Genetic use restriction technologies and possible applications in the integrated pest management

ABSTRACT: Genetic use restriction technologies (GURTs) were developed to preserve the intellectual property of genetically modified crops (GM) and ensure the return of investments made by industry to obtain technology delivered through seeds. The aims of this review are to discuss the GURTs and analyze their possible applications in integrated management of agricultural pests. There are two classes of GURTs: T-GURTs (trait-based GURTs), wherein the generated seed are viable, but the next generation does not express the trait of agronomic interest, and V-GURT (variety-based GURTs), in which plants produce non viable seeds. However, beyond the seed protection purpose, the GURTs could have also other application to solve agronomic problems. One of the most important is the use of GURTs as a tool to restrict gene flow of GM traits to relative weeds. In addition, it is proposed the use of this technology in integrated weed management by preventing the GMs seed germination, which produces volunteer plants that compete with the crop of interest. Also, these volunteer plants may serve as alternative hosts for insects and pathogens in between crop seasons. The GURTs could contribute to the control of undesirable agents in agricultural systems, reducing the use of pesticides and increasing crop yields.

Valuing new random genetically modified (GM) traits with real options

Purpose – The purpose of this paper is to develop an analytical model to value traits at different developmental phases and to determine the value of drought tolerance (DT) in wheat using GM technology. Design/methodology/approach – A stochastic binomial real-options model of GM traits was developed to estimate the value of a DT wheat trait. Findings – The results indicate that the value of DT wheat using GM technology is in-the-money at each development phase. The greatest value would accrue for the Prairie Gateway and Northern Great Plains regions in the USA. Research limitations/implications – The approach is useful for valuing high-cost risky investments in technology and results provide guidance for development strategies. Originality/value – The model is original and its applications to wheat are unique.

Assessment of Genetically Modified Soybean in Relation to Natural Variation in the Soybean Seed Metabolome

Genetically modified (GM) crops currently constitute a significant and growing part of agriculture. An important aspect of GM crop adoption is to demonstrate safety and equivalence with respect to conventional crops. Untargeted metabolomics has the ability to profile diverse classes of metabolites and thus could be an adjunct for GM crop substantial equivalence assessment. To account for environmental effects and introgression of GM traits into diverse genetic backgrounds, we propose that the assessment for GM crop metabolic composition should be understood within the context of the natural variation for the crop. Using a non-targeted metabolomics platform, we profiled 169 metabolites and established their dynamic ranges from the seeds of 49 conventional soybean lines representing the current commercial genetic diversity. We further demonstrated that the metabolome of a GM line had no significant deviation from natural variation within the soybean metabolome, with the exception of changes in the targeted engineered pathway.

Use of hypergeometric distribution for estimating adventitious presence of GM traits in small seed lots may be misleading

Testing for the unintended or adventitious presence (AP) of genetically modified (GM) events in seed lots is a common practice to comply with regulatory requirements and good stewardship practices. A subsample of a seed lot is typically tested for AP levels, and then statistical methods are used to estimate the upper level of AP in the remainder of the lot with a given level of confidence. For large seed lots, a binomial distribution is typically assumed, but for seed lots where the tested sample is a substantial proportion of the overall seed lot, a hypergeometric distribution is typically assumed. Due to the destructive nature of AP seed testing, we suggest that this latter method may overestimate confidence of low AP in the remaining seed.