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Soil maps’ value is increasingly recognised for enabling the optimal management of ecosystems. Digital soil mapping (DSM) can overcome the cost constraints of traditional mapping methods, but requires local area-specific research. As South Africa is blessed with the land type survey, local DSM research should start with the disaggregation of this resource. This paper shows how two land types (Ea34 and Ca11) near Newcastle in KwaZulu-Natal were disaggregated using DSM methods. A series of soil maps were created. With each map, more information was incorporated when creating the map. For Map 1 only the land type inventory and terrain analysis were used. A reconnaissance field visit with the land type surveyor was added for the second map. Field work and a simplified soil association legend improved the map accuracy for Maps 3 and 4, which were created using 30% and 60%, respectively, of the observation points as training data. The accuracy of the maps increased when more information was utilised. Map 1 reached an accuracy of 35%, whereas Map 4 achieved a commendable accuracy of 67%. Thus DSM methods can be used to disaggregate land types into accurate soil association maps. Emerging principles include that lithology rather than hard geology should be used as parent material input, field work is critical to obtain acceptable results, and simplifying the map legend into soil associations improves the accuracy of the map.Keywords: digitial elevation model, digital soil mapping, soil survey, SoLIM softwareSouth African Journal of Plant and Soil 2013, 30(3): 123–129

In South Africa, the only soil resource available with full spatial coverage is the national resource inventory. Disaggregating this polygon-based inventory, is thus a logical step to create more detailed soil maps covering the entire country. The polygons are large in area encompassing complex soil-terrain patterns and research into disaggregation techniques has been limited. This study aimed to compare 10 algorithms, implemented through a modified DSMART (“Disaggregating and Harmonizing Soil Map Units Through Resampled Classification Trees”) model, in their ability to disaggregate two polygons into soil associations in two environmentally contrasting locations. One site had high relief and strong catenal sequences (eastern KwaZulu-Natal Province) and the other site had low relief and a strong geological control of soil types (northern Eastern Cape Province). The algorithms compared were based on previous studies which included k-nearest neighbour, nearest shrunken centroid, discriminatory analysis, multinomial logistics regression, linear and radial support vector machines, decision trees, stochastic gradient boosting, random forest, and neural networks. The method involves stratifying the polygons with landform elements, randomly sampling the landform elements, allocating the soil classes based on the resource inventory, and predicting soil associations across a stack of covariates. This was done in an iterative process, creating multiple realisations of the soil distribution. The performance of each algorithm was based on their kappa and uncertainties. It was found that in general, robust linear models which either utilise an embedded feature selection or regularise covariates, performed best. In the area with high relief and clear toposequences, nearest shrunken centroid was the top performing algorithm with a kappa of 0.42 and an average uncertainty of 0.22. In the area with relatively low relief and complex geology, 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, where algorithms can be chosen on expert knowledge, model averaging can be performed, the top performing algorithm can be chosen, and algorithm parameters can be optimised.

Accurate hydrological modelling to evaluate the impacts of climate and land use change on water resources is pivotal to sustainable management. Soil information is an important input in hydrological models but is often not available at adequate scale with appropriate attributes for direct parameterisation of the models. In this study, conducted in three quaternary catchments in the midlands of KwaZulu-Natal, three different soil information sets were used to configure SWAT+, a revised version of the Soil and Water Assessment Tool (SWAT). The datasets were: (i) the Land Type database (currently the only soil dataset covering the whole of South Africa), (ii) disaggregation of the Land Type database using digital soil mapping techniques (called DSMART), and (iii) a dataset where DSMART were complemented by field observations and interpretations of the hydropedological behaviour of the soils (DSMART+). Simulated streamflow was compared with measured streamflow at three weirs with long-term measurements, and the impact of the soil datasets on water balance simulations was evaluated. In general, the simulations were acceptable when compared to other studies, but could be improved through calibration and including small reservoirs in the model. The DSMART+ dataset yielded more accurate simulations of streamflow in all three catchments with Nash-Sutcliffe efficiencies increasing by between 9% and 67% when compared to the Land Type dataset. The value of the improved soil maps is, however, highlighted through the enhanced spatial detail of streamflow generation mechanisms and water balance components. The internal catchment processes are represented more accurately, and we argue that South Africa needs a detailed hydrological soil map for effective water resource management.

