A wine climate model : using climatic variables and GIS for viticulture potential.
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
There have been numerous attempts for measuring viticulture potential. In most cases, this involves the construction of a climate/viticulture based model which is then applied to a potential region. Although these climatic based models may perform well inside their original regions, they tend to fail elsewhere. Discrepancies between climatic indexes have been attributed to many factors including vineyard management, grapevine variety, phenology/climate misconceptions and regional macro/mesoclimate regimes. The aim of this study is to investigate the viticulture potential within two regions of the South Island of New Zealand, by implementing several climatic based models. These models have been designed to improve the predictive accuracy over other current climatic/viticulture models. A GIS, along with climatic data collected from over 80 stations within Canterbury and the surrounding region, are incorporated into several models which are used within this project as viticultural tools to improve the current understanding between topographic, climatic and grapevine relationships. Viticulture potential ratings are then assessed for two overlapping regions; the smaller sub-region of the lower Waipara catchment and the larger Canterbury region. Results show that many of the current vineyard plots in the Canterbury and lower Waipara regions do not fall within the optimal limits of the models, while there are large potential areas in both regions yet to be utilized for viticulture. However, not all of Canterbury and the lower Waipara catchment can be used for viticulture due to several limiting factors such as elevation, risk of frosts and low mean temperatures. Surprisingly, most of the Canterbury Plains fall within this 'no go' area. The model has proved to be a much more reliable tool than other existing climatic indexes such as the widely used degreeday and mean temperature of the warmest month indices.