Dhofar University Study: Oman's AI Farming Gap Isn't the Tech

Oman has no shortage of AI pilots in agriculture: sensor-fed greenhouses, hydroponic towers, nutrient-prediction models. But a new peer-reviewed study out of Dhofar University in Salalah argues that the technology was never really the hard part. What actually determines whether a farm adopts AI, the researchers found, is whether its people, leadership and budgets are ready for it.

🔑 Key Takeaways

• Dhofar University researchers published a qualitative study on September 8, 2026 in the Journal of Science and Technology examining why AI adoption in Omani smart farming lags behind the available technology.
• The study is based on 20 semi-structured interviews with agricultural engineers, hydroponic farm managers, researchers and technicians across the Sultanate.
• Organizational factors, workforce training, leadership vision and financial capacity, generated 90 coded references in the analysis, more than the technological (72) or environmental (72) dimensions.
• Water scarcity and climate stress emerged as the strongest motivators pushing Omani farms toward AI-enabled systems despite the organizational barriers.
• The researchers point to government-university collaboration as the main channel currently enabling AI adoption to move forward at all.

🌱 Inside the Study: 20 Interviews Across Oman's Farms

The paper, titled "AI Integration in Omani Smart Farming Ecosystems: A Qualitative TOE-Based Exploration," was written by Duaa Al Ghafri, Abdulaziz Aborujilah and Samir Hammami of the Department of Management Information Systems at Dhofar University. According to the Journal of Science and Technology, the researchers conducted 20 semi-structured interviews with agricultural engineers, hydroponic farm managers, researchers and technicians working on smart farming ecosystems across the Sultanate.

The team analyzed the interviews using Braun and Clarke's six-phase thematic analysis method, coded in NVivo software, and mapped the results onto the Technology-Organization-Environment (TOE) framework, a model widely used to study why organizations adopt or resist new technology.

🏢 Organizations, Not Algorithms, Are the Bottleneck

The headline finding is a numbers story. Of the coded references pulled from the interview transcripts, the organizational dimension of the TOE framework generated 90 references, compared with 72 each for the technological and environmental dimensions, as reported in the published study. In plain terms, farm managers and technicians spent more time talking about workforce training gaps, unclear leadership direction and tight budgets than they did about sensors, models or connectivity.

That framing matters for a country that has spent heavily on AI infrastructure and strategy documents. It suggests that closing Oman's agri-tech gap will depend as much on training programmes for farm staff as it does on procuring better hardware, a theme that echoes the workforce push already underway in schools under Oman's new AI-focused school year framework.

💧 Water Scarcity Is Forcing the Issue Anyway

Despite the organizational friction, the study found that environmental pressure, chiefly water scarcity and climate stress, is a powerful motivator pushing farms toward AI-enabled systems regardless of readiness gaps. Hydroponic and controlled-environment methods can cut water use dramatically compared with traditional soil farming, which explains why Omani operators keep experimenting with AI-driven nutrient and irrigation systems even when their organizations are not fully prepared to manage them.

This is not the first time Dhofar University researchers have flagged the gap between Oman's agricultural technology ambitions and the constraints on the ground. An earlier paper by two of the same authors, published on February 4, 2026 in the same journal and titled "Towards an Integrated IoT and AI Architecture for Sustainable Agriculture: The Case of Oman," identified insufficient water, soil salinity, technology fragmentation and weak cybersecurity as the sector's core structural problems. The new study builds on that engineering-focused work by asking what happens once the technology is actually placed in front of the people expected to run it.

🤝 Government-University Partnerships as the "Enabling Channel"

The researchers describe collaboration between government bodies and universities as the main mechanism currently allowing AI adoption in Omani agriculture to progress at all, even as organizational readiness lags. That finding lines up with the broader pattern in Oman's innovation ecosystem, where applied research increasingly moves forward through structured academia-industry links rather than isolated pilots.

For policymakers, the practical implication is straightforward: funding a sensor network or an AI model is the easy part. Building the training pipelines, leadership buy-in and financing structures that let farm staff actually use these tools is the harder, longer-term task, and one that the study suggests has received comparatively less attention so far.

🇴🇲 Why This Matters for Oman

Oman Vision 2040 treats food security and technology diversification as linked priorities, and agriculture remains one of the sectors where AI adoption is expected to do real economic work rather than serve as a showcase. This study is a reminder that progress on that front depends less on the next AI product launch and more on whether Omani farm managers, technicians and engineers have the training, leadership support and budgets to put these tools to work. For a Sultanate investing heavily in AI infrastructure and talent programmes elsewhere, that is a useful, if less glamorous, lesson from the ground up.