Cargill aims to enhance salmon nutrition with its Effective Energy program

USA – Cargill is setting new industry standards with its latest salmon feed concept, designed to optimise nitrogen utilisation while improving fish performance, health, and cost-efficiency for farmers. 

The Effective Energy program leverages commercial data to fine-tune nutritional strategies based on specific farming conditions, including location, stocking seasons, life stages, and economic factors such as raw material prices. 

By integrating this approach into its digital platform, Cargill aims to deliver customised feed programs tailored to the unique needs of individual farms.

This program builds on the success of Essential Nitrogen, which Cargill says laid the foundation for optimising feed composition. 

“Effective Energy marks the next step in our journey toward precision nutrition. While Essential Nitrogen optimised the balance of nitrogenous nutrients, Effective Energy takes it further by incorporating digital tools and real-world data to refine nutritional planning,” Christian De Santis, principal scientist at Cargill Animal Nutrition and Health, explained.

This engine models fish physiology and nutrition requirements with remarkable accuracy, and when combined with real-world farming data, it enables the creation of customised nutritional plans that enhance feed efficiency and production outcomes.

This new phase utilises sophisticated nutritional models to optimise feeding strategies, helping farmers maximise production per license, improve fish performance, and make more informed feed decisions that balance cost and effectiveness.

Integrating with bluViu

Ketil Christensen, category lead for digital at Cargill Animal Nutrition and Health, elaborated on how Effective Energy integrates with Cargill’s bluViu digital platform, saying, 

“bluViu is a proprietary platform designed to help salmon farmers make smarter, data-driven decisions by simulating, optimising, and comparing feed programs. It houses one of the largest salmon production databases and continuously gathers data from diverse sources.” 

He added that with real-world inputs such as fish weight, temperature, seasonality, and feed composition, bluViu enables complex analyses and simulations at the touch of a button or even automatically. 

“By integrating Effective Energy, farmers can test different feeding strategies and make informed decisions, driving both sustainability and profitability.”

Christensen noted that farmers often have different priorities—some focus on sustainability, others on reducing costs, and some on maximising growth.

“The bluViu scenario planner helps farmers compare feeding programs based on cost, performance, and sustainability. Farmers can simulate potential outcomes and assess key performance indicators by inputting farm-specific data and various feed options,” Christensen added regarding scenario planning and economic considerations. 

Performance metrics

De Santis highlighted that success with Effective Energy is measured through biological, environmental, and economic performance indicators:

“While farmers often focus on weight gain and feed conversion ratio (FCR), Effective Energy also tracks carcass yield—an important but often overlooked metric. Some farms measure performance based on head-on and gutted (HOG) weight, which provides a more accurate economic perspective on feed decisions.” 

He added that sustainability is also a priority: the carbon footprint of feed and fish is tracked to align with environmental goals. 

“On the economic side, key metrics include feed costs, feed cost of growth, and feed cost of HOG growth, all of which help farmers enhance efficiency and profitability.”

Addressing variability in farming conditions, De Santis added that while controlled trials provide a strong scientific foundation, real-world data—such as water quality, stocking density, and seasonal variations—ensure that our models remain relevant and accurate. 

Predictive modelling and scenario planning provide flexible, customised solutions for conditions not fully covered by existing data.

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