Jean François Soussana

Next generation of elevated [CO2] experiments with crops: A critical investment for feeding the future world

A rising global population and demand for protein-rich diets are increasing pressure to maximize agricultural productivity. Rising atmospheric [CO(2)] is altering global temperature and precipitation patterns, which challenges agricultural productivity. While rising [CO(2)] provides a unique opportunity to increase the productivity of C(3) crops, average yield stimulation observed to date is well below potential gains. Thus, there is room for improving productivity. However, only a fraction of available germplasm of crops has been tested for CO(2) responsiveness. Yield is a complex phenotypic trait determined by the interactions of a genotype with the environment. Selection of promising genotypes and characterization of response mechanisms will only be effective if crop improvement and systems biology approaches are closely linked to production environments, that is, on the farm within major growing regions. Free air CO(2) enrichment (FACE) experiments can provide the platform upon which to conduct genetic screening and elucidate the inheritance and mechanisms that underlie genotypic differences in productivity under elevated [CO(2)]. We propose a new generation of large-scale, low-cost per unit area FACE experiments to identify the most CO(2)-responsive genotypes and provide starting lines for future breeding programmes. This is necessary if we are to realize the potential for yield gains in the future.

The coordination of leaf photosynthesis links C and N fluxes in C3 plant species

Photosynthetic capacity is one of the most sensitive parameters in vegetation models and its relationship to leaf nitrogen content links the carbon and nitrogen cycles. Process understanding for reliably predicting photosynthetic capacity is still missing. To advance this understanding we have tested across C3 plant species the coordination hypothesis, which assumes nitrogen allocation to photosynthetic processes such that photosynthesis tends to be co-limited by ribulose-1,5-bisphosphate (RuBP) carboxylation and regeneration. The coordination hypothesis yields an analytical solution to predict photosynthetic capacity and calculate area-based leaf nitrogen content (N a). The resulting model linking leaf photosynthesis, stomata conductance and nitrogen investment provides testable hypotheses about the physiological regulation of these processes. Based on a dataset of 293 observations for 31 species grown under a range of environmental conditions, we confirm the coordination hypothesis: under mean environmental conditions experienced by leaves during the preceding month, RuBP carboxylation equals RuBP regeneration. We identify three key parameters for photosynthetic coordination: specific leaf area and two photosynthetic traits (k3, which modulates N investment and is the ratio of RuBP carboxylation/oxygenation capacity () to leaf photosynthetic N content (N pa); and J fac, which modulates photosynthesis for a given k 3 and is the ratio of RuBP regeneration capacity (J max) to). With species-specific parameter values of SLA, k 3 and J fac, our leaf photosynthesis coordination model accounts for 93% of the total variance in Na across species and environmental conditions. A calibration by plant functional type of k 3 and J fac still leads to accurate model prediction of N a, while SLA calibration is essentially required at species level. Observed variations in k3 and Jfac are partly explained by environmental and phylogenetic constraints, while SLA variation is partly explained by phylogeny. These results open a new avenue for predicting photosynthetic capacity and leaf nitrogen content in vegetation models.

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.

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.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1xa0SD of observed N2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2 O emissions. Yield-scaled N2 O emissions (N2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N2 O emissions at field scale is discussed.

Evaluation of greenhouse gas emissions and design of mitigation options: a whole farm approach based on farm management data and mechanistic models

Agricultural activities are important sources of atmospheric nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2). In the European Union, the agricultural sector contributes approximately 10% of the total emitted greenhouse gases (GhG). We search for evaluating GhG emissions at the farm level and designing mitigation options from a biotechnical point of view. As GhG emissions occur at several stages within the farm operation, it is essential to combine nutrient cycling and farm working through an integrated farm approach to reach a full accounting of processes. Our proposal comprises the following characteristics: n- Our whole farm approach (FarmSim) combines empirical and mechanistic modelling through describing of above and below ground C and N fluxes; with respect to GhG, emissions can be also calculated with emissions factors, comparable to the IPCC methodology when no model is available yet as for manure management for instance. - Rebuilding farmers practices description from available data (on farm and through farmsnetwork databases) needs to choose a level of information to translate real situations to generic ones. It requires thus to choose a set of farm components and compatible spatio-temporal scales in respect with the data needed by the models. - At the moment, we coupled tow models allowing to quantify the emissions of the different lend uses (in our case PaSim, a grassland ecosystem model and CERES-EGC, a crop model). - The system boundaries are limited to the farm gate, or may include pre- and post-chain effects according to more and less high inputs and the difficulty to forecast produce fate if databases are available. - Building plausible scenarios requires to control a set farm operation coherences to account for stranding mitigation options. In a first step, we used a case study formalized by the French Livestock Institute (IE) and some farm working data from an experimental dairy farm belonging to the French Institute for Agricultural Research (INRA), both located in semi-continental conditions (Mirecourt-North-Eastern France). The simulated farming system is a mixed dairy and crops system so as to account for a rather generic farming system.