Methods have been developed to detect transgenic presence in non-GM maize fields. These detection methods may be used to determine whether the regulatory transgenic rate threshold (0.9%) is exceeded, but the results are likely to depend on the grain sample size and on the sampling strategy used to collect grains within agricultural fields. Until now, no clear sampling strategy and sample size have been defined for implementing detection methods.[br/] This study aims to compare four types of sampling strategies for maize grains in agricultural fields—i) random sampling, ii) systematic sampling, iii) stratified sampling, and iv) regression sampling. The first approach simply randomly samples maize ears in the considered field. The second approach consists of selecting ears according to a regular grid. The two final approaches use an auxiliary variable correlated with the real transgene distribution in order to define strata with contrasted presence rates or to reweight a sample of ears selected at random.[br/] The auxiliary variable considered in this study corresponds to the output of a gene-flow model simulating cross-pollination in function of wind speed, wind direction, and distance to the closest GM maize field. Data collected in the Montargis (France) experiments in 1998 and 1999 were used to compare the four sampling strategies and to determine the sample size (i.e., number of ears) required to detect transgene presence with a good level of accuracy.[br/] Results showed that a sample of 2,000 ears is needed to reach a sensitivity or a specificity of 0.95 with random sampling when the true presence rate differs by 0.2% from the regulatory threshold of 0.9%. We showed that this sample size could be strongly reduced (up to 25 to 100 ears depending on the siteyear) by using stratified sampling. Regression led to intermediate sample sizes, and systematic sampling was found to be very sensitive to the position of the first sampled plant.