Delineating Natural Terroir Units in Wine Regions Using Geoinformatics

Nikolaos Karapetsas, Thomas K. Alexandridis (), George Bilas, Serafeim Theocharis and Stefanos Koundouras
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Nikolaos Karapetsas: Laboratory of Remote Sensing, Spectroscopy and Geographic Information Systems, Department of Hydraulics, Soil Science and Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Thomas K. Alexandridis: Laboratory of Remote Sensing, Spectroscopy and Geographic Information Systems, Department of Hydraulics, Soil Science and Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
George Bilas: Laboratory of Remote Sensing, Spectroscopy and Geographic Information Systems, Department of Hydraulics, Soil Science and Agricultural Engineering, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Serafeim Theocharis: Laboratory of Viticulture, Department of Horticulture, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Stefanos Koundouras: Laboratory of Viticulture, Department of Horticulture, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Agriculture, 2023, vol. 13, issue 3, 1-18

Abstract: The terroir effect refers to the interactions between the grapes and their natural surroundings and has been recognized as an important factor in wine quality. The identification and mapping of viticultural terroir have long been relying on expert opinion coupled with land classification and soil/climate mapping. In this study, the data-driven approach has been implemented for mapping natural terroir units based on spatial modeling of public-access geospatial information regarding the three most important environmental factors that make up the terroir effect on different scales, climate, soil, and topography. K-means cluster analysis was applied to the comprehensive databases of relevant spatial information, and the optimum number of clusters was identified by the Dunn and CCC indices. The results have revealed ten clusters that cover the agricultural area of Drama (Greece), where it was applied, and displayed variable conditions on the climate, soil, and topographic factors. The implications of the resulting natural terroir units on the vini-viticultural management of the most common vine varieties are discussed. As more accurate and detailed input spatial data become available, the potential of such an approach is highlighted and paving the way toward a true understanding of the drivers of terroir.

Keywords: terroir effect; spatial modelling; k-means clustering; viticulture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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