Predicting Microbial Protein Synthesis in Cattle: Evaluation of Extant Equations and Steps Needed to Improve Accuracy and Precision of Future Equations.

Abstract

Predictions of microbial crude protein (MCP) synthesis for beef cattle generally rely on empirical regression equations, with intakes of energy and protein as key variables. Using a database from published literature, we developed new equations based on the intake of organic matter (OM) and intakes or concentrations of crude protein (CP) and neutral detergent fiber (NDF). We compared these new equations to several extant equations based on intakes of total digestible nutrients (TDN) and CP. Regression fit statistics were evaluated using both resampling and sampling from a simulated multivariate normal population. Newly developed equations yielded similar fit statistics to extant equations, but the root mean square error of prediction averaged 155 g (28.7% of the mean MCP of 540.7 g/d) across all equations, indicating considerable variation in predictions. A simple approach of calculating MCP as 10% of the TDN intake yielded MCP estimates and fit statistics that were similar to more complicated equations. Adding a classification code to account for unique dietary characteristics did not have significant effects. Because MCP synthesis is measured indirectly, most often using surgically altered animals, literature estimates are relatively few and highly variable. A random sample of individual studies from our literature database indicated a standard deviation for MCP synthesis that averaged 19.1% of the observed mean, likely contributing to imprecision in the MCP predictions. Research to develop additional MCP estimates across various diets and production situations is needed, with a focus on developing consistent and reliable methodologies for MCP measurements. The use of new meta-omics tools might improve the accuracy and precision of MCP predictions, but further research will be needed to assess the utility of such tools.