Early detection project – make a GTD infection visible without disease symptoms

Grapevine (vitis spp.) Is one of the major and most economically important fruit crops worldwide. Unlike other cropping systems, viticulture has ancient historical connections with the development of human culture and with the socio-cultural background of grape-growing areas. The vitis genus is characterised by high levels of genetic diversity, as result of natural genetic mutations, which are common in grapevines and further assisted by ongoing vegetative propagation.

Since 2012, EU Commission approved compulsory labeling of wines treated with allergenic additives or processing aids “if their presence can be detected in the final product” (EU Commission Implementing Regulation No. 579/2012 of 29 June 2012). The list of potential allergens to be indicated on wine labels comprises sulphur dioxide and milk- and egg- derived fining agents, including hen egg lysozyme, which is usually added in wines as preservative. In some non-EU countries, the list includes gluten, tree nuts and fish gelatins. With the exception of lysozyme, all these fining proteins were long thought to be totally removed by subsequent winemaking processings (e.g. bentonite addition).

he use of high-power ultrasound (US) is proving of great interest to the oenological industry due to its effects in the improvement of wine organoleptic characteristics, especially in terms of color [1, 2].

Ozone is a potent oxidizing compound that quickly decomposes into oxygen without residues. Previous works reported that ozone is not only a disinfectant that directly harms the pathogens of the vine but also activates systemic defense systems in the plant by activating oxidative stress. We assume these systemic defense mechanisms are essential to the vines’ resistance to downy and powdery mildew (Plasmopara viticola & Erysiphe necator, respectively). The goals of the research are to examine the effect of spraying with ozone water on the plant’s resistance against the mentioned pathogens as well as to characterize the metabolic profile of the plants treated with ozone as well as physiological characteristics in the vines such as the level of Photosynthesis and crop yield. Vines in the vineyard sprayed with ozone water at concentrations of 2 and 4 PPM weekly and biweekly, untreated control & conventional spray. Leaves were taken from vines 2,4,7,9 and 11 days after exposure to ozone and inoculated with the pathogens.

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.