Doctoral Degrees (Soil Science)

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The contribution of indigenous vegetables to food security and nutrition within selected sites in South Africa
(Stellenbosch : Stellenbosch University, 2013-12) Mavengahama, Sydney; De Clercq, W. P.; McLachlan, Milla; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: South Africa is rich in biodiversity among which are semi-domesticated vegetable species which are known as wild or indigenous vegetables. These wild indigenous vegetables have been reported to be good in nutritional qualities such as macro and micronutrients. However, there is still a high prevalence of malnutrition; especially micronutrient deficiencies among low or marginal income bracket of the population. The use of indigenous vegetables has been proposed as part of the solutions to the problems of micronutrient malnutrition among these populations. Indigenous vegetables are an important source of food in the maize based subsistence farming sector of rural South Africa. Their main role is as relish as they are used as an accompaniment for staple cereal based diets. They are also generally reported to be rich in micronutrients. Although they may be consumed in small quantities, they influence the intake of cereal staples, manage hunger and play a central role in household food security for the poorer rural groups. Mixing several indigenous vegetables species in one meal contributes to dietary diversity in terms of more vegetable types as well as in terms of choice of relish. For some very poor families indigenous vegetables are substitutes for some food crops. The seasonal occurrence of these vegetables leaves many families without a food source during the off-season. Indigenous vegetables increase agro-biodiversity at the household level. This agro-biodiversity helps in buffering against the accumulation and multiplication of pests and diseases and provides important cover for the soil. Further research on agronomic, social and economic dimensions is required to understand the roles of IV in subsistence farming systems in South Africa. The survey study revealed that indigenous vegetables were important in the diets of most rural people in the study area. They were consumed as relish although they were not being cultivated. Their method of acquisition was gathering from homesteads and the wild. These vegetables were also believed to be medicinal. The local naming of wild vegetables varied among villages in the same district such that a vegetable in one village was assigned to a different species of vegetable in another village. They were reportedly abundant during summer and there was a decrease in availability off-season leaving vulnerable people who rely on them with a food shortage. The utilisation of wild vegetables among South Africans is reported to be declining due to over reliance on introduced temperate species. Efforts to domesticate and cultivate wild vegetables could be hampered by several factors including seed dormancy and premature flowering. In this present study dormancy was observed in C. olitorius. The response of wild genotypes of C. olitorius with different seed sizes to various dry heat and hot water treatments was evaluated. Steeping seeds in boiling water (95oC) for ten seconds and soaking seeds in a hot water bath at 80oC for ten minutes resulted in the highest response to germination in this species. The study also recorded significant interactions between heat treatment and seed sizes. We concluded that C. olitorius seeds of different sizes require diverse durations of exposure to heat treatment methods to break dormancy caused by an impermeable seed coat. Cleome gynandra is another species that is consumed as a vegetable in various parts of the world including Africa. The plant is also used as a medicinal herb for the treatment of various human diseases. Among the wild vegetables, C. gynandra has been reported to flower prematurely, a phenomenon known as bolting and common in many vegetable crops. Premature flowering (bolting) can be as a response to temperature extremes and photoperiod and affects many other leafy vegetables such as lettuce (Lactuca sativa), spinach (Spinacea oleracea) and mustard rape (Brassica juncea). Bolting leads to production losses in leaf vegetable crops as they flower before they have produced an economic yield. The removal of flowers and nitrogen application resulted in significant increases in the fresh and dry weight of cleome leaves. Removal of flowers resulted in a 46% increase in fresh weight of leaves. The observed positive response of leaf yield to removal of flowers offers a possible way to deal with the problem of bolting. The continuous removal of the flowers leads to increased utilisable leaf yield. The application of incremental amounts of nitrogen top dressing results in increased leaf yield in C. gynandra. The response of selected indigenous vegetables (Corchorus olitorius and Amaranthus cruentus) to micronutrients added to the soil was compared with the response of a reference crop; Swiss