Open AccessUsing mixed methods to construct and analyze a participatory agent-based model of a complex Zimbabwean agro-pastoral system
Author(s) -
M. V. Eitzel,
Jon Solera,
Kirsty Wilson,
Kleber Neves,
Aaron Fisher,
André Veski,
Oluwasola E. Omoju,
Abraham Mawere Ndlovu,
Emmanuel Mhike Hove
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0237638
Subject(s) - context (archaeology) , psychological intervention , computer science , citizen journalism , process (computing) , qualitative property , agent based model , process management , matching (statistics) , data science , field (mathematics) , management science , risk analysis (engineering) , machine learning , psychology , artificial intelligence , business , engineering , geography , mathematics , statistics , archaeology , psychiatry , world wide web , operating system , pure mathematics
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-based-model 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.