La pianificazione del paesaggio agrario vitivinicolo del basso Monferrato

Micro(mi)RNAs are small non-coding RNAs that regulate several pathways and are widely recognised as key players in plant development, tissue differentiation, and many other important physiological processes, including plant adaptation to biotic and abiotic stresses. The release of plant genomes and the application of high throughput sequencing have considerably extended miRNA discovery across many species, including grapevine (Vitis spp.). Despite their relevance in plant development, functional studies in grapevine to clarify the function of miRNAs are not yet available. Through the grapevine genetic improvement platform IMPROVIT at CNR-IPSP (http://www.ipsp.cnr.it/en/thematics/turin-headquarter-thematics/improvit/), we developed integrated approaches to discover miRNA function in grapevine.

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).

It is well known that lactic acid bacteria modify the wine volatile compound. However, very few data are available regarding metabolite changes that occurred during the malolactic fermentation (MLF).

This study was conducted on soil-climate-plant relations (terroir) and their impact on grape composition and wine quality in the canton of Vaud by Agroscope Changins-Wädenswil ACW

The wine industry requires coexistence between tradition and innovation to meet consumers’ preferences. Sensory science allows the objective quantification of consumers’ understanding of a product and subjective feedback of consumer’s perception through acceptance or rejection of stimulus or even describing emotions evoked [1]. To measure sensations, emotions and liking, and their dynamics over time, time-intensity methods are crucial tools with growing interest in sensory science [2].