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Gully erosion affects land and water resources, resulting in serious environmental and socio-economic consequences. To aid mitigation and rehabilitation efforts, gully susceptibility mapping of broader gully-prone regions should be augmented by the rapid detection of existing gully features. Numerous works have been published on (semi-)automated approaches to detect gully erosion, most recently incorporating machine learning. However, upscaling and transferability capabilities of these approaches are rarely investigated. Establishing algorithms that are scalable and transferrable will constrain uncertainties when conducting quantitative analysis, allowing comparable results at different landscape scales and/or geo-environmental settings. Here, we aim to develop and apply a semi-automated approach based on Object-Based Image Analysis (OBIA) with low data needs, at different scales and geo-environmental regions. The segmentation process is underpinned by two gully morphological properties: 1) Height Above Nearest Drainage (HAND) and normalised slope, calculated from a Digital Elevation Model (DEM) with a spatial resolution of 2 m, with 93% coverage of South Africa’s 1.22 million km2 expanse. HAND is a terrain model that normalises topography according to local relative heights above a drainage channel (herein, a gully channel). While this has been implemented in flood mapping studies for river systems, it remains unused in gully detection algorithms. Slope, which is often used as a gully predictor variable, is used to confine HAND and implemented here as a normalised slope input, calculated by subtracting a convolved mean slope value with a designated filter size from the DEM-derived slope. Detected gully features are refined using expert knowledge, merging, and pixel-based growing and shrinking. Preliminary development at a local gully scale suggests good performance, with an overall accuracy of 82.3% (includes a user accuracy of 65.5% of gully and 99.0% for non-gullied areas, and a producer accuracy of 98.5% for gully and 74.2% for non-gullied areas) and a kappa index of 0.65. We also discuss the broader performance of our approach when upscaling and implemented in other geo-environmental settings covered by the 2 m-DEM.

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI [ADD1_IRSTEA]Gestion intégrée de la ressource et des infrastructures; International audience; Agriculture in developing countries will have to transform and increase production by an estimated 70% in order to meet demands by 2050. Although well-managed commercial farms offer little manoeuvring space for increasing agricultural water productivity, smallholder farms usually operate at low input costs and therefore provide ample opportunities to reduce the potential yield gap through agricultural intensification. The aim of this paper is to analyse and compare yields and water productivities obtained in field and modelling experiments in Ethiopia (maize, garlic, onion), South Africa (tomato) and Tunisia (tomato, potato, wheat). Innovative agricultural practices were introduced on smallholder farms: irrigation scheduling and NPS Zn fertilization in Ethiopia; high-yielding cultivar, drip irrigation, mulching and organic amendments in South Africa; and crop water modelling in Tunisia. In general, crop yields increased up to eight times with innovative practices compared to current conventional farming practices. Crop water productivities were fairly stable within the same experiments, but increased with innovations, indicating that intensive farming can be more environmentally sustainable than conventional farming. Intensive farming systems in a resource-rich environment (high radiation levels, relatively fertile, deep and well-drained soils), combined with technology transfer and capacity building could be seen as viable strategies to secure food for smallholders and communities in African rural areas, as well as to improve water utilization in water-scarce catchments.

SI; International audience; Boosting the productivity of smallholder farming systems continues to be a major need in Africa. Challenges relating to how to improve irrigation are multi‐factor and multisectoral, and they involve a broad range of actors who must interact to reach decisions collectively. We provide a systematic reflection on findings from the research project EAU4Food, which adopted a transdisciplinary approach to irrigation for food security research in five case studies in Ethiopia, Mali, Mozambique, South Africa and Tunisia. The EAU4Food experiences emphasize that actual innovation at irrigated smallholder farm level remains limited without sufficient improvement of the enabling environment and taking note of the wider political economy environment. Most project partners felt at the end of the project that the transdisciplinary approach has indeed enriched the research process by providing different and multiple insights from actors outside the academic field. Local capacity to facilitate transdisciplinary research and engagement with practitioners was developed and could support the continuation and scaling up of the approach. Future projects may benefit from a longer time frame to allow for deeper exchange of lessons learned among different stakeholders and a dedicated effort to analyse possible improvements of the enabling environment from the beginning of the research process. © 2020 The Authors. Irrigation and Drainage published by John Wiley & Sons Ltd on behalf of International Commission for Irrigation and Drainage

