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The benefits of sensitivity analysis in an interdisciplinary environment, a case study: the ecomeristem model

Soulie J.C., Luquet D., Rouan L.. 2013. In : Piantadosi J. (ed.), Anderssen R.S. (ed.), Boland J. (ed.). Adapting to change: the multiple roles of modelling. 20th International Congress on Modelling and Simulation (MODSIM2013), Adelaide, Australia, 1-6 December 2013. Adelaide : MSSANZ, p. 368-368. International Congress on Modelling and Simulation. 20, 2013-12-01/2013-12-06, Adelaide (Australie).

The models developed by scientists are more and more complex. Indeed, these models are com- posed of a large number of sub-models than can interact with other models and sub-models. These processes and interactions define non linear and non derivable outputs. This is why we need to analyze the properties of such models if we want to qualitatively validate them, infer new knowledge or evaluate the impact of an event on the system. In this context, the use of sensitivity analysis seems fundamental and is a tool that allows having a better understanding of our models. This paper presents why and how we use sensitivity analysis on the Ecomeristem model. Ecomeristem is a whole-plant, deterministic, dynamic, radiation and temperature- driven crop model within the category of Functional Structural Plant Modeling. It includes also soil and plant water balance (to study, for instance, drought stress) modules. The main distinguishing mark of this model is its capability to simulate competition for assimilates (supply) among growing organs (demand). The plant is simulated as an average individual of a population forming a canopy. Plant organogenetic and morphogenetic processes are driven by incremental carbon assimilate source and sink depending on genotypic parameters and environmental conditions. This model has been developed in an interdisciplinary environment and is a result of collaborations and works between scientists involved in ecophysiology, genetics, computer science, and ap- plied mathematics. For this study, we used the extended Fourier Amplitude Sensitivity Test (called fast99 and comes from the sensitivity R package). This method allows the estimation of first order and total Sobol' indices for all the factors. One of the main advantage of this method is that if n is the sample size and p the number of factors, the number of simulations needed to produce the sensitivity indices is: n x p. Using fast99 we carried out a large number of sensitivity analysis. These

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