Abstract
Uncertainty analysis is rarely considered in the application of predictive models in agriculture, resource planning and land suitability analysis. Uncertainty in modeling land suitability for agricultural production arises from a variety of sources. An important source of error is due to uncertainty in model inputs and parameters, especially in the case of multi-criteria analysis requiring data from physical measurements or expert opinion from regional workshops. The concept, scope and taxonomy of uncertainty analysis are discussed in the context of resource planning and land suitability analysis. The model used for land suitability was derived using the Analytic Hierarchy Process (AHP) originally introduced by Saaty in the mid 1970s. The general approach is also appropriate to modeling suitability to pasture and forestry as well as agricultural crops. The deterministic AHP approach produces point estimates only, with no indication of error or confidence in the output. We have integrated the AHP approach with a stochastic simulation model for uncertainty assessment. Since the AHP approach is deterministic, procedural adjustments are required to estimate uncertainty in predictions. The approach taken was to represent expert judgements and ratings by probability distributions and to implement a graded series of stochastic simulations. Variable weight values were subject to constraints of range and unit-sum for each level of the hierarchy in the AHP model. Results for uncertainty analysis are presented for land-use suitability in south-west Victoria in Australia for the crops ryegrass/sub-clover and winter wheat. The work was carried out in the context of a program supporting climate change adaptation funded by the Victorian Government. Estimates of uncertainty for the AHP approach were conservative in nature and a primary objective was to explore and develop further a generalised approach to uncertainty assessment for the AHP model and similar multi-criteria evaluation techniques.
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References
Benke KK, Hamilton AJ, Lowell KE (2007) Modeling Uncertainty and Risk Assessment in the Management of Environmental Resources. Australasian Journal of Environmental Management 14:16–22.
Benke KK, Hamilton AJ (2008a) Quantitative Microbial Risk Assessment: Uncertainty and Measures of Central Tendency for Skewed Distributions. Stochastic Environmental Research and Risk Assessment, 22:533–539.
Benke KK, Lowell KE, Hamilton AJ (2008b) Parameter Uncertainty, Sensitivity Analysis and Prediction Error in a Water-Balance Hydrological Model. Mathematical and Computer Modeling, 47:1134–1149.
Burgman MA (2001) Flaws in Subjective Assessments of Ecological Risks and Means for Correcting Them. Australian Journal of Environmental Management 8:219–226.
Gamini H, Prato T (2006) Using Multi-Criteria Decision Analysis in Natural Resource Management, Ashgate Publishing Limited, Hampshire, England.
Hacking I (2001) An Introduction to Probability and Inductive Logic, Cambridge University Press, Cambridge (UK).
Hahn ED (2003) Decision Making with Uncertain Judgements: A Stochastic Formulation of the Analytic Hierarchy Process. Decision Sciences 34:443–466.
Haines LM (1998) A Statistical Approach to the Analytic Hierarchy Process with Interval Judgements. (I). Distributions on Feasible Regions. European Journal of Operational Research 110:112–125.
Helton JC (1993) Uncertainty and Sensitivity Analysis Techniques for Use in Performance Assessment for Radioactive Waste Disposal. Reliability Engineering and System Safety 42:327–367.
Helton JC, Burmaster DE (1996) Guest Editorial: Treatment of Aleatory and Epistemic Uncertainty in Performance Assessments for Complex Systems. Reliability Engineering and System Safety 54:91–94.
Hossain H, Sposito V, Evans C (2006) Sustainable Land Resource Assessment in Regional and Urban Systems. Applied GIS 2(3):24.1–24.21.
Iman RL, Conover WJ (1983) A Modern Approach to Statistics. New York, Wiley.
Jablonsky J (2005) Measuring Efficiency of Production Units by AHP Models. ISAHP 2005. Honolulu, Hawaii.
McKay MD, Conover WJ, Beckman RJ (1979) A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics 21:239–245.
Mustajoki J, Hamalainen RP, Lindstedt MRK (2006) Using Intervals for Global Sensitivity and Worst-Case Analyses in Multiattribute Value Trees. European Journal of Operational Research 174:278–292.
Oberkampf WL, Helton JC, Joslyn CA, Wojtkiewicz SF, Ferson S (2004) Challenge Problems: Uncertainty in System Response given Uncertain Parameters. Reliability Engineering and System Safety 85:11–19.
Saaty TL (1994) Fundamentals of Decision Making, RWS Publications, Pittsburgh, USA.
Soanes, C., Hawker, S. Compact Oxford English Dictionary, Oxford University Press, United Kingdom.
Sposito VJ (2006) A Strategic Approach to Climate Change Impacts and Adaptation. Applied GIS 2(3): 23.1–23.26.
Vose D (2000) Risk Analysis, John Wiley & Sons, Chichester, England.
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© 2009 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
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Benke, K.K., Pelizaro, C., Lowell, K.E. (2009). Uncertainty in Multi-Criteria Evaluation Techniques When Used for Land Suitability Analysis. In: Cao, W., White, J.W., Wang, E. (eds) Crop Modeling and Decision Support. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01132-0_32
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DOI: https://doi.org/10.1007/978-3-642-01132-0_32
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