Spatial information on soil particle size distribution and soil organic carbon (SOC) are important for land-use management, environmental models and policy-making. Digital soil mapping (DSM) techniques can quantitatively predict these soil properties using minimal resources. However, DSM has not been adequately evaluated at the farm-scale. The aim of this study was to optimise the DSM framework to produce farm-scale soil maps for 366 ha in the Sandspruit catchment, Western Cape, South Africa. 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%, R 2 0.75), sand (support vector machine, RMSE 4.69%, R 2 0.67), clay (quantile regression, RMSE 2.38%, R 2 0.52) and SOC (ridge regression, RMSE 0.19%, R 2 0.41). Random forest was the best predictive model for silt content (RMSE 4.12%, R 2 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 are required.

Knowledge of soil depth spatial variability is important for land use management especially in dryland agriculture regions, which rely on climate and soils to provide adequate water and nutrients during the growing season. Soil spatial variability can be predicted from legacy soil data through machine learning techniques producing quantitative soil maps requiring minimal resources. South Africa has a country wide 1:250,000 scale resource map known as the Land Type Survey (LTS) which includes soil properties such as soil depth, soil class, root limiting layer, clay content, and texture. Each LTS polygon (land type), is comprised of unique soil – terrain patterns and is therefore, not a true soil map. This study aims to disaggregate the LTS into a farm-scale soil depth class map through a two-step disaggregation approach. First, landform elements were predicted through a pattern recognition algorithm known as geomorphons. Geomorphons, together with the original LTS were overlaid to produce polygons with unique distributions of soil. The polygons were disaggregated further to produce a raster map of soil depth classes through a soil map 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. The result of this study is significant as it produced a soil depth class map from a national resource map at a scale and resolution (10 m) suitable for farm management.

Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon. Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.

Increasing pressure on water resources is driving the development of technology to improve water-use efficiency in irrigation. Uptake of these technological advances are essential to ensure long-term water security in catchments, particularly in water-scarce regions and where agricultural activities and urban centres compete for the same resources. Research suggests that uptake of technology lags far behind the development of new products. The study presented in this paper interviewed 29 commercial farmers from the water-scarce Central Breede River Valley in South Africa to investigate their reasons behind the use or non-use of irrigation technology for scheduling, and in particular the uptake of a free, government-funded remote-sensing service called FruitLook. Evaluating the uptake of a free service eliminates monetary cost as one key barrier to uptake. In-depth interviews revealed a high uptake of technology (83%), but use of only one type – soil water measurement. Among the farmers that use water-use efficiency technology, 78% use the same probe service provider. Perceived accuracy and ease of use, as well as personalised after-sales service are the key reasons for this probe’s popularity. While 86% of the farmers have heard about FruitLook, only one farmer uses it for irrigation purposes. The non-use of the free service can mainly be attributed to the time cost associated with the product’s initial set-up, use, and interpretation of information. The study revealed that the integration of information from various products is essential for farmers – too much information in different formats is too time-costly. Developers of new technology should focus on these latter two findings to improve the likelihood of new product uptake.

CITATION: Watson, A., et al. 2018. Estimation of groundwater recharge via percolation outputs from a rainfall / runoff model for the Verlorenvlei estuarine system, west coast, South Africa. Journal of Hydrology, 558:238-254.

