Journal Articles

Permanent URI for this collection

http://hdl.handle.net/10204/11487

Browse

Now showing 1 - 20 of 48

A review of feedstock diversification for methanol production: From fossil fuels to renewable resources
(2026-03) Reddy, Trishen; Seodigeng, T
Methanol is a critical platform chemical and an increasingly important energy carrier. While global production is currently dominated by fossil-based pathways primarily natural gas (average 65%) and coal (average 35%), there is however an urgent industrial mandate to decarbonize the supply chain. This review provides a rigorous quantitative evaluation of conventional and emerging carbonaceous feedstocks, including biomass, agricultural residues, municipal solid waste, and captured carbon dioxide (CO2). Quantitative analysis reveals that while traditional biogas offers methane concentrations averaging 50–80%, emerging substrates can also provide superior methane yields. A significant contribution of this work is the integration of the latest 2025 findings on semolina processing waste, which demonstrates a high-hydrogen (H2) potential (average 23.0% H2) for biomethanol synthesis. Furthermore, the paper delves into the relevance of process intensification, identifying membrane reactor technology as a primary solution to thermodynamic equilibrium constraints. By addressing critical technical hurdles such as membrane fouling often cited as a major barrier to the 0.2% renewable share in global supply. This review serves as a vital roadmap for industries aiming to transition toward carbon-negative methanol production and enhanced energy resilience.
A systematic literature review: The influence of technical, operational and structural factors on the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa
(2026-07) Chesi, ALC; Cho, Moses A; Cho, MNA; Ramoelo, A
This systematic review paper examines how technical, operational, and structural factors influence the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa. Guided by PRISMA protocols, the study applies a hybrid thematic synthesis across six dimensions: technical, operational, policy and regulatory, governance, social and cultural, and environmental. The findings indicate that digital tools can generate substantial benefits, including yield increases of 10–30% (documented primarily for mobile-based advisory services and precision input management in East African horticulture and West African cocoa value chains) and price gains of 15–25%, with adoption rates of 70–80% in settings characterised by robust infrastructure, strong institutional support, and effective value chain integration. However, these benefits are unevenly distributed and tend to concentrate in “islands of adoption” characterized by robust infrastructure, strong institutional support, and effective value chain integration. While technical (94.9%) and operational (91.5%) factors dominate the literature, their impact is constrained by persistent structural barriers, including weak policy implementation (79.7%), fragmented governance systems (76.3%), and socio-cultural exclusion—such as gender disparities, age-related digital divides, and language misalignment (71.2%). The review identifies five minimum conditions for meaningful adoption: (i) affordable connectivity and access to digital devices; (ii) context-specific digital literacy; (iii) culturally relevant, user-centred design; (iv) robust institutional ecosystems; and (v) enabling policy and financial frameworks. Overall, the findings underscore that digital agriculture adoption is a socio-technical process shaped not only by technological innovation but also by institutional arrangements and user capabilities. Comparative cases, such as Kenya’s Farm.ink and the less successful EZ Farm initiative, further highlight the importance of integrated, context-responsive approaches to ensure that digital agriculture enhances, rather than marginalizes, small-scale farmers.
Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
(2022-03) Duncanson, L; Kellner, JR; Armston, J; Dubayah, R; Minora, DM; Hancock, S; Healey, SP; Patterson, PL; Main, Russell S; Naidoo, Laven
NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
Appraising waste from fed aquaculture animals as a food source for sea cucumbers
(2025-09) Onomu, Abigail J; Suleman, Essa
Nutrient-rich solid waste and effluent water from aquaculture remain a major problem for aquaculture in terms of environmental impact. Integrated multitrophic aquaculture (IMTA), where lower trophic level species consume the waste of fed animals, has been proposed as an alternative for sustainable aquaculture. The use of deposit-feeding sea cucumbers as extractive species in IMTA has attracted research and commercial interest in recent times, due to their low trophic level, high commercial value as food for humans, and ability to ingest sediment containing organic matter, bacteria, protozoa, diatoms, and detritus. Still, the suitability of using faecal waste from fed aquaculture animals as a potential feed requires further studies to ensure not only palatability but also nutritional value, health, and immune responses of the cultured organism. This review discusses various performance indices, such as palatability, ingestion rate, assimilation rate, faecal production rate, feed conversion ratio, growth, and survival of sea cucumber species fed various faecal wastes from different aquaculture animal sources. It further discusses various IMTA applications of sea cucumbers with selected animals. The compatibility, viability and efficacy of sea cucumbers and some aquatic animals in IMTA are summarised.
