Feed Efficiency

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Historically, measures of feed utilization incorporated both feed consumption and measures of body weight gain ; however, when expressed in a linear form (e.g. selection index) application of values for costs and returns results in outcomes more closely associated with net return (value – cost) and therefore a selection index considering both cow/calf performance, postweaning growth and carcass merit measures is likely optimum for the beef industry . Application of either approach could result in genetic change in feed utilization. However, for genetic improvement programs selection should be based on EPD (or EBV) resulting from multiple-trait genetic evaluation of feed intake .

Accurate feed utilization testing in beef cattle is dependent on collecting reliable and sufficiently precise measures of daily feed intake and body weight gain. Measurement of both phenotypes is subject to some degree of error. Therefore, much care should be given to the development and implementation of testing procedures that systematically minimize the errors associated with measuring these two components.

Dry Matter Intake vs Residual Feed Intake and Residual Gain

Organizations producing genetic predictions for feed consumption and partial efficiency differ in the expression of the EPD. While some EPD are expressed as measured daily dry matter intake (DMI), others are published in index form to quantify partial efficiency such as residual feed intake (RFI) and residual average daily gain (RADG).

RFI and RADG

Phenotypic-based RFI attempts to adjust observed intake for phenotypically correlated sources of variation, so RFI is not correlated with indicator traits. Most commonly these include gain and metabolic mid-weight, although measures of body composition have also been used. This process creates a restricted selection index based on phenotypes, whereby selection for RFI will reduce intake without changing gain. Alternately, RADG is a restricted index that allows change in gain whilst holding feed intake constant. To generate EPD for RFI, two alternative methods have been proposed. A phenotypic-based RFI can be calculated using estimated relationships between maintenance requirements, and anticipated requirements for growth and fat deposition, and this phenotype becomes the dependent variable in the genetic evaluation. More commonly, the DMI phenotype is a dependent variable in a model that includes correlated factors as covariables - e.g., weight, gain, fat thickness, etc. The resulting genetic prediction of RFI is intended to be genetically independent of the covariates included in the model. Alternately, RFI can be obtained using an index that includes DMI EPD and the EPD of the RFI covariables. The same issues and approaches exist for producing genetic predictions of RADG.