Local farmers’ engagement and contributions are increasingly underscored in resources restoration policy. Yet, empirical context-situated understanding of the environmental impacts of farmer-led restoration remains scant. Using six Agent-based Modeling (ABM) simulations that integrate multi-type data, we explore the potential spatial-temporal aggregate patterns and outcomes of local restoration actions in Central Malawi. Findings uncover a 10-year positive trend and spatially explicit potential restoration extent and intensity, greenness, and land productivity, all varying by farmer’s participation level. Landscape regreening is modestly promising with fluctuating greenness levels and low, slightly incremental, then steady land-productivity levels. Findings also show appropriate incentives, restoration knowledge, and inspiring local leadership as propitious management options for boosting local restoration. Bundling these enabling management and policy options would maximize local restoration. Findings suggest empowering bottom-up restoration efforts for enhanced environmental impacts. We also demonstrate the potential of using ABM to offer insights for spatially targeted, evidence-based restoration policy implementation and monitoring.

Ida N. S. Djenontin, Arika Ligmann-Zielinska & Leo C. Zulu (2022) Landscape-scale effects of farmers’ restoration decision making and investments in central Malawi: an agent-based modeling approach, Journal of Land Use Science, 17:1, 281-306, DOI: 10.1080/1747423X.2022.2076948

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