The Application of Land Evaluation Techniques in Jeffara Plain in Libya using Fuzzy Methods

This research compares three approaches to land suitability evaluation, Boolean, Fuzzy AHP and Ideal Point, for barley, wheat and maize crops in the north-western region of Jeffara Plain in Libya. A number of soil and landscape criteria were identified to accommodate the three cash crops under irrigation conditions and their weights specified as a result of discussions with local experts. The findings emphasised that soil factors represented the most sensitive criteria affecting all the crops considered. In contrast, erosion and slope were found to be less important in the study area. Using Boolean logic the results indicated only four suitability classes (highly suitable, moderately suitable, marginally suitable and currently not suitable) for all crops. In contrast, the results obtained by adopting the Fuzzy AHP and Ideal Point approaches revealed that the area of study has a greater degree of subdivision in land suitability classes. Overall, the results of the three approaches indicated that the area under consideration has a good potential to produce barley, wheat and maize under irrigation provided that the water and drainage requirements are met. Comparing the three models showed that each suitability class derived from the Boolean approach is associated with low and high values for joint membership functions when derived from Fuzzy AHP and Ideal Point approaches respectively. In other words, the two fuzzy approaches have shown their ability to explore the uncertainties associated with describing the land properties. The richer overall picture provides an alternative type of land suitability evaluation to Boolean approaches and allows subtle variations in land suitability to be explored. The Fuzzy AHP approach was found to be better than the Ideal Point approach; the latter was biased towards positive and negative ideal values. In the future, field trial plots will be needed to evaluate and validate the results further.