1. Consistent Risk Assessment Outcomes from Agronomic Characterization of GE Maize in Diverse Regions and as Single‐Event and Stacked Products.
- Author
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Clawson, Ernest L., Perrett, Jamis J., Cheng, Lulu, Ahmad, Aqeel, Stojšin, Duška, McGowan, Yan, Díaz, Oscar Heredia, Asim, Muhammad, Vertuan, Hallison, Quddusi, Murtaza, and Soares, Daniel J.
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ENVIRONMENTAL risk assessment , *RISK assessment , *PLANT parasites , *CORN breeding - Abstract
In commercializing a genetically engineered (GE) crop, agronomic characterization studies that contribute to environmental risk assessment (ERA) may be repeated in different global regions. Likewise, these studies may be done both for single‐event GE products and for traditional breeding crosses that combine GE events (breeding stacks). The objectives of this research were to assess the need for de novo agronomic characterization if previously done in another region or for each event in a breeding stack. Data were obtained for the GE maize (Zea mays L.) products MON 89034 (insect protected), NK603 (herbicide tolerant), and the breeding stack MON 89034 × NK603. The field trials were done from 2004 to 2014 in Argentina, Brazil, Mexico, Pakistan, and/or the United States. Sources of environmental diversity among the regions (i.e., countries) included differences in the prevalent climate classes of their sites. Although values for the agronomic characteristics varied among regions, event × region interactions caused <1% of the total variability for each GE product. Within each region, comparisons of GE products and near‐isogenic conventional controls were largely nonsignificant. When considering agronomic characteristics, a consistent risk assessment outcome—no evidence of increased potential to become a plant pest—was found in each region and for the single‐event products and the breeding stack. The results support ERA policies that provide for (i) acceptance of agronomic characterization data from other regions (data transportability) and (ii) exemption of breeding stacks from agronomic characterization, based on case‐by‐case assessments of plausible risks. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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