When the Barn Gets a Vote: Understanding Value Proposition in Animal Nutrition

Good Science Is Not Always Good Value

Dr. Casey Bradley’s perspective on value proposition was shaped long before boardrooms, sales meetings, or product development discussions. She grew up on a commercial swine farm. Later, she managed research farms and countless trials involving many different types of feed additives and nutritional strategies. That background forced her to see the gap between scientific intent and real-world application.

One of her clearest early lessons came during her PhD work. The project built from Zinpro’s work in dairy cows, where researchers measured feet and evaluated lesions. In dairy operations, cows can often be managed through systems such as turntables or other handling equipment that allow easier access to the feet. But sows are not dairy cows.

When a modified version of that approach was attempted in sows, the first attempt failed horribly. The science made sense. The measurement objective made sense. But the application did not fit the animal, the facility, or the people expected to execute the work. Sows are not as calm as cows, and the handling system needed to reflect that reality.

What stayed with Bradley was not only that the first system failed. It was that when those doing the work with the sows suggested a practical way to immobilize them, the idea was initially dismissed. Later, the chute that was ultimately designed was based on the very concept the sow team had proposed from the beginning. The people closest to the animal and the facility understood the implementation challenge before the broader scientific or commercial team did.

That experience became a foundation for how Bradley thinks about value. A value proposition cannot be built from the conference room alone. It has to be tested against the barn, the animal, the labor reality, the feed system, the budget, and the people who must make it work.

In livestock production, the barn always gets a vote.

Application Has to Come First

One common mistake in value proposition development is designing around the ideal biological window without asking whether the producer can actually implement the strategy. For example, a product may be designed to be fed during the last 30 days of gestation. Scientifically, that may be logical. But if the barn only has one feed line, how does the producer deliver that product to only the intended animals?

The industry may wish every sow system had multiple feed lines, blending technology, and precision-feeding capabilities. Some systems are moving in that direction, but integration remains slow, and even advanced systems may only allow two feeds or blends. That is still not perfect.

This is where value proposition must shift from “What is the ideal protocol?” to “What can the customer actually do?” If the return on investment is similar whether the product is fed throughout gestation or only prior to farrowing when sows move into crates, then the more practical feeding strategy may become the stronger commercial value proposition.

That difference matters. Instead of asking the customer to redesign their system around the product, the value proposition should be redesigned around the customer’s system.

Significant Is Not Always Valuable

Another place value proposition often goes wrong is in the way data are interpreted. Statistical significance matters, but it is not the same as biological relevance or commercial value. In production agriculture, there is rarely enough time, labor, facility flexibility, or replication to answer every question perfectly. At the same time, results can be significant on paper but difficult to believe, difficult to repeat, or difficult to monetize.

This is especially true when the wrong metric hides the real value.

Footpad lesions in poultry are a good example. Many researchers analyze footpad lesion scores as an average. Statistically, that can be convenient. But from a welfare, production, and commercial standpoint, an average score can hide what matters most. If a bird has no lesion, it receives a score of zero. There is meaningful value in understanding not only the average score, but also the number of birds affected and the severity of the lesions.

A flock where most birds have no lesions and a small number have severe lesions may tell a different story than a flock where many birds have mild to moderate lesions. Both scenarios may produce an average, but the biological and commercial implications are not the same. Poultry research on footpad dermatitis commonly emphasizes both prevalence and severity, and visual scoring systems are used because footpad dermatitis is tied to welfare assessment and commercial monitoring.

This was a real-life example where Bradley’s swine background helped support poultry colleagues. She had spent five years of her PhD research studying lesions in sows. That experience helped her recognize that lesion data are not just numbers. Lesions represent biology, welfare, environment, management, facility interaction, and ultimately customer value.

The wrong metric can make value disappear. Averages may simplify analysis, but they can also hide the biological and commercial story the customer actually needs to understand.

A Feature Is Not a Value Proposition

Another simple example is enzyme thermostability. Thermostability can be an important technical feature, especially when feed is pelleted or exposed to high processing temperatures. Feed processing research supports that enzyme activity can be affected by pelleting conditions, including pressure and temperature, and that enzyme recovery may decline as conditioning temperatures increase.

But if the customer is feeding meal diets, thermostability may not be the primary value driver.

That does not mean thermostability is useless. It means it may not be the most relevant value proposition for that customer. In a meal-fed system, the more important questions may be efficacy, substrate availability, consistency of response, diet formulation, cost per ton, and value per pig or bird.

This is one of the most common mistakes in technical sales and product development. Companies assume that a feature is valuable because it is scientifically interesting or different from the competitor’s product. But a feature only becomes valuable when it solves a meaningful problem in the customer’s system.

Do not build the value proposition around what makes the product interesting. Build it around what makes the product matter.

The Metric That Changes the Story

Xylanase in the U.S. swine industry is a strong example of learning to find the right value metric. If the conversation only focused on average daily gain and feed conversion ratio, the return on investment was not always enough. But when mortality and livability were included, the value proposition became much stronger.

The product did not become valuable because the biology suddenly changed. It became more valuable because the industry learned how to measure the value more completely. A review of xylanase supplementation in corn-based swine diets reported that digestibility responses may occur more frequently than improvements in growth performance, while also noting observations related to improved health markers and reduced finishing pig mortality.

That shift matters. The value was not only in the feed saved. The value was in the pig that made it to the truck.

Today, mortality and livability have become key drivers in many swine trials because they often determine whether the economics truly work. A small change in FCR may matter, but one more pig marketed can change the entire return calculation.

The Napkin Math of Value

Bradley’s “napkin math” approach started very practically. Someone would show her data from a significant trial, and she would immediately determine whether there was return on investment. Sometimes that calculation literally happened on a piece of scrap paper or a napkin.

The point was not to replace statistics. The point was to translate the result into the customer’s economic reality.

A trial may show significance, but the customer still needs to know what changed, what it cost, how consistent the response was, who captured the value, whether the system could implement the solution, and whether the benefit exceeded the cost. Over time, that napkin math evolved into models and calculators. Bradley developed tools that allowed sales teams to adjust customer-specific inputs and see how value changed under different conditions. She brought that approach to DSM for Hy-D, and it became more than a return-on-investment calculator. It became a tool to capture real-world customer data and insights.

That is an underappreciated part of value proposition work. A calculator should not only prove value. It should teach the company what the market values.

When sales teams can adjust inputs with the customer, they begin to see which assumptions matter most. They learn whether the customer is most sensitive to feed cost, mortality, carcass weight, replacement rate, labor, formulation space, ingredient price, or another driver. Those insights can come back into the business and help refine the message, improve customer segmentation, and even identify future product opportunities.

In swine and poultry, value often comes back to five practical questions: What did it cost? Did they grow? Did they convert? Did they live? Did they create more saleable product?

Those questions usually point back to a few core metrics: cost of goods sold, average daily gain, feed conversion ratio, mortality or livability, and carcass weight. The right metric depends on the customer’s problem, the production phase, and who captures the value.