Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. (May 2021)

• Record Type: Journal Article
• Authors: Shang, Linmei; Heckelei, Thomas; Gerullis, Maria K.; Börner, Jan; Rasch, Sebastian
• Is Part Of: Agricultural systems. Volume 190(2021)
• Publication Date: 2021-05
• Subjects: Technology adoption -- Innovation diffusion -- Digital farming -- Agent-based modeling -- Farm level -- systematic review; Q16 -- Q12 -- C61; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2021.103074
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: 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. SIGNIFICANCE: Our empirically grounded conceptual framework is the first holistic approach to connect the dots between the wealth of empirical research on technology adoption with more model-driven investigation of innovation diffusion in agent-based studies. Focusing on digital farming technologies, it may serve as a reference for those studying the adoption and diffusion of such technologies beyond farm scale. Furthermore, this framework can be the basis for contextual applications to inform policy-makers trying to foster the diffusion of suitable digital technologies through interventions as it highlights where policy can impact important aspects of adoption via relevant processes of diffusion. Graphical abstract: Unlabelled Image Highlights: Empirical literature on adoption of digital farming is not well connected with agent-based models on innovation diffusion. We connect empirical adoption studies and system approaches of innovation diffusion by review and synthesis. Agent-based models can explicitly represent complex systemic relationships but lack empirical validation. We converge the insights from our reviews into a conceptual framework. We provide a reference to study the adoption of digital farming and guide future analyses informing policy-makers.