Doctoral Degrees (Soil Science)

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Deficit irrigation and canopy management practices to improve water use efficiency and profitability of wine grapes
(Stellenbosch : Stellenbosch University, 2024-03) Lategan, Eugene Lourens; Hoffman, Josias Eduard; Myburgh, Philipus Albertus; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Grapevines irrigated at low plant available water (PAW) depletion levels required more than double the irrigation volumes compared to those irrigated at high depletion levels. The accelerated sugar accumulation of sprawling grapevine canopies resulted in earlier harvest dates, reducing pre-harvest irrigation requirements. Diﬀerent canopy manipulations did not aﬀect total leaf area (LA) per grapevine within an irrigation strategy, but negatively aﬀected LA as less water was applied. Non-suckered grapevines produced more shoots and more vigorous shoot growth, while non-suckered vertical shoot positioned (VSP) grapevines tended to produce lower cane mass. The LA distribution provides a good indication of canopy orientation, and non-destructive measurements of primary and secondary shoots can estimate winter pruned cane mass. This would allow viticulturists, producers, or irrigation consultants to estimate the maximum cane mass and use the VINET© model to predict grapevine water requirements in real-time throughout the season, as the LA is estimated using cane mass. Grapevines with sprawling canopies had lower mid-day leaf water (ΨL) and stem water (Ψs) potentials compared to those with VSP canopies. Grapevines experiencing severe water constraints ripened more rapidly than those without or with medium water constraints. Low frequency irrigation increased water constraints compared to high frequency irrigation. Diurnal ΨL cycles showed that grapevines with sprawling canopies had lower ΨL after 18:00 and throughout the night, suggesting that their water status could not recover as fast as VSP grapevines. High irrigation frequencies led to higher grapevine row evapotranspiration (ETGR) losses, with losses from sprawling grapevines, particularly those irrigated at ca. 30% plant available water (PAW) depletion, being higher in January and February than those with VSP canopies. Seasonal full surface evapotranspiration was more sensitive to irrigation frequency than to canopy manipulations. Grapevines irrigated at ca. 30% PAW depletion had higher mean full surface crop coeﬃcient (Kc) values compared to other strategies, with those irrigated at ca. 90% PAW depletion having the lowest Kc values. The mean peak Kc was generally obtained in February for grapevines irrigated at frequencies, while the lowest Kc was found during the same period for low frequency irrigation applications. The fraction of soil wetted during irrigation applications under grapevine row (Kc,GR) could be a more realistic coeﬃcient than Kc for producers and consultants to use in irrigation scheduling requirements. Irrigation frequency had a more signiﬁcant impact on yield than canopy manipulation. Higher rainfall in 2013/14 increased vegetative growth and yield, with low frequency irrigations resulting in higher production water use eﬃciency compared to medium and high frequency irrigations. The incidence of grey rot was higher during the wetter season, with grapevines with sprawling canopies experiencing higher yield losses due to sun burn and less frequent irrigation. The highest incidences and yield loss to grey rot were found in grapevines left un-suckered and irrigated at ca. 30 PAW depletion, while irrigation at around 90 PAW depletion resulted in the absence of grey rot. Grapes were harvested near the target total soluble solids level of 24ºB, with severe water constraints enhancing berry maturation. Non-suckered VSP grapevines produced poorer quality at lower levels (30% and 60% depletion levels), with the highest overall wine quality obtained when irrigated at ca. 90% PAW depletion. Less frequent irrigations reduced summer canopy management requirements, but grapevines with more shoots required higher labour inputs at harvest. Pruning labour input requirements were aﬀected by the number of shoots produced per grapevine and the mass per individual shoot. Sprawling canopy grapevines generally required lower labour costs, and pump costs were aﬀected by the frequency of irrigation applications. During low to normal rainfall seasons, grapevines with sprawling canopies irrigated at ca. 60% PAW depletion produced the highest gross margins incomes, followed by box pruned grapevines irrigated at ca. 90% PAW depletion. In high summer rainfall seasons, box pruned grapevines and non-suckered VSP canopies had the highest gross margins. Grapevines with sprawling canopies, particularly those irrigated at ca. 60% PAW depletion, produced the best balance between yield and quality, ensuring the best gross margin incomes. The gross margin