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Farmers’ decision-making for landscape restoration in Central Malawi: insights and implications for decision-rules for LUCC-ABM

Authors: Ida Nadia Djenontin*, Michigan State University, Leo C Zulu, Department of Geography, Environment, and Spatial Sciences; Michigan State University, Arika Ligmann-Zielinska, Department of Geography, Environment, and Spatial Sciences; Michigan State University
Topics: Human-Environment Geography, Development, Africa
Keywords: Environmental behavior, Restoration decision-making processes, Farmer-stakeholders, Demand-side management approach, LUCC-ABM, Central Malawi
Session Type: Poster
Presentation File: No File Uploaded


The momentum for restoring landscapes to address exacerbating environmental degradation and to sustainably supply ecosystem services for socio-ecological well-being is increasing in Sub-Sahara Africa. Engaging local stakeholders, particularly farmers, is crucial in implementing restoration schemes (Mansourian, 2017). While, farmers’ land-use decisions shape land-use covers and changes (LUCC) and the associated ecological outcomes (Villamor et al., 2014), such social dimensions remain neglected for landscape restoration (Djenontin et al., 2018). Why and how farmers decide to embrace restoration activities are poorly-understood and this hinders demand-side management approach as part of the various strategies to enhance delivery of restoration pledges.

We analyze the processes and nature of farmers’ decision making for restoring degraded forest-agricultural landscapes both individually and collectively in Central Malawi. Using a mixed-method analysis, we draw insights from role-playing games conducted within seven focus groups and from a household survey (N=480) in Dedza and Ntchisi Districts.

We characterize the rationales, motives, and incentives underlying farmers’ decisions to engage in different restoration activities in selected forest-agricultural landscapes. Emerging decision toward both tree and non-tree-based restoration behaviors appear diverse, reflecting nuanced goal frames and are categorized as problem-solving oriented, resources/materials-constrained, benefits-oriented, incentive-based, peers/leaders-influenced, knowledge/skill-dependent, altruistic-oriented, rules/norms-constrained, economic capacity-dependent, awareness-dependent, and risk averse-oriented.

Such findings are valuable for computational Agent-Based Modeling (ABM) approaches that explore outcomes of restoration investments by modeling individual decision-making processes and potential policy scenarios. The findings will contribute to parameterizing decision-making rules for a future LUCC-ABM and effectively circumventing often-ad hoc representations of farmers’ decisions and behavioral responses in LUCC-ABMs.

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