Landscape Ecology

, Volume 25, Issue 2, pp 201–215 | Cite as

Combining process-based models for future biomass assessment at landscape scale

  • C. Gaucherel
  • S. Griffon
  • L. Misson
  • T. Houet
Research Article


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.


Formal 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).


  1. Anselmi S, Chiesi M, Giannini M, Manes F, Maselli F (2004) Estimation of Mediterranean forest transpiration and photosynthesis through the use of an ecosystem simulation model driven by remotely sensed data. Glob Ecol Biogeogr 13:371–380CrossRefGoogle Scholar
  2. Bishop ID, Bruce Hull R IV, Stock C (2005) Supporting personal world-views in an envisioning system. Environ Model Softw 20:1459–1468CrossRefGoogle Scholar
  3. Bloom C (2000) Terrain texture composition by blending in the frame buffer (a.k.a. “Splatting Textures”).
  4. Butler SJ, Vickery JA, Norris K (2007) Farmland biodiversity and the footprint of agriculture. Science 315:381–384CrossRefPubMedGoogle Scholar
  5. Connor D, Minguez I (2006) Looking at biofuels and bioenergy. Science 312:1743–1744PubMedGoogle Scholar
  6. Cormeau J, Gosse G (2008) Les biocarburants de deuxième génération. Economie et stratégies agricoles. Club Demeter, Paris, pp 167–246Google Scholar
  7. 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
  8. 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
  9. Deque M, Dreveton C, Braun A, Cariolle D (1994) The Arpege/Ifs atmosphere model—a contribution to the French community climate modeling. Clim Dyn 10:249–266CrossRefGoogle Scholar
  10. Ervin SM (2001) Digital landscape modeling and visualization: a research agenda. Landsc Urban Plan 54:49–62CrossRefGoogle Scholar
  11. Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P (2008) Land clearing and the biofuel carbon debt. Science 319:1235–1238CrossRefPubMedGoogle Scholar
  12. Gaucherel C, Giboire N, Viaud V, Houet T, Baudry J, Burel F (2006) A domain specific language for patchy landscape modelling: the Brittany agricultural mosaic as a case study. Ecol Model 194:233–243CrossRefGoogle Scholar
  13. Gaucherel C, Campillo F, Misson L, Guiot J, Boreux JJ (2008a) Parameterization of a process-based tree-growth model: comparison of optimization, MCMC and particle filtering algorithms. Environ Model Softw 23:1280–1288CrossRefGoogle Scholar
  14. Gaucherel C, Guiot J, Misson L (2008b) Changes of the potential distribution area of French Mediterranean forests under global warming. Biogeosciences 5:1–12CrossRefGoogle Scholar
  15. 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
  16. 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
  17. Guiot J (1986) ARMA techniques for modelling tree-ring response to climate and for reconstructing variations of palaeoclimates. Ecol Model 33:149–171CrossRefGoogle Scholar
  18. Guiot J, Hély C, Haibin W, Gaucherel C (2008) Interactions between vegetation and climate variability: what are the lessons of models and paleovegetation data. CRAS Geosci 340:595–601CrossRefGoogle Scholar
  19. 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
  20. 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
  21. JRC Europe (2006) Well-to-wheels analysis of future automotive fuels and powertrains in European context. EUCAR, JRCGoogle Scholar
  22. Lambin EF, Rounsevell MDA, Geist HJ (2000) Are agricultural land-use models able to predict changes in land-use intensity? Agric Ecosyst Environ 82:321–331CrossRefGoogle Scholar
  23. Lindenmayer A (1968) Mathematical models for cellular interaction in developments, parts I and II. J Theor Biol 18:280–315CrossRefPubMedGoogle Scholar
  24. Misson L (2004) MAIDEN: a model for analyzing ecosystem processes in dendroecology. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 34:874–887CrossRefGoogle Scholar
  25. Misson L, Rathgeber C, Guiot J (2004) Dendroecological analysis of climatic effects on Quercus petraea and Pinus halepensis radial growth using the process-based MAIDEN model. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 34:888–898CrossRefGoogle Scholar
  26. Monticino M, Acevedo M, Callicott B, Cogdill T, Lindquist C (2007) Coupled human and natural systems: a multi-agent-based approach. Environ Model Softw 22:656–663CrossRefGoogle Scholar
  27. Nassauer JI, Corry RC (2004) Using normative scenarios in landscape ecology. Landscape Ecol 19:343–356CrossRefGoogle Scholar
  28. Prusinkiewicz P (2004) Modeling plant growth development. Curr Opin Plant Biol 7:79–83CrossRefPubMedGoogle Scholar
  29. Rambal S, Joffre R, Ourcival JM, Cavender-Bares J, Rocheteau A (2004) The growth respiration component in eddy CO2 flux from a Quercus ilex mediterranean forest. Glob Chang Biol 10:1460–1469CrossRefGoogle Scholar
  30. Sheppard SRJ (2005) Landscape visualisation and climate change: the potential for influencing perceptions and behaviour. Environ Sci Policy 8:637–654CrossRefGoogle Scholar
  31. Stokstad E (2008) Dueling visions for a hungry world. Science 319:1474–1476CrossRefPubMedGoogle Scholar
  32. Thenail C, Baudry J (2004) Variation of farm spatial land use pattern according to the structure of the hedgerow network (bocage) landscape: a study case in northeast Brittany, France. Agric Ecosyst Environ 101:53–72CrossRefGoogle Scholar
  33. 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
  34. Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SSA (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30:391–405CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.INRA, EFPA, UMR AMAPMontpellier Cedex 5France
  2. 2.CIRAD, UMR AMAPMontpellierFrance
  3. 3.CNRS, CEFE, UMR 5175 CNRSMontpellier Cedex 5France
  4. 4.CNRS, GEODE UMR 5602 CNRS, Université Toulouse 2 Le Mirail, Maison de la RechercheToulouse Cedex 9France

Personalised recommendations