91¿´Æ¬Íø

Industry:

Sugar Cane Processing

Industrial Representative:

Richard Loubser, Sugar Milling Research Institute NPC,

c/o University of KwaZulu-Natal, Durban.

Moderator:

Student Moderators:

Problem Statement:

In South Africa, the diffuser is a popular method for extracting sugar from sugar cane. It is a counter current washing machine with shredded cane added at one end and imbibition added at the other end of a chain (ladder) conveyor system. The water percolates through the cane bed and is pumped forward and reintroduced to the cane bed closer to the end where the cane is added. This is how the counter current effect is achieved.

The system relies on the free percolation of the water or juice through the cane bed. It was found during the development of the cane diffuser, however, that fine particles tend to form a layer in the bed which retards the free flow of the juice through the bed.

To ensure that the flow through the bed is restored, lifting screws are used. These are arranged across the diffuser bed as shown in Figure 1.

Figure 1. Lifting screws in a diffuser

Most lifting screws rotate at 40 revs per minute and are of a similar design regardless of make of diffuser.

As the cane passes the screws, they churn up the cane to disrupt any layers of fine particles that may have formed on the cane bed. Adjacent screws turn in opposite directions to enhance the effect of the screws.

The mounting of the lifting screw is designed with a shear pin that fails to protect the screw itself from failure. Given the design of the screw, it is possible to determine the strength of the pin.

Many factories are operating with lifting screws that are not working because the shear pin has failed. There is very little in the literature to guide the design of the lifting screw to handle the forces experienced in churning up the bed.

It is tempting to replace the pin with stronger components. This, however, has disastrous consequences. It would be far more acceptable if the lifting screws could be designed according to the maximum forces that would be experienced.

It would be useful if a method for estimating the load on the lifting screw could be determined. A typical cane preparation is shown in Figure 2. It consists of long strands with lengths in the order of 7 cm and thickness of a few millimetres. In between the long strands are shorter, thinner strands and then there is the fines and pith component. To churn this up, it is required that the bed is torn apart as it passes the lifting screws.

Figure 2. Prepared cane

Anecdotal evidence indicates that the effort required by the lifting screws reduces with increasing the amount of juice held up in the bed. Does the lubricating and buoyancy effect outweigh the mass effect? Is there a balance point?

The desired output from this study would be a way of estimating the worst-case design load for the lifting screw. The model would need to estimate how factors such as strand length, packing density and juice hold-up would interact and influence the design load.

Supporting Material

Forces on a lifting screw - Problem outline

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A dream to come true, or irreversible damage about to hit Coastal-Marine Tourism & Biodiversity and threaten Climate Change Resilience

Industry: Tourism Sector (Coastal & Marine Tourism), Robotics

Industry Representative: Dr Lombuso Precious Shabalala,

Department of Applied Management, UNISA

Student Moderators:

Problem Statement:

The sea or ocean hosts a unique, largely unexplored biodiversity that is incredibly fragile. The impacts of deep-sea mining could be irreversible, resulting in habitat destruction, species extinction, climate disruption, and disruption of oceanic food chains, as well as an impact on tourism activities.  Fast-advancing technologies have now granted access to even the deepest parts of the ocean, fuelling interest in commercial exploration of deep-sea resources, such as metals and rare earth elements. Deep-sea mining (DSM) activities are currently planned in the economic zones of several national jurisdictions, particularly Pacific Island States, and in areas beyond national jurisdiction (Kaikkonen & van Putten, 2021). Ma et al. (2022) define Deep-sea mining as the utilisation of hydrodynamic or mechanical methods to transport mineral ores from the seabed to the ocean surface and then transport the ores to land-based processing plants by ship. The scholars further note that, despite the vast amount of research and development that has made significant progress, there are still many obstacles to its industrial development activities, which include concerns about environmental pollution and sustainable development issues, regardless of the employment of technological advancements such as ore exploration, robotics, and hydrodynamic lifting (See Figure 1). 

