Modelling of the blood coagulation cascade in an in vitro flow system.

We drive a mathematical model of a part of the blood coagulation cascade set up in a perfusion experiment. Our purpose is to simulate the influence of blood flow and diffusion on the blood coagulation pathway. The resulting model consists of a system of partial differential equations taking into account the spatial distribution of the biochemical species. An important issue is inclusion of a dynamic boundary condition describing adhesion of activated platelets on a collagen coated top lid in the perfusion chamber. The validity of the model is established through criteria on the reaction diffusion and flow equations, which guarantee non negative concentrations at all times. The criteria is applied to the model of the blood coagulation cascade

Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment

Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvment. iCROPM2016 International Crop Modelling Symposiu

Statistical analysis of large simulated yield datasets for studying climate effects.

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMI