ISSN 2756-3251
Advanced Journal of Environmental Science and Technology ISSN 7675-1686 Vol. 3 (3), pp. 001-008, March, 2012. © International Scholars Journals
Full Length Research Paper
Delineation of management zones by classification of soil physico-chemical properties in the Northern Savanna of Nigeria
Salami, Biola Titilope1*, Okogun, Jacob Ade2 and Sanginga, Ntenranya3
1Department of Crop Production, Soil and Environmental Management, Bowen University, Iwo, Osun State, Nigeria.
2Department of Agronomy, University Of Ibadan, Ibadan, Nigeria.
3Tropical Soil Biology and Fertility Institute of Ciat (TSBF-CIAT) P. O. Box 30677, Nairobi Kenya.
Accepted 21 November, 2011
Abstract
This study was designed to identify properties that explained variability in soils of farmers’ fields in Kaya, within the northern Guinea savanna benchmark and group the soils on the basis of the identified properties. Ridge and furrow soil samples from farmers’ fields were analyzed for determination of physical and chemical properties. Principal component analysis of soil properties extracted and characterized factors that described the overall soil fertility variation; potential fertility, available phosphorus, organic matter, acidity and sand-silt. Component scores were computed and used for cluster analysis. Five soil cluster groups corresponding to potential management zones were identified. In Cluster A soils were sandy, low in phosphorus, organic matter and exchangeable bases. Cluster B was characterized by acidic and silty soils with low exchangeable bases, organic matter and phosphorus. Soils within Cluster C were acidic, sandy in texture, with low exchangeable bases, phosphorus and organic matter. In the single member Cluster D, the soil was rich in organic matter and phosphorus. Cluster E soils were high in exchangeable bases and organic matter but low in phosphorus. The identified soil clusters are delineated management zones and this determines their suitability or otherwise for specific agronomic management practices.
Key words: Cluster analysis, northern guinea savanna, principal component analysis.