Learning from remote sensing data: a case study in the Trentino region 

In wine growing regions around the world, climate change has the potential to affect vine transpiration and overall vineyard water use due to related changes in atmospheric demand and soil water deficits. Grapevines control their transpiration in response to a changing environment by regulating conductance of water through the soil-plant-atmosphere continuum. Most vineyard water use models currently estimate vine transpiration by applying generic crop coefficients to estimates of reference evapotranspiration, but this does not account for changes in vine conductance associated with water stress, nor differences thought to exist between varieties. The response of bulk stomatal conductance to daily weather variability and seasonal drought stress was studied on Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon vines in a non-irrigated vineyard in Bordeaux France. Whole vine sap flow, temperature and humidity in the vine canopy, and net radiation absorbed by the vine canopy were measured on 15-minute intervals from early July through mid-September 2020, together with periodic measurement of leaf area, canopy porosity, and predawn leaf water potential. From this data, bulk stomatal conductance was calculated on 15-minute intervals, and multiple regression analysis was performed to identify key variables and their relative effect on conductance. Attention was focused on addressing multicollinearity and time-dependency in the explanatory variables and developing regression models that were readily interpretable. Variability of vapor pressure deficit over the day, and predawn water potential over the season explained much of the variability in conductance, with relative differences in response coefficients observed across the five varieties. By characterizing this conductance response, the dynamics of vine transpiration can be better parameterized in vineyard water use modeling of current and future climate scenarios.

Vitis vinifera L. cv. Pinot noir can be challenging to manage in the winery as its thin skins require careful handling to ensure sufficient extraction of wine colour to promote colour stability during ageing.1 Literature has shown that fermentation with flocculent yeasts can increase red wine colour density.2 As consumers prefer greater colour density in red wines,3 the development of tools to increase colour density would be useful for the wine industry. This research explored the impact of interspecies sequential inoculation and co-flocculation of commercial yeast on Pinot noir wine colour.

Depuis 1973, une commission statutaire administre la législation qui régit le zonage vitivinicole en Afrique du Sud. La province «Le Cap de l’ouest» cerne toutes les zones viticoles sauf quatre unités. Pour la plupart, le Cap de l’ouest a un climat méditerranéen. Les zones viticoles – qui produisent les «vins d’origine» – sont des régions, des districts, des quartiers et des domaines. Les régions sont vastes, séparées par la topographie, par ex. des chaînes de montagnes et des fleuves. Généralement, chaque région représente une zone climatique. Le climat de chaque district est plus homogène. Les quartiers sont exactement délimités par le climat, la topographie et la géologie. Les domaines sont les plus petits. Chaque domaine doit avoir un seul propriétaire.

In this work, these four red varieties were characterized in terms of phenolic composition. Thus, the anthocyanin accumulation and the extractability evolution during ripening were compared.

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.