Reliable and harmonised soil information remains critically limited across Africa, constraining soil monitoring, climate-resilient agriculture, and evidence-based land management. Existing soil resources are often fragmented, spatially uneven, outdated, or derived from legacy observations, limiting their usefulness for contemporary continental-scale assessment. The Soils4Africa project implemented a coordinated field campaign across 33 African countries between 2022 and 2025 to establish a harmonised soil monitoring framework for agricultural lands. Using a hierarchical probabilistic sampling design, 24,951 soil samples were collected from 14,311 locations, supported by standardised field protocols, digital data capture, QR-based sample traceability, and centralised quality control. This paper presents the conceptual, operational, and data-management framework underpinning the survey and reports baseline field observations on farming systems, land management, vegetation structure, and soil physical constraints. The framework achieved more than 70% of planned sampling coverage despite major logistical, environmental, and security-related constraints. Baseline observations show that African agricultural landscapes remain dominated by smallholder systems, low external input use, limited soil and water conservation, and widespread dependence on rainfed production. Field indicators also reveal sparse woody vegetation cover and common physical constraints, including compaction, coarse fragments, shallow effective rooting depth, and subsoil barriers. Unlike earlier continental resources based largely on legacy profiles or site-based surveillance, Soils4Africa provides a contemporary, harmonised, spatially structured field-survey framework designed to support future laboratory-based soil assessment, digital soil mapping, land suitability analysis, and long-term soil monitoring. The study therefore provides a scalable model for coordinated soil monitoring across diverse African agroecosystems and establishes an operational baseline for subsequent analytical studies.

ABSTRACT Background Understanding the relative importance of climatic, edaphic, and management controls on soil organic carbon (SOC) is essential for improving SOC prediction and land management in data‐scarce agroecosystems. Aim This study quantified SOC drivers across Southern African agroecosystems using 655 observations covering climatic variables, soil physicochemical properties, and management indicators. Methods Linear regression, hierarchical modeling, response‐scale comparison, variance partitioning, and nonlinear sensitivity analyses were applied to evaluate the robustness of SOC driver attribution. Results SOC variability was primarily explained by edaphic properties. The edaphic‐only model explained 33.2% of SOC variation, closely matching the full additive model including climate, edaphic, and management variables ( R 2 = 0.349; adjusted R 2 = 0.338). Aluminum oxides, clay content, cation exchange capacity, and pH were the most consistent edaphic predictors, while management indicators were not significant in the full model. Variance partitioning confirmed the dominance of edaphic controls, with a unique edaphic contribution of 29.9%, compared with 1.0% for climate and no independent contribution from management. Nonlinear models improved fit, with the generalized additive model explaining 38.6% of deviance, but did not alter the dominance of edaphic controls. Conclusion These findings show that SOC variability across Southern African agroecosystems is structured mainly by soil physicochemical conditions rather than by broad climatic gradients or coarse management categories. SOC prediction and management in these systems should therefore prioritize edaphic constraints, particularly mineral reactivity, exchange capacity, and soil chemical conditions.

Regolith is an important component of the Earth's critical zone. Little investigative work has been done in southern Africa on regolith characteristics as a component of the critical zone, and in particular the importance to forestry plantation productivity in the eastern hinterland of South Africa. Regolith cores were extracted from five positions across two slopes underlain by sandstone and tillite to characterize these regolith profiles and ascertain the effect of slope position and parent material on regolith development. Regolith profiles were characterized and analysed on hill crest and lower midslope positions at both sites. The profile descriptions noted the degree of weathering, rooting activity, and mineral composition. The deepest in situ weathering of regolith was found on the hill crest positions; sandstone crest (24.0 m) and tillite crest (8.2 m) respectively. The accumulation of colluviated sediment on lower slopes also resulted in a thicker regolith at the tillite site, despite the less intensely weathered bedrock. These findings were consistent with earlier geophysical and pedohydrological studies of the area and the regolith profiles are consistent with those described for a range of other sedimentary lithologies in similar regions in sub-tropical and tropical landscapes The results show that tree roots utilize the saprolite and saprock below soil horizons and the nature of these layers indicate the full regolith profile should be considered in the water and nutrient supply available to forest plantations. Current concepts of ‘effective rooting depth’, as widely used by South African forestry, are inadequate for understanding ecosystem processes in forest plantations.

