are being looked at as solutions rather than problems. Aiming to develop a sustainable methodology for musts acidity correction in the process of winemaking, much needed in warm regions, the present study was performed according to Circular Economy values.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.