I problemi ambientali stanno aumentando progressivamente e pertanto è necessario analizzare linfluenza dellattività umana e cercare strategie per preservare lambiente, per portare al concetto di sviluppo sostenibile. Gli ecosistemi naturali sono stati considerati per un lungo tempo come supporto per la produzione primaria delle esigenze dellagricoltura (MA CHE VUOL DIRE? Originale: [Natural ecosystems have been considered for a long time as supports for primary production for agricultural needs.]).
La ricerca è stata pertanto focalizzata sulla valutazione della produttività dei campi erbosi (praterie) e sugli effetti della gestione sui valori agronomici delle praterie.
DA CONTINUARE (sinceramente il testo non mi sembra molto significativo
è tanto generico!)
The identification of other roles of prairies in ecosystems functioning has been detected while prairies were degraded to be converted into croplands especially after 1950 (through erosion, eutrophication, and biodiversity loss) (Vitousek et al., 1994).
International policies delimitated new goals for these ecosystems due to ecological services in particular for the two major changes for the following decades which are the availability of fresh unpolluted water and the regulation of carbon emission (through carbon storage, finding energy leading to low carbon emission). In order to achieve these goals, we tend now to create new natural systems as surrogates of the degraded natural systems to provide these ecological services (conf. Rio, 1992). Recent works have for example demonstrated the use of natural prairies to provide alternative biofuels (carbon positive biofuels) (Tilman et al., 2006), or their role in carbon storage (Ni, 2002; Purakayastha et al., 2008).
The design of these new grassland ecological systems has to respond efficiently to these ecological goals. These new systems are elaborated by sowing mixed-species seeds. Questions are raised therefore on the temporal evolution of these plants which are characterised by different life-strategies and which are in constant interactions with one another (Grime, 1977). Such systems are also managed by farmers and are dependent on the environment. Proposing precise design of these systems need to take into account all these complex interactions. The urgent need for short term responses makes impossible to respond to this sociological demand through the only classical experimental approach which may necessitate long-term surveys.
Ecological problems have the specificity of being dependent on biological elements with complex interactions and which response may be delayed at a year or pluri-year scales due to biological cycles. New tools are therefore needed to go past over biological constraints and take into account the complexity of living ecosystems. The ViP project aims at using modelling for (i). providing extensive virtual experiments for testing solutions in a shorter time that would have been possible through real experimentation, (ii). optimizing experimental designs to access to the most efficient results.
For our application, we focused on an example of the European agricultural policy which compel farmers to establish herbal strips in agricultural landscapes. These linear systems have a key role in restoring water quality (to achieve this goal, they need to be located in priority along water courses) (De Cawer et al. 2006). An indirect effect of these systems is also to maintain and restore biodiversity by providing ecological refuges for animal or plant species of high ecological values (Field et al., 2005; Reeder et al. 2005). These herbal strips need to cover 3% of the farm surface into cultures such s cereals. The 2005 environmental policy do not provide precise technical guidelines for creating such buffering systems. Our ViP project aims at providing ecological guidelines on the design of prairies with the best potential for water purification.
How does a grass grow?
An original aspect of this project is to take into account plant vegetative reproduction e.g. the ability of each perennial grass to colonize space through cloning (numerous reiterations of new shoots connected through aerial or subterranean connexions)(ref. figure 1&2). The nodes of this network share information of stress and resources (for example nitrate). If many modelling works have considered them as points in space, we know that this simplification of the complex reality may give birth to misleading results as long as prairial ecosystems are considered.
Figure 1&2 : Example of one clone in a prairie
Three major steps in the project
- Elaborating an Individual-Based Model for simulating one plant (IBMplant)
This step is based on rules linked with plant metabolism, architecture and resource strategy (ref. figure 3).
Figure 3 : Simulation of one plant with IBMplant
- Elaborating and IBM model for simulating a prairie which can be considered as interactions of plant populations (IBMprairie).
This step is based on rules linked with plant competition. We will start at first to simulate the interaction of two plant populations and then extent to a multispecies prairie (ref. figure 4&5).
Figure 4&5 : Example of two interacting populations
- Elaborating a coupling of the prairie model with a model simulating water nitrate or pesticides in the soil (IBMprairie-PDE)
This step is based on rules linked with plant uptake and release of nutrient or pesticides (ref. figure 6).
Figure 6 : Calculation of the ground flow
How does it work for one step?
Achieving one step needs different skills: - ecologists will at first delimitate through experimentations how a plant grow, develop and reproduce, - mathematician will convert this knowledge into theorical equations and probabilistic rules and compute them using a mathematical language, - computational scientists will adapt this program to the BOINC plateform and develop tools for analysing them (for instance webinterface, - simulations will be done thanks to volunteers, - the results will be analyzed and compared to real surveys in the field or in long-term experiments (ref. figure 7).
Figure 7 : A plant grow analysis