Making effective use of existing data for case-by-case risk assessments of genetically engineered crops
Version of Record online: 26 MAY 2009
© 2009 Blackwell Verlag, GmbH
Journal of Applied Entomology
Volume 133, Issue 8, pages 571–583, September 2009
How to Cite
Romeis, J., Lawo, N. C. and Raybould, A. (2009), Making effective use of existing data for case-by-case risk assessments of genetically engineered crops. Journal of Applied Entomology, 133: 571–583. doi: 10.1111/j.1439-0418.2009.01423.x
- Issue online: 13 AUG 2009
- Version of Record online: 26 MAY 2009
- Received: December 4, 2008; accepted: April 30, 2009.
- Bt crops;
- Cry proteins;
- GM crops;
- transgenic plants
Many crops in developing countries suffer devastating attacks from insect pests. Expression of insecticidal proteins in genetically engineered (GE) crops is a potentially powerful means of controlling such pests. Potentially harmful effects of these crops on non-target organisms (NTOs) is of major concern as many of those provide important ecological functions such as pest regulation. Consequently, the likelihood of adverse effects of insect-resistant GE crops on NTOs is assessed case-by-case as part of environmental risk assessments that inform regulatory decision-making. While risk assessments should be rigorous, it is vital that regulatory barriers do not unnecessarily restrict or prevent the application of genetic engineering to important crops in those countries. Efficient regulatory decision-making should make effective use of published information on the biology and ecology of the crop in the country where approval is sought, along with regulatory data produced for GE insect-resistant crops that have received regulatory approvals elsewhere. Just as the risks are assessed for each GE crop individually, the amount of new regulatory data required for a GE crop should vary between crops depending on the amount of existing data and the severity of the perceived risks: new data should be collected only if existing data do not corroborate identified risk hypotheses with sufficient certainty. In this paper, we illustrate how such an approach could work using risks to NTOs from insect-resistant GE pigeonpea in India as an example.