Based on a factorial experimental design (three locations × three cultivars × five harvest times × four replicates) conducted with the objective of investigating variations in fuel characteristics of cassava stem, a multivariate data matrix was formed which was composed of 180 samples and 10 biomass properties for each sample. The properties included as responses were two different calorific values and ash, N, S, Cl, P, K, Ca, and Mg content. Overall principal component analysis (PCA) revealed a strong clustering for the growing locations, but overlapping clusters for the cultivar types and almost no useful information about harvest times. PCA using a partitioned data set (60 × 10) for each location revealed a clustering of cultivars. This was confirmed by soft independent modelling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA), and indicated that the locations gave meaningful information about the differences in cultivar, whereas harvest time was not found to be a differentiating factor. Using the PLS technique, it was revealed that ash, K, and Cl content were the most important responses for PLS-DA models. Furthermore, using PLS regression of fuel and soil variables it was also revealed that fuel K and ash content were correlated with the soil P, Si, Ca, and K content, whereas fuel Cl content was correlated with soil pH and content of organic carbon, N, S, and Mg in the soil. Thus, the multivariate modelling used in this study reveals the possibility of performing rigorous analysis of a complex data set when an analysis of variance may not be successful.