SMARTscape: An agent-based model to analyse rural landscapes multifunctionality

Abstract

Land-use changes and behavioural adaptation will be essential in global efforts to address environmental crises. For this, landscape multifunctionality will become critical to support a wide range of ecosystem services (ES), land productivity, and economic profitability. This study aims to explore pathways towards achieving sustainable food production while enabling multifunctionality. We developed SMARTscape, an empirically stylized agent-based model (ABM) developed to simulate interactions between land-use patterns, ES supply, and decision-making across a range of stylized landscapes inspired by New Zealand rural systems. SMARTscape combines three agent behavioural types—business-as-usual (BAU), profit-oriented (PO), and environment-oriented (EO)—with three decision-making rules of increasing social complexity. Agents interact with four collective entities (neighbours, peers, industry bodies, government), allowing simulation of bottom-up and top-down influence mechanisms. We designed a factorial experiment of 60 scenarios, from the combination of five landscape compositions, three decision rules, four agent-population behaviour mixes, and tested cluster size effect (on 10 cluster sizes from a very fragmented landscape to very aggregated) on 15 out of the 60 scenarios. Results show that land-use intensity and fragmentation are primary drivers of multifunctionality. However, profit-oriented behaviours can outperform environment-oriented strategies in intensive contexts, particularly through adoption of perennial systems, and behavioural diversity enhances trade-off management. Combined decision-making consistently led to higher multifunctionality, highlighting the importance of social influence. SMARTscape demonstrates the value of integrating social and spatial dynamics in a flexible modelling platform, offering both theoretical insights and a tool for exploring sustainable land-use transitions in data-poor but policy-relevant contexts.