Il Lambrusco reggiano e il territorio di pianura: un modello efficace

Combined improvements in sequencing technologies and assembly algorithms have led to staggering improvements in the quality of grape genome assemblies.

In vineyard management, the block is considered today as the technical work unit. However, considerable variability can exist inside a block with regard to physiological parameters, such as vigour, particularly because of soil heterogeneity. To represent this variability spatially, many measurements have to be taken, which is costly in both time and money. High resolution remote sensing appears to be an efficient tool for mapping intra-block heterogeneity.

Nitrogen (N) is quite important nutrient in grapevine development and must quality, but under Mediterranean climatic conditions, available soil water (ASW) during grapevine development can also influence vigour and must quality. The aim was to determine the influence of soil nitrate (NO3-) availability on N foliar, yield, and must quality in vineyards with similar available water holding capacity (AWC). For this purpose, four cv. Tempranillo (Vitis vinifera L.) vineyards were selected. All of them are placed in Uruñuela municipality (La Rioja, Spain), separated less than 2.5 km and in a slope <1 %, in soils with similar soil chemistry properties and with similar rooting depth (ranging between 105 cm and 110 cm).

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, is one of the most destructive diseases of Vitis vinifera worldwide. All V. vinifera cultivars are susceptible to P. viticola infection, and epidemics can spread across an entire vineyard within a matter of weeks. Severe outbreaks cause substantial reductions in yield and fruit quality. Tracking GDM spread by manual scouting is time-consuming and unfeasible over large spatial extents.