Aquaculture and biodiversity in global food systems
(2025) Bremner, J; Stentiford, G; Suleman, Essa; Warham, E
A One Health approach will be essential to ensure future food systems can address the trade-offs between interventions needed to produce more food of higher nutritional value, with a smaller ecosystem footprint. This will involve consideration of hazards that link or spread between different supply chains, taking a whole-systems approach. Here, we consider how biodiversity maps to aquaculture across the One Health space. Aquaculture poses ecosystem health risks through the biodiversity impacts of disease spread, non-native introductions, farm-level pollution and habitat damage. Less well recognised, biodiversity also links strongly to animal/plant health, through increased risk of hazards (pathogen and pest diversity can create significant stock health challenges) but also by providing opportunities – genetic diversity underpins stock fitness and mixed-species farming improves resilience. Application of One Health principles will allow aquaculture to grow sustainably, but this needs the buy-in of policymakers, farmers and the scientific community.
Can Sentinel-2-derived spectral indices improve the accuracy of retrieving optically active water quality parameters using machine learning algorithms?
(2026-06) Rathupetsane, EM; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
Study region: This study was conducted in the Cradle of Humankind World Heritage Site (COHWHS), South Africa, an area characterised by interconnected surface waters and sensitive dolomitic aquifers. The region is subject to increasing pressure from land use change, tourism, and nutrient enrichment, making reliable and spatially explicit water quality monitoring essential for protecting its ecological, cultural, and hydrological integrity. Study focus: The study aimed to assess whether Sentinel-2-derived spectral indices improve the retrieval accuracy of optically active water quality parameters, namely Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS). Three input configurations were tested: traditional Landsat-like bands, Sentinel-2 bands, and Sentinel-2 bands combined with spectral indices. These inputs were used within Random Forest and Gaussian Process Regression models to evaluate model performance across wet (summer) and dry (winter) seasons. New hydrological insights for the region: The results show that integrating Sentinel-2 spectral indices substantially improves Chl-a estimation during wet conditions, while TSS retrieval benefits mainly from Sentinel-2 red, red-edge, and SWIR bands. Model performance was strongly seasonal, with reduced accuracy during dry periods due to lower optical variability. The findings provide new insight into how seasonal hydrological conditions and spectral sensitivity influence water quality retrievals in optically complex inland waters of the COHWHS. This approach supports improved regional water quality monitoring and contributes to the protection of connected surface water-groundwater systems in this vulnerable heritage landscape.
Cannabis sativa L. biomass valorization as a strategic bioresource for South Africa’s circular bioeconomy: Integrating biorefinery pathways and green extraction
(2026-07) Motsa, Vuiswa L; Seedat, N; Mekuto, L; Sekoa, P
South Africa’s legalized Cannabis Sativa L. industry generates substantial biomass waste, including post-harvest materials such as stalks, leaves, roots, and seeds, as well as post-extraction residues rich in lignocellulosic components (cellulose, lignin, hemicellulose) and functional compounds (fibers, phenolic compounds, and proteins) which remain largely unvalorized. This review critically and systematically examines valorization pathways for Cannabis sativa L. biomass within the context of a circular bioeconomy, focusing on its potential as a bioresource amid South Africa’s evolving regulatory, agricultural, and industrial landscape. A systematic scoping review of 65 peer-reviewed studies (2018–2025) was conducted using international databases to evaluate biochemical and thermochemical conversion, integrated biorefinery strategies, and green extraction methods such as supercritical CO2 extraction, microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE). Compositional analysis indicates that lignocellulosic fractions from Cannabis sativa L., particularly bast fibers and hurds, are well-suited to cascaded biorefinery applications, enabling the recovery of cannabinoids, carbon-based materials, and bioenergy. However, several challenges persist, including biomass recalcitrance to enzymatic hydrolysis, inconsistent feedstock availability, and the lack of a standardized protocol within South Africa’s regulated environment. Techno-economic assessments (TEA) highlight the need for financial incentives, decentralized infrastructure, and cohesive policies among government entities, including DALRRD, SAHPRA, and DTIC. The review proposes a South African collaboration framework linking policy with research-based process design. Key gaps include the need for life cycle assessment (LCA) and TEA validation at the pilot scale, characterization of landrace cultivars, and development of accessible pretreatment technologies for smallholders.