Figure 1:  Schematic diagram of the mining system. Source: Yao et al., 2025.

Additionally, these activities have garnered attention from governments, companies, and scientific research institutions.  Figure 2 presents the multifaceted impacts of plume generation. A deep-sea plume is a cloud of fine sediment suspended in the water, primarily caused by human activities like deep-sea mining or natural events like storms. These plumes can spread over large areas, reduce water clarity, and potentially smother or poison marine life by burying them or releasing toxic compounds.

Figure 2:  Schematic of plume impacts. Source: Yao et al., 2025.

Coastal and Marine tourism are affected by deep-sea mining.  For instance, a study conducted in Fiji found that pristine coral reefs possess a tremendous potential for contributing to tourism and economic development (Folkersen et al., 2018).  In addition, the study revealed that its tourism economy relies heavily on diving and coastal activities. Unfortunately, despite the importance of coral reefs to the Fijian tourism sector, the Fijian Government has granted exploration licenses to mining companies to assess the viability of deep-sea mining (DSM). Understanding divers’ perceptions of coral reefs and environmental issues is, therefore, paramount to sustaining the tourism sector. There is concern that DSM may negatively impact reef-related tourism due to tourists’ perception that DSM activities degrade Fiji's coral reefs. A study by Kaikkonen and van Putten (2021) confirms that economic development and human activities in the ocean are accelerating, with maritime activities at the forefront of numerous government, research, and industry initiatives aimed at expanding the ‘Blue Economy’. The deep sea is being promoted as the new frontier for resource extraction (Kaikkonen & van Putten, 2021).

Tourism is one of the top sectors that contributes to Gross Domestic Product and creates jobs in South Africa and globally. A call to halt deep-sea mining in Africa (Greenpeace, 2025) before it begins has already garnered over 4,822,507 signatures (on petitions) opposing deep-sea mining. These individuals argue that the deep sea is a treasure trove of biodiversity and home to countless wonders and possibilities. It is also one of our best allies against climate change. Kaikkonen and van Putten (2021) concur regarding the deep sea as the largest ecosystem on the planet.

The problem to investigate

It is understood that oceans produce approximately 50% of the oxygen we breathe, absorb around 25% of global COâ‚‚ emissions, and capture approximately 90% of the excess heat generated by those emissions (Greenpeace Africa, 2025). This suggests that oceans are our first line of defence against climate change. Destabilising such could lead to a disaster against humanity.

• Currently, there is no guarantee of a sustainable mining system that optimises the conservation of life forms (biodiversity) and tourism activities, including climate change resilience.
• Secondly, there is no assurance that benefits yielded from the blue economy will reach local communities and contribute to genuine sustainable development.

Building on the previously presented Figures 1 and 2, this problem seeks to develop:

1. Mathematical models for the creation and spread of plumes in deep-sea mining.
2. A mathematical model that supports a sustainable deep-sea mining system or method, which, upon its implementation, could optimise the conservation of life forms and tourism activities and contribute to climate change resilience.

References

Folkersen, M.V., Fleming, C.M. and Hasan, S., 2018. Deep-sea mining's future effects on Fiji's tourism industry: A contingent behaviour study. Marine Policy, 96, pp.81-89.

Greenpeace, 2025. Stop deep sea mining before it starts. Available from:   . Access Date:10 Nov 2025.

Kaikkonen, L. and van Putten, I., 2021. We may not know much about the deep sea, but do we care about mining it?. People and Nature, 3(4), pp.843-860.

Ma, W., Zhang, K., Du, Y., Liu, X. and Shen, Y., 2022. Status of Sustainable Development of Deep-Sea Mining Activities. Journal of Marine Science and Engineering, 10(10), p.1508.

Yao, W., Tian, C., Teng, Y., Diao, F., Du, X., Gu, P. and Zhou, W., 2025. Development of deep-sea mining and its environmental impacts: a review. Frontiers in Marine Science, 12, p.1598584.

