Modeling adaptive policy feedbacks in conservation agriculture adoption: a system dynamics perspective

Purpose This study develops a system dynamics framework to examine how behavioral, institutional and structural feedbacks shape the adoption of conservation agriculture (CA) among smallholder farmers. It approaches CA adoption as a complex adaptive process driven by multiple, interacting feedback loops rather than linear cause–effect relations. Design/methodology/approach Drawing on data from the 2024 Agricultural Census of Khuzestan Province, Iran, the research builds a causal loop model and a stock–flow simulation to capture interactions among financial incentives, awareness diffusion, perceived risk and social influence. The model's reliability was assessed through extreme-condition tests, boundary adequacy and expert validation. Four policy scenarios, namely subsidy provision, land consolidation, capacity building and integrated policy design, were simulated for the period 2025–2035. Findings Results indicate that isolated interventions produce limited and short-lived effects, whereas coordinated policies combining economic, institutional and educational levers create reinforcing feedbacks that strengthen adoption and weaken behavioral resistance. Peer learning and social influence emerge as decisive factors in overcoming initial skepticism. Originality/value By embedding behavioral and institutional feedback into a single dynamic framework, the study contributes to the field of social cybernetics and adaptive policy modeling. The framework provides a transferable simulation tool for exploring feedback-driven strategies to accelerate sustainable innovation in agriculture. The study conceptually frames conservation agriculture adoption through a cybernetic perspective on adaptive feedback, highlighting how interactions among policy, behavioral and institutional subsystems maintain dynamic balance and support sustainability transitions.