To make quantitative predictions about the pollen dispersal of a plant species under different environmental conditions, it is necessary to determine its individual pollen dispersal function, i.e., the two-dimensional density function describing the probability that a pollen grain emitted in (0, 0) fertilizes an ovule in (x, y). This function will depend on biological and climate parameters. We present models for the individual dispersal function of corn. These models are based on Brownian motion with drift and integrate biological (difference of height between male and female flowers) and aerodynamic (settling velocity, wind speed, air turbulence) parameters. The models presented differ in the importance of vegetation in stopping the paths of pollen grains. The models were fitted to data from two large field experiments of corn using the color of kernels as a phenotypic marker for pollen dispersal. The resulting estimations for the parameters of the models and comparisons between models indicate that (1) these models can provide good predictions of the observed data, (2) vegetation is not the major obstacle that stops pollen paths, and (3) there is a benefit in considering the difference in height between male and female flowers. Furthermore, values of the parameters estimated from dispersal data appear consistent with meteorological and biological data acquired independently.