Additional Material

Greenpeace Africa. URGENT petition telling African Govts to deliver a firm NO’ to deep sea mining before it starts. . Access Date: 11 Nov 2025.

PHYS.ORG.  Deep-sea mining risks disrupting the marine food web, study warns. Available from:. Access Date: 11 Nov 2025.

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Industry: Meteorology 

Industry Representative: Puseletso Mofokeng, South African Weather Service

Moderator: 

Student Moderator(s): 

Problem Statement

The sudden development of tornadoes with their localised but severe devastating destruction of property and environment remains a mayor challenge for meteorologists, disaster management practitioners and the public, to name a few. Collaterally, injuries with potential or real loss of human and animal lives form part of the challenge.  In South Africa, the problem further extends to inconsistent reports of tornadic events which are sometimes unreliable. Moreover, centuries of lack of traceable raw data of tornadic events, followed by their scarcity decades after the advent of satellite and RADAR technology, has slowed down research and effective operational decision-making.  

Regardless of this compounded challenge, the forecasting desk of the national weather authority (i.e. the South African Weather Service) only once issued a tornado warning at very short lead-time to the public on the afternoon of 30 December 2017. Then, about 5-minute updates of RADAR images were used and a meagre lead-time of less than 5-minutes was achieved. This time difference was worked out by comparing the time of issue of the tornado warning (i.e. 16:42 SAST) and time from the call log (16:46 SAST) of the residents of Protea Glen (Soweto), in the City of Johannesburg. The resident made a call at the instance of tornado touchdown.

Thereafter, a study of 34 tornadoes which occurred across South Africa was conducted between June 2016 to June 2021. This bulk study used images of meteorological satellite updating on 15-minute intervals. It was then deduced that slightly over 75% of tornadoes developed during the cross-intersection of a thunderstorm with fast velocities of lifted horizontal winds of another thunderstorm. The formation mechanisms of these tornadoes differed with the majority of those which occur across the south-eastern region (i.e. east of Rocky Mountains) of the United States of America (USA). Such USA tornadoes typically occur in association within the occluded fronts. 

Approximations of the increase (decrease) in the rate of change of the vorticity advection with the Equation 1 below:

However, occluded fronts are every rare in South Africa. Scrapping through historical records shows that their occurrences are decadal; they occur under exceptionally deep cut-off low pressure systems. Nonetheless, the deduction was that pseudo-occluded fronts occasionally occur and seem to be the cause of majority of tornadoes in South Africa. Pseudo-occluded fronts appear to be favourable for tornado development in South Africa when one thunderstorm deviated from the mean wind flow (i.e., steering winds) onto the strong outflow (i.e., gust front) of adjacent or approaching storm. The storm deviation could be the result of blocking significant topography (i.e., notably uneven features of land surface) or the influence of strong gust fronts change the steering winds of adjacent newly developed cells towards the outflow of the approaching storms. Such resulting quantum theory is illustrated in Figure 1 with a schematic model of two thunderstorms. One of these idealised thunderstorms deviate (due to significant topography) to temporally fixed point and onto the strong gust in front of an approaching storm. 

Figure 1 – Schematic Tornado Model: Tornadogenesis resulting from the cross-intersection of one thunderstorm with the gust front of another storm (circled) in proximity (e.g. within 1 km) . Such situations typically result when one storm (S₂) deviate onto strong gust front of the approaching storm (S₁).

Upon contact with lifted moist fluids (i.e., thunderstorm), fast velocities of horizontal dry fluids (i.e., winds) are assumed to undergo a degree of detour.

Figure 2: Thunderstorm activities from meteorological satellite along with the associated critical vectors.

Their sustenance into moist fluids typically experience counter-detour from pre-existing flows, resulting in downward spiral (oscillatory motion) of the lower sector of the moist fluids (i.e. drop of the portion of cloud base) (see Figure 2).Additionally, this portion of the cloud base do so due to the accumulation of cold temperatures from their advection by the winds (vectors). In optimal conditions the downward spiral of moist fluid typically develops into a funnel cloud or a tornado. The rapid decrease of radii towards the land surface is accepted to exponentially increase the speed of rotation within a tornado (see Figure 3).

Figure 3: A strong tornado and its enhanced-Fujita (EF) rating.

Theoretical results of further study suggest geographic locations of at least two thunderstorms could be used in the idealised straight lines together with approximation procedure to determine the potential of passing through a temporally fixed point (i.e. common geographic location). Such temporally fixed point seems important to find the subregion of less than 50 km across which a tornadic development is possible. This background and partitioning of constituent factors suggest the differential vector calculus as the best approach to develop mathematical tornado models.  

The Mathematics in Industry Study Group is requested to consider the scenario below and answer the questions below:

A short-lived tornado hit oThongathi in KwaZulu-Natal on the late afternoon of 3^rd June 2024. The phenomenon left considerable damage to property and reports have it that 16 residents lost their lives. Further, local businesses were halted and by extension local economy was negatively impacted. Images of meteorological satellite and RADAR show moments before, during and after the occurrence of a tornado.

Figure 4: Focus on Storm B and C. The 15-minute updates of top-view meteorological satellite images (© EUMETSAT). These storms were approximated with bottom-view RADAR images, as supplied by the South African Weather Service.

Figure 5: Evidence of impact across subregions of oThongathi, in KwaZulu-Natal.

Retrospectively, and given the two positions for each of these thunderstorms at 16:00 SAST and 16:15 SAST, coordinates were collected as follows,

Storm_B = {(-29.286, 30.761), (-29.450, 30.849)}

Storm_C = {(-29.264, 31.061), (-29.394, 30.947)}

Assuming straight propagation of these storms, attempt to address the following research questions:

1. Where could the theoretical temporally fixed point (i.e., common geographic position) be for each storm to pass through?
2. Which of these two thunderstorms (Storm_B or Storm_C) will reach the idealized temporally fixed point, first?
3. Upon cross-intersection with fast velocities (e.g., constant of 20 m/s) of the supporting storm, the horizontal speed of the main storm will be retarded. As influenced by the strong outflow of the supporting storm, detect the speed of the downward spiral (i.e., rotation of cloud) around the z-component with decreasing radii at lower height?
4. Could we use these constituent factors to develop a functional mathematical model for tornado genesis?

An alternative post-analysis study could be conducted with the questions above. A tornado that hit open field on the afternoon of 6 November 2025 could be used. For the time intervals 16:15 and 16:30, coordinates of storms labelled S1 and S2 are respectively given as: 

S1 = {(-28.460, 29.112), (-28.422, 29.145)} 

S2 = {(-28.359, 27.953), (-28.298, 28.203)}

Key words: Moist fluids, thunderstorms, dry fluids, gust front, vorticity, fluid spin  

References

• Boyer, C. H., and Dahl, J. M. (2020). The mechanisms responsible for large near-surface vertical vorticity within simulated supercells and quasi-linear storms. Monthly Weather Review, 4281–4297. doi:10.1175/mwr-d-20-0082.1
• Elsom, D. M. (1993). The thunderstorm gust front as a trigger for tornado formation: the long Stratton tornado of 14 December 1989. Journal of Meteorology, 18(175), 3-12.
• Jiang, Q., Dawson II, D. T., Li, F. & Chavas, D. R. (2025). Classifying synoptic patterns driving tornadic storms and associated spatial trends in the United States. Climate and Atmospheric Science, 8(7), 1-10. doi:10.1038/s41612-025-00897-1
• Lekoloane, L. E., Bopape, M.-J. M., Rambuwani, T. G., Ndarana, T., Landman, S., Mofokeng, P., Gijben, M., and Mohale, N. (2021). A dynamic and thermodynamic analysis of the 11 December 2017 tornadic supercell in the Highveld of South Africa. Weather and Climate Dynamics, 373-393. doi:10.5194/wcd-2-373-2021
• Mofokeng, S. P. (2024). Study of the influence gust fronts and topographical features in the development of severe thunderstorms across South Africa. Dissertation, 91¿´Æ¬Íø.
• Sibolla, B. H., Van Zyl, T., and Coetzee, S. (2020). Determining real-time patterns of lightning strikes from sensor observations. Journal of Geovisiualization and Spatial Analyis, 5(4), 1-18. doi:10.1007/s41651-020-00070-7 2021
• Stewart, J. (1998). Multivariable calculus: concepts and contexts. Carlifonia: Brooks/Cole Publishing.
• Trafalis, T. B., Adrianto, I., Richman, M. B., and Lakshmivarahan, S. (2013). Machine-learning classifiers for imbalanced tornado data. Computational Management Science. doi:10.1007/s10287-013-0174-6

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Industry: Carbon capture

Industry representative: Professor Tim Myers, Centre de Recerca

Matematica, Barcelona, Spain

Moderator:

Student Moderator:

Problem Statement:

Carbon capture and storage is considered essential in the fight to achieve global net- zero emissions targets and limit the effects of climate change. A key difficulty concerns the storage and transport of CO2 after capture.

The combination of water with CO2, as well as with common exhaust gas impurities such as SOx and NOx, can lead to the formation of carbonic, sulfuric, and nitric acids, which corrode transport and storage vessels. To combat this, there is a strong push to understand and thus reduce the corrosive effects. Current corrosion mitigation strategies rely heavily on the use of large volumes of chemical inhibitors to create protective films that form a barrier against corrosive attack. In addition to the enormous financial costs, there are serious concerns about the toxicity and degradability of existing inhibitor formulations, as well as the lack of effective inhibitors for use at the high temperatures required to effectively exploit sustainable energy sources, such as geothermal engineering. This has motivated significant research worldwide aimed at creating the next generation of sustainable inhibitors.

Corrosion inhibition in industrial systems requires a combination of components, typically at least five, which has led to a vast array of inhibitor formulations. A poor understanding of the corrosion process, coupled with a lack of systematic optimization methodologies, has resulted in the commercial adoption of suboptimal inhibitor formulations.

Whilst models exist, they are generally tackled numerically, consequently, the main objectives of this project will be to develop and analyse accurate corrosion inhibition models and combine them with experimental data to determine their parameters and optimize their efficiency.

Goals

1. To advance the theoretical understanding of corrosion inhibition through developing a series of validated mathematical models for corrosion inhibition, starting with the simple reaction between a single corrosive and a single inhibitor, progressing to multiple corrosives and inhibitors.
2. To use the models to help understand and optimize corrosion inhibition.

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Industry: Logistics

Industry Representative: Prof Stephan Visage/Dr Ian Campbell

Department of Logistics, University of Stellenbosch

Student Moderators:

Problem Statement:

The problem occurs at a fast-moving consumer goods (FMCG) central distribution warehouse, where stock is delivered from suppliers for distribution to retail stores. Stock arrives to be delivered to many stores (measured in cubic meters – they refer to it as cubes) daily. The number of cubes that arrive for each store is on average proportional to store size, but random. This leads to an inventory accumulation on the floor. At the end of each day a planner decides which stores will be serviced the next day. This is based on stock accumulation, into store dates of cartons and service levels agreement (SLA, number of visits a store gets per week). A fixed (homogeneous) delivery fleet is used, and more vehicles can temporarily be hired in at a higher cost per vehicle if needed. This is a vehicle routing problem known as the Vehicle Routing Problem with Inventory Management and Selective Delivery (VRPIMSD).

However, the company believes they can reduce logistics costs by changing to fixed delivery routes. Fixed, master routes can be determined, using the average inventory accumulation rates as well as the service level agreement requirements. This way, routes will never be changed, and delivery routes are triggered to run if a store on the route is at it’s SLA requirement, that is, carton into store deadline comes up on that route. Then all stock accumulated over all stores on that route get delivered, subject to the truck’s capacity. If there is more stock on the floor for the fixed route, each store will only receive a fraction of its oldest stock to scale down to one truckload.

