Plant regeneration via somatic embryogenesis and preliminary trials for the application of the DNA-free genome editing in grapevine cv. Corvina veronese

Viral diseases are reported to cause several detrimental effects on grapevine. Among them, leafroll, due to single or mixed infection of GLRaV1 and GLRaV3, and rugose wood, associated to GVA, are considered the most widespread and dangerous.

This study used climatic and topographic data to characterize the sub-appellations that have been recently delineated in the Niagara Peninsula viticulture area in order to assess their potential for ripening early to late season Vitis vinifera varieties. No major differences were found in the ripening-period mean temperatures, but major differences in the diurnal temperature ranges were observed.

In contrast to fruit and grape berry ripening, the biological processes causing ripening disorders are often much less understood, although shriveling disorders of fruits are manifold and contribute to yield losses and reduced fruit quality worldwide. Shrinking berries are a common feature for all shriveling disorders in grapevine although their timing of appearance during the berry ripening process and their underlying induction processes distinct them from each other. The sugar accumulation disorder Berry Shrivel (BS) is characterized by a suppression of sugar accumulation short after veraison resulting in berries low in sugar content and anthocyanins in berry skins, while the organic acid content is similar. Recent studies analyzed the biochemical, morphological and molecular processes affected in BS berries and linked early changes to the period of ripening onset [1,2].

“Flavescence dorée” (FD) is a grapevine quarantine disease associated with phytoplasmas and transmitted to healthy plants by insect vectors, mainly Scaphoideus titanus. Infected plants usually develop symptoms of stunted growth, unripe cane wood, leaf rolling, leaf yellowing or reddening, and shrivelled berries. Since plants can remain symptomless up to four years, they may act as reservoirs of FD contributing to the spread of the disease. So far, conventional management strategies rely mainly on the insecticide treatments, uprooting of infected plants and use of phytoplasma-free propagation material. However, these strategies are costly and could have undesirable environmental impacts. Thus, the development of sustainable and noninvasive approaches for early detection of FD and its management are of great importance to reduce disease spread and select the best cultural practices and treatments. The present study aimed to evaluate if multispectral/hyperspectral technologies can be used to detect FD before the appearance of the first symptoms and if infected grapevines display a spectral imaging fingerprint. To that end, physiological parameters (leaf area, chlorophyll content and photosynthetic rate) were collected in concomitance to the measurements of plant reflectance (using both a portable apparatus and a remote sensing drone). Measurements were performed in two leaves of 8 healthy and 8 FD-infected grapevines, at four timepoints: before the development of disease symptoms (21st June); and after symptoms appearance (ii) at veraison (2nd August); at post-veraison (11th September); and at harvest (25th September). At all timepoints, FD infected plants revealed a significant decrease in the studied physiological parameters, with a positive correlation with drone imaging data and portable apparatus analyses. Moreover, spectra of either drone imaging and portable apparatus showed clear differences between healthy and FD-infected grapevines, validating multispectral/ hyperspectral technology as a potential tool for the early detection of FD or other grapevine-associated diseases.

The selection of ancient grapevine varieties aims to achieve genetic gains in several important traits that can make the variety more interesting for the objectives of the producers. Traditionally, yield and quality traits of the must have been considered for selection, but many others can be taken into account. Linear mixed models are fitted to the data to predict the empirical best linear unbiased predictors (EBLUPs) of genotypic effects for each evaluated trait, which will be the basis for selection.