water use eﬃciency (WUEGM) increased with an increase in PAW depletion level irrigation, with box pruned grapevines consistently having the highest WUEGM. The study found that grapevines with sprawling canopies experienced lower diurnal and cumulative evaporation losses compared to VSP grapevines, regardless of PAW depletion levels. The higher mean leaf area per grapevine resulted in denser canopies, and treatments irrigated at approximately 30% PAW depletion were always within stage 1 of evaporation. Grapevines irrigated at around 60% PAW depletion occasionally went into stage 2, particularly in sprawling canopies. The water content of soil under grapevines irrigated at around 90% PAW depletion spent most of the season in stage 2. The vegetation coeﬃcient (Kv) of sprawling canopies was lower than VSP grapevines, irrespective of PAW depletion. The VINET© model generally underestimated transpiration rates in wet soil regimes and overestimated them during dry soil regimes. Adjusting the model by addition of Kv and adapted transpiration water predictions can be done using two multilinear regressions after a few grapevine canopy measurements inputs have been considered.
Determination and modelling of evapotranspiration of bearing and non-bearing apple trees at Grabouw in the Western Cape
(Stellenbosch : Stellenbosch University, 2024-03) Meyer, Aline; Van Zyl, J. L.; Hoffman, J. E.; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.
ENGLISH ABSTRACT: The irrigation of apple trees is important to ensure sustainable production and good quality fruit, especially in regions where rainfall does not contribute adequately to the water demand. Effective scheduling is the key to efficient water use to ensure profitability and sustainability on farms. Quantitative knowledge of the water use and the effect of irrigation application on young apple trees will improve on-farm decision making regarding scheduling. A study was conducted on Malus domestica “Bigbucks‟ (a mutation of “Corder Gala‟) trees grown in a gravelly soil at Grabouw in the Western Cape to determine the effect of three irrigation cycles on the water use, root growth characteristic and tree performance over four growing seasons (October to May) from planting to the first year of bearing. Treatment one (T1) was a short irrigation cycle receiving ca. 15 mm of water per irrigation with an average of 42 irrigations through the growing season, treatment two (T2) was a medium irrigation cycle receiving ca. 27 mm of water with an average of 21 irrigations through the growing season and treatment three (T3) was a long irrigation cycle receiving ca. 37 mm of water with an average of 13 irrigations through the growing season. Crop evapotranspiration (ETC) was determined for all three treatments based on the soil water balance. The ETC of all three treatments increased from the first to the fourth growing season as the leaf area index (LAI) of apple trees increased. T1 had a higher consumptive water use than T2 and T3. Studies done using micro-lysimeters to determine the orchard floor evaporation revealed that T1 lost more water through evaporation compared to T2 and T3, but water loss from the soil mainly occurred through transpiration, irrespective of the treatment. In situ and destructive root studies revealed that both root length density (RLD) and the number of fine roots within the soil profile is strongly related to soil water extraction (SWE). SWE increased with an increase in RLD and the number of fine roots. These results revealed that growing roots can continuously grow into moist regions of the soil. Significantly more fine roots penetrated deeper soil layers and at a greater distance from the tree for the two drier irrigation cycles (T2 and T3). The root index (RI) of T2 and T3 was also higher in deeper soil layers compared to T1 suggesting that soil moisture conditions of T2 and T3 were more favourable in deeper soil layers. It was concluded that short irrigation cycles will favour shallow root growth while longer irrigation cycles promote roots into deeper soil layers. There were no significant differences among treatments in terms of diurnal plant water status, vegetative growth, yield and fruit quality. These results suggest that longer irrigation cycles can be used to save water while simultaneously increasing root growth to deeper soil layers without compromising tree performance. Statistical analysis performed on ETC and RLD data revealed that there is a strong, positive correlation (R² = 0.741) between ETC and RLD. The data was used to develop a statistically significant model in which ETC can accurately be predicted using RLD data or vice versa. The model can be used as a reference for apple producers in South Africa to encourage more precise and effective irrigation scheduling while simultaneously increasing RLD for better water and nutrient uptake resulting in optimal crop production and quality.
