Modeling pollen dispersal to predict cross-pollination is of great
importance for the ongoing discussion of adventitious presence of
genetically modified material in food and feed. Two different modeling
approaches for pollen dispersal were used to simulate two years of data for
the rate of cross-pollination of non-GM maize (Zea mays (L.)) fields by pollen from a
central 1 ha transgenic field. The models combine the processes of wind
pollen dispersal (transport) and pollen competition. Both models used for
the simulation of pollen dispersal were Lagrangian approaches: a stochastic
particle Lagrange model and a Lagrangian transfer function model. Both
modeling approaches proved to be appropriate for the simulation of the
cross-pollination rates. However, model performance differed significantly
between years. We considered different complexity in meteorological input
data. Predictions compare well with experimental results for all
simplification steps, except that systematic deviations occurred when only
main wind direction was used. Concluding, it can be pointed out that both
models might be adapted to other pollen dispersal experiments of different
crops and plot sizes, when wind direction statistics are available. However,
calibration of certain model parameters is necessary.