Advancing grapevine science through genomic research

Vineyards are home to a wide diversity of microorganisms that interact with plants and with each other.

Plants have been shown to redistribute water between root sections and soil layers along a gradient of decreasing water availability. One benefit of this hydraulic redistribution is that water can be transported from roots in wet soil to others in dry soil, delaying the onset of water stress and increasing root longevity in dry environments. Grapevines are thought to redistribute water laterally across the trunk from wet to dry portions of the root system. However, it is unknown whether the phloem contributes to such water redistribution.

The foundation of wine production lies in the use of high-quality grapes. To produce wines that meet the highest standards, a fast and reliable analytical assessment of grape quality is essential. Many wineries currently employ Fourier-Transform Middle-Infrared Spectroscopy (FTIR) for this purpose.

Lugana Protected Designation of Origin (PDO) wines are made from Turbiana grapes. The aroma of Lugana wines results from the combined contribution of esters, terpenes, norisprenoids, sulfur compounds and the benzenoid methyl salicylate. This study aims to investigate how volatile aroma compounds are affected by different nitrogen supplies and yeast strains.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.