Abstract

Abstract for: Coupling Fast Biophysical and Slow Socio-Economic Dynamics to Model Resilience in the UK Poultry System

Introduction Over recent decades, poultry production has become highly efficient through advances in breeding, nutrition, and supply chain integration. However, this has created a specialised socio-ecological system potentially vulnerable to both acute and chronic disruptions such as trade shocks, environmental stressors, and biological limits, which are not well captured in existing modelling approaches.
Approach We develop a data-driven system dynamics framework coupling two empirically grounded models: a daily-resolution model of broiler growth and metabolism, and an annual-resolution model of the UK poultry supply chain. The models are linked through feedbacks connecting biological performance, production, market dynamics, and welfare and environmental outcomes across timescales.
Results Preliminary scenario experiments exploring feed supply disruptions show how shocks originating in the annual socio-economic model influence metabolic growth dynamics in the daily biophysical model. These responses then propagate back to production, prices, and environmental pressures, revealing cross-timescale feedbacks and the emergence of non-linear system behaviour.
Discussion While coupling fast and slow dynamics remains work in progress, the fully integrated model is expected to reveal cross-scale feedbacks not observable in isolated representations. By linking micro-level biological processes with macro-level socio-economic dynamics, the framework offers a novel approach to analysing resilience in intensive food systems, with implications for policy design under uncertainty.

Introduction	Over recent decades, poultry production has become highly efficient through advances in breeding, nutrition, and supply chain integration. However, this has created a specialised socio-ecological system potentially vulnerable to both acute and chronic disruptions such as trade shocks, environmental stressors, and biological limits, which are not well captured in existing modelling approaches.
Approach	We develop a data-driven system dynamics framework coupling two empirically grounded models: a daily-resolution model of broiler growth and metabolism, and an annual-resolution model of the UK poultry supply chain. The models are linked through feedbacks connecting biological performance, production, market dynamics, and welfare and environmental outcomes across timescales.
Results	Preliminary scenario experiments exploring feed supply disruptions show how shocks originating in the annual socio-economic model influence metabolic growth dynamics in the daily biophysical model. These responses then propagate back to production, prices, and environmental pressures, revealing cross-timescale feedbacks and the emergence of non-linear system behaviour.
Discussion	While coupling fast and slow dynamics remains work in progress, the fully integrated model is expected to reveal cross-scale feedbacks not observable in isolated representations. By linking micro-level biological processes with macro-level socio-economic dynamics, the framework offers a novel approach to analysing resilience in intensive food systems, with implications for policy design under uncertainty.