Clay mineralogy of the fluvial plains reflects the complexity of geological setting, material weathering and transport. Clay minerals produced in the weathering process are closely associated with the parent rock and serve as an indicator of the material sources. Soil clay mineralogy of South African river valleys is insufficiently studied and this research can make a meaningful contribution towards advancing the understanding the history of planation surfaces in Africa and their evolution. Here we examine the valley of the Letaba river – one of the many Limpopo tributaries originating in the Drakensberg escarpment and flowing through the basement complex of the Lowveld geomorphic province. Nine soil profiles were selected from a total of 37 profiles represented by Ustifluvents, Haplustepts, and Haplustalfs for mineralogical study of the The upstream section of the study area is dominated by kaolinite associated with the general dominance of granite and granitic gneiss lithology in the upper reaches of the river. The occurrence of mafic (dolerite) dikes along the river course immediately shows in soils as presence of smectite trail in the clay fraction. As the river cuts through the greenstone belt the clay mineralogy of the fluvial terraces immediately reflects this change as an increase in talc and chlorite proportions and the appearance of vermiculite. The traces of talc originating from greenstones are present throughout the valley within the middle reaches of the river and may be showing the contribution of small streams and tributaries transporting the material from the water divide to the valley of the Letaba. The changes in soil clay mineralogy of alluvial soils across the lithological boundaries indicate the prevalence of locally-sources material over long-distance-transported sediment in aggradation of these terraces. The analysis of clay mineralogy in fluvial environments may be a good indicator of local sediment source and short distance transport contribution towards formation of fluvial terraces in the valley of the Letaba river system. This contradicts the pediplane theory of Lowveld planation and emphasizes the role of localized erosion/sedimentation processes.

Accurate DEM-derived streamlines and catchment boundaries are essential for hydrological modelling. Due to the popularity of hydrological parameters derived mainly from free DEMs, it is essential to investigate the accuracy of these parameters. This study compared the spatial accuracy of streamlines and catchment boundaries derived from available digital elevation models in South Africa. Two versions of Stellenbosch University DEMs (SUDEM5 and DEMSA2), the second version of the 30 m advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM2), the 30 and 90 m shuttle radar topography mission (SRTM30 and SRTM90 DEM), and the 90 m Water Research Commission DEM (WRC DEM) were considered. As a reference, a 1 m GEOEYE DEM was generated from GeoEye stereo images. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module. A reference catchment boundary was generated from the GEOEYE DEM and verified during field visits. Reference streamlines were digitised at a scale of 1:10 000 from the 1 m orthorectified GeoEye images. Visual inspection, as well as quantitative measures such as correctness index, mean absolute error, root mean squares error and figure of merit index were used to validate the results. The study affirmed that high resolution (

River systems that support high biodiversity profiles are conservation priorities worldwide. Understanding river ecosystem thresholds to low-flow conditions is important for the conservation of these systems. While climatic variations are likely to impact the streamflow variability of many river courses into the future, understanding specific river flow dynamics with regard to streamflow variability and aquifer baseflow contributions is central to the implementation of protection strategies. While streamflow is a measurable quantity, baseflow has to be estimated or calculated through the incorporation of hydrogeological variables. In this study, the groundwater components within the J2000 rainfall–runoff model were distributed to provide daily baseflow and streamflow estimates needed for reserve determination. The modelling approach was applied to the RAMSAR-listed Verlorenvlei estuarine lake system on the west coast of South Africa, which is under threat due to agricultural expansion and climatic fluctuations. The sub-catchment consists of four main tributaries, Krom Antonies, Hol, Bergvallei and Kruismans. Of these, Krom Antonies was initially presumed the largest baseflow contributor, but was shown to have significant streamflow variability attributed to the highly conductive nature of the Table Mountain Group sandstones and Quaternary sediments. Instead, Bergvallei was identified as the major contributor of baseflow. Hol was the least susceptible to streamflow fluctuations due to the higher baseflow proportion (56 %) as well as the dominance of less conductive Malmesbury shales that underlie it. The estimated flow exceedance probabilities indicated that during the 2008–2017 wet cycle average lake inflows exceeded the average evaporation demand, although yearly rainfall is twice as variable in comparison to the first wet cycle between 1987 and 1996. During the 1997–2007 dry cycle, average lake inflows are exceeded 85 % of the time by the evaporation demand. The exceedance probabilities estimated here suggest that inflows from the four main tributaries are not enough to support Verlorenvlei, with the evaporation demand of the entire lake being met only 35 % of the time. This highlights the importance of low-occurrence events for filling up Verlorenvlei, allowing for regeneration of lake-supported ecosystems. As climate change drives increased temperatures and rainfall variability, the length of dry cycles is likely to increase into the future and result in the lake drying up more frequently. For this reason, it is important to ensure that water resources are not over-allocated during wet cycles, hindering ecosystem regeneration and prolonging the length of these dry cycle conditions.

