M. Lisa Yeo ^* Catherine Keske

• University of California, Merced, Merced, United States

Digital agriculture supports farmers' decision making to improve productivity and profitability. However, adoption of digital technology is uneven. Through interviews with 21 medium acreage almond growers and crop consultants in California's Central Valley, we examine barriers to adoption through the lens of technology acceptance models, Technology Acceptance Model (TAM-3) and Unified Theory of Acceptance and Use of Technology (UTAUT-2). Not surprisingly, farmers are willing to adopt technology when profitability and ease of use are shown, with economic returns (either anticipated or demonstrated) from the technology investment serving as the primary factor influencing adoption. Trust operates as a moderating factor to the desire for economic returns that influences adoption. There may be trust, or lack of trust, in technology performance or in the advisors who recommend it. Producer trust is affected by expectations of technology relevance and usefulness, and it is influenced by prior experience. Concerns about data management (e.g. governance, quality, privacy, security) take a back seat to more practical issues such as profitability, leaving producers in an imbalanced position with tech companies who have an interest in their agricultural data. We assert that producer acceptance of data management practices (despite their uncertainty in how to utilize the data being generated) implies that there is a basic level of trust in tech companies' data management practices that is consistent with Gardezi and Stock's (2021) models of moralistic trust behaviors for precision ag adoption. Our findings contribute to the growing research on digital agriculture that debates the benefits and downsides of digital agriculture.