GIS‑Based Land Suitability Assessment for Rice Cultivation in Rafin Kada Floodplains, Wukari, Nigeria

Sustainable rice intensification in tropical floodplains requires precise evaluation of land resources to guide expansion and management decisions. This study integrated Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) to assess land suitability for rainfed rice (Oryza sativa) cultivation in the Rafin Kada Floodplain, Taraba State, Nigeria. Eleven criteria, including soil physical and chemical properties, topography, climate, and remote‑sensing indices, were standardised and weighted to generate a composite suitability map. Rainfall (0.258) and temperature (0.184) received the highest AHP weights, reflecting their controlling influence on paddy systems in tropical environments. Texture (0.142), depth (0.098), and slope (0.064) followed in importance, while chemical attributes such as pH and CEC were uniform but critical to long‑term soil fertility. The results showed that 27% (220.5 ha) of the area was highly suitable (S1), 46% (379.4 ha) moderately suitable (S2), and 27% (224.6 ha) marginally suitable (S3), with constraints primarily related to drainage, CEC, and NDVI variability. Sensitivity analysis showed strong model stability, with the S1 class remaining unchanged under 20% weight variation and spatial disagreement limited to 0.2% of the study area. The integration of NDVI and TWI improved the spatial representation of vegetation vigour and wetness conditions beyond conventional FAO guidelines, confirming that combining GIS and AHP can effectively reveal spatial heterogeneity even within physiographically uniform floodplains. The study recommends targeted management interventions, such as bunding, drainage improvement, and organic matter incorporation, to enhance marginal areas. Future research should integrate multi‑seasonal remote sensing data and machine learning to improve predictive reliability across tropical lowland systems.