ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Modelling in the Agriculture 4.0 Era and Beyond: Merging Mechanistic Knowledge and Artificial Intelligence with Hybrid Modelling

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ID: 329849
2026
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Abstract
Abstract With the arrival of the 4th Agricultural Revolution (Agriculture 4.0) and the influsion of animal production systems with sensors, automation, and advanced analytics, new opportunities have arisen to improve sustainability, efficiency, and the precision with which we feed animals. It is possible that the barriers of the past such as lack of automation, efficient means of data capture, and appropriate analytical tools with which to drive decision support and system optimization will be mitigated in our lifetime. Increasingly, we will see the blending of mechanistic understanding with technological advancement to guide decision-making. The emerging frontier of hybrid models representing frameworks that integrate mechanistic understanding with the pattern recognition of machine learning, offers considerable promise for application in animal systems. As illustrative case studies, research conducted in other fields where modelling approaches have been successfully integrated will be explored (for example, bioprocess engineering, weather, human health), as well as recent research in agriculture. Several architectures for hybrid modelling may be considered for application in animal biosciences: (1) Serial hybridization, where a machine learning (ML) model provides inputs into a white box model (could be mechanistic or empirical), or vice versa, (2) Parallel hybridization, where models act in parallel and ML and white box model outputs are aggregated or weighted to enhance model performance, (3) Physical constraint hybridization, where domain knowledge (such as conservation laws) typically embedded in mechanistic models are integrated into the structure, parameters or outputs of a ML model, and (4) mechanistic learning, where ML model(s) are embedded within a mechanistic model to predict specific pools or fluxes. Key challenges including data quality and integration, model interpretability, and computational efficiency are discussed alongside practical strategies to overcome them. This review examines how hybrid models can bridge the gap between data-driven insights and biological causality to support real-time decision-making in feed formulation, emissions management, and health monitoring. By positioning hybrid modelling as a cornerstone of precision livestock farming, Agriculture 4.0, and beyond, this review outlines a vision for next-generation tools that are scientifically grounded, causally robust, and prepared to support digital decision-making.
Reference Key
openalex_W7214453206 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors J.L. Ellis, Jessica Bode, Maureen Sahar, Jihao You, Dan Tulpan, Noud Aldenhoven, Luis O Tedeschi
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Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag311
URL
https://doi.org/10.1093/jas/skag311
Keywords Keywords not found

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