Simulating the Gene Flow of Genetically Modified Maize in Taiwan

Abstract

A field experiment was conducted in Taiwan to measure the cross-pollination (CP) rate of maize pollen recipients from pollen sources using phenotypic marker and to determine the isolation dis- tance between the 2 maize varieties. A waxy variety (Black Pearl) with purple kernels simulated the genetically modified (GM) pollen donor, and another waxy variety (White Pearl) with white kernels simulated the non-GM recipient. For the first crop, the total area was approximately1.5 hawith a pollen source and recipient acreage ratio of approximately 1:32. For the second crop, the total area was approximately1.83 hawith a ratio of approximately 1:17.3. The source fields were surrounded by the recipient fields for 2 crop seasons. The results showed that the rate of CP was <0.05% beyond15 mupwind and84.8 mdownwind in all crop seasons. The CP rate was below 5% at a distance of10min the downwind direction. A sample with 0.24% CP was recorded at107.3 mdownwind; however, the CP rate was 0% at68 mupwind. Three empirical models were used, that is, exponential, log/log and log/log, and a simplified Gaussian Plume model, to examine the relationship between the CP rates and the source-field distances. All of the models were appropriate for predicting CP rates, and the Gaussian Plume model performed better compared to the empirical models. The results show that it is possible to control CP from foreign pollen by using an appropriate isolation distance.

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Kuo, B. , Nieh, S. , Shieh, G. and Lin, W. (2014) Simulating the Gene Flow of Genetically Modified Maize in Taiwan. Agricultural Sciences, 5, 440-453. doi: 10.4236/as.2014.55045.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] James, C. (2013) Global Status of Commercialized Biotech/GM Crops: 2012. ISAAA Brief, ISAAA, Ithaca.
[2] Ma, B.L., Subedi, K.D. and Reid, L.M. (2004) Extent of Cross-Fertilization in Maize by Pollen from Neighboring Transgenic Hybrids. Crop Science, 44, 1273-1282.
http://dx.doi.org/10.2135/cropsci2004.1273
[3] Palaudelmás, M., Melé, E., Pennas, G., Pla, M., Nadal, A., Serra, J., Salvia, J. and Messeguer, J. (2008) Sowing and Flowering Delays Can Be an Efficient Strategy to Improve Coexistence of Genetically Modified and Conventional Maize. Crop Science, 48, 2404-2413.
[4] Marceau, A., Loubet, B., Andrieu, B., Dur, B., Foueillassar, X. and Huber, L. (2011) Modeling Diurnal and Seasonal Patterns of Maize Pollen Emission in Relation to Meteorological Factors. Agricultural and Forest Meteorology, 151, 11-21. http://dx.doi.org/10.1016/j.agrformet.2010.08.012
[5] Devos, Y., Demont, M., Dillen, K., Reheul, D., Kaiser, M. and Sanvido, O. (2009) Coexistence of Genetically Modified (GM) and Non-GM Crops in the European Union. A Review. Agronomy for Sustainable Development, 29, 11-30. http://dx.doi.org/10.1051/agro:2008051
[6] Luna, S.V., Figueroa, J.M., Baltazar, B.M., Gomez, R.L., Townsend, R. and Schoper, J.B. (2001) Maize Pollen Longevity and Distance Isolation Requirements for Effective Pollen Control. Crop Science, 41, 1551-1557.
http://dx.doi.org/10.2135/cropsci2001.4151551x
[7] Loos, C., Seppelt, R., Meier-Bethke, S., Schiemann, J. and Richter, O. (2003) Spatially Explicit Modeling of Transgenic Maize Pollen Dispersal and Cross-Pollination. Journal of Theoretical Biology, 225, 241-255.
http://dx.doi.org/10.1016/S0022-5193(03)00243-1
[8] Gustafson, D.I., Horak, M.J., Rempel, C.B., Metz, S.G., Gigax, D.R. and Hucl, P. (2005) An Empirical Model for Pollen-Mediated Gene Flow in Wheat. Crop Science, 45, 1286-1294.
http://dx.doi.org/10.2135/cropsci2004.0137
[9] Halsey, M.E., Remund, K.M., Davis, C.A., Qualls, M., Eppard, P.J. and Berberich, S.A. (2005) Isolation of Maize from Pollen-Mediated Gene Flow by Time and Distance. Crop Science, 45, 2172-2185.
http://dx.doi.org/10.2135/cropsci2003.0664
[10] Messeguer, J., Penas, G., Ballester, J., Bas, M., Serra, J., Salvia, J., Palaudelmas, M. and Mele, E. (2006) Pollen-Mediated Gene Flow in Maize in Real Situations of Coexistence. Plant Biotechnology Journal, 4, 633-645.
http://dx.doi.org/10.1111/j.1467-7652.2006.00207.x
[11] Della Porta, G., Ederle, D., Bucchini, L., Prandi, M., Verderio, A. and Pozzi, C. (2008) Maize Pollen Mediated Gene Flow in the Po Valley (Italy): Source-Recipient Distance and Effect of Flowering Time. European Journal of Agronomy, 28, 255-265. http://dx.doi.org/10.1016/j.eja.2007.07.009
[12] Frederick, C. (2011) Taiwan Biotechnology Annual Report. USDA Foreign Agricultural Service.
[13] Pasquill, F. (1974) Atmospheric Diffusion, 2nd Edition, Wiley, New York.
[14] Yao, K., Hu, N., Cheng, W., Li, R., Yuan, Q., Wang, F., Qian, Q. and Jia, S. (2008) Establishment of a Rice Transgene Flow Model for Predicting Maximum Distances of Gene Flow in Southern China. New Phytologist, 180, 217-228.
http://dx.doi.org/10.1111/j.1469-8137.2008.02555.x
[15] Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W. (1996) Applied Linear Statistical Models. 4th Edition, McGraw-Hill Professional Publishing, New York.
[16] Aylor, D.E., Schultes, N.P. and Schields, E.J. (2003) An Aerobiological Framework for Assessing Cross-Pollination in Maize. Agricultural and Forest Meteorology, 119, 111-129.
http://dx.doi.org/10.1016/S0168-1923(03)00159-X
[17] Jarosz, N., Loubet, B., Durand, B., Foueillassar, X. and Huber, L. (2005) Variations in Maize Pollen Emission and Deposition in Relation to Microclimate. Environmental Science & Technology, 39, 4377-4384.
http://dx.doi.org/10.1021/es0494252

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