Faculty of Computing, Health and Science
School of Computer and Security Science
This paper presents the final investigation within the series of qualitative and quantitative investigations carried out for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of crop and cereal production land use profiles. These were linked to previously recorded climatic data from fixed weather stations in Australia that was interpolated using ordinary krigeing to fit a grid surface. In this study, the profiles for the stochastic average monthly temperature and rainfall for a selected study area were used to determine their simultaneous effects on crop production at the shire level. The temperature and rainfall were sampled for a selected decade of crop production for the years from 2001 to 2010. The evaluation was carried out using graphical, correlation and data mining regression techniques in order to detect the patterns of crop production in response to the climatic effect across the cropping shires of agricultural region. Data mining classification algorithms within the WEKA software package were used with location as the classifier to make comparisons between predicted and actual wheat yields. The predicted patterns suggested that crop production is affected by the climate variability especially at certain stages of plant growth for some shires.