The author, the chief economist of the UN Food and Agriculture Organization in Rome, argues that agricultural AI needs safeguards to avoid deepening inequalities in access. The author cites Pope Leo XIV's recent AI encyclical as calling for digital technology to be judged by whether opportunities are fairly accessible. The author contrasts widespread fears of AI-driven job losses with agriculture's problems of ageing farmers, shortages of skills and low labour productivity.
AI could improve productivity on large, highly mechanised farms. However, smaller farms unable to afford costly machinery, high-speed internet or AI subscriptions could fall further behind. Smallholders farming less than five acres represent 85% of farms worldwide. Smallholders produce 60% of food crops in poorer countries and are essential to food security there.
Agriculture faces particularly severe information gaps, leaving farmers to make consequential, long-term decisions about seeds and markets with limited information. Climate and geopolitical shocks compound these problems by rapidly changing growing conditions and fuel and fertiliser prices. Smallholders suffer most from these information gaps. Agricultural advisers in some places must try to reach millions of producers over extensive areas. AI could expand advisers' reach by translating information into local languages and combining weather, soil and market data. AI could also help identify crop diseases and make prices more transparent.
A 2026 study involving 240 agricultural advisers in Benin found that advisers using generative AI provided better advice. An AI tool in India advises cotton farmers on pesticide timing and quantities in eight Indian languages. More than 30,000 cotton farmers have used the tool, with revenue increases reaching 24%. A trial in China found that an AI system increased wheat yields by 7%. The trial found that maize yields rose by 4%. The system guided irrigation and pesticide application using weather data, soil data and computer vision.
The author endorses Pope Leo XIV's warning that technology can introduce new forms of exclusion. AI lending models may reject smallholders or impose higher interest rates because smallholders lack the financial records used for risk assessments. Subsidy systems may exclude farmers who lack formal land titles. AI systems developed using data from large farms may be unsuitable for small plots. Some AI tools have been found to systematically favour commercial crop-protection products over sustainable alternatives based on biodiversity. Such preferences reinforce dependence on purchased products rather than practices that farmers can adopt without payment and that benefit nature more.
The author argues that safeguards should become stronger as potential harm increases. The author regards crop-disease identification tools as relatively low-risk applications that should be straightforward to test and deploy. The author calls for rigorous testing, human oversight and transparency for systems influencing access to loans or insurance, harvest decisions or food safety. The author argues that farmers should have ways to contest decisions and obtain remedies. The author calls for binding legal agreements to support safeguards at the highest risk levels. Such agreements could impose shared obligations on the largest AI companies, including independent safety audits and protections against discrimination and misuse of market power. The author argues that these safeguards matter both to farmers and to global AI safety.
The author argues that regulation cannot by itself deliver AI access to farmers in Benin. Governments will still need to fund connectivity, digital skills, data and agricultural advisory services. The author argues that regulation can nevertheless help stop AI from widening inequalities in access. About 44% of young people rely on agrifood systems for their livelihoods. The author concludes that these young people will need AI to survive and prosper, while safeguards must prevent the exclusion of a generation.
AI in Agriculture: Balancing Productivity Gains and Inequality Risks
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Summary
A new report ties AI’s role in agriculture to the fear and greed index of global markets, showing how tech-driven gains can shift economic power. AI tools boosted Indian cotton revenues by 24% and Chinese wheat yields by 7%, but risks of exclusion remain. Experts urge legal safeguards and funding for smallholders to avoid a digital divide. Altcoins to watch may reflect this trend as blockchain solutions emerge in agri-tech. Transparency and access are key to balancing productivity and fairness.
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