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Strategic bidding behaviour in agricultural land rental markets: Reinforcement learning in an agent-based model

Author

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  • Njiru, Ruth Dionisia Gicuku
  • Dong, Changxing
  • Appel, Franziska
  • Balmann, Alfons

Abstract

Agricultural land markets are crucial for efficient land allocation, yet they face complexities arising from land characteristics and the heterogeneous nature of market participants. This study explores how to address heterogeneity in the modelling process for land markets models by integrating Deep Reinforcement Learning (DRL) into the agent-based model AgriPoliS, to model strategic bidding behaviour. The simulations demonstrates that a DRL agent adapts its bidding strategies based on long-term growth objectives, experience, competitive interactions and adaptive decision-making leading to increased land rental and farm growth compared to a standard agent using a fixed bidding strategy. The results reveal how strategic behaviour not only improve individual farm performance but also affect neighbouring farms, emphasizing the dynamic interactions within land markets. By capturing the agent’s strategic behaviour, this work contributes towards more realistic modelling of agricultural land market dynamics and offers insights into the implications of potential land market regulations. Future research will explore multi-agent frameworks to further refine these interactions and address the limitations of static bidding strategies.

Suggested Citation

  • Njiru, Ruth Dionisia Gicuku & Dong, Changxing & Appel, Franziska & Balmann, Alfons, 2025. "Strategic bidding behaviour in agricultural land rental markets: Reinforcement learning in an agent-based model," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 28(2), pages 392-422.
    • Handle: RePEc:zbw:espost:320715
    DOI: 10.22434/ifamr.1126
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    References listed on IDEAS

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    3. Heinrich, Florian & Appel, Franziska & Balmann, Alfons, 2019. "Can land market regulations fulfill their promises?," FORLand Working Papers 12 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    4. Franziska Appel & Alfons Balmann, 2023. "Predator or prey? Effects of farm growth on neighbouring farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 214-236, February.
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    6. Balmann, Alfons, 1997. "Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 24(1), pages 85-108.
    7. Kellermann, Konrad & Sahrbacher, Christoph & Balmann, Alfons, 2008. "Land Markets In Agent Based Models Of Structural Change," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6647, European Association of Agricultural Economists.
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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