Determination of optimal soil conditions and foliar nutrient levels in commercial rooibos tea production
(2023-03) Smith, Jacobus Francois Naude; Hardie-Pieters, Ailsa G.; Hoffman, J. E.; Stellenbosch University. Faculty of Agrisciences. Dept. of Soil Science.
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.
Using remote sensing and geographical information systems to classify local landforms using a pattern recognition approach for improved soil mapping
(Stellenbosch : Stellenbosch University, 2022-05) Atkinson, Jonathan Tom; De Clercq, W. P.; Rozanov, Andrei Borisovich; Stellenbosch University. Faculty of AgriSciences. Dept. of Soil Science.
ENGLISH ABSTRACT: Presently, a major focus of digital soil mapping (DSM) in South Africa is unlocking the soil-landscape relationships of legacy soil data by disaggregating the only source of contiguous soil information for South Africa, the National Land Type Survey (LTS) (ARC, 2003). Each land type is best defined as a homogenous mapping unit with a unique combination of terrain type, soil pattern and macroclimate properties (Paterson et al., 2015). One of the prevailing reasons for the LTS longevity and continual temporal-interoperability is that terrain description is expressly related to a suite of catenary soil property descriptions (Milne, 1936). These terrain types are further divided into terrain morphological units (TMUs) representing a sequence of patterns based on a 5-unit landscape model of 1-crest, 2-scarp, 3-midslope, 4-footslope and 5-valley bottom. Importantly, dominant soil distribution patterns are defined by terrain units relying on an elementary terrain topo-sequence pattern approach, with much of the work done on modelling soil variation related to variation in terrain (van Zijl, 2019). Whilst the LTS remains a source of national interest, there is immense opportunity to build on the existing soil inventory data rather than only focus on “breaking it down” (disaggregation). However, what is needed is a standard operating procedure that not only leverages the ability of digital elevation models (DEM) to explicate soil-landscape associations beyond the limited 5-unit landscape model but allows better refinement of soil descriptions with landscape features. Only once the nuances of optimal DEM parametrisation under controlled conditions are fully understood can the complete scope of DSM and digital geomorphological mapping (DGM) applications be explored. This dissertation attempts to synthesise knowledge on theory, methods, and applications of using remote sensing (RS) and geographical information systems (GIS) to classify local landforms using a pattern recognition approach for improved soil mapping in the context of multiscale problems of digital terrain analysis in KwaZulu-Natal. The dissertation is divided into three parts. Part one (Chapter 2) represents the DEM pre- processing and generalisation method and establishes the protocols for soil-landscape covariate application derived from various sensor platforms and spatial scales. Part two (Chapter 3) introduces the concept of improved terrain unit mapping through the geomorphon approach and describes DEM optimisation for standardised geomorphon representation for uniformly describing soil-landscape properties for inputs to DSM applications. Finally, part three (Chapters 4 & 5) looks at applications of DEM sources and geomorphons first from a holistic landscape context by linking digital terrain and soil-landscape analysis to geodiversity. Finally, the benefit of improved RS and GIS combined with quantitative modelling approaches on improving natural resource predictions are explored by modelling soil-ecotope and soil type mapping units and proposing improvements to an existing DSS designed for KwaZulu-Natal Natal. Specifically, this research is organised into four (4) research chapters with an overview of each chapter’s contribution outlined hereafter. Chapter 2 accounts for the recognition and requirements of DEM generalisation from high to medium resolution RS platforms and the influence these pre-processing approaches have on the extraction of a wide range of terrain attributes. Digital elevation data are elemental in deriving primary topographic attributes that are input variables to various regional soil-landscape models. DEMs' utility to extract different topographic indices as primary inputs to DSM allows the generalised soil-formative relationship between topography and soil characteristics to be measured quantitatively. Traditional landscape-scale approaches to extracting and analysing soils remain subjective and an expensive last resort for large-scale regional soil distribution and variability prediction. Selecting the right DEMs is a critical step in the development of any soil-landscape model. Therefore, the ability to represent soil-landscape relationships rapidly and objectively between soil properties and landscape position using emerging technologies and elevation data in a digital environment and