The development of cattle feeding has benefited from research and data-based decision making. To remain successful in an increasingly competitive global marketplace, scientists supporting the feeding industry must continue in this tradition. For research results to be useful to industry, experimental models should be relevant to the questions being asked, and production data should be reflective of commercial conditions. While much discussion has compared the advantages of commercial large-pen research and small-pen models more typical of university facilities, both are useful for contributing to new knowledge. In order to minimize both Type I and Type II errors, researchers should consider how to best control the random variation between experimental units treated alike in their own systems. Large-pen models have advantages in replicating “commercial conditions,” detecting smaller differences, understanding distributions and categorical outcomes, but these models may be limited in the number of treatments and the ability to take multiple measurements or samples from individual animals. A primary objective of university research is the training of the next generation of scientists and industry professionals. Cattle feeders must use data generated with biological methods and make economic decisions. Therefore, research results should be presented clearly so economic implications can be modeled and likely variation around means and differences between treatments understood. Predicting cattle growth, especially carcass growth, more accurately will continue to be important, as will ways to understand and manage individual animals within commercial facilities. New technologies, including sensors, genetic testing, and data management systems have potential, but value propositions need to be demonstrated and feasible implementation strategies developed. However, as animal types, feed ingredients, and market-driven endpoints have changed over time, old dogmas should continually be re-evaluated in contemporary conditions.
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