Machine d'essai en fatigue biaxiale disposant d'une éprouvette

Machine d'essai en fatigue disposant d'une éprouvette (10), cette machine comportant un poinçon mobile appliquant une charge axiale cyclique sur une face de l'éprouvette (14) perpendiculaire à l'axe du poinçon, cette éprouvette étant en appui sur une base fixe par une face opposée (12) à la première face, caractérisée en ce que la machine d'essai comporte deux appuis annulaires de rayons différents centrés sur l'axe du poinçon, appliquant la charge du poinçon ou de la base fixe sur chacune des faces opposées (12, 14) de l'éprouvette (10), la partie centrale de cette éprouvette comprenant une zone de contrainte bi-axiale (22) comportant deux surfaces parallèles, et la face (12) comportant l'appui annulaire de plus grand rayon (A), recevant sur la zone de contrainte bi-axiale (22) des jauges de déformation (20)

Machine d'essai en fatigue biaxiale disposant d'une éprouvette

Machine d'essai en fatigue disposant d'une éprouvette (10), cette machine comportant un poinçon mobile appliquant une charge axiale cyclique sur une face de l'éprouvette (14) perpendiculaire à l'axe du poinçon, cette éprouvette étant en appui sur une base fixe par une face opposée (12) à la première face, caractérisée en ce que la machine d'essai comporte deux appuis annulaires de rayons différents centrés sur l'axe du poinçon, appliquant la charge du poinçon ou de la base fixe sur chacune des faces opposées (12, 14) de l'éprouvette (10), la partie centrale de cette éprouvette comprenant une zone de contrainte bi-axiale (22) comportant deux surfaces parallèles, et la face (12) comportant l'appui annulaire de plus grand rayon (A), recevant sur la zone de contrainte bi-axiale (22) des jauges de déformation (20)

Modelling crop management effects on soil organic C stocks and pools dynamics using CENTURY

International audienceSoils constitute the major reservoir of organic carbon storing around 2500 Pg C in the top two meters which correspond to approximately more than three times the amount of C stored in the atmosphere (Jobbágy and Jackson, 2000; Tarnocai et al., 2009) and six times the amount of C stored in terrestrial vegetation (Prentice et al., 2001). Consequently, small changes in soil organic carbon (SOC) stocks will greatly affect the global ecosystem carbon cycling and potentially the global climate (Davidson and Janssens, 2006; Heimann and Reichstein, 2008). Beside its importance in the global terrestrial C cycle, SOC is also a key component for sustainable productivity in agro-ecosystems. Improving agricultural practices represents a win-win strategy that has the potential to enhance soil fertility and sequester C (Lal, 2004). However, there still uncertainties about agricultural practices impacts on SOC stocks dynamics and controversial and contradictory results in the literature are common (Luo et al., 2010; Virto et al., 2011). These uncertainties are mainly attributed to the lack of continuous SOC monitoring in long-term experiments, the diversity of climatic conditions (Powlson et al., 2014), and the antagonistic effects of some practices such as fertilization or irrigation. Modeling represents a valuable tool to simulate the spatial and temporal SOC stocks dynamics in response to the fast changes in policies, agriculture practices and their complex interaction with current and projected future climatic conditions. The main objectives of this work were: to analyze the interaction between crop management and climatic conditions in the long-term on SOC dynamics as simulated by theCentury model (Parton et al., 1987) across France and to quantify GHG emissions and SOC stocks at different scales in order to apply the Tier 3 methodology for the GHG inventories. We examined more than 20 sites with different Long-term experiments, ranging from 10 to 40 years, across the France. Different agricultural practices were studied such as tillage treatments, crop rotation, organic amendment, mineral fertilization, etc. We used the well-validated Century model which has been widely used all over the world to simulate SOC dynamics in agricultural systems. Model parameters calibration through inverse modeling using PEST (Doherty, 2010) was applied to estimate some of the parameter values. First results showed that the model gave satisfactory results for the SOC stocks dynamics over the layer 0-20 cm. Model parameters calibration improved the fit for to crop productivity and SOC. An important result emerged from this work emphasis that agricultural practices that maximize C input are more effective strategies for SOC sequestration than those that limit SOC mineralization. Further results will be given in the poster

Uncertainty functions of modelled soil organic carbon changes in response to crop management derived from a French long term experiments dataset

The land use, land-use change and forestry (LULUCF) activities and crop management (CM) in Europe could be an important carbon sink through soil organic carbon (SOC) sequestration. Recently, the (EU decision 529/2013) requires European Union's member states to assess modalities to include greenhouse gas (GHG) emissions and removals resulting from activities relating to LULUCF and CM into the Union's (GHG) emissions reduction commitment and their national inventories reports (NIR). Tier 1, the commonly used method to estimate emissions for NIR, provides a framework for measuring SOC stocks changes. However, estimations have high uncertainty, especially in response to crop management at regional and specific national contexts. Understanding and quantifying this uncertainty with accurate confidence interval is crucial for reliably reporting and support decision-making and policies that aims to mitigate greenhouse gases through soil C storage. Here, we used the Tier 3 method, consisting of process-based modelling, to address the issue of uncertainty quantification at national scale in France. Specifically, we used 20 Long-term croplands experiments (LTE) in France with more than 100 treatments taking into account different agricultural practices such as tillage, organic amendment, inorganic fertilization, cover crops, etc. These LTE were carefully selected because they are well characterized with periodic SOC stocks monitoring overtime and covered a wide range of pedo-climatic conditions. We applied linear mixed effect model to statistically model, as a function of soil, climate and cropping system characteristics, the uncertainty resulting from applying this Tier 3 approach. The model was fitted on the dataset yielded by comparing the simulated (with the Century model V 4.5) to the observed SOC changes on the LTE at hand. This mixed effect model will then be used to derive uncertainty related to the simulation of SOC stocks changes of the French Soil Monitoring Network (FSMN) where only one measurement is done in 16 Km regular grid. These simulations on the grid will be in turn used for NIR. Preliminary results suggest that the model do not adequately simulate SOC stocks levels but succeeds at capturing SOC changes due to management, despite the fact that the model does not explicitly simulate some management such as tillage. This is probably due to inappropriate model parametrization especially for crops and thus Cinput in the French context and/or model initialization