Evolución de los compuestos fenólicos durante el envero y la maduración en la DO Tarragona

Happens too often in scientific papers to find the same harvesting period of a cultivar, although the used treatment influence a maturity curve of investigated thesis.
This inevitably leads to wrong conclusions when comparing the treatment effects, since obtained on maturity stages more or less far from those technologically correct.

During wine alcoholic fermentation, yeasts produce volatile aroma compounds from sugar and nitrogen metabolism. Some of the metabolic pathways leading to these compounds have been known for more than a century.

The mechanization of pruning and harvesting in vineyards has increased the risk of soil compaction. To reclaim soil properties or avoid this degradation process, it is crucial to properly manage the soil organic matter, and the application of compost derived from the vines themselves is a strategy to achieve this. The objective of this study was to evaluate the properties of soil treated with different doses of compost applied both on the vine row and the inter rows of a Vitis vinifera crop.

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).

Aim: Climate modelling in viticulture introduced new challenges such as high spatio-temporal monitoring and the use of dependable time series and robustness modelling methods. Land surface temperature (LST) is widely used and particularly MODIS thermal satellite images due to their high temporal resolution (four images per day).