- 3.2Impact Factor
- 5.9CiteScore
- 16 daysTime to First Decision
Land, Volume 12, Issue 4
April 2023 - 210 articles
Cover Story: Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Our review finds that LU ABMs mainly rely on predefined behavioural rules to model individual farmers’ decision making, prioritising explanatory over predictive purposes and thus limiting their use for policy assessment. We propose a framework to develop data-driven ABMs for agricultural LU, aimed at improving their ability to predict policy outcome. This framework avoids predefined theoretical or heuristic rules and instead resorts to ML algorithms to learn agents’ behavioural rules from data, exploiting the increased availability of remote sensing products and agricultural micro-data. View this paper
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Land - ISSN 2073-445XCreative Common CC BY license