Agent-Based Modeling of Land Use and Agricultural Systems

Agent-based modelling (ABM) has emerged as a powerful computational approach to simulate the decisions and interactions of individual land managers, farmers and other stakeholders within complex agricultural landscapes. By representing each decision-maker as an autonomous ‘agent’ endowed with behavioural rules, preferences and adaptive capacities, ABMs capture heterogeneity in farm size, production goals, risk attitudes and social networks. This bottom-up framework reveals how local choices—such as crop rotations, stocking densities or technology adoption—scale up to influence land-cover change, ecosystem services, greenhouse-gas emissions and rural livelihoods. Applications range from exploring the impacts of subsidy schemes and market incentives on sustainable farming to assessing the resilience of pastoral systems under climate stress. Hybrid approaches combine ABMs with biophysical models or life-cycle assessment to quantify environmental footprints alongside economic outcomes. Across diverse biomes, these models have advanced understanding of policy trade-offs, guided landscape restoration, informed carbon-sequestration incentives and highlighted pathways to balance food security with biodiversity conservation.

Research from Nature Portfolio

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Agent-Based Modeling of Land Use and Agricultural Systems publication trend

The graph below shows the total number of articles in agent-based modeling of land use and agricultural systems across all publications each year (not limited to Nature Index journals).

Technical terms

Agent-based model (ABM): A simulation framework in which individual entities (‘agents’) operate according to defined behavioural rules and interact within an environment, producing emergent system-level outcomes.

Land-use change: The alteration of land cover and management practices—such as conversion between cropland, pasture, forest and urban areas—driven by human decisions and natural processes.

Life-cycle assessment (LCA): A systematic method to quantify environmental impacts of a product or system across all stages, from resource extraction to disposal or recycling.

Data-driven modelling: An approach that uses empirical observations and statistical or machine-learning techniques to infer model parameters and agent decision rules rather than relying solely on theory.

Transhumance: The seasonal migration of livestock between grazing areas—often between lowlands and highlands—to optimise resource use and cope with climatic variability.

References

1. Sustainable farming strategies for mixed crop-livestock farms in Luxembourg simulated with a hybrid agent-based and life-cycle assessment model. Journal of Cleaner Production (2023).
2. Data-driven agent-based modelling of incentives for carbon sequestration: The case of sown biodiverse pastures in Portugal. Journal of Environmental Management (2023).
3. Agent-Based Model for Analyzing the Impact of Movement Factors of Sahelian Transhumant Herds. Human-Centric Intelligent Systems (2024).