Doctoral Degrees (Soil Science)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/569

Browse

Now showing 1 - 8 of 8

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.
Effect of different biochars on inorganic nitrogen availability
(Stellenbosch : Stellenbosch University, 2018-03) Aghoghovwia, Makhosazana Princess; Hardie-Pieters, Ailsa G.; Rozanov, Andrei Borisovich; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Biochar (a fine pyrolysed organic material) is an amendment used to increase and sustain productivity, reduce environmental pollution and sequester carbon (C) in soils. Successes were reported in improving acidic, sandy and highly weathered soil. However, the effects are strongly influenced by biochar physico-chemical characteristics, which vary widely depending on feedstock and pyrolysis conditions. The main objective of this study was to determine the effects of six biochars (commercially-produced in South Africa under various pyrolysis conditions from maize stover, grape pip, grape skin, pine wood, rubber tyre and sugarcane pith) on nitrogen (N) interactions in a sandy soil. The physico-chemical properties of the above biochars were characterised, three main experiments were conducted to study the effects of biochar addition to soil on (1) inorganic (ammonium and nitrate) N adsorption and desorption of added ammonium and nitrate in aqueous solution; (2) soil C and N mineralisation; and (3) leaching of inorganic N fertiliser. Maize stover and grape skin chars were suggested to be imperfect biochars due to low total C contents. Characterisation suggested that the sugarcane pith char was either not a suitable raw material for biochar or it was not actually a biochar due to its low stability and high chemical reactivity. However, its high ash content (66%) suggests good nutrient delivery as a soil amendment. Pine wood biochar was the most recommendable because of its low ash (3.5%), high total C (80%) and high surface area (344 m2 g-1), which all aid nutrient and water holding. However, the grape pip biochar had a low surface area (9.8 m2 g-1) and the highest fixed-C content (87%) which can be good for soil C storage. This work shows that despite many positive effects of biochar application to soil reported in literature, the negative effects of such applications on N availability are clear. All six biochars had a stronger nitrate removal affinity (82-89%) compared to ammonium (33-39%). It was also shown that adsorbed nitrate was not desorbable (0.01-0.23%) compared to adsorbed ammonium removal which was around 50% desorbable with KCl. Based on the shape of the adsorption isotherms, physisorption was the suggested mechanism for this behaviour. Competing reactions such as redox reactions in nitrate adsorption and volatilisation of ammonium were also suggested to have influenced the adsorption study results. Laboratory incubation studies showed that biochars enhanced N immobilisation along with increase in absolute and suppression of relative soil respiration. Pine wood and sugarcane pith biochars were found to reduce inorganic N availability the most due to net N immobilisation. The following biochar property may be linked to N immobilisation: inherent inorganic N in the soil-biochar system. Suppression of relative soil respiration may be due to biochar fixed-C content. Sugarcane pith char had the least effect on relative respiration because of its low fixed-C content (15.6%). However, the remaining biochars were substantially limiting the relative CO2 emissions. Rubber tyre char was the best performer in this regard with 75% lower cumulative relative CO2 emissions compared to the control. Among the plant-derived biochars, grape pip had the lowest CO2 released with 59% lower cumulative relative CO2 release. The leaching column experiment showed that application of biochars at 2.5% (w/w) to sandy soil reduced cumulative leaching of NH4+ and NO3- by 15-26% and 11-54%, respectively, compared to unamended soil. Using 15N labelled ammonium nitrate, it was found that 0.77-10.81% of applied fertiliser N remained in soil-biochar treatments after leaching. Only the pine wood and sugarcane pith biochar treatments significantly increased N fertiliser retention by 136 and 157% compared to the control soil. Whereas, the rubber tyre biochar treatment significantly reduced N fertiliser retention by 81%. The study concludes that all six biochars make inorganic N less available by mechanisms such as nitrate capture which is related to aromaticity and metal content of the chars and by enhancing biological immobilisation.
Evaluating 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.
