DNA-Free genome editing confers disease resistance in grapevine

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.

The development of interspecific hybrid varieties (ihvs) resistant to diseases such as powdery mildew and downy mildew allows for a decrease in the use of inputs in vineyards. In this pers-pective, ihvs represent a response to societal demand for reducing environmental impact and are increasingly used in viticulture. At the same time, wines resulting from so-called sponta-neous fermentations, based on indigenous flora, have recently gained popularity.

In the 19th century, devastating outbreaks of phylloxera (Daktulosphaira vitifoliae Fitch), almost brought European viticulture to its knees. Phylloxera does not only take energy in form of sugars from the vine, but also affects the up- and down- regulations of genes, acts as a carbon sink and reprograms the physiology of the grapevines, including nutrient uptake and the defense system [1]. A key trait of rootstocks is the ability to perform well under high lime conditions as about 30 % of the land surface has calcareous soil. Iron deficiency not only causes the well-known problems of lime-induced chlorosis and stunted growth, but also affects the entire plant metabolism.

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.

Aim: to elucidate if it is possible to detect variations in the source of nitrogen (organic vs. inorganic) measuring nitrogen isotope ratio (δ15N) in berries and to examine the degree of variation occurring for this parameter naturally within a vineyard.