Simulation analysis of the impacts of climate change and scenarios of adapted cropping practices on the risk of brown rust development in winter wheat.
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- Master's theses (IPV) 
The challenge of climate change in agriculture is a global threat and to meet the demand for food of future generations and ensure global food security, there is a great need to use agroecological measures to reduce or eliminate this threat. Wheat, both a staple crop and an export crop in France, is not an exception to suffering the negative impacts of climate change. This research aimed at simulating the impacts of climate change on brown rust development in wheat. In order to do so, the information on brown rust severity embedded within warning bulletins combined with weather data of twenty regions from 1986–2010 was used to create a simple classifier to predict brown rust severity on wheat. The machine learning tool WEKA was used to create a simple J48 (C4.5) pruned decision tree model using the data of the warning bulletins and the Safran weather database (8 km * 8 km grid, Météo France). Temperatures above 15 °C were found to increase the severity of brown rust. Relative humidity between 70% and 90% were also predicted to affect brown rust development. For the simulation study, the risk of brown rust was quantified under climate change and an adaptation scenario that consisted of using mulch of pea residues for the 150 years using the dynamic model, STICS MILA. These simulated brown rust severity data were then used as input variables in the WHEATPEST model, to calculate the yield losses caused by the disease. Also, RUE values increased as temperature increased, and it was predicted that over the 150 years, temperature, RUE, and brown rust severity would continue to increase. Yield is predicted to be impacted either negatively or positively by climate change as in some cases, high temperature resulted in increased yield. For the decision tree model, the training set test option had a high performance as described by the ROC Area value of 0.974 whereas, in the cross-validation test option, the ROC Area value of 0.647 was recorded. Brown rust was predicted to cause yield losses for the simulated years and adapting agroecological cropping practices would be beneficial in suppressing these losses.