Mobilizing endogenous transposable elements for grapevine improvement: a genomic and epigenomic approach in New Zealand Sauvignon Blanc

Drought stress has a profound impact on grapevine productivity and significantly alters key quality-related traits of berries. Although research has been conducted on the effects of individual drought events, there is still a knowledge gap regarding the cumulative consequences of repeated exposure to water scarcity and the influence of the timing of stress imposition. To address this gap, a field experiment was conducted to investigate the impacts of repeated drought stress on yield, berry composition, and the phenolic profile of grape berries. The results indicate that yield is primarily influenced by pre-veraison water deficit. Although the number of clusters was only slightly reduced, a substantial decrease in berry size was observed, resulting in a notable reduction in overall yield.

In Portugal, and particularly in the Demarcated Region of Douro (DDR), wine production has a great tradition, producing the unique and worldwide famous Port wine as well as other remarkably good table wines. In this study the impact of projected climate change to wine production is analysed for the DDR. A statistical grapevine yield model (GYM) is developed using climate parameters as predictors.

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

The Occitanie Region is the first vine-growing area in France: 270 000 hectares of vineyard and an annual production of 15 million hectoliters. Its annual income reaches 1 900 million euros, of which 900 million euros in export.The vine and wine sector is facing many issues: inputs reduction, adaptation to climate change, maintaining the production competitiveness, digital tools integration in production and transformation processes, and the production of quality wines meeting the consumer demand.

When talking about wine, terroirs are never too far. The National Institute of Apellation d’Origine (INAO) defines it as a system inside of which interact a group of human factors, an agricultural production and a physical environment.