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Clay mineralogy of the fluvial plains reflects the complexity of geological setting, material weathering and transport. Clay minerals produced in the weathering process are closely associated with the parent rock and serve as an indicator of the material sources. Soil clay mineralogy of South African river valleys is insufficiently studied and this research can make a meaningful contribution towards advancing the understanding the history of planation surfaces in Africa and their evolution. Here we examine the valley of the Letaba river – one of the many Limpopo tributaries originating in the Drakensberg escarpment and flowing through the basement complex of the Lowveld geomorphic province. Nine soil profiles were selected from a total of 37 profiles represented by Ustifluvents, Haplustepts, and Haplustalfs for mineralogical study of the The upstream section of the study area is dominated by kaolinite associated with the general dominance of granite and granitic gneiss lithology in the upper reaches of the river. The occurrence of mafic (dolerite) dikes along the river course immediately shows in soils as presence of smectite trail in the clay fraction. As the river cuts through the greenstone belt the clay mineralogy of the fluvial terraces immediately reflects this change as an increase in talc and chlorite proportions and the appearance of vermiculite. The traces of talc originating from greenstones are present throughout the valley within the middle reaches of the river and may be showing the contribution of small streams and tributaries transporting the material from the water divide to the valley of the Letaba. The changes in soil clay mineralogy of alluvial soils across the lithological boundaries indicate the prevalence of locally-sources material over long-distance-transported sediment in aggradation of these terraces. The analysis of clay mineralogy in fluvial environments may be a good indicator of local sediment source and short distance transport contribution towards formation of fluvial terraces in the valley of the Letaba river system. This contradicts the pediplane theory of Lowveld planation and emphasizes the role of localized erosion/sedimentation processes.

Landform elements (LFEs) are commonly used in soil science to demark pedological boundaries and as a first indication of soil spatial variability. A novel LFE classification system known as geomorphons, has been shown to be able to overcome limitations of other automated LFE classifiers. The pattern recognition algorithm classifies the 10 most common LFEs, is computationally efficient, and is robust to changes in scale. However, due to their novelty, research into geomorphons has been limited. This study aimed to stratify the soil landscape through an aggregated geomorphon at the farm-scale (1:25 000) in the Western Cape, South Africa (33.25° S and 18.20° E). Twenty-four geomorphons were created at different resolutions and their association with soil classes were compared. The best fitting geomorphon was aggregated into a 5-unit system corresponding to the South African national resource inventory. The aggregation was based on a decision tree corresponding to soil type. The 5-unit system was evaluated on how well the system stratified soil associations, soil lightness, soil electrical conductivity (EC), soil organic carbon, effective rooting depth (ERD), depth to lithology, gravel, sand, silt, and clay. The prediction potential was compared between the original geomorphon, the aggregated geomorphon, and a manually delineated LFE system. It was found that the aggregated geomorphon stratified all soil attributes except EC. Additionally, the aggregated geomorphon predicted 6 out of 9 soil properties with the greatest accuracy (RMSE). This study shows that aggregating geomorphons can stratify the soil landscape even at the farm-scale and can be used as an initial indication of the soil spatial variability. This has implications in resource poor areas where an additional soil survey is not feasible or can be used to aid in the disaggregation of existing soil-terrain datasets.