New Study Examines Crop Advisors’ Preferences for AI-Enabled Decision Support Systems

Burlington, Vt. — Feb 25, 2026 — A groundbreaking peer-reviewed study, co-authored by researchers from Virginia Tech and the University of Vermont, sheds light on how Certified Crop Advisors (CCAs) across North America assess the next generation of artificial intelligence–enabled decision support systems (AI-DSS) in agriculture. The study, published in Technological Forecasting and Social Change by Elsevier, highlights the key design features influencing these trusted agricultural advisors in their adoption of AI tools and what challenges they face.

Conducted in collaboration with the American Society of Agronomy, the research was led by Maaz Gardezi, an Associate Professor at Virginia Tech’s School of Public and International Affairs. The team included co-authors from UVM: Professor Asim Zia, Professor Donna M. Rizzo, Research Associate Professor Scott C. Merril, along with UVM graduate students Benjamin E.K. Ryan and Halimeh Abuayyash, as well as Virginia Tech graduate students Indunil Dharmasiri, Pablo Carcamo, and Bhavna Joshi. Other contributors included David Clay, Distinguished Professor at South Dakota State University, and John McMaine, Extension Associate Professor at the University of Kentucky. Using a discrete-choice experiment, the researchers examined how crop advisors balance trade-offs among cost, accuracy, spatial precision, and data ownership when evaluating AI-based systems.

Key Findings

• Simplicity and usability matter most. Advisors consistently preferred systems that were user-friendly and integrated satellite data over more sophisticated tools requiring extensive data inputs.
• Trust depends on transparency and data governance. Cost and data ownership emerged as critical factors in adoption, with advisors favoring systems that allowed users to fully or partially control their data.
• AI shouldn’t replace professional judgment. Crop advisors leaned towards AI-DSS tools that support rather than automate their roles, valuing options for editable recommendations, local calibration, and field verification.
• Tech attitudes shape adoption. Advisors with a positive outlook on AI were more likely to embrace data-intensive systems, while those with privacy concerns were less inclined to adopt tools that demanded extensive farmer data.

Maaz Gardezi, the principal investigator of the study, encapsulates the research’s key insights: “While technical performance is crucial, cost and data ownership—particularly through shared or open models—are fundamental to selection. Crop advisors prefer systems that augment rather than supplant their expertise.”

A Turning Point for Agricultural AI

The timing of this study is notable, as AI-generated predictions, classifications, and recommendations are becoming more prominent in areas such as fertilizer application, pest and disease management, and irrigation scheduling. However, adoption has been slow, especially among mid-sized and smaller farms, due in part to concerns around privacy, affordability, transparency, and trust.

Asim Zia, a Professor of Public Policy and Computer Science at UVM, remarked, “Certified Crop Advisors are some of the most trusted technical experts for farmers in the US. It is vital to design AI decision tools that enhance, rather than replace, their knowledge to create agricultural systems that are productive, equitable, and resilient to climate change.”

A Socio-Technical Framework for Trustworthy AI

The authors advocate for a socio-technical approach from AI developers and policymakers, aligning algorithms with the real-world needs and values of expected users. Their findings suggest:

• Co-creation with crop advisors and farmers during the development process.
• Transparent cost structures and clear communication regarding trade-offs.
• User-controlled data governance models.
• Human-in-the-loop designs that maintain advisor autonomy.

Donna Rizzo, a co-author and Dorothean Chair and Professor of Civil & Environmental Engineering at UVM, noted, “These insights push AI for agriculture beyond mere performance metrics. The objective is to create trustworthy, context-sensitive tools that cater to a variety of farms and advisory systems.”

About the Study

The article, titled “A Socio-Technical Framework for Analyzing Crop Advisors’ Preferences for AI-Based Decision Support Systems,” appears in the May 2026 issue of Technological Forecasting and Social Change. The research received support from the National Science Foundation (Grant Nos. 2202706 and 2026431) and the USDA National Institute of Food and Agriculture (Award No. 2023‑67023‑40216).

This study offers valuable insights into the integration of AI in agriculture, highlighting the importance of creating supportive, user-friendly systems that respect the roles of agricultural advisors while boosting productivity in farming.

As we move forward, the challenge remains to build trust and usability into AI tools so that they can truly enhance agricultural practices, making them more effective and sustainable for future generations.