Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction

CONTEXT: Adoption and diffusion of digital farming technologies are expected to help transform current agricultural systems towards sustainability. To enable and steer transformation we need to understand the mechanisms of adoption and diffusion holistically. Our current understanding is mainly informed by empirical farm-level adoption studies and by agent-based models simulating systemic diffusion mechanisms. These two approaches are weakly integrated.

OBJECTIVE: Our objective is to build an empirically grounded conceptual framework for adoption and diffusion of digital farming technologies by synthesizing literature on these alternative approaches.
METHODS: We review 32 empirical farm-level studies on the adoption of precision and digital farming technologies and 27 agent-based models on the diffusion of agricultural innovations. Empirical findings are synthesized in terms of significance and partially standardized coefficients, and diffusion studies are categorized by their approaches and theoretical frameworks.
RESULTS AND CONCLUSIONS: We show that farm-level studies focus on farm and operator characteristics but pay less attention to attributes of technology, interactions, institutional and psychological factors. Agent-based models, despite their usefulness for representing system interaction, only loosely connect with empirical farm-level findings. Based on the identified gaps, we develop a conceptual framework integrating farm-level evidence on adoption with a systemic perspective on technology diffusion.