# AI Promises Gains for Agriculture, Risks Excluding Smallholders > Source: > Published: 2026-06-03 16:56:10.268946+00:00 # AI Promises Gains for Agriculture, Risks Excluding Smallholders The Conversation reports that AI tools offer potential productivity gains for agriculture but risk leaving smallholder farmers behind. The Conversation notes that smallholder farmers account for around **80%** of farmers in developing countries and contrasts maize yields of over **10 tons per hectare** in the US with about **2-3 tons per hectare** in parts of sub-Saharan Africa. Editorial analysis: Deploying AI for smallholders typically requires localized data, affordable sensors and inputs, reliable connectivity, and extension-service integration; without those enablers adoption and equitable benefits are unlikely. ### What happened The Conversation reports that AI tools could help increase agricultural productivity but also warns these tools risk excluding smallholder farmers if not tailored to local contexts. The Conversation states that smallholder farmers make up around **80%** of farmers in developing countries and highlights yield gaps-maize yields in the US often exceed **10 tons per hectare**, while yields in parts of sub-Saharan Africa remain around **2-3 tons per hectare**. The Conversation also documents common constraints for smallholders: limited access to improved seeds, fertilisers, irrigation, mechanisation and weaker infrastructure. ### Technical details Editorial analysis - technical context: Deploying AI for smallholders typically requires localized data, affordable sensors and inputs, reliable connectivity, and extension-service integration; without those enablers adoption and equitable benefits are unlikely. ### Context and significance Industry context: The combination of infrastructure gaps, input affordability, and limited extension services creates a socio-technical barrier that can amplify digital divides. When AI tools require subscriptions, constant connectivity, or calibrated sensors, wealthier, larger farms capture disproportionate benefits. Conversely, participatory design, open local datasets, and partnerships with local agricultural extension networks are recurring recommendations in the literature for equitable deployment. ### What to watch Track field trials that publish results disaggregated by farm size, the emergence of open, labeled datasets from African research institutions, low-cost sensor + offline-model solutions, and collaborations between AI developers and local extension services. Observers should also watch policy moves on data governance and subsidy programs that affect input affordability. ## Scoring Rationale Notable for practitioners working at the intersection of AI and agriculture because it highlights adoption barriers and data gaps affecting smallholders. The story is practical rather than frontier-model changing, so its impact is significant but not transformational. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)