Doctoral Degrees (Soil Science)
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Browsing Doctoral Degrees (Soil Science) by browse.metadata.advisor "Clarke, Catherine E."
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- ItemDigital 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.
- ItemEvaluating soil and terrain variables in a production environment: implications for agricultural land assessment(Stellenbosch : Stellenbosch University, 2022-12) Barichievy, Kurt Russell; Clarke, Catherine E.; Rozanov, Andrei Borisovich; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.ENGLISH ABSTRACT: Agricultural land in South Africa is under increasing pressure to produce more food from an ever-shrinking land base, as more land is being converted to non-productive uses. Additional to these pressures, is the concept of land reform and strategic land acquisition, aimed at agrarian transform within the rural landscape. It is estimated that less than 15% of South Africa is suitable for dryland cultivation. Consequently, the sustainable utilisation of these scarce resources and preservation of agricultural land is of paramount importance, to ultimately ensure some measure of national food security in the years to come. Agricultural land evaluation is a critical tool that can achieve this goal. Unfortunately, in recent decades the development of revised or novel land evaluation methodologies has stalled for South African farm-level assessments, the scale at which land release decisions are made. Further, the relationship between productivity and individual land assessment attributes has not been adequately quantified or incorporated into contemporary local assessment procedures. It is envisaged that this study would influence and help guide in-field methodologies, as well as draft legislation and best-practice strategies, with a view of both standardising and improving agricultural land assessment techniques. By emphasising the importance of agricultural land and the accurate assessment thereof, this research also aims to increase our understanding of production-based approaches at an operational scale, though the novel combination of traditional approaches and use of newer technologies. It is anticipated that this improved understanding will be employed to not only protect more agricultural land, which may have been undervalued by historical methods, but also as an intuitive assessment tool to highlight the yield gap between potential and actual production levels. A review of pertinent literature identified the need for local verification studies to evaluate the performance of land assessment methodologies currently used in industry. To address this, five methods were verified using land assessment polygons in a commercial production environment, in the Province of KwaZulu-Natal, South Africa. The resultant classifications, derived from 225 soil observations, were compared to actual land use and precision yields achieved by dryland maize and soybean, across five growing seasons (2016 - 2020). By comparing land use with broad arability, four of the five land assessment methods were found to adequately classify arable land. Additionally, land evaluation polygons, linked to dryland precision maize and soybean yields can provide a general overview of method performance. However, it was concluded that yield performance and variation, across land evaluation methods and classes, is only explicit on or near a soil observation point where measurements are taken. Accordingly, seasonal variograms for maize and soybean were developed, to establish a representative yield buffer around individual soil observation points. This, along with yield normalisation strategies were employed, to improve verification procedures across multiple growing seasons. To determine crop productivity drivers, significant land assessment attributes inter alia slope, effective rooting depth, soil texture, soil group and soil wetness limitations were analysed against maize and soybean yields. It was found that the two crops respond differently to individual land assessment attributes and these differences should be taken cognisance of in new, crop-specific land evaluation methodologies and weighted accordingly. In an attempt to improve productivity-based land classification 78 attributes; derived from land assessment methodologies, digital terrain analysis, the pedological survey and soil colour spectrophotometry were collated. From these attributes, three new approaches, one based on biophysical scoring criteria and two based on machine learning, were developed across two commercial farming operations, in northern KwaZulu-Natal. These new methodologies were then tested on three separate commercial operations, located in different regions of the province. The biophysical scoring classification generally outperformed machine learning models and was particularly accurate when classifying observations associated with either extremely poor or extremely advantageous soil and terrain attributes. The transferability of the models to other regions, with different resources produced mixed results, highlighting the need for wider calibration in some instances. The study also found that the new productivity-based approaches can have useful applications in commercial farm management, where crop specific classification can identify underperforming areas and yields gaps, which can be ringfenced for appropriate interventions. The newly developed biophysical scoring classification was used to demonstrate the utility of these approaches in broader agricultural land release applications. The study found the new approaches better reflect production potential and should be used to supplement existing methodologies in land release assessments. Ultimately, the application of these production- based approaches can assist the land assessor to better classify the production potential of the land, as well as the decision-making authority to justify preserving more land for agricultural purposes.
- ItemLipid humification by soil clays(Stellenbosch : Stellenbosch University, 2019-04) Adams, Adrian Richard; Clarke, Catherine E.; Hardie-Pieters, Ailsa G.; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.ENGLISH ABSTRACT: We studied three aspects of the natural polymerisation (humification) of lipids by soil clays – namely, the products formed, reaction mechanism and kinetics – at environmental temperatures (c. 20–50°C). Various clays were reacted with oleic acid (our chosen model lipid). The Mn-oxide birnessite was the most reactive toward oleic acid, polymerising it into quasi-solid polyesters. A probing of the birnessite-oleic acid reaction mechanism revealed that the formation of a surface exchange complex between oleic acid carboxyl groups and birnessite surface sites (>Mn(III)/>Mn(IV)) is a crucial first step of the reaction. Subsequent chelation and one-electron reduction of Mn(III) to Mn(II) forms radical oleic acid species which couple and thereby polymerise. Kinetic studies revealed that the birnessite-oleic acid reaction was near-linearly dependent on birnessite mass-loading (rate order ~ 0.75) but virtually independent of birnessite surface pH (rate order ~ 0.2). A determined activation energy for the reaction of 12.8 ± 4.2 kJ/mol revealed that it is energetically more spontaneous than the usual autoxidation pathways. These findings broaden our understanding of the role soil clays play in lipid humification in soils.