How Computer Models Simulate Our Future Protein Supply
Imagine a single family dinner table—once set with a simple cut of meat and some vegetables. Now picture that same table connected to millions of others around the world, each place setting linked through a complex web of environmental impacts, economic constraints, and cultural preferences.
Global demand for protein is projected to increase by 70% from today's levels by 2050 1 .
61% of consumers increased their protein intake in 2024, up from 48% in 2019 2 .
Originally developed in the 1950s at MIT by Jay Forrester, system dynamics helps scientists model nonlinear behavior in complex systems using stocks, flows, feedback loops, and time delays 3 .
Sophisticated visualization tools like ParaView and PyVista enable researchers to create detailed 3D environments that represent everything from individual buildings to regional landscapes 4 .
| Model Type | Key Features | Applications | Limitations |
|---|---|---|---|
| System Dynamics | Models feedback loops, time delays, stock-flow relationships | Simulating adoption of new protein sources, supply chain dynamics | Can become computationally complex with many variables |
| 3D Visualization | Creates photorealistic renderings, spatial analysis, scenario comparison | Land use planning, communicating protein production facilities | Focuses on spatial rather than temporal dynamics |
| Integrated Approach | Combines temporal dynamics with spatial visualization | Comprehensive assessment of protein systems across time and space | Requires interdisciplinary expertise and significant computational resources |
Agri-photovoltaic systems could potentially cover 362% of current energy demand and 181% of projected 2045 demand while still accommodating food production 9 .
The IN-SOURCE project extended the CityGML standard to create the FEW CityGML ADE (Application Domain Extension) for simulating food, energy, and water demand at municipal levels 9 .
Incorporated diverse datasets on building types, agricultural land, and infrastructure
Created four distinct land use scenarios for renewable energy generation
Modeled performance against current and projected 2045 energy demands
| Energy Scenario | Energy Production (% of 2045 Demand) | Food Production Capacity | Economic Viability | Key Trade-offs |
|---|---|---|---|---|
| Conventional Solar Farms | 210% | None | High | Complete loss of agricultural land |
| Agri-photovoltaic Systems | 181% | Moderate reduction | Medium | Crop type limitations due to shading |
| Rooftop PV Only | 45% | No impact | High | Limited generation capacity |
| Hybrid Approach | 165% | Minimal impact | Medium-High | Requires diversified infrastructure investment |
Essential research tools for protein system modeling and visualization
| Tool Name | Type | Primary Function | Application in Protein Research |
|---|---|---|---|
| VTK (Visualization Toolkit) | Software Library | 3D rendering and data processing | Creating visualizations of protein production facilities and their impacts 4 7 |
| ParaView | Standalone Application | Scientific visualization of large datasets | Analyzing spatial impacts of different protein supply chain configurations 4 |
| PyVista | Python Library | 3D data visualization | Creating interactive models of urban protein production scenarios 4 |
| CityGML | Data Standard | 3D city modeling information exchange | Simulating food-energy-water nexus in urban environments 9 |
| Cargill Protein Profile | Data Source | Consumer behavior and protein consumption trends | Informing model assumptions about future protein demand patterns 2 |
| ODE Solvers | Computational Tool | Solving differential equations numerically | Simulating how protein systems evolve over time 6 |
| FEW CityGML ADE | Modeling Framework | Food-Energy-Water nexus simulation | Assessing trade-offs between protein production and other urban needs 9 |
Specialized software enables the creation of complex system dynamics models and 3D visualizations that can simulate future scenarios.
Accurate models require diverse datasets on consumption patterns, agricultural production, environmental impacts, and economic factors.
The future of protein won't be determined by a single solution but through diverse systems working together. Integrated approaches like agri-photovoltaics allow both food and energy production on the same land 9 .
With 52% of consumers trying new foods after seeing them on social media 2 , and different generations embracing distinct protein preferences , the social dimensions of protein consumption cannot be overlooked.
The success of the IN-SOURCE project's Vistoolbox demonstrates that when citizens can see realistic 3D visualizations of different development scenarios, they can provide more meaningful input 9 .
Meeting growing protein demand will require embracing diversified production methods—from advances in traditional animal agriculture to innovations in aquaculture, plant-based proteins, and cultivated alternatives 8 .