chard (Beta vulgaris var. cicla). For all the levels of micronutrients applied, Swiss chard accumulated Cu, Zn and Mn in the leaves at significantly (p<0.01) higher concentrations than the wild vegetables. Variations between the vegetables in the micronutrients were greater for Zn (72–363 ppm) and Mn (97.9–285.9 ppm) for Cu (8.8–14 ppm). C. olitorius had the least capacity to concentrate Mn and Zn in the leaf, which suggested that this vegetable is a less attractive candidate for agronomic bio-fortification of these elements. However, C. olitorius accumulated Fe at a significantly higher concentration (327 ppm) in the leaves than did Amaranthus (222 ppm) or Swiss chard (295 ppm). Sulphur as a macronutrient varied little in the plant species tested. The mean S concentration in the leaves ranged from 0.26% in C. olitorius to 0.34% in Amaranthus cruentus and Swiss chard. We concluded that the different vegetables have different abilities to take up Cu and Zn in the order Swiss chard > Amaranthus > Corchorus, and that they responded to micronutrients added to the soil but only up to certain limits of supplementation. The results from this current study seem to contradict the belief that wild vegetables have the inherent ability to concentrate mineral micronutrients in their tissue. Factors such as environment, anti-nutrients, dietary diversity, plant parts, plant age, and varieties result in differences in reported nutritional composition of indigenous vegetables. Post-harvest handling, storage, cooking and preservation also alter the composition. The need to optimise protocols for each vegetable type and for different laboratories makes analysis expensive. Equipment and methods of analysis are varied and may not be comparable, making it difficult to generalise on the composition of these vegetables. The Agricultural Research Council of South Africa and other stake holders are conducting studies on some aspects of these vegetables. There are still many information gaps regarding many aspects of these vegetables which require research attention. These include; the selection and improvement of genotypes, seed biology and germination studies, agronomic (population, fertiliser, crop mixtures) studies and phyto-chemical evaluation of these important species in order to encourage the overall use of these important indigenous resources. Finally, there is need to promote their increased utilisation.
Digital soil mapping techniques across multiple landscape scales in South Africa
(Stellenbosch : Stellenbosch University, 2019-12) Trevan, Flynn; Clarke, Catherine E.; Rozanov, Andrei Borisovich; De Clercq, W. P.; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Digital soil mapping has seen increasing interest due to environmental concerns and increasing food security issues. Digital soil mapping offers a quantitative approach which is cost effective as less soil observations are needed to produce large area soil maps. However, digital soil mapping has only recently been addressed in South Africa. This research aimed to produce two digital soil mapping (DSM) frameworks with the available resources in South Africa. The methodologies incorporate advanced geostatistics and/or machine learning techniques to be able to produce quantitative soil maps from the farm to catchment scale. First, a framework that optimises both feature selection and predictive models was developed to produce farm-scale soil property maps. Four feature selection techniques and eight predictive models were evaluated on their ability to predict particle size distribution and SOC. A boosted linear feature selection produced the highest accuracy for all but one soil property. The top performing predictive models were robust linear models for gravel (ridge regression, RMSE 9.01%, R2 0.75), sand (support vector machine, RMSE 4.69%, R2 0.67), clay (quantile regression, RMSE 2.38%, R2 0.52), and SOC (ridge regression, RMSE 0.19%, R2 0.41). Random forest was the best predictive model for silt content with a recursive feature selection (RMSE 4.12%, R2 0.53). This approach appears to be robust for farm-scale soil mapping where the number of observations is often small but high-resolution soil data is required. Second, 24 geomorphons (landform classification) were evaluated on their association with soil classes. The geomorphon with the highest association was aggregated into a 5-unit system which was evaluated on how well the system stratified soil lightness, soil EC, SOC, effective rooting depth, depth to lithology, gravel, sand, silt, and clay. It was found that an aggregated geomorphon stratified all soil attributes except EC. Additionally, the aggregated geomorphon predicted 6 out of 9 soil properties with the greatest accuracy (RMSE) when compared to the original geomorphon (10-unit system) and a manually delineated system (5-unit system). 