The advantage of this system is that, having fixed routes, you can provide certainty to stores about when during a day they will receive stock, because the routes stay the same. This improves turnaround times at stores. Therefore you can fit in more stores per trip. It also improves driving times as drivers get to know the routes they deliver on, instead of using a new route every time a driver delivers, as is the case currently. The disadvantage is that you might sometimes deliver with trucks that are not fully loaded.

Currently the average turnaround time per store is about an hour, it is believed that if a store gets certainty about delivery time, this can be reduced to less than 30 minutes. In cities, delivery vehicles spend more time standing at stores than driving, so this could potentially more than double the number of drops a vehicle can make per day. In rural areas the effect would be smaller because the majority of time a vehicle will be driving and not standing at a store, due to the longer distances between stores.

The problem to investigate

The question that needs to be answered is: how much should the turnaround time decrease to make the fixed routes idea more profitable than the current VRPIMSD?

Additional Material

• Gribkovskaia, Irina & Laporte, Gilbert & Shyshou, Aliaksandr. (2008). The single vehicle routing problem with deliveries and selective pickups. Computers & Operations Research. 35. 2908-2924. 10.1016/j.cor.2007.01.007.
• Savelsbergh, Martin. (2006). Inventory Routing Problems.  

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Industry: Mining

Industry Representative: Professor T R Stacey, School of Mining Engineering, 91¿´Æ¬Íø, Johannesburg

Moderator: Professor D P Mason, School of Computer Science and Applied Mathematics, 91¿´Æ¬Íø, Johannesburg

Problem Statement:

This Problem was originally submitted to MISG 2017. The work on the Problem was not finished  and no Report was submitted to the Proceedings.

Rock failure criteria such as the Mohr-Coulomb failure criterion fail to describe mining excavations under low stress conditions especially near the surface of excavations. An alternative failure criterion based on the extension strain in the rock has been proposed (1):

Fracture of rock will initiate when the extension strain in the rock exceeds a critical value which is characteristic of that rock type.

The fractures will form in planes normal to the direction of the extension strain which corresponds with the least compressive principal stress. Extension fractures can form when all three principal stresses are compressive. The magnitude of the extension strain depends on all three principal stresses. Unlike in the Mohr-Coulomb failure criterion the effect of the intermediate principal stress is included.

In order to investigate the extension strain failure criterion a cylindrical excavation in the rock mass will be considered. The horizontal stress at depth H is determined by Heime’s rule which states that it is k times the vertical stress required to support the weight of the overburden rock. The constant k depends on the local geology and the depth of the excavation. The Study Group is asked to formulate  the cylindrical excavation in terms of the  plain strain theory of elasticity. Introduce the Airy stress function and reduce the problem to the solution  of Euler differential equations. Derive analytical solutions for the hoop stress, the axial stress and the radial stress in the surrounding rock.

• Investigate extension strain at the sidewall, especially in the radial direction near the surface of the excavation. Investigate if all three principal stresses can be compressive.
• If there is time, investigate extension strain at the hanging wall and floor.

References

1. A simple extension strain criterion for fracture of brittle rock. International J Rock Mechanics and Mining Sciences, 1981, 18, 469-474.

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Industry: Attractions (Tourism Sector)

Industry Representative: Dr. Lombuso Precious Shabalala, University of South Africa

Moderators: Dr. Simphiwe Simelane, University of Johannesburg

                       Dr. Matthews Sejeso, 91¿´Æ¬Íø  

Problem Statement:

The Bushbuckridge Nature Reserve (Figure 1) is geographically located in the north of Mpumalanga Province, under Bushbuckridge Local Municipality.  It shares a boundary with Kruger National Park and local communities.  The area is about 7000 hectares, co-managed by the Mpumalanga Tourism and Parks Agency (MTPA) and Communal Property Associations (CPA) through Integrated Management.  Interestingly, it is 100% land claimed by Sisonke CPA (95%) and Injaka Watervaal (5%). 

