Implications of the nature of organic mulches used in vineyards on grapevine water status, yield, berry quality and biological soil health  

Remote sensing technology captures spectral data beyond the visible range, making it useful for monitoring plant stress. Vis-NIR (Visible-Near Infrared) spectroscopy (400-1000 nm) is commonly used to indirectly assess plant status during drought. One example is the widespread use of normalized difference vegetation index (NDVI) that is strongly linked to green biomass. However, a knowledge gap exists regarding the applicability of this method to all the drought conditions and if it is a direct correlation to the water status of the plant.

The application of remote and proximal sensing is a fast and efficient method to monitor grapevine vegetative and physiological parameters and is considered valuable to derive information on associated yield and quality traits in the vineyard. Further details can be obtained by the application of molecular analysis at the gene expression level aiming at elucidating how pathways controlling the formation of different grape quality traits are influenced by spatial variability. This work aims at evaluating intra-vineyard variability in grape composition at harvest and at comparing this with remotely sensed canopy vegetation data and molecular-based approaches.

The mechanization of pruning and harvesting in vineyards has increased the risk of soil compaction. To reclaim soil properties or avoid this degradation process, it is crucial to properly manage the soil organic matter, and the application of compost derived from the vines themselves is a strategy to achieve this. The objective of this study was to evaluate the properties of soil treated with different doses of compost applied both on the vine row and the inter rows of a Vitis vinifera crop.

The main objective of the work was to develop a simple, robust and selective analytical tool that allows predicting the authenticity of Tempranillo wines from DOCa Rioja. The techniques of voltammetry and absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) spectroscopy have been applied in combination with machine learning (ML) algorithms to classify red wines from DOCa Rioja according to region (Alavesa, Alta or Oriental) and category (young, crianza or reserva).

Several Eurasian wild grapevine populations were found along Extremadura region (southwestern Spain). For conservation and study, one individual from four different populations (named L1, L2, L5 and L6) was vegetatively propagated and planted at Instituto de Investigaciones Agrarias Finca La Orden (CICYTEX), Badajoz. The aim of the present work was to characterize those conserved individuals from four different populations based on both an ampelographic description and a molecular analysis. Three vines per individual were studied.