Using mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system

Journal Article · Fri Aug 21 00:00:00 EDT 2020 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0237638· OSTI ID:1815641

Eitzel, M. V. ^[1]; Solera, Jon ^[2]; Wilson, K. B. ^[3]; Neves, Kleber ^[4]; Fisher, Aaron C. ^[5]; Veski, André ^[6]; Omoju, Oluwasola E. ^[7]; Mawere Ndlovu, Abraham ^[3]; Mhike Hove, Emmanuel ^[3]

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1. Univ. of California, Santa Cruz, CA (United States). Science and Justice Research Center; OSTI
2. Seven Points Consulting, Lafayette, CA (United States)
3. The Muonde Trust, Midlands Province (Zimbabwe)
4. Universidade Federal do Rio de Janeiro (Brazil)
5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
6. Tallinn Univ. of Technology (Estonia)
7. National Inst. for Legislative and Democratic Studies (National Assembly), Maitama, Abuja (Nigeria)

Complex social-ecological systems can be difficult to study and manage. Simulation models can facilitate exploration of system behavior under novel conditions, and participatory modeling can involve stakeholders in developing appropriate management processes. Participatory modeling already typically involves qualitative structural validation of models with stakeholders, but with increased data and more sophisticated models, quantitative behavioral validation may be possible as well. In this study, we created a novel agent-basedmodel applied to a specific context: Zimbabwean non-governmental organization the Muonde Trust has been collecting data on their agro-pastoral system for the last 35 years and had concerns about land-use planning and the effectiveness of management interventions in the face of climate change. We collaboratively created an agent-based model of their system using their data archive, qualitatively calibrating it to the observed behavior of the real system without tuning any parameters to match specific quantitative outputs. We then behaviorally validated the model using quantitative community-based data and conducted a sensitivity analysis to determine the relative impact of underlying parameter assumptions, Indigenous management interventions, and different rainfall variation scenarios. We found that our process resulted in a model which was successfully structurally validated and sufficiently realistic to be useful for Muonde researchers as a discussion tool. The model was inconsistently behaviorally validated, however, with some model variables matching field data better than others. We observed increased model system instability due to increasing variability in underlying drivers (rainfall), and also due to management interventions that broke feedbacks between the components of the system. Interventions that smoothed year-to-year variation rather than exaggerating it tended to improve sustainability. The Muonde trust has used the model to successfully advocate to local leaders for changes in land-use planning policy that will increase the sustainability of their system.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science (SC)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1815641
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 8 Vol. 15; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
59 BASIC BIOLOGICAL SCIENCES
60 APPLIED LIFE SCIENCES
Agent-based modeling
Crop management
Crops
Graphs
Grazing
Livestock
Simulation and modeling
Sustainable agriculture