What Can We Learn from Droppers and Non‐adopters About the Role of Advice in Agricultural Innovation?

Abstract

Data were collected using a simplified version of the Triggering Change Model of farm decision-making, developed by Sutherland et al. (2012). In this model, farmers' path dependency -i.e. where the decision options presented to them are dependent on decisions or experiences encountered in the pastis conceptualised as being interrupted by a trigger event (such as a period of low profitability, or entrance of a farm successor to the business) which leads to active information seeking about potential innovations and development options. This is followed by assessment of potential innovations, and if these innovations are deemed promising, a period of implementation while the innovation becomes fully embedded in farming practice. The model is idealised and recognises that farmers may skip or repeat steps, depending on specific situations. It has similarities to Rogers' Diffusion of Innovations Model, which identified a process of knowledge, persuasion, decision, implementation and confirmation (Rogers, 2003).

Bridging the gap : Understanding farmer-system dynamics in the transition toward sustainable coffee production in Costa Rica

“…In chapter 4, I empirically show the usefulness of studying CSA technologies as interrelated, since understanding the factors that determine the complementary adoption of technologies deepens the debates of rethinking adoption a single decision but seeing it as a dynamic process (Kiptot et al, 2007;Sutherland et al, 2022). It shows that interdependencies among technologies and practices are an important determinant for adoption and non adoption.…”

“…The study is limited by the fact that the data were captured within the constraints of a set of temporal and spatial frameworks and the data analysis only allowed for correlations, not cause-and-effect relationships. A more nuanced explanation of the complexity of technological change dynamics may be useful for understanding individual adoption paths over time (as proposed by Montes de Oca Munguia et al, 2021 andSutherland et al, 2022). Further research could benefit from this type of long-term analysis for analyzing current technology adoption paths or modelling future pathways.…”