Principal component and cluster analyses based characterization of maize fields in southern central Rift Valley of Ethiopia

Abstract Soil management decisions should consider physical potential of the environment, weather variability, and requirements crops to maximize production limits. This calls for characterization environments using selected input variables. Such studies are scanty in southern central Rift Valley Ethiopia due which area is considered homogeneous identical agricultural planning, extension, delivery programs. Thus, investigate scenario, we employed principal component, clustering, GIS analysis on geo-referenced physiographic climatic attributes, their statistical variables obtained from 43 stations with objective identifying units similar physiography, pattern, scheduling. The components (PCs) indicated that three PCs explained 74.7% variance October, November, December, January (ONDJ), four 79.3% February, March, April, May, 80.5% June, July, August, September (JJAS). Cluster-I was characterized by high altitude low temperature ONDJ season. Cluster-II across most seasons. Cluster-III intermediate altitude, temperature, rainfall. Cluster-IV rainfall JJAS. In all clusters, PC1 mean component strong association longitude, while PC2 component. PC3 influence Thus factors determine formation clusters reduced 12 5 ( T , latitude, RF ) grouped into 4 (Shamana, Bilate, Hawassa, Dilla) geographically ecologically distinct. These require different sets agro-meteorology advisory, maize management, strategies.