Canned complementary porridges for infants and young children (6–23 months) based on African indigenous crops; nutritional content, consistency, sensory, and affordability compared to traditional porridges based on maize and finger millet
(2024-11) Løvdal, T; Skaret, J; Drobac, G; Okole, Blessed N; Sone, I; Rosa-Sibakov, N; Varela, P
Child malnutrition is a major health problem in Sub-Saharan Africa. Complementary foods made from African indigenous and locally available raw materials are often low in protein and nutrients. It is, therefore, important to supply complementary foods that are nutritious and affordable, and with an acceptable consistency and taste. The objective of this study was to develop, on a pilot scale, food-to-food fortified, convenient, canned complementary porridges based on blends of African indigenous crops, i.e., orange fleshed sweet potato (OFSP) flour, and leguminous (i.e., cowpea, and Bambara groundnut) and cereal flours (i.e., teff, finger millet, maize, and amaranth), and milk powder. Plant-based, African complementary foods are often lacking in vitamin A, zinc, iron, and energy. Porridge with OFSP on a 32% dry weight (dw) basis achieved recommended levels of vitamin A (530 µg per 100 g dw). Satisfactory energy (431 Kcal per 100 g dw) was obtained by supplementation of vegetable oil. A nutritious, low-cost porridge (costing 0.15 € per 100 g can) that fulfills consistency constraints was obtained by including supplements of zinc and iron salts as ingredients. The solids content and thus protein/energy could be significantly increased using protein fractionated or germinated cowpea flours without compromising on viscosity. The sensory profile was characterised by more intense vegetable, leguminous, and malty flavours as compared to traditional reference porridges.
Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor
(2025-07) Ngamile, S; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
Introduction: Water quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS. Methods: High-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions. Results: The results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. Specifically, Model 2 achieved an R2 of 0.88 and an RMSE of 1.37 for DO, while Model 1 achieved an R2 of 0.63 and an RMSE of 291.48 for EC. For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions using Model 2 (R2p = 0.55; RMSE = 118.19). Due to the over-pixelation of other smaller water bodies within the COHWHS in Sentinel-2 imagery, Cradlemoon Lake was selected to show distinct seasonal (high- and low-flow) and spatial variations in optically and non-optically active water quality parameters. Discussion: Variations in the results were influenced by runoff dynamics and upstream pollution: lower Temperatures and suspended solids under low-flow conditions increased DO concentrations, whereas higher suspended solid concentrations under high-flow conditions likely reduced light penetration, resulting in lower spectral reflectance and chlorophyll-a levels. These findings highlight the potential of Sentinel-2 MSI data and machine learning models for monitoring dynamic water quality variations in freshwater ecosystems.
Dammarane-type triterpenoids with anti-cancer activity from the leaves of Cleome gynandra
(2021-06) Mzondo, Buntubonke; Dlamini, Nomusa; Malan, FP; Labuschagne, Philip W; Bovilla, VR; Madhunapantula, SV; Maharaj, V
Three dammarane-type triterpernoids including two new ones, cleogynones A and B (1 and 2), were isolated from the leaves of Cleome gynandra. The structures of the new triterpenoids were elucidated by spectroscopic data analysis and confirmed by single crystal X-ray crystallography. All three compounds showed moderate cytotoxicity against breast cancer (MDA-MB-468), cleogynone B (2) and compound (3) further showed cytotoxicity against colorectal cancer (HCT-116 & HCT-15). Cleogynone B was also moderately active against lung cancer (A549).
