Research Unveils Crop Advisors' AI Tool Preferences

Study identifies key design features that influence whether trusted agricultural advisors will choose AI-powered decision support systems.

Published on Feb. 26, 2026

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A new peer-reviewed study co-authored by researchers from Virginia Tech and the University of Vermont offers insights into how Certified Crop Advisors (CCAs) across North America evaluate the next generation of artificial intelligence-enabled decision support systems (AI-DSS) for agriculture. The study, published in Technological Forecasting and Social Change, identifies the specific design features that most influence whether trusted agricultural advisors will choose AI tools—and what might hold them back.

Why it matters

As AI-generated predictions, classifications, and recommendations become increasingly used to guide agricultural decisions, understanding the preferences and concerns of trusted crop advisors is crucial for driving widespread adoption, especially among mid-sized and smaller farms. The study's findings can help AI developers and policymakers design trustworthy, context-sensitive tools that work for diverse farms and advisory systems.

The details

The research team used a discrete-choice experiment to analyze how crop advisors weigh trade-offs among cost, accuracy, spatial precision, and data ownership when evaluating AI-based systems. Key findings include: Advisors consistently favored systems that were easy to use and incorporated satellite data over more complex, ultra-accurate tools requiring intensive data inputs; cost and data ownership emerged as major determinants of adoption, with advisors preferring systems that allow users to retain full or shared control over their data; and advisors favored AI-DSS tools that augment rather than automate their work, valuing editable recommendations, local calibration, and field-verification options.

  • The study was published in the May 2026 issue of Technological Forecasting and Social Change.
  • The research was conducted in collaboration with the American Society of Agronomy and supported by the National Science Foundation and the USDA National Institute of Food and Agriculture.

The players

Maaz Gardezi

An Associate Professor in the School of Public and International Affairs at Virginia Tech and the study's principal investigator.

Asim Zia

A Professor of Public Policy and Computer Science at the University of Vermont and a co-author of the study.

Donna M. Rizzo

The Dorothean Chair and Professor of Civil & Environmental Engineering at the University of Vermont and a co-author of the study.

American Society of Agronomy

The organization that collaborated with the research team.

National Science Foundation

The organization that provided funding for the research.

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What they’re saying

“Technical performance of AI tools matters in agriculture, but cost and data ownership—especially shared or open models—are pivotal to selection. Crop advisors prefer systems that augment rather than replace professional judgment.”

— Maaz Gardezi, Study Principal Investigator (Technological Forecasting and Social Change)

“Certified crop advisors are among the most trusted technical experts that farmers in the US turn to. Designing AI decision tools that enhance, not replace, their expertise is essential for building agricultural systems that are productive, equitable, and climate‑resilient.”

— Asim Zia, Professor of Public Policy and Computer Science, University of Vermont (Technological Forecasting and Social Change)

“These insights help move AI for agriculture beyond performance metrics. The goal is trustworthy, context-sensitive tools that work for diverse farms and advisory systems.”

— Donna Rizzo, Dorothean Chair and Professor of Civil & Environmental Engineering, University of Vermont (Technological Forecasting and Social Change)

What’s next

The research team plans to continue working with the American Society of Agronomy and other stakeholders to further refine the design of AI-powered decision support systems that meet the needs and preferences of certified crop advisors.

The takeaway

This study highlights the importance of aligning AI-based agricultural tools with the real-world values and constraints of the trusted experts who advise farmers. By prioritizing simplicity, transparency, and user control, AI developers can create decision support systems that enhance, rather than replace, the professional judgment of certified crop advisors.