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The paper elaborates upon a hybrid approach of data mining and statistical methods, being investigated to modelling climate effects i.e., arising from year-to-year variability in weather conditions, on grape crop of three different varieties cultivated in northern New Zealand. Recent research using iterative χ 2 method based approach to modelling climate effects on "high" and "low' yearly yield (of perennial crops) with both data at regional (macro) scale, and then grape yield at meso i.e., vineyard, with climate data at macro scale, the latter work by Geoinformatics Research Centre are briefly outlined. The grape varieties studied are Chardonnay, Pinot Noir and Pinot Gris. The results show interesting patterns in the nexuses between extreme daily weather conditions and grape crop data, in terms of daily maximum, temperature observed over time against "low" and "high" yields, within the macro and meso scale data covering a period of less than ten years, which is considered to be inadequate for any conventional statistical data analysis, such as the iterative χ 2 method, based approach.

Data Mining and c2 Test Based Hybrid Approach to Modelling Climate Effects on Grape Crop in Varieties of Kumeu, New Zealand