Technology alone fails to drive AI uptake in farming without inclusive policies

The transition to sustainable farming increasingly depends on artificial intelligence, but technology alone cannot drive change. A new study highlights the crucial role of fairness, inclusion, and farmer-led networks in driving AI uptake in non-chemical agriculture.

The study, titled “Artificial Intelligence Adoption in Non-Chemical Agriculture: An Integrated Mechanism for Sustainable Practices”, published in Sustainability, examines how smallholder farmers in Tamil Nadu adopt AI tools when they go beyond conventional chemical farming. The researchers propose an Integrated Mechanism for Sustainable Practices (IMSP) that combines technology acceptance with justice-based factors, providing a clearer picture of what enables or hinders AI adoption.

Shifting the focus from technology alone to fair access

The fieldwork, carried out between September and October 2024 with 57 farmers from 18 districts in Tamil Nadu, reveals that technology acceptance hinges not just on performance and ease of use, but on broader structural and social conditions.

Using a combination of fuzzy-set qualitative comparative analysis (fsQCA) and thematic inquiry, the study demonstrates that labour shortages, mobile technology use, and cost efficiency are necessary but not sufficient to drive adoption. Farmers often face barriers in training, access, and support, making inclusive extension services and equitable communication central to adoption.

The researchers found that active practitioners of non-chemical agriculture exhibit higher perceived usefulness (PU), perceived ease of use (PEU), and user acceptance (UA) of AI tools compared with marginal farmers. However, willingness to adopt AI and previous user experience did not vary significantly across groups.

These findings underscore that while farmers recognize the value of AI, consistent access to tools and supportive structures, such as tailored training, field-level demonstrations, and affordable mobile applications, determine how effectively they integrate AI into daily agricultural practices.

Community networks and justice-driven frameworks prove critical

One of the most striking findings is the role of peer influence and community validation. The study reports that around 60 percent of farmers attribute their decisions to adopt AI tools to peer encouragement, while farmer-led networks provide about 70 percent of the practical support needed for implementation. These networks often outperform institutional extension programs in reaching rural communities and addressing context-specific challenges.

By applying a justice-centered lens to the Technology Acceptance Model (TAM), the study highlights that economic, cultural, and political justice influences adoption more than previously recognized. Issues such as equitable access to AI training, gender inclusivity, and the capacity to participate in decision-making processes affect adoption rates.

The IMSP framework developed by the authors links traditional TAM variables with these justice-based considerations. This integration reveals why marginalized farmers often lag behind in adoption despite the availability of AI technologies. It also emphasizes the need for participatory governance to ensure that AI tools align with local practices and do not exacerbate existing inequalities.

Policy directions to scale sustainable AI use in agriculture

The study’s findings carry significant implications for policymakers, agricultural extension agencies, and technology developers. Amalan and Aram recommend that programs aimed at promoting AI adoption in sustainable farming focus on inclusive extension models, AI literacy training, and village-level tech hubs that provide real-time support to smallholders.

The researchers also stress the importance of addressing structural barriers. Labour shortages and cost concerns remain major hurdles, but targeted subsidies, collective labour strategies, and locally adapted AI solutions can bridge these gaps.

Crucially, the study calls for participatory governance frameworks that give farmers, particularly those practicing non-chemical methods, a stronger voice in the development and rollout of AI tools. Such approaches align with Tamil Nadu’s Organic Farming Policy 2023 and global Sustainable Development Goals (SDGs) focused on zero hunger, responsible consumption, and climate action.

By embedding fairness and participation into AI deployment strategies, policymakers can ensure that the benefits of technology reach all farmers, rather than widening existing disparities.