Impact models in agriculture : from seasonal forecast to long-term estimations, including annual estimates ; Modèles d'impact statistiques en agriculture : de la prévision saisonnière à la prévision à long terme, en passant par les estimations annuelles

In agriculture, weather is the main factor of variability between two consecutive years. This thesis aims to build large-scale statistical models that estimate the impact of weather conditions on agricultural yields. The scarcity of available agricultural data makes it necessary to construct simple models with few predictors, and to adapt model selection methods to avoid overfitting. Careful validation of statistical models is a major concern of this thesis. Neural networks and mixed effects models are compared, showing the importance of local specificities. Estimates of US corn yield at the end of the year show that temperature and precipitation information account for an average of 28% of yield variability. In several more weather-sensitive states, this score increases to nearly 70%. These results are consistent with recent studies on the subject. Mid-season maize crop yield forecasts are possible from July: as of July, the meteorological information available accounts for an average of 25% of the variability in final yield in the United States and close to 60% in more weather-sensitive states like Virginia. The northern and southeastern regions of the United States are the least well predicted. Predicting years for which extremely low yields are encountered is an important task. We use a specific method of classification, and show that with only 4 weather predictors, 71% of the very low yields are well detected on average. The impact of climate change on yields up to 2060 is also studied: the model we build provides information on the speed of evolution of yields in different counties of the United States. This highlights areas that will be most affected. For the most affected states (south and east coast), and with constant agricultural practice, the model predicts yields nearly divided by two in 2060, under the IPCC RCP 4.5 scenario. The northern states would be less affected. The statistical models we build can help for management on the short-term (seasonal forecasts) or to quantify the quality of the ...