Adapting agroforestry to upland farming systems: narratives from smallholder farmers in Northwest Vietnam

Fruit tree–based agroforestry has been promoted as an alternative farming practice in upland Northwest Vietnam to replace monocultures of staple crops. Although many studies have focused on evaluating the performance of agroforestry systems at the plot level, research on how farmers perceive and evaluate agroforestry considering whole-farm contexts is limited. We explored the perceptions and reasoned management decisions of agroforestry farmers to uncover challenges that hinder the wider use of agroforestry, and we assessed farmers’ strategies for effective management of adoption challenges. We combined the Q methodology and the systems thinking approach. With the Q methodology, we explored prevalent discourses among the members of the farming community on the impact of agroforestry. Systems thinking elucidated a system-wide understanding of farmers’ adaptive decision-making processes. By combining the two approaches, we uncovered the dynamics that shape farmers’ perceptions and the rationale behind their management of the adoption process. Through the Q method, we identified three distinct discourses among participants. Two of these discourses are in favor of agroforestry, highlighting its beneficial impacts on livelihoods and the environment, e.g., through diversification of household income and through soil erosion control. We also generated a collective development pathway outlining how farmers navigated and adapted agroforestry practices to overcome adoption challenges through a whole-system approach to farm resource management. We identified structural barriers, such as unstable farm-gate prices, that may need high-level interventions. Our study adds a new dimension to the assessment of agroforestry through farmers’ perspectives and contributes to the existing body of research on knowledge systems in agroforestry. Considering farmers’ views and their ways of reasoning during innovation processes may allow tailoring appropriate innovations by accounting for unique farm situations and local farming systems. Such locally generated knowledge will have relevance for real-world contexts and therefore be useful for guiding actions.