In situ denitrification on nitrate rich groundwater in South Africa
(Stellenbosch : Stellenbosch University, 2015-12) Israel, Sumaya; Rozanov, Andrei Borisovich; Jovanovic, Nebojsa; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science
ENGLISH ABSTRACT: South Africa is a water scarce country and in certain regions the quantity of surface water is insufficient to provide communities with their domestic water needs. In many arid areas groundwater is often the sole source of water. This total dependence means that groundwater quality is of paramount importance. A high nitrate concentration in groundwater is a common cause of water being declared unfit for use and denitrification has been proposed as a potential remedy. In many areas of South Africa nitrate levels exceed the recommended maximum concentration of 40 mg/L NO3- as N. Concentrations of 100 mg/L NO3- as N or even greater than200 mg/L NO3- as N are found in various places. Water with nitrate concentrationsexceeding 40 mg/L NO3- as N, belongs to the category of “dangerous” drinking waterquality (“purple”, i.e. Class IV) according to DWA (1996, 1998) water quality guidelines. Concentrations in this range have been reported in case studies to cause conditions like methaemoglobinaemia (“blue baby syndrome”), spontaneous abortions, stomach cancers and livestock deaths. The purpose of the study includes laboratory experiments to compare the denitrification efficiency, reaction rates and reaction mechanisms between woodchips, biochar and a mixture of woodchips and biochar. Further work included modelling of denitrification using the PHREEQC-2 1D reactive transport model. Field implementation of a denitrification technique was tested at a site which previously experienced some NH4NO3 spills, to determine the lifespan of the woodchips used during the experiment based on available data. The underlying intended purpose of this research is to contribute to the wellbeing of rural South Africans in areas where groundwater is plentiful, but elevated nitrate levels prevent the use of this water. The purpose of the laboratory experiment was to establish the efficiency of carbon sources and compare their rates, sorption properties and processes by which they react. Laboratory experiment consisted of three leaching columns containing two layers of building sand on either side of a carbon containing layer. The carbon containing layers were made of about 600g of woodchips, biochar and woodchip and biochar mixture respectively. Parameters analysed from the effluent from the columns included NO3-, NO2-, SO42-, NH4+, Alkalinity, DOC (dissolved organic carbon content) and Phosphate. The purpose of the field experimental work was to install a barrier containing a cheaply available carbon source to treat groundwater and to monitor changes with time in order to determine the efficiency and life span of carbon source used for the experiment. Experimental work was done at a site in Somerset West (South Africa) that had experienced spills in the past from agrochemical storage factory premises. Somerset West normally receives about 568 mm of rain per year. It receives most of its rainfall during winter; it thus has a Mediterranean climate. It receives the lowest rainfall (10 mm) in February and the highest (96 mm) in June. The “reactor”/ tank with dimensions- 1,37m height, 2.15m diameter used for the experiment was slotted for its entire circumference by marking and grinding through the 5mm thick plastic material. The top section was left open to allow for filling and occasional checking of filled material during the experiment. The tank was packed with Eucalyptus globulus woodchips which was freely available at the site. Concentrations of groundwater nitrate at the site were well over what could be expected in any naturally occurring groundwater systems, and would result only by major anthropogenic activities in unconfined aquifer areas of South Africa. Nitrate levels in monitoring boreholes at the site ranged from about 20 mg NO3--N/L at background boreholes up to about 600 mg/L NO3--N. Woodchips used to denitrify groundwater in the field experiment were sampled after 27 months and 35 months of being active in the treatment zone. Various depths of samples were collected namely the top section, bottom of the tank and a full core sample of the tank. Main results from the laboratory studies showed that biochar on its own as a carbon source for nitrate removal would not be viable, however, the presence of biochar in the mixture of woodchips and biochar increased