MSc (Environmental Sciences), North-West University, Potchefstroom Campus Precision agriculture (PA) has been named as a cultivation method which could help alleviate food shortages in the future. However, PA relies on knowing the spatial distribution of soil properties, which requires repeated, quick, accurate, and cost-effective soil analysis. Conventional methods of soil analysis are slow and costly, reducing the application of PA in South Africa. Soil spectroscopy can fulfil the need for quick cost-effective soil analysis, but depend on robust calibration curves, of which none exist openly for South Africa. It is expected that mid-infrared soil spectroscopy can be used to analyse soil samples for soil properties pH, Effective Cation Exchange Capacity and Phosphorus within the Western Highveld summer grain area. Data for this study was provided by Noord Wes Kooperasie and Griekwaland Wes Kooperasie which resulted in a selection pool of ± 5 180 samples already analysed for soil properties. Conditioned Latin Hypercube sampling (cLHS) was then used to select 1 000 samples based on the pH, Effective CEC and P values of the dataset. The samples were then prepared and scanned with Mid Infrared spectroscopy (4 000 to 400 cm−1) using a Bruker Alpha II with DRIFTS module attached to create a spectral library. The soil property database and spectral library was combined and the R programming language was used to create calibration models using Cubist, Partial Least Square regression (PLSR) and Random Forest (RF). Each calibration algorithm was also applied onto two types of spectral datasets which includes spectra with pre-processing and spectra with minimal to no pre-processing applied. These models were validated using statistical performance measures including root mean square error (RMSE), squared correlation coefficient ( r2), standard deviation, bias and ratio of performance to deviation (RPD). Results show that models created for pH with Cubist and pre-processed spectral data had the best performance (R2=0.86,RMSE=0.3,RPD=2.66) along with ECEC with Cubist (R2=0.86,RMSE=0.3,RPD=2.66) and RF (R2=0.85,RMSE=0.72) and then P with some success using RF (R2=0.57,RMSE=13.48,RPD=1.51). Overall performance increase was observed with Cubist, PLSR and RF models using pre-processed spectral data compared to spectra with no pre-processing and produced acceptable prediction models to be able to predict pH and ECEC from soil spectra. Findings are consistent with other studies conducted worldwide but with little to no data to compare from South Africa more research and data is needed to create models that include all soil properties used for PA and that is representative of the whole of South Africa. Masters

MSc (Environmental Sciences), North-West University, Potchefstroom Campus Cotton (Gossypium hirsutum) production in South Africa relies on rainfed cultivation, and as such the availability of water is a key factor in the production of the crop. Cotton production in South Africa is declining gradual annually. The decline is marked by the competition with traditional crops, high input costs, international market prices and a lack of technical production information. The cotton produced in the 2019/2020 season consisted of 27 850 hectares in total from which 15 966 hectares is produced under dryland conditions, and 11 884 hectares under irrigated conditions. Dryland cotton production has been becoming more prominent, which establishes the need for technical information in the production under these conditions. Technical knowledge regarding soil moisture regimes is unknown in the industry and this uncertainty prevents the continued cultivation of the crop. This study aims to quantify the effect of the different soil forms and their associated water regimes on the rainfed cotton seed yield and quantity. The effect of the water table was duly visible as bedrock interflow soils exhibited a higher amount of soil water throughout the production season in comparison with sandy clay soils. It was expected that the sandy clay soils would indicate higher soil water contents throughout the production season, but certain interflow soils had a substantial amount of soil water more during the both the productions seasons. To determine the soil water regime the relationship between the soil water and the soil matric potential was established. The soil matric potential was determined by means of undisturbed core sampling and suction under pressure by using a pressure plate apparatus. By determining the soil water capacity at 33 and 1500 kPa, the drained upper limit (DUL) and lower limit (LL) was established for each of the various soil forms. Through the utilization of these parameters, the amount of planting-available water was determined which is critical in the production of cotton. Considering the DUL and LL the state of the soil water content throughout the production season was assessed for each of the soil forms. It was evident that sandy clay loam soils tend to be above DUL during a high rainfall period, but due to the drainage capacity of the soils, the period above DUL does not occur over a long period. The sandy clay soils indicated the opposite effect by being above DUL during the entire production season. The cotton quality was assessed with the High-Volume Instrument (HVI) system and the focus was on the main quality parameters such as length, strength and micronaire. It was evident that the sandy clay soils produced the best quality cotton in comparison with the sandy clay loam soils. Even though these soil’s soil water content was above DUL it produced thicker and stronger fibres with an optimal spinning consistency in comparison with the sandy clay loam soils. The cotton yield maps for each of the sites was obtained and the yield per soil form was extracted for the specific sites of measurement. Although no statistical differences occurred between the different soil forms it was evident that sandy clay loam soils produced a higher yield in comparison with the sandy clay soils. The ability of cotton roots to shoot in sandy clay loam soils with a less dense compaction, in association with the ability of soil water to be drained enabled the cotton to produce more cotton bolls, resulting in a higher yield. It was evident that the amount of soil water did vary between the various soil forms due to the variation in the soil physical properties such as the soil profile depth, clay percentage of the soil horizons, and soil moisture retention capabilities. Sandy clay soils tend to remain above DUL for longer periods than sandy clay loam soils, which causes anoxic conditions within the soil profile. These anoxic conditions result in stress induced effects in the rooting system which causes a lowered yield in comparison with the sandy clay loam soils which provides an oxidised environments for the rooting system, resulting in increased yields. Sandy clay loam soils tend to drain freely due to the large amount of macro pores, which in turn creates an oxidized environment for roots to develop and extract soil water. It is recommended that the sites for both seasons remains the same to ensure consistent data with less variation. In association with the sites the same cultivar should be produced over two cropping seasons to ensure that all possible variation is excluded from the results. By including a weather system that measures the evaporative demand, the soil water balance can be defined more accurately. Masters

