Aim: Terroir is a French concept relating the qualities and quality of agricultural products to their physical and socio-cultural place of origin. It is increasingly used by business and policymakers as a marketing technique to provide economic benefits (e.g. Lenglet, 2014; Wine Australia, 2015), and to potentially preserve cultural heritage (e.g. Bauer, 2009) and the environment (e.g. Bowen, 2010)
Aims: Most consumer research about terroir has focused on wine, particularly with French or other European wine drinkers, rather than those in the Anglo-Saxon world. In Europe, whilst there is no agreement amongst consumers as to what terroir actually is, there is a general recognition of the word and an acceptance that it represents something important
Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.
Aims: The aims of this study were to i) characterize the impact of geographical origin and viticulture treatments on Chilean Cabernet-Sauvignon, and ii) develop machine learning models to predict its quality.
Currently, the greatest challenge for vine growers is to improve the yield and quality of grapes by minimizing costs and environmental impacts. This goal can be achieved through a better knowledge of vineyard spatial variability. Traditional platforms such as airborne, satellite and unmanned aerial vehicles (UAVs) solutions are useful investigation tools for vineyard site specific management.