Defining brightness-shape-moisture soil parameters for Southern Africa from Hyperion Hyperspectral Imagery
(2024-08) Bonnet, Wessel J; Cho, Moses A; Masemola, Cecilia R
An effective methodology is needed to simulate soil spectra on a large scale. The brightness-shape-moisture (BSM) radiative transfer model (RTM) is used to simulate soil spectra for different semiarid and arid biomes within Southern Africa based on hyperspectral imagery obtained from the Hyperion satellite. Such simulation based on hyperspectral data is especially relevant in light of newer hyperspectral missions, such as Prisma providing ongoing data streams. In this particular study, Hyperion’s data are cleaned using the SUREHYP procedure, segmented using the simple linear iterative clustering (SLIC) algorithm, filtered to exclude photosynthetic and senescent vegetation, and parameterized via a Hyperion band calibrated BSM model lookup table to obtain simulation parameter distributions for different biomes. This provides a means to better simulate soil spectra using each biome’s obtained parameter distributions in the BSM forward model.
Determining the sensitivity of Sentinel-2 bands to optically and non-optically active water quality parameters under high- and low-flow conditions in the cradle of Humankind World Heritage Site, South Africa
(2026-04) Ngamile, S; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
Access to clean water is a global challenge, particularly in the Global South, where scarcity and pollution threaten human and ecosystem health. Traditional water quality monitoring is limited in spatial and temporal coverage, while satellite remote sensing provides an alternative for assessing optically and non-optically active water quality parameters (WQPs). This study evaluated the sensitivity of Sentinel-2 MSI bands to WQPs in the Cradle of the Humankind World Heritage Site, South Africa, an area impacted by acid mine drainage. In situ measurements of DO, EC, pH, temperature, chlorophyll-a, and suspended solids were collected from 40 sites under high- and low-flow conditions. The Sentinel-2 images were atmospherically corrected, resampled to 10 m, and the values were extracted to the sampling points. Using multiple linear regression, random forest, and partial dependence plots methods, results showed that DO strongly influenced most bands, while suspended solids dominated the optically active band responses.
Determining the value of different wavelength ranges of non-imaging hyperspectral reflectance to estimate carotenoid content using the PROSPECT-5 model
(2024-12) Sibiya, Bongokuhle; Cho, Moses A; Mutanga, O; Ondidi, J; Masemola, Cecilia R; Bonnet, Wessel J
Carotenoids are important plant attributes offering valuable insights into the physiological condition of vegetation and serve as essential indicators for early identification of plant stress. Generally, carotenoids can be extracted using radiative transfer model (RTM) remote sensing techniques like PROSPECT that utilize the entire spectral domain (400–2500 nm) to retrieve carotenoid information. However, such inversions suffer from ill-posed due to model uncertainties. Literature suggests that selecting appropriate bands improves the RTM inversion. Hence, this study proposed a wavelength selection approach using various regions in the visible portion of the electromagnetic spectrum and bands selected by the random forest algorithm to estimate carotenoids using the PROSPECT-5 model. This study utilized three distinct datasets – savanna, tropical forest, and a combination of two. The green spectral region demonstrated the strongest performance in the tropical forest dataset (R2 = 0.90, RMSE = 0.71) than the savanna (R2 = 0.70, RMSE = 1.19) and combined (R2 = 0.72, RMSE = 1.11) datasets, respectively. The bands (green, yellow, and red-edge) selected by the random forest model produced the highest accuracy in the savanna dataset (R2 = 0.84, RMSE = 0.99), followed by combined (R2 = 0.80, RMSE = 1.20) and tropical forest (R2 = 0.78, RMSE = 1.33), respectively. Lastly, the visible region demonstrated strong performance in the tropical forest (R2 = 0.84, RMSE = 0.85), followed by combined datasets (R2 = 0.72, RMSE = 1.15) and savanna (R2 = 0.68, RMSE = 1.27), respectively. The findings suggest that carotenoid retrieval should be limited to the visible portions of the spectrum as it exhibited strong performance in estimating carotenoid content across the savanna, tropical forest, and combined datasets.
