Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320715 
Year of Publication: 
2025
Citation: 
[Journal:] International Food and Agribusiness Management Review [ISSN:] 1559-2448 [Volume:] 28 [Issue:] 2 [Publisher:] Brill [Place:] Leiden [Year:] 2025 [Pages:] 392-422
Publisher: 
Brill, Leiden
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.
Subjects: 
AgriPoliS
agent-based modelling
bidding strategy
deep reinforcement learning
farm growth
strategic interactions
JEL: 
C63
D21
Q18
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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