ABSTRACT
The area under study has witnessed series of violent communal clashes between farmers and pastoralists as a result of encroachment into grazing reserves and farmlands by the two parties. Thus, there is a need to settle these pastoralists and their livestock on suitable land for grazing, with basic infrastructures provided on such lands. Therefore, land suitability analysis and mapping is required to find the most befitting localities for establishing, developing and maintaining grazing reserves. The aim of this research was to determine and map appropriate locations for siting grazing reserves within Kaduna and Niger states using a geospatial technique with GIS serving a platform for evaluation. The various datasets collected and processed for the suitability mapping were categorized as biophysical, environmental and socioeconomic criteria. A mosaicked Landsat 8 imagery of the study area was classified by supervised classification. Accuracy assessment was carried out on the classification with a 56.59% Kappa coefficient and overall classification accuracy of 60.88%. Ten factors and two constraints were used as criteria and weighted by Analytical Hierarchical Process (AHP) for the Multicriteria evaluation (MCE) in a GIS environment, to come up with the final suitability map which was classified according to FAO specifications. Results showed that 8% of the total area (9374.58 km2) is highly suitable, 62% (72212.59 km2) is moderately suitable, 1% (615.09 km2) is marginally suitable whereas 29% (33325.92 km2) of the total area under consideration is not suitable for siting grazing reserves. Findings indicate that unsuitability or low suitability is as a result of steep slopes, unavailability of water resources, unfavourable climatic condition, unavailability of forage and nearness to settlements. Southern parts of Kaduna and south eastern area of Niger state were found to be suitable locations for establishing grazing reserves. The study showed that GIS-based Multicriteria evaluation provides a great advantage to analyze multiple layers of data.