Estimation and mapping of soil organic carbon storage in three dimensions for large areas is increasingly in demand for climate change reporting, but such estimates at large spatial scales and different soil depths are generally time consuming and expensive. This paper investigates the potential to reduce the number of required soil observations to one point close to the soil surface by applying negative exponential vertical depth functions of soil carbon distribution. This approach assumes that the carbon content at a depth of 1 m, under relatively stable vegetation conditions, can be functionally related to the concentration at the soil surface in the absence of major recent disturbances (e.g. landslides, soil stock piling, etc.). Vertical distribution functions were developed for 38 soil profiles in the Midlands area of KwaZulu-Natal, South Africa, and normalized using the value observed close to the surface, followed by the grouping of soils with similar vertical distribution patterns. Normalization reduced the number of model parameters and enabled the multiplication of the exponential decline curve characteristics with a soil carbon stock value observed at the surface to represent 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 and raster layers were developed for each of the parameters in the integral to map soil carbon storage to 1 m. The exponential decline function adequately fits the soil carbon data and is shown to relate to soil type. The resultant maps predicted soil organic carbon stocks to 1 m in forest plantations and grasslands to vary from 19 to 46 km · m − 2 .

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 digital elevation models (DEMs). Digital geomorphic mapping (DGM) approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units as well as a range of spatial scales, therefore, offer great potential for improved topographic and landscape analysis. One such approach is the mapping of landform elements using the concept of the Geomorphon (geomorphological phonotypes). The output of the geomorphon approach is the stratification of the landscape into ten unique but recognisable landform elements: peak, ridge, shoulder, spur, and slope, hollow, foot slope, valley, depression and flat. Equally appealing is the way the model self-adapts to local topography using a line-of-sight principle enabling better matching of landform elements to computational spatial scale. The purpose of this paper is to observe the effects that different pixel resolution (grain size) and digital elevation model source (DEM) would have on the replication of observed geomorphic spatial patterns and representation of terrain selected parameters within the landscape. This paper provides a comprehensive exploratory assessment of digital terrain representation and relief classification using an automated geomorphometric mapping approach, by evaluating three different digital surface models (SUDEM, SRTM, ASTER GDEM2) and different spatial resolution (30 m & 90 m) for an 11,200 ha catchment in KwaZulu-Natal, South Africa. To test the self-adapting ability of the geomorphon approach under regional conditions, we use 4750 gridded terrain samples to quantitatively analyse how the choice of terrain model and scale influence the extraction, generalisation and representation of digitally-derived terrain attributes such as slope, elevation and terrain unit feature extent. We further show how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities of selected elementary soil association distribution, soil texture and soil depth. We also introduce the results of a Similarity Index used to gauge the degree of recall and precision between the different geomorphic landscape features. Finally, the findings of the regional geomorphon-soil relationships are presented in a readily interpretable and qualitative manner, providing a “quasi-landscape signature” for potential localised geomorphons. The application of the study findings may be beneficial to practitioners looking to align or refine modelled terrain classification approaches with expert perception and formalised heuristic approaches.

