Grapevine productivity modelling in the Portuguese Douro Region

«L’Observatoire Grenache» est un réseau de parcelles qui a été mis en place par l’Institut Rhodanien en Vallée du Rhône sur les millésimes de 1995 à 1999. Composé de 24 parcelles de Vitis vinifera L. cv Grenache noir, ce réseau vise à étudier l’influence du terroir (sol, climat et vigneron) sur la qualité des vins. Les parcelles ont été choisies afin de représenter différentes situations géographiques et géopédologiques de la vallée du Rhône. Le matériel végétal (clone, porte-greffe), la taille (cordon de Royat), la densité et l’âge de la parcelle ont été encadrées. Ainsi les conditions de milieu (sol, climat) et les pratiques du vigneron étaient les principales sources de variations.

Smoke taint (ST) is a grape-wine off-flavour that may occur when grapes absorb volatile phenols (VPs) originating from wildfire smoke (1). ST is associated with the negative sensory attributes such as smoky and ashy notes. VPs are glycosylated in the plant and thus present in both free and bound forms (2; 3). Wildfire smoke has resulted in a decline in grape and wine quality and financial losses which has become a prominent issue for the global wine industry.

The disruptive effect exerted by high-power ultrasound (US) on plant cell walls, natural barriers to the diffusion of compounds of interest during the maceration of red wines, is established as the reason behind the chromatic improvement that its treatment causes. However, sometimes this improvement is not observed, especially with short maceration times. The presence of a high quantity of suspended cell wall material, which formation is favored by the sonication, could be the cause of this lack of positive results since this cell wall material has a high affinity for phenolic compounds.

Agriculture, natural resource management and the production and sale of products such as wine are increasingly data-driven activities. Thus, the use of remote and proximal crop and soil sensors to aid management decisions is becoming commonplace and ‘Agtech’ is proliferating commercially; mapping, underpinned by geographical information systems and complex methods of spatial analysis, is widely used. Likewise, the chemical and sensory analysis of wines draws on multivariate statistics; the efficient winery intake of grapes, subsequent production of wines and their delivery to markets relies on logistics; whilst the sales and marketing of wines is increasingly driven by artificial intelligence linked to the recorded purchasing behaviour of consumers. In brief, there is data everywhere!

Opinions will vary on whether these developments are a good thing. Those concerned with the ‘mystique’ of wine, or the historical aspects of terroir and its preservation, may find them confronting. In contrast, they offer an opportunity to those interested in the biophysical elements of terroir, and efforts aimed at better understanding how these impact on vineyard performance and the sensory attributes of resultant wines. At the previous Terroir Congress, we demonstrated the potential of analytical methods used at the within-vineyard scale in the development of Precision Viticulture, in contributing to a quantitative understanding of regional terroir. For this conference, we take this approach forward with examples from contrasting locations in both the northern and southern hemispheres. We show how, by focussing on the vineyards within winegrowing regions, as opposed to all of the land within those regions, we might move towards a more robust terroir zoning than one derived from a mixture of history, thematic mapping, heuristics and the whims of marketers. Aside from providing improved understanding by underpinning terroir with data, such methods should also promote improved management of the entire wine value chain.

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions.