Human-wildlife conflict in Kenya poses a critical challenge that undermines both conservation efforts and the livelihoods of local populations. This issue emerges from the overlap between wildlife needs and human activities, leading to competition over space and resources. Such conflicts result in crop destruction, livestock losses, and sometimes human casualties. This situation complicates wildlife conservation and underscores the intricate relationship between economic growth and environmental protection.
One major issue is habitat loss driven by agricultural expansion, urban development, and infrastructure projects. As the human population in Kenya expands, more land is transformed into farms, urban areas, and roads, encroaching upon natural habitats and disrupting wildlife migratory paths. This encroachment diminishes available space for wildlife and fragments their habitats, compelling animals to roam into human areas in search of food, which in turn leads to further conflict.
Addressing human-wildlife conflict in Kenya demands a nuanced approach that balances conservation needs with community well-being. Effective management of these conflicts requires collaborative efforts among the government, conservation groups, and local communities to create sustainable solutions that benefit both humans and wildlife. World Wildlife Fund [WWF], Vodafone and Safaricom have introduced a technological solution named m-Twiga.
m-Twiga utilizes a 360-camera system that integrates Internet of Things [IoT] and artificial intelligence [AI] technologies to detect and identify nearby wildlife. Upon detecting an animal, the system sends an SMS to alert wildlife rangers and activates deterrents like flashing lights or specific sounds to repel the animals.
The overarching challenge of human-wildlife conflict involves animals and humans encroaching into each other’s territories or competing for shared resources, leading to adverse outcomes such as animal attacks on humans, crop destruction, or livestock predation. These incidents can provoke retaliatory killings of wildlife. Exacerbated by climate change and habitat reduction, these conflicts have severe consequences.
Recently, after initial tests at the UK’s Longleat Safari Park, the m-Twiga team conducted their first field tests in Kenya. WWF Kenya notes that over 60% of Kenya’s wildlife resides in community and private lands outside protected areas, where conflicts are most prevalent. Trials in Kenya’s Mara Siana Community Conservancy have allowed the development team to gather extensive data on wildlife behavior, enhancing the AI model’s speed and accuracy in animal identification.
