DNA-Free genome editing confers disease resistance in grapevine

The “Geoviticulture Multicriteria Climatic Classification (MCC) System” was developed to characterize the climate of the wine producing regions of the world.

Due to the central role of the ripening timing in the evaluation of the varietal response to the environmental resources, a method to manage maturation curves has been developed. The method produces an index of veraison precocity and overcomes several methodological problems, like the visual evaluation of the veraison point and the multi-annual and multi-varieties data processing. It is based on a statistical and mathematical processing of the sugar ripening curves.

AIM: the aim of this study was to develop a method for fine-scale temperature zoning. The effect of temperature variability on vine phenology and grape composition was assessed in the production area of Saint-Emilion

The objective of this work is to characterize the different fractions obtained, based on their malic, tartaric and total acidity. Twenty fractions of must were discriminated in order during the pressing for further physical-chemical analysis by Fourier Transform (FTIR).

Increasingly warm and dry climate conditions are challenging the viticulture and winemaking sector. Digital technologies and crop modelling bear the promise to provide practical answers to those challenges. As viticultural activities strongly depend on harvest date, its early prediction is particularly important, since the success of winemaking practices largely depends upon this key event, which should be based on an accurate and advanced plan of the annual cycle. Herein, we demonstrate the creation of modelling tools to assess grape ripeness, through sugar concentration monitoring. The study area, the Portuguese Côa valley wine region, represents an important terroir in the “Douro Superior” subregion. Two varieties (cv. Touriga Nacional and Touriga Franca) grown in five locations across the Côa Region were considered. Sugar accumulation in grapes, with concentrations between 170 and 230 g l-1, was used from 2014 to 2020 as an indicator of technological maturity conditioned by meteorological factors. The climatic time series were retrieved from the EU Copernicus Service, while sugar data were collected by a non-profit organization, ADVID, and by Sogrape, a leading wine company. The software for calibrating and validating this model framework was the Phenology Modeling Platform (PMP), version 5.5, using Sigmoid and growing degree-day (GDD) models for predictions. The performance was assessed through two metrics: Roots Mean Square Error (RMSE) and efficiency coefficient (EFF), while validation was undertaken using leave-one-out cross-validation. Our findings demonstrate that sugar content is mainly dependent on temperature and air humidity. The models achieved a performance of 0.65