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Last update: May 2021

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Context and objectives


At the turn of the millennium, 40-50% of the land surface on earth was devoted to agricultural production (IPCC, 2001). This land area currently feeds more than 6 billion people, but by mid-century, it will be expected to feed about 9 billion people, Processes that are advocated to make this possible involve expanding agricultural land to currently uncultivated areas, and increasing food production on land currently under cultivation. Food security can be achieved only if land use is sustainable and soil functions are maintained by careful soil management. At the same time, there is also significant concern related to the release by agriculture fields to the atmosphere of significant amounts of greenhouse gases (GHG) like CO2 and N2O (Cole et al., 1997; IPCC, 2001; Paustian et al., 2004). CO2 is released largely from microbial mineralization of soil organic matter (Smith, 2004b; Janzen, 2004) and N2O is generated by the microbial transformation of nitrogen in soils, especially under wet conditions (Oenema et al., 2005; Smith and Conen, 2004). Agricultural emissions of greenhouse gases could increase to 7.9-8.5 Pg CO2 eq/year by 2050 (Global Research Alliance). Reduction of GHG emissions by improving the efficiency of agricultural systems through robust ecologically-based management practices represents the most important challenge facing agriculture today, but GHG fluxes are highly heterogeneous. Simulation models are needed to decipher the relative effects of soil properties, climate, and agricultural management practices for a wide range of circumstances, since soil is the most complex biological system on the planet. This, however, requires better integration of the currently fragmented knowledge in soil science by bridging the sub-disciplines and time-space scales in order to enable modelling and up-scaling for accurate predictions. Current models should be improved by using recent technological advances made to observe and understand the spatial environment of microorganisms responsible of GHG at the microscopic scales (Baveye and Laba, 2015). We believe that the results of our project will improve predictions of soil models and contribute in the future to help first soil scientists and then land managers to develop novel soil management practices able to maintain soil functions with limited environmental impacts.


The goal of this new project is now to go further by using the 3D models resulting from Mepsom to upscale heterogeneities identified at the scale of microhabitats to the soil profile scale. In Soilµ-3D project, MEPSOM’s 3D models will pass the baton to simpler models able to run at the field scale for a better prediction of organic matter decomposition, nitrous oxide emission and organic pollutants impacted by climate and environmental changes. The general question we intend to answer in the proposed research is whether information on the spatial heterogeneity of soils at the microscale can be used to predict the processes observed at the macroscale in soils (Friedlingstein et al., 2006, Addiscott. 2010, Baveye et al., 2010). To answer this question we need to find the key descriptors of soil structure that can explain the nature and extent of CO2 and N2O emissions, and the specific spatial distributions of organic compounds within the soil structure, and their descriptors that explain their accessibility and availability to microorganisms.


The aims of the project are to:

  • Analyze experimentally the dependency of CO2 and NO2 emissions on the microscale heterogeneity of soils in various conditions (we propose to extend our approach to N2O emission compare to MEPSOM project which dealt only on carbon mineralization)
    • Develop new descriptors of the pore scale 3D soil heterogeneitythat explain the fluxes measured at the core scale
    • Improve the performance of 3D pore scale models to simulate processes from pores to cores with a reduction of the computational time
    • Develop new simple models describing the soil micro-heterogeneity and integrating these micro-features into field-scale models
  • Use our 3D models to connect the µ-scale heterogeneity and the measured macroscale fluxes