Landform elements (LFEs) are commonly used in soil science to demark pedological boundaries and as a first indication of soil spatial variability. A novel LFE classification system known as geomorphons, has been shown to be able to overcome limitations of other automated LFE classifiers. The pattern recognition algorithm classifies the 10 most common LFEs, is computationally efficient, and is robust to changes in scale. However, due to their novelty, research into geomorphons has been limited. This study aimed to stratify the soil landscape through an aggregated geomorphon at the farm-scale (1:25 000) in the Western Cape, South Africa (33.25° S and 18.20° E). Twenty-four geomorphons were created at different resolutions and their association with soil classes were compared. The best fitting geomorphon was aggregated into a 5-unit system corresponding to the South African national resource inventory. The aggregation was based on a decision tree corresponding to soil type. The 5-unit system was evaluated on how well the system stratified soil associations, soil lightness, soil electrical conductivity (EC), soil organic carbon, effective rooting depth (ERD), depth to lithology, gravel, sand, silt, and clay. The prediction potential was compared between the original geomorphon, the aggregated geomorphon, and a manually delineated LFE system. It was found that the aggregated geomorphon stratified all soil attributes except EC. Additionally, the aggregated geomorphon predicted 6 out of 9 soil properties with the greatest accuracy (RMSE). 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. This has implications in resource poor areas where an additional soil survey is not feasible or can be used to aid in the disaggregation of existing soil-terrain datasets.

The water-energy-food nexus has emerged as a useful concept to understand the multiple interdependencies that exist between the water, energy and food sectors. The nexus is an ambitious attempt to work across disciplines and scales to understand the workings of these complex systems. It is, however, criticised for being more of a general framework than a practical methodology because of the vast amount of data it would need to make real-life contributions to sustainable development. We show how the nexus approach, when used within a farm budget model, can transform the problem focus in water governance. By changing the relationship among water, energy and food production of a farm, profitability is significantly changed. The water-energy-food nexus debate is discussed within the context of the South African water sector, particularly the Breede River Catchment. Working from within the farm budget model, we demonstrate the impact of moving from an irrigation canal system that requires electricity for pumping, to a gravity-fed piped irrigation system in the Middle Breede River. The finding is that the water-energy-food nexus has the potential to unlock groundbreaking solutions to complex problems in agricultural water management when used in appropriate modelling systems.Significance: The water-energy-food nexus approach can lead to an entirely new framing of water governance problems and therefore solutions to these problems. The water-energy-food nexus when used in farm budget models can identify ways of altering farm profitability. By addressing the energy cost of farming through an irrigation pipeline system in parts of the Breede Catchment Area, farm profitability could significantly increase. A gravity-fed closed pipeline system in parts of the Breede River can improve water availability and reduce farm and management costs.

River systems that support high biodiversity profiles are conservation priorities world-wide. Understanding river eco-system thresholds to low flow conditions is important for the conservation of these systems. While climatic variations are likely to impact the streamflow variability of many river courses into the future, understanding specific river flow dynamics with regard to streamflow variability and aquifer baseflow contributions are central to the implementation of protection strategies. While streamflow is a measurable quantity, baseflow has to be estimated or calculated through the incorporation of hydrogeological variables. In this study, the groundwater components within the J2000 rainfall/runoff model were distributed to provide daily baseflow and streamflow estimates needed for ecological reserve determination. The modelling approach was applied to the RAMSAR-listed Verlorenvlei estuarine lake system on the west coast of South Africa which is under threat due to agricultural expansion and climatic fluctuations. The sub-catchment consists of four main tributaries, the Krom Antonies, Hol, Bergvallei and Kruismans. Of these, the Krom Antonies tributary was initially presumed the largest baseflow contributor, but was shown to have significant streamflow variability, attributed to the highly conductive nature of the Table Mountain Group sandstones and quaternary sediments. The Bergvallei tributary was instead identified as the major contributor of baseflow. The Hol tributary was the least susceptible to streamflow fluctuations due to the higher baseflow proportion (56 %), as well as the dominance of less conductive Malmesbury shales which underlie this tributary. The estimated flow exceedance probabilities indicated that during the wet cycle (2007–2017) the average inflow supported the evaporative demands if the lake was at 40 % capacity, while during the dry cycle (1997–2008), only 15 % of the lake’s capacity would be met. The exceedance probabilities estimated in this study suggest that inflows from the four main tributaries are not enough to support the lake during dry cycles, with the evaporation demand of the entire lake being met only 38 % of the time. This study highlighted the importance of low occurrence events for filling up the lake, allowing for regeneration of lake supported ecosystems. While the increased length of dry cycles are likely to result in the lake drying up more frequently, it is important to ensure that water resources are not overallocated during wet cycles, hindering ecosystem regeneration and prolonging the length of these dry cycle conditions.

Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1–10, 11–40, and 41–80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.

Water is a critical resource necessary to support social and economic development. Economic growth and, in particular, the growth of urban and peri-urban areas, however results in declining water quality which threatens water-dependent industries. In developing countries this is a particular concern due to the rapid rate of urbanisation and the limited financial resources and technical capabilities to adequately maintain and upgrade wastewater treatment works. This is particularly relevant in catchments with a high dependence on export-orientated agriculture. This study considered water quality risks in the Breede River catchment as an area which experiences significant urban and peri-urban growth, focusing on economic risks associated with declining water quality, estimates of the costs needed to rehabilitate existing wastewater treatment works, and alternative strategies such as the use of artificial wetlands, the rehabilitation and protection of natural wetlands, as well as the clearing of invasive alien plants. A major conclusion is that the financial risk associated with a declining economy and social instability outweighs the costs that will be needed for rehabilitation of existing treatment plants. Together with more pronounced fluctuations in precipitation anticipated with climate change, these risks due to declining water quality are likely to increase in future with continued urban and peri-urban growth.Keywords: economics, development, water quality, pollution risks, waste water treatment works

The consumption of semi-domesticated indigenous vegetables such as Corchorus olitorius is being promoted in South Africa. Presently, cultivation of indigenous vegetables is hampered by the absence of cultivation guidelines due to lack of agronomic research on the various production aspects. The current study evaluated the yield of C. olitorius in response to cattle manure (5 000 kg ha−1) and NPK inorganic fertiliser (500 kg ha−1). Plant height, number of branches, marketable fresh yield and shoot dry mass responded significantly (p < 0.05) to the applied fertilisers. Growing Corchorus without basal fertiliser gave significantly the lowest yield even when top-dressing nitrogen fertiliser was applied. There were significant interactions between the different basal and nitrogen top-dressing fertiliser. It was concluded that the application of basal soil amendments and the interaction of basal and top-dressing resulted in increased marketable yield for C. olitorius. The highest marketable yield for both cattle manure and NPK fertiliser were obtained when these were combined with 200 kg ha−1 lime ammonium nitrate.Keywords: Corchorus olitorius, nitrogen top-dressing, soil amendment, wild vegetables, yield

Soil and stream salinisation is a major environmental problem because it reduces the productivity of landscapes and degrades water quality. The Berg River (South Africa) has been exhibiting a trend of increasing salinity levels, which has primarily been attributed to the manifestation of dryland salinity. Dryland salinity occurs as a result of changes in land use (indigenous vegetation to agriculture and/or pasture), which cause a change in the water and salt balance of the landscape, consequently mobilising stored salts. The quantification of salinity fluxes at the catchment scale is an initial step and integral part of developing dryland salinity mitigation measures. The objective of this study was to quantify the salinity fluxes in the Sandspruit catchment, a tributary catchment of the Berg River. This included the quantification of salt storage, salt input (rainfall) and salt output (in run‑off). The results of the catchment salt balance computations indicate that the Sandspruit catchment is exporting salts, i.e. salt output exceeds salt input, which may have serious implications for downstream water users. Interpolated regolith salt storage generally exhibited increasing storage with decreasing ground elevation. A salinity hotspot was identified in the lower reaches of the catchment. It is envisaged that the data presented in this study may be used to classify the land according to the levels of salinity present; inform land management decisions; and provide a guide and framework for the prioritisation of areas for intervention and the choice and implementation of salinity management options. The data which were generated may also be used to calibrate hydrosalinity models.Online Supplementary Material: Quantifying the catchment salt balance: An important component of salinity assessments

