R. Barillot1 – C. Chambon1 – B. Andrieu1
1 UMR ECOSYS, INRA, 78850 Thiverval-Grignon, bruno.andrieu@grignon.inra.fr Introduction
Improving crops requires to better link traits and metabolic processes to whole plant performance. We present CN-Wheat model that provides a comprehensive and mech-anistic representation of carbon (C) and nitrogen (N) metabolism within a wheat culm during grain filling.
Materials and Methods
Culm structure is composed of a root compartment, a set of photosynthetic organs and the grains. Each module includes structural, storage and mobile materials. Fluxes of C and N among modules take place through the communication with a common pool and/or through the transpiration flow. Physiological activities modelled are the acquisition of C and N, the synthesis and degradation of primary metabolites (sucrose, fructans, starch, amino acids, proteins, nitrate), and C loss by respiration, exudation and tissue death. Assimilation of C is calculated using Farquhar model applied at organ scale with parameter dependency to tissue N (Braune et al., 2009). Nitrogen uptake is modelled as the resultant of activities of HATS and LATS systems regulated by root concentrations in nitrate and sucrose. A central role is given to metabolite concentra-tions, as drivers of physiological activities through Michaelis-Menten equations and as driver of transfers between organs through resistance analogy. Finally the plant func-tioning is represented as a set of differential equations. The model is initialized at flowering and simulates the post flowering stage with a time step of 1 hour. To evalu-ate overall consistency, we estimevalu-ated model parameters by compiling various biblio-graphic sources, so that they do not represent a specific genotype but represent plau-sible values.
Results and Discussion
We illustrate model behaviour by simulating the grain filling period for two contrasted treatments corresponding to no fertilisation (H0) at flowering and 15 kg N/ha (H15) brought at flowering (Bertheloot et al., 2011). Figure 1 shows the dynamics of non-structural C and N in the main plant parts. Stem and laminae accumulated large amounts of non-structural C until the rapid growth of grains triggered the remobilisa-tion. Diurnal variations of C were observed for laminae due to the balance between daily sucrose accumulation and the phloem loading. These variations were less pro-nounced for stem that mainly accumulated storage forms of C such as fructans. Non-structural C in laminae and stem decreased faster for H0 than H15 due to the early decrease of leaf protein in H0, triggering leaf senescence, while leaf in N15 accumulat-ed N until 600 hours post-flowering. The N treatments inducaccumulat-ed contrastaccumulat-ed dynamics of
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root non-structural N. The shortage lack of N in H0 roots resulted in a decrease of organic N synthesis that therefore reduced the consumption of C, which explains the larger accumulation of C in H0 roots than for H15. Finally, the shorter live span of pho-tosynthetic tissue in N0 treatment resulted in a less acquisition of C and a grain dry mass lower of 0.5 g compared to H15. Similarly, the lower N availability impacted N accumulation in grains which reached 27mg in H0 vs 33 mg in H15.
Figure 1. Dynamics of non-structural C and N (a) and dynamics of total C and N in grains (b), simulated for H0 (dashed line) and H15 (solid line) treatments. Vertical line shows the start of fast grain filling.
Conclusions
Modelling the functions based on an explicit description of the pools of metabolites provided original insights on the interactions that take place within the plant. We ex-pect that this approach will strengthen our capacity to integrate in plant and crop models the knowledge in physiology and investigate plant traits adapted to changes in practices or environmental conditions.
Acknowledgements
The model is developed within the frame of the “Investissements d’Avenir” Breedwheat Project References
Bertheloot, J., Q. Wu, P.-H. Cournède et al., (2011). Annals of Botany, 108: 1097-1109.
Braune, H., J. Müller, and W. Diepenbrock. (2009). Ecological Modelling, 220:1599-1612.
