A new study co-authored by researchers from Virginia Tech and the University of Vermont offers one of the first large-scale, empirical examinations of how Certified Crop Advisors (CCAs) across North America evaluate next-generation artificial intelligence–enabled decision support systems (AI-DSS) in agriculture. Published in Technological Forecasting and Social Change, the research identifies the key design features that influence whether trusted agricultural advisors adopt AI tools—and what barriers may prevent their use. The study, led by Maaz Gardezi, Associate Professor in the School of Public and International Affairs at Virginia Tech, was conducted in collaboration with the American Society of Agronomy and included co-authors from UVM: Professor Asim Zia, Professor Donna M. Rizzo, Research Associate Professor Scott C. Merril, and graduate students Benjamin E.K. Ryan and Halimeh Abuayyash. Additional contributors were 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 research team analyzed how crop advisors weigh trade-offs among cost, accuracy, spatial precision, and data ownership when considering AI-based systems. The findings reveal that while technical performance is important, cost and data ownership—particularly the availability of shared or open models—are decisive factors in adoption. Gardezi summarized the core insight: “Technical performance 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.” The study comes at a critical moment as AI-generated predictions and recommendations become more common in guiding decisions around fertilizer use, pest and disease control, irrigation scheduling, and carbon and nutrient accounting. Despite this, adoption remains low, especially among mid-sized and smaller farms, due to concerns about privacy, affordability, transparency, and trust. “Certified crop advisors are among the most trusted technical experts that farmers in the U.S. rely on,” said Asim Zia, Professor of Public Policy and Computer Science at UVM. “Designing AI decision tools that enhance, not replace, their expertise is essential for building agricultural systems that are productive, equitable, and climate-resilient.” The authors advocate for a socio-technical framework that aligns AI development with the real-world values, workflows, and constraints of agricultural professionals. Their recommendations emphasize the need for transparent, affordable, and customizable tools that preserve the advisor’s role as a trusted intermediary. “These insights help move AI for agriculture beyond performance metrics,” said study co-author Donna Rizzo, Dorothean Chair and Professor of Civil & Environmental Engineering at UVM. “The goal is trustworthy, context-sensitive tools that work for diverse farms and advisory systems.”