The Laetoli hominid footprints dating back some 3.6 million years discovered by Leakey in 1978 is an archaeological site of great importance in understanding the humanHuman evolutionEvolution. The footprints of hominids, animals, and birds cast in the volcanic ash consolidated into tuff deposits are also an insight into the evolutionEvolution of the whole biogeosystem of this area dominated by volcanic activity. The volcanic ash deposits consolidated into tuffs are a marker which sets the base line for tracking the further sediment transport in the catchmentCatchment of this historic site. The surface of the tuff exposure along the Garusi river carrying the footprints shows no signs of weatheringWeathering and the soils of the area form in subsequent sediments that filled the valley after the deposition of the tuff material. Following the deposition and consolidation of the airfall tuffs, the biogeosystem of the Laetoli gorge and its surroundings experienced a complex evolutionEvolution which led to formation of the present-day soil cover. The study of clay mineralogyMineralogy of these soils has provided interesting insights into the evolutionEvolution of this system.

Earth-systems modelling of the critical zone often require soil maps that represent in-field conditions. Some developing regions, such as southern Africa, do not have detailed soil data for all areas and are often reliant on data from global soil maps and data sets. This raises the question of global and regional soil map and soil data accuracy at local levels. Although previous studies have indicated that global soil maps do not always provide an accurate representation of in-field soil conditions, it is not clear how inaccurate these maps and data are for southern Africa. This study assessed the accuracy of widely used global and continental-scale soil maps for southern Africa. The accuracy of the soil maps was assessed by comparing profile and mapped soil data. Three soil maps of global, continental, and regional scale (Harmonized World Soil Database version 1.2, SOTER for southern African, and AfSoilGrids250m) were compared to observation data from two profile data sets (Africa Soil Profile Database and South African Soil Profile Database). Furthermore, maps of selected soil properties of the AfSoilGrids250m rasters were compared to the Harmonized World Soil Database map. Soil properties were statistically compared through Lin's Concordance Correlation Coefficient, Root Mean Square Error, and Mean Absolute Error. Investigated maps and point data generally correlated poorly and large Root Mean Square Error and Mean Absolute Error values were observed for most of the properties. Of the investigated soil maps, the digital AfSoilGrids250m soil property raster layers provided the most accurate estimate of in-field soil conditions for selected soil properties in the study region.

This study involved the evaluation of farm-scale digital soil classification in the Sandspruit catchment of the Western Cape Province, South Africa. The study aimed to evaluate a digital soil mapping (DSM) method, from feature selection, spatial predictions and sample design. The results showed that feature selection with the least absolute shrinkage and selection operator (LASSO) technique is a robust method as it had a high relative efficiency and achieved the highest accuracy for three out of the four soil classes predicted. This implies that covariate selection is the most notable aspect in DSM at the farm-scale. The top-performing predictive models achieved satisfactory results for soil associations (kappa = 0.64, accuracy = 74%), presence of a bleached topsoil (kappa = 0.64, accuracy = 74%) and soil depth (kappa = 0.48, accuracy = 74%), whereas only moderate results were achieved for soil texture (kappa = 0.43, accuracy = 66%). Lastly, the expert sampling locations had a higher average probability of occurrence (geographic and feature space distribution coverage) yet achieved similar performance to conditioned Latin hypercube sampling (cLHS).

The change from grasslands and natural shrubs to afforested arable land has a major impact on soil organic carbon (SOC) stocks. Grasslands are known to be SOC sinks as seen in the Chernozems of North America, Eurasia and South Africa. However, determining the SOC stocks of soils can be financially costly as each location must be sampled in depth increments. This study aimed to estimate the SOC stocks for the Mvoti catchment (30 degrees 48 ' E and 29 degrees 18 ' S) in Kwa-Zulu Natal, South Africa by developing depth functions on a limited number of soil samples and expanding these functions to known land-uses and soil types. The results showed that splines captured the short-term vertical distribution of SOC better than exponential decay functions, which has major implications on arable lands. Long-term forest plantations showed a positive correlation with SOC stocks (32.7 kg m(-2)), while annual crop cultivation (27.0 kg m(-2)) showed a negative correlation when compared to natural grasslands (28.8 kg m(-2)). The Cubist algorithm predicted the total SOC stock of the catchment area at between 12 248 and 17 624 Mg depending on the depth function used. Soils with yellow-brown subsoils tend to have higher SOC stocks and the lowest degree of uncertainty.

