Modélisation de l’impact des systèmes de culture sur la pollinisation croisée chez le maïs dans le cadre de l’établissement de règles de coexistence
Notice bibliographique
Résumé
Consumer demand for food free of GMOs is growing and if production standards are to be met, food and non-food chains must be separated. Indeed, European Commission regulations 1829/2003 and 1830/2003 stipulate that food and feed thought to be GMO-free but found to be containing more than a 0.9% portion of an adventitious presence of authorized GMOs have to be distinguished, traced and labelled as such. Moreover, to ensure that producers have a choice a choice among differing types of production, the European Commission has issued recommendations that permit the coexistence of non-GM and GM crops. This poses the problem of how to deal with coexistence in an agricultural supply chain dedicated to handling a single crop species. To help in the elaboration of coexistence rules, and then assess their feasibility and their consequences as well as for setting up monitoring and control schemes, specific field experiments, even if necessary, are not sufficient as their predictive value remains restricted to a given context. It is necessary to be able to forecast the fate of GM crops at the landscape level taking into account the various cropping systems and agricultural practices that may occur across Europe. The key to forecasting spread and behaviour of GM plants and seeds as well as their impacts under a wide range of agro-ecosystems is modelling. Models reproduce the functioning of agro-systems and take into account the relevant factors and processes as well as their interactions. They thus make it possible to simulate the behaviour of agro-systems in non-observed situations and on a long term basis.This doctoral thesis establishes a methodological framework for the development of biological flow models thanks to the dynamic interactions of modelling, evaluation and experimentation. In a first phase, the work involved the design of the MAPOD® model developed in relationship to literature and varietal experiments carried out by GEVES. MAPOD® simulates gene flow between maize crops at the landscape scale. It is based on an individual dispersal function which depends on biological and climatic parameters. It calculates the probability of fecundation at a (x, y) point as a function of distance from the pollen emitter (efficient pollination). Its flowering dynamics module makes it possible to take into account of the consequences of flowering time - lags on GM adventitious presence in harvests. MAPOD® evaluates the effect of the spatial distribution of maize plots, varietal characteristics, and climate as well as agricultural practices on cross-pollination. In this investigation, in a second phase the initial version of MAPOD® was evaluated with another dataset provided by GEVES. Ways to improve the algorithm were thereby defined. In a third phase, the predictive quality of MAPOD® was estimated by comparing model outputs to cross-pollination rates which were obtained by monitoring farmers' fields in Catalonia (Spain) over a 5-year period. The relevance of decisions made according to model output was also evaluated.In a fourth phase, MAPOD® was used to simulate different scenarios involving the introduction of GM varieties into European cropping systems. The efficiency of individual coexistence measures was tested. Afterwards, the effect of combining different types of practices was simulated, leading to a set of decision-support tables elaborated according to the cropping context. At the scale of the collecting basin, with the enhanced version of MAPOD®, the efficiency of segregation strategies (spatial or temporal) that could be implemented by collecting and storing organisations was also studied. Lastly, model outputs were used as a basis for the design of a decision-support tool for use by farmers and extension workers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».