Agricultural Land Suitability Evaluator Using GIS, Multi Criteria and Sensitivity Analysis
There is a pressing need to develop an optimal land evaluation method to identify in which part of a region selected crops could provide high yields. Matching the requirement of landuse with land’s capability ensures production levels would not exceed the capacity which the land can support. The pu...
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| Format: | Thesis |
| Language: | English |
| Published: |
2011
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| Summary: | There is a pressing need to develop an optimal land evaluation method to identify in which part of a region selected crops could provide high yields. Matching the
requirement of landuse with land’s capability ensures production levels would not exceed the capacity which the land can support. The purpose of this study is to develop
an agricultural land suitability evaluation system using Geographic Information System and Multi Criteria. The system works on reducing the subjectivity of weights based on Sensitivity Analysis. Sensitivity Analysis was used to display the spatial change and test the output variation when weights of parameters are systemically varied over a range of interest. The land suitability assessment relied on the FAO framework of 1976, with some necessary modifications to suit the local environmental conditions. The developed model for land suitability is based on a classification structure rather than a set of guidelines as in the FAO framework. Fourteen land characteristics and their threshold values were determined for the study area, Terengganu state in Malaysia, and grouped into nine land qualities. The land suitability model was constructed with GIS capabilities. Mango was selected as the crop for which the land suitability map will be produced. Soil type, climate, slope, erosion hazard and flood were created and integrated in GIS environment as information layers and then overlaid to produce the land suitability map for mango crop. The model specifications were assessed through
verification, validation, and sensitivity analysis. The sensitivity analyses and variation of function were used to determine the level of importance for each defined criterion in order to reduce the subjectivity in weights. The influence of each criterion was visualized spatially for each scenario and the variations of function were used to test the stability of the result. The findings of the weighting schemes emphasized that soil, slope and erosion are the most important factors in the study area. Conversely, climate and flood were found to be less important in the study area. The results of this analysis
indicated that 31% of the study area is considered as the most suitable place for mango cultivation, 55% of the area as moderately suitable and 9% percent as marginally
suitable. The study found 5% of the study area to be not suitable for mango agriculture. The model was utilized and accepted by the Department of Agriculture in Putrajaya. It
showed that it can serve as a decision support tool for farmers and decision makers. |
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