Allocation of soil profiles to one or another soil class depends on the surveyor's experience and knowledge. Inconsistent class designations can affect the downstream uses of soil information such as hydrological and ecological applications. Therefore, numerical classifications can be useful in creating property-based soil clusters. Quantitative classification of soil horizons can also be beneficial for guiding physicochemical thresholds used in taxonomic criteria. This study aimed at taking a step in applying numerical classification techniques to the South African soil legacy database to quantitatively cluster horizons. For each master horizon, the database was clustered into new "diagnostic" horizons through Partitioning Around Medoids (PAM) implemented in the Clustering Large Applications algorithm (CLARA). This algorithm has the benefit of using multiple subsets of the data making a more reliable initiation of medoids and it is a non-parametric approach making it more robust to outliers. To determine the optimal number of clusters, the silhouette width for 1 through 11 clusters was calculated. The number of clusters with the largest silhouette width was taken as the optimal number of clusters. The results of this study show that for O, E, and G (gleyed) horizons, the algorithm performs relatively well with the first two principle components capturing almost half of the variation in the data. However, for the A, B, and C horizons, the algorithm struggled to separate clusters sufficiently. The A horizon clusters only broadly corresponded to environmental factors (geography, climate, elevation, and geology) and the large clusters straddled numerous taxonomic thresholds. Moreover, the B horizon clusters correspond relatively well with environmental factors. Limitations identified, include bias in geographic location of modal soil profiles of the database, bias in the South African classification system, how to handle non-globular clusters, and outliers which add leverage to the clusters.

Establishing continuity of fluvial terrace remnants in eroded landscapes is often limited to resource intensive field interpretations of in-situ stratigraphic and physiographic features. Soil NIR spectra are an integrative property of soil that have been successfully combined with digital terrain data for soil-landscape modelling purposes. This study assesses the viability of soil NIR spectroscopy as a rapid and cost-effective way to differentiate between various fluvial terrace levels. First, the correlation between the NIR spectra of 88 soil samples and the height above river channel (HARC) of various fluvial terrace levels were investigated using pre-processed spectra, principle component analysis (PCA) and partial least square regression (PLSR). A strong correlation (R-2 = 0.78, RPD = 2.11) was achieved using NIR-spectra located between 7500 and 5446 cm(-1), which remained strong (R-2 = 0.55, RPD = 1.49) after cross-validation. Next, the Scikit-learn random forest (RF) machine learning library was used to classify two sets of fluvial terrace level classes. The classification of two and three fluvial terrace classes produced, respective, validation scores of 0.73 and 0.76 when using the entire unprocessed spectral dataset as input. Interpretation of the subsequent feature importances indicated that spectral wavelength bands associated with the absorption characteristics of smectite, kaolinite, carbonates and talc were most important. Final validation scores of 0.74 (two fluvial terrace classes) and 0.76 (three fluvial terrace classes) were achieved by isolating specific wavelength bands associated with smectite (5334 cm(-1) and 5269 cm(-1)) and kaolinite (3664 cm(-1) and 3699 cm(-1)) fractions. This study demonstrates the clear potential of NIR spectroscopy as a data-driven alternative to in-situ stratigraphic and physiographic differentiation of fluvial terrace levels. (C) 2021 Published by Elsevier B.V.

