The research, co-led by the University of Cape Town (UCT), focused on 11 reserves in South Africa’s Greater Kruger region from 2017 to 2023 – a critical conservation area that is home to approximately 25% of Africa’s remaining rhino population.

The study was the result of a powerful collaboration between reserve managers, represented by the Greater Kruger Environmental Protection Foundation (GKEPF), and scientists from UCT’s Centre for Statistics in Ecology, Environment and Conservation, Nelson Mandela University, Stellenbosch University and the University of Oxford. Key partners who supported the study include the South African National Parks, the World Wildlife Fund South Africa and the Rhino Recovery Fund.

“We documented the poaching of 1 985 rhinos (about 6.5% of the population annually) across 11 Greater Kruger reserves over seven years. This landscape is a critical global stronghold that conserves around 25% of all Africa’s rhinos,” said Dr Tim Kuiper, lead author, who conducted the work as a postdoctoral researcher at UCT and is now a senior lecturer at Nelson Mandela University.

“Dehorning rhinos to reduce incentives for poaching (2 284 rhinos were dehorned across eight reserves) was found to achieve a 78% reduction in poaching using just 1.2% of the overall rhino protection budget,” added Dr Kuiper 

Using a comparative approach between dehorned and horned rhino populations and tracking changes over time, the study shows that dehorning is a highly cost-effective intervention – though not without challenges. Some poaching for horn stumps and regrowth continued, and evidence from 2024–2025 indicates this remains a growing concern. The potential displacement of poaching to horned populations elsewhere also remains a critical consideration.

Between 2017 and 2021, the reserves invested approximately $74 million (R1 billion) in anti-poaching strategies – including ranger patrols, tracking dogs, helicopters and surveillance technology. Despite this massive investment, the study found no statistically significant evidence that these traditional law enforcement measures alone reduced poaching.

“Finally, ineffective criminal justice systems mean that arrested offenders often escape punishment, with evidence from our study area of multiple repeat offenders,” said Dr Kuiper.

This study stands out not only for its findings but also for its model of science-policy collaboration. The research was sparked by GKEPF reserve managers themselves, who sought to evaluate the effectiveness of their anti-poaching investments.

“The true value of this innovative study, conceived by GKEPF operational managers, lies in its collective critical thinking. Ensuring not only that operations are guided by science, but also that science is grounded in real experience from the frontline,”
said Sharon Hausmann, CEO of GKEPF.

Dr Markus Hofmeyr of the Rhino Recovery Fund said: “From a donor perspective this study has given excellent insight where conservation donor funding can be spent and where to avoid funding.”

“This collaboration is a brilliant example of how the effectiveness of conservation interventions can be assessed quantitatively, even in challenging and complex situations, and how important the participation of on-the-ground practitioners is in initiating, and interpreting, such research,” said Professor Dame E.J. Milner-Gulland of the University of Oxford.

“It’s important to check that our conservation interventions work as intended, and keep working that way. For me, this project has again highlighted the value of collecting detailed data, both on the interventions that were applied and the outcome. It’s such data that makes robust quantitative analyses possible,” said UCT Professor Res Altwegg, who supervised the statistical analysis.

The findings open a timely window for governments, funders, conservation NGOs and the private sector to rethink and recalibrate strategies in addressing wildlife crime, particularly rhino poaching, with a renewed focus on data-driven and cost-effective interventions.

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University of Pretoria electronic engineering student Neil-John Lord, speaking at the recent SA Radar Interest Group conference at the Council for Scientific and Industrial Research, discussed the problem of wildlife poaching and described previous efforts at identifying poachers. These included the use of camera traps in the Pilanesberg game reserve and the use of patrols and other methods.

These methods were not satisfactory due to false alarms with the camera traps: wind moving trees, leaves falling in front of the sensors or the movement of animals. Of the useable 10 percent of data, only a small subset were poachers and not animals.

He said most motion-detection models were developed in urban environments, which did not translate well to the bush and the difficulty of operating at night exacerbated the problem.

A University of Pretoria team then augmented the camera traps with additional sensors including lidar and radar sensors to detect motion (such as that of poachers) at night. The added sensors could give the observers distance to target, velocity and other extended information (size, orientation and shape) of the target.

Lord explained that with earlier radars, a target was picked up as a single return. With the advent of millimetre band radar, numerous returns were received from the same target. The target points on the radar screen were further clarified by use of an algorithm to create a ‘point cloud’. This can be averaged to get a very accurate measurement of where the target is at a given time. But a lot more can be done.

Target tracking and target classification are critical problems in battlefield surveillance systems, Lord explained. Traditionally, military radars detected and tracked targets, but classifying them is a new development in the digital age.

In World War II, for example, radars could detect enemy aircraft, but could not tell the number or type, only height and vector (size and direction). Modern systems, using improved radar and digitised software, including artificial intelligence (AI), can separate out the height, vector and often, the general type of aircraft, such as a fighter, passenger aircraft, cargo plane, or a missile. The software’s ability to ‘classify’ radar targets can make all the difference to success in the battlespace.

Getting back to the poaching problem, Lord explained that the difficulty was that, unlike ships or aircraft, sizes of animals and poachers were unknown. He said that sizes varied drastically, from elephants to impalas, but both are animals. He pointed out that separate classes could be created for separate species, but ‘that could get complicated quite quickly’.

Lord and his team discovered that the main difference between humans and animals was that humans exhibited bipedal motion, giving a different radar return to animals with quadrupedal motion. Once this difference was fed into the radar computer, it could create two ‘classes’, one for humans, one for animals. Two possible classifications were created, one based on orientation, and one on size.

Lord said he hoped the findings could be incorporated into existing poacher detection and tracking systems.

Drone detection

William Bourn, representing the University of Cape Town, told conference delegates of the promising use of millimetre band radar, specifically Frequency Modulated Continuous Wave (FMCW) radar, to detect drones and small targets.

With the increased use of drones used by terrorists, armed forces and civilian troublemakers, such as those who disrupted air traffic at Gatwick Airport, London in 2018, drone detection has become a priority, he explained.

He described existing challenges as ‘teaching’ software with machine learning to distinguish between small objects, such as small drones and birds. While much work needs to be done, the study of millimetre band radar promises solutions for a host of tasks such as assisting civil aviation at airports, catching poachers or preventing terrorists using drones to reconnoitre or attack military or civilian targets.

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