Until recently the description of dynamic biological processes was done using static models even if those biological processes such as lactation or growth provided us with longitudinal data. A classical example was the use of lactational milk yields even if individual test-days describing the underlying lactation curves were available. Similarly for growth, weights were corrected phenotypically to fit into categories like weaning or yearling weights. Several recent developments stimulated the research on alternative methods describing the evolution of the mean and the variances of continuos dynamic biological processes. These developments were especially the extension of repeatability models towards random regressions and the development of the (co)variance function approach, but the development of better computers allowing the storage and the processing of a huge quantity of data. Despite this the analysis of certain types of longitudinal data as test-day yields in large populations and/or international settings is still a major challenge. But a very important aspect of the analysis of longitudinal data is often forgotten: they give us other information than the one classically extracted from genetic evaluation systems. In fact, the detailed modeling of dynamic biological processes provides opportunities for the development of advanced management tools. This may have a large influence on the way genetic evaluation systems may evolve in the future, making them integrated systems for the management and selection of animals