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Handling uncertainties with multi-ensemble and multi-model simulations in the LandCaRe-DSS
M. Berg1 – W. Mirschel 1, B. Köstner2
1 Institute for Landscape Systems Analysis, Leibniz-Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374 Müncheberg, Germany, e-mail: michael.berg@zalf.de
2 Chair of Meteorology, TU Dresden Introduction
The LandCaRe-DSS (Wenkel et al., 2013) is a model-based decision support system for impact assessment and adaptation strategy development of agriculture to climate and land use changes, designed to offer a reasonable easy way to explore this complex problem space in an interactive and dynamic manner. It offers access to a multitude of different climate simulations, different sets of geographical raster data, soil profile data and a range of included statistical and process-oriented models to simulate crop yields, soil-processes and landscape indicators like erosion risk etc. The whole system is easily extensible in all these aspects and tries hard to make them transparent for the average user of the system. As a result of the framework like character of the Land-CaRe-DSS, multi-ensemble and multi-model simulations are supported and due to their ever growing need especially the use of multiple climate ensembles has been made easy. Model results are presented to the end user geo-located, for instance as overlay maps, and the LandCaRe-DSS aggregates multiple runs to visualize climate data de-pendent uncertainties in histograms and box-plots.
Materials and Methods
The LandCaRe-DSS has two ways to handle the uncertainties inherent in climate data and different models treating the same state variables. The first way is to simply run models for different sets of climate realizations, aggregating the results and giving the end user result visualizations containing for instance the average value plus the stand-ard deviation of a particular variable, e.g. crop yield. This process is completely trans-parent to the user, as she might simply choose a predefined climate simulation and after running a model just has to interpret the results with the attached uncertainty information. Even though an end user might not be aware of the parts that make up a multi-ensemble simulation, a scientist or an advanced user can easily define which climate realizations a single climate simulation is comprised of or even define virtual climate simulations, creating real ensembles, by choosing realizations of different cli-mate simulations and scenarios. The other way to treat uncertainties is more involved and directed at the scientific use case. Here the LandCaRe-DSS is used to run possibly multiple models (for the same state variables, e.g. crop yield from the process-based MONICA model (Nendel et al., 2011) and the statistical hybrid-model YIELDSTAT (Mirschel et al., 2014)) using possibly different climate simulations. The results of these multi-ensemble multi-model runs are stored into model-specific local SQLite databases
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and standard post-processing methods are used to extract and interpret the results according to the requirements. The LandCaRe-DSS displays results in two broad cate-gories, as map overlays for model simulations running in whole regions and as a col-umn visualization for point models. For both visualizations there are diagrams available containing further statistical information. In the case of regional results usually four diagrams are displayed, a box-plot and histogram of the spatial distribution of the average result map being displayed and a box-plot and histogram of the yearly spatial averages of the used climate realizations in the present model run (see YIELDSTAT in figure 1, right). In the case of point results at the local scale, every column displays the average value of the according state variable and upon hovering with the computer mouse over the mark representing the value, a rectangle will appear visualizing the standard deviation of all involved values. In the case of the MONICA model further diagrams are available visualizing for instance the yearly dynamics of ground water recharge or the soil organic carbon, where possible including uncertainty bands show-ing the standard deviation of the aggregated values (see MONICA in figure 1, left).
Figure 1. Left: MONICA model displaying results of single run at local scale Right: YIELDSTAT model displaying winter wheat yields in a region Acknowledgements
The development of the LandCaDSS was funded by the German Federal Ministry of Education and Re-search (BMBF) within the klimazwei reRe-search program (grant: 01 LS 05109) and the Ministry of Infrastructure and Agriculture of the Federal State of Brandenburg (Germany).
References
Mirschel, W., R. Wieland, K.-O. Wenkel, C. Nendel, C. Guddat (2014). YIELDSTAT - a spatial yield model for agricultural crops. European Journal of Agronomy 52: 33-46.
Nendel, C., M. Berg, K.C. Kersebaum, W. Mirschel, X. Specka, M. Wegehenkel, K.-O. Wenkel, R. Wieland (2011). The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics.
Ecological Modelling 222: 1614-1625.
Wenkel, K.-O., M. Berg, W. Mirschel, R. Wieland, C. Nendel, B. Köstner (2013). LandCaRe DSS – An interactive decision support system for climate change impact assessment and the analysis of potential agricultural land use adaptation strategies. Journal of Environmental Management 127, Supplement, S168-S183.
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