Organic mulches improve vine vigour, yield and physiological response in a semi-arid region

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.

Winemaking is influenced by micro-organisms, which are largely responsible for the quality of the product. In this context, Non-Saccharomyces and Saccharomyces species are of great importance not only because it influences the development of alcoholic fermentation (AF) but also on the achievement of malolactic fermentation (MLF). Among these yeasts, Torulaspora delbrueckii allows in sequential inoculation with strains of S. cerevisiae shorter MLF realizations [5] . Little information is available on the temporal effect of the presence of T. delbrueckii on (i) the evolution of AF and (ii) the MLF performance.

Traceability of wine is today a consumer demand and a scientific challenge. The methods of analysis must be able to control three fundamental parameters: the geographical origin, the grape varieties, and the vintage.

Il Barbera rappresenta sicuramente uno dei più importanti vitigni autoctoni del Piemonte occu­pando circa il 50% della superficie vitata regionale. Esso è ancora diffuso su un’area molto vasta, che si estende per oltre 200.000 ha, dando origine a diverse produzioni vinicole tutelate da denominazioni d’origine.

Estimation of vineyard leaf area index (LAI) is an important aspect for the winegrowers. However, tracking and monitoring are difficult tasks due to time constraints. Satellite and unmanned aerial vehicle (UAV) imaging have become a practical monitoring method for LAI. Nevertheless, for a proper LAI determination, the image’s spatial resolution is a key factor, since low-resolution images are incapable of distinguishing between adjacent vines due to the large area covered in each pixel, this leads to misinterpretation or generalisation of vineyard information.