Figure 1: Bushbuckridge Nature Reserve 
Source:  Bushbuckridge Local Municipality, 2022.

This nature reserve presents an opportunity to restore and conserve the ecosystem because it used to be one of the province's well-operational and well-preserved natural reserves.  Through eco-tourism, the preservation and enhancement of the restored natural wonders could be realised.  Eventually, sustainable benefits will come into life.   Partnerships with local communities that honor their traditions and values, including discussions around human-wildlife conflict, become paramount. Creating employment in restoration and conservation while integrating scientific activities and fully reopening doors to tourists can be considered a significant contributor to the Tourism Sector as part of South Africa's tourism Gross Domestic Production (GDP) from this region.

The Bushbuckridge Nature Reserve aims to be a world-class Nature Reserve that maintains the integrity of its biodiversity through conservation while permitting sustainable utilisation of its natural resources by neighboring communities. In addition, it plans to offer its visitors and future generations a safe and unique nature experience at its best (Bushbuckridge Nature Reserve Management Plan, 2016-2022).   Biodiversity is described by Noss (1990) as the composition, structure, and function inferred at four levels of organisation – regional-landscape, community-ecosystem, population-species, and genetic. Therefore, through management authorities such as Mpumalanga Tourism and Parks Agency (MTPA), South Africa has recognised the importance of biodiversity protection through legislation.   Hence, effective management of declared protected areas must consider biodiversity conservation. 

Areas adjacent to the Reserve have undergone habitat losses, and the size and densities of human settlements have increased in Bushbuckridge (Coetzee et al., 2010). The activities of impoverished communities are seen as a major driver of landscape change. The increases seen in human-impacted vegetation suggest rural settlements depend on adjacent communal lands for cattle grazing, fuelwood, and medicinal plants, amongst other ecosystem services. These further stress the importance of the nature reserve as a conservation area and the need for detailed terrestrial biodiversity data.

In addition, Bushbuckridge Nature Reserve has the potential to contribute to conservation as mandated by the National Biodiversity Act (Act No. 10 of 2004) and the Mpumalanga Nature Conservation (Act No. 10 of 1998), and the Reserve's value as a significant catchment area, its position in the Kruger to Canyons Biosphere Reserve, as an ecological corridor should be viewed as an added advantage. The envisaged activities in the nature reserve will link the Blyde River Canyon Nature Reserve and the Kruger National Park in order to maintain and improve the movement of animals and plants through reserves and potentially improve the resilience of ecosystems to climate change.

On the other hand, as is experienced in other nature reserves, human-wildlife conflict and coexistence are serious challenges. This is not an exception for Bushbuckridge Nature Reserve, which includes livestock of the communities residing in the boundary that enters the Reserve since some of the fence is down. Nyhus (2016) is under the impression that human interactions with wildlife are a defining experience of human existence, although these interactions can be positive or negative. It is understood that naturally, people compete with wildlife for food and resources and have eradicated dangerous species, co-opted and domesticated valuable species, and applied a wide range of social, behavioral, and technical approaches to reduce negative interactions with wildlife (Nyhus, 2016:147).   If such conflicts are not addressed, unfortunately, they eventually lead to the extinction and reduction of numerous species and uncountable human deaths and economic losses.

 The problem to investigate:

• Design or develop a mathematical model to determine what can be done to revive the ecosystem of Bushbuckridge Nature Reserve since there is currently no life or meaningful activity.
• This design should also assist with presenting viable tools for controlling the ecosystem versus human-wildlife conflict.

