Seeds & Traits Safety Manager
Bayer Crop Science
Area of Expertise
As an Agronomist with a Master’s degree in crop production, Hallison conducts studies to develop and assess new crop traits, herbicides, and field management practices. Taken together, the data he generates is then used to enrich Bayer’s regulatory submissions to approve new biotech traits in Brazil.
Hallison loves his work as a scientist because it provides the opportunity to contribute—even in small but significant ways—to keep humanity moving forward. As a researcher and agronomist, he is particularly inspired to develop technologies that empower farmers to produce more, more sustainably.
Education: MA in Crop Production
Comparing agronomic and phenotypic plant characteristics between single and stacked events in soybean, maize, and cotton
Genetically modified (GM) crops are one of the most valuable tools of modern agriculture, especially in Brazil, which is one of the largest food producers in the world. Farmers use GM Crops to combat insects and weeds which can negatively impact yield by up to 40%. This work investigates whether stacking single GM traits would have a significant impact on agronomic and phenotypic plant characteristics, how the crop grows or looks, in soybean, maize, and cotton planted from 2008 to 2017 in crop field sites in Brazil. Data analyzed over 10 years confirms that the tests previously performed on crops that contain single traits can be used to demonstrate the safety of the stacked products, which deliver significant benefits to growers and to the environment.
In the News
Consistent Risk Assessment Outcomes from Agronomic Characterization of GE Maize in Diverse Regions and as Single-Event and Stacked Products
Consideration of familiarity accumulated in the confined field trials for environmental risk assessment of genetically modified soybean (Glycine max) in Japan
Stacked Trait Products Are As Safe As Non-Genetically Modified (GM) Products Developed By Conventional Breeding Practices
Single-Event Transgene Product Levels Predict Levels in Genetically Modified Breeding Stacks