ABSTRACT: The overall motivation for the development
of an information system for beef cattle improvement
is the belief that knowledge of breeding values
and heterosis effects allows one to determine the consequences
of alternative selection and mating options.
With this information, livestock managers can easily
shift populations in a desirable direction. The foundation
principles for establishing a sound breeding program,
including the prediction of animal performance
for economically relevant traits and their incorporation
into a single index of aggregate economic merit, have
been well established over the last half century. Rather
than this goal-based approach, the industry adopted a
data-driven approach to the production of genetic evaluations
that has been characterized by an overemphasis
on the evaluation of productive traits, notably BW at
various ages, with inadequate regard for other economically
important traits, such as reproduction, animal
health, and feed requirements. Production of evaluations
is breed association centered, and this has delayed
the introduction of national across-breed evaluations
for all breeds and crosses of cattle. The computational
aspects of producing evaluations are now migrating
from land-grant universities to breed associations, but
not yet to a single entity. The introduction of genomic
information in the form of high-density SNP panels will
introduce threats, challenges, and new opportunities for
the production of evaluations, and represents the largest
force to alter the structure of the beef improvement
industry since the advent of AI. The use of evaluations
has, until recently, stopped short of the provision of
index merit as a basis for selection. Accordingly, the
value propositions associated with annual improvement
or the selection of alternative sires has not been well
communicated. Technology, along with economic and
other issues related to stakeholder acceptance, will collectively
determine the future nature of the industry in
terms of the production and use of evaluations.