MSc (Environmental Sciences), North-West University, Potchefstroom Campus Bush encroachment (BE) is a serious form of land degradation and South Africa alone has lost an estimated 8 million hectares (ha) of grazing or cultivation land due to BE. This consequently leads to decreased food security. To prevent BE, one needs to understand the drivers and mechanisms that control the process and to advise when and where certain management actions should be implemented. Unfortunately, the proposed drivers for BE in African savannas are still widely debated given that the causes for this process is still poorly understood. The focus of this study was to understand the effect of soil type and certain soil properties on BE in the North West Province (NWP) between 1993 and 2018. For this study, the main driving factors of BE extent and spread were identified in the study area for the specified period by taking a GIS approach on provincial (NWP) and regional scales (four significant areas). Maps indicating the percentage (%) of woody cover for the years 1993, 1998 and 2018 were sourced from Symeonakis et al. (2020). The layers indicating the % woody cover in the NWP were used for calculating the spread of bush and bush spread maps were created for time frames, 1993-1998, 1998-2018 and 1993-2018. Potential driving factors of BE were sourced from various sources and used to analyse the bush spread and determine the driving factor/s of the specific bush spread from 1993 to 2018 on a provincial scale and regional scale. On a provincial scale, mean annual precipitation (MAP) was the main driving factor of BE, while in land-managed areas, land-use and MAP together with soil, were important driving factors of bush encroachment from 1993 to 2018. Therefore, soil can be regarded as a minor driving factor of BE in the NWP from 1993 to 2018. Vegetation surveys were also carried out at the study sites, characterising different soil types, soil properties and degrees of BE. The belt-transect method was used for the vegetation survey to determine the composition, density, and structure (height classes) of the woody component (tree- and shrub species). Soil profiles were described per soil horizon, soil samples were taken within each transect, which were analysed at the laboratory to determine the soil particle distribution (soil texture), pH, electrical conductivity (EC), and water retention of the soil. From the vegetation surveys, Dichrostachys cinerea and Diospyros lycioides were found to be the main woody encroacher species as D. cinerea occurred at all study sites with D. lycioides mainly occurring at the Kgomo-Kgomo study site. The other recognized woody encroacher species included Grewia flava, Grewia flavescens, Senegalia mellifera, Vachellia karroo, Vachellia tortillis, and Ziziphus mucronata. Soil types and properties did not have a significant influence on all the woody species identified at each study site, but rather on specific encroacher species causing BE in the NWP. The results indicated that D. cinerea mostly occurred on soils with low clay content, while G. flava favoured soils with higher clay content. The highest extent of BE occurred at the Legkraal and Kgomo-Kgomo study sites, where the soil was characterised as deep soils with sandy loam texture. Species such as Combretum apiculatum, Combretum inberbe, and Combretum hereroense, occurred on shallow soils, while Vachellia tortillis preferred deep soil types. The encroacher species, D. lycioides, occurred on the subsoil with an alkaline pH, while both D. lycioides and D. cinerea preferred soils with EC higher than 25 mS/m. D. lycioides and G. flava both occurred in soils with high dry bulk densities (Pbs), especially at the Kgomo-Kgomo study site. The highest Pbs was also recorded at the Kgomo-Kgomo study site. It therefore seems that soil types with specific soil properties, influence the occurrence of specific woody species causing BE in the NWP. It is recommended that land-managed areas that experience BE should in general be considered as important future restoration and/or research study sites. Areas where deep soils occur, with predominantly sandy or sandy loam textures, should be regarded as priority areas. Restoration actions that could be considered in the priority areas include the application of manual, mechanical, chemical, biological or a combination of these methods in BE areas to stimulate the growth of grasses. To improve soil condition for grass growth, soil organic matter in the form of livestock manure could be added to the topsoil instead of fertilizer, as fertilizers are usually too expensive for land managers. A knowledge, training and skills development program should also be impleemted for land managers. It is recommended that future research be conducted on determining the main driving factors of BE of other Provinces, such as Limpopo and Northern Cape and using different GIS methods for determining the main driving factors of BE on provincial and regional scale. Future research should also be done on the effect of other soil properties, such as soil temperature and soil organic matter, on woody species causing BE. Masters

