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Background and Aims: Vineyards are variable. However, to date, no spatial analysis of vineyard variability has been conducted in New Zealand. We were interested to quantify variability in a Marlborough vineyard and to produce a spatial platform onto which modelled information on phenology and juice composition could be integrated. Methods and Results: A combination of remote and proximal sensing of vine vigour, direct measurement of trunk circumference, yield mapping and high resolution electromagnetic induction (EM38) soil survey was used to examine vineyard variability in a 5.9 ha Marlborough vineyard planted to Vitis vinifera L. cv. Sauvignon Blanc. Yield variation was little more than twofold, in spite of substantial variation in vine vigour which was associated with variation in the land (soil, topography) underlying the vineyard. Conclusions: A focus on tools that facilitate enumeration of variation in vine vigour may offer the greatest value to Marlborough practitioners interested in adopting Precision Viticulture approaches to grapegrowing and winemaking. EM38 soil survey appeared to be useful for describing vineyard soil variation, but because the soils that predominate over the alluvial Wairau Plains in Marlborough are shallow and stony, very low values of apparent electrical conductivity (ECa) over a narrow range were observed. However, ECa was closely correlated with trunk circumference, an index of vine vigour. In contrast to Australian studies, neither ECa, plant cell density (derived from remotely sensed imagery) nor trunk circumference were good predictors of grapevine yield. It is hypothesised that this is largely a reflection of differences in vine training systems (hand cane pruning in Marlborough vs mechanical pruning in Australia) and the greater degree of selection of buds when vines are hand pruned. Significance of the Study: This is the first such study conducted in New Zealand and provides results that contrast with similar studies conducted in Australia. Nevertheless, the maps produced are expected to provide a valuable platform for a follow-up study aimed at understanding spatial variation in vine phenology and juice composition. The study also highlighted the power of kriging as a means of interpolating useful vineyard maps from relatively sparse, unevenly distributed sampling data.
Bramley et al. (2011) studied this question.