Summary
This study presents a methodology for deriving data-driven farm typologies suitable for agent-based modelling of agricultural land use decisions. By integrating participatory methods, multivariate statistical analysis, and 20 years of spatiotemporal parcel-level land cover data, the authors identified farm size, private land ownership, crop mix, and EU scheme participation as primary drivers of land use decisions. The typology reveals that family tradition and return on investment were the strongest motivations for maintaining current practices, whilst income support and diversification dominated aspirations for change.
UK applicability
The methodology is directly transferable to UK farm typology research, particularly given the UK's participation in Common Agricultural Policy schemes (pre-Brexit) and current agri-environment stewardship programmes. UK farm-scale questionnaire and land cover datasets could be subjected to the same clustering approach to inform landscape-scale policy design and agent-based modelling of responses to subsidy reform or environmental regulation.
Key measures
Principal component analysis scores; k-means cluster membership; farm size; share of private land; dominant crop types; participation in EU environmental schemes; motivations for land use decisions (ranked)
Outcomes reported
The study identified a data-driven farm typology using principal component analysis and k-means clustering of questionnaire data, spatiotemporal land cover analysis (2000–2020), and participatory methods. Key influencing factors on land use decisions were farm size, private land share, dominant crops, and participation in EU schemes (NATURA2000 and agri-environment-climate measures).
Topic tags
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