Managing gene flow: a prerequisite for recombinant DNA biotechnology
Over centuries, crop domestication and improvement has led to modern commercial agriculture. Agricultural biotechnology is considered by many a natural step in the course of crop improvement by utilizing genetic engineering. Currently, the global production of biotech crops is approximately 34% of global agriculture. The major biotech crops in terms of production volumes are canola, cotton, maize and soybean. In Africa, South Africa is the only country to accept and commercially produce genetically modified (GM) crop. The 2007, GM traits per crop with environmental release status in South Africa included insect resistant (IR) and herbicide tolerant (HT) cotton (including the stack for both traits) (90% of total cotton production), IR and HT maize (including the stack for both traits) (57% of total production) and HT soybean (80% of total production). There are several factors that impact on the application of this technology in terms of commercial as well as small scale farming. These include: intellectual property rights, socio-economics, regulatory frameworks, agriculture, environment, niche markets and cost benefit. Of all of these aspects, gene flow from GM to non-GM or organic products, land races and wild relatives is a critical consideration. In this study, the impact of potential pollen mediated gene flow (PPMGF) and pollen mediated gene flow (PMGF) was studied in GM soybean and maize, two of the most important GM food crops in terms of production volumes. In this study, GM gene flow was found to have occurred up to 0.9 m from a GM source at two locations over two seasons, despite being considered a selfpollinating crop (Greytown 2005/2006 and Delmas 2006/2007, respectively). However, it was also found that GM soybean pollen was not wind borne and we suggest that the gene flow observed was due to insect-mediation. Future studies of PPMGF in South Africa should include a survey of insects present with the potential to act as a pollen vector in soybean. In the maize component of this study, molecular technology was used to detect GM maize pollen up to 400 m from a GM pollen source. Furthermore, it was found that out-crossing of GM to non-GM maize was possible at a distance of 300 m from the GM field. Based on the statistical analysis of out-crossing data, I have determined that the average theoretical zero (0.0001%) level of out-crossing was between 1.3 km and 2.0 km over different geographic locations. However, what was unexpected is the difference in out-crossing per location for a specific direction. For example, in Bainsvlei (2005/2007) for the ENE direction, the calculated distance to achieve 0.01% out-crossing is 79 km, yet the average is 113 m. Similarly in the second season for the same direction, the calculated distance is 956 m and the average is 135 m. The implication of these data is that it is not possible to establish a one size fits all isolation distance to minimize or prevent gene flow. Different threshold levels of commingling require different isolations distances and should be determined by the acceptable level of tolerance for commingling. For non-GM production in South Africa, based on the 1.0% threshold applied by the Department of Agriculture, I suggest a minimum isolation distance of between 120 m up to 200 m, assuming that the weather patterns are comparable to those of the current study as well as that the non-GM seed being planted contains 0% GM. However, for more stringent thresholds, the isolation distance would need to be extended. For organic crop production, at 0% adventitious GM, as well as field trials of second and third generation GMOs, it is suggested that the isolation distance be set at a minimum of 1.5 km and 2.0 km, respectively. In addition, for non-GM seed production (with a mandatory 0% tolerance so as not to contravene patents) I recommend a 1.5 km isolation distance. These suggested isolation distances are based on the absence of time isolation. It is hoped that this study will help to inform regulatory as well as on farm decision making and that it could be used as a blueprint for other GM crops, especially indigenous African crops such as sorghum and cassava.