Farmers Buy Digital Tools but Often Ignore the Data They Generate

Digital agriculture has promised a revolution in how farms are managed, from GPS-guided machinery and IoT sensors to sophisticated farm management platforms that stream real-time information about crops, soil, and livestock. Yet a new study from Western Canada suggests that the hardest part of this transformation may not be convincing farmers to buy the technology, but getting them to actually use the data it produces. A survey of hundreds of commercial producers across Alberta, Saskatchewan, and Manitoba reveals a striking gap between adoption and engagement: many farmers who have invested in digital agricultural technologies make only limited use of the insights those tools generate, and the factors that determine whether data ends up informing daily decisions are largely psychological and behavioral rather than demographic.

The research, published in Smart Agricultural Technology, was led by Sabrina Gulab, Hanan Ishaque, and Guillaume Lhermie, who surveyed 587 commercial crop and livestock producers operating farms larger than 700 acres across the three Prairie provinces. After restricting the analysis to producers who had adopted at least one digital agricultural technology, or DAT, the final analytical sample comprised 392 operations. The team used a binary logit regression model to identify which factors were associated with what they call data-use intensity, defined as the degree to which producers integrate technology-generated data into day-to-day farm management decisions. Respondents were classified as making high use of data or low or no use, and the model estimated how behavioral, psychological, and institutional variables shifted the probability of falling into the high-use category.

The theoretical foundation of the study is an extension of two influential frameworks in technology acceptance research: the Technology Acceptance Model, or TAM, and the Unified Theory of Acceptance and Use of Technology, known as UTAUT. Both frameworks were originally designed to explain why people adopt technology in the first place, emphasizing constructs such as perceived usefulness, effort expectancy, and facilitating conditions. The authors argue that these models fall short in the post-adoption phase, where the relevant question is not whether a farmer expects a tool to perform, but whether the continuous stream of data it generates is perceived as actionable, trustworthy, and valuable for ongoing decisions. They systematically remapped UTAUT constructs to post-adoption equivalents: performance expectancy became the perceived usefulness of data insights, facilitating conditions became data handling support, and social influence was extended to capture emerging norms around data sharing and collective contribution to agricultural data ecosystems.

The descriptive findings alone are sobering for the digital agriculture industry. Only 40.3 percent of surveyed producers reported high use of data in farm decision-making, while 54 percent reported low use and 6 percent reported no use at all. In other words, a majority of farmers who had already paid for and installed digital technologies were not meaningfully integrating the resulting information into their management choices. Producers operating larger farms showed higher levels of data integration than those on smaller operations, a pattern the authors attribute to economies of scale, where data-driven management produces clearer efficiency gains on complex, resource-intensive enterprises.

The regression results sharpen the picture considerably. Perceived usefulness of data emerged as the single strongest predictor: a one-unit increase in the belief that data helps optimize resources and inputs was associated with a 20.4 percentage point higher probability of high data use. Openness and proactive behavior came next, with roughly a one-standard-deviation increase associated with a 14.9 percentage point rise in the probability of high data use. Producers who actively seek information about new technologies, experiment with unfamiliar tools, and attend workshops appear to sustain engagement with data long after the initial purchase. Altruism also played a meaningful role, with producers comfortable sharing farm data with providers, researchers, companies, and government institutions showing an 8.7 percentage point higher probability of high data use, consistent with the idea that contributing to a shared data ecosystem both reflects and reinforces deeper engagement with one’s own data.

On the negative side, risk perception significantly suppressed data engagement. Producers who felt the risks of digital technologies outweighed their benefits had an 8.1 percentage point lower probability of high data use. Notably, the study uncovered a moderating effect: the negative influence of risk perception was stronger among high adopters who used many technologies, suggesting that farmers with the most at stake operationally and financially are also the most sensitive to concerns about data privacy, system reliability, and technological risk. Lack of digital literacy was another substantial barrier, associated with a 9.8 percentage point lower probability of high data use. Drawing on cognitive load theory, the authors argue that when interpreting complex dashboards and agronomic recommendations exceeds a producer’s cognitive resources, the data simply never makes it into the decision-making process.

Perhaps the most counterintuitive finding concerns social support. Producers who frequently consulted neighbors when they ran into difficulties with their technologies were less likely to use data intensively, with each unit increase in reliance on peer consultation associated with a 4.3 percentage point decrease in the probability of high data use. The authors interpret this as evidence that dependency-oriented support may substitute for independent engagement: farmers with lower technological self-efficacy lean on neighbors rather than developing the autonomous problem-solving skills needed to work with data themselves. This challenges a long-standing assumption in agricultural extension literature that peer networks uniformly accelerate technology uptake, and it suggests that building independent digital competence may matter more than facilitating help-seeking.

Equally striking is what did not predict data use. Concerns around data governance, including privacy, ownership, and commercial exploitation of farm data, showed no statistically significant association with data-use intensity, nor did trust in technology providers or the lack of facilitating conditions such as technical support and system interoperability. These factors dominate the adoption literature and are frequently cited in policy debates, yet in this sample of commercially established producers they did not shape what happened after adoption. The authors propose two explanations: established commercial producers may already operate within sufficiently developed governance arrangements, or governance concerns may function primarily as barriers at the adoption stage rather than during ongoing data integration. Either way, the finding underscores a stage-based view of technology engagement in which different barriers operate at different points along the adoption continuum.

The implications extend beyond farm gates. High-quality, farm-level data are the raw material for the next generation of AI-enabled agriculture, feeding the predictive models and recommendation systems that promise precision forecasting and optimized input use. If most adopters leave their data unexamined, both current returns on technology investment and future innovation pipelines are constrained. The authors argue that policy should therefore prioritize behavioral and cognitive enablers alongside infrastructure spending: strengthening digital literacy, reducing perceived risk through experiential learning, and framing data sharing as a contribution to the collective resilience of the agricultural community. Crop producers, who showed a 29.3 percentage point lower probability of high data use than livestock producers, may deserve particular attention, since livestock systems tend to depend on continuous monitoring that makes data value more immediately visible. As governments pour money into rural broadband and digital agriculture subsidies, this study offers a clear warning: the bottleneck is no longer the technology itself, but the human capacity and motivation to turn its output into decisions.

Subject of Research: Post-adoption data use behavior among commercial crop and livestock producers using digital agricultural technologies in Western Canada.

Article Title: Beyond adoption: Understanding data use behavior in digital agriculture

Article References: Gulab, S., Ishaque, H., & Lhermie, G. (2026). Beyond adoption: Understanding data use behavior in digital agriculture. Smart Agricultural Technology, 15, Article 102546. https://doi.org/10.1016/j.atech.2026.102546

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102546

Keywords: digital agriculture, data use behavior, precision agriculture, technology adoption, UTAUT, data governance, digital literacy, farm decision-making, risk perception, altruism, Western Canada, agricultural policy