With rising population,decline in soil productivity and land-based conflicts,the per-capita land availability for cultivation is rapidly decreasing within Benue State,a largely agrarian and small-holder setting.This study attempts a local-level support for the actualisation of Sustainable Development Goal Number 2 (end hunger,achieve food security and improved nutrition,and promote sustainable agriculture) by 2030.Using Multi-riteria Decision Making (MCDM) method,remote sensing data from Climate Research Unit (CRU) and in-situ data from Nigeria Meteorological Agency (NIMET) were analyzed by GIS techniques to map the suitability of rice cultivation in the study area,with the integration of Normalized Difference Vegetation Index (NDVI),land cover,slope,temperature,precipitation and soil parameters (cation exchange capacity,pH,bulk density,organic carbon).We apply the various statistical parameters that include mean spatial NDVI;correlation coefficient,standard deviation and Root Mean Square (RMS) between CRU and NIMET data.Spatial regression trend analysis is conducted between CRU precipitation and NDVI and between CRU temperature and NDVI from 1985 to 201.5.The results reveal that NDVl in highly suitable rice planting regions is higher than marginally suitable regions except in the months of October and November,which shows that the highly suitable regions will yield better than the marginally suitable regions during the dry season.Additionally,NDVI is seasonally bimodal in response to precipitation,meaning that vegetation vigor is more dependent on precipitation than temperature.Finally,the correlation coefficient,standard deviation and RMS between CRU and NIMET precipitation data shows 0.42,108,and 110,respectively,while these three factors between CRU and NIMET temperature data shows 0.88,1.60,and 0.86,respectively.In conclusion,the MCDM approach reveals that upland is more suitable for rice cultivation in Benue State when comparing with the area provided by the Global Land Cover and National Mappings Organization (GLCNMO) data.