Digital elevation model (DEM) data are elemental in deriving primary topographic attributes which serve as input variables to a variety of hydrologic and geomorphologic studies. There is however still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape characterisation. While elevation data for South Africa are available from several major sources and resolutions: Shuttle Radar Topographic Mission (SRTM), Earth ENV and Stellenbosch University DEM (SUDEM). Limited research has been conducted in a local context comparing the extraction of terrain attributes to high resolution Digital Terrain Data (DTM) such as LiDAR (Light Detection and Ranging) that are becoming increasing available. However, the utility of LiDAR to topographic analyses presents its own challenges in terms of operational-relevant resolution, processing demands and limited spatial coverage. There is a need to quantify the impact that generalisation approaches have on simplifying detailed DEMs and to compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localized topographic variables. In this regional study, we analyse the accuracy on selected local terrain attributes: elevation, slope and topographic wetness index derived from DEMs from varying sources, at different spatial resolutions and using three generalisation algorithms, namely: mean cell aggregation, nearest neighbour and hydrological corrected topo-to-raster. We show that topographic variable extraction is highly dependent on DEM source and generalisation approach and while higher resolution DEMs may represent the “true“ surface more accurately, they do not necessarily offer the best results for all extracted variables. Our results highlight the caveats of selecting DEMs not “fit-for-purpose” for topographic analysis and offer a simple yet effective solution for reconciling the selection of DEMs based on neighbourhood size resolution prior to terrain analyses and topographic feature characterization .

Little is known about the effects of residue burning or retention on nutrient leaching during the inter-rotation of clonal Eucalyptus grown on the sandy soils of subtropical Zululand, South Africa. A study compared zero-tension nutrient leaching through the top metre of soil at depths of 0.15, 0.5 and 1.0 m in an undisturbed crop with adjacent clearfelled areas subjected to residue burning and residue retention. Leaching at 1.0 m in the undisturbed crop was 80% less than at 0.15 m leaching due to high water use of the mature trees. Loss of nutrients past 1.0 m in the undisturbed crop amounted to 7.0, 13.1, 6.6, 15.1 and 60.7 kg ha−1 of nitrogen (N), potassium (K), calcium (Ca), magnesium (Mg) and sodium (Na) over the period between felling and new crop canopy closure (22 months). Annualised 1.0 m leaching amounted to 4.2, 7.8, 4.0, 9.0 and 36.3 kg ha−1 of N, K, Ca, Mg and Na, respectively. Clearfelling induced an increase in N and cation leaching that was apparent five months after clearfelling and persisted for nine months. Leaching loss declined rapidly in the new crop after planting to levels similar to the undisturbed crop by six months of age. Leaching past 1.0 m soil depth under residue retention amounted to 30.6, 132.0, 82.5, 108.7 and 299.1 kg ha−1 of N, K, Ca, Mg and Na, respectively, between felling and canopy closure. Although some weakly significantly differences were found between residue burning and retention, residue burning did not substantially alter leaching past 1 m soil depth. Burning rather induced a large loss of N (121 kg ha−1) through oxidisation, around half the residue N content. Residue retention or burning followed by rapid re-establishment can therefore be practiced to retain most nutrients on this site. Burning of residues should be practiced conservatively on low N soils or be followed by N fertilisation.Keywords: commercial plantation forestry, nutritional sustainability, residue burning, soil solutionSouth African Journal of Plant and Soil 2014, 31(3): 153–162

Land component boundaries often coincide with transitions in environmental land properties such as soil, climate and biology. Image segmentation is an effective method for delineating terrain morphological units from digital elevation models (DEMs). This paper compares the land components derived from five DEMs. The second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were considered. The SRTM DEM and the ASTER GDEM2 were upsampled to 5-m resolution for comparison purposes. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The resulting land components were visually inspected and quantitatively analyzed using the slope gradient standard deviation (SGSD) measure and the mean slope gradient local variance (MSGLV) ratio. The results show that the GEOEYE DEM and SUDEM L2 yielded land components with relatively low SGSD values and that their boundaries often coincide with morphological discontinuities. The GEOEYE DEM produced land components with the highest MSGLV ratio, followed by SUDEM L2, ASTER GDEM2, SRTM DEM and SUDEM L1. Although the land components derived from SRTM DEM and SUDEM L1 were relatively homogeneous internally, their boundaries did not always trace morphological discontinuities. The ASTER GDEM2 failed to incorporate many of the morphological discontinuities in the study area. It is concluded that, although the SRTM DEM is more suitable than the ASTER GDEM2 for generating land components, higher-resolution DEMs such as the GEOEYE DEM and SUDEM L2 are required for delineating meaningful land components.

