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Delineating Natural Terroir Units in Wine Regions Using Geoinformatics

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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)

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.

Suggested Citation

  • Nikolaos Karapetsas & Thomas K. Alexandridis & George Bilas & Serafeim Theocharis & Stefanos Koundouras, 2023. "Delineating Natural Terroir Units in Wine Regions Using Geoinformatics," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:629-:d:1089382
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    References listed on IDEAS

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    1. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    2. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    3. Simone Priori & Roberto Barbetti & Giovanni L'Abate & Pierluigi Bucelli & Paolo Storchi & Edoardo A.C. Costantini, 2014. "Natural terroir units, Siena province, Tuscany," Journal of Maps, Taylor & Francis Journals, vol. 10(3), pages 466-477, July.
    4. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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