Farm-specific nutrition models
Hybrid models combine production data with precision-feeding knowledge to estimate nutrient requirements and utilization efficiency over time.
cSBO Research Project
A digital twin for sustainable pig production
NutriTwin connects farm, carcass, diet, and chain data to support feed producers, farmers, processors, and software developers with data-driven feeding strategies that reduce nutrient losses and environmental footprint while strengthening sustainable pork production.
cSBO project 2025-2029
Strategic basic research with a 48-month roadmap for industry-ready knowledge transfer.
Project objectives
Turning nutrient flows into actionable farm intelligence.
The project investigates how dietary nutrient flows on pig farms can be modelled as dynamic digital representations of production. As farm-specific data is added, the generic architecture evolves into a digital twin tailored to each production context.
The expected result is better insight into feed composition, nutrient utilization, environmental impact, and decision support for farmers, feed producers, processors, software developers, and sector organizations.
48 Months
Project duration
3 Domains
Nutrition, LCA, data integration
5 Research lines
From farm data to decision support
>2% Target
Feed-cost reduction potential
Research
From production signals to decisions.
Dynamic and consequential LCA
Near real-time footprint calculations connect feed choices with carbon, nitrogen, phosphorus, and supply-chain consequences.
Privacy-preserving collaboration
Federated learning and decentralized data frameworks help partners train models without exposing raw chain data.
Multi-objective feed formulation
Optimization tools balance feed cost, animal performance, and environmental impact for practical scenario testing.
How NutriTwin works
A self-learning loop for precision feeding.
Measurements from farm operations, carcass performance, and diet composition feed the digital twin. Model updates estimate farm-specific pig profiles, compare predicted and measured outcomes, and refine future decisions.
Farm data
Carcass data
Diet data
Digital twin
Federated learning
Scenario testing
Feed decisions
Consortium
A chain-facing research partnership.
Ghent University - LANUPRO Ghent University - Data Analysis and Mathematical Modelling ILVO - Food Sciences and Technology ILVO - Animal Sciences Flanders' FOOD
Shared expertise
NutriTwin brings together expertise in animal nutrition, data analysis, mathematical modelling, food technology, animal sciences, and cluster-based industry valorization.
Precision animal nutrition Mathematical modelling Food technology Animal sciences Data infrastructure Industry valorization
Visibility and transfer
Built for adoption across the pig-to-pork value chain.
Stakeholder engagement
Clear project positioning for stakeholders in feed, farming, processing, software, and sustainability reporting.
Industry workshops
Knowledge transfer around test beds, ontology, dynamic LCA, dashboards, API use, and decision support.
Future dissemination
Results, tools, project outputs, and scientific publications can be added later as the research matures.
Contact
