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A key challenge of land-use modelling for supporting sustainable land management is to understand how environmental feedback that emerges from land-use actions can reshape land-use decisions in the long term. To investigate this issue, we apply the Human–Environment System framework formulated by Scholz (2011) as a conceptual guide to read typical feedback loops in land-use systems. We use an agent-based land-use change model (LUDAS) developed by Le et al. (2008, 2010) to test the sensitivity of long-term land-use dynamics to the inclusion of secondary feedback loop learning with respect to different system performance indicators at different levels of aggregation. Simulation experiments were based on a case study that was carried out in the Hong Ha watershed (Vietnam). We specified two model versions that represent two mechanisms of human adaptation in land-use decisions to environmental changes that emerged from land-use actions. The first mechanism includes only primary feedback loop learning, i.e. households adapt to the annual change in socio-ecological conditions and direct environmental response to land-use activities. The second mechanism includes the first one and secondary feedback loop learning, in which households can change their behavioural model in response to changes in socio-ecological conditions at the landscape-community level in the longer term. Spatial-temporal patterns of land-use and interrelated community income changes driven from the two feedback mechanisms are compared in order to evaluate the added value of the inclusion of secondary feedback loop learning. The results demonstrate that the effect of the added secondary feedback loop learning on land-use dynamics depends on domain type, time scale, and aggregation level of the impact indicators.
