Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

The application of a visual data mining framework to determine soil, climate and land-use relationships

In this research study, the methodology of action research dynamics and a case study was employed in constructing a visual data mining framework for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of a digital elevation model (DEM), soil and land use profiles that were juxtaposed with previously captured climatic data from fixed weather stations in Australia. In this pilot study, monthly rainfall profiles for a selected study area were used to identify areas of soil variability. The rainfall was sampled for the beginning (April) of the rainy season for the known ‘drought’ year 2002 for the South West of Western Australia. The components of the processing framework were a set of software tools such as ArcGis, QuantumGIS and the Microsoft Access database as part of the pre-processing layer. In addition, the GRASS software package was used for producing the map overlays. Evaluation was carried out using techniques of visual data mining to detect the patterns of soil types found for the cropping land use. This was supported by analysis using WEKA and Microsoft Excel for validation. The results suggest that agriculture in these areas of high soil variability need to be managed differently to the more consistent cropping areas. Although this processing framework was used to analyse soil and rainfall climate data pertaining to agriculture in Western Australia; it is easily applicable to other datasets of a similar attribution in different areas

A Data Mining Perspective of the Dual Effect of Rainfall and Temperature on Wheat Yield

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

An Investigation into the effect of stochastic annual rainfall on crop yields in South Western Australia

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