From narratives to numbers: Quantifying Euro2040 farm profiles using FADN data

Area of Science:

• Agricultural Economics
• Farm Management Systems
• Policy Analysis

Background:

• European agriculture exhibits increasing diversity, yet policy and statistical systems use outdated, simplified farm classifications.
• Existing systems fail to capture the heterogeneity and emerging models of contemporary farming.
• The Euro2040 profiles offer a narrative-based segmentation but lack empirical applicability in standard datasets.

Purpose of the Study:

• To translate the qualitative Euro2040 farm profiles into measurable variables for empirical analysis.
• To bridge the gap between narrative farm typologies and observable farm-level data.
• To support more targeted agricultural policy design by improving farm classification.

Main Methods:

• Structured expert elicitation to define relevant indicators for farm profiles.
• Development of an indicator-profile matrix using Farm Accountancy Data Network (FADN)-based data.
• Analysis of key dimensions: economic size, land use, labor structure, diversification, and subsidy dependence.

Main Results:

• A framework linking qualitative Euro2040 profiles with quantitative, observable farm data was developed.
• Systematic differentiation between farm types is enabled through key economic and structural dimensions.
• Certain emerging farm types (urban, controlled-environment, social-care, cellular) remain poorly captured in current datasets, highlighting statistical system limitations.

Conclusions:

• The developed framework facilitates more detailed empirical analysis of farm diversity.
• Improved farm classification supports more targeted and effective agricultural policy design.
• There is a critical need to update and extend current indicators to encompass emerging farming models and enhance policy relevance.