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.
Highlights
‘Terroir zoning’ has traditionally relied on qualitative ‘expert’ opinion of wines and/or fruit and heuristic views of the biophysical factors that might impact them, coupled with classical approaches to land classification and cartography, to describe the influence of soil conditions and climate on wine composition and vine management
With few exceptions (e.g. Fraga et al, 2017; Lacorde, 2019), recent terroir research has remained reliant on heuristic opinion and conformity to previously defined terroir units (Carey et al, 2009; Vaudour et al, 2010; Bonfante et al, 2011; Bonfante et al, 2018), rather than employing purely data-driven approaches
A further difficulty is presented by the notion of terroir zones being homogenous (e.g. Fraga et al, 2017), despite variation being evident at scales ranging from between-regions to a few metres within individual vineyards (Johnson and Robinson, 2019; Bramley et al, 2011a, 2017)
Summary
‘Terroir zoning’ has traditionally relied on qualitative ‘expert’ opinion of wines and/or fruit and heuristic views of the biophysical factors that might impact them, coupled with classical approaches to land classification and cartography, to describe the influence of soil conditions and climate on wine composition and vine management. Using examples from the Padthaway and Murray Valley winegrowing regions of Australia, they demonstrated how a combination of yield monitoring and mapping, remotely sensed imagery, a digital elevation model and spatial analysis, coupled with targeted sampling of vines, could promote an understanding of variation in terroir at the within-vineyard scale. When used as an input to the standard sugar industry N fertiliser recommendations, the ‘block yield potential’ derived from these regional scale maps provided the basis for more targeted use of N fertiliser and a consequent reduced risk of N loss to the Great Barrier Reef compared to when the ‘district yield potential’ was assumed. Two key questions are: Can knowledge of yield at one location be used to infer yield at another? If it can, may an estimate of yield made in one location, be used to infer the likely yield at another? To answer both questions requires understanding of patterns of spatial variability in yield and of their temporal stability
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