Running head: Computer models for beef systems.

Brazil is a major beef producer and exporter with most of its production obtained from tropical grazing systems based on Urochloa (Brachiaria) pastures and Nellore (Bos indicus) animals. Despite of major differences to the production systems of developed countries, adaptation of foreign models has been the most successful strategy to develop our own decision support systems (DSSs). In Brazil, DSSs have been more straightforwardly developed and adopted in feedlot operations, probably due to the easier tuning of the available models and the better information and control related to feedlots. An example of successful Brazilian computer model for feedlots is RLM, which includes a model of animal growth based on NRC adjusted for Nellore and crossbred animals. Further, RLM includes a modified feed intake equations, a Brazilian feed library and diet optimization methods for least cost of dry matter or minimum production cost. The development of computer models for our grazing conditions has been comparatively slower. Greater changes in model structure and parameter values were required as most process-based grazing system models have been developed based on temperate or rangelands pasture species. Brazilian research on pasture production, feed intake and diet selection seems more distant to modeling, possibly due to the absence of a reference model to help driving the experimental procedures. From the manager perspective, planning occurs on a longer time horizon in grazing systems and much more risk is associated to the effects of variable climate conditions on the dynamics of pasture production and quality. Besides, higher costs and lower accuracy in monitoring the pasture may also discourage adoption. However, great interest by the Brazilian extensionists and farmers has been perceived for Invernada, a dynamic DSS model recently released Brazilian for grazing systems. Experiences with Invernada training courses indicated that adoption may be slowed down due to the higher complexity of this tool compared with feedlot DSSs and due to the lack of acquaintance of the users with other similar tools. Drivers and future options for the development of DSSs for beef production are discussed.