Invasive weed managers are presented with a complicated and ever-enlarging set of management alternatives. Identifying the optimal weed management strategy for a given set of conditions requires predicting how candidate strategies will affect plant community composition. Although field experiments have advanced our ability to predict postmanagement composition, extrapolation problems limit the prediction accuracy achieved by interpreting treatment means as predictions. Examples of extrapolation problems include nonlinear relationships between competing plants, site-to-site variation in plant population growth rates, and the carrying capacities of desired species and weeds. Our objective was to develop a model that improves predictions of weed management outcomes by overcoming a subset of these problems. To develop the model, we used data from two field experiments in which four Kentucky bluegrass, six western wheatgrass, and six invasive plant (i.e., leafy spurge) densities were combined in field plots. Graphs of our model's predictions vs. observed field experiment data indicate that the model predicted the data accurately. Our model may improve predictions of plant community response to invasive weed management actions.