An introduction to multivariate adaptive regression splines for the cane industry

Abstract

Industries strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties and growing conditions throughout each region. A challenge that the cane industry faces is how to accurately extract useful information from this vast array of data to better understand and improve the production system. Data mining methods have been developed to search large data sets for hidden patterns. This paper introduces a powerful data mining method known as Multivariate Adaptive Regression Splines (MARS). By applying the MARS methodology to model CCS production data from the Herbert district, a model was produced for the 2005 harvest period. This model produced a north-south geographic separation between low and high CCS producing farms in line with recorded CCS values. The model was also able to identify farm groupings which contributed to lower, modelled CCS values, relative to other farms. A brief investigation on the isolated effects of variety was also conducted.