A GIS-based multivariate clustering for characterization and ecoregion mapping from a viticultural perspective
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Moral, Francisco J. [1] ; Rebollo, Francisco J. [1] ; Paniagua, Luis L. [1] ; García-Martín, Abelardo [1]
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[1] Universidad de Extremadura
Universidad de Extremadura
Badajoz, España
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- Localización: Spanish journal of agricultural research, ISSN-e 2171-9292, ISSN 1695-971X, Vol. 14, Nº. 3, 2016
- Idioma: inglés
- Enlaces
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Resumen
In wine-growing regions, zoning studies define areas according to their potential to produce specific wines and also identify the key drivers behind their variability and optimize vineyard management for sustainable viticulture. However, delineation of homogeneous zones is difficult because of the complex combination of factors which could affect zone classifications. One possibility to capture potential variability is the use of natural environmental properties as they are related to success in grape growing. With the aim of characterizing the spatial variability of the main vine-related environmental variables and determining different zones, climate and topographical data were obtained for Extremadura (southwestern Spain), an important wine region. Firstly, accurate maps of all climate indices were generated by using regression-kriging as the most suitable algorithm in which exhaustive secondary information on elevation was incorporated, and maps of topography-derived variables were obtained using GIS (Geographical Information System) tools. Secondly, principal component analysis and multivariate geographic classification were used to define homogeneous classes, resulting in three zones. Each zone was further characterized by overlaying the zonation map with a geology map and all enviromental layers. It was obtained that although a wide part of the Extremaduran territory has warm climate characteristics, the zones have different viticultural potential and a high proportion of the region lays on suitable substrate. This zonation in Extremadura is the basis for further zoning studies at more detailed field scale and the modeling of vineyard response to climate change.
