A multivariate clustering approach for a gis based territorial characterization of the montepulciano d’abruzzo DOCG “Colline Teramane”

Climate change is impacting wine production in all parts of the world in highly variable ways that may change the expression of terroir, from rapid loss of viability right through to highly beneficial aspects that increase suitability

The occurrence of aroma and macromolecule constituents in sparkling wines, directly influencing their organoleptic characteristics, is affected by several factors, including the grape cultivar, base-wine particularities, inoculated yeasts, the aging time, and winemaking practices [1].

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.

The wine industry works to minimize pesticides and adapt to climate change. Breeding programs have developed disease-resistant grape varieties, particularly against downy and powdery mildew, to minimize pesticide applications [1]. However, their enological potential remains underexplored.

Grapevine nitrogen (N) monitoring is essential for efficient N management plans that optimize fruit yield and quality while reducing fertilizer costs and the risk of environmental contamination. Unlike traditional vegetative-tissue sampling methods, remote sensing technologies, including hyperspectral imaging, have the potential to allow monitoring of the N status of entire vineyards at a per-vine resolution. However, differential N partitioning, variable spectral properties, and complex canopy structures hinder the development of a robust N retrieval algorithm. The present study aimed to establish a solid understanding of vine spectroscopic response at leaf and canopy levels by evaluating the different nitrogen retrieval approaches, including the radiative transfer model.