OENO One (Nov 2020)
Making sense of a sense of place: precision viticulture approaches to the analysis of terroir at different scales
Abstract
Traditional ‘terroir zoning’ has largely relied on heuristic ‘expert’ opinion coupled with approaches to land classification based on thematic mapping to describe the influence of soil conditions and climate on wine composition. Recent advances in geographical information systems (GIS) and digital mapping have enabled more robust quantitative methods to be developed, but with few exceptions recent terroir research has remained reliant on heuristic opinion and conformity to previously defined terroir units, rather than employing data-driven approaches. Using two case studies at regional scale, the aim of this paper is to illustrate how the use of methods of quantitative spatial analysis, as used to guide understanding of production system variability and to underpin precision viticulture (PV), may assist in better understanding terroir at a range of scales. In the Barossa region of Australia, cluster analysis of indices of soil physical and chemical fertility (available water capacity and cation exchange capacity), with critical climate variables (growing season rainfall, mean January temperature and growing degree days), clearly delineates differences between the Barossa and Eden Valleys but does not robustly promote further sub-division. Meanwhile, in the Marlborough region of New Zealand, interpolation of data supplied by wine companies from over 450 vineyards over several seasons suggests a consistent and characteristic regional ‘terroir’ in terms of vine yield and harvest date. Similarly consistent results were obtained for sub-regions of the Wairau Valley and a comparison of the Wairau and Awatere valleys. Thus, with scale-dependent modification, the methods of spatial analysis used to underpin PV and studies of within-vineyard variability offer much potential for terroir analysis and the identification of terroir zones. Importantly, these methods are unbiased, data-driven, and not reliant on heuristic opinion.
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