Determining the wetland-dryland boundary of depressions using littoral gradient analysis of soil edaphic factors
(2021-08) Nondlazi, Basanda X; Cho, Moses A; Van Deventer, Heidi; Sieben, EJ
Depressional wetlands are highly vulnerable to changes in land surface temperature and rainfall but little is known about their responses to future climate change. This study assessed the variation in edaphic factors between wetlands and along their littoral gradients to detect the boundary between the endorheic wetlands and upland zones. A sample of 202 paired measurements of three edaphic factors were collected (Soil Moisture Content – SMC-g/g, Bulk Density – BD-g/cm 3 and Salinity as Electrical Conductivity – EC-dS/m) in 10 m plots along 14 belt transects in eight representative wetlands in the Mpumalanga Lake District, South Africa. In general, there were significant differences between the eight wetlands for SMC and BD but not for EC.SMC and BD generally showed negative trends along the littoral gradients. The trends occurred over short distances, ranging from 30 to 70 m, reflecting the extent of the wetlands. Understanding of the spatial variation of edaphic factors helps in the management and monitoring of depressional wetlands under a changing climate. In addition, the study showed that the current wetland buffer zone stipulated in local legislation was too narrow and recommended that this be extended to 100 m.
Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets
(2021-07) Naidoo, Laven; Main, Russell S; Cho, Moses A; Madonsela, Sabelo; Majozi, Nobuhle, P
Sentinel-1 and Sentinel-2 have provided consistent hyper-temporal information (5–7 days or earlier) at high spatial resolutions (10m) on biophysical composition, structural and physiological conditions of crops in a variety of environments. Unmanned aerial vehicles (UAVs) can provide sufficient calibration and validation data for model upscaling and regional extrapolation. Of the numerous maize crop parameters which require regular and accurate modelling, maize above ground biomass (AGB) is important for yield estimates. The aim of this study was to evaluate the Random Forest modelling performance of Sentinel 1 SAR C-band and Sentinel 2 multispectral imagery for maize AGB estimation whilst utilising UAV-derived maize AGB for model upscaling. Results illustrated that Sentinel 2 reflectance bands predicted more accurate estimates of maize AGB than the VV and VH polarisation bands of Sentinel 1 (R2 = 0.91; RMSE = 355.11g/m 2 ; rRMSE = 21.28% versus R2 = 0.31; RMSE = 974.72g/m 2 ; rRMSE = 59.04%).
Ethnomedicinal and phytochemical properties of sesquiterpene lactones from Dicoma (Asteraceae) and their anticancer pharmacological activities: A review
(2021-09) Mangisa, Mandisa; Peter, Xolani K; Khosa, Mbokota; Fouche, Gerda; Nthambeleni, Rudzani; Senabe, Jeremiah V; Tarirai, C; Tembu, VJ
Dicoma species belonging to the Asteraceae family are commonly utilized as traditional medicine in Southern Africa. Dicoma anomala, Dicoma capensis, Dicoma schinzii and Dicoma zheyeri are the most common ethnomedicinal plant species used in Southern Africa. The plant species of Dicoma genus are identified as the main source of sesquiterpene lactones. Dicoma species are associated with pharmacological properties such as antiviral, antibacterial, antihelminthic, antispasmodic antiplasmodial, as analgesic, antiinflammatory, anticancer, and wound healing properties. The plant species of Dicoma genus are identified as the main source of sesquiterpene lactones. In this review, the authors report the ethnomedicinal and phytochemical properties, and pharmacology of sesquiterpene lactones from the genus Dicoma from 1978 to 2020. There are over eighty (80) reported sesquiterpene lactones isolated from Dicoma species including, germacronolides, eudesmanolides, melampolides, guaianolides and pseudoguaianolides. Sesquiterpene lactones possess antimalarial, anticancer and antiinflammation activities due to their structural diversity. The diagnostic search on phytochemistry of sesquiterpene lactones from Dicoma carried out in the 70’s has limited pharmacological screening activities; hence these may need to be revisited and explored. Furthermore, the literature search conducted in this review showed that out of the 35 Dicoma species, seven species were investigated, and their medicinal uses, pharmacology and photochemistry reported. The recommendation drawn is that Dicoma species that are not investigated and not fully exploited should be studied for their phytochemicals and efficacy. The information compiled in this review on the pharmacological, phytochemistry and ethnomedicinal activities of genus Dicoma was obtained from relevant literature sources, including books, book chapters, websites, theses, reviews and research articles from databases such as Web of Science, Scopus, Science Direct, BioMed Central, Springer link, PubMed, and Google Scholar.
