Assessing and Mapping Land Suitability Units for Maize ( Zea mays L) Production Using Integrated DEMATEL-ANP Model and GIS in the Foumbot Agricultural Basin (Cameroon Western Highlands)

Abstract

Land suitability assessment (LSA) is an essential step in the process of determining environmental limits for sustainable crop production. Up to date, studies on LSA for crop production in Cameroon have been based on empirical methods which are limited as they consider similar singnificance levels for all evaluation criteria and do not consider the interrelationships of criteria in the best-fit models. In the present study a qualitative land suitability evaluation by an integrated multi-criteria decision-making (MCDM) approach and geographic information system (GIS) was tested to assess and map suitable land units for maize (Zea mays L) production in Cameroon Western highland. Eight environmental criteria identified as the most relevant for maize production in the area of interest (AOI) saw their thematic maps prepared using ArcGIS 10.8. The relationship between criteria was considered by the DEMATEL method. The criteria were weighted using the ANP method. Thereafter, the land suitability map was obtained using the weighted overlay analysis (WOA) in ArcGIS. The results obtained indicated that slope has the highest specific weight and consequently the greatest influence on land suitability for maize production in the locality. The land suitability map generated showed that Foumbot’s agricultural land suitability for maize production varies from very high to marginally suitable (99% of the surface area). Specifically, 11% (8056 ha) is very highly suitable, 29% (21,119 ha) is highly suitable, 38% (27,405 ha) are moderately suitable and 20% (14,422 ha) are marginally suitable. The remaining 1% that falls under non suitable class represents 606 ha and is located on the steep slopes around the Mount Mbappit. The kappa analysis reveals a total overall accuracy of 78.67% and a kappa value of 0.7256 with an asymptotic error of 0.058 which is good. Then the model used in this research is highly recommended for future land evaluation works in Cameroon and similar ecosystems around the world.