Combining process-based models for future biomass assessment at landscape scale
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We need an integrated assessment of the bioenergy production at landscape scale for at least three main reasons: (1) it is predictable that we will soon have landscapes dedicated to bioenergy productions; (2) a number of “win–win” solutions combining several dedicated energy crops have been suggested for a better use of local climate, soil mosaic and production systems and (3) “well-to-wheels” analyses for the entire bioenergy production chain urge us to optimize the life cycle of bioenergies at large scales. In this context, we argue that the new generation of landscape models allows in silico experiments to estimate bioenergy distributions (in space and time) that are helpful for this integrated assessment of the bioenergy production. The main objective of this paper was to develop a detailed modeling methodology for this purpose. We aimed at illustrating and discussing the use of mechanistic models and their possible association to simulate future distributions of fuel biomass. We applied two separated landscape models dedicated to human-driven agricultural and climate-driven forested neighboring patches. These models were combined in the same theoretical (i.e. virtual) landscape for present as well as future scenarios by associating realistic agricultural production scenarios and B2-IPCC climate scenarios depending on the bioenergy type (crop or forest) concerned in each landscape patch. We then estimated esthetical impacts of our simulations by using 3D visualizations and a quantitative “depth” index to rank them. Results first showed that the transport cost at landscape scale was not correlated to the total biomass production, mainly due to landscape configuration constraints. Secondly, averaged index values of the four simulations were conditioned by agricultural practices, while temporal trends were conditioned by gradual climate changes. Thirdly, the most realistic simulated landscape combining intensive agricultural practices and climate change with atmospheric CO2 concentration increase corresponded to the lowest and unwanted bioenergy conversion inefficiency (the biomass production ratio over 100 years divided by the averaged transport cost) and to the most open landscape. Managing land use and land cover changes at landscape scale is probably one of the most powerful ways to mitigate negative (or magnify positive) effects of climate and human decisions on overall biomass productions.
KeywordsFormal grammar Landscape modeling Heterogeneity Agricultural production system Tree-growth model Mediterranean forests Evergreen oak Dendrochronology CO2 fertilization effect
We gratefully thank S. Cadoux for providing us information about bioenergy. This research has been funded by the Agence Nationale de la Recherche (project “BiodivAgriM”, ANR-biodiversité 2007).
- Bloom C (2000) Terrain texture composition by blending in the frame buffer (a.k.a. “Splatting Textures”). www.cbloom.com/3d/techdocs/splatting.txt
- Cormeau J, Gosse G (2008) Les biocarburants de deuxième génération. Economie et stratégies agricoles. Club Demeter, Paris, pp 167–246Google Scholar
- De Coligny F (2006) Efficient building of forestry modelling software with the capsis methodology. In: PMA06—plant growth modelling and applications. IEEE Computer Society, Los Alamitos, pp 216–222Google Scholar
- de Noblet-Ducoudre N, Gervois S, Ciais P, Viovy N, Brisson N, Seguin B, Perrier A (2004) Coupling the soil–vegetation–atmosphere-transfer scheme ORCHIDEE to the agronomy model STICS to study the influence of croplands on the European carbon and water budgets. Agronomie 24:397–407CrossRefGoogle Scholar
- Gibelin AL, Deque M (2003) Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Clim Dyn 20:327–339Google Scholar
- Griffon S, Auclair D (2009) Visualising changes in agricultural landscapes. In: Brouwer F, Van Ittersum M (eds) Environmental and agricultural modelling: integrated approaches for policy impact assessment. Springer, New YorkGoogle Scholar
- Houet T, Gaucherel C (2005) Simulation dynamique et spatialement explicite d’un paysage agricole bocager: validation sur un petit bassin versant breton sur la période 1981–1998. European Journal of GIS and Spatial Analysis Revue Internationale de Géomatique 17:491–516Google Scholar
- Houet T, Hubert-Moy L (2006) Modelling and projecting land-use and land-cover changes with a cellular automaton considering landscape trajectories: an improvement for simulation of plausible future states. In: EARSeL eProceedings, pp 63–76Google Scholar
- JRC Europe (2006) Well-to-wheels analysis of future automotive fuels and powertrains in European context. EUCAR, JRCGoogle Scholar
- Tyrväinen L, Tahvanainen L (2000) Landscape visualisation in rural land-use planning. In: XXI IUFRO world congress. Forests and society: the role of research. Kuala Lumpur, pp 338–347Google Scholar