doi: 10.7763/IJMLC.2012.V2.181
Data Mining and χ2 Test Based Hybrid Approach to Modelling Climate Effects on Grape Crop in Varieties of Kumeu, New Zealand
Subana Shanmuganathan1
,
Philip Sallis1 ,
A. Narayanan1,2
- 1Geoinformatics Research Centre, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand.
- 2School Computing and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand.
Abstract
The paper elaborates upon a hybrid approach consisting of data mining and statistical methods, to modelling seasonal 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 an iterative χ2 method based approach to modelling climate effects on “high” and “low” yearly yields (of perennial crops) with data at the regional (macro) and grape yield from different vineyards, with climate data at macro scale, 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 for “low” and “high” yields, and within the macro and meso scale data, covering a period of less than ten years.
Keywords
- Year-to-year variability
- seasonal patterns
- extreme weather conditions
How to Cite
Subana Shanmuganathan, Philip Sallis, and A. Narayanan, "Data Mining and χ2 Test Based Hybrid Approach to Modelling Climate Effects on Grape Crop in Varieties of Kumeu, New Zealand," International Journal of Machine Learning, vol. 2, no. 4, pp. 521-525, 2012. https://doi.org/10.7763/IJMLC.2012.V2.181
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).