Evaluation of minerals, trace elements, and antinutritional factors in selected legume fodder species (Fabaceae) with the potential to improve cattle nutrition and gastrointestinal health
(2024-08) Lebeloane, MM; Famuyide, Ibukun M; Elgorashi, EE; McGaw, LJ; Kgosana, KG
The study aimed to investigate the nutritional composition, trace elements and anti-nutritional factors of fodder species belonging to the family Fabaceae potentially used as an alternative feed for cattle. The proximate composition, particularly dry matter, moisture, fats, crude proteins (CP), carbohydrates, crude fibre (CF), and neutral detergent fiber (NDF), were analysed, thereby, nonfibre carbohydrate (NFC) and gross energy (GE) were calculated. Thirty-three trace elements were determined from chemically digested dried plant material using ICP-MS (Inductively Coupled Plasma Mass Spectrometry) and ICP-OES (ICP-Optical Emission Spectrophotometry). The tannin levels, a known antinutritional factor, were estimated using Folin–Ciocalteu method. The methods were validated by the relative standard deviation (RSD) values and acceptable recovery percentage, linearity, limit of quantification (LoQ), and limit of detection (LoD). The proximate composition analysis estimated levels of dry matter (> 90 %), ash (3.77–26.98 %), crude proteins (8.22–22.19 %), carbohydrates (54.00–86.79 %), crude fibre (10.54–40.10 %), NDF (22.26–59.20 %) and GE (< 100 Kcal kg−1 DM) in leguminous species. Essential elements were detected in recommended levels including Zn (21.20–50.30 mg/kg), Co (0.06–0.045 mg kg), Cr (0.5–5.08 mg kg−1), Mn (9.02–197 mg kg−1), Mg (0.10–0.52 mg kg−1), Fe (42.40–812 mg kg−1) and Na (72.00–1721 mg kg−1). The concentration of toxic elements was below critical levels and tannin occurred at a safe level (< 50 mgTAE kg−1) for ruminant consumption. Therefore, the selected fodder can effectively contribute to cattle dietary requirements for smallscale farmers in Onderstepoort, Gauteng Province, South Africa.
