A GIS-based multivariate clustering for characterization and ecoregion mapping from a viticultural perspective

Francisco J. Moral, Francisco J. Rebollo, Luis L. Paniagua, Abelardo García-Martín


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.


regression-kriging; zonation; viticulture; Extremadura

Full Text:



Almarza C, 1984. Fichas hídricas normalizadas y otros parámetros hidrometeorológicos, tomo II. National Meteorological Institute, INM. Madrid, Spain.

Bishop TFA, McBratney AB, 2001. A comparison of prediction methods for the creation of field-extend soil property maps. Geoderma 103: 149-160. http://dx.doi.org/10.1016/S0016-7061(01)00074-X

BOE, 1999. Order of 16 April that ratified the regulation of the regulatory board of the Ribera del Guadiana Denomination of Origin. Boletín Oficial del Estado No. 105, 03/05/99.

Goovaerts P, 1999. Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34 (3-4): 227-242. http://dx.doi.org/10.1016/S0341-8162(98)00116-7

Hall A, Jones GV, 2010. Spatial analysis of climate in winegrape-growing regions in Australia. Aust J Grape Wine R 16 (3): 389-404. http://dx.doi.org/10.1111/j.1755-0238.2010.00100.x

Herrera-Nuñez JC, Ramazzotti S, Stagnari F, Pisante M, 2011. A multivariate clustering approach for characterization of the Montepulciano d'Abruzzo Colline Teramane area. Am J Enol Viticult 62 (2): 239-244. http://dx.doi.org/10.5344/ajev.2010.10008

Huglin P, 1978. Nouveau mode d´évaluation des possibilités héliothermiques d´un milieu viticole. CR Acad Agr France 64: 1117-1126.

Liu M, Samal A, 2002. A fuzzy clustering approach to delineate agroecozones. Ecol Model 149: 215-228. http://dx.doi.org/10.1016/S0304-3800(01)00446-X

Mackenzie DE, Christy A, 2005. The role of soil chemistry in wine grape quality and sustainable soil management in vineyards. Water Sci Technol 51: 27-37.

MAGRAMA, 2015. Estadísticas agrarias: Agricultura. Ministerio de Agricultura, Alimentación y Medio Ambiente, Gobierno de España. http://www.magrama.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/default.aspx. [April 7 2015].

Moral FJ, 2010. Comparison of different geostatistical approaches to map climate variables: application to precipitation. Int J Climatol 30: 620–631. DOI: 10.1002/joc.1913.

Moral FJ, Rebollo FJ, Paniagua LL, García A, Honorio F, 2016. Integration of climatic indices in an objective probabilistic model for establishing and mapping viticultural climatic zones in a region. Theor Appl Climatol 124: 1033-1043. http://dx.doi.org/10.1007/s00704-015-1484-0

Munier B, Birr-Pedersen K, Schou JS, 2004. Combined ecological and economic modelling in agricultural land use scenarios. Ecol Model 174: 5-18. http://dx.doi.org/10.1016/j.ecolmodel.2003.12.040

Odeh I, McBratney A, Chittleborough D, 1994. Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma 63 (3-4): 197-214. http://dx.doi.org/10.1016/0016-7061(94)90063-9

Odeh I, McBratney A, Chittleborough D, 1995. Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67 (3-4): 215-226. http://dx.doi.org/10.1016/0016-7061(95)00007-B

OEMV, 2012. Estadísticas. Observatorio Español del Mercado del Vino. http://www.oemv.es/esp/estadisticas-3p.php. [May 23, 2015].

OIV, 2010. Summary of resolutions adopted by the Eight General Assembly of the Organisation Internationale de la Vigne et du Vin. June 2010. Tbilisi, Georgia.

Tonietto J, Carbonneau A, 2004. A multicriteria climatic classification system for grape-growing regions worldwide. Agr Forest Meteorol 124: 214-251. http://dx.doi.org/10.1016/j.agrformet.2003.06.001

Williams CL, Hargrove WW, Liebman M, James DE, 2008. Agro-ecoregionalization of Iowa using multivariate geographical clustering. Agr Ecosyst Environ 123: 161-174. http://dx.doi.org/10.1016/j.agee.2007.06.006

Zhou Y, Narumalani S, Waltman WJ, Waltman SW, Palecki MA, 2003. A GIS-based spatial pattern analysis model for ecoregion mapping and characterization. Int J Geophys Inform Sci 17: 445-462. http://dx.doi.org/10.1080/1365881031000086983

DOI: 10.5424/sjar/2016143-9323