Spatial Assessment of Biophysical Suitability for Climate-Smart Agriculture across Nigeria

Abstract Climate change continues to threaten agricultural productivity and food security in Nigeria, highlighting the need for spatially targeted Climate-Smart Agriculture (CSA) interventions. This study evaluated environmental suitability for CSA across Nigeria using an integrated Remote Sensing, Geographic Information System (GIS), and Multi-Criteria Decision Analysis (MCDA) framework. Five environmental indicators, Normalized Difference Vegetation Index (NDVI), mean annual rainfall, mean temperature, soil organic carbon, and surface-water occurrence, were derived from Sentinel-2, CHIRPS, ERA5-Land, SoilGrids, and JRC Global Surface Water datasets and processed using Google Earth Engine and ArcGIS Pro. The indicators were standardized and integrated using a Weighted Linear Combination (WLC), while criterion weights were determined using the Analytic Hierarchy Process (AHP). The pairwise comparison matrix produced a consistency ratio (CR) of 0.000, indicating acceptable consistency. Results revealed a pronounced south–north gradient in CSA suitability, with relatively higher suitability concentrated in southern Nigeria and parts of the Middle Belt. Very Low and Low suitability accounted for 42.33% and 37.77% of Nigeria, respectively, while High and Very High suitability covered 5.91% and 0.89%. Overall, 80.10% of the country was classified as Very Low or Low suitability, compared with 6.80% classified as High or Very High suitability. The findings provide a national-scale biophysical baseline for prioritizing context-specific climate adaptation, sustainable land management, and agricultural planning.