Raphaël Martin

Priming effect and microbial diversity in ecosystem functioning and response to global change: a modeling approach using the SYMPHONY model.

Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions.

Disentangling coordination among functional traits using an individual-centred model: impact on plant performance at intra- and inter-specific levels.

Background Plant functional traits co-vary along strategy spectra, thereby defining trade-offs for resource acquisition and utilization amongst other processes. A main objective of plant ecology is to quantify the correlations among traits and ask why some of them are sufficiently closely coordinated to form a single axis of functional specialization. However, due to trait co-variations in nature, it is difficult to propose a mechanistic and causal explanation for the origin of trade-offs among traits observed at both intra- and inter-specific level. Methodology/Principal Findings Using the Gemini individual-centered model which coordinates physiological and morphological processes, we investigated with 12 grass species the consequences of deliberately decoupling variation of leaf traits (specific leaf area, leaf lifespan) and plant stature (height and tiller number) on plant growth and phenotypic variability. For all species under both high and low N supplies, simulated trait values maximizing plant growth in monocultures matched observed trait values. Moreover, at the intraspecific level, plastic trait responses to N addition predicted by the model were in close agreement with observed trait responses. In a 4D trait space, our modeling approach highlighted that the unique trait combination maximizing plant growth under a given environmental condition was determined by a coordination of leaf, root and whole plant processes that tended to co-limit the acquisition and use of carbon and of nitrogen. Conclusion/Significance Our study provides a mechanistic explanation for the origin of trade-offs between plant functional traits and further predicts plasticity in plant traits in response to environmental changes. In a multidimensional trait space, regions occupied by current plant species can therefore be viewed as adaptive corridors where trait combinations minimize allometric and physiological constraints from the organ to the whole plant levels. The regions outside this corridor are empty because of inferior plant performance.

Regional-scale analysis of carbon and water cycles on managed grassland systems

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213?g?C?m-2?yr-1, which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185?g?C?m-2?yr-1 on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006?g?C?m-2?yr-1, respectively, in agreement with observed average GPP (1230?g?C?m-2?yr-1) and RECO (1046?g?C?m-2?yr-1). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ~5-16?g?C?week-1 across the study sites, while the goodness of fit (R2) was ~0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415?mm?yr-1) for all sites and years is close to the average value of the observed ET (451?mm?yr-1) by flux towers (on a weekly basis, RMSE~2-8?mm?week-1; R2?=?0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. A mechanistic biogeochemical pasture simulation model (PaSim) is improved by using a common set of eco-physiological parameters.PaSim was evaluated at 12 grassland sites in Europe, performing regional-scale analysis of carbon and water cycles.PaSim estimated that European grasslands are a carbon sink of 213?g?C?m-2?yr-1.PaSim overestimated the soil water content during dry periods.

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.

Sensitivity analysis for models of greenhouse gas emissions at farm level. Case study of N2O emissions simulated by the CERES-EGC model

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N(2)O emissions were mainly sensitive to the fraction of N(2)O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.

Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961–2010

About 25% of European livestock intake is based on permanent and sown grasslands. To fulfill rising demand for animal products, an intensification of livestock production may lead to an increased consumption of crop and compound feeds. In order to preserve an economically and environmentally sustainable agriculture, a more forage based livestock alimentation may be an advantage. However, besides management, grassland productivity is highly vulnerable to climate (i.e., temperature, precipitation, CO2 concentration), and spatial information about European grassland productivity in response to climate change is scarce. The process-based vegetation model ORCHIDEE-GM, containing an explicit representation of grassland management (i.e., herbage mowing and grazing), is used here to estimate changes in potential productivity and potential grass-fed ruminant livestock density across European grasslands over the period 1961–2010. Here “potential grass-fed ruminant livestock density” denotes the maximum density of livestock that can be supported by grassland productivity in each 25 km × 25 km grid cell. In reality, livestock density could be higher than potential (e.g., if additional feed is supplied to animals) or lower (e.g., in response to economic factors, pedo-climatic and biotic conditions ignored by the model, or policy decisions that can for instance reduce livestock numbers). When compared to agricultural statistics (Eurostat and FAOstat), ORCHIDEE-GM gave a good reproduction of the regional gradients of annual grassland productivity and ruminant livestock density. The model however tends to systematically overestimate the absolute values of productivity in most regions, suggesting that most grid cells remain below their potential grassland productivity due to possible nutrient and biotic limitations on plant growth. When ORCHIDEE-GM was run for the period 1961–2010 with variable climate and rising CO2, an increase of potential annual production (over 3%) per decade was found: 97% of this increase was attributed to the rise in CO2, -3% to climate trends and 15% to trends in nitrogen fertilization and deposition. When compared with statistical data, ORCHIDEE-GM captures well the observed phase of climate-driven interannual variability in grassland production well, whereas the magnitude of the interannual variability in modeled productivity is larger than the statistical data. Regional grass-fed livestock numbers can be reproduced by ORCHIDEE-GM based on its simple assumptions and parameterization about productivity being the only limiting factor to define the sustainable number of animals per unit area. Causes for regional model-data misfits are discussed, including uncertainties in farming practices (e.g., nitrogen fertilizer application, and mowing and grazing intensity) and in ruminant diet composition, as well as uncertainties in the statistical data and in model parameter values.

