Council for Scientific and Industrial Research

The effectiveness of combining biological agents, comprising Telenomus remus, Beauveria bassiana, and Metarhabditis rainai, against the fall armyworm (Spodoptera frugiperda J. E. Smith) in maize fields across two ecological zones in Ghana was evaluated. The four treatments tested were sole T. remus, T. remus + M. rainai, T. remus + B. bassiana, and T. remus + M. rainai + B. bassiana, with the entomopathogens applied 24 h after T. remus release. Parameters collected included parasitized egg masses and larval mortality of S. frugiperda, maize leaf and cob damage, re-infestation after treatment and yield. Egg parasitism rates (43.24–61.53%) were similar across treatments, indicating the compatibility of the parasitoid and entomopathogens. However, larval mortality was significantly higher (75.00–87.65%) in combined treatments compared to sole T. remus (4.03–7.89%) with increased yield. Leaf damage varied, with the highest levels recorded in plots treated with T. remus, while cob damage remained consistently low across treatments. Ecological zone type influenced egg parasitism, re-infestation, and yield. Incorporating T. remus with these entomopathogens into Integrated Pest Management (IPM) strategies as one of the components significantly reduced fall armyworm damage in Ghana.

Abstract Crop and varietal diversification are essential for African smallholder farmers to adapt to the complex and unprecedented challenges posed by climate change. Although African genebanks maintain seed collections of numerous crops, with thousands of varieties collected from their countries’ farmers, the direct use of these collections by farmers is very limited. Five African national genebanks therefore explored ways to strengthen farmers’ access to and use of these collections through a longer-term collaborative process. The genebanks and their partners engaged with ‘Germplasm User Groups’ as a basis for facilitating sustained joint learning with farmers for use of conserved germplasm. The structure of these groups and the methods they used for identifying and testing germplasm accessions, although differing by country context, all enabled a diversity of farmers to learn about a wide range of germplasm under relevant field conditions. The large number of accessions that farmers selected, their diverse advantages and the requests by numerous farmer groups to continue exploring additional crops and varieties indicated the usefulness of these approaches. These experiences revealed the feasibility and unique roles and opportunities for national genebanks to facilitate farmers’ direct use of the diversity conserved in their crop collections. National genebanks thus have unique responsibilities for adapting their operating procedures and partnering with research and development practitioners to facilitate farmers’ discovery and use of their conserved crop diversity.

Abstract Conventional approaches for metal-organic framework synthesis have shortcomings that may be circumvented through unconventional approaches, which offer fast reactions and scale-up opportunities. This work reports the successful preparation of iron-based MIL-101 utilizing unconventional precursors: iron extracted from acid mine water and terephthalic acid derived from waste polyethylene terephthalate. Three methods were employed including conventional solvothermal, unconventional microwave-assisted and unconventional sonochemical-assisted synthesis and the resulting Fe-MIL-101 was evaluated as an adsorbent for hydrogen. Unconventional synthesis drastically reduced synthesis duration from 20 h (solvothermal) to 4 h (sonochemical-assisted) and 2 h (microwave-assisted). The prepared materials displayed comparable surface areas, with the sample from microwave-assisted synthesis exhibiting a surface area of 512 m 2 g − 1 while that from sonochemical and conventional solvothermal methods exhibited surface areas of 702 and 717 m 2 g − 1 , respectively. Correspondingly, the highest hydrogen uptake (1.03 wt% at 1 bar, 77 K) was attained for the solvothermal Fe-MIL-101. The feasibility of synthesizing Fe-MIL-101 holistically from these specific waste sources using unconventional methods, with an advantage of shortened reaction times, has been proven in this study. Graphical Abstract A drive towards sustainable synthesis of metal-organic frameworks as potential gas storage materials.

South Africa is developing a network of flux observations to support studies on coupled ecological and social systems. Currently, eight flux measurement sites are operated by the South African Environmental Observation Network (SAEON) and the Council for Scientific and Industrial Research (CSIR). Data from these observations are openly available online in near real-time, following standardised quality control and FAIR data principles. Each of the flux observations forms part of a landscape-scale research infrastructure (RI) co-designed with researchers, land managers and policy stakeholders to ensure long-term relevance for both science and decision making. Landscape RI sites include suites of standard continuous meteorological, hydrological, and repeated manual measurements covering biodiversity, productivity, ecosystem condition, and ecosystem service provision and use. These landscapes encompass a diverse suite of biomes across South Africa, including arid shrubland, subtropical savanna, tropical grassland, high-altitude mesic grassland, Afromontane forests, and fynbos, with a range of land-use types and tenure systems represented. These long-term research infrastructure platforms have been utilised in numerous national and international projects supporting scientific development and informed societal decision-making. This presentation will focus on infrastructure co-design and development with broad stakeholder groups and showcase results highlighting activities in the RI, outputs, lessons learned, and future priorities

Displacement-based early warning of deep-seated landslides requires displacement forecasts that are not only accurate but also physically consistent under rapidly changing hydrological conditions. Variations in rainfall infiltration, pore-water pressure, and subsurface moisture dynamics can modify the effective stress state and creep rates, leading to complex and often nonlinear displacement responses. Despite extensive modeling efforts, reliable and physically interpretable displacement forecasting remains challenging. Data-driven models often lack process consistency, while physics-based approaches are limited by uncertain parameters and simplified assumptions when applied to real-world conditions. In this study, we develop a physics-informed machine learning model that integrates physical process constraints with landslide monitoring data. A hydrologically driven deformation relationship dominated by seepage-related effects is incorporated as a model constraint to guide the prediction of displacement. The model is trained using cumulative displacement observations and hydrological forcing from an IoT-enabled in situ monitoring system deployed on a landslide, and is subsequently applied to forecast displacement over unseen periods. Results show that embedding physical constraints improves the temporal generalization and physical plausibility of predicted displacement trajectories, particularly during hydrologically triggered acceleration phases. The inferred model parameters exhibit physically interpretable and internally consistent behavior, indicating that dominant hydrological controls on deformation are captured. This framework improves both robust displacement forecasting and physical interpretability, thereby supporting the development of operational landslide early-warning systems.

Tracking meanders of the Agulhas Current and their associated cyclonic eddies is essential for understanding episodic upwelling along the southeast coast of South Africa, particularly near Port Alfred where the inshore jet interacts strongly with the shelf. We developed a new temperature-gradient-based current-core tracker, the Port Alfred Current Tracker (PACT) and applied it alongside a sea surface height-derived tracker (an adapted version of Location of the Agulhas Current Core and Edges, LACCE) and the PY-Eddy-Tracker algorithm to detect meander events and associated cyclonic activity using 22 years of high-resolution CROCO output (WOES36). Using these methods, we detected 2.3 large meander events per year from PACT, 1.7 events per year from adapted LACCE, 0.6 events per year from eddy amplitude, 1 event per year from eddy area, and 1.3 events from eddy radius. Upon inspection of the link between Agulhas Current meanders and upwelling in the Port Alfred region, long-term analysis showed that most upwelling events occur in the absence of a meander and it is weakly correlated with meander or cyclonic eddy activity. Nonetheless, individual case studies reveal that long-lived meanders or cyclonic eddies can locally enhance upwelling. These results indicate that Agulhas Current meanders arise from a combination of coherent eddies and shifts in the core jet but upwelling at Port Alfred is primarily driven by strong, smooth current flow rather than meander-induced variability. Combining current-core and eddy-tracking diagnostics provides a more complete understanding of both meandering and upwelling dynamics along the southeast African margin.

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R 2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R 2 = 0.61, with typical point differences &amp;lt;0.10 m ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"> <mml:mrow> <mml:mo>∼</mml:mo> </mml:mrow> </mml:math> 5% of the 2 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely ( R 2 = 0.86). A <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"> <mml:mrow> <mml:mo>∼</mml:mo> </mml:mrow> </mml:math> 26 min phase lag, determined using a lag-correlation analysis, reduces the TPXO–SWOT RMSE from 0.18 m to 0.11 m, indicating that phase differences explain some of the mismatch, with remaining differences likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

Efficient fertilizer application is vital for enhancing maize production and profitability in Sub-Saharan Africa, where soil fertility varies widely across regions. This study aimed to develop a machine learning approach for generating site-specific fertilizer recommendations for maize production in Ghana and to evaluate its performance against conventional and semi-mechanistic approaches. A random forest machine learning model was trained on 482 maize yield experiments, consisting of 3136 yield observations collected from 1991 to 2020, to predict maize yield response to different fertilizer rates. The model incorporated multiple explanatory variables, including soil properties, climate conditions, and management practices, to generate fertilizer response curves from which fertilizer recommendations were derived for 14 sites across three agro-ecological zones in Ghana where field validation experiments were conducted. On these sites, the recommendations were compared with recommendations derived from the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS), Conventional Fertilizer Dose Response (CFDR), and Updated Conventional Fertilizer Dose Response (UCFDR) approaches and validated through field experiments. The machine learning approach generally recommended lower rates of phosphorus and potassium than the other approaches, while nitrogen recommendations were comparable. In the Guinea Savanna zone, the recommendations from the machine learning approach outperformed those from the other approaches, producing higher mean yields for three out of the four sites in the zone. In the Forest-Savanna Transition (FST) zone, the machine learning model recommendations led to higher mean yields at four sites, while the approaches based on QUEFTS and UCFDR performed best at two other sites. In the Semi-deciduous Forest zone, the recommendations of the QUEFTS approach resulted in the highest mean yields at three sites, and CFDR at one site. Despite high input prices during the period of experimentation, the machine learning approach-based recommendations demonstrated higher net profit margins in the FST zone, suggesting cost-effectiveness in this zone. These findings indicate that site-specific fertilizer recommendations are more efficient than blanket recommendations and that machine learning approaches offer a promising and innovative approach for generating cost-effective, site-specific fertilizer recommendations in tropical climates. • ML and QUEFTS approaches provided site-specific fertilizer recommendations. • ML and QUEFTS outperformed conventional fertilizer recommendation approaches. • CFDR and UCFDR were constrained by generalized, non-site-specific recommendations. • Machine learning supports sustainable fertilizer use.