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. Third, a framework to disaggregate the Land Type Survey (LTS) through machine learning was developed. Geomorphons, together with the original LTS were overlaid to produce terrain morphological units. The polygons were disaggregated further to produce a raster map of soil depth classes through a 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. Forth, a study aimed to compare 10 algorithms, implemented through a modified DSMART model, in their ability to disaggregate two polygons into soil associations in two environmentally contrasting locations (Cathedral Peak, KwaZulu-Natal Province and Ntabelanga, Eastern Cape Province). At Cathedral Peak (high relief with clear toposequences), nearest shrunken centroid was the top performing algorithm with a kappa of 0.42 and an average uncertainty of 0.22. At Ntabelanga (low relief with strong geological control), the results were unsatisfactory. However, a regularised multinomial regression was the top performing algorithm, achieving a kappa of 0.17 and an average uncertainty of 0.84. The results of this study highlight the versatility of a technique to disaggregate South Africa’s national resource inventory. Disaggregation was then used to simultaneously disaggregate 20 land types in the Mvoti catchment covering 317 km2 in KwaZulu Natal province. First, the optimal geomorphon was chosen through a spatially resampled Cramer’s V test to determine the association between the soil legacy polygons and the geomorphon units. Second, feature selection algorithms were embedded into DSMART. Third, the feature selection techniques 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. Feature selection algorithms achieved the same accuracy as using all covariates but had greater computational efficiency. It is recommended that 10 to 20 times the amount of soil classes be used for the number of resamples per polygon.
Mapping soil organic carbon stocks by combining NIR spectroscopy and stochastic vertical distribution models : a case study in the Mvoti River Catchment, KZN, South Africa
(Stellenbosch : Stellenbosch University, 2019-03) Wiese, Liesl; Rozanov, Andrei Borisovich; De Clercq, W. P.; Seifert, Thomas; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: The agricultural and environmental importance of maintaining and increasing soil organic carbon (SOC) has been increasingly recognized globally. To a large extent, this recognition can be attributed to soil being the largest terrestrial carbon pool, as well as to soil’s responsiveness to land use and management. Land use and land use change are major factors affecting SOC levels with changes from natural vegetation (forests, grasslands and wetlands) to croplands, for example, causing significant SOC losses. The topsoil (0-30 cm depth) is especially sensitive to changes in land use and management and the highest variation in SOC levels is observed in this zone. In this study SOC stocks in the first meter of soil were quantified and mapped under different land uses and management systems using a vertical SOC distribution model, applying near-infrared (NIR) spectroscopy for SOC analysis and estimating the uncertainty of the maps created using different approaches. The study area was chosen as a quaternary catchment of 317 km-2 south and southeast of Greytown in the Midlands area of KwaZulu-Natal, South Africa. The catchment exhibits complex topography and predominantly shale and dolerite parent material. Soils in the area have high organic carbon content ranging from 0.08 to 22.85 % (mean = 3.48 %), with clay content ranging from 3 to 49 % (mean = 14.7 % clay) and pH(H20) between 3.3 and 6.7 (mean pH(H20) = 4.5). Vertical SOC distribution functions were developed for 69 soil profiles sampled from different land uses (mainly forestry plantations, grasslands and croplands) in and around the study catchment. Bulk density samples were taken at 2.5, 7.5, 12.5, 17.5, 30, 40, 50, 75 and 100 cm depths. The aim was to reduce the number of soil observations required for SOC accounting to one point close to the soil surface by applying negative exponential vertical depth functions of SOC distribution. To achieve this, the exponential functions were normalized using the volumetric SOC content observed close to the surface and grouped as a function of land use and soil types. Normalization reduced the number of model parameters and enabled the multiplication of the exponential decline curve characteristics with the SOC content value observed at the surface to present an adequately represented value of soil carbon distribution to 1 m at that observation point. The integral of the exponential function was used to calculate the soil carbon storage to 1 m. The vertical SOC distribution functions were refined for soils under maize production systems using reduced tillage and conventional tillage. In these soils, the vertical SOC distributions are described by piecewise, but still continuous functions where the distribution within the cultivated layer (0-30 cm) is a linear decline under reduced tillage or a constant value under conventional tillage, followed by an exponential decline to 1 m (30-100 cm). The value of predicting SOC concentrations in soil samples using wet oxidation (WalkleyBlack method) and