AI in agriculture: Structural inequities risk deepening smallholder exclusion without decolonial design
Original framing: “AI offers promise for agriculture, but smallholder farmers risk being left behind” — bing news
The Green Revolution’s introduction of high-yield varieties in the 1960s-70s created dependency on corporate inputs, leading to soil degradation and farmer suicides in India, a pattern now repeating with AI-driven 'smart' farming. Colonial land enclosure acts in Africa and Asia displaced indigenous farming systems, creating the 'smallholder' category as a residual category of dispossession. Structural adjustment programs in the 1980s-90s dismantled public agricultural research, leaving farmers vulnerable to tech monopolies.
The narrative of AI in agriculture as a neutral tool obscures how colonial land dispossession, corporate data extraction, and energy-intensive tech transfer models reproduce dependency under a new guise.
Historical precedents like the Green Revolution and structural adjustment programs demonstrate that technological 'solutions' without land and knowledge justice deepen inequality, a pattern now repeating with AI. Indigenous systems—from Andean *ayni* to West African *zaï*—offer proven alternatives to AI’s extractive paradigms, yet are systematically excluded from mainstream discourse. The solution lies not in scaling AI uncritically, but in decolonial co-design hubs that center smallholder and indigenous leadership, coupled with land and data reforms that dismantle the structural inequities AI risks exacerbating. Without this, AI will become another vector of 'digital colonialism,' with smallholders as the latest victims of a centuries-old pattern of dispossession repackaged as progress.