Knowledge Gaps in the Nutrient Requirements of Beef Cattle

Abstract

The 8th revised edition of the Nutrient Requirements of Beef Cattle was released in 2016, with the recommendations provided in the publication being used extensively in both research and production settings. In the context of research needs identified in that publication, our objective was to review research on beef cattle nutrient requirements published since 2016 and identify knowledge gaps that should be addressed. Relative to energy requirements, the effects of environmental temperature and grazing activity, along with stress and disease, on maintenance requirements are inadequately characterized or defined. In addition, relationships between retained energy and protein should be more fully elucidated, and additional guidance on body weight at a target compositional endpoint is needed. Areas of continuing concern include accurately and precisely predicting microbial protein supply, predicting N recycling, and the metabolizable protein requirements for maintenance. Mineral and vitamin requirements are often challenging because of a lack of consistency in models used to determine requirements and potential effects of unique production settings on requirements. Based on recent research with feedlot cattle, zinc and chromium requirements should be examined more closely. Because predictions of dry matter intake are critical to supplying nutrients, additional development of prediction equations is needed, especially for beef cows and grazing beef cattle in general. Given considerable research in prediction of greenhouse gases, reevaluation of 2016 recommendations is warranted, along with a need for the updating of equations to predict excretions of N and P. Composition of feeds, particularly byproducts from ethanol production or other industrial streams, represents a knowledge gap, with obtaining reliable energy values of these feeds being a notable challenge. Nutritional models provide the means to integrate nutrient requirement recommendations into practice, and moving towards mechanistic models that take advantage of artificial intelligence and precision livestock farming technologies will be critical to developing future modeling systems.