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Soybean MON 87708 × MON 89788 × A5547‐127 (three‐event stack soybean) was produced by conventional crossing to combine three single events: MON 87708, MON 89788 and A5547‐127. The GMO Panel previously assessed the three single events and did not identify safety concerns. No new data on the single events, leading to modification of the original conclusions on their safety have been identified. The molecular characterisation, comparative analysis (agronomic, phenotypic and compositional characteristics) and the outcome of the toxicological, allergenicity and nutritional assessment indicate that the combination of the single soybean events and of the newly expressed proteins in the three‐event stack soybean does not give rise to food and feed safety and nutritional concerns. The GMO Panel concludes that the three‐event stack soybean, as described in this application, is as safe as and nutritionally equivalent to its conventional counterpart and the non‐GM reference varieties tested. The nutritional impact of food/feed derived from the three‐event stack soybean is expected to be the same as that of food/feed derived from the conventional counterpart and non‐GM reference varieties. In the case of accidental release of viable seeds of the three‐event stack soybean into the environment, this would not raise environmental safety concerns. The post‐market environmental monitoring plan and reporting intervals are in line with the intended uses of the three‐event stack soybean. Post‐market monitoring of food/feed is not considered necessary. The GMO Panel concludes that the three‐event stack soybean is as safe as its conventional counterpart and the tested non‐GM reference varieties with respect to potential effects on human and animal health and the environment.

Background and aims: In order to analyse root system architectures (RSAs) from captured images, a variety of manual (e.g. Data Analysis of Root Tracings, DART), semi-automated and fully automated software packages have been developed. These tools offer complementary approaches to study RSAs and the use of the Root System Markup Language (RSML) to store RSA data makes the comparison of measurements obtained with different (semi-) automated root imaging platforms easier. The throughput of the data analysis process using exported RSA data, however, should benefit greatly from batch analysis in a generic data analysis environment (R software). Methods: We developed an R package (archiDART) with five functions. It computes global RSA traits, root growth rates, root growth directions and trajectories, and lateral root distribution from DART-generated and/or RSML files. It also has specific plotting functions designed to visualise the dynamics of root system growth. Results: The results demonstrated the ability of the package’s functions to compute relevant traits for three contrasted RSAs (Brachypodium distachyon [L.] P. Beauv., Hevea brasiliensis Müll. Arg. and Solanum lycopersicum L.). Conclusions: This work extends the DART software package and other image analysis tools supporting the RSML format, enabling users to easily calculate a number of RSA traits in a generic data analysis environment.

International audience; The soil science community is facing a growing demand of regional, continental, and worldwide databases in order to monitor the status of the soil. However, the availability of such data is very scarce. Cost-effective tools to measure soil properties for large areas (e.g., Europe) are required. Soil spectroscopy has shown to be a fast, cost-effective, environmental-friendly, nondestructive, reproducible, and repeatable analytical technique. The main aim of this paper is to describe the state of the art of soil spectroscopy as well as its potential to facilitating soil monitoring. The factors constraining the application of soil spectroscopy as an alternative to traditional laboratory analyses, together with the limits of the technique, are addressed. The paper also highlights that the widespread use of spectroscopy to monitor the status of the soil should be encouraged by (1) the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and (2) the scanning of existing soil archives, reducing the need for costly sampling campaigns. Finally, routine soil analysis using soil spectroscopy would be beneficial for the end users by a reduction in analytical costs, and an increased comparability of results between laboratories. This ambitious project will materialize only through (1) the establishment of local and regional partnerships among existent institutions able to generate the necessary technical competence, and (2) the support of international organizations. The Food and Agriculture Organization (FAO) of United Nations and the Joint Research Centre of the European Commission are well placed to promote the use of laboratory and field spectrometers for monitoring the state of soils.

International audience; The development of models describing water and nutrient fluxes to and through 3-D spatially resolved root structures in soils brings along the need to predict or describe the root architecture and root growth in detail. However, detailed data to calibrate and validate such architecture and growth models is typically not available. Here, we investigate the sensitivity of the root architecture model RootTyp to changes in its model parameters and reconstructed the root system architecture of barley (Hordeum vulgare L.) growing in an undisturbed lysimeter using minirhizotron images at four different depths. Root arrival curves from a series of minirhizotron images were used to parameterize RootTyp using a range of realistic architectures. We adjusted a simple architecture to the data, which contained only long primary roots starting from the seed. This simple model unfortunately could not reproduce the observed increase of root density with depth. The model was subsequently improved by allowing root branching and elongation to be horizon-dependent and by making reiteration of root tips possible. Reiteration is an alternative form of branching, where secondary roots can become as long and thick as primary roots. Our results show that minirhizotron data do not contain enough information to warrant identification of the parameters governing these processes, as the additional parameters act similarly on data characteristics as the initial ones. Therefore, different experimental techniques should be combined to constrain the model parameters better in the future.

International audience; In the context of plant leaf roughness analysis for precision spraying, this study explores the capability and the performance of some combinations of pattern recognition and computer vision techniques to extract the roughness feature. The techniques merge feature extraction, linear and nonlinear dimensionality reduction techniques, and several kinds of methods of classification. The performance of the methods is evaluated and compared in terms of the error of classification. The results for the characterization of leaf roughness by generalized Fourier descriptors for feature extraction, kernel-based methods such as support vector machines for classification and kernel discriminant analysis for dimensionality reduction were encouraging. These results pave the way to a better understanding of the adhesion mechanisms of droplets on leaves that will help to reduce and improve the application of phytosanitary products and lead to possible modifications of sprayer configurations