Cocoa agroforestry is a major landuse type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroforests of southwest Nigeria. Plots where natural forests have been converted to cocoa plantations of ages 1–10 years, 11–40 years and 41–80 years, respectively representing young cocoa plantations (YCP), mature cocoa plantations (MCP) and senescent cocoa plantations (SCP) were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0–20 cm in each plot and analysed in terms of their physical, chemical and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes, decline in soil nutrient, loss of soil organic matter, increase in soil acidity and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, SOM, CEC, available phosphorus, total porosity, pH, and clay). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc; cation exchange capacity; soil organic matter and clay) have the highest power to separate the studied soils into YCP, MCP and SCP. In this way, we hope to have controlled sufficiently for redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, CSDI was developed and used to classify selected cocoa soils into three (3) different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. Finally, the value of the CSDI as an objective index of soil degradation under cocoa agroforests was statistically validated.

A detailed water balance and conceptual flow model was calculated and developed for the Sandspruit catchment for the period 1990 to 2010 on a winter rainfall water-year (1 April - 31 March) basis. The Sandspruit catchment (quaternary catchment G10J) is located in the Western Cape Province of South Africa and is a tributary of the Berg River. It contributes significantly to the salinisation of the mid- to lower-reaches of the Berg River and thus the hydrological drivers need to be quantified and conceptualised in order to develop salinity management strategies. Various components of the water balance, i.e. precipitation, evaporation, streamflow, recharge, etc., were monitored and quantified. In addition, stable environmental isotopes and water balance modelling were used to perform hydrograph separation as well as to quantify components of the water balance. Annual streamflow in the catchment during the period of observation was variable, ranging between 0.026 mm·a-1 and 75.401 mm·a-1. Streamflow volumes also exhibit high variability between water years. Catchment annual rainfall varied between 351 and 655 mm·a-1, averaging at 473 mm·a-1. On average, 6.5% of rainfall was converted to streamflow during the period of observation. Evapotranspiration was found to be the dominant component of the water balance, as it comprises, on average, 94% of precipitation in the catchment. Groundwater recharge was calculated to average at 29 mm·a-1. The water balance model (J2000) performed well during the simulation period with all measures of performance exhibiting acceptable values. Simulation results indicate that streamflow is driven by interflow from the soil horizon (94.68% of streamflow), followed by overland flow (4.92% of streamflow). These results, together with the physiographic conditions evident in the catchment, were used to develop a conceptual flow model. Streamflow is interpreted to be driven by quickflow, i.e. overland flow and interflow, with minimal contribution from groundwater, and is also more dependent on the rainfall distribution in time rather than on the annual volume. The correlation between average annual streamflow and average rainfall was observed to be poor, suggesting that alternative factors, e.g. the spatial distribution of winter wheat, the temporal distribution of rainfall, climatic variables (temperature), etc., exert a greater influence on streamflow. The water balance and conceptual flow model will form the basis for the application of distributed hydrological modelling in the Sandspruit catchment and the development of salinity management strategies. Results from this investigation, e.g. ET estimates, methods to quantify groundwater recharge, hydrograph separation, etc., could potentially be extrapolated to other semi-arid areas.

Wild vegetables are an important commodity in the subsistence farming sector. They are considered to be rich in micronutrients and can therefore be used to overcome inadequate nutrition. However, research on micronutrients in wild vegetables remains limited and sporadic. In this study, we evaluated the responses of two wild vegetables – Corchorus olitorius and Amaranthus cruentus var. Arusha – to micronutrients added to the soil in comparison with a reference crop, Swiss chard (Beta vulgaris var. cicla). Swiss chard concentrated significantly (p A. cruentus > C. olitorius. These results seem to contradict the belief that wild vegetables have an inherent ability to concentrate mineral micronutrients in their tissues.