Joël Léonard

Nitrous Oxide Emission by Agricultural Soils: A Review of Spatial and Temporal Variability for Mitigation

Abstract This short review deals with soils as an important source of the greenhouse gas N2O. The production and consumption of N2O in soils mainly involve biotic processes: the anaerobic process of denitrification and the aerobic process of nitrification. The factors that significantly influence agricultural N2O emissions mainly concern the agricultural practices (N application rate, crop type, fertilizer type) and soil conditions (soil moisture, soil organic C content, soil pH and texture). Large variability of N2O fluxes is known to occur both at different spatial and temporal scales. Currently new techniques could help to improve the capture of the spatial variability. Continuous measurement systems with automatic chambers could also help to capture temporal variability and consequently to improve quantification of N2O emissions by soils. Some attempts for mitigating soil N2O emissions, either by modifying agricultural practices or by managing soil microbial functioning taking into account the origin of the soil N2O emission variability, are reviewed.

SoDA project: A simulation of soil surface degradation by rainfall

Abstract The main objective of the SoDA (Soil Degradation Assessment) project is to realize a simulator of soil surface degradation by rainfall at the meter scale and including visualization. Soil surface structure and morphology deeply influence a lot of processes of high agronomic and environmental relevance, such as mass and heat transfer through the soil–atmosphere interface, runoff and erosion, seed germination and seedling emergence. The soil surface structure of agricultural field is in continuous evolution: it is strongly affected by tillage, and in between tillage operations, erosion by rainfall and runoff causes a progressive degradation of the structure whose intensity and speed partly depend on the initial state associated to tillage modalities. A soil surface degradation model could allow one to predict this evolution of the soil surface structure, and even to help choosing adequate tillage practices and sowing dates. Erosion modeling has been addressed by soil scientists but also by computer graphic scientists in order to add realism to virtual landscapes. Mixing both of these points of view would be interesting to simulate and visualize the evolution of the soil surface of a cultivated soil. Based on a 3D cellular automata approach using the knowledge accumulated by soil scientists about the physical processes involved in erosion, the principles of our simulator and its first implementation are presented in this paper.

The diversity of the N2O reducers matters for the N2O:N2 denitrification end-product ratio across an annual and a perennial cropping system.

Agriculture is the main source of terrestrial emissions of N2O, a potent greenhouse gas and the main cause of ozone layer depletion. The reduction of N2O into N2 by microorganisms carrying the nitrous oxide reductase gene (nosZ) is the only biological process known to eliminate this greenhouse gas. Recent studies showed that a previously unknown clade of N2O-reducers was related to the capacity of the soil to act as an N2O sink, opening the way for new strategies to mitigate emissions. Here, we investigated whether the agricultural practices could differently influence the two N2O reducer clades with consequences for denitrification end-products. The abundance of N2O-reducers and producers was quantified by real-time PCR, and the diversity of both nosZ clades was determined by 454 pyrosequencing. Potential N2O production and potential denitrification activity were used to calculate the denitrification gaseous end-product ratio. Overall, the results showed limited differences between management practices but there were significant differences between cropping systems in both the abundance and structure of the nosZII community, as well as in the [rN2O/r(N2O+N2)] ratio. More limited differences were observed in the nosZI community, suggesting that the newly identified nosZII clade is more sensitive than nosZI to environmental changes. Potential denitrification activity and potential N2O production were explained mainly by the soil properties while the diversity of the nosZII clade on its own explained 26% of the denitrification end-product ratio, which highlights the importance of understanding the ecology of this newly identified clade of N2O reducers for mitigation strategies.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

A Dynamic Model of Cracks Development Based on a 3D Discrete Shrinkage Volume Propagation

We attempt to model and visualize the main characteristics of cracks produced on the surface of a desiccating crusted soil: their patterns, their different widths and depths and their dynamics of creation and evolution. In this purpose we propose a method to dynamically produce three‐dimensional (3D) quasi‐static fractures, which takes into account the characteristics of the soil. The main originality of this method is the use of a 3D discrete propagation of ‘shrinkage volumes’ with respect to 2D precalculated paths. In order to get realistic cracks, we newly propose to take into account a possibly inhomogeneous thickness of the shrinking layer by using a watershed transformation to compute these paths. Moreover, we use the waterfall algorithm in order to introduce in our simulation a hierarchy notion in the cracks appearance, which is therefore linked with the initial structure of the surface. In this paper, this method is presented in detail and a validation of the cracks patterns by a comparison with real ones is given.

