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<p>Given the evolving nature of agricultural systems, advanced modelling approaches are crucial for understanding dynamics and filling the knowledge gaps in complex agricultural decision-making processes. This systematic review examines the integration of agent-based modelling (ABM) and mathematical programming (MP) in agricultural contexts. We analyzed selected studies, characterizing model structures, decision processes, agent interactions and comparative strengths/limitations of both modelling techniques. Findings reveal diverse applications spanning resource management, policy analysis and production dynamics. Key strengths of integration include enhanced spatial representation and optimization capabilities. Limitations involve computational intensity and challenges in capturing real-world complexity. The review highlights opportunities for improving behavioural realism, standardizing methods and extending them to wider and different agricultural domain dynamics. By synthesizing current approaches and identifying research gaps, this work aims to guide future development of integrated models supporting sustainable agricultural decision-making.</p>

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Agricultural systems modelling: an exploration of agent-based modelling and mathematical programming integration through systematic literature review

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Kadriye Kardelen Guzel,
  • Amélie Bourceret,
  • Sophie Drogue

摘要

Given the evolving nature of agricultural systems, advanced modelling approaches are crucial for understanding dynamics and filling the knowledge gaps in complex agricultural decision-making processes. This systematic review examines the integration of agent-based modelling (ABM) and mathematical programming (MP) in agricultural contexts. We analyzed selected studies, characterizing model structures, decision processes, agent interactions and comparative strengths/limitations of both modelling techniques. Findings reveal diverse applications spanning resource management, policy analysis and production dynamics. Key strengths of integration include enhanced spatial representation and optimization capabilities. Limitations involve computational intensity and challenges in capturing real-world complexity. The review highlights opportunities for improving behavioural realism, standardizing methods and extending them to wider and different agricultural domain dynamics. By synthesizing current approaches and identifying research gaps, this work aims to guide future development of integrated models supporting sustainable agricultural decision-making.