the rate of denitrification. Biochar on its own was able to remove some nitrate, but results showed incomplete denitrification and limited reactivity. The results also confirmed that different processes were in play, while the redox reaction of denitrification was taking place in woodchips and biochar and woodchip mixtures, the biochar treatment followed a physical process and had only a small percentage of incomplete denitrification. This was confirmed by sulphate reduction and increased alkalinity in the woodchips and biochar and woodchip mixture treatments. Rates deduced from the data also showed that the woodchip and biochar mixture would take a shorter period to affect total denitrification. Main results from the field work showed that nitrate was totally removed at the treatment zone and surrounding boreholes, and even sulphate and NH4+ were removed during the experiment. This shows that the woodchips were successful in affecting denitrification for 35 months. Data also shows that boreholes further downstream from the tank had reduced NO3-, SO42- and NH4+ levels. This would relate to higher permeability flow paths possibly present on the downstream side of the treatment zone. This became evident when pumping boreholes during sampling and noting that upstream boreholes had to be allowed for a recovery period, while downstream boreholes could be pumped continuously for 30 minutes without any reduced yields. This shows that not only did the treatment zone work at removing nitrate, but migration of excess available carbon from the tank may have further treated nitrate rich areas on the site. During monitoring on the site, woodchips were sampled and analysed for their components at time period 27 months and 35 months of the experiment respectively. Results showed that woodchips were considerably more degraded than a) woodchips of the same species of tree that had undergone natural degradation on the floor and b) un-degraded woodchips of the same tree species. Comparing data from the two time series samples, a rate of woodchip degradation could be calculated. Using the available biodegradable carbon for the woodchips based on its composition, a barrier lifespan could be determined. The results of calculations show that the barrier would be effective for at least another 6.9 years from the period of the last sampling date. A total lifespan of about 10 years can thus be estimated. These calculations are tree species composition specific and rate specific. PHREEQC-2 modelling was used to estimate the use of carbon in the experiment by adding incremental moles of carbon to the influent composition. Saturation indices from PHREEQC-2 showed that mineral phases of iron may precipitate from solution during the experiment. Experimental data were plotted against results of intermittent carbon reactions in PHREEQC-2 and it was found that initial rates in the experiment were higher and agreed with up to 100mg/L of carbon consumption when a 24 hour residence time was used while later stages agreed with about 37.5 mg/L carbon consumption, where a 72 hr residence time was used. It was concluded that biochar and woodchips combined are more effective than woodchips on their own at denitrifying groundwater. Also woodchips successfully denitrified groundwater at the Somerset West site for 35 months, with added removal of sulphate and NH4+. Barrier life span calculations show that the barrier could remain active for an additional 6.9 years which relates to a total period of about 10 years of denitrification should the rates remain constant. It was concluded that nitrate removal and barrier lifespan would be extended by testing variable lignin content in different tree species prior to use in a denitrification barrier as lignin is unlikely to degrade in an anaerobic environment. It was recommended that implementation or field test should be done using a biochar and woodchip mixture. Improved results may be achieved by analysing wood or plant material for comparative lignin content, cellulose content and hemicellulose contents. Wood types or plant species with higher lignin content would be more resistant to degradation in anaerobic conditions.
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.
Mitigation of soil and ground water pollution caused by on-land disposal of olive mill wastewater
(Stellenbosch : Stellenbosch University, 2016-03) Umeugochukwu, Obiageli; Rozanov, Andrei Borisovich; Sigge, G. O.; Hardie-Pieters, Ailsa G.; Stellenbosch University. Faculty of Agrisciences. Dept of Soil Science.
