Vineyard yield estimation using image analysis: assessing bunch occlusions and its dependency on fruiting zone canopy features

Grapevine resilience to climate change has become one of the most pressing topics in the Viticulture & Enology field. Vineyard health demands understanding the mechanisms that explain the direct and indirect interactions between environmental stressors. The current climate change scenario, where drought and heat-wave are more frequent and intense, strongly demands improving our knowledge of environmental stresses. During a heatwave, the ambient temperature rises above the plant’s average tolerance threshold and, generally, above 35 oC plant’s adaptation to heat stress is activated.

The presence of acetic acid bacteria in wine can lead to the appearance of acetic acid at concentrations above the perception threshold, causing the wine rejection by the consumer. During the winemaking process, avoiding the presence of acetic acid bacteria is very difficult, as there is always a residual population accompanying the wine[1], and the problem arises with the significant development of these microorganisms that metabolizes large amounts of acetic acid.
The concern of wineries to control the presence of acetic acid bacteria in wines during their conservation is due to the absence of simple and effective analyses that allow the detection of these microorganisms in the initial stages.

Red grape pomace can be an important source of polysaccharides, but currently they are little studied and even less with viable and environmental extraction processes (green extraction). These green techniques must be able to break the cell wall so that the compounds contained in the cells, including polysaccharides, are released and can have a great influence on extraction yields, the chemical structure of polysaccharides and applications in wines. Amongst the emerging green techniques most applied to the extraction of bioactive compounds, such as polysaccharides, high-power ultrasound (US) and enzyme-assisted extraction stand out.

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.