Application of Multivariate Principal Component Analysis for Characterization of Leaf Litters in Northern Nigeria

This study was to objectively describe the interrelationship between mass loss and leaf chemical parameters of litter species: Khaya senegalensis, Mangifera indica, Gmelina arborea, and Eucalyptus camaldulensis using principal component analysis. Leaf litters were analyzed for chemical compositions. Mass loss and ten litter chemical parameters such as organic carbon, total nitrogen, total phosphorus, potassium, magnesium, calcium, sodium, total soluble polyphenol, total sulphur and carbon to nitrogen ratio were investigated. Principal Component Analysis (PCA) was used to identify the variation of litter chemical properties. The results showed significant relationships between mass loss and litter chemical parameters in PC1 and PC2 axes. Khaya senegalensis has the highest loadings 84% followed by Gmelina arborea 77%, Eucalyptus camaldulensis 75.3% and Mangifera indica 64.5%. In conclusion, Khaya senegalensis showed a propensity to have a faster response in driving biogeochemical cycling during decomposition.