ENGLISH ABSTRACT: Olive mill wastewater (OMW) is generated in large quantities, particularly in the regions with a Mediterranean climate where olive oil is produced on a commercial scale. Some producers collect the effluent and dispose of it as hazardous waste at significant expense, while others dispose of it directly on land, claiming the potential benefits to productivity from the plant nutrients present in the OMW. It was shown that the OMW also contains some phytotoxic phenols, which may have both immediate and cumulative negative effects on plant growth. The long-term effects on the soil and crop growth have been shown to be detrimental. Sandy soils are of particular concern due to the possibility of phenol penetration into deeper soil layers and potential ground water contamination. The study explores in-situ (soil amendment with biochar prior to the OMW disposal) and ex-situ (OMW filtration through a biochar bed) options to mitigate the negative effects of the OMW on-land disposal. A laboratory batch sorption experiment was set up using 0.2 g pinewood biochar to explore the possibilities of removing the phenols from 50 mL of the OMW or gallic acid (GA) solutions at different concentrations. The results showed that the sorption process was rapid and stabilized within one hour. The kinetic process followed a pseudo-second-order model and was described by the Freundlich multi-layer isotherm. The pinewood biochar had a sorption capacity of 30 mg·g-1 and 100 % removal was obtained with 300 g·l-1 of the OMW load. It was found that pinewood biochar could be used to remove the phenols contained in the effluent. A column experiment was set up to determine the effectiveness of biochar and biochar-soil mixtures in removal of phenol and Chemical Oxygen Demand (COD) from the OMW compared to sand filtration. The breakthrough curves for phenol and COD were determined, while the pH and EC of the filtrates were monitored. Ten PVC columns of 30 cm height and 5 cm diameter were filled with five different materials: sand, sand + biochar, Hutton clay loam soil, Hutton clay loam soil + biochar and biochar alone. Two different treatments were given to the columns; five of the columns were prewashed with 2 liters of deionized water and the other five were not washed before the OMW filtration. The performance of the columns was determined in respect of the phenol and COD removal capacities, hydraulic conductivities and porosity changes. The results showed that washing enhanced the phenol sorption but not the COD sorption. The addition of the biochar at 2%wt load significantly improved the effectiveness of the filtration. The best performance was achieved in terms of COD removal in pure biochar columns, but in terms of the phenol, the best performance was on a pre-washed Hutton clay loam soil with 2%wt biochar addition. Both the washing and biochar addition affected the porosity and reduced the hydraulic conductivity of the columns. The greenhouse experiments were conducted to confirm the above statement using pot trials laid out in a 4 x 4 factorial Randomized Complete Design (CRD) to determine the effect of effluent and biochar on wheat and green beans on alkaline sand. Results showed that the increasing effluent rate up to 200 m3·ha-1 gave significantly negative results on wheat growth, even with fertilizer application. But the effect was different for beans where low effluent loads gave positive results though not significant while with fertilizer (N and P) 50 m3·ha-1 performed better. With the addition of biochar there was no significant effect on wheat, but it significantly affected beans at the application rate of 2.5 and 5%wt. The interaction of biochar and effluent showed that the best performance was at 5% biochar application and effluent loads of 50 and 100 m3·ha-1, but increased effluent rate decreased production even with a 5% wt biochar application rate. It was suggested that a leguminous crop should tolerate OMW application better compared to wheat even in the adverse conditions of the alkaline sand. A second greenhouse experiment was conducted with another legume, an indigenous African crop, the bambara groundnut, on an acidic Hutton clay-loam soil (Oxisol) sourced locally. The experiment was laid out in a 2 x 6 CRD factorial design to determine the effect of the biochar and effluent combination on the yield and growth parameters of bambara as well as the effect on soil conditions and nutrient availability. The result showed that biochar addition improved seed germination, which was retarded by effluent loading. The effluent rate of 200 m3·ha-1 and biochar 2% gave the best yield performance. The biochar addition increased the pH and hence affected the release of P and N whereas Na and K availability were reduced. We conclude that biochar may be used for both ex-situ filtration to treat the OMW, and as a soil amendment to allow safe on-land disposal of the OMW. The estimations of safe disposal loads and the required application rates of the biochar should be made individually for a specific soil type. Pinewood biochar was proven to be a cheaper source of activated carbon for the treatment of olive mill wastewater organic contaminants in South Africa.