The soil science community needs to communicate about soils and the use of soil information to various audiences, especially to the general public and public authorities. In this global review article, we synthesis information pertaining to museums solely dedicated to soils or which contain a permanent exhibition on soils. We identified 38 soil museums specifically dedicated to soils, 34 permanent soil exhibitions, and 32 collections about soils that are accessible by appointment. We evaluate the growth of the number of museums since the early 1900s, their geographical distribution, their contents, and their attendance. The number of museums has been continuously growing since the early 1900s. A noticeable increase was observed from 2015 to 2019. Europe (in a geographical sense), Eastern and South-East Asia have the highest concentration of soil museums and permanent exhibitions related to soils. Most of the museums' attendance ranged from 1000 to 10,000 visitors per year. Russia has the largest number of soil monoliths exhibited across the world's museums, whereas the ISRIC-World Soil Museum has the richest and the most diverse collection of soil monoliths. Museums, collections, and exhibitions of soil play an important role in educating the population about this finite natural resource that maintains life on the planet, and for this reason, they must be increasingly supported, extended, and protected.

Recent climate instability necessitates a fresh approach to water cycle services in the Hessequa municipal region. Attention is drawn to impacts on water storage in this region, and an assessment of the current status is necessary. Land-use change and soil properties are focal points of a runoff assessment. Defining Land Type soils information is necessary to support agricultural needs, concentrating on depth-limiting materials, mechanical limitations and texture. It is evident in the area under study that mountainous regions are not well-described. With most dams located in mountain regions and the land increasingly being used for agroforestry, the demand for better supporting information has increased. Furthermore, the available Land Type polygons for the region are too coarse for the catchment, which is primarily undulated. Enhanced Land Type mapping resolution may be defined through terrain morphological segmentation. The process indicates that the terrain prediction capability is acceptable, with 62% and 74% within 4 and 15 m search windows, respectively. This information has provided a broader platform to enhance our ability to deal with the impacts of climate and land-use change in the Korentepoort mountain catchment.

The biotic-abiotic interactions are particularly challenging in alkaline conditions. Growth of plants is an ultimate indicator of such interactions. The on-land disposal of olive mill wastewater (OMW) negatively affects plant growth due to its high phytotoxic organic polyphenol content. Our previous study has shown that phenols may be successfully sorbed on biochar—one of the most promoted soil amendments. A greenhouse experiment was conducted to determine the combined and separate effect of OMW (applied at 50, 100 and 200 m3 ha−1) and pinewood biochar (applied at 0.5, 2.5 and 5%) on the growth of wheat and green beans in an alkaline sand. Results showed that increasing OMW rate significantly suppressed wheat growth especially the above ground phytometrics, and that biochar addition did not significantly mitigate this effect. This was mainly attributed to unsuitable high pH growing conditions of the wheat, which was enhanced by application of OMW and biochar. In contrast, the lowest OMW only and 5% biochar only treatments positively affected bean phytometrics, though not statistically significant. A significant positive interaction was obtained in the bean total biomass when 2.5 and 5% biochar was applied on soil that received 100 m3 ha−1 OMW. Findings showed that pinewood biochar application at 2.5 and 5% enhanced tolerance of beans to OMW applied at 100 and 200 m3 ha−1 likely due to not only reduction of phenol toxicity but also due to increased available soil P and K.

Biochar amendment of soils is an ancient technology which has attracted a lot of recent attention from soil scientists and environmentalists as a possible way to sequester carbon from the atmosphere in the soil, whilst increasing soil fertility. Wheat (Triticum aestivum) was grown for twelve weeks in pots were pine derived biochar was placed in two distinct layers within a sandy soil. The sandy and biochar layers were separated at harvesting to assess plant root growth, microbial biomass and degree of mycorrhizal root colonization. The biochar layers formed preferred zones for root development (P = 0.039) and microbial proliferation (P < 0.001) compared to the sandy layers. However, the degree of root mycorrhizal colonization decreased slightly in the two biochar layers and in the sandy layer between them, relative to the sandy layers above and below. The decrease in mycorrhizal colonization was possibly due to the enhancing effect that biochar has on water and nutrient retention. Furthermore, the physical and chemical characteristics of the biochar layers differed markedly from the sandy layers in terms of pH, cation exchange capacity, total C and available P. These factors have a strong influence on the micro-climate and nutrient status of each layer.

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 degrees S and 18.20 degrees 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.

