Agricultural and Biosystems Engineering
Journal or Book Title
Applied Engineering in Agriculture
Research Focus Area(s)
Advanced Machinery Engineering and Manufacturing Systems
The current rate of population growth necessitates the use of viable technologies like genetic modification to address estimated global food and feed requirements. However, in recent years, there has been an increase in resistance against the diffusion of genetic modification technology around the world. Many countries have adopted coexistence policies to allow a certain percentage of adventitious presence in non-genetically modified crops. However, the tolerance percentage for adventitious presence has been a bottleneck to free trade in some cases. It is a challenging task to fix a tolerance percentage considering the level of permeation of genetic modification technology in agriculture. This article introduces a software developed to serve as a decision-making tool to predict the probability distribution of genetically modified (GM) contamination in non-GM grain lot using user inputs such as final quantity of processed corn, overall tolerance level, and moisture content. The output from the software includes the mass of corn in each processing stage, the tolerance level and the probability distribution of potential GM contamination. The software predicted the probability of contamination with adventitious presence at tolerance levels of 5.0%, 3.0%, 1.0%, 0.9%, 0.5%, and 0.1% as 0.05, 0.07, 0.11, 0.12, 0.16, and 0.36, respectively. The predictions from the model were compared to a similar study wherein the effect of tolerance levels incurred in the costs of segregation was studied. The mean absolute percentage error for the predictions was found to be 3.07%. This software can be used as a tool in testing GM contamination in non-GM grain against a desired threshold levels in a grain elevator.
American Society of Agricultural and Biological Engineers
Salish, Karthik; Mosher, Gretchen A.; and Ambrose, R. P. Kingsly, "Developing a Graphical User Interface (GUI) to Predict the Contamination of GM Corn in Non-GM Corn" (2020). Agricultural and Biosystems Engineering Publications. 1126.