Machine Learning and Earth Observation — A Beacon of Hope for a Climate-Resilient Africa
As recorded by the Intergovernmental Panel on Climate Change (IPCC), Africa’s contribution is among the lowest of historical greenhouse gas (GHG) emissions responsible for human-induced climate change, yet it remains the most vulnerable continent in the face of climate change.
This change, coupled with Africa’s high dependence on climate-sensitive sectors such as agriculture, poses significant challenges to livelihoods, food security, and socio-economic development. However, the advent of machine learning (ML) and earth observation (EO) technologies offers a glimmer of hope, providing data-driven solutions that foster resilience and adaptability.
Fostering Climate Resilience in Agriculture
Agriculture is the backbone of many African economies, providing livelihoods for a significant proportion of the population. More than 60 percent of the population of sub-Saharan Africa is smallholder farmers, and about 23 percent of sub-Saharan Africa’s GDP comes from agriculture. According to a report by McKinsey, agriculture accounts for about 23% of Africa’s GDP and about 60% of its jobs. However, due to the sector’s reliance on rainfall and changing weather patterns, climate change poses a significant threat to agriculture and, by extension, food security.
ML and EO can predict weather patterns with increased accuracy, aiding farmers in making informed decisions regarding planting and harvesting times. For example, satellite-based remote sensing can provide real-time information on soil moisture, crop health, and weather conditions. This information is crucial for smallholder farmers who rely on rain-fed agriculture.
Through these technologies, crop yields can also be predicted more accurately. A study conducted by a group of researchers including the Climate Hazards Center, Department of Geography, University of California et al has indicated that machine learning algorithms showed an impressive accuracy rate of over 85% in some regions. Such predictive capabilities could significantly enhance food security and contribute to economic stability in the region. It allows for better planning, from predicting potential food shortages to providing valuable data for agricultural policy development.
Another aspect of climate resilience in agriculture is the ability to monitor and adapt to land use changes. Africa is experiencing rapid urbanization, which often comes at the expense of agricultural lands. EO technologies can track these changes, allowing for more effective land use planning and helping ensure that agricultural productivity is not undermined.
Furthermore, ML and EO can also play a vital role in pest and disease management, a challenge exacerbated by climate change. By identifying signs of pest infestations or disease outbreaks early, preventative measures can be implemented promptly, thereby protecting crop yields and the livelihoods of farmers.
Biodiversity plays a vital role in ensuring the overall health of the planet and has a direct impact on human lives. However, the biodiversity crisis we are facing is as alarming as the climate crisis. The African continent is home to a wealth of biodiversity, including approximately 30% of the planet’s remaining rainforest, such as the Congo Basin, the second-largest rainforest in the world.
Through EO technology, changes in land cover — a significant driver of biodiversity loss — can be detected and analyzed in real-time. This allows for rapid responses to threats such as deforestation, illegal logging, and encroachment. For example, the United Nations has shown that Africa loses 3.9 million hectares of forest per year, a trend that can be combated more effectively with the use of these technologies.
Additionally, machine learning and earth observation technologies provide powerful tools for monitoring and conserving biodiversity. Satellite imagery, combined with machine learning algorithms, can track changes in ecosystems, identify threats to endangered species, and monitor protected areas’ effectiveness. This provides invaluable data for crafting and implementing effective conservation strategies.
Natural disasters such as droughts, floods, and cyclones are becoming increasingly frequent and intense due to climate change, posing a significant threat to African communities. Timely and effective disaster management can reduce these impacts, saving lives, and preserving livelihoods. Here too, machine learning and earth observation technologies can be of immense value. By analyzing historical and real-time data, ML algorithms can be used to predict the likelihood of natural disasters, providing crucial early warnings to communities at risk.
Once a natural disaster has occurred, ML and EO can also aid in damage assessment and recovery efforts. Satellite imagery can quickly provide an overview of the affected areas, identifying infrastructure damage, flooded areas, or landslides. Machine learning algorithms can then process this data, aiding in the efficient allocation of resources where they are most needed.
In this transformative landscape, startups like Amini are taking the lead. Amini has established itself as the single source of truth for African environmental data, addressing the critical issue of Africa’s environmental data scarcity. Through a holistic solution that harnesses AI and space technologies, Amini aims to bridge this data gap, foster economic inclusivity for farmers, and enhance supply chain resilience across the continent and beyond.
Significant investments, policy support, and capacity building remain essential to fully exploiting the potential of these technologies. Leveraging these powerful tools can enable us to mitigate the impacts of climate change, fostering a more resilient and sustainable future for Africa. As the African proverb says, “Tomorrow belongs to the people who prepare for it today.” By acting now and embracing the transformative potential of these technologies, we can prepare for and secure a resilient tomorrow for Africa. The promise of this future depends on the actions we take today.