Exploring the utility of a multivariate soil hyperspectral reflectance model for estimating soil moisture using sentinel-2 Data
(2026-05) Atyosi, Yonwaba; Cho, Moses A; Majozi, Nobuhle P; Bonnet, Wessel J; Ramoelo, A
Accurate and spatially transferable estimation of soil moisture is critical for sustainable agriculture, water resource management, and drought monitoring, particularly in data-scarce semiarid regions. However, soil moisture retrieval from optical satellite data remains challenging due to heterogeneous soil conditions and limited model generalizability, especially when interactions between soil moisture and clay content are neglected. This study presents a physically informed, simulation-based multivariate framework for estimating soil moisture from freely available Sentinel-2 multispectral imagery that explicitly accounts for soil clay content and its interaction with moisture. A Monte Carlo look-up table comprising 100,000 synthetic soil reflectance spectra was generated under varying soil moisture and clay conditions and resampled to Sentinel-2 spectral bands. Soil moisture-sensitive spectral band combinations, ratios, and newly developed soil moisture indices were derived and used to train machine learning models, which were evaluated using group-aware cross-validation to assess spatial robustness and transferability. Model application across multiple agricultural sites in South Africa’s Eastern Cape and Limpopo provinces, regions geographically distinct from calibration areas and spanning contrasting ecological and climatic conditions demonstrated high predictive performance (R² up to 0.91; RMSE as low as 0.71) and strong spatial transferability. The results indicate that explicitly integrating soil property interactions within a synthetic spectral modeling framework substantially improves Sentinel-2–based soil moisture estimation. The proposed approach advances operational optical remote sensing of soil moisture by bridging physically consistent spectral simulations and scalable multispectral observations, providing a transferable methodology for precision irrigation, drought early warning, and sustainable agricultural water management in semiarid environments.
From species to pixels: Monitoring rangeland quality & productivity by leveraging the NDVI-RCI relationship
(2025-01) Nondlazi, Basanda X; Cho, Moses A; Mantlana, Khanyisa B; Ramoelo, A
Grasslands are highly vulnerable to climate and changes in grazing management, yet little is known about the national rangeland response to long-term (>18 years) grazing management that may confound climate effects. This study assessed the correlation between Normalized Difference Vegetation Index (NDVI), i.e., productivity and Rangeland Condition Index (RCI) i.e., quality and predicted historical grazing management (26 years) using Ecological Index Method (EIM) analysis of 72 relevés in the Highland Sourveld (HSV). Relationships between 150 NDVI and 72 RCI samples showed a rate of 0.125 change in NDVI for every 12.5% change in RCI. In 1983, the HSV’s rangeland carrying capacity (RCC) ranged from 2.0 - 2.2 ha/AU/yr (land required to support one mature bovine for 1 year), with an NDVI of 0.43, like the benchmark. site. By 2009, the RCC decreased to 3.2 ha/AU/yr, with NDVI <0.30. Selective overgrazing, reduced RCC by increasing Increaser II species and reducing Decreaser species presence. Findings suggest combining NDVI and RCI is more effective than using either alone. Integrating remote sensing with traditional ecological data (Ecological Remote Sensing - eRS) improves our understanding of rangeland vulnarability, thus, ideal for permanent monitoring of public rangelands in South Africa.
Geospatial analysis of meteorological drought impact on Southern Africa biomes
(2021-01) Cho, Moses A; Chirwa, PW; Marumbwa, FM
Within Southern African biomes, droughts are recurrent with devastating impacts on ecological, economic, and human wellbeing. In this context, understanding the drought impact on vegetation is of extreme importance. However, information on drought impact on natural vegetation at the biome level is scanty and remains poorly understood. Most studies of drought impact on vegetation have largely focussed on crops. The few existing studies on natural vegetation are based on experiments and field measurements at individual tree level which are not representative of biomes. In this study, we mapped the spatial extent and severity of drought using the Standardized Precipitation Evapotranspiration Index (SPEI) and then quantified the drought impact on Southern African biomes using the Vegetation Condition Index (VCI) for the period 1998 to 2017. To compare drought impact across the biomes, we computed the percentage area of the biome with seasonal VCI <30. The drought trend for each biome was computed for each pixel using a linear regression model in R software using the seasonal VCI images from 1998 to 2017. Our result showed that extreme drought impact on vegetation was mainly confined to the southwestern biomes (i.e. the Nama karoo and desert biomes) with most drought occurring during the first half of the season. We also observed an increasing trend of VCI (1998 to 2017) across all biomes and this increasing VCI trend might be explained by woody encroachment which is prevalent in the Savannah and Grassland biomes. The results of this study provide baseline information on drought hotspots.

Browse

Browsing Journal Articles by browse.metadata.cluster "Advanced Agriculture & Food"

Results Per Page

Sort Options