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This research project investigates how potential disturbances due to extreme variations in biophysical or economic environments can affect systems of human activities. Many studies of environmental processes assume normal/average conditions. However, many events considered ‘rare’ until now, such as seasonal peaks in temperature, tend to occur more frequently and may cause great damage when they do occur. The increasing probability of such extreme events occurring challenges the robustness of models describing environmental interactions. This project thus aims to integrate statistical tools for risk analysis (e.g., extreme-value theory) with tools for assessing environmental impacts of human activities (e.g., life cycle assessment). Amongst a variety of systems, cattle production will be studied in detail, since it is a crucial sector in agriculture with significant local and global economic and social impacts. In this project, we try to identify a few key factors of livestock production systems, such as time spent indoors vs. on pastures and quality and quantity of grass from pastures, which may be influenced directly by extreme fluctuations in environmental conditions. Analysis of variability in the key factors identified will provide information about risks to stakeholders to help them make decisions about cattle management and future environmental impacts of their systems (e.g., greenhouse gas emissions). It will also assess the robustness of the systems studied by investigating their behaviour under extreme conditions. Integrating risk analysis and environmental assessment aims to help livestock systems make the transition toward agro-ecological systems.
I am a statistician interested in both theoretical developments and applied works concerning statistical methods. My research area concerns the coupling of agricultural models and statistical methods to study and understand mechanisms within agricultural models, e.g. influence of inputs variables, interactions and linear/non-linear relationships. For instance, my research relies on statistical sensitivity analysis methods to qualify and quantify influences of input variables of models. Thus, I collaborate to combine Morris and Sobol sensitivity analysis methods with Life Cycle Assessment approach to evaluate environmental impacts of civil engineering and agricultural models. From a more theoretical point of view, I contribute to propose a framework for nonparametric estimation and regression of count data distributions using discrete kernel approach. My current research interests focus now on applying techniques and recent advances from Extreme Value Theory to analyze environmental impacts of agro systems affected by extreme climate events.
Ta V.L, Senga Kiessé, T., Bonnet S., Ventura, A., 2018. Application of sensitivity analysis in the life cycle design for the durability of reinforced concrete structures in the case of XC4 exposure class. Cement and Concrete Composites 87, 53-62.
Thévenot A., Rivera L.J., Wilfart A., Maillard F., Hassouna M., Senga Kiessé T., Le Féon S., Aubin J, 2017. Mealworm meal for animal feed: environmental assessment and sensitivity analysis to guide future prospects. Journal of Cleaner Production 170, 1260-1267.
Senga Kiessé T. 2017. On finite sample properties of nonparametric discrete asymmetric kernel estimators. Statistics: A Journal of Theoretical and Applied Statistics. Doi: 10.1080/02331888.2017.1293060.
Senga Kiessé T., Ventura, A., van der Werf H.M.G, Cazacliu,B., Rachida,I., 2017. Andrianandraina. Introducing economic actors and their action possibilities in LCA using sensitivity analysis: application to hemp-based insulation products for buildings. Journal of Cleaner Production 142, 3905-3916.
Senga Kiessé T., Ventura, A., 2016. Discrete nonparametric kernel estimation for global sensibility analysis. Reliability Engineering and System Safety 146, 47-54.