In the regions of Asia and Africa geophagic clays are traditionally used by population based on historical and traditional motivations including ethno-medical ones. The aim of the present research was to reveal the general common properties of some geophagic clay occurrences based on their mineral composition and landscapes specificity, in which they were sampled. In addition to assess the potential source of the minerals in geoghagic clays, the mineral association of the hard rocks, located in geophagic loose material, was studied too. Mineralogy of the samples was investigated using X-ray diffractometry, FTIR spectroscopy, and optical microscopy. The studied samples were collected in two provinces of South Africa: Free State and Limpopo. It was shown that location of geophagic materials is spread widely in the studied provinces. Using material as geophagic is more historically and traditionally determined than by the type or specificity of a landscape, which cannot be characterized by common features. Whereas it was revealed that despite the differences in location and rock geneses, the most common mineral in the fine size fraction of the studied geophagic samples is smectite—a fine-sized clay mineral with high specific surface area being able to play a role as adsorbent.

Techniques that disaggregate complex soil-terrain polygons from legacy maps are becoming more relevant, as cost effective highly detailed soil information is required to advise agriculture, hydrology, ecology, engineering, and a variety of other disciplines. Disaggregation involves the spatial placement of individual soil classes from soil legacy polygons which have multiple soil classes, while specifying the approximate proportion of each soil class and verbally or diagrammatically explaining their distribution in the landscape. One of the most common disaggregation approaches is known as DSMART ("Disaggregation and Harmonisation of Soil Map Units through Resampled Classification Trees"). However, DSMART is computationally intensive and has many parameters that must be optimised. This study aimed to address these drawbacks including input map selection, feature selection, and resample size optimisation. The research site was selected in the upper reaches of the Mvoti river catchment covering 317 km2 in KwaZulu Natal province, South Africa. The catchment consists of 20 soil-terrain polygons drawn at a 1:250,000 scale from the South African Land Type Survey (LTS). First, the optimal input map derived from landform elements (geomorphons) was selected through a spatially resampled Cramer's V test to determine the association between the legacy polygons (proportion of terrain) and the geomorphon units. This was done for five different aggregated geomorphons with different parameters. Second, three feature selection algorithms (FSAs) were embedded into DSMART to determine if the algorithms could improve accuracy and computational efficiency. Third, the FSAs were compared using 25, 50, 100, and 200 resamples per polygon. The results indicate that the Cramer's V test is a rapid method to determine the optimal input map. All FSAs achieved a significantly greater accuracy then when disaggregating the original legacy polygons and were more computationally efficient than when using all 52 covariates. This study has implications when disaggregating large and small datasets by improving computational efficiency while maintaining an acceptable accuracy.

Boosting the productivity of smallholder farming systems continues to be a major need in Africa. Challenges relating to how to improve irrigation are multi‐factor and multisectoral, and they involve a broad range of actors who must interact to reach decisions collectively. We provide a systematic reflection on findings from the research project EAU4Food, which adopted a transdisciplinary approach to irrigation for food security research in five case studies in Ethiopia, Mali, Mozambique, South Africa and Tunisia. The EAU4Food experiences emphasize that actual innovation at irrigated smallholder farm level remains limited without sufficient improvement of the enabling environment and taking note of the wider political economy environment. Most project partners felt at the end of the project that the transdisciplinary approach has indeed enriched the research process by providing different and multiple insights from actors outside the academic field. Local capacity to facilitate transdisciplinary research and engagement with practitioners was developed and could support the continuation and scaling up of the approach. Future projects may benefit from a longer time frame to allow for deeper exchange of lessons learned among different stakeholders and a dedicated effort to analyse possible improvements of the enabling environment from the beginning of the research process. © 2020 The Authors. Irrigation and Drainage published by John Wiley & Sons Ltd on behalf of International Commission for Irrigation and Drainage

Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geocomputational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality digital elevation models (DEMs). Digital geomorphic mapping (DGM) approaches that can simplify and translate the inclusion of "human knowledge" to automatic terrain classification across a broader spectrum of terrain morphological units as well as a range of spatial scales, therefore, offer great potential for improved topographic and landscape analysis. One such approach is the mapping of landform elements using the concept of the Geomorphon (geomorphological phonotypes). The output of the geomorphon approach is the stratification of the landscape into ten unique but recognisable landform elements: peak, ridge, shoulder, spur, and slope, hollow, foot slope, valley, depression and flat. Equally appealing is the way the model self-adapts to local topography using a line-of-sight principle enabling better matching of landform elements to computational spatial scale. The purpose of this paper is to observe the effects that different pixel resolution (grain size) and digital elevation model source (DEM) would have on the replication of observed geomorphic spatial patterns and representation of terrain selected parameters within the landscape. This paper provides a comprehensive exploratory assessment of digital terrain representation and relief classification using an automated geomorphometric mapping approach, by evaluating three different digital surface models (SUDEM, SRTM, ASTER GDEM2) and different spatial resolution (30 m & 90 m) for an 11,200 ha catchment in KwaZulu-Natal, South Africa. To test the self-adapting ability of the geomorphon approach under regional conditions, we use 4750 gridded terrain samples to quantitatively analyse how the choice of terrain model and scale influence the extraction, generalisation and representation of digitally-derived terrain attributes such as slope, elevation and terrain unit feature extent. We further show how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities of selected elementary soil association distribution, soil texture and soil depth. We also introduce the results of a Similarity Index used to gauge the degree of recall and precision between the different geomorphic landscape features. Finally, the findings of the regional geomorphon-soil relationships are presented in a readily interpretable and qualitative manner, providing a "quasi-landscape signature" for potential localised geomorphons. The application of the study findings may be beneficial to practitioners looking to align or refine modelled terrain classification approaches with expert perception and formalised heuristic approaches. (c) 2020 Elsevier B.V. All rights reserved.

Biochar is known to be a highly adsorptive material, especially when the biochar is altered by activation to further increase its sorption ability. Little information, however, is available on the potential reversibility of both ammonium (NH4+) and nitrate (NO3-) sorption on the inherent biochar pH. The objective of our study was to characterise biochars made using different pyrolysis conditions from five various plant materials and rubber tyre, and to use them to investigate the biochar properties responsible for NH4+ and NO3- adsorption and desorption. The rubber tyre, maize stover and sugarcane pith were the weakest adsorbing biochars (5.77.8 mg g(-1)) and best described by the Freundlich adsorption isotherm. The grape pip, grape skin and pine wood biochars had adsorption capacities in the range 8.3-9.4 mg NH4+ g(-1) and best described by a linear adsorption isotherm at 100 mg L-1. The NH4+ adsorption results were associated with physisorption which implies that they can act as slow release NH4+ fertilisers if NH4+ is bioavailable. The six biochars had NO3- adsorption capacities in the range 15.215.9 mg g(-1) and were well fitted to the linear adsorption isotherm at 100 mg L-1. All six biochars had a stronger NO3- removal affinity (82-89%) compared to NH4+ (33-39%). Adsorbed nitrate was not desorbable (0.01-0.23%) compared to adsorbed NH4+ which was 53-60% desorbable. The desorption result was possibly due to NO3- competing redox reactions or NO3- being too strongly adsorbed for extraction. Desorption of NH4+ was associated with biochar net negative pH values and volatilisation of ammonia.

Knowledge of soil depth spatial variability is important for land use management especially in dryland agriculture regions, which rely on climate and soils to provide adequate water and nutrients during the growing season. Soil spatial variability can be predicted from legacy soil data through machine learning techniques producing quantitative soil maps requiring minimal resources. South Africa has a country wide 1:250,000 scale resource map known as the Land Type Survey (LTS) which includes soil properties such as soil depth, soil class, root limiting layer, clay content, and texture. Each LTS polygon (land type), is comprised of unique soil - terrain patterns and is therefore, not a true soil map. This study aims to disaggregate the LTS into a farm-scale soil depth class map through a two-step disaggregation approach. First, landform elements were predicted through a pattern recognition algorithm known as geomorphons. Geomorphons, together with the original LTS were overlaid to produce polygons with unique distributions of soil. The polygons were disaggregated further to produce a raster map of soil depth classes through a soil map disaggregation algorithm known as DSMART. The first most probable class raster achieved an accuracy of 68% and for the two most probable class rasters, an accuracy of 91% was achieved. The two-step approach proved necessary for producing a farm-scale soil map. The result of this study is significant as it produced a soil depth class map from a national resource map at a scale and resolution (10 m) suitable for farm management.