This paper is a continuation of 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 surface grid. In this investigation, the stochastic average monthly temperature profiles for a selected study area were used to determine the effects on crop production. The areas within the study area were spatially scaled to correspond to individual shires within the South West Agricultural region of Western Australia. The temperature was sampled for three selected years of crop production for 2002, 2003 and 2005. The evaluation was carried out using graphical, correlation and data mining regression techniques in order to detect the patterns of crop production. The patterns suggested that crop production can generally be expected to increase with an increase in temperature during the wheat growing season for some shires
Abstract-In this research, the methodology of action research dynamics and a case study using both qualitative and quantitative methods was employed for the analysis of geographic data in an agricultural context. The geographic data was made up of land use profiles that were juxtaposed with previously captured rainfall data from fixed weather stations in Australia which was interpolated using ordinary krigeing to fit a grid surface. The resultant stochastic annual rainfall profiles for a selected study area within the South West Agricultural region of Western Australia were used to identify areas of high crop production. The areas within the study area were spatially scaled to individual shires. The rainfall was sampled for the years 2002, 2003, 2005 as a mix of low and high rainfall and high production attributes. The patterns suggested that crop production was closely linked to the annual rainfall for some shires, with location being of significance at other shires.
Abstract-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.
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