Characterizing graft union formation in different scion/rootstock combinations of grapevine 

Traceability of agro-alimentary products is very important to certify their origin. This work aimed to characterize wines obtained by the same cultivar (Nero d’Avola and Fiano) – grown in regions with different soil and climate conditions during three vintages (2003-2005) – employing isotopic analyses (NMR and IRMS) and sensory analyses.

The production of grated vines is a complex process from grafting to final sorting in nurseries. To reach the market, grafted grapevines must meet three criteria by law in France: resistance to a manual graft union test (named thumb test), a minimum number of three roots and a woody shoot of at least 2 cm long.

In the context of sustainable viticulture, modern and efficient techniques to determine water status are required to optimize irrigation practices. Proximal techniques such as thermography and spectroscopy have shown promising results. When these techniques are incorporated into mobile systems is possible to evaluate the water status on-the-go, offering the possibility to generate variability maps. However, in most cases, complex protocols of data acquisition and analysis are required. Also, the inherent physiological behaviour of the plants under certain water stress conditions needs to be considered. Therefore, the aim of this study was to evaluate the effect of scion-rootstock combinations on the performance of a predefined plant-based method based on proximal near-infrared (NIR) spectroscopy.

In recent years red wines are being produced in Andalusia from indigenous and foreign grape varieties, one of which is the Spanish variety Tempranillo.

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.