This study describes how farmers manage climate variability in dryland crop production, and aims to contribute to the theory and practice of decision support for managing climate variability. The intent was to study farmer decision making to see how DSS could be used to deliver information and procedures on climate risk to farmers more effectively. The study investigated whether there are significant differences between farmers' subjective distributions of seasonal rainfall and its derivatives (such as crop yield and fallow recharge) and a probability distribution derived from long-term records and simulation models, and whether these differences in risk assessment lead to changes in the optimum decision. Subjective probability distributions of rainfall and its derivatives were collected from farmers and advisers and it was found the overall match between these and long term records and simulation models was close. This study found little evidence to support the role of DSS for routine decision making, but this does not lessen the value of distributions derived from simulation models. Rather, it provides an opportunity for both farmers and scientists to learn. / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:ADTP/181678 |
| Date | January 2001 |
| Creators | Hayman, Peter Theodore, University of Western Sydney, Hawkesbury, College of Science, Technology and Environment, School of Environment and Agriculture |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Source | THESIS_CSTE_EAG_Hayman_P.xml |
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