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Objective was to improve the accuracy of a new model that optimizes breeding and replacement decisions for dairy cows to support dairy farmer decision making. In most recently published models, cows are characterized by a limited number of discrete states, a combination of (at least) lactation number, level of milk production, stage of lactation, and reproductive status. The number of states is necessarily limited to make computations feasible. Cows currently in the herd may not be well represented by a state and the models decisions may be significantly different from what is optimal in practice. For example, in practice every cow has a unique lactation curve. An improvement in accuracy of prediction of cow performance can be obtained when the states for the remainder of the current lactation of cows currently in the herd are separated from the states for their next lactations and replacement heifers. This allows for more accurate estimates of milk production in the remainder of the lactation and other inputs than previously available. Consequently, optimized breeding and culling decisions are more accurate. To illustrate, optimal breeding and replacement decisions were calculated for 596 cows at the University of Florida Dairy Research Unit. Milk production for the remainder of the lactation was predicted by Best Prediction. Optimal decisions were sometimes significantly different as a result of improved accuracy. The model currently runs as a Microsoft Excel add-on to an existing dairy herd information system to make these optimal decisions available to dairy farmers.