Modelling the transfer of 14C from the atmosphere to grass: A case study in a grass field near AREVA-NC La Hague

Radioactive (14)C is formed as a by-product of nuclear power generation and from operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. Since the autumn of 2006, (14)C activity concentrations in samples from the terrestrial environment (air, grass and soil) have been monitored monthly on grassland 2 km downwind of the reprocessing plant. The monitoring data provides an opportunity to validate radioecology models used to assess (14)C transfer to grassland ecosystems. This article compares and discusses the ability of two different models to reproduce the observed temporal variability in grass (14)C activity in the vicinity of AREVA-NC La Hague. These two models are the TOCATTA model which is specifically designed for modelling transfer of (14)C and tritium in the terrestrial environment, and PaSim, a pasture model for simulating grassland carbon and nitrogen cycling. Both TOCATTA and PaSim tend to under-estimate the magnitude of observed peaks in grass (14)C activity, although they reproduce the general trends. PaSim simulates (14)C activities in substrate and structural pools of the plant. We define a mean turn-over time for (14)C within the plant, which is based on both experimental data and the frequency of cuts. An adapted PaSim result is presented using the 15 and 20 day moving average results for the (14)C activity in the substrate pool, which shows a good match to the observations. This model reduces the Root Mean Square Error (RMSE) by nearly 40% in comparison to TOCATTA.

The TOCATTA-χ model for assessing 14C transfers to grass: an evaluation for atmospheric operational releases from nuclear facilities

Radioactive (14)C is formed as a by-product of nuclear power generation and from the operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. This article evaluates a recently improved radioecology model (TOCATTA-χ) to assess (14)C transfers to grassland ecosystems under normal operating conditions. The new version of the TOCATTA model (TOCATTA-χ) includes developments that were derived from PaSiM, a pasture model for simulating grassland carbon and radiocarbon cycling. The TOCATTA-χ model has been tested against observations of (14)C activity concentrations in grass samples collected monthly from six plots which are located around the periphery of the reprocessing plant. Simulated (14)C activities are consistent with observations on both intensively managed and poorly managed grasslands, but an adaptation of the mean turn-over time for (14)C within the plant is necessary in the model to account for different management practices. When atmospheric (14)C activity concentrations are directly inferred from observations, TOCATTA-χ performs better than TOCATTA (the root mean square error is decreased by 45%), but when atmospheric (14)C activity concentrations are not known and must be calculated, the uncertainty associated with the TOCATTA-χ model outcomes is estimated to be larger than the standard deviation of the observations.

Vuln-Indices

Vulnerability assessment to climate change is an issue of concern.We develop Java-based software for vulnerability assessment to climate change.We illustrate the software in vulnerability assessments of European grasslands. Vuln-Indices Java-based software was developed on concepts of vulnerability to climate change of agro-ecological systems. It implements the calculation of vulnerability indices on series of state variables for assessments at both site and region levels. The tool is useful because synthetic indices help capturing complex processes and prove effective to identify the factors responsible for vulnerability and their relative importance. It is suggested that the tool may be plausible for use with stakeholders to disseminate information of climate change impacts.

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.