AI promises higher farm yields, but risks widening the gap for smallholders
A review of global research finds artificial intelligence could boost agricultural productivity across Africa and the developing world, but only if governments invest in infrastructure and ensure equitable access—otherwise the technology may deepen inequality among farming communities.
Artificial intelligence is reshaping agriculture in wealthy nations, delivering measurable gains in crop yields and resource efficiency. But a comprehensive study of AI adoption across developed and developing countries warns that smallholder farmers in Africa and other low-income regions risk being left further behind without deliberate policy action.
The gap in farm productivity is stark. Maize yields in the United States often exceed 10 tons per hectare, driven by mechanisation, improved seeds, irrigation and precision agriculture tools. In much of sub-Saharan Africa, the same crop typically yields just 2-3 tons per hectare, constrained by limited access to inputs, rain-fed farming systems and weak infrastructure.
Smallholder farmers—who account for around 80% of all farmers in developing countries—face particular pressure. They operate with low-yielding local seed varieties, limited fertiliser and agrochemical use, minimal mechanisation, and heavy reliance on manual labour. Many are highly vulnerable to climate shocks. These conventional approaches are increasingly insufficient to meet 21st-century food demand.
The AI opportunity
Recent research has shown that artificial intelligence tools can meaningfully improve agriculture. In technologically advanced systems, AI has demonstrated the ability to:
- Improve input-output efficiency and enable real-time crop and livestock monitoring - Conserve soil and water resources - Reduce post-harvest losses
Evidence from the US, China and Europe shows the practical value of precision farming, disease detection, yield prediction and smart irrigation systems powered by AI.
The critical conditions
A recent study comparing AI adoption between developed and developing countries examined evidence from Europe, the US, Australia and Japan alongside research from Africa, South Asia and Latin America. The core finding is sobering: AI has strong potential to improve agricultural productivity and resilience, but only if certain conditions are met.
Without supportive policies, reliable infrastructure and equitable access, the technology risks reinforcing existing inequalities rather than reducing them.
The research identified key gaps that determine whether smallholders can access these tools. These include the availability of electricity and broadband connectivity, digital literacy support, data management systems and smart devices. Institutional support and extension services also matter.
In regions where these foundations are weak, AI-driven agriculture may become another driver of inequality—concentrating productivity gains among larger, better-resourced farms while smallholders fall further behind. The gap between 10 tons per hectare and 2-3 tons could widen if access to technology remains unequal.
What comes next
The implication for policymakers across Africa and the developing world is clear: simply introducing AI tools without addressing the underlying infrastructure and access barriers will not solve the productivity crisis. Governments must invest in broadband and power systems, build digital literacy, and create inclusive pathways for smallholders to adopt and benefit from these technologies. Without deliberate action, AI in agriculture will be yet another advance that bypasses those who need it most.