Edith Cowan University Research Online Theses: Doctorates and Masters Theses 2013 Mining climate data for shire level wheat yield predictions in Western Australia Yunous Vagh Edith Cowan University Recommended Citation Vagh, Y. (2013). Mining climate data for shire level wheat yield predictions in Western Australia. Retrieved from https://ro.ecu.edu.au/ theses/695 This Thesis is posted at Research Online. https://ro.ecu.edu.au/theses/695 Edith Cowan University Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorize you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. 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Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. pro Edith Cowan University Mining Climate Data for Shire Level Wheat Yield Predictions in Western Australia A research thesis submitted as fulfillment of the by requirements for the degree of Doctor of Philosophy Yunous Vagh BSc Hons Faculty of Computing, Health and Science School of Security and Computer Science Principal Supervisors: Dr. Jitian Xiao Professor Craig Valli Dr. Mike Johnstone 2013 USE OF THESIS The Use of Thesis statement is not included in this version of the thesis. ABSTRACT 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 ii 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. iii DECLARATION I certify that this thesis does not, to the best of my knowledge and belief: (i.) incorporate without acknowledgment any material previously submitted for a degree or diploma in any institution of higher education; (ii.) contain any material previously published or written by another person except where due reference is made in the text of this thesis; (iii.) contain any defamatory material; or (iv.) contain any data that has not been collected in a manner consistent with ethics approval. I also grant permission to the Library at Edith Cowan University to make duplicate copies of my thesis as required. Student signature Date 22 November 2013 iv ACKNOWLEDGMENTS I am grateful in the first and last instance to God, the Creator and Fashioner who has endowed me with all faculties; physical, mental and spiritual. This is both general and specific. I realize that my family has supported me despite my research consuming so much time and attention that would have otherwise been reserved for them. Their patience and support were invaluable in an arduous journey that inevitably comes with undertaking doctoral research. This is a debt that can never be repaid and which warrants no less than my eternal gratitude. The primary acknowledgement is to Dr. Jitian Xiao who, as a supervisor, was a academically kindred spirit in this project. Special thanks accrue to him for agreeing to take me despite other demands on his research time, and for providing guidance, encouragement and assurance, particularly when the end of the project was near. Very special thanks are also directed to Professor Craig Valli for the belief and support he afforded me especially during the challenging times in 2011. In addition, Dr. Tapan Rai, the Faculty of Health, Engineering and Science statistician provided valuable assistance with design and analysis issues. I am also grateful to Phil Goulding from the Department of Agriculture and Food WA (DAFWA) for supplying information needed for the project, as well as to Dr. Dean Diepeveen for facilitating cooperation from DAFWA. Thanks go to Edith Cowan University for hosting this research and specifically to the Graduate Research School, for their numerous orientation and training services and general support for researchers. In this regard, special thanks are afforded to Dr. Greg Maguire for his invaluable editorial advice. Finally, thanks must be directed to my fellow students who provided much needed companionship on this sometimes lonely journey. In this regard, I specifically acknowledge Dr. Usman Farooq Kayani, Dr. Sunsern Limwiriyakul, Dr. Panida Subsorn, Vinh Dang, Rajeswari Chelliah, Pervaiz Ahmed, and Samaneh Rastegari. v LIST OF SUPPORTING PUBLICATIONS 1. Vagh, Y. (2012). The application of a visual data mining framework to determine soil, climate and land-use relationships.3rd International Science, Social Science, Engineering and Energy Conference ISEEC-2011, Thailand, Feb 2-5, 2012, NPR University, 37-42. Upgraded to journal article in Procedia Engineering, 2012, 32(2012), Elsevier, 299-306. 2. Vagh, Y. (2012). An Investigation into the effect of stochastic annual rainfall on crop yields in South Western Australia. International Conference of Knowledge Discovery 2012, Bali, May 26-27, IACSIT, 227-232. Upgraded to article in International Journal of Information and Education Technology, 2012, 2(3), IACSIT, 227-232. 3. Vagh, Y., Armstrong, L., & Diepeveen, D. (2010). Application of a data mining framework for the identification of agricultural production areas in WA. Proceedings of the 14th Pacific-Asia Conference Pacific Asia Knowledge Discovery and Data Mining 2010, Hyderabad, Jun 21-24, 2010, 11-22. Upgraded to journal article in Edith Cowan University Research Online, 2010, 1(2010),