Sanjeevak as a source of nutrients and phytohormones for production and propagation of plants
(Stellenbosch : Stellenbosch University, 2012-03) Orendo-Smith, Richard; Rozanov, Andrei Borisovich; Kate, T.; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: The use of cowdung as an organic fertilizer in Asian and African agriculture is an ancient practice. This explains its renewed interest, partly due to the financial inability of most farmers to purchase agrochemicals but also the ever increasing need to adopt greener technologies that do not adversely affect soil health, water quality, biodiversity and promote sustained or even increased food production. In this context, many innovative farmers have developed their own novel technologies based on the use of local resources. One such innovation is Sanjeevak (a mix of cow dung, cow urine, water and a handful of sugar); which showed very promising boosting effect on crop productivity. However, very little scientific work has so far been conducted to evaluate its effect as an organic product for soil amendments. The present study was subdivided into three main objectives. (i) To assess the fertilizing value, human health and ecological risk profiles of Sanjeevak; (ii) To screen Sanjeevak for phytohormones content using Salkowski colorimetric method and liquid chromatography – mass spectrometry (LC-MS) (iii) To evaluate Sanjeevak application at various rates on growth parameters and yield of various crops cultivated in glasshouse and field conditions. Sanjeevak was assessed for its micro and macro nutrients contents. The analysis showed the presence of micronutrients such as Mg, Na, Ca and Zn at variable concentrations and phosphorus (P) (0.007%) and potassium (K) (0.063%). However, Sanjeevak content in total nitrogen (TN) (0.11%), and total organic carbon (TOC) (0.71%) was very low; suggesting that it may be a viable source of nutrients only if applied at higher and consistent rates or alternatively by improving its formulation. Also, Sanjeevak was analysed for its microbiological characteristics and level of heavy metals content in comparison to the strictest legislations that regulate the use and application of wastewater sludge to agricultural land in South Africa. The findings showed that heavy metals, which averaged from 0.03±0.01 for Arsenic (As) to 4.74±0.92 mg/kg for Zinc (Zn) and feacal coliform was estimated at 1.2×102 CFU/g dry matter measured were considerably below the threshold (for Arsenic between 40 to 75 mg/kg dry weight; for Zinc between 2800 to 7500 mg/kg dry weight) and faecal coliform bacteria between 1000 to 1×107 CFU/g dry weight for application as a source of soil amendments. Studies investigating the detection and concentration of phytohormones in Sanjeevak were carried out. In using the Salkowski colorimetric method to detect and quantify auxins from Sanjeevak and its composites (cow urine and dung), the results showed the presence of indole-3-acetic acid (IAA) at variable concentrations ranging from 20.38±2.1 ppm in cow urine, 20.1±6.6 ppm in cow dung, Sanjeevak 17.90±1.1 ppm to up to 138.31±12.6 ppm when LTRP was added to Sanjeevak bacterial cultures and by varying parameters such as incubaton time and temperature. Screening of the above mentioned samples for IAA using LC-MS analysis validated earlier findings. Further analysis of these results strongly emphasized the influence of bacteria in Sanjeevak in producing IAA. Trials were carried out both in the glasshouse and the field. In the greenhouse, different Sanjeevak application rates consistently confirmed its root promoting effect on crops such as tomato, cucumber and grapevine and increased wheat yield independent of the nutrients it contains. Marginal increases were recorded between treatments under field conditions; for example compost and compost + Sanjeevak 20.35 and 20.61 t/ha; and 2.46 and 2.60 t/ha compared to the control 11.67 t/ha and 1.29 t/ha respectively for tomato and maize. However, statistical analysis of the results obtained, revealed that there was no difference between treatments (control, compost, Sanjeevak and compost + Sanjeevak) for the same crop tested due to the high coefficient of variation of the data. Therefore, the use of Sanjeevak as an organic source of soil amendments may be considered as a cheaper alternative to effective microorganisms (EM) technology made up of local and natural resources. As observed in the study, it may be best used in combination with a reliable source of plant nutrients.
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.

Browse

Browsing Doctoral Degrees (Soil Science) by browse.metadata.advisor "Rozanov, Andrei Borisovich"

Results Per Page

Sort Options