Open AccessSpatiotemporal land use modelling to assess land availability for energy crops – illustrated for M ozambique
Author(s) -
Hilst Floor,
Verstegen Judith A.,
Karssenberg Derek,
Faaij André P. C.
Publication year - 2012
Publication title -
gcb bioenergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.378
H-Index - 63
eISSN - 1757-1707
pISSN - 1757-1693
DOI - 10.1111/j.1757-1707.2011.01147.x
Subject(s) - bioenergy , land use , agricultural land , land use, land use change and forestry , environmental science , energy crop , scenario analysis , production (economics) , portfolio , agriculture , population , agroforestry , agricultural engineering , renewable energy , natural resource economics , geography , ecology , business , economics , engineering , demography , macroeconomics , archaeology , finance , sociology , biology
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 M ozambique 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 km 2 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 M onte C arlo 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.