Estimation of stomatal conductance and chlorophyll fluorescence in Croatian grapevine germplasm under water deficit    

Candida zemplinina (synonym Starmerella bacillaris) is frequently isolated in grape must in different vitivinicultural areas. The enological significance of C. zemplinina strains used in combination with S. cerevisiae has been demonstrated, being wines produced by the above-mixed starter, characterized by higher amounts of glycerol and esters.

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.

Climate change is increasing the frequency and severity of environmental hazards (e.g., prolonged drought), and even non-extreme climate events (e.g., a period of slightly warmer temperatures) can lead to extreme impacts when they occur simultaneously with other (non-extreme) events.

ine nitrogen deficiency can negatively influence the aroma profile and ageing potential of white wines. Canopy management can alter vine microclimate, affect the nitrogen availability and influence the response of leaf senescence. Increasing the nitrogen availability to vines can increase the Yeast Assimilable Nitrogen (YAN) levels in harvested fruit and wine. Studies show that foliar nitrogen and sulphur applications at véraison, on low YAN Sauvignon blanc grapes have an effect on the level of amino acids (Jreij et al. 2009) and on S-containing compounds such as glutathione and thiols (Lacroux et al. 2008), which in turn can influence the formation of major volatiles and the aroma profile of the wine.

The study of the physiological behaviour of the grapevine (cv. Chasselas), and of plant hydraulics in particular, was conducted on various « terroirs » in the Canton of Vaud (Switzerland) between 2001 and 2003 by Agroscope Changins-Wädenswil ACW, in collaboration with the firm I. Letessier (SIGALES) in Grenoble and the Federal Polytechnic School of Lausanne (EPFL). An evaluation of the vine plant hydraulics was made by means of physiological indicators (leaf and stem water potentials, transpiration and leaf stomatal conductance, carbon isotope discrimination and a model of transpirable soil water), in relation to estimations of the soil water reservoir and climatic factors.