Impact of the ‘Pinot’-family on early ripening in cool climate viticulture varieties

Over the last years, the potential use of non-Saccharomyces yeasts to modulate the production of target metabolites of oenological interest has been well recognized. Among non-Saccharomyces yeasts, Starmerella bacillaris (synonym Candida zemplinina) is considered one of the most promising species to satisfy modern market and consumers preferences due to its peculiar characteristic (enhance glycerol and total acidity contents and reduce ethanol production). Mixed fermentations using Starm. bacillaris and Saccharomyces cerevisiae starter cultures represent a way to modulate metabolites of enological interest, taking advantage of the phenotypic specificities of the former and the ability of the latter to complete the alcoholic fermentation. However, the consumption of nutrients by these species and their produced metabolites may inhibit or stimulate the growth (and malolactic activity) of lactic acid bacteria (LAB).

One of the effects of warming trends is the advance of budburst, increasing the frequency of spring frost-related damage. In April 2021, a severe frost event affected central and northern italian viticulture. In a cv. Barbera vineyard located in the Colli Piacentini wine district, after such occurrence, vines were tracked and growth of primary bud shoots (PBS), secondary bud shoots (SBS), and suckers (SK) was monitored, as well as their fruitfulness and fruit composition. Vine performances were then compared to those of the previous year, when no post-budburst freezing temperatures occurred. The goal of the study was to evaluate the efficacy of SBS in restoring yield loss due to PBS injuries and analyze respective contribution to fruit composition.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions. 

Soil is important in the carbon cycle and the dynamics of greenhouse gases (CO2, CH4 and N2O). Key soil characteristics, such as organic matter content, texture, structure, pH and microbial activity, play a determining role in GHG emissions[1]. The objective of the study is to delimit different types of soil, with different soil management and to be able to verify the differences in CO2, CH4 and N2O emissions. The study was carried out in a vineyard of Bodegas Campo Viejo in Logroño (La Rioja), whose plant material is Vitis vinifera L. cv. Tempranillo.

“Ribeira Sacra” is an origin denomination located between the provinces of Lugo and Ourense, in Galicia (northwest of Spain).