PhD (Environmental Sciences), North-West University, Potchefstroom Campus The Griqualand West Centre (GWC) of plant endemism harbours a unique flora of which 24 species are endemic. Heterogeneous geology, climate and topography are considered drivers of the unique flora and local endemism. However, these drivers have not yet been investigated and our understanding of the effects thereof on vegetation dynamics remains poor. Four mountain ecosystems, each underlain by different rock types and with distinct climatic patterns, provided a setting to investigate the effects of ecological drivers shaping vegetation dynamics of this semi-arid area. Therefore, the primary aim of this study was to disentangle the effects of rainfall and geology, through soil properties related to the underlying geological parent material, as drivers of floristic patterns, plant diversity and structure, biomass production, and the relationship between diversity and biomass production. The objectives of this study were to (i) redefine the borders of GWC to establish which mountain ranges fall within the centre by using a MaxEnt spatial model based on geology, climate and topography in combination with distribution data of GWC endemics, (ii) describe the flora within the newly redefined borders of GWC based on dominant plant families and -species, indicator plant species, endemic species and species composition, (iii) compare soil properties, rainfall, plant diversity and structure between mountain ecosystems to test whether mountains, within the newly defined borders of GWC, differ significantly from each other, (iv) determine whether soil properties, rainfall or a combination thereof act as drivers of plant diversity and structural differences between mountains, (v) test for differences in total biomass production (above ground green plant material and debris), live biomass production (only live green above ground plant material) and respective plant functional group (PFG) biomass production between the four mountain rangelands, (vi) relate differences to specific soil properties and rainfall to identify the strongest drivers of biomass production, (vii) investigate diversity-biomass relationships for total plant species and for species representing different PFGs, and (viii) present an optimal range of biomass production at which herbaceous species diversity can be maintained at regional scale. Results obtained from this study revealed that each mountain plant community was characterised by unique herbaceous plant communities with specific indicator plant species, driven by soil properties and rainfall. Herbaceous plant composition, density, height, cover and shrub frequencies were related to a combination of soil properties and mean annual rainfall. However, plant diversity, and grass, lignified forb and tree frequencies, as well as woody plant height and canopy area, could only be related to soil properties. Grasses, lignified forbs and herbaceous forbs contributed to biomass production in descending order. At regional and local scales, diversity-productivity relationships followed non-linear trends. However, optimum biomass production was reached at highest diversity. Semi-arid mountain landscapes in GWC provide important ecosystem services through their unique plant diversity. It is necessary to follow a holistic, multi-disciplinary conservation and management approach to not only manage for species diversity, but to conserve the underlying environmental drivers in semi-arid mountain plant communities. Doctoral

Soil information is critical in watershed-scale hydrological modelling, however, it is still debated which level of complexity the soil data should contain. In the present study, we have compared the effect of two levels of soil data on the hydrologic simulation of a mesoscale, urbanised watershed (630 km2) in central South Africa. The first level of soil data, land type (LT) data, is currently the best, readily available soil information that covers the whole of South Africa. In the LT database, the entire study area is covered by only two soil types. The second level of soil data (DSM) was created by means of digital soil mapping based on hydropedological principles. It resulted in six different soil types with different hydrological behaviour (e.g., interflow, recharge, responsive). The two levels of soil data were each included in the revised version of the Soil and Water Assessment Tool (SWAT+). To compare the effects of different complexity of soil information on the simulated water balance, the outputs of the uncalibrated models were compared to the three nested gauging stations of the watershed. For the LT scenario, the simulation efficiencies calculated with the Kling&ndash, Gupta efficiency (KGE) for the three nested gauging stations (640 km2, 550 km2, 54 km2) of 0, 0.33 and &minus, 0.23 were achieved, respectively. Under the DSM scenario, KGE increased to 0.28, 0.44 and 0.43 indicating an immediate improvement of the simulation by integrating soil data with detailed information on hydrological behaviour. In the LT scenario, actual evapotranspiration (aET) was clearly underestimated compared to MODIS-derived aET, while surface runoff was overestimated. The DSM scenario resulted in higher simulated aET compared to LT and lower surface runoff. The higher simulation efficiency of DSM in the smaller headwater catchments can be attributed to the inclusion of the interflow soil type, which covers the governing runoff generation process better than the LT scenario. Our results indicate that simulations benefit from more detailed soil information, especially in smaller areas where fewer runoff generation processes dominate.