at varying scales is fundamental for using soil-landscape mapping as a regional planning tool. There is, however, still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape and geomorphic characterisation within South Africa. However, Atkinson et al. (2017) have shown that topographic variable extraction is highly dependent on the DEM source and generalisation approach. However, while higher resolution DEMs may represent the “true” landscape surface more accurately, they do not necessarily offer the best results for all extracted terrain variables for modelling soil-landscape outputs. Given the convenience of a wide range of open-source elevation data for South Africa, there is a need to quantify the impact that DEM generalisation approaches have on simplifying detailed DEMs and compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localised topographic variables as a primer for soil-landscape or digital soil models. Chapter 3 explores the harmonisation of geomorphons derived from various RS platforms to define the landscape character in central KwaZulu-Natal. Robust DGM approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units and a range of DEM spatial scales offer great potential for improved topographic and landscape analysis and must have their utility investigated. Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality DEMs. Therefore, a thorough assessment of the effects that different pixel resolution (grain size) and DEM sources have on replicating observed geomorphic spatial patterns and representing selected terrain parameters using advanced automated geomorphometric mapping approaches is necessary. Specifically, it would be valuable to interrogate the self-adapting ability of these automated mapping approaches under regional conditions to quantitatively analyse how the choice of terrain model and scale influences the extraction, generalisation, and representation of digitally derived terrain attributes such as slope gradient, elevation and terrain unit feature extent. Equally important is understanding how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities that ultimately influence the extraction and representation of elementary soil properties. Chapter 4 is a shift from the technical aspects of digital terrain preprocessing and modelling and instead attempts to explore the contribution of gridded soil-landscape products to the abiotic landscape development agenda. It would be worthwhile to contextualise and decode these technical aspects of terrain and soil analyses to a holistic landscape development agenda. It is argued that current global environmental problems and questions demand exploration into new scientific perspectives and improved related paradigms and methodologies. Geodiversity (abiotic complexity) has not received the same level of attention as biodiversity (biotic complexity) despite its intrinsic and indivisible linkages to ecosystem and landscape richness characterisation. The ability to better describe the substrate in which biological and human activities occur is of top standing and must have its potential explored. To date, only one landmark study has successfully investigated the influence of environmental factors on geodiversity mapping in South Africa (Kori et al., 2019). Using an array of multimodal environmental covariates, including hydrographic, lithostratigraphic, pedological, climatic, topographic, solar morphometric and geomorphic variables, I aim to provide further confirmation to regional and international geodiversity research agendas. Chapter 5 culminates in applying quantitative DSM methods, with improved terrain representation, to classify productive soil units (ecotopes) as a proposed methodology to improve the current Bioresource Report Writer (BRW) soil-landscape recommendations. In KwaZulu-Natal, it has been accepted that detailed natural resource information based on scientifically accurate and relevant criteria is required to develop spatial layers that planners, developers, local government, and other stakeholders can use to guide future development. At present, the KwaZulu-Natal Department of Agriculture and Rural Development (KZNDARD) can provide high-level crop production approximations for various crops based on BioResource Units (BRU). However, the BRW has not seen a significant revision for over two decades. Still, the natural resource information it contains provides land managers, policymakers and farmers with invaluable access to regional and farm level qualitative estimations of agricultural productivity. There is a need to preserve this information while simultaneously providing modern measures of land management recommendation at multiple scales to the end-user. Against this backdrop, access to readily interpretable soil and crop information is increasingly being prioritised by provincial planning commissions as critical inputs to DSS for sustainable land management within KwaZulu-Natal.

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