Irrigation informatics: addressing poor irrigation decision support system uptake
AuthorCar, Nicholas John
Document TypePhD thesis
Access StatusOpen Access
© 2018 Nicholas John Car
Irrigation Decision Support Systems (DSS), in the first decade and a half of the 21st century, suffered from a lack of uptake by their target audience, namely farmers, according to a number of researchers in Australia and overseas. Reasons for this were proposed and solutions to overcome them explored. These included a "gap" between DSS research/development practice and on-farm practice with a solution being to involve irrigation practitioners in the planning and development of DSS; so-called participatory action research. The wider irrigation research community also undertook forms of social science investigation, such as surveys and networked interviewing, to better understand real-world irrigation decision making. That work did not evaluate, in any depth, the utility to farmers of new styles of DSS delivery based on the profound telecommunications and information technology changes in the early 2000s. Nor did that work much look to how decision-making in general may be better catered for via DSS. This thesis does these things by investigating approaches taken to assist irrigation DSS uptake in the field of informatics -- the science of information. The fundamental hypothesis being tested is that it is supposed there are real gains to be made in both improving the technical delivery mechanics of irrigation DSS' advice to farmers and the better staging of that advice through better technical handling of aspects of decision theory. Related here are several experiments conducted over the decade 2007 -- 2017. They look at how: * information has been, and could be, presented to irrigators to help them use it; * more sources of knowledge used by irrigators, perhaps available but not currently used, could be incorporated into DSS to make them more relevant to real-world practice; * the decisions that DSS hope to assist with can be modelled themselves, as distinct from biophysical irrigation modelling, in order to empirically determine irrigation best-practice and communicate decision norms. Additionally, work over time for this thesis has shed some insights into how changing technology availability, use and general acceptance on Australian farms has shifted the goalposts of DSS uptake. This thesis shows that: * there have been, and are likely to continue to be, real gains to be made in the adoption of DSSs through technical system design such as user interface design; * through both improving DSS design and growing data availability via technology change, the range of data sources available for and used by DSS is growing and this is likely to assist with their uptake. This is due to them being able to incorporate more data sources used in real-world decision making; * some issues preventing DSS uptake have disappeared due to technology change on Australian farms, some remain, and new ones have appeared; * decision modelling, as opposed to biophysical or economic modelling, has not really been undertaken by irrigation DSS designers and yet there are both existing decision modelling systems that designers could use and the potential to create other, better, models which may help with uptake. Recommended future investigations from this work are: * addressing the newly emergent issue of 'data deluge' which is beginning to plague DSS designers; which source of weather or commodity price should they use? A utility assessment of similar sources of data could be conducted; * building of collections of real-world irrigation decisions made, modelled using approaches from this thesis, and the testing of automated approaches for assessing their outcome. This would test the practicality of empirical decision-modelling-based DSS; * testing of the utility to irrigators of decision-modelling-enabled DSS. If current problems facing farmers can be matched to real-world best-practice, does this offer superior utility to calculated, theoretical best-practice?
Keywordsirrigation; decision theory; decision support system; DSS; ontology; advice; case-based reasoning; decision modelling
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