Abstract
Land suitability analysis is employed to evaluate the appropriateness of land for a particular purpose whilst integrating both qualitative and quantitative inputs, which can be continuous in nature. However, in agricultural modelling there is often a disregard of this contiguous aspect. Therefore, some parametric procedures for suitability analysis compartmentalise units into defined membership classes. This imposition of crisp boundaries neglects the continuous formations found throughout nature and overlooks differences and inherent uncertainties found in the modelling. This research will compare two approaches to suitability analysis over three differing methods. The primary approach will use an Analytical Hierarchy Process (AHP), while the other approach will use a Fuzzy AHP over two methods; Fitted Fuzzy AHP and Nested Fuzzy AHP. Secondary to this, each method will be assessed into how it behaves in a climate change scenario to understand and highlight the role of uncertainties in model conceptualisation and structure. Outputs and comparisons between each method, in relation to area, proportion of membership classes and spatial representation, showed that fuzzy modelling techniques detailed a more robust and continuous output. In particular the Nested Fuzzy AHP was concluded to be more pertinent, as it incorporated complex modelling techniques, as well as the initial AHP framework. Through this comparison and assessment of model behaviour, an evaluation of each methods predictive capacity and relevance for decision-making purposes in agricultural applications is gained.
Highlights
There is a growing consensus in natural resource management that agricultural policy will need to address likely impacts foreseen by climate change science and suggest and support adaptation actions [1]
Multi Criteria Analysis (MCA) is a suite of methodologies that are primarily used for making decisions from complex input data and has been used numerous times for modelling suitability, where the main concern is how to combine multiple points of biophysical related data to arrive at a suitability decision [6,7,8]
For the purposes of this study, all area within the Study Region will be considered for analysis in spatial analysis and total area counts
Summary
Decisions taken can influence agricultural or environmental practices, other linked components, such as infrastructure and transportation. These impacts will potentially occur across all spatial domains and have ramifications on the adaptive capacities of all linked components. Evaluation of suitability is principally done to assess an area, or region, for optimal crop production and involves the interpretation of agricultural based data, such as soils, landscape, climate and water, in an effort to match land characteristics with crop requirements [4,5]. In an agricultural frame, the determination of what areas of land are deemed suitable for a particular use can be complex due to the multiple, and often disparate, streams of data. Two main distinguishing features of a MCA is that it can combine objective and subjective inputs, as well as absolute or relative criteria, and it is flexible in terms of adjustment [9]
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