Spatiotemporal land use modelling to assess land availability for energy crops – illustrated for Mozambique

A method and tool have been developed to assess future developments in land availability for bioenergy crops in a spatially explicit way, while taking into account both the developments in other land use functions, such as land for food, livestock and material production, and the uncertainties in the key determinant factors of land use change (LUC). This spatiotemporal LUC model is demonstrated with a case study on the developments in the land availability for bioenergy crops in Mozambique in the timeframe 2005–2030. The developments in the main drivers for agricultural land use, demand for food, animal products and materials were assessed, based on the projected developments in population, diet, GDP and self‐sufficiency ratio. Two scenarios were developed: a business‐as‐usual (BAU) scenario and a progressive scenario. Land allocation was based on land use class‐specific sets of suitability factors. The LUC dynamics were mapped on a 1 km2 grid level for each individual year up to 2030. In the BAU scenario, 7.7 Mha and in the progressive scenario 16.4 Mha could become available for bioenergy crop production in 2030. Based on the Monte Carlo analysis, a 95% confidence interval of the amount of land available and the spatially explicit probability of available land was found. The bottom‐up approach, the number of dynamic land uses, the diverse portfolio of LUC drivers and suitability factors, and the possibility to model uncertainty mean that this model is a step forward in modelling land availability for bioenergy potentials.

[1]  John H. Perkins,et al.  Special Report on Renewable Energy Sources and Climate Change Mitigation: 2011. Intergovernmental Panel on Climate Change, Working Group III—Mitigation of Climate Change. Cambridge University Press, Cambridge, England. 1,088 pp. $100.00 hardcover (ISBN13: 9781107607101). Also available for free at h , 2012 .

[2]  T. Beringer,et al.  Bioenergy production potential of global biomass plantations under environmental and agricultural constraints , 2011 .

[3]  T. Sang,et al.  China's bioenergy potential , 2011 .

[4]  Carlos Alberto Cuvilas,et al.  Energy situation in Mozambique: A review , 2010 .

[5]  H. W. Elbersen,et al.  Potential, spatial distribution and economic performance of regional biomass chains: The North of the Netherlands as example , 2010 .

[6]  Peter Salamon,et al.  A software framework for construction of process-based stochastic spatio-temporal models and data assimilation , 2010, Environ. Model. Softw..

[7]  A. Faaij,et al.  Bioenergy revisited: Key factors in global potentials of bioenergy , 2010 .

[8]  A. Bondeau,et al.  Indirect land-use changes can overcome carbon savings from biofuels in Brazil , 2010, Proceedings of the National Academy of Sciences.

[9]  A. Faaij,et al.  European biomass resource potential and costs , 2010 .

[10]  Wim Turkenburg,et al.  Large-scale bioenergy production from soybeans and switchgrass in Argentina: Part A: Potential and economic feasibility for national and international markets , 2009 .

[11]  Wim Turkenburg,et al.  Large-scale bioenergy production from soybeans and switchgrass in Argentina: Part B. Environmental and socio-economic impacts on a regional level , 2009 .

[12]  K. Overmars,et al.  Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model , 2009, Landscape Ecology.

[13]  L. Jansen,et al.  Analysis of land-cover/use change dynamics in Manica Province in Mozambique in a period of transition (1990–2004) , 2008 .

[14]  Peter H. Verburg,et al.  Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model , 2007 .

[15]  B. D. Vries,et al.  Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach , 2007 .

[16]  A. Faaij,et al.  A bottom-up assessment and review of global bio-energy potentials to 2050 , 2007 .

[17]  J. Bakkes,et al.  Scenario development to explore the future of Europe's rural areas , 2006 .

[18]  Mark Rounsevell,et al.  The limitations of spatial land use data in environmental analysis , 2006 .

[19]  André Faaij,et al.  Biomass and bioenergy supply from Mozambique , 2006 .

[20]  A. Faaij,et al.  Potential of biomass energy out to 2100, for four IPCC SRES land-use scenarios , 2005 .

[21]  L. Fox,et al.  Poverty in Mozambique: unraveling changes and determinants , 2005 .

[22]  Bas Eickhout,et al.  The land-use projections and resulting emissions in the IPCC SRES scenarios scenarios as simulated by the IMAGE 2.2 model , 2004 .

[23]  A Veldkamp,et al.  Modelling land use change and environmental impact. , 2004, Journal of environmental management.

[24]  Stephen P. Slinsky,et al.  Bioenergy Crop Production in the United States: Potential Quantities, Land Use Changes, and Economic Impacts on the Agricultural Sector , 2003 .

[25]  J. Muir,et al.  Status of range and forage research in Mozambique , 2003 .

[26]  J. W. Bruce,et al.  The causes of land-use and land-cover change: moving beyond the myths , 2001 .

[27]  Wim Turkenburg,et al.  Potentials for electricity production from wood in Ireland , 2001 .

[28]  E. Lambin,et al.  Predicting land-use change , 2001 .

[29]  J. Bouma,et al.  A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use , 1999 .

[30]  Wim Turkenburg,et al.  Exploration of the land potential for the production of biomass for energy in the Netherlands. , 1998 .

[31]  A. Veldkamp,et al.  CLUE: a conceptual model to study the Conversion of Land Use and its Effects , 1996 .

[32]  André Faaij,et al.  Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique , 2012, Comput. Environ. Urban Syst..

[33]  D. Macqueen,et al.  Small and medium forest enterprises in Mozambique. , 2009 .

[34]  Wim Turkenburg,et al.  Exploration of regional and global cost–supply curves of biomass energy from short-rotation crops at abandoned cropland and rest land under four IPCC SRES land-use scenarios , 2009 .

[35]  Lars J Nilsson,et al.  Assessment of the potential biomass supply in Europe using a resource-focused approach , 2004 .

[36]  Paul Schot,et al.  Land use change modelling: current practice and research priorities , 2004 .

[37]  F. D. Gatto.  Forest law enforcement in Mozambique: An Overview Mission Report , 2003 .

[38]  Charcoal Potential in Southern Africa CHAPOSA , 2003 .

[39]  R. H. Williams,et al.  The contribution of biomass in the future global energy supply : a review of 17 studies , 2003 .

[40]  Cynthia Donovan,et al.  Gaps and Opportunities for Agricultural Sector Development in Mozambique , 2003 .

[41]  J. Bruinsma.  World Agriculture: Towards 2015/2030: An Fao Perspective , 2002 .

[42]  R. Fao,et al.  Cattle and small ruminant production systems in sub-Saharan Africa. A systematic review , 2002 .

[43]  Timothy Evans,et al.  A Review and Assessment of Land-Use Change Models Dynamics of Space, Time, and Human Choice , 2002 .

[44]  R. Leemans,et al.  Global change scenarios of the 21st Century : results from the IMAGE 2.1 model , 1998 .