As the climate crisis bites, Africa is at a crossroads: how will it feed the world’s fastest-growing population in the decades ahead?
At GLF Africa 2024, we gathered a panel of experts to debate the role of AI and digital tools in shaping the continent’s food future.
You had plenty of questions for our panelists – far more than we had time for during the discussion.
So, we reached back out to Catherine Nakalembe, an assistant professor of geographical sciences at the University of Maryland, Africa director at NASA Harvest and a member of the NASA SERVIR Applied Sciences team, to answer a few more.
AI has significant potential to transform agriculture and food systems in Africa by enhancing crop monitoring, enabling precision agriculture, improving weather forecasting, optimizing supply chains and providing market intelligence.
However, several barriers exist, including limited infrastructure, data scarcity, lack of technical expertise, high costs and digital literacy challenges.
Context matters when implementing these technologies. Overcoming these challenges requires collaborative efforts from governments, NGOs, tech companies and local communities to develop locally relevant solutions and supportive policy frameworks.
That requires a thoughtful, collaborative approach and recognition that eroding native and traditional approaches will not solve the problems we are trying to solve.
This involves participatory design that includes local farmers in developing AI tools, creating hybrid knowledge systems that bridge modern technology with time-tested practices and ensuring cultural sensitivity in solutions.
To strike this balance, we can use AI for tasks like data analysis or early warning systems, while maintaining traditional methods for culturally significant practices.
We can train local organizations in the development of the tools and provide education and training to farmers to help bridge the gaps between the insights we get from AI and enhancing their existing knowledge.
AI can also be used to document and preserve traditional farming knowledge. By implementing AI-driven tools flexibly and allowing for customization to fit diverse farming contexts and cultural norms, they can serve as a supportive tool that enhances rather than erodes local agricultural heritage and expertise.
While we inevitably ‘stand on the shoulders of giants’ by leveraging existing knowledge and expertise, it’s crucial to adapt and localize these tools for specific African challenges.
African developers should focus on learning from global AI advancements, sharing knowledge within the continent, and developing expertise in customizing and deploying AI tools for local environments.
By taking the lead in implementing AI solutions that address uniquely African problems, developers can create models that excel in tasks critical to local contexts.
For instance, an AI model developed this way might be particularly adept at improving productivity for native vegetables – a use case that may not be prioritized by foreign researchers or even by teams working just a few hundred kilometers away.
This approach ensures AI solutions are truly tailored to the continent’s diverse needs and conditions, fostering innovation that directly benefits African communities.
AI can benefit African farmers irrespective of their literacy levels and limited internet access if the intention is to serve through creative implementation strategies.
Voice-based interfaces and visual AI tools can deliver crucial agricultural information via basic mobile phones, bypassing literacy barriers. SMS services and community knowledge hubs can disseminate AI-driven insights locally.
Importantly, AI-enhanced policymaking and early warning systems can improve agricultural support programs and disaster preparedness, benefiting farmers indirectly.
It goes without saying: farmers don’t need to be literate to benefit from improved policies and responsive support programs informed by AI insights.
By focusing on accessible technologies and leveraging existing communication channels, AI can enhance agricultural productivity, increase resilience to climate challenges and improve market access for African farmers – regardless of their technological literacy or reading ability.
One major concern is the potential to widen socioeconomic disparities, as those with access to AI technologies will and are gaining disproportionate advantages. This digital divide could exacerbate existing inequalities, particularly in developing regions.
Research also indicates that AI systems, especially large language models, have a substantial environmental impact and generate significant carbon emissions. As AI adoption grows, so does this cumulative energy demand.
The environmental cost of widespread AI use may often outweigh its benefits, particularly when less energy-intensive alternatives exist. This raises serious questions about the sustainability of current AI practices and highlights the need for more energy-efficient AI technologies and computing methods to reduce their contribution to climate change.
Governance issues arise from the need for robust regulatory frameworks to ensure ethical AI development and deployment, protecting privacy and preventing misuse.
There’s also a widespread fear of job displacement and the societal impacts of AI automation.
Lastly, bad actors could exploit AI insights for malicious purposes, such as manipulating markets or creating artificial scarcities in agricultural sectors, potentially harming vulnerable populations like small-scale farmers.
Addressing these challenges requires multi-stakeholder collaboration, focusing on inclusive AI development, sustainable computing practices, comprehensive regulatory approaches, and ethical guidelines that prioritize human welfare and the equitable distribution of AI benefits.
It’s important to recognize that you’re a vital partner in agricultural innovation, not a passive beneficiary. AI already influences agriculture globally, so understanding its potential impacts on your livelihood is crucial.
While AI can offer tools for improving yields and market access, it’s equally important to be aware of how your data is collected and used.
Stay informed about who accesses information on your farming practices, as this data could influence markets and policies affecting you. Seek out advocates and farmer organizations that can represent your interests in AI-related policy discussions.
Your practical knowledge and experience are invaluable in shaping how AI is applied in agriculture. By actively engaging with these technologies and the organizations developing them, you can help ensure AI serves the needs of smallholder farmers, rather than solely benefiting large agricultural corporations.
Smallholder farmers are a vital partner in agricultural innovation – not passive helpless beneficiaries.
Anyone who doesn’t recognize this sparks wonder for whom and why they are innovating in agriculture.
It is imperative that tech innovators figure out how to engage and meet farmers where they are, rather than the other way around.
For example, in delivering insights in the most accessible medium, local language support should be number one on our to-do list, not number 1,000. AI can help with this.
This interview has been edited for length and clarity.
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