MAASSD: Methodology for Agent-based modelling for Agricultural System Simulation in Developing Countries

Modelling agricultural systems in developing countries is attracting particular attention, as the models provide policy-makers with a relevant framework for ex ante analysis of their decision-making processes, enabling them to develop policies to address future challenges such as climate change and...

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Vydáno v:Food and Ecological Systems Modelling Journal Ročník 6
Hlavní autor: Belem, Mahamadou
Médium: Journal Article
Jazyk:angličtina
Vydáno: 27.10.2025
ISSN:2815-3197, 2815-3197
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Abstract Modelling agricultural systems in developing countries is attracting particular attention, as the models provide policy-makers with a relevant framework for ex ante analysis of their decision-making processes, enabling them to develop policies to address future challenges such as climate change and population growth. Agent-based modelling (ABM) is increasingly being used for this purpose. ABM enables the simulation of the heterogeneity of farmers’ decision-making processes and the interactions between human decision-making processes and the environment. However, despite the wide use of ABM in simulating agricultural systems in developing countries, a clear methodology is still lacking. Current applications of ABM in agricultural system simulations are disparate and cannot be replicated in other contexts. This study aims to propose a unique methodology for agent-based modelling of agricultural systems in developing countries. The resulting methodology, ‘MAASSD’ (Methodology for Agent-based Modelling for Agricultural System Simulation in Developing Countries), is generic and multi-scale, taking into account multi-stakeholder engagement for knowledge integration and sharing. Our methodology is based on existing methodologies and provides a unique approach to agricultural system modelling using ABM. However, MAASSD methodology differs from these existing methodologies with regard to agricultural systems. The methodology has been tested in the simulation of the feedback loop between agricultural dynamics and migration in Burkina Faso, demonstrating its robustness. In future, the MAASSD methodology will be tested in a large number of contexts to demonstrate its effectiveness in representing agricultural systems in developing countries.
AbstractList Modelling agricultural systems in developing countries is attracting particular attention, as the models provide policy-makers with a relevant framework for ex ante analysis of their decision-making processes, enabling them to develop policies to address future challenges such as climate change and population growth. Agent-based modelling (ABM) is increasingly being used for this purpose. ABM enables the simulation of the heterogeneity of farmers’ decision-making processes and the interactions between human decision-making processes and the environment. However, despite the wide use of ABM in simulating agricultural systems in developing countries, a clear methodology is still lacking. Current applications of ABM in agricultural system simulations are disparate and cannot be replicated in other contexts. This study aims to propose a unique methodology for agent-based modelling of agricultural systems in developing countries. The resulting methodology, ‘MAASSD’ (Methodology for Agent-based Modelling for Agricultural System Simulation in Developing Countries), is generic and multi-scale, taking into account multi-stakeholder engagement for knowledge integration and sharing. Our methodology is based on existing methodologies and provides a unique approach to agricultural system modelling using ABM. However, MAASSD methodology differs from these existing methodologies with regard to agricultural systems. The methodology has been tested in the simulation of the feedback loop between agricultural dynamics and migration in Burkina Faso, demonstrating its robustness. In future, the MAASSD methodology will be tested in a large number of contexts to demonstrate its effectiveness in representing agricultural systems in developing countries.
Author Belem, Mahamadou
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