Modeling FAO Land Suitability Assessment for Wheat Cultivation in Semi-Arid Environments Using Fuzzy Logic and GIS Based Approach

Sustainable land management and accurate land suitability assessment are fundamental pillars for ensuring long-term agricultural productivity and environmental health.. In this study, the FAO's sustainable agricultural land suitability assessment for wheat was carried out using the criteria of slope from topographical factors and pH, calcium carbonate (CaCO₃) and OM from critical soil factors. The aim is to assess the suitability of the land through the integration of geospatial data with fuzzy logic models and to contribute to sustainable agricultural activities. Soil samples were analysed for basic properties. Digital soil maps were produced using ordinary kriging. The Mamdani fuzzy inference technique was used for classification of the land into appropriate classifications in accordance with FAO standards. The study area was classified moderately (59.41%) and slightly (40.58%) suitable for wheat. High calcium carbonate (CaCO₃) concentration, low organic matter were identified as limiting factors. Soil management is recommended to rehabilitate marginally suitable sites and to use them with care. This research highlights the possibility of facilitating sustainable agricultural planning in semi-arid environments through the combination of fuzzy logic with GIS-based techniques for proper land assessment.