Urbanization and hydrology have an interactive relationship, as urbanization changing the hydrology of a system and the hydrology commonly causing structural damage to the infrastructure. Hydrological modelling has been used to quantify the water causing structural impacts, and to provide solutions to the issues. However, in already-urbanized areas, creating a soil map to use as input in the modelling process is difficult, as observation positions are limited and visuals of the natural vegetation which indicate soil distribution are unnatural. This project used historical satellite images in combination with terrain parameters and digital soil mapping methods to produce an accurate (Kappa statistic = 0.81) hydropedology soil map for the Cosmo City suburb in Johannesburg, South Africa. The map was used as input into the HYDRUS 2D and SWAT hydrological models to quantify the water creating road damage at Kampala Crescent, a road within Cosmo City (using HYDRUS 2D), as well as the impact of urbanization on the hydrology of the area (using SWAT). HYDRUS 2D modelling showed that a subsurface drain installed at Kampala Crescent would need a carrying capacity of 0.3 m3.h&minus, 1.m&minus, 1 to alleviate the road damage, while SWAT modelling shows that surface runoff in Cosmo City will commence with as little rainfall as 2 mm.month&minus, 1. This project showcases the value of multidisciplinary work. The remote sensing was invaluable to the mapping, which informed the hydrological modelling and subsequently provided answers to the engineers, who could then mitigate the hydrology-related issues within Cosmo City.

Soil erosion is probably the most common form of land degradation, leading to on and off site detrimental effects, such as an increase in river sediment load which have been shown to drastically decrease dam storage capacity. The South African Government has announced plans to build two large dams within an area with a very high erosion rate, thus necessitating soil erosion mitigation efforts. These efforts should consider the mechanism of gully formation, as it determines which conservation measures could be used. With overland flow gullies the kinetic energy of flowing water needs to be curbed, while gullies form by piping in dispersive soils where free water could accumulate. This paper describes how a soil association map for the quarternary catchments T35DE was created with digital soil mapping methods, in order to indicate areas where piping could occur. Soil were described and classified at 600 locations determined with the conditioned Latin hypercube sampling method, within a 500 m buffer around the available road network. This soil database was then used with the multinomial logistic regression algorithm to create an initial soil association map with seven soil associations, based on their soil erosion properties. Additionally, a soil depth map was created using the cubist algorithm. The soil association map was then created again, but with the soil depth map added as an additional covariate. Including the soil depth map as a covariate improved the accuracy (68% vs 64%) and level of detail of the final soil association map, even though the soil depth map was a poor reflection of reality. The final soil association map was compared to the best current available spatial soil dataset, and found to contain 123 times more detail. Superimposing the gully extent of the soil association map confirmed that the planosols are the most susceptible to gully erosion and mitigation efforts should avoid increasing free water in the subsoil on the planosol area, and the as yet ungullied planosol should be prioritized as it could become a hotspot for gully formation if it degrades.

There is an increasing demand worldwide for spatial soil information. Unfortunately, gathering new soil data is expensive, leading to a focus on extracting detailed information from existing datasets. The most extensive dataset in South Africa is that of the land type survey. This paper proposes a method of disaggregating land type inventories into a soil association map, using a pedogenetic approach. The method is illustrated by the disaggregation of land types in landscapes with simple (Cathedral Peak) and complex (Ntabelanga) soil distribution patterns. The maps were validated with independent datasets. The Cathedral Peak map was produced with a Kappa value of 0.66, indicating a substantial agreement with reality, but the Ntabelanga soil association map only achieved a Kappa value of 0.20, indicating a slight agreement with reality. These values, comparable to results obtained using the automated disaggregated algorithm DSMART, show that the method is useful in landscapes with a simple soil distribution pattern, but not in areas where there is a complex soil distribution pattern. The pedogenetic method would be useful to soil surveyors with a moderate level of GIS skills and access to pedogenetic knowledge of the study area. Sites consisting of numerous land types are better analysed by the automated DSMART method