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An updated empirical approach is proposed for specifying coexistence requirements for genetically modified (GM) maize (Zea mays L.) production to ensure compliance with the 0.9% labeling threshold for food and feed in the European Union. The model improves on a previously published (Gustafson et al., 2006) empirical model by adding recent data sources to supplement the original database and including the following additional cases: (i) more than one GM maize source field adjacent to the conventional or organic field, (ii) the possibility of so‐called “stacked” varieties with more than one GM trait, and (iii) lower pollen shed in the non‐GM receptor field. These additional factors lead to the possibility for somewhat wider combinations of isolation distance and border rows than required in the original version of the empirical model. For instance, in the very conservative case of a 1‐ha square non‐GM maize field surrounded on all four sides by homozygous GM maize with 12 m isolation (the effective isolation distance for a single GM field), non‐GM border rows of 12 m are required to be 95% confident of gene flow less than 0.9% in the non‐GM field (with adventitious presence of 0.3%). Stacked traits of higher GM mass fraction and receptor fields of lower pollen shed would require a greater number of border rows to comply with the 0.9% threshold, and an updated extension to the model is provided to quantify these effects.

This work explores the uncertainty of the inferred maize pollen emission rate using measurements and simulations of pollen dispersion at Grignon in France. Measurements were obtained via deposition of pollen on the ground in a canopy gap; simulations were conducted using the two-dimensional Lagrangian Stochastic Mechanistic mOdel for Pollen dispersion and deposition (SMOP). First, a quantitative evaluation of the model's performance was conducted using a global sensitivity analysis to analyse the convergence behaviour of the results and scatter diagrams. Then, a qualitative study was conducted to infer the pollen emission rate and calibrate the methodology against experimental data for several sets of variable values. The analysis showed that predicted and observed values were in good agreement and the calculated statistical indices were mostly within the range of acceptable model performance. Furthermore, it was revealed that the mean settling velocity and vertical leaf area index are the main variables affecting pollen deposition in the canopy gap. Finally, an estimated pollen emission rate was obtained according to a restricted setting, where the model studied includes no deposition on leaves, no resuspension and with horizontal pollen fluctuations either taken into account or not. The estimated pollen emission rate obtained was nearly identical to the measured quantity. In conclusion, the findings of the current study show that the described methodology could be an interesting approach for accurate prediction of maize pollen deposition and emission rates and may be appropriate for other pollen types.

We present numerical results obtained on the CEMRACS project Predictive SMS proposed by Safety Line. The goal of this work was to elaborate a purely statistical method in order to reconstruct the deceleration profile of a plane during landing under normal operating conditions, from a database containing around 1500 recordings. The aim of Safety Line is to use this model to detect malfunctions of the braking system of the plane from deviations of the measured deceleration profile of the plane to the one predicted by the model. This yields to a multivariate nonparametric regression problem, which we chose to tackle using a Bayesian approach based on the use of gaussian processes similar to the one presented in [6]. We also compare this approach with other statistical methods. Résumé. Nous présentons des résultats numériques obtenus sur le projet CEMRACS Predictive SMSproposé par Safety Line. L'objectif de ce travailétait d'élaborer une méthode purement statistique afin de reconstruire le profil de décélération d'un avion durant son atterissage,à partir d'une base de données contenantà peu près 1500 enregistrements. Le but de Safety Line est d'utiliser ce modèle pour détecter des anomalies du système de freinage de l'avionà partir de l'écart entre le profil de décélération de l'avion mesuré et celui prédit par le modèle. Ceci mèneà un problème de régression multivarié non paramétrique que nous avons choisi de traiter via une approche bayésienne utilisant des processus gaussiens similaireà celle présentée dans [6]. Nous comparonségalement cette approche avec d'autres méthodes statistiques classiques.