U.S. feed industry professionals excel at generating and accumulating enormous amounts of data. Over the years, organizations like the Institute for Feed Education and Research (IFEEDER) have been able to gather and connect stratified data to yield novel insights and tangible action plans. IFEEDER’s 17-year history in advancing research, education and sustainability across the animal food industry makes it particularly well positioned to explore how modern artificial intelligence (AI) could help researchers ask more sophisticated questions about ingredient availability, environmental footprints and future feed demand — and examine the implications across species, ingredients and geographies.
Modeling the ripple effects of ingredient constraints
Feed formulation is an interconnected system. When the availability, price or use of one ingredient changes, the effects can extend throughout feed formulations, nutrient levels and the animal protein market.
Considering synthetic amino acids, these inputs allow nutritionists to meet animals’ nutritional requirements more precisely while reducing the amount of high-protein ingredients required in a ration. But, when certain synthetic amino acids become constrained because of a supply chain disruption, as IFEEDER explored in a research paper last year, there can be severe impacts on the production of animal protein, such as decreased gain and meat yield, but increased feed cost due to using alternative ingredients. While IFEEDER’s paper highlights scenarios, AI-supported modeling could help IFEEDER dive deeper into the cascading effects.
A model could examine what happens to demand for corn, soybean meal, distillers dried grains with solubles and other protein sources for animal feed when the availability of synthetic amino acids or other critical inputs is reduced. Researchers could vary the severity or duration of a constraint and evaluate how ingredient relationships change across different livestock and poultry sectors.
The same approach could be used for other scenarios. What happens if supplies of a commonly used ingredient decline? How could alternative ingredients compensate? Which sectors experience the greatest changes in limiting ingredient availability?
While researchers and industry experts would still need to establish appropriate assumptions and validate the results, AI could dramatically expand the number and complexity of scenarios they can explore, and identify relationships worthy of deeper economic or nutritional analysis.
Developing more representative environmental footprints
A second opportunity for AI integration is combining feed consumption information with environmental lifecycle assessment data.
IFEEDER’s past report lifted the veil on which feed ingredients are most consumed, in what quantities and by which animal sectors. Separately, resources such as the Global Feed LCA Institute (GFLI) database provide life cycle assessment information that can be used to understand the environmental footprint of feed ingredients.
AI and advanced data analytics could help connect these datasets at a much greater scale.
Stakeholders are able to generate weighted feed footprints by drawing upon individual ingredient environmental data from GFLI, but we could use IFEEDER’s feed ingredient consumption volumes to segment those footprints by species or geography, where the underlying data support that level of analysis.
This could provide a more nuanced picture of where environmental impacts occur within the feed system and how they vary depending on what animals consume and where ingredients originate or are produced.
Looking ahead to future ingredient needs
Perhaps the most powerful application is moving from understanding today’s feed system to exploring what it could look like tomorrow.
AI-supported models could combine existing relationships between animal populations, production and feed ingredient consumption with projections from organizations such as the U.S. Department of Agriculture, the Organization for Economic Co-operation and Development, and the Food and Agriculture Organization of the United Nations to estimate ingredient use outlooks under different productivity assumptions.
For example, one scenario might assume that animal productivity continues improving at historical rates. Another could examine slower productivity gains, while a third could explore accelerated improvements driven by advances in genetics, animal nutrition, health or management. The resulting ingredient needs could differ substantially.
Agricultural markets are influenced by herd size, weather, trade, consumer demand, technology, policy and countless other factors. Scenario modeling would give IFEEDER and the broader industry a structured way to examine several plausible futures and better understand the variables that could shape them.
Turning data into industry intelligence
Consistency, productivity and sustainability are what producers, feed mill managers and animal nutritionists aim for. AI offers organizations like IFEEDER an opportunity to strengthen the work of supporting the industry by turning increasingly complex datasets into actionable research questions and scenario-based insights.
Across all three applications, human expertise remains essential. Nutritionists, life-cycle assessment experts, economists, data scientists and industry professionals would need to determine assumptions, establish guardrails and validate findings.
Used this way, AI could help IFEEDER get more value from trusted data, explore relationships that were previously difficult to model and provide the animal food industry with a clearer understanding of how decisions made today could influence the future. Stay tuned, as IFEEDER uses AI to turn insights into action.
