Effect of drought on grapevine wood fungal pathogen communities using a metatranscriptomics approach

Global warming is accelerating the technological ripening of the grape, with a loss of acidity, which requires that vineyard management can delay ripening to avoid it. The source-sink relation is essential for grape ripening, since it affects the distribution of photosynthates and substances derived from plant metabolism. A work is proposed to know the response of the vineyard to the drastic reduction of the foliar surface by trim down the shoots in cv.

Ultraviolet radiation (UVR) is a minor part of the solar spectrum, but it represents an important ecological factor that influences many biological processes related to plant growth and development. In recent years, the application of UVR in agriculture and food production is emerging as a clean and environmentally friendly technology.
In grapevine, many studies have been conducted on the effects of ambient levels of UVR, but there are few considering the effects of UV-B application on grape phenolic composition under commercial growing or postharvest conditions.

Wine, a sensitive and intricate agricultural product, is being affected by climate change, which accelerates grapevine phenological stages and alters grape composition and ripening. This influences the synthesis of key aroma compounds, shaping wine’s sensory attributes [1]. The complex aroma profile, resulting from compound interactions, presents a metabolomics challenge to identify these indicators and their environmental change responses, which is being addressed using diverse analytical techniques.

Performing accurate vineyard yield estimation is of upmost importance as it provides important benefits to the whole vine and wine industry. Recently, image-analysis approaches have been explored to address this issue however this approach has as main challenge the bunch occlusion, mostly by vegetation but also by neighboring bunches. The present work aims at assessing the magnitude of bunch occlusion by neighboring bunches and to evaluate its dependency on a selection of vegetative and reproductive vine parameters assessed at fruiting zone. Forty vine segments (1 m) of two vineyard plots of the white cultivars ‘Alvarinho’ and ‘Arinto’ were assessed for vegetative and reproductive features at fruiting zone and imaged with a 2D camera.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).