dry near-infrared (NIR) spectrometry was assessed by comparing them to the dry combustion method. NIR spectrometry is considered to be an especially promising method, since it may be used in both proximal and remote sensing applications. In addition, the effect of using paired samples with single SOC determination versus paired samples with replicated (three times) analysis by all (reference and test) methods was tested. It was shown that the use of paired tests without replication dramatically decreases the precision of SOC predictions of all methods, possibly due to high variability of SOC content in reference values analysed by dry combustion. While reasonable figures of merit were obtained for all the methods, the analysis of non-replicated paired samples has shown that the relative RMSE for the SOC NIR method only falls below 10 % for values above ~8 % SOC. For the corrected SOC Walkley Black method the relative RMSE practically never falls below 10 %, rendering this method as semi-quantitative across the range. It was concluded that for method comparison of soil analysis, it is essential that reference sample analysis be replicated for all methods (reference and test methods) to determine the “true” value of analyte as the mean value analysed using the reference method. Finally, the above elements of vertical SOC distribution models as a function of land use and soil type, predicting SOC stocks to 1 m using only a surface (0-5 cm) sample, and the use of NIR spectroscopy as SOC analysis method were combined to assess the changes in SOC stock prediction errors through mapping. Results indicated a dramatic improvement in precision of SOC stock predictions with increasing detail in the input parameters using vertical SOC distribution functions differentiated by land use and soil grouping. Still, the relative error mostly exceeded 20 % which may be seen as unacceptably high for carbon accounting, trade and tax purposes, and the SOC stock accuracy decreased in terms of map R 2 and RMSE. The results were generally positive in terms of the progressive increase in complexity associated with SOC stock predictions and showed the need for a substantial increase in sampling density to maintain or increase map accuracy while increasing precision. This would include an increase both in surface samples for the prediction of SOC stocks using the vertical SOC distribution models, as well as an increase in the sampling of profiles to include more soil types and increase the profile density per land use to improve the vertical SOC prediction models.
Modeling and regulating hydrosalinity dynamics in the Sandspruit river catchment (Western Cape)
(Stellenbosch : Stellenbosch University, 2014-04) Bugan, Richard D. H.; De Clercq, W. P.; Jovanovic, N.; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Bugan, R.D.H. Modelling and regulating hydrosalinity dynamics in the Sandspruit River catchment (Western Cape). PhD dissertation, Stellenbosch University. The presence and impacts of dryland salinity are increasingly become evident in the semi-arid Western Cape. This may have serious consequences for a region which has already been classified as water scarce. This dissertation is a first attempt at providing a methodology for regulating the hydrosalinity dynamics in a catchment affected by dryland salinity, i.e. the Sandspruit catchment, through the use of a distributed hydrological model. It documents the entire hydrological modelling process, i.e. the progression from data collection to model application. A review of previous work has revealed that salinisation is a result of land use change from perennial indigenous deep rooted vegetation to annual shallow rooted cropping systems. This has altered the water and salinity dynamics in the catchment resulting in the mobilisation of stored salts and subsequently the salinisation of land and water resources. The identification of dryland salinity mitigation measures requires thorough knowledge of the water and salinity dynamics of the study area. A detailed water balance and conceptual flow model was calculated and developed for the Sandspruit catchment. The annual streamflow and precipitation ranged between 0.026 mm a-1 - 75.401 mm a-1 and 351 and 655 mm a-1 (averaging at 473 mm a- 1), respectively. Evapotranspiration was found to be the dominant component of the water balance, as it comprises, on average, 94% of precipitation. Streamflow is interpreted to be driven by quickflow, i.e. overland flow and interflow, with minimal contribution from groundwater. Quantification of the catchment scale salinity fluxes indicated the Sandspruit catchment is in a state of salt depletion, i.e. salt output exceeds salt input. The total salt input to and output from the Sandspruit catchment ranged between 2 261 - 3 684 t Catchment-1 and 12 671 t a-1 - 21 409 t a-1, respectively. Knowledge of the spatial distribution of salt storage is essential for identifying target areas to implement mitigation measures. A correlation between the salinity of sediment samples collected during borehole drilling