C and N models Intercomparison – benchmark and ensemble model estimates for grassland production

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international bench- marking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions (ensemble modelling) to generate expanded envelopes of uncertainty in model predictions (Asseng et al. , 2013). Simulations of C and N fluxes, in particular, are inherently uncertain because they are driven by complex interactions (Sandor et al. , 2016) and complicated by considerable spatial and temporal variability in the measurements. In this context, the Integrative Research Group of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases promotes a coordinated activity across multiple international projects (e.g. C and N Models Inter-comparison and Improvement to assess management options for GHG mitigation in agrosystems worldwide (C-N MIP) and Models4Pastures of the FACCE-JPI, https://www.faccejpi.com) to benchmark and compare simulation models that estimate C – N related outputs (including greenhouse gas emissions) from arable crop and grassland systems (http://globalresearchalliance.org/e/model- intercomparison-on-agricultural-ghg-emissions). This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

A discrete modelling of soil fragments transport by runoff

We aim to model and visualize the evolution of the surface structure of a cultivated soil surface during rainfall. In this paper,we briefly present our model, based on an Extended Cellular Automaton, and the different simulated processes. Among these processes, we focus on runoff which is of high relevance as it drives the evolution of the soil surface structure by transporting and depositing the detached fragments of soil and thus inducing an evolution in the granulometry of the surface material. We propose a simple algorithm to model, in a discrete way, runoff and also the transport and deposition of soil fragments according to their size. In that way we are able to derive information about the evolution of soil surface granulometry. A validation of the runoff model is proposed, based on the comparison of the results obtained with results from a numerical solution of the Saint Venants equations. Although no validation was attempted for transport, simulations yielded visually promising results.

A Non-Modular Cellular DEVS Model Of The Degradation Of A Cultivated Soil Surface By Rainfall

We aim to model and simulate the evolution of the surface structure of a cultivated soil surface during rainfall. The surface degradation is mainly the consequence of the creation and the transport of soil fragments, which are caused by the circulation of water, rainfall and runoff in particular. Our first intent was to use Cellular Automata (CA), but these processes cannot easily be modelled in a pure CA model because they are both discrete and continuous, local and global. We explain in this paper how non modular cellular DEVS can efficiently model this natural system and we present in detail the coupled model of the simulator and the atomic model of the terrain, and we give a sketch of the way we model the processes involved.

A preliminary approach of 3D simulation of soil surface degradation by rainfall

Soil surface structure and morphology deeply in uence a lot of processes of high agronomic and environmental relevance, such as mass and heat transfer through the soil-atmosphere interface, runoff and erosion, seed germination and seedling emergence. The soil surface structure of agricultural eld is in continuous evolution: it is strongly affected by tillage, and in between tillage operations, erosion by rainfall and runoff causes a progressive degradation of the structure whose intensity and speed partly depend on the initial state associated to tillage modalities. A soil surface degradation model could allow to predict this evolution of the soil surface structure, and even to help choosing adequate tillage practices and sowing dates. Erosion modelling has been addressed by soil scientists but also by computer graphic scientists in order to add realism to virtual landscapes. Mixing both of these points of view would be interesting to simulate and visualize the evolution of the soil surface of a cultivated soil. In this paper, we present our project of a simulator of soil surface degradation by rainfall at a small spatial scale (1 m2 or less), including visualization, and which is mainly based on a 3D cellular automata approach with a speci c type of cell. The choices made for the implementation of our model are discussed in the light of the results found in the literature with different modelling approaches.