Cost, trust and data fears slow AI adoption on Midwest farms

A new study suggests that artificial intelligence (AI) could help Midwestern farmers manage climate stress, rising costs and labor shortages, but adoption remains sharply uneven because many producers still face financial, infrastructure and trust barriers. Farmers are more likely to consider AI tools when they see clear performance gains, find the systems easy to use and trust the technology, while cost pressures and data security fears reduce adoption intent.

The study, titled Drivers and Barriers to Artificial Intelligence Adoption in Agriculture: A Socio-Technical Analysis of Midwestern United States Farmers, was published in Sustainability. Based on survey data from 489 farmers in the U.S. Midwest and analyzed through partial least squares structural equation modeling, the paper finds that performance expectancy, effort expectancy and trust are the strongest positive predictors of AI adoption intention, while economic constraints and data security concerns are major deterrents.

Farmers see AI's value, but adoption depends on practical payoff

Farmers are operating under climate volatility, labor shortages, higher production costs and growing demand for efficiency. AI systems promise help through crop yield prediction, disease detection, autonomous machinery, drone imagery, irrigation guidance, pest monitoring and decision-support tools that use real-time data.

The research states that potential alone is not enough to drive adoption. Farmers do not evaluate AI as a broad innovation trend, but they judge it against farm-level needs, cost exposure, technical demands and risk. AI differs from earlier digital farming tools because it can automate or influence decisions, not merely display information, raising a different level of concern for farmers who must trust algorithmic recommendations that can affect yield, input use and profit.

The Midwest is a critical region for this question because of its role in corn, soybean and grain production. The paper notes that the region contributes heavily to U.S. agricultural output and global food security, yet AI adoption remains far more common among large agribusinesses than among small and midsized family farms. Many smaller operations face limited capital, weaker broadband access, lower digital readiness and uncertainty over whether AI investments will produce returns quickly enough.

The authors built their model by combining the Unified Theory of Acceptance and Use of Technology with Task-Technology Fit, then adding agriculture-specific factors. These included environmental risk, broadband access, economic constraints, policy support, trust and data security concerns. The result is a socio-technical model that treats adoption as a product of both technology perception and the farming environment in which decisions are made.

All 11 hypothesized relationships in the model were supported. The finding shows that adoption intent is not shaped by a single driver. Farmers are more likely to adopt AI when they believe it improves farm performance, is easy to learn, fits their actual tasks, is supported by infrastructure and policy, and can be trusted. They are less likely to adopt when cost burdens and data-security concerns outweigh expected gains.

Performance expectancy had the strongest positive effect on adoption intention. Farmers are more inclined to consider AI when they believe it can raise productivity, improve efficiency, reduce costs, increase profitability or support environmental sustainability. The finding aligns with a basic business reality in agriculture: tools must prove their worth under tight margins and uncertain conditions.

Effort expectancy also played a major role. Farmers were more open to AI when they believed the tools would be easy to learn and use. This is especially important in farming communities where digital literacy levels vary and time for training is limited. If an AI platform is difficult to operate, requires heavy technical support or fails to integrate with existing systems, its value weakens even when its performance claims are strong.

Task-technology fit was another key factor because farmers want AI tools that match specific farm operations. AI adoption becomes more likely when systems support decisions around irrigation, planting, spraying, yield prediction, crop stress, nutrient application or pest detection. Tools that appear generic, poorly aligned with machinery or disconnected from daily work are less likely to gain traction.

Environmental risk increased perceived usefulness. Farmers facing drought, pests, soil degradation and climate variability were more likely to view AI as valuable. The study suggests that environmental pressure can shift AI from being seen as an optional technology to being viewed as a risk-management tool. In this context, predictive analytics and early-warning systems become part of how farmers evaluate resilience.

Cost, broadband and data control remain major barriers

The strongest barriers identified in the study are not abstract resistance to technology, but practical constraints that shape whether farmers can use AI even when they see its value.

Economic constraints had a negative effect on both performance expectancy and effort expectancy. High costs, uncertain returns and limited farm income reduce confidence in AI adoption. Farmers may believe a tool could be useful in theory but still judge it as financially unrealistic. This is particularly significant for small and midsized farms that operate with tighter margins and less room for risky capital investments.

