Soil management of interrow spacing as an important factor to protect the vineyard soils from runoff and erosion under the Mediterranean climate

CropManage (CM) is an online decision support service (DSS) developed by the University of California, Division of Agriculture and Natural Resources for assisting farmers with efficiently managing water and nitrogen fertilizer to match the site-specific needs of their crops.

The Matrix Assisted Laser Desorption/Ionization–Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) technology is commonly used in food and medical sector to identify yeast or bacteria species isolated from a nutritive culture media. Since a decade, brewery and oenology industries have been attracted to this method which combines fast analysis times, reliability and low cost of analysis. Briefly, this method is based on the comparison of the MALDI-TOF/MS protein spectra of an isolated colony of yeast or bacteria with those contain in a manufacturer’s reference protein spectra database. Initiated in 2015, the creation of the first oenological mass spectra database has proved to be essential for increase quality of species identification.

Knowledge of the nitrogen nutrition status of grapevines is essential for the sustainable management of their nutrition for the production of quality grapes. The measurement of the chlorophyll index is a rapid, non-destructive and relatively inexpensive method that provides a good approximation of the nitrogen nutrition status of the vine during the season. Interpretation thresholds are currently insufficient or non-existent for some chlorophyll meters. Ideally, they should be available for each variety and each phenological stage. In order to popularize the use of chlorophyll-meters, measurements were carried out at Agroscope in Switzerland to establish the correlation between the indices obtained by the devices N-tester and SPAD 502.

Inactive dry yeast treatments in the vineyard are a tool used with the aim to improve the concentration and quality of secondary metabolites in grapes, leading to a better differentiation of the wines made from grapes differently treated. In this work, a foliar spraying treatment with yeast derivatives specifically designed to be used with the patent pending application technology of Lallemand Inc. Canada (LalVigne® Mature, Lallemand Inc., Montreal, Canada) was tested on Vitis vinifera L. cv. Barbera and Nebbiolo black winegrapes. The aim was to evaluate the effect of this treatment on the phenolic compounds accumulation, the skin physical-mechanical properties and the related phenolic extractability. Prior to analysis, the berries were sorted by flotation in order to evaluate their distribution by density class, and to determine the skin texture parameters of berries with different sugar contents, thus understanding also the ripening effect.

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