Understanding variability in winegrape production systems

Abstract

Spatial variability in winegrape yield was studied over several vintages in blocks planted to Cabernet Sauvignon, Merlot and Ruby Cabernet in the Coonawarra, Clare Valley and Sunraysia regions of Australia using new yield monitoring technology, a differentially corrected global positioning system (GPS), a geographical information system and some simple methods of spatial analysis. In any given year, yield was highly variable and typically of the order of 10 fold (i.e. 2 to 20 t/ha). However, through the use of k-means clustering and a method based on assessment of the probability of achieving yield targets relative to the mean annual block yield, temporal stability in the patterns of yield variation was demonstrated, even though there were substantial year to year differences in mean annual yield in these blocks. The methods used to demonstrate temporal stability in the patterns of yield variation also promote identification of zones of characteristic performance within variable vineyard blocks. Of significance in this work was the finding that, whilst k-means clustering is the more statistically robust of the two methods used, the ability to incorporate expert knowledge into the yield target method enhances the ability of the manager to accommodate the effects of abnormal events (e.g. an unusually cold flowering period) in the zone identification process. Targeted harvesting of different zones, followed by comparison between commercial lots of wine, provided indication that wine characteristics vary from zone to zone. However, the ranking of wine scores for the various zones changed between seasons. Our results have important implications for the adoption of Precision Viticulture. In particular, they support the introduction of a system of zonal vineyard management. Thus, rather than being managed uniformly, individual blocks can be split into zones in which the management of both inputs to, and outputs from the production system can be applied differentially.