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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".