Temperature variability assessment at vineyard scale: control of data accuracy and data processing protocol

The browning index (BI), based on the absorbance at 420 nm, is a common oxidation marker in white wines, typically measured after thermal stress (50–60 °C for 5 up to 12 days) in air-saturated wines.

The overall quality of aged wines is in part due to the development of complex aromas over a long period (1.) The apparition of this aromatic complexity depends on multiple chemical reactions that include the liberation of odorous compounds from non-odorous precursors. One example of this phenomenon is found in dimethyl sulphide (DMS) which, with its characteristic odor truffle, is a known contributor to the bouquet of premium aged wine bouquet (1). DMS supposedly accumulates during the ten first years of ageing thanks to the hydrolysis of its precursor dimethylsulfoniopropionate (DMSp.) DMSp is a possible secondary by-product from the degradation of S-methylmethionine (SMM), an amino acid iden- tified in grapes (2), which can be metabolized by yeast during alcoholic fermentation.

Climate influence on grapevine physiology is prevalent and this influence is only expected to increase with climate change. Although governed by a general determinism, climate influence on grapevine physiology may present variations according to the terroir. In addition, these site-specific differences are likely to be enhanced when climate influence is studied using farm data. Indeed, farm data integrate additional sources of variation such as a varying representativity of the conditions actually experienced in the field. Nevertheless, there is a real challenge in valuing farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes a framework to site-specifically study climate influence on grapevine physiology using farm data. It focuses on improving the analysis of time series of weather data. The analytical framework includes the synchronization of time series using site-specific thermal indices computed with an original method called Extended Growing Degree Days (eGDD). Synchronized time series are then analyzed using a Bayesian functional Linear regression with Sparse Steps functions (BLiSS) in order to detect site-specific periods of strong climate influence on yield development. The article focuses on temperature and rain influence on grape yield development as a case study. It uses data from three commercial vineyards respectively situated in the Bordeaux region (France), California (USA) and Israel. For all vineyards, common periods of climate influence on yield development were found. They corresponded to already known periods, for example around veraison of the year before harvest. However, the periods differed in their precise timing (e.g. before, around or after veraison), duration and correlation direction with yield. Other periods were found for only one or two vineyards and/or were not referred to in literature, for example during the winter before harvest.

Among nitrogen compounds included in white wines, the peptide fraction is certainly the least studied, however this fraction is quantitatively the most important (Feuillat, 1974). Existing studies concern the fraction below 1 kDa and only for white and sparkling wines (Bartolomé et al, 1997, Desportes et al 2000). In this report, we have developed methods to isolate peptides from reference white wines. Then, we have applied this methodology with bitter wine to answer a research question: is there a relation between peptides and the bitterness of white wine as for some cheese for example (Furtado, 1984)?

With alcohol consumption steadily declining, the growing popularity of dry january, a fiercely competitive environment, high dry matter inflation, economic upheavals, commercial uncertainties… The wine industry must adapt and offer products that meet consumer expectations, without denying their historical singularities.