and the groundwater EC (r2 = 0.75) allowed for the point data of salt storage to be interpolated. Interpolated salt storage ranged between 3 t ha-1 and 674 t ha-1, exhibiting generally increasing storage with decreasing ground elevation. The quantified water and salinity fluxes formed the basis for the application of the JAMS/J2000-NaCl hydrological model in the Sandspruit catchment. The model was able to adequately simulate the hydrology of the catchment, exhibiting a daily Nash-Sutcliffe Efficiency of 0.61. The simulated and observed salt outputs exhibited discrepancies at daily scale but were comparable at an annual scale. Recharge control, through the introduction of deep rooted perennial species, has been identified as the dominant measure to mitigate the impacts of dryland salinity. The effect of various land use change scenarios on the catchment hydrosalinity balance was evaluated with the JAMS/J2000-NaCl model. The simulated hydrosalinity balance exhibited sensitivity to land use change, with rooting depth being the main factor, and the spatial distribution of vegetation. Revegetation with Mixed forests, Evergreen forests and Range Brush were most effective in reducing salt leaching, when the “salinity hotspots” were targeted for re-vegetation (Scenario 3). This re-vegetation strategy resulted in an almost 50% reduction in catchment salt output. Overall, the results of the scenario simulations provided evidence for the consideration of re-vegetation strategies as a dryland salinity mitigation measure in the Sandspruit catchment. The importance of a targeted approach was also highlighted, i.e. mitigation measures should be implemented in areas which exhibit a high salt storage.
Remote sensing of salt-affected soils
(Stellenbosch : Stellenbosch University, 2013-03) Mashimbye, Zama Eric; De Clercq, W. P.; Van Niekerk, Adriaan; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western Cape Province of South Africa. Soil salinization is a global land degradation hazard that negatively affects the productivity of soils. Timely and accurate detection of soil salinity is crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River catchment. The goal of this study was to investigate less tedious, accurate and cost effective techniques for better monitoring. Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated. Spectral reflectance of dry soil samples was measured using an analytical spectral device FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These soil samples were not ground or sieved and the spectra were measured using the sun as a source of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral reflectance of dry soil samples was measured using the Bruker multipurpose analyser spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land components to map soil properties, the value of digital elevation models (DEMs) to delineate accurate land components was investigated. Land components extracted from the second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components were visually inspected and quantitatively analysed using the slope gradient standard deviation measure and the mean slope gradient local variance ratio for accuracy. Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2 and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a new Euclidean distance index and figure of merit index were used to validate the results. Finally, the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater was investigated. Soil salinity regression predictive models were developed using CurveExpert software. In addition, stepwise multiple linear regression soil salinity predictive models based on annual evapotranspiration, the aridity index and terrain attributes were developed using Statgraphics software. The models were validated using R2, standard error and correlation coefficients. The models were also independently validated using groundwater hydro-census data covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive models were achieved. Furthermore, it was established that reliable predictions of EC, pH, soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2 for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the potential to be used as a quick, reliable and less expensive method for evaluating salt-affected soils. As regards hydrological parameters, the study concluded that valuable hydrological parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a reliable tool to compare raster data sets. Regarding land components, it was concluded that higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling and that they can be integrated with other data sets to map soil salinity more accurately at catchment level. In the case of terrain attributes, the study established that promising soil salinity predictions could be made based on slope, elevation, evapotranspiration and terrain wetness index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity. Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR spectroscopy, land components, hydrological parameters and terrain attributes.