References

1. Bushbuckridge Local Municipality. Bushbuckridge Nature Reserve Management Plan 2016-2022.
2. Coetzee, M. and Van Averbeke, W., 2010. Poverty, land and food production in South African townships. In: Reforming Land and Resource Use in South Africa, Routledge, pp. 291-309.
3. Mpumalanga Nature Conservation (Act No. 10 of 1998). Available from: .
4. National Environmental Management Biodiversity Act, 2004 (No. 10 of 2004). Available from: .
5. Nyhus, P.J., 2016. Human–wildlife conflict and coexistence. Annual review of environment and resources, 41(1), pp.143-171.

Supporting material

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MISG 2025 Study Group Problem 1 Report-back presentation

Industry: Conservation, Tourism, and Sustainability

Industry Representative: Mr Mukhtaar Waja, 91¿´Æ¬Íø, Ms Thea Juanita Earnest, 91¿´Æ¬Íø

Moderators:

Student Moderators:

Problem Statement:

Climate change poses a major threat to the survival of the African penguin population – which has already been classified as Critically Endangered – as it may adversely impact the species’ access to food, potentially disrupting the African penguin’s breeding patterns, while also exposing African penguins to dangerous levels of heat stress, and habitat degradation. 

Climate change is inducing changes in sea temperatures, ocean currents, and upwelling, resulting in changes to the timing of the movement of sardines and anchovies, which are the African penguin’s primary source of food. The annual sardine run – which occurs along the coast of South Africa between May and July – is a crucial feeding event for African penguins, which rely on the sardine run to satisfy their energy requirements during the breeding season, when adult penguins must feed their offspring and themselves. Research suggests that the sardine run is occurring at a later period with each passing year, due to warming ocean temperatures. African penguins may thus struggle to synchronise their feeding and breeding patterns with the changing timing of the sardine run, resulting in a species mismatch. Increasing ocean temperatures and acidification may also lead to a decline in fish stocks which African penguins rely on as a food source, potentially forcing African penguins to have to travel greater distances to locate sufficient food during critical periods. 

Rising ocean and air temperatures caused by climate change will likely place the African penguin at risk of experiencing prolonged heat stress. While African penguins can survive in relatively warm, temperate coastal climates, they are still vulnerable to extreme heat, which has the potential to overwhelm their thermoregulatory mechanisms, potentially resulting in reduced survival rates and lower reproductive success. African penguins’ eggs are especially vulnerable to temperature fluctuations, and an increase in the frequency and intensity of heat waves may severely compromise the ability of adult penguins to maintain suitable conditions for their eggs and offspring. 

Lastly, climate change may threaten the African penguin’s habitat, due to an increase in the intensity and frequency of extreme weather events. Tropical cyclones and intense storms may result in the flooding of breeding colonies and nesting sites, and the destabilisation of penguin colonies, forcing African penguins to relocate to areas with suboptimal conditions.

The problem to Investigate:

Model a projection of the year at which the African penguin species will likely face a significant threat to its survival, potentially leading to its extinction.

References:

Sherley, R.B., Crawford, R.J., de Blocq, A.D., Dyer, B.M., Geldenhuys, D., Hagen, C., Kemper, J., Makhado, A.B., Pichegru, L., Tom, D. and Upfold, L., 2020. The conservation status and population decline of the African penguin deconstructed in space and time. Ecology and Evolution, 10(15), 8506-8516. 
(Download PDF: Sherley et al. 2020. The conservation status and population decline of the African Penguin)

Sherley, R.B., Underhill, L.G., Barham, B.J., Barham, P.J., Coetzee, J.C., Crawford, R.J., Dyer, B.M., Leshoro, T.M. and Upfold, L., 2013. Influence of local and regional prey availability on breeding performance of African penguins Spheniscus demersus. Marine Ecology Progress Series, 473, 291-301.
(Download PDF: Sherley et al. 2013. Influence of local and regional prey availability)

Welman, S., Green, J.A., Ryan, P.G., Parsons, N.J. and Pichegru, L., 2024. Body temperature and thermoregulatory behaviour in the Endangered African Penguin Spheniscus demersus. Bird Conservation International, 34, 1-8.
(Download PDF: Welman et al. 2024. Body temperature and thermoregulatory behaviour)

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