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Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields

Authors :
Makowski, David
Bancal, Rémi
BENSADOUN, Arnaud
Monod, Herve
Messean, Antoine
Agronomie
AgroParisTech-Institut National de la Recherche Agronomique (INRA)
Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE)
Institut National de la Recherche Agronomique (INRA)
Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV)
Institut National de la Recherche Agronomique (INRA)-AgroParisTech
Source :
Risk Analysis, Risk Analysis, Wiley, 2017, Version of Record online : 23 FEB 2017, 13 p. ⟨10.1111/risa.12745⟩
Publication Year :
2014

Abstract

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non-genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non-GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene-flow model (a zero-inflated Poisson model) simulating cross-pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene-flow model.

Details

ISSN :
15396924 and 02724332
Volume :
37
Issue :
9
Database :
OpenAIRE
Journal :
Risk analysis : an official publication of the Society for Risk Analysis
Accession number :
edsair.pmid.dedup....a8db29a086b072d190befdaec5648073