Turning Data into Dollars

Friday, July 24, 2026

Using farm data to reduce cost per kilogram in grow-finish production

By Fiona Tansil

Today’s swine producers operate in an environment where margins are tight and variability is the norm. Volatile feed prices, animal health pressures, labour challenges, and increasing sustainability expectations mean that even small management decisions can have a big impact on profitability. To stay competitive, producers need approaches that go beyond spreadsheets and intuition. One solution is tools that convert complex, farm-specific data into clear insights and informed nutritional and management decisions.

Modern production systems generate large volumes of data, but the value of this information comes from integrating it in a way that reflects how biology, nutrition, management, and economics interact on commercial farms. Digital models can incorporate how these factors interact on real farms, which allows producers to evaluate decisions with greater precision and confidence.

One important advantage of digital models is the ability to test changes virtually before they are implemented on the farm. Some models have the ability to optimize key performance and economic indicators, including cost per kilogram of gain, margin over feed cost, average daily gain (ADG), feed conversion ratio (FCR), and shipping strategy. Some advanced models are also capable of generating fully customized feeding programs tailored to a farm’s specific conditions.

Producers can compare scenarios such as weaning at different ages, marketing pigs at alternative weights, or evaluating the economic impact of lighter or heavier weaned pigs. These are possible when the model incorporates the nutrients in feed, animal genetics, environment, health status, and market conditions.

Reducing Cost per Kilogram in a Grow-Finish Operation:

A Real-World Example

A grow-finish producer approached me with a clear objective in mind: reduce cost per kilogram of gain without sacrificing pig performance. The farm was digitally recreated to mirror real-world conditions, including starting pig weights, genotype, feeding program, facility environment, and health status.

Once the simulation was set up, the model was tasked with optimizing the feeding strategy specifically for the lowest possible cost/kg of gain. It evaluated multiple nutrient specifications and identified a feeding program that minimized cost while still supporting strong growth performance. This optimized program was then re-run through the model to predict its production and economic outcomes.

The predicted results from the simulation

Compared to the producer’s original feeding program, the model-optimized program delivered clear improvements:

• +20 g ADG
• 0.04 unit lower FCR
• $0.09/kg lower cost of gain

The higher ADG also translated into pigs reaching market weight sooner, resulting in an average reduction of three days on feed. Fewer days on feed further improved efficiency and reduced overhead costs.

With an average starting weight of 25 kg, an average market weight of 135 kg, and savings of $0.09 per kg of gain, this equated to nearly $10 saved per pig. In a commercial operation, savings at this level quickly add up, especially across large groups of pigs.

Experience from commercial evaluations shows that when programs are assessed using farm-specific data, predicted performance and economic outcomes are typically closely aligned with observed on-farm results. This reinforces the value of data-driven decision-making, giving producers confidence that modelled improvements will translate into real-world gains.

In a high-cost, margin-tight production environment, precision nutrition is no longer optional. By creating a digital twin of their operation, producers can evaluate strategies virtually, reduce uncertainty, and uncover new opportunities for efficiency and profitability.

For those focused on lowering cost per kilogram while maintaining or even improving performance, using digital models can truly turn data into dollars. BP