Understanding the Adoption of Innovations in Agriculture: A Review of Selected Conceptual Models

Models can provide a structured way to think about adoption and provide a method to investigate the impacts of different factors in the adoption process. With at least 70 years of research in the adoption of agricultural innovations, there has been a proliferation of adoption models, both conceptual and numerical. This diversity has resulted in a lack of convergence in the way adoption is defined, explained, and measured, causing agricultural extension and policy to rely on a body of literature that is often not able to offer clear recommendations on the variables or mechanisms that can be used to design interventions. We conducted a review of conceptual models to clarify the concepts and approaches used in the practice of modeling adoption in agriculture. We described general adoption conceptual models originating from sociology, psychology, economics, and marketing and reviewed examples of models specifically defined for the study of adoption in agriculture. We also broadly assessed the ability of conceptual models to support building numerical models. Our review covered a range of modeling approaches for diffusion and individual adoption, illustrating different perspectives used in the literature. We found that key elements that should be used in adoption models for agriculture include: a way to assess the performance of the proposed new technology (e.g., relative advantage, both economic and non-economic) in relation to the existing technology or practice in place, the process of learning about this advantage, the interaction between individual decision-making and external influences, and characteristics of potential adopters affecting their attitudes towards the technology. We also detected inconsistencies in how different elements are treated in different conceptual models, particularly behavioral elements such as attitudes, motivations, intentions, and external influences. In terms of modeling, the main implication of these inconsistencies is the difficulty to generate quantitative evidence to support these models since multiple interpretations make it difficult to achieve consistency in the definition of observable, measurable variables that can be used to quantify cause-effect relationships. Suggestions for further research in the field include: questioning whether the adoption of all technologies and practices can be represented by the same adoption or learning process, exploring the dynamics in the relationship between adopters and technology before and after adoption, and questioning the basic assumptions behind the process of individual decision-making models and the role of collective decision-making. Findings from this review can be considered by adoption researchers and modelers in their work to assist policy and extension efforts to improve the uptake of future beneficial agricultural innovations.