The cost issue also affects perceived ease of use. When farmers lack resources for training, technical support, hardware upgrades or digital infrastructure, AI systems feel more difficult to adopt. In this sense, financial pressure does not only limit purchasing power. It also increases the perceived burden of learning and integrating new systems.

Broadband access had a positive effect on effort expectancy, showing that digital infrastructure changes how easy AI feels to use. Reliable internet is necessary for cloud dashboards, real-time analytics, sensor networks, autonomous machinery and GPS-linked systems. When connectivity is weak, farmers face delays, data interruptions and frustration. When broadband is stable, AI tools appear more manageable.

The study highlights rural broadband as a foundational condition for AI adoption. Without connectivity, even well-designed systems struggle to deliver value. This is especially relevant for rural areas where broadband quality remains uneven. The findings suggest that investment in rural internet is not a separate policy issue from farm technology adoption. It is part of the adoption pathway.

Data security concerns had a clear negative effect on adoption intention. Farmers are worried about how farm data are collected, stored, accessed and used. AI systems can gather sensitive operational information, including soil conditions, crop production, machinery activity, drone images and yield data. That information may have commercial value, and farmers may fear misuse by agribusiness firms, insurers, government agencies or other third parties.

The paper treats data control as a serious barrier rather than a secondary concern. For farmers, data are tied to competitiveness, privacy and autonomy. If producers believe they are losing control over farm-level information, they may reject AI tools even when those tools promise productivity gains. This finding places transparency, ownership rules and security standards at the center of ag-tech adoption.

Trust in technology was one of the strongest positive predictors of adoption intention. This is important because AI often operates through complex models that users cannot easily inspect. The black-box nature of algorithmic recommendations can make farmers cautious, especially when decisions affect planting, irrigation, pest control or input spending. Trust improves when farmers believe AI tools are reliable, accurate and aligned with their interests.

The study also found that social influence matters. Farmers are more likely to consider AI when trusted peers, advisers, extension agents, cooperatives or farming networks support it. In rural communities, technology diffusion often depends on local proof, peer experience and practical demonstration. Adoption is not only an individual decision. It is shaped by the credibility of people and institutions around the farmer.

Facilitating conditions also increased adoption intention. These include training, vendor support, technical assistance, resources and compatibility with existing tools. Farmers are more willing to adopt AI when they believe help is available if something goes wrong. This finding points to the importance of extension services, cooperative networks and support systems after purchase, not only sales activity before adoption.

Policy support could decide whether AI widens or narrows the farm technology gap

Policy support had a positive effect on facilitating conditions, showing that public programs can shape the environment in which farmers evaluate AI. Subsidies, broadband expansion, training programs, research funding, extension services and tax incentives can reduce barriers and make adoption more realistic.

On the whole, AI adoption in agriculture will not become equitable through market forces alone. Without targeted support, large agribusinesses with capital, connectivity and technical teams are more likely to benefit first, while smaller farms remain behind. That could deepen the rural technology divide.

The authors argue that policy should focus on the practical barriers farmers face. Broadband expansion is one priority because AI-intensive applications depend on high-quality connectivity. Financial incentives are another because upfront costs and uncertain returns deter smaller producers. Training and extension support are also critical because perceived ease of use depends on whether farmers feel capable of learning and managing new systems.

For ag-tech developers, the findings point to a need for farmer-centered design. AI systems must be transparent, task-specific and easy to integrate into existing farm operations. Developers should avoid assuming that technical sophistication alone will drive adoption. Farmers want clear proof of value, reliable support, strong data protections and tools that fit the work they already do.

The research also suggests that vendors need to address trust directly. This includes explaining how recommendations are generated, giving farmers control over data, offering clear privacy terms and allowing trial use before full adoption. The study notes that farmers often evaluate technology through trialability, testing systems on limited areas before scaling up. That process can help reduce perceived risk.

The paper's findings are especially relevant as agriculture moves further into data-driven systems. AI can support yield forecasting, input optimization, nitrogen reduction, weed detection and resource efficiency. But adoption will depend on whether systems can work under real farm conditions, including weather variability, limited time, broadband gaps and tight budgets.

The study also acknowledges some limits. Because the survey was distributed online and through extension networks, the sample may lean toward farmers already more digitally engaged than the most disconnected producers. This means barriers may be even stronger among farmers with little digital exposure or poor connectivity. The research also measured adoption intention, not actual long-term use, so future studies will need to track whether intent turns into sustained adoption.