Models are important tools that can help managers and researchers understand the population dynamics of a species and how different habitat or population management scenarios impact that species. We used radio-telemetry data from northern bobwhites (Colinus virginianus) in southern Texas from 2000 to 2005 to develop a stochastic simulation model for bobwhite populations. Our model is based on difference equations, with stochastic variables drawn from normal and Weibull distributions. We simulated bobwhite populations to 100 yr and evaluated our model by comparing results with independent estimates of 4 population parameters (spring and fall density, finite rate of increase in the fall population [λ], and winter juv:ad age ratios). Using a quasi-extinction criterion of ≤40 birds (density = ≤0.05 birds/ha), probability of persistence to 100 yr was 88.3% (106 of 120 simulations) for the spring population and 96.7% (116 of 120 simulations) for the fall population. Using a less restrictive quasi-extinction criteria (≤ 14 birds), probability of persistence was 93.3% (112 of 120 simulations) for the spring population and 98.3% (118 of 120 simulations) for the fall population. Simulated population parameters were similar to independent estimates for 4 of 4 population parameters. Winter age ratios differed between our model ( = 4.98 juv:ad, n = 120, SE = 0.32) and empirical age ratios from harvested bobwhites on our study area ( = 2.85 juv:ad, n = 25, SE = 0.24). However, when we corrected harvest age ratios for bias in juvenile harvest ( = 3.85 juv:ad, n = 25, SE = 0.32) simulated and empirical estimates were similar. Our model appears to be a reliable predictor of bobwhite populations in the southern Texas. Our simulation results indicate that bobwhite hunters and managers can expect excellent bobwhite hunting (fall populations ≥2.2 birds per ha) in about one of 10 yr.