AI Summary & Key Takeaways
AI InsightThe suitability maps of wheat, clover, sugar beet and corn crops are generated for the projected area using 63 Land Mapping Units (LMU) with considering pH, lime, texture, salinity, organic matter, electrical conductivity, permeability, slope, aspect and the distance to settlements and roads within chemical, physical, topological and socio-economic criteria.
📌 3 Key Takeaways
Abstract
Crop selection for sustainable and effective agricultural land management has to take into accounts several issues such as chemical, physical, environmental, economic and social conditions. Especially after land consolidation projects, sustainable agricultural crop management should be investigated for each crop which are suitable for the project area to benefit from the land consolidation contributions such as irrigation, roads, modified parcel boundaries and surfaces. Thus, Geographical Information Systems (GIS) aided suitability analysis techniques are required to determine the suitable crops for the consolidated areas. In this study, Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision techniques are integrated with GIS to determine most suitable crops for parcels. The suitability maps of wheat, clover, sugar beet and corn crops are generated for the projected area using 63 Land Mapping Units (LMU) with considering pH, lime, texture, salinity, organic matter, electrical conductivity, permeability, slope, aspect and the distance to settlements and roads within chemical, physical, topological and socio-economic criteria.