Influence of Partial Rootzone Drying on grape and wine anthocyanin composition

Downy mildew, caused by Plasmopara viticola, is the most economically impactful disease affecting grapevines. This polycyclic pathogen triggers both primary and secondary infection cycles, resulting in significant yield losses when effective disease control measures are lacking. Over the winter, the pathogen survives by forming resting structures, the oospores, derived from sexual reproduction, which produce the inoculum for primary infections. To optimize grapevine downy mildew control and obtain the desired levels of production while minimizing chemical inputs, it is crucial to optimize the timeframe for fungicide application. Disease forecasting models are useful to identify the infection risk.

The definition of terroir has evolved throughout history, from something clearly negative in the XVIth-XVIIIth century to a complex multi-parametric construct with positive connotations but also with many scientific unknowns. Terroir has always been linked more or less explicitly to the sensory properties of the resulting products.

Introduction: Malolactic fermentation (MLF) is a common process in winemaking to reduce wine acidity, maintain microbial stability and modify wine aroma. However, successful MLF is often hampered by their sluggish or stuck activity of malolactic bacteria (MLB) which may be caused by nutrient deficiency, especially when MLB are inoculated after alcoholic fermentation (Alexandre et al., 2004; Lerm et al., 2010). Identification and characterization of essential nutrients and growth factors for MLB allows for production of highly efficient nutrient supplements for MLF.

Unlike table wines, Madeira Wine (MW,17-22% ABV) benefits from a long aging period under thermo-oxidative aging conditions, during which it gains its unique and complex flavour. A broad study is ongoing and aims to assess if the differences in the storage conditions impact significantly the evolution of MWs during canteiro aging. Considering that polyphenols have a significant role in the wine aging, we intended to appraise if there are significant differences in the evolution trends of polyphenols of MWs aging in different cellars under canteiro. Different MWs were aged into brand-new oak casks in two different wine cellars, one in Funchal (B) and other in Caniçal (Z). Temperature and humidity data were sensor recorded. RP-HPLC-DAD was used to perform the identification and quantification of polyphenols [1]. CIELab parameters were also assessed, using an UV-Vis spectrophotometer.

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.