Appropriate sampling methods and statistics can tell apart fraud from pesticide drift in organic farming

Abstract Pesticide residues are much lower in organic than in conventional food. The article summarizes the available residue data from the EU and the U.S. organic market. Differences between samples from several sources suggest that organic products are declared conventional, when they have residue...

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Autores principales: Albrecht Benzing, Hans-Peter Piepho, Waqas Ahmed Malik, Maria R. Finckh, Manuel Mittelhammer, Dominic Strempel, Johannes Jaschik, Jochen Neuendorff, Liliana Guamán, José Mancheno, Luis Melo, Omar Pavón, Roberto Cangahuamín, Juan-Carlos Ullauri
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/a1975fe9d4c64410a594f0afcf980600
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Sumario:Abstract Pesticide residues are much lower in organic than in conventional food. The article summarizes the available residue data from the EU and the U.S. organic market. Differences between samples from several sources suggest that organic products are declared conventional, when they have residues—but the origin of the residues is not always investigated. A large number of samples are being tested by organic certifiers, but the sampling methods often do not allow to determine if such residues stem from prohibited pesticide use by organic farmers, from mixing organic with conventional products, from short-range spray-drift from neighbour farms, from the ubiquitous presence of such substances due to long-distance drift, or from other sources of contamination. Eight case studies from different crops and countries are used to demonstrate that sampling at different distances from possible sources of short-distance drift in most cases allows differentiating deliberate pesticide application by the organic farmer from drift. Datasets from 67 banana farms in Ecuador, where aerial fungicide spraying leads to a heavy drift problem, were subjected to statistical analysis. A linear discriminant function including four variables was identified for distinguishing under these conditions application from drift, with an accuracy of 93.3%.