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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(4): 521-525 ISSN: 2010-3700
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

S. Shanmuganathan, P. Sallis, and A. Narayanan
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.

Index Terms—Year-to-year variability, seasonal patterns, extreme weather conditions.

Subana Shanmuganathan and Philip Sallis are with Geoinformatics Research Centre, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand (e-mail: subana.shanmuganathan@aut.ac.nz; philip.sallis@aut.ac.nz).
Ajit.Narayanan is with School Computing and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand (e-mail: ajit.narayana@aut.ac.nz).

[PDF]

Cite:S. Shanmuganathan, P. 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 and Computing vol.2, no. 4, pp. 521-525, 2012.

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