Using remote sensing and geographical information systems to classify local landforms using a pattern recognition approach for improved soil mapping
(Stellenbosch : Stellenbosch University, 2022-05) Atkinson, Jonathan Tom; De Clercq, W. P.; Rozanov, Andrei Borisovich; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Presently, a major focus of digital soil mapping (DSM) in South Africa is unlocking the soil-landscape relationships of legacy soil data by disaggregating the only source of contiguous soil information for South Africa, the National Land Type Survey (LTS) (ARC, 2003). Each land type is best defined as a homogenous mapping unit with a unique combination of terrain type, soil pattern and macroclimate properties (Paterson et al., 2015). One of the prevailing reasons for the LTS longevity and continual temporal-interoperability is that terrain description is expressly related to a suite of catenary soil property descriptions (Milne, 1936). These terrain types are further divided into terrain morphological units (TMUs) representing a sequence of patterns based on a 5-unit landscape model of 1-crest, 2-scarp, 3-midslope, 4-footslope and 5-valley bottom. Importantly, dominant soil distribution patterns are defined by terrain units relying on an elementary terrain topo-sequence pattern approach, with much of the work done on modelling soil variation related to variation in terrain (van Zijl, 2019). Whilst the LTS remains a source of national interest, there is immense opportunity to build on the existing soil inventory data rather than only focus on “breaking it down” (disaggregation). However, what is needed is a standard operating procedure that not only leverages the ability of digital elevation models (DEM) to explicate soil-landscape associations beyond the limited 5-unit landscape model but allows better refinement of soil descriptions with landscape features. Only once the nuances of optimal DEM parametrisation under controlled conditions are fully understood can the complete scope of DSM and digital geomorphological mapping (DGM) applications be explored. This dissertation attempts to synthesise knowledge on theory, methods, and applications of using remote sensing (RS) and geographical information systems (GIS) to classify local landforms using a pattern recognition approach for improved soil mapping in the context of multiscale problems of digital terrain analysis in KwaZulu-Natal. The dissertation is divided into three parts. Part one (Chapter 2) represents the DEM pre- processing and generalisation method and establishes the protocols for soil-landscape covariate application derived from various sensor platforms and spatial scales. Part two (Chapter 3) introduces the concept of improved terrain unit mapping through the geomorphon approach and describes DEM optimisation for standardised geomorphon representation for uniformly describing soil-landscape properties for inputs to DSM applications. Finally, part three (Chapters 4 & 5) looks at applications of DEM sources and geomorphons first from a holistic landscape context by linking digital terrain and soil-landscape analysis to geodiversity. Finally, the benefit of improved RS and GIS combined with quantitative modelling approaches on improving natural resource predictions are explored by modelling soil-ecotope and soil type mapping units and proposing improvements to an existing DSS designed for KwaZulu-Natal Natal. Specifically, this research is organised into four (4) research chapters with an overview of each chapter’s contribution outlined hereafter. Chapter 2 accounts for the recognition and requirements of DEM generalisation from high to medium resolution RS platforms and the influence these pre-processing approaches have on the extraction of a wide range of terrain attributes. Digital elevation data are elemental in deriving primary topographic attributes that are input variables to various regional soil-landscape models. DEMs' utility to extract different topographic indices as primary inputs to DSM allows the generalised soil-formative relationship between topography and soil characteristics to be measured quantitatively. Traditional landscape-scale approaches to extracting and analysing soils remain subjective and an expensive last resort for large-scale regional soil distribution and variability prediction. Selecting the right DEMs is a critical step in the development of any soil-landscape model. Therefore, the ability to represent soil-landscape relationships rapidly and objectively between soil properties and landscape position using emerging technologies and elevation data in a digital environment and at varying scales is fundamental for using soil-landscape mapping as a regional planning tool. There is, however, still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape and geomorphic characterisation within South Africa. However, Atkinson et al. (2017) have shown that topographic variable extraction is highly dependent on the DEM source and generalisation approach. However, while higher resolution DEMs may represent the “true” landscape surface more accurately, they do not necessarily offer the best results for all extracted terrain variables for modelling soil-landscape outputs. Given the convenience of a wide range of open-source elevation data for South Africa, there is a need to quantify the impact that DEM generalisation approaches have on simplifying detailed DEMs and compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localised topographic variables as a primer for soil-landscape or digital soil models. Chapter 3 explores the harmonisation of geomorphons derived from various RS platforms to define the landscape character in central KwaZulu-Natal. Robust DGM approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units and a range of DEM spatial scales offer great potential for improved topographic and landscape analysis and must have their utility investigated. Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality DEMs. Therefore, a thorough assessment of the effects that different pixel resolution (grain size) and DEM sources have on replicating observed geomorphic spatial patterns and representing selected terrain parameters using advanced automated geomorphometric mapping approaches is necessary. Specifically, it would be valuable to interrogate the self-adapting ability of these automated mapping approaches under regional conditions to quantitatively analyse how the choice of terrain model and scale influences the extraction, generalisation, and representation of digitally derived terrain attributes such as slope gradient, elevation and terrain unit feature extent. Equally important is understanding how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities that ultimately influence the extraction and representation of elementary soil properties. Chapter 4 is a shift from the technical aspects of digital terrain preprocessing and modelling and instead attempts to explore the contribution of gridded soil-landscape products to the abiotic landscape development agenda. It would be worthwhile to contextualise and decode these technical aspects of terrain and soil analyses to a holistic landscape development agenda. It is argued that current global environmental problems and questions demand exploration into new scientific perspectives and improved related paradigms and methodologies. Geodiversity (abiotic complexity) has not received the same level of attention as biodiversity (biotic complexity) despite its intrinsic and indivisible linkages to ecosystem and landscape richness characterisation. The ability to better describe the substrate in which biological and human activities occur is of top standing and must have its potential explored. To date, only one landmark study has successfully investigated the influence of environmental factors on geodiversity mapping in South Africa (Kori et al., 2019). Using an array of multimodal environmental covariates, including hydrographic, lithostratigraphic, pedological, climatic, topographic, solar morphometric and geomorphic variables, I aim to provide further confirmation to regional and international geodiversity research agendas. Chapter 5 culminates in applying quantitative DSM methods, with improved terrain representation, to classify productive soil units (ecotopes) as a proposed methodology to improve the current Bioresource Report Writer (BRW) soil-landscape recommendations. In KwaZulu-Natal, it has been accepted that detailed natural resource information based on scientifically accurate and relevant criteria is required to develop spatial layers that planners, developers, local government, and other stakeholders can use to guide future development. At present, the KwaZulu-Natal Department of Agriculture and Rural Development (KZNDARD) can provide high-level crop production approximations for various crops based on BioResource Units (BRU). However, the BRW has not seen a significant revision for over two decades. Still, the natural resource information it contains provides land managers, policymakers and farmers with invaluable access to regional and farm level qualitative estimations of agricultural productivity. There is a need to preserve this information while simultaneously providing modern measures of land management recommendation at multiple scales to the end-user. Against this backdrop, access to readily interpretable soil and crop information is increasingly being prioritised by provincial planning commissions as critical inputs to DSS for sustainable land management within KwaZulu-Natal.

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