Objective 1 of SOWtrack demonstrates how computational optimisation can make EFSA’s risk-based framework operational at scale. By identifying and weighting iceberg indicators, the project ensures that the final set of ABMs is not only feasible on farm but also maximises welfare coverage.
This approach sets a new benchmark in indicator selection methodology, providing a solid foundation for EU-wide risk assessment and practical, science-based welfare monitoring.
Introduction
The first objective of SOWtrack is to identify a robust set of animal-based measures (ABMs) that effectively assess the welfare of sows and piglets. This requires balancing scientific validity, feasibility and consistency across Europe’s diverse farming systems.
The SOWtrack approach is rooted in EFSA’s risk-based framework, which links welfare hazards (e.g. flooring, feeding, housing design) to welfare consequences (e.g. lameness, injuries, mortality). This framework, together with EFSA’s published scientific opinions on pig welfare, provide the scientific foundation for SOWtrack’s work.
A key innovation of SOWtrack is the use of optimisation algorithms to identify the most efficient combination of indicators, ensuring broad coverage of welfare hazards while keeping the protocol feasible.
Iceberg Indicators
Not all ABMs are equal in terms of their links to welfare consequences. Some indicators are linked to only one or two welfare consequences, while others are associated with many. The latter can be described as “iceberg indicators”. An iceberg indicator is a single measure that reveals information about multiple underlying welfare consequences.
For example:
Lameness may reflect issues with flooring, feeding, or group management.
Body condition can indicate long-term feeding problems, illness, or competition for food.
Piglet mortality may reveal both maternal welfare and environmental challenges.
By capturing multiple welfare dimensions in one observation, iceberg indicators can be highly efficient tools.
Optimisation Approach
To make best use of iceberg indicators and other ABMs, SOWtrack applies a structured optimisation process:
Data acquisition
A data table will be built which links each ABM to the known welfare hazards and consequences
The coverage strength of each ABM will be calculated by counting the number of unique linked welfare consequences (an index of ‘icebergyness’.
Constraints
Sensitivity and feasibility will be considered as being limiting factors.
Objective function
Maximise coverage of welfare consequences, giving higher weight to iceberg indicators that cover several issues at once.
Minimise redundancy, so the same welfare consequence is not repeatedly measured without added value.
Algorithms
Linear programming and combinatorial optimisation will be used to identify the most efficient combination of ABMs.
The algorithm will effectively balance coverage with practical feasibility, ensuring that the chosen set captures maximum information within the time available for farm visits.
Repository and Transparency
The selected ABMs will be recorded in a structured repository, which documents:
Definitions and scoring protocols
Links to hazards and consequences
Feasibility and reliability notes
Supporting references
This repository will ensure consistency, transparency, and reproducibility in welfare data collection.
Outcomes and Significance
The methodology will produce a scientifically validated, optimised set of ABMs that:
Capture the key welfare consequences highlighted in EFSA’s expert opinions and elsewhere
Adopt iceberg indicators that efficiently cover multiple welfare dimensions
Provide maximum coverage for minimum on-farm effort
Are transparent, reproducible, and grounded in risk-based assessment
By combining EFSA’s risk framework with optimisation algorithms, SOWtrack will advance welfare science beyond simple indicator lists. The recognition and systematic use of iceberg indicators is central to achieving a balanced, efficient, and defensible protocol.
