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Risk Assessment of Genetically Engineered Maize Resistant to Diabrotica spp.: Influence on Above-Ground Arthropods in the Czech Republic.

ABSTRACT: Transgenic maize MON88017, expressing the Cry3Bb1 toxin from Bacillus thuringiensis (Bt maize), confers resistance to corn rootworms (Diabrotica spp.) and provides tolerance to the herbicide glyphosate. However, prior to commercialization, substantial assessment of potential effects on non-target organisms within agroecosystems is required. The MON88017 event was therefore evaluated under field conditions in Southern Bohemia in 2009-2011, to detect possible impacts on the above-ground arthropod species. The study compared MON88017, its near-isogenic non-Bt hybrid DK315 (treated or not treated with the soil insecticide Dursban 10G) and two non-Bt reference hybrids (KIPOUS and PR38N86). Each hybrid was grown on five 0.5 ha plots distributed in a 14-ha field with a Latin square design. Semiquantitative ELISA was used to verify Cry3Bb1 toxin levels in the Bt maize. The species spectrum of non-target invertebrates changed during seasons and was affected by weather conditions. The thrips Frankliniella occidentalis was the most abundant species in all three successive years. The next most common species were aphids Rhopalosiphum padi and Metopolophium dirhodum. Frequently observed predators included Orius spp. and several species within the Coccinellidae. Throughout the three-year study, analysis of variance indicated some significant differences (P<0.05). Multivariate analysis showed that the abundance and diversity of plant dwelling insects was similar in maize with the same genetic background, for both Bt (MON88017) and non-Bt (DK315) untreated or insecticide treated. KIPOUS and PR38N86 showed some differences in species abundance relative to the Bt maize and its near-isogenic hybrid. However, the effect of management regime on arthropod community was insignificant and accounted only for a negligible portion of the variability.

SUBMITTER: Svobodova Z

PROVIDER: S-EPMC4470634 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Project description:Background:Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. Results:In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. Conclusions:These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

Dataset Information

Risk Assessment of Genetically Engineered Maize Resistant to Diabrotica spp.: Influence on Above-Ground Arthropods in the Czech Republic.

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